Power supply gap evaluation method based on multi-time scale production simulation

Abstract

The invention discloses a power supply gap evaluation method based on multi-time scale production simulation, and belongs to the field of power systems. The invention discloses a power supply gap evaluation method based on multi-time scale production simulation. The method comprises the following steps: establishing a provincial power system multi-time scale production simulation model considering a complex grid structure; comprehensively considering the power supply risk of wind and light uncertainty and the power purchase quantity of a tie line, determining a power supply gap evaluation index based on the power purchase quantity, and performing power gap evaluation based on a production simulation result obtained by the multi-time scale production simulation model; according to the invention, a power supply gap evaluation index integrating hard power shortage and power shortage risk of the system is provided; and power supply gaps of different time scales of the provincial power grid in an extreme scene can be effectively quantified.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for evaluating power supply gap based on multi-time scale production simulation. Background Art

[0002] The penetration rate of renewable energy such as wind power and photovoltaics in the power system has increased year by year. Due to the high volatility of wind and solar power, extremely low output scenarios are prone to occur. If the power grid is under heavy load at the same time, a huge power gap may occur. In actual production scheduling, if the power gap can be quantified, it can guide the formulation of scheduling plans, thereby promoting the safe and stable operation of the power system. Summary of the invention

[0003] The purpose of the present invention is to solve the problems existing in the prior art and to propose a method for evaluating power supply gap based on multi-time scale production simulation.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for assessing power supply gap based on multi-time scale production simulation includes the following steps:

[0006] Establish a multi-time scale production simulation model for provincial power systems taking into account complex grid structures;

[0007] The power supply risk of wind and solar power uncertainty and the power purchase volume of interconnection lines are comprehensively considered, the power supply gap assessment index is determined based on the power purchase volume, and the power gap is assessed based on the production simulation results obtained based on the multi-time scale production simulation model.

[0008] Preferably, the multi-time-scale production simulation model includes a weekly scheduling model, a day-ahead scheduling model and a daily scheduling model, and the scheduling periods and time resolutions of the weekly scheduling model, the day-ahead scheduling model and the daily scheduling model are 7d / 4h, 1d / 1h and 4h / 15min respectively.

[0009] Furthermore, the weekly scheduling model is based on the total operating cost of the system during the week F w Minimize the objective function:

[0010] min F w =C w,tg +C w,li +C w,ab

[0011]

[0012] Where, T w is the number of time periods included in the weekly scheduling cycle; Δt is the length of the time period; C w,tgRepresents the operating cost of thermal power units; C w,li represents the power purchase cost of the inter-provincial interconnection line; C w,ab represents the total abandoned electricity cost of wind, solar and hydropower; N tg 、N li 、N wi 、N pv and N hy They are the number of thermal power units, inter-provincial tie lines, wind farms, photovoltaic power stations and hydropower stations in the system; and They are the output of thermal power unit i in time period t in the weekly dispatch plan, the power received by the inter-provincial tie line j, the abandoned power of wind farm k and photovoltaic power station p, and the abandoned water volume of hydropower station h; is a Boolean variable, indicating the start and stop status of thermal power unit i in time period t (start is 1, stop is 0); and are the fuel price of thermal power in time period t, the startup cost of thermal power unit i, and the unit price of electricity purchased by tie line j; w wi 、w pv and w hy are the penalty factors for abandoning wind, solar and hydro respectively, The linearized thermal power unit i has an output of The coal consumption coefficient at that time.

[0013] Furthermore, the coal consumption coefficient It is obtained by the following formula:

[0014]

[0015] Where P i max , P i min , and are the maximum, minimum, technical output and coal consumption coefficient under the corresponding output of thermal power unit i respectively.

[0016] Furthermore, the system-related constraints of the intra-week dispatch model include power balance constraints, reserve constraints, and power line transmission limit constraints, where both the positive and negative reserve rates are taken as 0.05.

