Method, system and equipment for verifying clearing boundary data of provincial power grid and medium

Abstract

The invention relates to the technical field of power spot market clearing boundary data verification, in particular to a provincial power grid clearing boundary data verification method, system and device and a medium, and the method comprises the steps: carrying out the manual verification of a provincial power grid operation scheme, and carrying out the electric quantity verification and peak regulation verification; constructing a typical scene set based on the prediction data, and performing multi-dimensional feasibility verification on all typical scenes through a pre-clearing model; generating a random scene set based on time sequence modeling of a new energy prediction error, and performing multi-dimensional feasibility verification on a random scene through a pre-clearing model; if the feasibility verification is passed, determining that the clearing boundary data of the provincial power grid is feasible; and if not, adjusting the non-market unit and hydropower output plan. The provincial power grid clearing boundary data checking method has the beneficial effects that the reasonability and feasibility of the provincial power grid clearing boundary data are comprehensively and meticulously checked by sequentially implementing manual simple checking, typical scene checking and new energy random scene checking.

Description

Technical Field

[0001] The present invention relates to the technical field of boundary data verification for electricity spot market clearing, and in particular to a provincial power grid clearing boundary data verification method, system, equipment and medium. Background Art

[0002] With the gradual advancement of the electricity spot market, provincial power grids need to submit boundary data including model parameters, load forecasts, renewable energy output forecasts, equipment maintenance plans, etc. to the regional dispatch center during the regional market clearing process. The rationality of these data directly affects the distribution of interests of market entities within the province and the clearing results of the regional market. However, the existing verification methods for clearing boundary data of provincial power grids have problems such as a single verification dimension, insufficient scenario coverage, lack of multi-dimensional evaluation, and weak dynamic adjustment capabilities. As a result, provincial power grids face the challenges of insufficient verification and insufficient risk prediction when submitting clearing boundary data, which in turn affects the stable operation of the regional power market and the efficiency of resource allocation. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0004] In a first aspect, the present invention provides a provincial power grid clearing boundary data verification method, comprising manual verification of the provincial power grid operation plan, including power verification and peak load verification;

[0005] Build a set of typical scenarios based on forecast data, and conduct multi-dimensional feasibility verification of all typical scenarios through pre-clearing models;

[0006] Generate a set of random scenarios based on time series modeling of new energy forecast errors, and verify the feasibility of the random scenarios in multiple dimensions through a pre-clearing model;

[0007] If the feasibility verification is passed, it is determined that it is feasible for the provincial power grid to clear boundary data.

[0008] As a preferred solution of the provincial power grid clearing boundary data verification method of the present invention, the power verification is carried out by verifying whether the maximum power generation of the starting unit meets the load power in the province;

[0009] Peak load calibration is to verify whether the total system output at load peak and trough moments meets the corresponding load demand.

[0010] As a preferred solution of the provincial power grid clearing boundary data verification method of the present invention, wherein: the typical scenario set includes a forecast deviation scenario and a plan adjustment scenario;

[0011] The forecast deviation scenario is generated by adjusting the forecast values ​​of renewable energy output, load forecast, hydropower output and market quotes;

[0012] The plan adjustment scenario is generated by adjusting the western power plan, non-market unit output and grid equipment status.

[0013] As a preferred solution of the provincial power grid clearing boundary data verification method of the present invention, the construction of the pre-clearing model includes:

[0014] Based on boundary data, combined with grid topology, unit quotation information, and grid safety thresholds, a safety-constrained unit combination model for provincial boundary scenarios is established.

[0015] The establishment of a safety constraint unit combination model under provincial boundary scenarios includes:

[0016] Construct the objective function of the safety-constrained unit commitment model under the prediction scenario;

[0017] Construct the constraint function of the safety constraint unit commitment model under the provincial boundary scenario;

[0018] The safety constraint unit combination model is solved by the solver to obtain the start-up and shutdown plan of each unit.

[0019] As a preferred solution of the provincial power grid clearing boundary data verification method of the present invention, wherein: time series modeling includes using kernel density estimation method and Markov chain method to generate a simulation sequence of prediction errors;

[0020] Random scenarios are generated by superimposing the prediction error simulation sequence onto the predicted output sequence.

