A new energy consumption capacity evaluation method based on timing state

Abstract

The application discloses a new energy consumption capacity evaluation method based on time sequence state, takes the thermal power unit output as a variable, and takes the maximum new energy output consumption as a target, first obtains the time sequence state of part of elements in a power system, including the time sequence output of load, lines, wind power and photovoltaic power generation, arranges the time sequence output of the hydropower unit according to the electricity quantity and output size of the hydropower unit, and then iteratively solves the starting sequence of the thermal power unit after arranging the unit output to realize the maximum new energy consumption, finds whether there is an optimal solution of the receiving capacity constraint analysis model meeting the multi-objective optimization, if yes, the system can receive the existing new energy power generation, if not, takes the maximum new energy consumption as a target function, solves the multi-objective optimization model, obtains the new energy power generation that can be consumed by the system, analyzes the reason why the new energy cannot be consumed, finally calculates and counts a series of new energy consumption capacity evaluation indexes, and outputs the calculation result.

Description

Technical Field

[0001] This invention relates to the field of multi-energy complementary coordinated operation technology of power systems, and specifically to a method for assessing the renewable energy absorption capacity based on time-series status. Background Technology

[0002] With the continuous grid connection of new energy sources, represented by wind and solar power, my country has formed a new pattern of multi-source coordinated and complementary power generation, including wind, solar, hydro, and thermal power. However, constrained by natural factors, wind and solar power generation exhibit significant uncertainties and uncontrollability. The construction of redundant hydropower or thermal power units to meet the reliability requirements of the power supply system has resulted in substantial resource waste and certain environmental problems. Therefore, under the new circumstances of large-scale integration of new energy sources into the power system, accurately, quickly, and effectively assessing the capacity of multi-source coordinated power generation systems to accommodate new energy sources is of significant theoretical and practical importance for promoting the efficient utilization of new energy sources and preventing over-investment and resource waste in the power system. Summary of the Invention

[0003] This invention aims to at least partially solve some of the technical problems in the prior art. Based on the inventor's understanding of the following facts and issues, wind and solar energy have become renewable energy sources that can be supported for development and utilization. Wind and solar energy have advantages such as wide energy distribution, large reserves, and short infrastructure construction cycles. However, the output of wind and solar power is highly susceptible to weather conditions and has strong uncertainty. The high proportion of wind and solar power connected to the grid also poses a significant challenge to the stable operation of the power grid. Therefore, effectively assessing the absorption capacity of new energy sources, and thus determining the power generation and reserve requirements of thermal power, hydropower, pumped storage, etc., can save a significant amount of traditional energy input and address potential new energy power generation gaps.

[0004] In view of this, the present invention discloses a method for evaluating the renewable energy absorption capacity based on time-series status, in order to solve the technical problems in related technologies.

[0005] The technical solution proposed in this invention is as follows:

[0006] This invention provides a method for assessing the renewable energy absorption capacity based on time-series status, comprising: taking the output of thermal power units as a variable and the maximum absorption of renewable energy as the objective; firstly, obtaining the time-series status of some components in the power system, including the time-series output of loads, lines, wind power, and photovoltaic power generation; arranging the time-series output of hydropower units according to their power generation and output; after arranging the unit output to achieve maximum renewable energy absorption, iteratively solving the start-up sequence of thermal power units to find whether there is an optimal solution to the multi-objective optimization constraint analysis model for absorption capacity; if there is, the system is considered to be able to absorb the existing renewable energy generation; if there is no optimal solution, the multi-objective optimization model is solved with the maximum renewable energy absorption as the objective function to obtain the renewable energy generation that the system can absorb, and analyzing the reasons why renewable energy cannot be absorbed; finally, calculating and statistically analyzing a series of renewable energy absorption capacity assessment indicators, and outputting the calculation results.