[0017] Furthermore, the day-ahead scheduling model uses the total operating cost F da The minimum is the goal. The constraints of the day-ahead scheduling model include intra-weekly-day-ahead coupling constraints and conventional constraints. The intra-weekly-day-ahead coupling constraints indicate that the decision variables such as the maintenance status of thermal power units, the start and stop status of heating units, and the storage capacity of hydropower stations in the day-ahead scheduling model need to meet the operating boundaries determined by the intra-weekly scheduling results.

[0018] Furthermore, the intra-weekly-day-ahead coupling constraint is as follows:

[0019]

[0020] In the formula, Ω t is the set of day-ahead scheduling periods contained in period t on the intra-week time scale; is the maintenance status of thermal power unit i in time period t on a weekly time scale; and are the maintenance status and start-up and shutdown status of thermal power unit i in the period τ on the day-ahead time scale; Ω chp A collection of thermal power units for heating; and are the storage capacity of hydropower unit h in the weekly and day-ahead time scales respectively; T da is the day-ahead scheduling period.

[0021] Furthermore, the intraday scheduling model is executed every 15 minutes, and the operation plan for the next 4 hours is formulated based on the latest forecast data, retaining the current system status as the real plan. The constraints of the intraday scheduling model include basic constraints and day-ahead-intraday coupling constraints.

[0022] Preferably, when the fluctuation of wind and solar power supply exceeds the upper limit of the system regulation capacity, the absorbable interval is introduced and P is defined t new,min is the lower limit of renewable energy that can be absorbed by the regional power grid at time t, P t new,max is the upper limit that can be absorbed, and is obtained by the following formulas:

[0023] P t new,min =P t new -P t rsvup

[0024] P t new,max =P t new +P t rsvdown

[0025] Where P t rsvup and P t rsvdown are the maximum positive and negative regulation capabilities in the time zone at time t; P t new To predict the output of wind and solar energy at time t.

[0026] Furthermore, based on the absorbable range, the potential power supply gap index is further proposed, and the potential power supply gap index P is analyzed. t shed It is obtained by the following formula:

[0027]

[0028] Therefore, the power supply gap of the provincial power system in period t can be calculated as follows: t :

[0029]

[0030] Where N li is the number of inter-provincial contact lines; P j,t is the power purchase amount of the jth interconnection line at time t.

[0031] Compared with the prior art, the present invention provides a method for assessing power supply gap based on multi-time scale production simulation, which has the following beneficial effects:

[0032] The parts not involved in the device are the same as the existing technology or can be implemented by the existing technology. The present invention does not take into account the hard power shortage that may occur in the system in the economic dispatch plan when evaluating the power gap, and lacks a quantification method for the power supply gap at different time scales; a provincial power grid power supply gap evaluation method based on multi-time scale production simulation is proposed; firstly, a multi-time production simulation model of the provincial power system within the week, the day before, and the day is established; then, considering the uncertainty of wind and solar power output, a power supply gap evaluation index that integrates the hard power shortage and power shortage risk of the comprehensive system is proposed; the present invention can effectively quantify the power supply gap of the provincial power grid at different time scales under extreme scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A multi-time scale production simulation framework diagram in a power supply gap assessment method based on multi-time scale production simulation proposed in the present invention;

[0034] Figure 2 This is a diagram of multi-time scale production simulation results of a power supply gap assessment method based on multi-time scale production simulation proposed in the present invention under a high-load scenario in summer;

[0035] Figure 3 This is a diagram of multi-time scale production simulation results of a power supply gap assessment method based on multi-time scale production simulation proposed in the present invention under a winter heavy load scenario;

[0036] Figure 4 A power supply gap diagram at different time scales under a high-load scenario in summer based on a power supply gap assessment method based on multi-time-scale production simulation proposed in the present invention;

[0037] Figure 5 This invention proposes a method for evaluating power supply gap based on multi-time-scale production simulation, and shows power supply gap diagrams at different time scales under a high-load scenario in winter. DETAILED DESCRIPTION

[0038] In the description of the present invention, it needs to be understood that the terms "up", "down", "front", "back", "left", "right", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are only for the convenience of describing the present invention and simplifying the description, and therefore cannot be understood as limiting the present invention.

