New energy access system-oriented long-period production cost model construction method

Abstract

The invention discloses a long-period production cost model construction method for a new energy access system, and the method comprises the following steps: S1, obtaining load data, new energy output data and cost data, comprehensively considering the continuity of long-period simulation, and setting the period of a simulation model as 72 hours; s2, setting different new energy permeability ratio scenes; s3, establishing an objective function capable of ensuring system cost minimization under the condition of relatively high consumption rate of new energy; and S4, according to the system load, the conventional unit parameters and the network parameter conditions, in combination with the power balance constraint, the conventional thermal power unit constraint, the new energy unit constraint and the system safe and stable operation constraint, constructing a long-period time sequence production analog simulation model of the power system. Compared with the prior art, the method has the advantages that the system cost increment caused by high-proportion new energy access can be quantitatively analyzed; and calculating the unit start-stop cost, the system standby cost, the system frequency modulation cost and the system safe operation cost under different new energy permeability.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and in particular to a method for constructing a long-term production cost model for a new energy access system. Background Art

[0002] At present, the new power system focuses on achieving a shift from a supply structure dominated by fossil energy power generation to a reliable power support provided by new energy, and building a high-proportion new energy supply and consumption system. Therefore, new energy has ushered in a step-by-step development, achieving a high or even ultra-high proportion of grid connection across the country. However, the high proportion of new energy connected to the power system makes the system face the operating characteristics of strong fluctuations and weak inertia, and urgently needs more flexible resources to provide support, and then the system cost is facing a significant increase. The traditional on-grid electricity price calculation method is no longer suitable for a high-proportion new energy system, and the effective guidance of the transformation cost of the new power system is challenging.

[0003] In the current context of high proportion of new energy access, the system cost research model urgently needs to be fully upgraded. In China, the economic feasibility of new energy development and utilization is generally evaluated by indicators such as levelized cost of energy (LCOE) or operating period electricity price. Although it focuses on the direct cost of new energy power generation, it often ignores the comprehensive cost considerations at the system level, especially the construction, consumption and transmission costs of new energy. For example, Zhang Cong's team at Hunan University proposed a new energy power system interval economic dispatch model and algorithm based on safety limit definition [Jia Wanqing, Zhang Cong, Liu Qian, et al. New energy power system interval economic dispatch model and algorithm based on safety limit definition [J]. Power System Technology, 2024, 48(02): 509-518.], which regards wind power output as an interval number, considers the power balance constraints in extreme scenarios and the participation of transferable and reducible flexible loads to establish an interval economic dispatch model that takes into account wind power uncertainty and power balance. The model takes into account the cost of traditional unit power generation, the cost of flexible load electricity consumption, the cost of reducible load compensation, and the cost of wind curtailment for the optimization target, and focuses on the consumption and transmission costs, but does not take into account the construction costs. The Zhou Bin team of Hunan University proposed a multi-subject responsibility allocation model for system grid connection costs and strengthening costs [Yan Jiong, Lu Shengwei, Zhang Tao, et al. Multi-subject allocation model for grid transmission and distribution costs under high proportion of new energy access [J]. Electric Power Construction, 2024, 45(01):138-146.]. For grid connection costs, considering the differences in the cost-bearing capacity of new energy enterprises, the new energy enterprise clusters are identified by clustering based on the heterogeneity of enterprises, and the grid connection costs are allocated based on the internal cost responsibility measurement of the new energy enterprise clusters. For the strengthening cost, the strengthening cost of the grid on the source-grid side is calculated based on the strengthening cost-effectiveness, the risk factors of new energy grid connection are analyzed, and the two-layer cost sharing structure of new energy enterprise aggregate-alliance aggregate is constructed considering the shared attributes of the strengthening cost. The risk factor is introduced to quantify the impact of the new energy grid connection risk on the strengthening cost, and then the indivisible cost is used to measure the new energy grid connection risk, and the strengthening cost sharing model based on the EANS-Owen value is proposed. This model is more detailed in the division of the new energy transmission cost, and there is also the problem of insufficient attention to the new energy construction cost and consumption cost. In contrast, foreign scholars mostly use the method of power economics, load continuity curve for analysis, and power system time series production simulation model for estimation. Although the methods are diverse, due to the lack of comprehensive consideration of the causes of system costs, its measurement method has not yet been widely popularized and applied.

[0004] Therefore, for the purpose of stimulating the sustainable development of new energy, it has become a necessary research topic to carry out revolutionary and innovative design for the problems existing in the above-mentioned production cost models, which has important theoretical significance and practical application value. On the basis of comprehensively considering the evolution law of system cost, from the perspective of the cost increment generated by the development of new energy in the power system, a new energy power generation equivalent on-grid electricity price calculation model based on system cost is constructed to scientifically identify the cost and composition of new energy development and utilization, so as to comprehensively evaluate the impact of new energy access on system cost, thereby providing strong economic incentives and scientific guidance for the sustainable development of the new energy industry.

[0005] Therefore, it is necessary to provide a method for constructing a long-term production cost model for new energy access systems to solve the above technical problems. Summary of the invention

[0006] The technical problem to be solved by the present invention is that in view of the deficiencies in the above-mentioned prior art, a method for constructing a long-term production cost model for a new energy access system is disclosed, which comprehensively considers various causes of system costs and clarifies the system's new costs as new energy costs, system safety operation costs, new energy consumption costs, and renewable energy transmission costs. Accurately reflect the impact of new energy access on system costs and provide an important reference for the design of power market mechanisms. Construct a scientific equivalent on-grid electricity price calculation model to encourage the sustainable development of new energy.

[0007] The technical solution adopted by the present invention to solve the technical problem is: a method for constructing a long-cycle production cost model for a new energy access system, comprising the following steps:

[0008] S1. Obtain load data, new energy output data and cost data, comprehensively consider the continuity of long-term simulation, and set the simulation model cycle to 60-84 hours;

[0009] S2. Set different scenarios for new energy penetration ratio;

[0010] S3. Establish an objective function that can minimize system costs under a high consumption rate of new energy;

[0011] S4. According to the system load, conventional unit parameters, network parameter conditions, combined with power balance constraints, conventional thermal power unit constraints, new energy unit constraints, and system safe and stable operation constraints, a long-term time series production simulation model of the power system is constructed;

[0012] S5. Use CPLEX to solve the constructed long-period time-series production simulation model of the power system to obtain the start-up and shutdown and output information of the units, and calculate the evaluation indicators of the start-up and shutdown and auxiliary service costs of conventional thermal power units based on this;

[0013] S6. Based on the long-cycle production cost model, a reasonable cost relief mechanism is designed to distribute the system cost to the power supply side, grid side and user side according to the principles of "whoever benefits, pays" and "whoever causes, pays", so as to encourage all parties to actively participate in the consumption of new energy and improve system flexibility.