[0007] Optionally, the timing state includes:

[0008] (1) Output power time curves of wind power and photovoltaic power generation;

[0009] (2) Output regulation capability curves of conventional generator sets at different time periods;

[0010] (3) Power load curves with different response characteristics.

[0011] Optionally, the power generation priority order of the generating unit is:

[0012] (1) Wind power, photovoltaic power and hydropower units without regulation capacity;

[0013] (2) Hydropower units with regulating capabilities;

[0014] (3) Gas turbine unit;

[0015] (4) Conventional coal-fired power units.

[0016] Optionally, the objective function of the multi-objective optimization acceptance capacity constraint analysis model is:

[0017] minC(P f )

[0018] In the formula: P f For the output of all thermal power units; C(P f The operating cost of thermal power units;

[0019] The constraints of the objective function include:

[0020] (1) Power balance constraints

[0021]

[0022] In the formula: P f P represents the output value of thermal power plants. H P represents the hydropower output value. R Represented as the output value of new energy sources; P represents the wind power output value. L Represented as load value; P tran This refers to surplus renewable energy power being exported.

[0023] (2) Output constraints of thermal power units

[0024] P down ≤P f ≤P up

[0025] In the formula: P down P represents the minimum technical output of a thermal power unit. up This is expressed as rated output.

[0026] (3) Climbing constraints of thermal power units

[0027] P f(t) -P f(t-1) >-R down Δt

[0028] P f(t) -P f(t-1) <R up Δt

[0029] In the formula: R down R represents the generator's downhill gradient. up This indicates the generator's uphill gradient.

[0030] (4) Power transmission constraints

[0031] P tran ≤P line

[0032] In the formula: P tran This indicates surplus renewable energy power being exported; P line This represents the maximum power of the transmission line.

[0033] (5) Constraints on the output cut-off of new energy units:

[0034]

[0035] In the formula: P represents the assessment value of the optimal acceptance capacity of new energy sources during the corresponding time period. R，t This indicates the maximum capacity of new energy sources during the corresponding time period.

[0036] Optionally, a series of new energy consumption capacity assessment indicators are calculated and statistically analyzed, including:

[0037] Electricity consumption by new energy sources:

[0038]

[0039] In the formula: n represents the number of time periods; This represents the average power output of new energy sources within time period i; This represents the average load during time period i; This represents the minimum output of the system during time period i; Indicates the system's peak-shaving margin;

[0040] When the output of new energy sources exceeds the system load, the excess output of new energy sources is discarded.

[0041]

[0042] In the formula: Q aban This indicates the excess output value of new energy sources.

[0043] Optionally, probabilistic assessment indicators for renewable energy absorption capacity include:

[0044] (1) Expected when peak shaving is insufficient:

[0045]

[0046] In the formula: LHPR represents the expected value when peak shaving is insufficient; T represents the minimum technical output of all units in wind power scenario s and photovoltaic scenario n; year P represents the total number of moments contained in a horizontal year. Ws P represents the wind power output in scenario s; Vn P represents the photovoltaic power in scenario n; L (t) represents the load power at time t; K W Represents a typical wind power scenario; K V This represents a typical photovoltaic scenario;

[0047] (2) Probability of insufficient peak shaving:

[0048]

[0049] In the formula: LPPR represents the probability of insufficient peak shaving;

[0050] (3) Expected peak-shaving depth of thermal power units:

[0051]

[0052] In the formula: D year Indicates the number of days in a horizontal year; P represents the power output of all thermal power units at time t on day d, under wind power scenario s and photovoltaic scenario n. AGmax (d) represents the operating capacity of the thermal power unit on day d;

[0053] (4) Expected power generation from new energy sources:

[0054]

[0055] In the formula: EENP represents the expected power generation from new energy sources; This represents the wind power output at time t in a horizontal year, under wind power scenario s and photovoltaic scenario n. This represents the photovoltaic power at time t in the horizontal year, under wind power scenario s and photovoltaic scenario n.