[0039] Embodiment 1:

[0040] Reference Figure 1-Figure 5 , a method for assessing power supply gap based on multi-time scale production simulation, comprising the following steps:

[0041] Establish a multi-time scale production simulation model for provincial power systems taking into account complex grid structures;

[0042] The power supply risk of wind and solar power uncertainty and the power purchase volume of interconnection lines are comprehensively considered, the power supply gap assessment index is determined based on the power purchase volume, and the power gap is assessed based on the production simulation results obtained based on the multi-time scale production simulation model.

[0043] In actual production, because the accuracy of forecast information at different time scales is different, and the regulation inertia and response speed of different resources are also different, the power system usually follows the principle of "multi-level dispatch" to give full play to the regulation capacity of various flexible resources; therefore, considering the operating characteristics of the power system at different time scales, a multi-time scale production simulation model of the provincial power system considering the complex grid structure is established.

[0044] Combined with actual production needs, the present invention establishes a three-level scheduling model of intra-week-day-day and intra-day. The model framework diagram is as follows: Figure 1 shown.

[0045] Among them, multiple time scales include intra-week time scale-day-ahead time scale-intra-day time scale. The multi-time scale production simulation model includes intra-week scheduling model, day-ahead scheduling model and intra-day scheduling model; the multi-time scale production simulation model includes intra-week scheduling model, day-ahead scheduling model and intra-day scheduling model. The scheduling period and time resolution of the intra-week scheduling model, day-ahead scheduling model and intra-day scheduling model are 7d / 4h, 1d / 1h and 4h / 15min respectively. The solution result of the superior scheduling model determines the operating boundary of the subordinate scheduling model. The subordinate scheduling model obtains more accurate scheduling results based on more accurate prediction data under this boundary. The specific introduction of each level of scheduling is described as follows.

[0046] On the weekly time scale, the objective function is to minimize the operating cost, and the constraints such as system balance reserve constraints, constraints on various types of units, and line flow constraints are considered in the constraints; in the provincial power system, the regulation inertia of resources such as heating units and hydropower stations is relatively large, and their weekly operation plans need to be determined in advance and used as boundary constraints in the lower-level scheduling model.

[0047] The weekly scheduling model is based on the total operating cost of the system during the week F w Minimize the objective function:

[0048] min F w =C w,tg +C w,li +C w,ab

[0049]

[0050] Where, T w is the number of time periods included in the weekly scheduling cycle; Δt is the length of the time period; C w,tg Represents the operating cost of thermal power units; C w,li represents the power purchase cost of the inter-provincial interconnection line; C w,ab represents the total abandoned electricity cost of wind, solar and hydropower; N tg 、N li 、N wi 、N pv and N hy They are the number of thermal power units, inter-provincial tie lines, wind farms, photovoltaic power stations and hydropower stations in the system; and They are the output of thermal power unit i in time period t in the weekly dispatch plan, the power received by the inter-provincial tie line j, the abandoned power of wind farm k and photovoltaic power station p, and the abandoned water volume of hydropower station h; is a Boolean variable, indicating the start and stop status of thermal power unit i in time period t (start is 1, stop is 0); and are the fuel price of thermal power in time period t, the startup cost of thermal power unit i, and the unit price of electricity purchased by tie line j; w wi 、w pv and w hy are the penalty factors for abandoning wind, solar and hydro respectively, The linearized thermal power unit i has an output of The coal consumption coefficient at that time.

[0051] Coal consumption coefficient It is obtained by the following formula:

[0052]

[0053] Where Pi max , P i min , and are the maximum, minimum, technical output and coal consumption coefficient under the corresponding output of thermal power unit i respectively.