[0014] Preferably, in step S3, the objective function includes a first sub-function F 1 and the second subfunction F 2 , where F 1 represents the combined power generation cost of the units, F 2 represents the cost of auxiliary services provided by conventional thermal power units, from which the system minimum operating cost F under different new energy penetration rates can be obtained:

[0015] F = F 1 + F 2 (1)

[0016] Among them, the first subfunction F 1 The expression is:

[0017]

[0018] In the formula, f 1 represents the operating cost of thermal power units, f 2 represents the operating cost of wind turbines, f 3 Indicates the operating cost of the photovoltaic unit; Represents the electricity price of thermal power units, P i，m，t represents the output of thermal power unit i at time t during period m; c i U , and They represent the startup, shutdown and no-load operation costs of thermal power units, v i，t represents the start-up action of thermal power unit i at time t, i is a 0-1 variable; w i，t Indicates the shutdown action of thermal power unit i at time t, i is a 0-1 variable; u i，t Indicates whether it is in no-load running state, i is a 0-1 variable; c j W represents the bid of wind power plant station j, represents the price offered by photovoltaic plant k, P j，t represents the output power of the wind turbine in period j, P k，t Represents the output power of the photovoltaic unit in time period t;

[0019] The second subfunction F 2 The expression is as follows:

[0020]

[0021] In the formula, f 1 The cost of providing up and down backup for conventional thermal power units, f 2 represents the cost of deep peak load regulation auxiliary services provided by thermal power units, f 3 It represents the cost of providing primary and secondary frequency regulation by thermal power units; represents the upper reserve quotation of thermal power unit i at time t; represents the next reserve price of thermal power unit i at time t, represents the upper reserve power provided by thermal power unit i; represents the lower reserve power provided by thermal power unit i; Indicates the deep peak load regulation quotation of thermal power units. Indicates the deep peak-shaving capacity of the thermal power unit that won the bid; and Respectively represent the primary / secondary frequency regulation mileage price of thermal power units, and Respectively represent the price of primary / secondary frequency regulation capacity of thermal power units, and Respectively represent the primary / secondary frequency regulation performance indicators of thermal power units.

[0022] Preferably, in step S4, the power balance constraint means that the output of the new energy and conventional units is equal to the load size, and the power of the system needs to be balanced at every moment. The power balance constraint is described as:

[0023]

[0024] Where N G Indicates the number of thermal power units; N L Indicates the number of load nodes; N W Indicates the number of wind power plants; N S Indicates the number of photovoltaic plants; P i，t represents the output power of thermal power unit i in period t, P k，t represents the output power of the wind turbine in period k, P j，t Represents the output power of the photovoltaic unit in time period t.

[0025] Preferably, in step S4, the conventional thermal power unit constraints include unit power constraints, unit ramp constraints and unit start and stop time constraints;

[0026] The unit power constraint means that the output level of the generator unit should be between its minimum technical output and maximum technical output:

[0027]

[0028] In the formula, u i，tIndicates the online status of unit i in time period t, 1 means in operation, and 0 means off-grid operation; P i and Respectively represent the maximum output power and minimum output power of thermal power unit i; P i，t Represents the output power of thermal power unit i in period t;

[0029] The unit climbing constraint means that the output change value of the unit in two adjacent time periods should be within the allowable range of its up and down climbing:

[0030]

[0031] In the formula, and R represent the maximum output rate and maximum output rate of the unit when climbing up and down respectively; P i，t represents the output power of unit i in time period t; P i，t represents the output power of unit i in time period t; P i，t-1 represents the output of unit i in t-1;

[0032] The unit start and stop time constraint means that the unit must be guaranteed to run for a period of time when it is started. i At this time TU i The internal unit can only be turned on once; similarly, when the unit is turned off, it must be stopped for a period of time. i , at this time TD i The internal unit can only be turned on once:

[0033]

[0034] The relationship between the three Boolean variables is as follows:

[0035] u i,t -u i,t-1 =v i,t -w i,t (9)

[0036] In the formula, v i，t Indicates the start-up action of unit i in time period t, 1 means start-up, 0 means other; u i，t represents the online status of unit i in time period t, 1 means in operation, 0 means off-grid operation; w i，t Indicates the shutdown action of unit i in period t, 1 means shutdown, 0 means others; TU i and TD i All are minimum running times.

[0037] Preferably, in step S4, the new energy unit constraints include: wind power constraints, photovoltaic power constraints, new energy output constraints and new energy consumption rate constraints.

[0038] Preferably, in step S4, the wind power constraint means that the wind power output is mainly affected by the wind speed, and the wind speed distribution is described by a two-parameter Weibull distribution model, and the probability density function expression of the two-parameter Weibull distribution model is as follows:

[0039]

[0040] In the formula, v represents wind speed; k represents shape parameter, and c represents size parameter. The specific expression is:

[0041]

[0042] In the formula, μ w , σ w are the mean and standard deviation of wind speed respectively;

[0043] Furthermore, the probability cumulative density function of the two-parameter Weibull distribution model is:

[0044]

[0045] This gives the wind speed:

[0046]

[0047] In the formula, r is a random variable uniformly distributed between [0, 1], which can be randomly generated by Monte Carlo simulation;

[0048] Furthermore, the characteristic curve between wind speed and wind power can be expressed by the following piecewise function:

[0049]

[0050] In the formula, Indicates the active power output by the fan; P G，w，N Indicates the rated output of the fan; v in 、v N 、v out They are cut-in wind speed, rated wind speed, and cut-out wind speed respectively.

[0051] Preferably, in step S4, the photovoltaic power constraint means that the photovoltaic output level is mainly related to the light radiation of the location; similar to wind speed, light radiation itself has a certain randomness and generally obeys beta distribution, and its probability density expression is as follows:

[0052]

[0053] In the formula, q and q maxare the actual light radiation intensity and the maximum light radiation intensity, respectively; Γ(·) is the gamma function; α and β are the shape parameters of the beta distribution, which can be obtained by the following equations (17) and (18), respectively:

[0054]

[0055] In the formula, μ pv With σ pv are the mean and standard deviation of sunlight radiation, respectively. Generally speaking, the sampling period for sunlight intensity measurement is 1 minute or an integer multiple of 1 minute. It can calculate its mean and variance based on a large number of sampling results in one day.

[0056] Furthermore, the gamma function is an extension of the factorial function, which is defined in the real number field as:

[0057]

[0058] Using formula (20), the output power of photovoltaic cells under any light intensity can be obtained:

[0059]

[0060] Where P G，pv (q) represents the output value of photovoltaic power generation; P G，pv，N is the rated output power of the photovoltaic cell; s is the temperature coefficient, which is generally -0.47; q N and T pv，N are the light radiation intensity and temperature under standard conditions, which are 100W / m 2 and 25℃; T pv is the actual temperature of the photovoltaic cell, which is calculated by formula (21):

[0061]

[0062] Where, T en Indicates the ambient temperature.