[0056] (5) Expected utilization hours of new energy power generation:

[0057]

[0058] In the formula: C W Indicates wind power installed capacity; C V Indicates photovoltaic installed capacity; C W +C V This indicates the total installed capacity of new energy sources;

[0059] (6) Expected amount of electricity to be abandoned by new energy sources:

[0060]

[0061] In the formula: AEEN represents the expected amount of electricity discarded by new energy sources; This represents the amount of wind power curtailment at time t in a horizontal year, under wind power scenario s and photovoltaic scenario n. This represents the amount of abandoned photovoltaic power at time t in a horizontal year, under wind power scenario s and photovoltaic scenario n.

[0062] (7) Curtailment rate of renewable energy:

[0063]

[0064] In the formula: AERN represents the renewable energy curtailment rate;

[0065] (8) Upper limit of daily peak-shaving depth for thermal power units:

[0066]

[0067] In the formula: PDMT represents the maximum daily peak shaving depth of thermal power units;

[0068] (9) Maximum curtailment of renewable energy:

[0069]

[0070] In the formula: APMN represents the maximum amount of electricity abandoned by new energy sources. Attached Figure Description

[0071] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0072] Figure 1 This is a schematic diagram illustrating the implementation process of a time-series-based method for assessing the renewable energy absorption capacity, according to an embodiment of this disclosure. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] The technical solutions of the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] This invention provides a method for assessing the renewable energy absorption capacity based on time-series status, such as... Figure 1 As shown, the method includes:

[0076] Using thermal power unit output as the variable and maximizing the absorption of renewable energy as the objective, this study first obtains the time-series status of some components in the power system, including the time-series output of loads, lines, wind power, and photovoltaic power generation. Based on the power generation and output of hydropower units, the time-series output of hydropower units is arranged. After arranging unit output to achieve maximum renewable energy absorption, the thermal power unit start-up sequence is iteratively solved to find the optimal solution of a multi-objective optimization model that satisfies the capacity constraint analysis. If an optimal solution exists, the system is considered capable of absorbing existing renewable energy generation. If no optimal solution exists, the multi-objective optimization model is solved with the maximum renewable energy absorption as the objective function to obtain the renewable energy generation that the system can absorb. The reasons for the inability to absorb renewable energy are analyzed. Finally, a series of renewable energy absorption capacity evaluation indicators are calculated and statistically analyzed, and the calculation results are output.

[0077] Specifically, the time-series output of new energy sources is first obtained, along with time-series data on loads and power transmission via tie lines in the power system.

[0078] Then:

[0079] (1) Arrange the output of hydropower units in accordance with the principle of making full use of hydropower resources;

[0080] (2) Set the initial output values ​​of the thermal power unit start-up sequence;

[0081] (3) Establish a multi-objective iterative optimization method to assess the system’s capacity to accept new energy power generation. If the model’s calculation results can obtain the optimal thermal power unit output solution and the amount of new energy power generation to be cut off is zero, then the system is considered to be able to accept the existing new energy power generation and proceed to step (5). Otherwise, proceed to step (4).

[0082] (4) Identify the factors limiting the acceptance of new energy sources, including line power flow constraints, ramp rate constraints, peak shaving capacity constraints, etc. The method of identification is to eliminate the corresponding constraint equations one by one, and then solve the constraint analysis of the acceptance capacity model. If the model has an optimal solution after removing a certain constraint, it can be determined that this constraint is the main constraint on the acceptance capacity of new energy sources under the current system state, and the output of new energy generation is reduced proportionally until the optimal solution of the multi-objective iteration is obtained again, which is taken as the maximum new energy consumption of the system.

[0083] (5) Statistically analyze and output a series of calculated values ​​of indicators for evaluating the system’s new energy acceptance capacity.