[0021] As a preferred solution of the provincial power grid clearing boundary data verification method of the present invention, wherein: time series modeling includes using kernel density estimation method and Markov chain method, generating a simulation sequence of prediction error includes,

[0022] Divide the prediction box and use Bayesian optimization to determine the predicted output range;

[0023] Fit the error probability distribution within each prediction box based on kernel density estimation;

[0024] The state transition matrix of the Markov chain is used to generate a time-dependent simulation sequence of prediction errors.

[0025] As a preferred solution of the provincial power grid clearing boundary data verification method of the present invention, the multi-dimensional feasibility verification indicators include:

[0026] Safety indicators: N-1 pass rate, line load rate and section load rate, power shortage and maximum power shortage, and spare capacity;

[0027] Economic indicators: grid loss rate, power generation cost, average market electricity price, and extreme electricity price;

[0028] Policy indicators: proportion of renewable energy power generation and proportion of renewable energy consumption;

[0029] Fairness indicators: HHI index, Top-m share index.

[0030] In a second aspect, the present invention provides a provincial power grid clearing boundary data verification system, comprising: a verification module for manually verifying the provincial power grid operation plan, including power verification and peak load verification;

[0031] The first verification module is used to construct a set of typical scenarios based on the forecast data and perform multi-dimensional feasibility verification on all typical scenarios through the pre-clearing model;

[0032] The second verification module is used to generate a set of random scenarios based on the time series modeling of new energy forecast errors, and to perform multi-dimensional feasibility verification on the random scenarios through the pre-clearing model;

[0033] The determination module is used to determine whether the provincial power grid clearing boundary data is feasible if the feasibility verification is passed; if not, the non-market unit and hydropower output plan is adjusted.

[0034] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0035] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when the computer program is executed by a processor.

[0036] Compared with the existing technology, the beneficial effects of the present invention are as follows: by sequentially implementing manual simple verification, typical scenario verification and new energy random scenario verification, the rationality and feasibility of the provincial power grid clearing boundary data are comprehensively and meticulously checked. Manual simple verification is used as a basis to quickly screen out solutions that obviously do not meet the system requirements; on this basis, the typical scenario verification constructs a typical scenario covering a variety of uncertainty factors, and uses the pre-clearing model to deeply evaluate the applicability of boundary data in different situations from the dimensions of economy, security, policy, etc., thereby making up for the limitations of manual verification; the new energy random scenario verification further focuses on the random fluctuation characteristics of new energy output, and uses the kernel density estimation method and Markov chain to generate a large number of random scenarios close to reality, so that the boundary data can more accurately respond to the uncertainty of new energy, thereby ensuring that the final boundary data can achieve economic efficiency and effective absorption of new energy while ensuring the safe and stable operation of the system, thereby improving the operational efficiency and reliability of the entire power market. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 This is a flowchart of the provincial power grid boundary data verification method.

[0039] Figure 2 This is a flow chart of the provincial power grid boundary clearing data verification method.

[0040] Figure 3 It is an evaluation indicator for feasibility verification of typical scenarios. DETAILED DESCRIPTION

[0041] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0042] Example 1, with reference to Figures 1 to 3 , which is the first embodiment of the present invention, provides a provincial power grid clearing boundary data verification method, comprising:

[0043] S1. Manually verify the provincial power grid operation plan, including power verification and peak load verification.

[0044] Furthermore, the power verification is carried out by verifying whether the maximum power generation of the started units meets the load power in the province;

[0045] It should be noted that the power verification must satisfy formula (1):

[0046]

[0047] Where: NG represents the number of conventional thermal power units, NH represents the number of conventional hydropower units, N represents the number of new energy units (wind power, photovoltaic), and NT represents the total number of tie lines; α g,t represents the start and stop status of thermal power unit g in time period t, α g,t =0 means the unit is shut down, α g,t =1 means the unit is on; is the maximum output of conventional thermal power unit g in period t, P h,t is the planned output of conventional hydropower units, P n,tThe predicted output of the new energy unit on the operating day; D t represents the provincial system load in period t, T j,t It represents the planned power of western electricity of inter-provincial tie line j in time period t (output is positive, input is negative).