[0063] Preferably, in step S4, the new energy output constraint includes: wind power output constraint and photovoltaic output constraint, and the wind power output constraint is:

[0064] 0<P W,t <P W,t,Max (twenty two)

[0065] Where P w，t represents the wind farm output at time t, P w，t，Max represents the predicted maximum output of the wind power plant at time t;

[0066] The photovoltaic output constraint is:

[0067] 0<PPV,t <P PV,t,Max (twenty three)

[0068] Where P PV，t represents the photovoltaic field output at time t, P PV，t，Max Indicates the predicted maximum output of the photovoltaic power plant at time t.

[0069] Preferably, in step S4, the new energy consumption rate constraint is:

[0070]

[0071] In the formula, and They represent the actual maximum possible output of the i-th wind turbine and the j-th photovoltaic unit in time period t; h 1 and h 2 They are the lowest new energy consumption rates for wind power and photovoltaic systems respectively.

[0072] Preferably, in step S4, the system safe and stable operation constraints include: frequency stability constraints, upper and lower standby constraints, node constraints, bus phase angle constraints and power flow safety constraints;

[0073] In the frequency stability constraint: to ensure the frequency safety of the system, after disturbances occur on the new energy and load sides, the frequency rise or fall must be within the safe range that the system can withstand, so the minimum frequency constraint can be constructed as follows.

[0074] f a ≤f N +Δf≤f b (25)

[0075] In the formula, f a Indicates the frequency lower limit, which is usually set to the system frequency lower limit;

[0076] f b Indicates the upper frequency limit value, which is usually set to the upper frequency limit of the system;

[0077] f N Indicates the frequency reference value;

[0078]

[0079] In the formula, △P NE Indicates the active power fluctuation caused by the new energy unit, △P L Indicates the active power fluctuation caused by the load, K G Indicates the power-frequency static characteristic coefficient of a conventional generator set;

[0080] In the upper and lower reserve constraints, as the uncertainty in the source and load sides of the high-proportion renewable energy system increases, fluctuations on either side will cause power imbalance, and the system reserve constraints need to be considered from both the power generation side and the load side.

[0081] On the one hand, due to the volatility and uncertainty of wind and solar power output, the fluctuation of its output will also cause certain disturbances in the power system, so the system needs to reserve spinning reserves to deal with the uncertainty caused by the two; on the other hand, in order to deal with the uncertainty caused by load prediction deviation and load fluctuation on the load side and avoid its impact on power quality, the system usually also sets a certain reserve to smooth out this part of the unbalanced power, so as to ensure the safe and stable operation of the system. First, the upper and lower reserve requirements of the system are determined by the following formula:

[0082]

[0083] In the formula, κ t U and κ t D They are the upward and downward reserve demand coefficients of the system at time t, which are determined according to the forecast of the fluctuation of new energy units and uncontrollable loads in each period. When the proportion of fluctuating loads and new energy units is relatively large, the upward and downward reserve demand coefficients are correspondingly large; represents the standard deviation of the uncontrollable load power; δ j W It represents the standard deviation of wind turbine output; Represents the standard deviation of the photovoltaic unit;

[0084] Furthermore, in addition to considering the volatility of new energy and load side, we should also consider the situation where conventional thermal power units are out of operation due to failures, and reserve corresponding spare capacity. Indicates the output of the largest thermal power unit in the system at maximum load. It represents the maximum capacity unit output in the system at the minimum load, so the upper and lower reserve requirements of the system can be expressed as:

[0085]

[0086] Therefore, the upper spare constraint of the model is:

[0087]

[0088] The following alternate constraints are:

[0089]

[0090] In the formula, R up is the upper reserve demand of the system, R dnis the system's lower reserve demand; u i，t Indicates the online status of unit i in time period t, 1 means in operation, and 0 means off-grid operation; and P i Respectively represent the maximum output power and minimum output power of unit i; R t U and They represent the upper standby demand and lower standby demand required by the system respectively;

[0091] The node constraints include node power balance constraints and node voltage constraints, wherein the node power balance constraint means that the injected power is equal to the outflow power at any node in the power system at any time:

[0092]

[0093] In the formula, K p，n，i represents the node-generator association matrix element; P nm，t represents the power flow of line nm in period t; D n，t represents the load of node n in the tth period; θ n，t represents the phase angle of node n in the tth period;

[0094] The node voltage constraint is:

[0095] V min,n ≤V n ≤V Max,n (34)

[0096] Where V n Represents the voltage of node n, V Max，n Represents the maximum voltage of node n; V min，n represents the minimum voltage value of node n;

[0097] The bus phase angle constraint refers to the value range of the bus phase angle. The phase angle of the balanced bus is taken as 0 as a reference. During normal operation, the phase angles of other buses should be close to 0. The specific equation constraint is as follows:

[0098] -π≤θ(s,t)≤π(35)

[0099] The power flow safety constraint means that the line power flow should be less than the allowable limit of the line power flow. Currently, the most widely used method is to use the DC power flow model to solve the line power flow. The specific model is as follows:

[0100]

[0101] Where θ(s, t) represents the phase angle of bus s at time t; x l It represents the reactance parameter of the transmission line; andP i are the maximum power constraint and the minimum power constraint of line l respectively;

[0102] After calculating the power flow transfer distribution factor matrix G, the power flow safety constraint is rewritten as

[0103]

[0104] Compared with the prior art, the present invention solves the problems that the traditional existing system cost research model under the condition of high proportion of new energy access focuses on the power generation cost of new energy itself and ignores the system cost of new energy; the various causes of system cost are not considered comprehensively, and the calculation method is difficult to promote and apply on a large scale. The present invention considers multi-dimensional boundary conditions such as power structure, grid parameters, and new energy penetration rate, considers conventional unit constraints, new energy unit constraints, system safety and stability constraints, and power balance constraints, and constructs a long-term production cost simulation model for a system with high proportion of new energy access. It can quantitatively analyze the incremental system cost caused by high proportion of new energy access; calculate the unit start-up and shutdown cost, system standby cost, system frequency regulation cost, and system safe operation cost under different new energy penetration rates, and analyze the growth of each cost under different new energy penetration rates. Secondly, it can not only calculate the system safe operation cost under different new energy penetration rates, but also quantify the incremental system safe operation cost under different new energy penetration rates. It can comprehensively and dynamically evaluate the changes in system costs, provide a method for system cost calculation under the condition of high proportion of new energy access, and provide an important reference for the design of power market mechanism, adjustment of electricity price mechanism, and sustainable development of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 Constructing a flow chart for the long-term production cost model of the new energy access system of the present invention;

[0106] Figure 2 It is a schematic diagram of a solution framework of a method for constructing a long-cycle production cost model of the present invention;

[0107] Figure 3 It is a load curve diagram of an example of the present invention;

[0108] Figure 4 A three-day photovoltaic output curve diagram of the new energy access system of the present invention;