[0054] It should be noted that the system-related constraints of the intraweek scheduling model include power balance constraints, reserve constraints, and power line transmission limit constraints, among which the positive and negative reserve rates are both taken as 0.05, and the line flow is obtained using the DC flow method. The DC flow method can use existing technology, and the specific expressions will not be repeated here; the unit-related constraints include the upper and lower limits of output, climbing, start and stop, and other conventional constraints for pure condensing thermal power, heating thermal power, inter-provincial interconnection lines, wind and photovoltaic power generation, hydropower stations, and pumped storage power stations. The specific expressions use existing technology and will not be repeated here.

[0055] The day-ahead scheduling solves the system's optimal daily operation plan based on the day-ahead forecast data and the operation boundary obtained based on the superior model. The objective function is similar to the intra-week model, and the constraints are based on the superior model by adding intra-week-ahead coupling constraints.

[0056] The day-ahead dispatch model aims to minimize the total operating cost for the whole day, including the operating cost of thermal power units, start-up and shutdown costs, interconnection line power purchase costs, and wind and solar power abandonment costs. The specific expression is the same as the intra-week dispatch model and will not be repeated here.

[0057] The constraints include intra-weekly-day-ahead coupling constraints and conventional constraints. Conventional constraints are the same as intra-weekly scheduling and will not be described here. The day-ahead scheduling model is based on the total operating cost F for the whole day. da The minimum is the goal. The constraints of the day-ahead scheduling model include intra-weekly-day-ahead coupling constraints and conventional constraints. The intra-weekly-day-ahead coupling constraints indicate that the decision variables such as the maintenance status of thermal power units, the start and stop status of heating units, and the storage capacity of hydropower stations in the day-ahead scheduling model need to meet the operating boundaries determined by the intra-weekly scheduling results.

[0058] The intra-weekly-day-ahead coupling constraint is as follows:

[0059]

[0060]

[0061] In the formula, Ω t is the set of day-ahead scheduling periods contained in period t on the intra-week time scale; is the maintenance status of thermal power unit i in time period t on a weekly time scale; and are the maintenance status and start-up and shutdown status of thermal power unit i in the period τ on the day-ahead time scale; Ω chp A collection of thermal power units for heating; and are the storage capacity of hydropower unit h in the weekly and day-ahead time scales respectively; T da is the day-ahead scheduling period.

[0062] Intraday rolling scheduling is executed every 15 minutes. The operation plan for the next 4 hours is formulated based on the latest forecast data, and the current system status is retained as the real plan. The unit start and stop costs are no longer considered in the objective function. The constraints include basic constraints and day-ahead-intraday coupling constraints, and no decisions are made on the unit start and stop status.

[0063] During the intraday rolling stage, no decision will be made on the start and stop status of the unit, so the start and stop cost of the unit is no longer considered in the objective function. Other aspects are the same as the intra-week and day-ahead models and will not be repeated here. The constraints include day-ahead-intraday coupling constraints and conventional constraints, which are the same as the day-ahead scheduling coupling, and the specific expressions will not be repeated.

[0064] In step 2, considering the power supply risk of wind and solar uncertainty and the power purchase of interconnection lines, the power supply gap assessment index is proposed and the power gap assessment is performed based on the production simulation results. The specific operations are as follows:

[0065] The power gap of the provincial power system can be divided into two parts. One part is the hard power shortage, that is, the power shortage caused by the inability of the internal power supply of the system to supply the load, which can be represented by the power purchase amount of the inter-provincial interconnection line; the other part is the potential power gap, that is, due to the fluctuation of wind and solar power, the system may be short of power, which can be calculated by the probability density function of the wind and solar power output of the system and the system reserve size; when the power system is in operation, it is necessary to adjust the output of the adjustable unit according to the actual wind and solar power output to maintain the balance of the system. When its output is too high, it is necessary to call the system's positive regulation capacity to increase the output of the adjustable unit, and vice versa; however, in actual operation, there is an upper limit to the adjustable capacity of the system. If the fluctuation of wind and solar power output exceeds the upper limit of the system's regulation capacity, a power gap or power abandonment will occur; for this reason, the concept of the absorbable interval is introduced, and the definition of P t new,min is the lower limit of renewable energy that can be absorbed by the regional power grid at time t, P t new,max is the upper limit of the absorption capacity, which is obtained by the following formula:

[0066] P t new,min =P t new -P t rsvup

[0067] Pt new,max =P t new +P t rsvdown

[0068] Where P t rsvup and P t rsvdown are the maximum positive and negative regulation capabilities in the time zone at time t; P t new To predict the output of wind and solar energy at time t.

[0069] Based on the absorbable range, a potential power supply gap index is further proposed. From the above analysis, it can be seen that when the output of renewable energy is less than the predicted value, the system is at risk of power shortage. Combined with its probability density function, the potential power supply gap index P is analyzed. t shed It can be obtained by the following formula:

[0070]

[0071] Therefore, the power supply gap of the provincial power system in period t can be calculated as follows: t :

[0072]

[0073] Where N li is the number of inter-provincial contact lines; P j,t is the power purchase amount of the jth interconnection line at time t.

[0074] In order to verify the effectiveness and accuracy of the proposed provincial power grid power supply gap assessment method based on multi-time scale production simulation, a case analysis of the provincial 500kV and above power system topology of a province is carried out. The system has a total of 73 nodes (including 5 UHV points) and 160 lines. The power generation resources in the system include unified dispatching coal power plants, of which 36 undertake heating tasks, and the maximum power generation during the non-heating period is 64486MW; unified dispatching gas power plants, with a maximum power generation of 3340MW and a maximum peak regulation rate of 66 8MW / min; unified dispatching hydropower station, the maximum power generation is 2390MW; unified dispatching pumped storage power station, the maximum power generation is 2520MW; wind power installed capacity is 20000MW, of which centralized type is 15034.8MW; photovoltaic installed capacity is 35000MW, of which centralized type is 6219.7MW; inter-provincial interconnection line, the total capacity is 21110MW; due to space limitations, the detailed parameters of each unit are no longer listed; two extreme weekly scenarios are selected for analysis, namely summer high load and winter high load, the characteristics of the two scenarios are as follows:

[0075] (1) Heavy load + extremely hot and windless in summer

[0076] The characteristics of this week are that the photovoltaic output is normal, but the wind power output continues to be low. At the same time, it is also a period of high load in summer. Since photovoltaic power cannot output at night, the power supply pressure during the evening peak is extremely high.

[0077] (2) Heavy load in winter + extreme cold and no light

[0078] The characteristics of this week were continued low output of photovoltaic and hydropower, and low output of wind power in the second half of the week. In addition, during the heating period, the output of some coal-fired power units was hindered, and the system's adjustable resources were reduced.

[0079] The rationality of the proposed method is analyzed. Figure 2 and Figure 3 The multi-time scale production simulation diagrams under different extreme scenarios are shown in the analysis Figure 2 and Figure 3 It can be seen that under the high-load scenario in summer, the base load of the system is mainly borne by coal-fired power. In the first few days, due to the certain output of wind power and the relatively low system load, the overall power supply pressure of the system was not large; starting from August 16, the wind power output continued to be low, the system load was gradually increasing, and the system power supply pressure increased suddenly. After losing the photovoltaic output at night, support from out-of-province interconnection lines was needed to ensure the balance of the system. At this time, the system had a power gap. The largest power gap occurred during the evening peak period on August 17. Within 2.5 hours of the evening peak, the power gap in the province reached 3336.45MWh, and the largest power gap reached 2784.3MW; under the high-load period in winter, the load is obviously smaller than that in summer. Therefore, although the wind and solar output in the power system is small, the overall power supply pressure is not large. Only within 3 hours of the evening peak on December 15, there was a power supply gap of 2022.75MWh. The maximum power gap during this period was 1750.2MW.