[0084] In an optional embodiment, the timing states described above include:

[0085] (1) Output power time curves of wind power and photovoltaic power generation, focusing on the impact of output fluctuations on power in each period;

[0086] (2) Daily power generation start-up and shutdown plan for conventional generating units other than thermal power, with a focus on the output regulation capacity of each conventional generating unit at different times;

[0087] (3) Electricity load curves with different response characteristics, with a focus on load capacity that can be transferred, adjusted, interrupted and reduced.

[0088] In an optional embodiment, the power output of the generating units is arranged to maximize the absorption of new energy sources, and the power generation priority order of the generating units is as follows:

[0089] 1) Wind power, photovoltaic power, and hydropower units without regulation capacity shall be given priority in power generation;

[0090] 2) Hydropower units with regulation capabilities have good dispatchability and flexible working positions, which can make up for the uncertainty of new energy power generation output. By generating electricity in the second order, they can make full use of their capacity benefits and power benefits.

[0091] 3) Gas turbine units are flexible in starting and stopping, making them ideal peak-shaving power sources. They can improve the absorption of new energy sources and generate electricity in the third order of power generation.

[0092] 4) Conventional coal-fired power units consume a lot of energy, cause a lot of pollution, and are the last in the power generation sequence; the start-up and shutdown costs of conventional coal-fired power units are very high, and in actual dispatching, conventional coal-fired power units are generally not considered to participate in intraday start-up and shutdown peak shaving; for conventional coal-fired power units, there is only one start-up and shutdown state within an operating dispatching cycle T, that is, only one start-up and shutdown plan for conventional coal-fired power units is formulated in one operating dispatching cycle.

[0093] In an optional embodiment, the objective function of the multi-objective optimization capacity constraint analysis model is to minimize the system operating cost and the renewable energy output. Since the set values ​​for hydropower output, load, tie line transmission, thermal power unit start-up, and renewable energy output are already given, the model only needs to consider minimizing the operating cost of the thermal power unit. The objective function is as follows:

[0094] minC(P f )

[0095] In the formula: P f For the output of all thermal power units; C(P f The operating cost of thermal power units;

[0096] The specific constraints of the objective function include:

[0097] (1) Power balance constraints

[0098]

[0099] In the formula: P f P represents the output value of thermal power plants. HP represents the hydropower output value. R Represented as the output value of new energy sources; P represents the wind power output value. e P represents the output value of other types of power generation; t This is expressed as external power; P L Represented as load value; P tran This refers to surplus renewable energy power being exported.

[0100] (2) Output constraints of thermal power units:

[0101] While fulfilling the system's power generation tasks, thermal power generating units also need to undertake the system's peak-shaving tasks. Based on the predicted maximum load and considering a certain reserve capacity, the start-up mode of the thermal power units is determined, and the minimum allowable technical output of each thermal power unit is determined through comprehensive analysis of thermal power units of different capacities. Considering the ramp-up constraints of each thermal power unit, the output range of each thermal power unit is determined as follows, under the premise of meeting load demand:

[0102] P down ≤P f ≤P up

[0103] In the formula: P down P represents the minimum technical output of a thermal power unit. up This is expressed as rated output.

[0104] (3) Climbing constraints of thermal power units:

[0105] The output of new energy sources can fluctuate significantly within a short period, while the output of thermal power units, constrained by the ramp rate, cannot quickly keep up with these changes, leading to phenomena such as wind and solar power curtailment. The ramp rate constraint for thermal power units is determined by the following formula:

[0106] P f(t) -P f(t-1) >-R down Δt

[0107] P f(t) -P f(t-1) <R up Δt

[0108] In the formula: R down R represents the generator's downhill gradient. up This indicates the generator's uphill gradient.

[0109] (4) Power transmission constraints:

[0110] When a region has sufficient renewable energy output, it can consider transmitting the surplus electricity to other regions. The power transmission of renewable energy is subject to the power constraints of the transmission lines as shown in the following formula:

[0111] P tran ≤P line

[0112] In the formula: P tran This indicates surplus renewable energy power being exported; P line This represents the maximum power of the transmission line.