[0048] It should be further explained that equation (1) cannot be equal, because the system needs to reserve a portion of the available power generation capacity to cope with the random fluctuations of load and renewable energy output.

[0049] Peak load calibration is to verify whether the total system output at load peak and trough moments meets the corresponding load demand.

[0050] It should be noted that, for peak load, equation (2) must be satisfied; for valley load, equation (3) must be satisfied:

[0051]

[0052] Where, Indicates the maximum load value of the operating day. Indicates the time t at which the maximum load value occurs; is the minimum technical output of conventional thermal power unit g in period t, Indicates the minimum load value of the operating day. Indicates the time t at which the minimum load value occurs.

[0053] S2. Construct a set of typical scenarios based on forecast data, and conduct multi-dimensional feasibility verification on all typical scenarios through the pre-clearing model. The forecast data includes the forecast boundary data of the operation day.

[0054] It should be noted that the manual verification method in S1 only performs basic feasibility checks on power and electricity consumption, and cannot fully cover the complex situations encountered in actual system operation. For example, in actual operation, due to uncertainties such as weather, the actual output of renewable energy units may sometimes far exceed the predicted value. This may result in the inability to shut down thermal power units during certain periods to maintain system power balance, resulting in positive power imbalance and the need to curtail wind and solar power. In this case, the overall energy utilization efficiency of the system is reduced. Therefore, using forecast boundary data for operating days, we construct typical scenarios that incorporate multiple sources of grid-load uncertainty, including renewable energy output fluctuations, supply and demand conditions, water supply conditions, price quotes, and grid equipment failures. We also construct a pre-clearing model for the provincial power grid, and conduct comprehensive feasibility verification of these scenarios from multiple perspectives, including economic feasibility, security, and policy adaptability.

[0055] Furthermore, the typical scenario set includes forecast deviation scenarios and plan adjustment scenarios;

[0056] The forecast deviation scenario is generated by adjusting the forecast values ​​of renewable energy output, load forecast, hydropower output and market quotes;

[0057] It should be noted that the generation of forecast deviation scenarios includes applying preset positive and negative deviation adjustments to wind turbines, photovoltaic units, load forecasts, hydropower output, and market quotations. Specifically:

[0058] (1) Wind turbine prediction deviation scenario

[0059] 1) Wind power output increase scenario: generated by multiplying the predicted values ​​of all wind turbines in the province at all times of the operating day by 1.1;

[0060] 2) Wind power output reduction scenario: generated by multiplying the predicted values ​​of all renewable energy units in the province at all times of the operating day by 0.9;

[0061] (2) PV unit prediction deviation scenario

[0062] 1) PV output increase scenario: generated by multiplying the predicted values ​​of all PV units in the province at all times of the operating day by 1.1;

[0063] 2) PV output reduction scenario: generated by multiplying the predicted values ​​of all PV units in the province at all times of the operating day by 0.9;

[0064] (3) Load forecast deviation scenario

[0065] 1) Load increase scenario: generated by multiplying the predicted value of the provincial system load at all times of the operating day by 1.1;

[0066] 2) Load reduction scenario: generated by multiplying the predicted value of the provincial system load at all times of the operating day by 0.9;

[0067] (4) Hydropower forecast deviation scenario

[0068] 1) Scenario of abundant water inflow to hydropower units: Abundant water inflow leads to increased hydropower output. This is calculated by multiplying the planned output of hydropower units in the province at all times of the operating day by 1.1.

[0069] 2) Hydropower unit water inflow low scenario: Low water inflow leads to a decrease in hydropower output. This is calculated by multiplying the planned output of hydropower units in the province at all times of the operating day by 0.9.