[0080] It is assumed that the wind and solar power output satisfies the normal distribution, and the mean is equal to the wind and solar power forecast output, that is, the wind and solar power output data used in the production simulation; the power supply gap of the power system at different time scales under the scenarios of high load in summer and high load in winter is evaluated respectively, and the evaluation results are as follows Figure 4 and Figure 5As shown in the figure, it can be seen that the time distribution of power gap is closely related to the system's new energy output and dispatch plan. When new energy is generated and the system has sufficient reserve, the power gap is small, otherwise it is easy to have a gap; from the seasonal characteristics, the power gap of heavy load in summer is significantly higher than that of heavy load in winter, which is also consistent with the production simulation results. Analysis of the power gap characteristics at different time scales shows that the smaller the time scale, the larger and more frequent the system power gap. This is because the forecast data at a long time scale is often more conservative, with a large time resolution, and the system adjustment time is longer, so the power gap is small; the forecast data at a short time scale is more volatile and random, and requires a higher system adjustment ability, so it is easy to have a large power gap.

[0081] In summary, the present invention aims at the problem that the risk of power system supply and demand balance is aggravated and power gap is more likely to occur under the background of large-scale access of highly volatile wind and solar resources. A multi-time scale production simulation model of the provincial power system considering the grid structure is established; then, considering the uncertainty of wind and solar resources, a power supply risk index is proposed to quantify the potential power gap; further, combined with the power purchase volume of the interconnection line, a power supply gap assessment index is proposed; the present invention can effectively quantify the power supply gap of the provincial power system at different time scales under extreme scenarios.

[0082] Embodiment 2:

[0083] A power supply gap assessment system based on multi-time scale production simulation includes a power supply gap assessment method based on multi-time scale production simulation.

[0084] Embodiment three:

[0085] A storage medium stores a computer program, which, when executed by a processor, implements the aforementioned method for assessing power supply gap based on multi-time scale production simulation.

[0086] Embodiment 4:

[0087] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the aforementioned method for assessing the power supply gap based on multi-time-scale production simulation is implemented.

[0088] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for assessing power supply gap based on multi-time scale production simulation, characterized in that: The following steps are involved: Establish a multi-time scale production simulation model for provincial power systems taking into account complex grid structures; The power supply risk of wind and solar power uncertainty and the power purchase volume of interconnection lines are comprehensively considered, the power supply gap assessment index is determined based on the power purchase volume, and the power gap is assessed based on the production simulation results obtained based on the multi-time scale production simulation model.

2. The method for assessing power supply gap based on multi-time scale production simulation according to claim 1, characterized in that: The multi-time scale production simulation model includes intra-week scheduling model, day-ahead scheduling model and intra-day scheduling model; The scheduling period and time resolution of the intra-week scheduling model, day-ahead scheduling model and intra-day scheduling model are 7d / 4h, 1d / 1h and 4h / 15min respectively.

3. The method for assessing power supply gap based on multi-time scale production simulation according to claim 2 is characterized in that: The weekly scheduling model is based on the total operating cost of the system during the week F w Minimize the objective function: minF w =C w,tg +C w,li +C w,ab Where, T w is the number of time periods included in the weekly scheduling cycle; Δt is the length of the time period; C w,tg Represents the operating cost of thermal power units; C w,li represents the power purchase cost of the inter-provincial interconnection line; C w,ab N represents the total abandoned electricity cost of wind, solar and hydropower; tg 、N li 、N wi 、N pv and N hy They are the number of thermal power units, inter-provincial tie lines, wind farms, photovoltaic power stations and hydropower stations in the system; and They are the output of thermal power unit i in time period t in the weekly dispatch plan, the power received by the inter-provincial tie line j, the abandoned power of wind farm k and photovoltaic power station p, and the abandoned water volume of hydropower station h; is a Boolean variable, indicating the start and stop status of thermal power unit i in time period t (start is 1, stop is 0); and are the fuel price of thermal power in time period t, the startup cost of thermal power unit i, and the unit price of electricity purchased by tie line j; w wi 、w pv and w hy are the penalty factors for abandoning wind, solar and hydro respectively, The linearized thermal power unit i has an output of The coal consumption coefficient at that time.