[0113] (5) Constraints on the output cut-off of new energy units:

[0114]

[0115] In the formula: P represents the assessment value of the optimal acceptance capacity of new energy sources during the corresponding time period. R，t This indicates the maximum capacity of new energy sources during the corresponding time period.

[0116] Solve the above model, based on the objective function value and the output cut-off amount of the new energy units. It is possible to assess the optimal acceptance capacity of new energy sources during the corresponding time period: if The optimal solution of the model is the output and corresponding cost of each thermal power unit when the system can accommodate all new energy sources. Because renewable energy cannot be fully absorbed at this time, and some of its output is cut off, thermal power plants will have to bear a corresponding increase in load. Therefore, the optimal solution in the model is the reduction of renewable energy output by P. R，t When the system accepts other new energy sources, the output and corresponding operating costs of each thermal power unit are considered.

[0117] In an optional embodiment, the assessment indicators and calculation process for new energy absorption capacity include:

[0118] The amount of electricity consumed by renewable energy can be expressed as the sum of the renewable energy output within the peak-shaving margin, using the following formula:

[0119]

[0120] In the formula: n represents the number of time periods; This represents the average power output of new energy sources within time period i; This represents the average load during time period i; This represents the minimum output of the system during time period i; Indicates the system's peak-shaving margin;

[0121] When the output of new energy sources exceeds the system load, the excess output will be discarded to maintain power balance constraints. The discarded output of new energy sources is represented as follows:

[0122]

[0123] In the formula: Q aban This indicates the excess output value of new energy sources.

[0124] In one optional embodiment, the probabilistic assessment indicators of renewable energy absorption capacity include:

[0125] The Expectation of Lacking Hours of Peaking Regulation (LHPR) represents the expected number of hours of insufficient peaking regulation that will occur in the system's annual capacity under all renewable energy output scenarios.

[0126]

[0127] In the formula: LHPR represents the expected value when peak shaving is insufficient; T represents the minimum technical output of all units in wind power scenario s and photovoltaic scenario n; year P represents the total number of moments contained in a horizontal year. Ws P represents the wind power output in scenario s; Vn P represents the photovoltaic power in scenario n; L (t) represents the load power at time t; K W Indicates a typical wind power scenario; K V This represents a typical photovoltaic scenario;

[0128] (2) Lacking Probability of Peaking Regulation (LPPR):

[0129]

[0130] In the formula: LPPR represents the probability of insufficient peak shaving;

[0131] (3) Peak Regulating Depth Expectation of Thermal Power (PDET) characterizes the average peak regulating depth of thermal power on each day of the system's horizontal year under all new energy power generation scenarios:

[0132]

[0133] In the formula: D year Indicates the number of days in a horizontal year; P represents the power output of all thermal power units at time t on day d, under wind power scenario s and photovoltaic scenario n. AGmax (d) represents the operating capacity of the thermal power unit on day d;

[0134] (4) Generation Energy Expectation of New Energy Power (EENP): This represents the expected annual power generation of new energy sources under all new energy output scenarios.

[0135]

[0136] In the formula: EENP represents the expected power generation from new energy sources; This represents the wind power output at time t in a horizontal year, under wind power scenario s and photovoltaic scenario n. This represents the photovoltaic power at time t in the horizontal year, under wind power scenario s and photovoltaic scenario n.

[0137] (5) Generation Hours Expectation of New Energy Power (HENP), representing the ratio of the expected annual power generation of new energy sources to the installed capacity of new energy sources under all new energy output scenarios:

[0138]

[0139] In the formula: C W Indicates wind power installed capacity; C V Indicates photovoltaic installed capacity; C W +C V This indicates the total installed capacity of new energy sources;

[0140] Similarly, the expected number of generating hours for wind power / solar power / hydropower / thermal power can be calculated.