[0070] (5) Scenario of unit quotation forecast deviation in the provincial market

[0071] 1) Quotation is too high: The result is generated by multiplying the quotation of the market-oriented units in the province on the day before the operation date by 1.1 times;

[0072] 2) Low quotation scenario: generated by multiplying the quotation of the provincial market-oriented units on the day before the operation date by 0.9.

[0073] The plan adjustment scenario is generated by adjusting the western power plan, non-market unit output and grid equipment status.

[0074] It should be noted that (1) the scenario of adjusting the output plan of Xidian

[0075] 1) Scenario of increased electricity transmission from western China: When the province is a province that transmits electricity from western China, the data is generated by multiplying the planned electricity transmission volume from western China at all times of the operation day by 1.1.

[0076] 2) Scenario of reduced electricity output from western China: When the province is a province that outputs electricity from western China, the data is generated by multiplying the planned electricity output from western China at all times of the operation day by 0.9;

[0077] 3) Scenario of increased western power intake: When the province is a recipient of western power, the data is generated by multiplying the planned western power intake at all times of the operation day by 1.1.

[0078] 4) Scenario of reduced western power intake: When the province is a recipient of western power, the data is generated by multiplying the planned western power intake at all times of the operation day by 0.9;

[0079] (2) Scenario for adjusting the output plan of non-market units within the province

[0080] 1) Scenario for increasing the output plan of non-market units within the province: generated by multiplying the output of non-market units within the province at all times of the operating day by 1.1;

[0081] 2) Scenario of planned reduction of provincial non-market unit output: generated by multiplying the provincial non-market unit output at all times of the operating day by 0.9;

[0082] (3) Scenarios for adjusting maintenance plans for transmission and transformation lines and equipment

[0083] The disconnection of the corresponding transmission lines in the grid structure is adjusted accordingly, and the sensitivity distribution matrix of the network topology is recalculated.

[0084] (4) Failure scenarios of key power grid lines

[0085] The disconnection of the corresponding key lines in the grid structure is adjusted accordingly, especially the status of the inter-provincial DC transmission lines, and the sensitivity distribution matrix of the network topology is recalculated.

[0086] It should be further explained that based on the above-mentioned forecast deviation scenarios and plan adjustment scenarios, typical complex scenarios can be generated based on the actual operation of the system. In actual operation, power grid companies pay more attention to the power supply and demand situation, the consumption of new energy, and the peak load regulation of power. Therefore, the following typical complex scenarios can be formed:

[0087] 1) Scenario of tight power supply and demand within the province: formed through a combination of scenarios of reduced wind power output, reduced photovoltaic output, increased load, low water inflow to hydropower units, and increased power transmission from the west.

[0088] 2) Scenarios of difficulties in absorbing new energy: formed through a combination of scenarios of increased wind power output, increased photovoltaic output, reduced load, abundant water inflow to hydropower units, and reduced power transmission from the west.

[0089] 3) Difficult power peak-shaving scenarios: By determining the time when the load peak occurs, a combination of different scenarios such as reduced wind power output, reduced photovoltaic output, increased load, reduced water inflow to hydropower stations, and increased power transmission from the west is considered; for the load valley moments, a comprehensive consideration is given to scenarios such as increased wind power output, increased solar photovoltaic power output, decreased electricity demand, abundant water inflow to hydropower stations, and reduced power output from the west.

[0090] Other typical complex scenarios can be generated based on actual conditions. The above scenarios use extreme values. In practice, an adjustment factor between 0.9 and 1.1 can be used based on system conditions.

[0091] Furthermore, the construction of the pre-clearing model includes:

[0092] Based on boundary data, combined with grid topology, unit bidding information, and grid security thresholds, a safety-constrained unit commitment (SCUC) model is established for provincial boundary scenarios. The boundary data includes data on renewable energy output, load conditions, and non-market unit output plans derived from the aforementioned typical scenarios.