[0109] Figure 5 A three-day wind power output curve diagram of the new energy access system of the present invention;

[0110] Figure 6 It is a simulation data diagram of each item under the new energy penetration rate of 15% of the present invention;

[0111] Figure 7It is the output level and output range diagram of each unit of the present invention;

[0112] Figure 8 It is a simulation data diagram of each item under the new energy penetration rate of 25% of the present invention;

[0113] Fig. 9 It is the output level and output range diagram of each unit of the present invention;

[0114] Fig.10 It is a simulation data diagram of each item under the new energy penetration rate of 30% of the present invention;

[0115] Fig.11 It is the output level and output range diagram of each unit of the present invention;

[0116] Fig.12 is the cumulative start and stop times of the unit under the three permeability conditions of the present invention;

[0117] Fig.13 is the average power generation hours of thermal power units under the three penetration rates of the present invention;

[0118] Fig.14 The system reserve demand curves under three penetration rate conditions of the present invention;

[0119] Fig.15 It is the system frequency modulation requirement under three penetration rate conditions of the present invention. DETAILED DESCRIPTION

[0120] The present invention is described in detail below in conjunction with the various embodiments shown in the accompanying drawings, but it should be noted that these embodiments are not limitations of the present invention, and any equivalent transformations or substitutions in functions, methods, or structures made by ordinary technicians in the field based on these embodiments are all within the scope of protection of the present invention.

[0121] The following is combined with Figure 1 To further illustrate the present invention, a method for constructing a long-cycle production cost model for a new energy access system includes the following steps:

[0122] See attached Figure 1 Step S1: Obtain load data, new energy output data and cost data, comprehensively consider the continuity of long-term simulation, and refer to the attached Figure 2 The simulation model cycle is set to 72 hours. The basic data are as follows: Load curve Figure 3 As shown, the photovoltaic output curve and wind power output curve are as follows: Figure 4 and Figure 5 As shown, the cost-related data are shown in Tables 1 and 2.

[0123] Table 1 Startup and shutdown costs of various types of units

[0124]

[0125]

[0126] Table 2 Unit cost of system backup and frequency regulation

[0127]

[0128] Step S2, setting different scenarios of new energy penetration ratios. In order to study the changes in system safe operation costs under different new energy penetration ratios, the present invention sets three groups of operation scenarios, scenario 1, scenario 2 and scenario 3, for comparison. The specific simulation scenario data settings are shown in Table 3.

[0129] Table 3 Simulation scenario data

[0130]

[0131] Step S3: Establish an objective function that can minimize system costs under a high new energy consumption rate.

[0132] Furthermore, the objective function in step S3 includes a first sub-function F 1 and the second subfunction F 2 Among them, the first subfunction F 1 The second sub-function F represents the power generation cost of the unit combination. 2 represents the cost of auxiliary services provided by conventional thermal power units, from which the system minimum operating cost F under different new energy penetration rates can be obtained:

[0133] F = F 1 + F 2 (1)

[0134] Among them, the expression of the first part objective function F1 is:

[0135]

[0136] In the formula, f 1 represents the operating cost of thermal power units, f 2 represents the operating cost of wind turbines, f 3 Represents the operating cost of a photovoltaic system. Represents the electricity price of thermal power units, P i，m，t represents the output of thermal power unit i at time t during period m. i U , and They represent the startup, shutdown and no-load operation costs of thermal power units, v i，t represents the start-up action of unit i at time t, i is a 0-1 variable; wi，t Indicates the shutdown action of unit i at time t, i is a 0-1 variable; u i，t Indicates whether it is in no-load operation state, i is a 0-1 variable. j W represents the bid of wind power plant station j, represents the price offered by photovoltaic plant k, P j，t represents the output power of the wind turbine in period j, P k，t Represents the output power of the photovoltaic unit in time period t.

[0137] The expression of the second part objective function F2 is as follows:

[0138]

[0139] In the formula, f 1 The cost of providing up and down backup for conventional thermal power units, f 2 represents the cost of deep peak load regulation auxiliary services provided by thermal power units, f 3 Represents the cost of providing primary and secondary frequency regulation by thermal power units. represents the upper reserve quotation of thermal power unit i at time t; represents the next reserve price of thermal power unit i at time t, Indicates the upper reserve power provided by unit i; Indicates the lower reserve power provided by unit i. Indicates the deep peak load regulation quotation of thermal power units. Indicates the deep peak regulation capacity of the thermal power unit that won the bid. and Indicates the price of primary / secondary frequency regulation mileage of thermal power units, and Indicates the price of primary / secondary frequency regulation capacity of thermal power units and Indicates the primary / secondary frequency regulation performance index of the unit.

[0140] Step S4: Based on system load, conventional unit parameters, network parameters and other conditions, combined with power balance constraints, conventional thermal power unit constraints, new energy unit constraints, system safe and stable operation constraints and other constraints, a long-term time series production simulation model of the power system is constructed.

[0141] Step S41, the power balance constraint means that the output of new energy and conventional units is equal to the load size, and the power of the system needs to be balanced at every moment. The equality constraint of the model is described as:

[0142]

[0143] Where N G Indicates the number of thermal power units; NL Indicates the number of load nodes; N W Indicates the number of wind power plants; N S Indicates the number of PV plants.

[0144] Step S42: In step S4, the conventional thermal power unit constraints include unit power constraints, unit ramp constraints, and unit start and stop time constraints.

[0145] Step S421: The power constraint of the generator set means that the output level of the generator set should be between its minimum technical output and maximum technical output:

[0146]

[0147] In the formula, u i，t Indicates the online status of unit i in time period t, 1 means in operation, and 0 means off-grid operation; P i Indicates the maximum / minimum output power of unit i; P i，t Represents the output power of unit i in time period t.

[0148] Furthermore, in step S421, the thermal power unit needs to participate in the electric energy market and the ancillary service market, and the electric energy and ancillary services provided by the thermal power unit also meet the following constraints:

[0149]

[0150] Furthermore, in step S421, the thermal power unit adopts segmented quotation, so the following power constraints are also met:

[0151]

[0152] Step S422: The unit climbing constraint means that the output change value of the unit in two adjacent time periods should be within the allowable range of its up and down climbing:

[0153]

[0154] In the formula, R Indicates the up and down climbing rate of the unit; P i，t represents the output power of unit i in time period t; P i，t represents the output power of unit i in time period t; P i，t-1 Represents the output of unit i in t-1.