[0141] (6) Abandoned Energy Expectation of New Energy Power (AEEN):

[0142]

[0143] In the formula: AEEN represents the expected amount of electricity discarded by new energy sources; This represents the amount of wind power curtailment at time t in a horizontal year, under wind power scenario s and photovoltaic scenario n. This represents the amount of abandoned photovoltaic power at time t in a horizontal year, under wind power scenario s and photovoltaic scenario n.

[0144] (7) Abandoned Energy Rate of New Energy Power (AERN), which represents the ratio of the expected amount of abandoned new energy power to the expected amount of available new energy power generation:

[0145]

[0146] In the formula: AERN represents the renewable energy curtailment rate;

[0147] (8) The upper limit of daily peak-shaving depth of thermal power units (PDMT) represents the maximum daily peak-shaving depth of thermal power units in a horizontal year under all new energy power generation scenarios, reflecting the maximum contribution of thermal power units to the peak-shaving of the power system:

[0148]

[0149] In the formula: PDMT represents the maximum daily peak shaving depth of thermal power units;

[0150] 10) Abandoned Power Maximum of New Energy Power (APMN): This represents the maximum value of abandoned power at any time during the year for all new energy generation scenarios. It reflects the most severe power abandonment situation for new energy due to insufficient system peak-shaving capacity.

[0151]

[0152] In the formula: APMN represents the maximum amount of electricity abandoned by new energy sources.

[0153] The aforementioned evaluation indicators include both expected values ​​and extreme cases under various power generation scenarios. After large-scale grid connection of new energy sources, they can achieve a comprehensive evaluation of the system's peak-shaving operation characteristics and new energy absorption capacity.

Claims

1. A method for assessing the renewable energy absorption capacity based on time-series status, characterized in that, include: Using thermal power unit output as a variable and maximizing the absorption of renewable energy as the objective, this approach first obtains the time-series status of some components in the power system, including loads, lines, wind power, and photovoltaic power generation. Based on the power generation and output of hydropower units, the time-series output of these units is arranged. After arranging unit output to achieve maximum renewable energy absorption, the thermal power unit start-up sequence is iteratively solved to find an optimal solution for a multi-objective optimization model that satisfies the capacity constraint analysis. If an optimal solution exists, the system is considered capable of absorbing existing renewable energy generation; otherwise, the optimal solution is not found. Using the maximum renewable energy absorption capacity as the objective function, a multi-objective optimization model is solved to obtain the renewable energy power generation that the system can absorb. The reasons for the inability to absorb renewable energy are analyzed. Finally, a series of renewable energy absorption capacity assessment indicators are calculated and statistically analyzed, and the calculation results are output. Among them, the series of renewable energy absorption capacity assessment indicators include: expected peak shaving hours, probability of insufficient peak shaving, expected peak shaving depth of thermal power units, expected renewable energy power generation, expected renewable energy power generation utilization hours, expected renewable energy curtailment, renewable energy curtailment rate, upper limit of daily peak shaving depth of thermal power units, and maximum renewable energy curtailment. The analysis of the reasons why renewable energy cannot be absorbed includes: identifying the factors that limit renewable energy absorption, including line power flow constraints, ramp rate constraints, and peak shaving capacity constraints; the method of identification is to eliminate the corresponding constraint equations in turn, and then solve the constraint analysis of the absorption capacity model. If the model has an optimal solution after removing a certain constraint, then this constraint is determined to be the main constraint on the renewable energy absorption capacity under the current system state, and the output of renewable energy generation is reduced proportionally until the optimal solution of multi-objective iteration is obtained again, which is taken as the maximum renewable energy absorption capacity of the system.

2. The method for assessing the capacity for renewable energy absorption according to claim 1, characterized in that, The timing states include: (1) Output power time curves of wind power and photovoltaic power generation; (2) Output regulation capability curves of conventional generator sets at different time periods; (3) Power load curves with different response characteristics.