[0093] The establishment of a safety constraint unit combination model under provincial boundary scenarios includes:

[0094] Construct the objective function of the safety-constrained unit commitment model under the prediction scenario;

[0095] It should be noted that in the process of constructing the objective function, it is assumed that the number of thermal power units in a certain power grid is N (n = 1, 2, …, N), the number of hydropower stations is M (m = 1, 2, …, M), the number of gas power units is R (r = 1, 2, …, R), the number of nuclear power units is D (d = 1, 2, …, D), the number of pumped storage power stations is C (c = 1, 2, …, C), the number of wind farms is J (j = 1, 2, …, J), and the number of photovoltaic power stations is K (k = 1, 2, …, K). Except for wind power and photovoltaic power, other units are collectively referred to as conventional units, that is, the number of conventional units is G = N + M + R + D (g = 1, 2, …, G), and the scheduling period is T (t = 1, 2, …, T).

[0096] The objective function includes the power generation costs of thermal power, gas power, and nuclear power, as well as the environmental costs of thermal power and gas power, and is expressed as:

[0097]

[0098] Where Min:F is the sum of the power generation costs of thermal power, gas power, and nuclear power under the prediction scenario of the objective function, and F g (n,t),F q (r,t),F e (d, t) are the power generation costs of thermal power unit n, gas power unit r, and nuclear power unit d in time period t. Their cost functions are all quadratic functions with the actual output of the unit as the decision variable. The variable coefficients are obtained through actual operation or experiments. S en The environmental costs of thermal power and gas power.

[0099] Construct the constraint function of the safety constraint unit commitment model under the provincial boundary scenario;

[0100] It should be noted that the constraint functions for constructing the safety-constrained unit commitment model under the provincial boundary scenario include system power balance constraints, pumped storage unit constraints, unit spinning reserve constraints, output upper and lower limits and ramping constraints of conventional units, total power constraints of hydropower stations and gas-fired power plants, storage capacity constraints and conversion efficiency constraints of pumped storage power stations, and line flow constraints. They are specifically expressed as follows:

[0101]

[0102] Wherein, formula (5) is the system power balance constraint, P g (n,t),P v (m,t),P q (r,t),P e (d,t),P x (c,t),P w (j,t),P f (k, t) are the outputs of thermal power unit n, hydropower station m, gas power unit r, nuclear power unit d, pumped storage power station c, wind farm j, and photovoltaic power station k in time period t, respectively. L (t) is the load in time period t.

[0103] P x (c,t)=P x,pm (c,t)+P x,gen (c,t) (6)

[0104] Among them, formula (6) is the constraint of the pumped storage unit. The pumped storage unit includes two states: pumping and power generation. When in the pumping state, it is equivalent to a running virtual motor with an output power of P x,pm (c,t),Px,pm (c, t)<0; when in power generation state, it is equivalent to a running virtual generator with an output power of P x,gen (c,t),P x,gen (c,t)>0, obviously, at most one virtual machine can be running at any time.

[0105]

[0106] Wherein, formula (7) is the unit spinning reserve constraint, L% is the load demand for spinning reserve, P max (g) is the output upper limit of conventional unit g, and P(g,t) is the output of conventional unit g in time period t.

[0107] P min (g)≤P(g,t)≤P max (g) (8)

[0108] Among them, formula (8) is the upper and lower limit constraints of the output of conventional units, P min (g) is the lower limit of the output of conventional unit g, P max (g) is the upper limit of output of conventional unit g.

[0109] -R d (g)×T 15 ≤P(g,t)-P(g,t-1)≤R u (g)×T 15 (9)

[0110] Wherein, formula (9) is the ramp speed constraint of conventional units, R d (g), R u (g) are the downward and upward climbing speeds of the conventional unit g, respectively, and the scheduling period T 15 =15min. Usually, the pumped storage unit pumps water at full power and is allowed to adjust its output only in the power generation state. At this time, the upper and lower limit constraints must be met. The hydropower unit and pumped storage unit have a fast adjustment speed, so the climbing speed constraint can be ignored.

[0111]

[0112] Among them, formula (10) and formula (11) are the total power constraints of hydropower stations and gas power plants respectively. Due to the limitation of water and gas volume, the allowed power generation of hydropower station m and gas power plant r in the scheduling period are W respectively. v (m), W q (r).