[0155] Furthermore, in step S422, when the minimum power for starting the unit is greater than the ramp rate, the unit ramp constraint will cause all the shut-down units to fail to start, so the ramp constraint is rewritten as:

[0156]

[0157] In order to simplify the calculation, the maximum startup rate and the maximum shutdown rate can be taken as:

[0158]

[0159] Step S423, the start-stop time constraint of the unit means that when the unit is turned on, it must be guaranteed to run for a period of time, and it can only be turned on once during this period of time (minimum running time); similarly, when the unit is turned off, it must also be guaranteed to stop running for a period of time (minimum shut-down time):

[0160]

[0161] The relationship between the three Boolean variables is as follows:

[0162] u i,t -u i,t-1 =v i,t -w i,t (13)

[0163] In the formula, TU i , TD i Indicates the minimum start / stop time of unit i; v i，t Indicates the start-up action of unit i in time period t, 1 means start-up, 0 means other; u i，t represents the online status of unit i in time period t, 1 means in operation, 0 means off-grid operation; w i，t Indicates the shutdown action of unit i in time period t, 1 means shutdown, and 0 means others.

[0164] Step S43: The constraints on the new energy units in step S4 include wind power constraints, photovoltaic power constraints, new energy output constraints, and new energy consumption rate constraints.

[0165] Step S431: The wind power constraint means that the wind power output is mainly affected by wind speed. At present, the two-parameter Weibull distribution model is widely used to describe the wind speed distribution. The probability density function expression of this type of model is as follows:

[0166]

[0167] In the formula, v represents wind speed; k represents shape parameter, and c represents size parameter. The specific expression is:

[0168]

[0169] In the formula, μ w , σ w are the mean and standard deviation of wind speed respectively.

[0170] Furthermore, the probability cumulative density function of the Weibull distribution in step S431 is:

[0171]

[0172] This gives the wind speed:

[0173]

[0174] Where r is a random variable uniformly distributed between [0, 1], which can be randomly generated by Monte Carlo simulation.

[0175] Furthermore, the characteristic curve between wind speed and wind power in step S431 can be represented by the following piecewise function:

[0176]

[0177] In the formula, Indicates the active power output by the fan; P G，w，N Indicates the rated output of the fan; v in 、v N 、v out They are cut-in wind speed, rated wind speed, and cut-out wind speed respectively.

[0178] Step S432: The photovoltaic power constraint means that the photovoltaic output level is mainly related to the light radiation of the location. Similar to wind speed, light radiation itself has a certain randomness and generally obeys beta distribution. Its probability density expression is as follows:

[0179]

[0180] In the formula, q and q max are the actual light radiation intensity and the maximum light radiation intensity, respectively; Γ(·) is the gamma function; α and β are the shape parameters of the beta distribution, which can be obtained by the following equations (21) and (22), respectively:

[0181]

[0182] In the formula, μ pv , σ pv They are the mean and standard deviation of sunlight radiation. Generally speaking, the sampling period for sunlight intensity measurement is 1 minute or an integer multiple of 1 minute. The system can calculate its mean and variance based on a large number of sampling results in one day.

[0183] Furthermore, the gamma function in step S432 is an extension of the factorial function, which is defined in the real number domain as:

[0184]

[0185] Formula (24) can be used to obtain the output power of photovoltaic cells under any light intensity.

[0186]

[0187] Where P G，pv (q) represents the output value of photovoltaic power generation; P G，pv，N is the rated output power of the photovoltaic cell; s is the temperature coefficient, which is generally -0.47; q N 、T pv，N are the light radiation intensity and temperature under standard conditions, which are 100W / m 2 and 25℃; T pv is the actual temperature of the photovoltaic cell, calculated by formula (25).

[0188]

[0189] Where, T en Indicates the ambient temperature.

[0190] Step S433: The new energy output constraint, including wind power output constraint:

[0191] 0<P W,t <P W,t,Max (26)

[0192] Where P w，t represents the wind farm output at time t, P w，t，Max It represents the predicted maximum output of the wind power plant at time t.

[0193] Photovoltaic output constraints:

[0194] 0<P PV,t <P PV,t,Max (27)

[0195] Where P PV，t represents the photovoltaic field output at time t, P PV，t，Max Indicates the predicted maximum output of the photovoltaic power plant at time t.

[0196] Step S434: the new energy consumption rate constraint:

[0197]

[0198] In the formula, and They represent the actual maximum possible output of the i-th wind turbine and the j-th photovoltaic unit in time period t; h 1 and h 2 These are the minimum new energy consumption rates for wind power and photovoltaic systems respectively.

[0199] Step S44: The system safe and stable operation constraints in step S4 include frequency stability constraints, upper and lower standby constraints, node constraints, bus phase angle constraints, and power flow safety constraints.

[0200] Step S441, the frequency stability constraint, in order to ensure the frequency safety of the system, after disturbances occur on the new energy and load sides, the frequency rise or fall must be within the safe range that the system can withstand, thereby constructing the minimum frequency constraint as follows.

[0201] f a ≤f N +Δf≤f b (29)

[0202] f a Indicates the frequency lower limit, which is usually set to the system frequency lower limit;

[0203] f b Indicates the upper frequency limit value, which is usually set to the upper frequency limit of the system;

[0204] f N Indicates the frequency reference value;

[0205]

[0206] In the formula, △P NE Indicates the active power fluctuation caused by the new energy unit, △P L Indicates the active power fluctuation caused by the load, K G Indicates the power-frequency static characteristic coefficient of a conventional generator set.

[0207] Step S442, the upper and lower reserve constraints. As the uncertainty in the source-load side of a system with a high proportion of renewable energy increases, fluctuations on either side will cause power imbalance. The present invention will consider the system reserve constraints from both the power generation side and the load side.

[0208] On the one hand, due to the volatility and uncertainty of wind and solar power output, the fluctuation of its output will also cause certain disturbances in the power system, so the system needs to reserve rotating reserves to deal with the uncertainty caused by these two. On the other hand, in order to deal with the uncertainty caused by load forecast deviation and load fluctuation on the load side and avoid its impact on power quality, a certain amount of reserve is usually set in the system to smooth out this part of the unbalanced power, thereby ensuring the safe and stable operation of the system. First, the upper and lower reserve requirements of the system are determined by the following formula:

[0209]

[0210] In the formula, κ t U , κ t DThey are the system's upward and downward reserve demand coefficients at time t, which are determined according to the forecast of new energy units and uncontrollable load fluctuations in each period. When the fluctuating load and new energy units account for a relatively large proportion, the upper and lower reserve demand coefficients are correspondingly larger. represents the standard deviation of the uncontrollable load power; δ j W It represents the standard deviation of wind turbine output; Represents the standard deviation of the PV system.

[0211] Furthermore, in addition to considering the volatility of new energy and load side in step S442, the situation where conventional thermal power units are out of operation due to failures should also be considered, and corresponding spare capacity should be reserved. Indicates the output of the largest thermal power unit in the system at maximum load. It represents the maximum capacity unit output in the system at minimum load, so the upper / lower reserve demand of the system can be expressed as:

[0212]

[0213] Therefore, the upper spare constraint of the model is:

[0214]

[0215] The following alternate constraints are:

[0216]

[0217] In the formula, R up is the upper reserve demand of the system, R dn is the system's lower reserve demand; u i，t Indicates the online status of unit i in time period t, 1 means in operation, and 0 means off-grid operation; P i Indicates the maximum / minimum output power of unit i; R t U , They respectively represent the upper and lower backup requirements of the system.