3. The method for assessing the capacity for renewable energy absorption according to claim 1, characterized in that, The power generation priority order of the generating units is: (1) Wind power, photovoltaic power generation and hydropower units without regulation capacity; (2) Hydropower units with regulating capabilities; (3) Gas turbine unit; (4) Conventional coal-fired power units.

4. The method for assessing the capacity for renewable energy absorption according to claim 1, characterized in that, The objective function of the multi-objective optimization model for accepting capacity constraints is: In the formula: For the output of all thermal power units; The operating costs of thermal power units; The constraints of the objective function include: (1) Power balance constraints In the formula: Expressed as thermal power output value; Expressed as hydropower output value; Represented as the output value of new energy sources; Expressed as wind power output value; Represented as load value; This refers to surplus renewable energy power being exported. (2) Output constraints of thermal power units In the formula: This represents the minimum technical output of a thermal power unit; This is expressed as rated output. (3) Climbing constraints of thermal power units In the formula: This indicates the generator's downhill gradient. This indicates the generator's uphill gradient. (4) Power transmission constraints In the formula: This refers to surplus renewable energy power being exported. This represents the maximum power of the transmission line. (5) Constraints on the output cut-off of new energy units: In the formula: This represents the assessment value indicating the optimal acceptance capacity of new energy sources for the corresponding time period; This indicates the maximum capacity of new energy sources during the corresponding time period.

5. The method for assessing the new energy absorption capacity according to claim 1, characterized in that, Calculate and statistically analyze a series of indicators for assessing the capacity for renewable energy absorption, including: Electricity consumption by new energy sources: In the formula: n represents the number of time periods; Indicates time period The average output value of new energy sources within the region; Indicates time period Average load within; Indicates the system during the time period Minimum output at that time; Indicates the system's peak-shaving margin; When the output of new energy sources exceeds the system load, the excess output of new energy sources is discarded. In the formula: This indicates the excess output value of new energy sources.

6. The method for assessing the capacity for renewable energy absorption according to claim 5, characterized in that, Probabilistic assessment indicators for renewable energy absorption capacity include: (1) Expected when peak shaving is insufficient: In the formula: This indicates the expected peak shaving time; Indicating wind power scenario Photovoltaic scenarios Below, the minimum technical output of all units; This indicates the total number of moments included in a horizontal year; Representing a scene Downstream wind power; Representing a scene Lower photovoltaic power; express Real-time load power; This represents a typical wind power scenario; This represents a typical photovoltaic scenario; (2) Probability of insufficient peak shaving: In the formula: Indicates the probability of insufficient peak shaving; (3) Expected peak-shaving depth of thermal power units: In the formula: Indicates the number of days in a horizontal year; In the context of wind power Photovoltaic scenarios Next, all thermal power units day Power generation output at all times; Indicates the first Operating capacity of Japanese thermal power units; (4) Expected power generation from new energy sources: In the formula: This indicates the expected power generation from new energy sources; Indicating wind power scenario Photovoltaic scenarios Below, horizontal year Wind power output at any time; Indicating wind power scenario Photovoltaic scenarios Below, horizontal year Photovoltaic power at any time; (5) Expected utilization hours of new energy power generation: In the formula: Indicates wind power installed capacity; Indicates photovoltaic installed capacity; This indicates the total installed capacity of new energy sources; (6) Expected amount of electricity to be abandoned by new energy sources: In the formula: This indicates the expected amount of electricity to be sacrificed from new energy sources; Indicating wind power scenario Photovoltaic scenarios Below, horizontal year Wind power curtailment at all times; Indicating wind power scenario Photovoltaic scenarios Below, horizontal year Real-time abandoned photovoltaic power generation; (7) Curtailment rate of renewable energy: In the formula: Indicates the rate of curtailment of renewable energy; (8) Upper limit of daily peak-shaving depth for thermal power units: In the formula: This indicates the maximum daily peak-shaving depth of thermal power units; (9) Maximum curtailment of renewable energy: In the formula: This represents the maximum amount of electricity that can be abandoned by renewable energy sources.

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