[0113] W min (c)≤W0(c)-L(c)×ΔP x (c,t)≤W max(c) (12)

[0114] Among them, formula (12) requires that the upper reservoir of the pumped storage power station should meet the storage capacity constraint in each period, W0(c), W min (c) W max (c) are the initial storage capacity and the upper and lower limits of the storage capacity of the pumped storage power station c, ΔP x (c, t) is the cumulative power generation of the pumped storage power station c from the initial time to time t, ΔP x (c,t) multiplied by the storage capacity to electricity conversion coefficient L(c) represents the cumulative water consumption at time t.

[0115]

[0116] Wherein, formula (13) represents ΔP x (c, t) is the equivalent cumulative power generation of the two processes of pumping and power generation, and η is the conversion efficiency of the pumped power station, which is generally taken as 80%.

[0117]

[0118] Among them, formula (14) indicates that the equivalent cumulative power generation of the pumped storage power station during the entire dispatching period is zero, that is, the upper reservoir is required to return to the initial water level after the dispatching is completed.

[0119]

[0120] Wherein, Equation (15) is the line power flow constraint, γ(g,l), γ w (j,l),γ f (k, l) are the power distribution factors of conventional unit g, wind farm j, and photovoltaic power station k on line l, respectively, and L(l) is the line flow limit.

[0121] The safety constraint unit combination model is solved by the solver to obtain the start-up and shutdown plan of each unit

[0122] It should be further explained that if the forecast deviation scenario adjusts the renewable energy output and load forecast, it is reflected in formula (5); if it adjusts the hydropower output, it is reflected in formula (10); if it adjusts the market quotation, it will affect the objective function; if the plan adjustment scenario adjusts the western power plan, non-market unit output and grid equipment status generation, it is reflected in formula (15).

[0123] The safety constraint unit combination model is solved by the solver to obtain the start-up and shutdown plan of each unit.

[0124] In an optional implementation, the solver adopts a CPLEX solver.

[0125] In another optional embodiment, the solver adopts the Gurobi solver.

[0126] Furthermore, in this model, boundary data such as renewable energy output, load conditions, and non-market unit output plans are derived from the aforementioned typical scenario set construction method. The remaining clearing boundary information that the provincial power grid needs to report to the regional dispatch center is set based on forecasted or planned values. By calculating the SCUC pre-clearing model for the provincial power grid, the start-up and shutdown plans and output plans of each market-based unit can be derived.

[0127] It should be further explained that if more than two typical scenarios need to be met, the SCUC model needs to be calculated for different scenarios separately to verify whether a reasonable and feasible unit start-up and shutdown plan can be obtained under the boundary of the scenario.

[0128] Further, the indicators for multi-dimensional feasibility verification include:

[0129] The indicators for multi-dimensional feasibility verification include:

[0130] Safety indicators: N-1 pass rate, line load rate and section load rate, power shortage and maximum power shortage, and spare capacity;

[0131] Economic indicators: grid loss rate, power generation cost, average market electricity price, and extreme electricity price;

[0132] Policy indicators: proportion of renewable energy power generation and proportion of renewable energy consumption;

[0133] Fairness indicators: HHI index, Top-m share index.

[0134] It should be noted that the construction of scenario feasibility verification and evaluation indicators focuses on the economic dimension, safety dimension, policy dimension and fairness dimension, and selects indicators such as power generation cost, clearing electricity price, section load rate, new energy consumption, and market concentration of power generation enterprises to construct a set of scenario feasibility verification and evaluation indicator system. Specifically:

[0135] (1) Safety indicators

[0136] 1) N-1 pass rate

[0137] In C (N-1) The percentage of times in which no overload occurs and neither the frequency nor the voltage exceeds the limit after any component (such as the generator or line) in the system is removed during the N-1 check test is defined as the N-1 pass rate:

[0138]

[0139] Where: I (N-1) N-1 passing rate; C (N-1),passThe number of times the verification passes.

[0140] For example, assume that in a power grid system, 100 N-1 calibration tests (i.e., C (N-1) =100), in these 100 tests, there were 95 tests in which the system did not experience overload after removing any component, and the frequency and voltage did not exceed the limit (i.e. C (N-1),pass =95).