[0218] Step S443: The node constraints include node power balance constraints and node voltage constraints, wherein the node power balance constraint means that the injected power is equal to the outflow power at any node in the power system at any time:

[0219]

[0220] K p，n，i Node-generator correlation matrix element; P nm，t represents the power flow of line nm in period t; D n，t represents the load of node n in the tth period; θn，t Represents the phase angle of node n in the tth period.

[0221] Node voltage constraints:

[0222] V min,n ≤V n ≤V Max,n (38)

[0223] Where V n Represents the voltage of node n, V Max，n Represents the maximum voltage of node n; V min，n Indicates the minimum voltage at node n.

[0224] Step S444: The bus phase angle constraint refers to the value range of the bus phase angle. The phase angle of the balanced bus is taken as 0 as a reference. During normal operation, the phase angles of other buses should be around 0. The specific equation constraint is as follows:

[0225] -π≤θ(s,t)≤π (39)

[0226] Step S445, the power flow safety constraint means that the line power flow should be less than the line power flow allowable limit. Currently, the most widely used method is to use the DC power flow model to solve the line power flow. The specific model is as follows:

[0227]

[0228] Where θ(s, t) represents the phase angle of bus s at time t; x l It represents the reactance parameter of the transmission line; P l is the maximum / minimum power constraint of line l.

[0229] After calculating the transfer distribution factor matrix G of the power flow, the power flow safety constraint is rewritten as:

[0230]

[0231] Step S5: CPLEX is used to solve the constructed mixed integer programming model, and finally the information such as the start and stop of the unit and the output is obtained, and the evaluation indicators such as the start and stop of the conventional thermal power unit and the auxiliary service cost are calculated.

[0232] Scenario 1: New energy penetration rate 15%

[0233] Through the simulation example, when the renewable energy power generation accounts for 15% of the system load, the system safe operation cost is 1,065,584 yuan. Fig.12, 4 units started and stopped during the simulation period, of which a 300MW unit started once, 3 50MW units started 18 times, and shut down 15 times, generating a total of 570,000 yuan in start-up and shutdown costs. In addition, when the new energy penetration rate is 15%, the system backup demand during the simulation period is 27281.63MW, and the backup cost is 272,816.3 yuan. Fig.15 , the system frequency regulation demand is 14851.18MW, and the frequency regulation cost is 222767.7 yuan. The simulation data under the penetration rate of 15% are as follows Figure 6 As shown in Figure 2, the output level and output range of each thermal power unit are as follows: Figure 7 shown.

[0234] Scenario 2: New energy penetration rate 25%

[0235] Through example simulation, when renewable energy power generation accounts for 25% of the system load, the system safe operation cost is 1,458,680.2 yuan. During the simulation period, 13 units were started and stopped, of which 1 300MW unit was started and stopped 3 times, and 12 100MW units were started and stopped 27 times, resulting in a total start-stop fee of 900,000 yuan. In addition, when the renewable energy penetration rate is 25%, the system backup demand during the simulation period is 29,708.02MW, the backup cost is 297,080.2 yuan, the system frequency regulation demand is 17,440MW, and the frequency regulation cost is 261,600 yuan. The simulation data under the penetration rate of 25% are as follows Figure 8 As shown in Figure 2, the output level and output range of each thermal power unit are as follows: Fig. 9 shown.

[0236] Scenario 3: New energy penetration rate 30%

[0237] Through example simulation, when the renewable energy power generation accounts for 30% of the system load, the system safe operation cost is 1940220.55 yuan. During the simulation period, 15 units were started and stopped, of which 1 300MW unit was started and stopped 3 times, 13 100MW units were started and stopped 39 times, and 1 80MW unit was started and stopped 3 times, resulting in a total start-stop fee of 1350000 yuan. In addition, when the renewable energy penetration rate is 30%, the system backup demand during the simulation period is 30921.22MW, the backup cost is 309212.2 yuan, the system frequency regulation demand is 18733.89MW, and the frequency regulation cost is 281008.35 yuan. The simulation data under the penetration rate of 30% are as follows Fig.10 As shown in Figure 2, the output level and output range of each thermal power unit are as follows: Fig.11 shown.

[0238] By comparing the simulation results of scenario 1, scenario 2 and scenario 3, it can be concluded that the system safety operation cost gradually increases with the increase of new energy penetration rate. The system safety operation costs under the three scenarios are 1,065,584 yuan, 1,458,680.2 yuan and 1,940,220.55 yuan respectively. The system safety operation cost of 25% penetration rate increased by 36.89% compared with 15% penetration rate; the system safety operation cost of 30% penetration rate increased by 33.01% compared with 25% penetration rate, and the system safety operation cost of 30% penetration rate increased by 82.08% compared with 15%. The cost situation and increase of each part are shown in Table 4 and Table 5.

[0239] Table 4 Cost of each part

[0240]

[0241] Table 5 Cost increase

[0242]

[0243] Note: In the table, 25%-15% indicates the growth rate of various costs in the scenario of new energy penetration rate of 25% compared with the scenario of new energy penetration rate of 15%. The increase in new energy penetration rate leads to an increase in the total safe operation cost of the system. The system safe operation cost specified in the present invention mainly includes three parts of costs: start-up and shutdown cost of thermal power units, system standby cost and system frequency regulation cost. The specific cost increases of each part are as follows.

[0244] (1) Start-up and shutdown costs of thermal power units

[0245] The increase in the proportion of renewable energy power generation has resulted in more start-up and shutdown costs for thermal power units. The start-up and shutdown costs of units at a penetration rate of 15% are 570,000 yuan, the start-up and shutdown costs of units at a penetration rate of 25% are 900,000 yuan, and the start-up and shutdown costs of units at a penetration rate of 30% are 1,350,000 yuan. The start-up and shutdown costs of new energy with a penetration rate of 30% increased by 780,000 yuan compared to 15%, an increase of 137%. In addition, the increase in renewable energy power generation has also led to a decrease in the average generating hours of thermal power units. The average generating hours of units at a penetration rate of 15% are 50h, the average generating hours of units at a penetration rate of 25% are 46h, and the average generating hours of units at a penetration rate of 30% are 43h. Compared with a penetration rate of 15%, the average generating hours of new energy with a penetration rate of 30% have decreased by 14%, as follows Fig.12 and 13 shown.

[0246] (2) System backup cost

[0247] As the penetration rate of new energy increases, the system's operating reserve demand increases simultaneously. The reserve demand curves under the three new energy penetration rates are as follows: Fig.14As shown. Among them, the standby cost of the penetration rate of 15% is 272816.3 yuan, the standby cost of the penetration rate of 25% is 297080.2 yuan, and the standby cost of the penetration rate of 30% is 309212.2 yuan. Compared with the penetration rate of 15%, the system standby cost of the penetration rate of 30% increased by 36395.9 yuan, an increase of 13.34%.