4. The method for assessing power supply gap based on multi-time scale production simulation according to claim 3 is characterized in that: Coal consumption coefficient It is obtained by the following formula: Where P i max , P i min , and are the maximum, minimum, technical output and coal consumption coefficient under the corresponding output of thermal power unit i respectively.

5. The method for assessing power supply gap based on multi-time scale production simulation according to claim 4 is characterized in that: The system-related constraints of the intra-week dispatch model include power balance constraints, reserve constraints, and power line transmission limit constraints, among which the positive and negative reserve rates are both 0.

05.

6. The method for assessing power supply gap based on multi-time scale production simulation according to claim 5, characterized in that: The day-ahead scheduling model is based on the total operating cost F for the whole day. da The minimum is the goal. The constraints of the day-ahead scheduling model include intra-weekly-day-ahead coupling constraints and conventional constraints. The intra-weekly-day-ahead coupling constraints indicate that the decision variables such as the maintenance status of thermal power units, the start and stop status of heating units, and the storage capacity of hydropower stations in the day-ahead scheduling model need to meet the operating boundaries determined by the intra-weekly scheduling results.

7. The method for assessing power supply gap based on multi-time scale production simulation according to claim 6, characterized in that: The intra-weekly-day-ahead coupling constraint is as follows: In the formula, Ω t is the set of day-ahead scheduling periods contained in period t on the intra-week time scale; is the maintenance status of thermal power unit i in time period t on a weekly time scale; and are the maintenance status and start-up and shutdown status of thermal power unit i in the period τ on the day-ahead time scale; Ω chp A collection of thermal power units for heating; and are the storage capacity of hydropower unit h in the weekly and day-ahead time scales respectively; T da is the day-ahead scheduling period.

8. The method for assessing power supply gap based on multi-time scale production simulation according to claim 7, characterized in that: The intraday scheduling model is executed every 15 minutes. The operation plan for the next 4 hours is formulated based on the latest forecast data, and the current system status is retained as the real plan. The constraints of the intraday scheduling model include basic constraints and day-ahead-intraday coupling constraints.

9. The method for assessing power supply gap based on multi-time scale production simulation according to claim 1, characterized in that: When the fluctuation of wind and solar power supply exceeds the upper limit of the system regulation capacity, the absorbable interval is introduced and P is defined t new,min is the lower limit of renewable energy that can be absorbed by the regional power grid at time t, P t new,max is the upper limit that can be absorbed, and is obtained by the following formulas: P t new,min =P t new -P t rsvup P t new,max =P t new +P t rsvdown Where P t rsvup and P t rsvdown are the maximum positive and negative regulation capabilities in the time zone at time t; P t new To predict the output of wind and solar energy at time t.

10. The method for assessing power supply gap based on multi-time scale production simulation according to claim 9, characterized in that: Based on the absorbable range, a potential power supply gap index is further proposed, and the potential power supply gap index P is analyzed. t shed It is obtained by the following formula: Therefore, the power supply gap of the provincial power system in period t can be calculated as follows: t : Where N li is the number of inter-provincial contact lines; P j,t is the power purchase amount of the jth interconnection line at time t.

11. A power supply gap assessment system based on multi-time scale production simulation, comprising a power supply gap assessment method based on multi-time scale production simulation as described in any one of claims 1-10.

12. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements a method for assessing electricity supply gap based on multi-time-scale production simulation as described in any one of claims 1-10.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements a method for assessing electricity supply gap based on multi-time-scale production simulation as described in any one of claims 1-10.

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