[0141] Calculate the N-1 pass rate according to the formula:

[0142]

[0143] This means that in 95% of the N-1 calibration tests, the system can operate normally after removing any component, without overload or frequency or voltage exceeding the limit.

[0144] 2) Line load factor index

[0145] The average value μ1 and standard deviation σ1 of the ratio of the actual line load to the line transmission limit are used to reflect the average level of the line load rate of the entire network and the degree of unevenness between the lines. The equivalent probability evaluation algorithm is used for quantitative calculation, as shown in the following formula:

[0146]

[0147] Where: I LR is the line load rate index; r is the independent variable;.

[0148] It should be noted that this formula represents the integral from negative infinity to 1. In practical applications, it can be calculated by looking up a table or using statistical software (such as R, Python's SciPy library, MATLAB, etc.). Furthermore, the calculated I LR Indicates the probability that the line load factor exceeds the average value μ1.

[0149] 3) Section load rate index

[0150] The average value μ2 and standard deviation σ2 of the ratio of the actual load of the section to the transmission limit of the line are used to reflect the average level of the section load rate of the entire network and the degree of unevenness between the lines. The equivalent probability evaluation algorithm is used for quantitative calculation, as shown in the following formula:

[0151]

[0152] It should be noted that this formula represents the integral from negative infinity to 1. In practical applications, it can be calculated by looking up a table or using statistical software (such as R, Python's SciPy library, MATLAB, etc.). Furthermore, the calculated I SRIndicates the probability that the section load factor exceeds the average value μ2.

[0153] 4) Power shortage

[0154] Definition I Q The actual value is the difference between the total power demand and the maximum power supply on that day, as shown in the following formula:

[0155]

[0156] Where: represents the total power demand in period t; It represents the sum of the maximum power output of all conventional thermal power units that are turned on in period t; represents the sum of the power output of all hydropower stations in period t; It represents the sum of the power generation output of all new energy units in the tth period.

[0157] For example, let's assume we have the following data: 24 time periods per day, three conventional thermal power plants, two hydropower plants, and two renewable energy units. Four interconnectors are available. We can create a table or use a programming language (such as Python) to calculate the power deficit for each time period and then sum them up to obtain the total power deficit. This calculated power deficit allows us to assess the difference between the grid's supply capacity and demand throughout the day.

[0158] 5) Maximum power shortage

[0159] Definition I V The maximum power deficit is the maximum difference between the power demand and the maximum power supplied at the same time in a day, as shown in the following formula:

[0160]

[0161] It should be noted that for each time period t (from 1 to 24), after calculating the power deficit of each time period, the maximum value of the power deficit in all time periods is found and determined as the maximum power deficit.

[0162] 6) Spare capacity

[0163] Definition I R is the minimum value of the spare capacity. Generally, the actual system needs to meet I R ≥0.05.

[0164]

[0165] It should be noted that for each time period t (from 1 to 24), after calculating the spare capacity of each time period, the minimum value of the spare capacity in all time periods is found and determined as the spare capacity.

[0166] (2) Economic indicators

[0167] 1) Network loss rate

[0168] The total network loss P loss Total load level The ratio is defined as the network loss rate I PL , as shown below:

[0169]

[0170] For example, assuming the total network loss P loss =10, the total load level is 1000, then the network loss rate I PL 1%.

[0171] 2) Power generation costs

[0172] The power generation cost of the generator is expressed as a quadratic function:

[0173]

[0174] Where: a k 、b k 、c k is the operating cost coefficient of generator k.