[0248] (3) System frequency modulation cost

[0249] The increase of fluctuating power sources such as wind power and photovoltaic power leads to more uncertainty in the power balance of the system, so more frequency regulation capacity needs to be reserved to ensure the frequency stability of the system operation. Through the simulation of the example, the frequency regulation cost is 222,767.7 yuan when the penetration rate is 15%, the frequency regulation cost is 261,600 yuan when the penetration rate is 25%, and the frequency regulation cost is 281,008.35 yuan when the penetration rate is 30%. Compared with the penetration rate of 15%, the frequency regulation cost of 30% has increased by 26.14%.

[0250] Step S6: Based on the long-cycle production cost model, design a reasonable cost relief mechanism to distribute the system cost to the power supply side, grid side and user side according to the principle of "whoever benefits, pays" and "whoever causes, pays", so as to encourage all parties to actively participate in the consumption of new energy and improve system flexibility.

[0251] In summary, the present invention solves the problems of the existing system cost research model under the condition of high proportion of new energy access, which focuses on the power generation cost of new energy itself and ignores the system cost of new energy; the various causes of system cost are not considered comprehensively, and the measurement method is difficult to promote and apply on a large scale. The present invention considers multi-dimensional boundary conditions such as power supply structure, grid parameters, and new energy penetration rate, and considers constraints such as conventional unit constraints, new energy unit constraints, system safety and stability constraints, and power balance, and constructs a long-term production cost simulation model for a high proportion of new energy access system. It can quantitatively analyze the incremental system cost caused by high proportion of new energy access; calculate the unit start-up and shutdown costs, system standby costs, system frequency regulation costs, and system safe operation costs under different new energy penetration rates, and analyze the growth of each cost under different new energy penetration rates accordingly.

[0252] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for constructing a long-term production cost model for a new energy access system, characterized in that: The steps include: S1. Obtain load data, new energy output data and cost data, comprehensively consider the continuity of long-term simulation, and set the simulation model cycle to 60-84 hours; S2. Set different scenarios for new energy penetration ratio; S3. Establish an objective function that can minimize system costs under a high consumption rate of new energy; S4. According to the system load, conventional unit parameters, network parameter conditions, combined with power balance constraints, conventional thermal power unit constraints, new energy unit constraints, and system safe and stable operation constraints, a long-term time series production simulation model of the power system is constructed; S5. Use CPLEX to solve the constructed long-period time-series production simulation model of the power system to obtain the start-up and shutdown and output information of the units, and calculate the evaluation indicators of the start-up and shutdown and auxiliary service costs of conventional thermal power units based on this; S6. Based on the long-cycle production cost model, a reasonable cost relief mechanism is designed to distribute the system cost to the power supply side, the grid side and the user side according to the principle of "whoever benefits, pays" and "whoever causes, pays", so as to encourage all parties to actively participate in the consumption of new energy and improve the flexibility of the system.

2. According to claim 1, a method for constructing a long-term production cost model for a new energy access system is characterized in that: In step S3, the objective function includes a first sub-function F1 and a second sub-function F2, wherein F1 represents the power generation cost of the unit combination, and F2 represents the cost of auxiliary services provided by the conventional thermal power unit, thereby obtaining the system minimum operating cost F under different new energy penetration rates: F=F1+F2 (1) Among them, the expression of the first sub-function F1 is: In the formula, f1 represents the operating cost of thermal power units, f2 represents the operating cost of wind power units, and f3 represents the operating cost of photovoltaic units; Represents the electricity price of thermal power units, P i，m，t represents the output of thermal power unit i at time t during period m; c i U , and They represent the startup, shutdown and no-load operation costs of thermal power units, v i，t represents the start-up action of thermal power unit i at time t, i is a 0-1 variable; w i，t Indicates the shutdown action of thermal power unit i at time t, i is a 0-1 variable; u i，t Indicates whether it is in no-load running state, i is a 0-1 variable; c j W represents the bid of wind power plant station j, represents the price offered by photovoltaic plant k, P j，t represents the output power of the wind turbine in period j, P k，t Represents the output power of the photovoltaic unit in time period t; The expression of the second subfunction F2 is as follows: In the formula, f1 is the cost of providing upper and lower reserve for conventional thermal power units, f2 is the cost of providing deep peak load auxiliary services for thermal power units, and f3 is the cost of providing primary and secondary frequency regulation for thermal power units. represents the upper reserve quotation of thermal power unit i at time t; represents the next reserve price of thermal power unit i at time t, i represents the upper reserve power provided by thermal power unit i; represents the lower reserve power provided by thermal power unit i; c Indicates the deep peak load regulation quotation of thermal power units. Indicates the deep peak-shaving capacity of the thermal power unit that won the bid; and Respectively represent the primary / secondary frequency regulation mileage price of thermal power units, and Respectively represent the price of primary / secondary frequency regulation capacity of thermal power units, and Respectively represent the primary / secondary frequency regulation performance indicators of thermal power units.

3. The method for constructing a long-term production cost model for a new energy access system according to claim 2, characterized in that: In step S4, the power balance constraint means that the output of new energy and conventional units is equal to the load size, and the power of the system needs to be balanced at every moment. The power balance constraint is described as: Where N G Indicates the number of thermal power units; N L Indicates the number of load nodes; N W Indicates the number of wind power plants; N S Indicates the number of photovoltaic plants; P i，t represents the output power of thermal power unit i in period t, P k，t represents the output power of the wind turbine in period k, P j，t Represents the output power of the photovoltaic unit in time period t.

4. The method for constructing a long-term production cost model for a new energy access system according to claim 3, characterized in that: In step S4, the conventional thermal power unit constraints include: unit power constraints, unit ramp constraints and unit start and stop time constraints; The unit power constraint means that the output level of the generator unit should be between its minimum technical output and maximum technical output: In the formula, u i，t Indicates the online status of unit i in time period t, 1 means in operation, and 0 means off-grid operation; P i and Respectively represent the maximum output power and minimum output power of thermal power unit i; P i，t Represents the output power of thermal power unit i in period t; The unit climbing constraint means that the output change value of the unit in two adjacent time periods should be within the allowable range of its up and down climbing: In the formula, and R represent the maximum output rate and maximum output rate of the unit when climbing up and down respectively; P i，t represents the output power of unit i in time period t; P i，t represents the output power of unit i in time period t; P i，t-1 represents the output of unit i in t-1; The unit start and stop time constraint means that the unit must be guaranteed to run for a period of time when it is started. i At this time TU i The internal unit can only be turned on once; similarly, when the unit is turned off, it must be stopped for a period of time. i , at this time TD i The internal unit can only be turned on once: The relationship between the three Boolean variables is as follows: u i,t -u i,t-1 =v i,t -w i,t (9) In the formula, v i，t Indicates the start-up action of unit i in time period t, 1 means start-up, 0 means other; u i，t represents the online status of unit i in time period t, 1 means in operation, 0 means off-grid operation; w i，t Indicates the shutdown action of unit i in time period t, 1 means shutdown, and 0 means others.

5. The method for constructing a long-term production cost model for a new energy access system according to claim 4, characterized in that: In step S4, the new energy unit constraints include: wind power constraints, photovoltaic power constraints, new energy output constraints and new energy consumption rate constraints.

6. A method for constructing a long-term production cost model for a new energy access system according to claim 5, characterized in that: In step S4, the wind power constraint means that the wind power output is mainly affected by the wind speed. The wind speed distribution is described by a two-parameter Weibull distribution model. The probability density function expression of the two-parameter Weibull distribution model is as follows: In the formula, v represents wind speed; k represents shape parameter, and c represents size parameter. The specific expression is: In the formula, μ w , σ w are the mean and standard deviation of wind speed respectively; Furthermore, the probability cumulative density function of the two-parameter Weibull distribution model is: This gives the wind speed: In the formula, r is a random variable uniformly distributed between [0, 1], which can be randomly generated by Monte Carlo simulation; Among them, the characteristic curve between wind speed and wind power can be expressed by the following piecewise function: In the formula, Indicates the active power output by the fan; P G，w，N Indicates the rated output of the fan; v in 、v N 、v out They are cut-in wind speed, rated wind speed, and cut-out wind speed respectively.

7. A method for constructing a long-term production cost model for a new energy access system according to claim 6, characterized in that: In step S4, the photovoltaic power constraint means that the photovoltaic output level is mainly related to the light radiation of the location; the light radiation itself has a certain randomness and generally obeys the beta distribution, and its probability density expression is as follows: In the formula, q and q max are the actual light radiation intensity and the maximum light radiation intensity, respectively; Γ(·) is the gamma function; α and β are the shape parameters of the beta distribution, which can be obtained by the following equations (17) and (18), respectively: In the formula, μ pv With σ pv are the mean and standard deviation of solar radiation respectively; Among them, the gamma function is an extension of the factorial function, which is defined in the real number field as: Using formula (20), the output power of photovoltaic cells under any light intensity can be obtained: Where P G，pv (q) represents the output value of photovoltaic power generation; P G，pv，N is the rated output power of the photovoltaic cell; s is the temperature coefficient; q N and T pv，N are the light radiation intensity and temperature under standard conditions, which are 100W / m 2 and 25℃; T pv is the actual temperature of the photovoltaic cell, which is calculated by formula (21): Where, T en Indicates the ambient temperature.

8. The method for constructing a long-term production cost model for a new energy access system according to claim 7, characterized in that: In step S4, the new energy output constraint includes: wind power output constraint and photovoltaic output constraint, and the wind power output constraint is: 0<P W,t <P W,t,Max (22) Where P w，t represents the wind farm output at time t, P w，t，Max represents the predicted maximum output of the wind power plant at time t; The photovoltaic output constraint is: 0<P PV,t <P PV,t,Max (23) Where P PV，t represents the photovoltaic field output at time t, P PV，t，Max Indicates the predicted maximum output of the photovoltaic power plant at time t.

9. The method for constructing a long-term production cost model for a new energy access system according to claim 8, characterized in that: In step S4, the new energy consumption rate constraint is: In the formula, and They represent the actual maximum possible output of the i-th wind turbine and the j-th photovoltaic unit in period t respectively; h1 and h2 are the minimum new energy absorption rates of wind power and photovoltaic systems respectively.

10. A method for constructing a long-term production cost model for a new energy access system according to claim 9, characterized in that: In step S4, the system safe and stable operation constraints include: frequency stability constraints, upper and lower reserve constraints, node constraints, bus phase angle constraints and power flow safety constraints; In the frequency stability constraint: to ensure the frequency safety of the system, after disturbances occur on the new energy and load sides, the frequency rise or fall must be within the safe range that the system can withstand, so the minimum frequency constraint can be constructed as follows. f a ≤f N +Δf≤f b (25) In the formula, f a Indicates the lower frequency limit, f b Indicates the upper frequency limit, f N Indicates the frequency reference value; In the formula, △P NE Indicates the active power fluctuation caused by the new energy unit, △P L Indicates the active power fluctuation caused by the load, K G Indicates the power-frequency static characteristic coefficient of a conventional generator set; In the upper and lower reserve constraints, the upper and lower reserve requirements of the system are determined by the following formula: In the formula, κ t U and κ t D They are the upward and downward adjustment coefficients of the system reserve demand at time t, which are determined according to the forecast of the fluctuation of new energy units and uncontrollable loads in each period; represents the standard deviation of the uncontrollable load power; It represents the standard deviation of wind turbine output; Represents the standard deviation of the photovoltaic unit; Consider the situation where conventional thermal power units are out of operation due to failures, and reserve corresponding spare capacity. Indicates the output of the largest thermal power unit in the system at maximum load. It represents the maximum capacity unit output in the system at the minimum load, so the upper and lower reserve requirements of the system can be expressed as: R up =Max{R t U ,R i G,Max } (29) Therefore, the upper spare constraint of the model is: The following alternate constraints are: In the formula, R up is the upper reserve demand of the system, R dn is the system's lower reserve demand; u i，t Indicates the online status of unit i in time period t, 1 means in operation, and 0 means off-grid operation; and P i Respectively represent the maximum output power and minimum output power of unit i; R t U and They represent the upper standby demand and lower standby demand required by the system respectively; The node constraints include node power balance constraints and node voltage constraints, wherein the node power balance constraint means that the injected power is equal to the outflow power at any node in the power system at any time: In the formula, K p，n，i represents the node-generator association matrix element; P nm，t represents the power flow of line nm in period t; D n，t represents the load of node n in the tth period; θ n，t represents the phase angle of node n in the tth period; The node voltage constraint is: In min,n ≤V n ≤V Max,n (34) Where V n Represents the voltage of node n, V Max，n Represents the maximum voltage of node n; V min，n represents the minimum voltage value of node n; The bus phase angle constraint refers to the value range of the bus phase angle. The phase angle of the balanced bus is taken as 0 as a reference. During normal operation, the phase angles of other buses should be close to 0. The specific equation constraint is as follows: -π≤θ(s,t)≤π (35) The power flow safety constraint means that the line power flow should be less than the allowable limit of the line power flow. The line power flow is solved by the DC power flow model. The DC power flow model is as follows: Where θ(s, t) represents the phase angle of bus s at time t; x l It represents the reactance parameter of the transmission line; and P i are the maximum power constraint and the minimum power constraint of line l respectively; After calculating the power flow transfer distribution factor matrix G, the power flow safety constraint is rewritten as

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