[0175] For example, it is assumed that the relevant data of the generator 1 are the operating cost coefficient a1=0.01, b1=10, c1=5 and the power output P g,1 =100; the relevant data of generator 2 are operating cost coefficient a1=0.05, b1=8, c1=3 and power output P g,1 =150;

[0176] Cost of Generator 1: 0.01×100 2 +10×100+5=1105

[0177] Generator 2 cost: 0.005×150 2 +8×150+3=1315.5

[0178] Total power generation cost: 1105 + 1315.5 = 2420.5

[0179] 3) Average market-based electricity price

[0180] The formula for calculating the weighted average electricity price of market-oriented units on the power generation side is:

[0181]

[0182] Where, represents the average market electricity price in hour t; Qm,t represents the winning bid power of market unit m at hour t, Q m,t,基数 represents the base contract power of Class B unit m at hour t; LMP m,t represents the settlement price of the node where unit m is located in the market at hour t; Represents the sum of all market-oriented units.

[0183] For example, the time period t is 1 hour, the number of market-based units m is 3, and the relevant data for unit 1 is the winning bid power: 50MWh, the base contract power: 20MWh, and the settlement price: 60$ / MWh; the relevant data for unit 2 is the winning bid power: 70MWh, the base contract power: 30MWh, and the settlement price: 65$ / MWh; the relevant data for unit 1 is the winning bid power: 30MWh, the base contract power: 10MWh, and the settlement price: 70$ / MWh;

[0184] Molecular calculation: Unit 1: (50-20)×60=1800;

[0185] Unit 2: (70-30)×65=2600;

[0186] Unit 3: (30-10)×70=1400

[0187] Numerator sum: 1800 + 2600 + 1400 = 5800;

[0188] Denominator calculation: Unit 1: 50-20=30;

[0189] Unit 2: 70-30=40;

[0190] Unit 3: 30-10=20

[0191] Sum of the denominators: 30 + 40 + 20 = 90;

[0192] The final average market electricity price was $64.44 / MWh.

[0193] 4) Extreme electricity prices

[0194] Count and analyze the times when electricity prices exceed 1,000 yuan / MWh.

[0195] (3) Policy indicators

[0196] 1) Proportion of renewable energy power generation

[0197] This indicator represents the proportion of renewable energy power generation, and to a certain extent reflects the operational level of new energy power sources. It is calculated using the following formula:

[0198]

[0199] Where: I res is the proportion of renewable energy generation; N res is the number of renewable energy sources, N G is the number of power nodes; W Gi is the amount of power output from the i-th power node to the system, W resi is the amount of electricity output by the i-th renewable energy source to the system.

[0200] For example, the number of renewable energy power sources is 3, specifically, the output power of the first renewable energy power source is 100MWh, the output power of the second renewable energy power source is 150MWh, and the output power of the third renewable energy power source is 200MWh; the number of power supply nodes is 5, specifically, the output power of the first power supply node is 50MWh; the output power of the second power supply node is 75MWh; the output power of the third power supply node is 100MWh; the output power of the fourth power supply node is 125MWh; and the output power of the fifth power supply node is 150MWh.

[0201] The numerator is calculated as: 100+1500+200=450MWh;

[0202] The denominator is calculated as: 450 + (50 + 75 + 100 + 125 + 150) = 450 + 450 = 900 MWh

[0203] Proportion of electricity generated from renewable energy:

[0204] 2) Renewable energy consumption ratio

[0205] This indicator indicates the proportion of electricity actually provided to the load by renewable energy sources in the system to the total electricity generated by renewable energy. It is calculated using the following formula:

[0206]

[0207] Where, I consumption is the proportion of renewable energy consumption; N res is the number of renewable energy sources; W resi is the amount of electricity output from the i-th renewable energy source to the system, W resGi is the total power generation of the i-th renewable energy source.

[0208] (4) Fairness Index

[0209] If the clearing boundary is not set reasonably, it may lead to a high degree of market concentration among the winning power generation companies, resulting in fairness issues.

[0210] 1) HHI index

[0211] The HHI index is based on the total number and size distribution of companies in a market. It is the sum of the squares of the market shares of all companies in the relevant market. The formula is:

[0212]

[0213] The criteria for judging market concentration through HHI are shown in the following table.

[0214] Table 1 HHI index range judgment

[0215] HHI index range Market Type HHI ≥ 1800 Highly oligopolistic market 1800>HHI≥1000 Low oligopoly market HHI<1000 Competitive market

[0216] The larger the HHI value, the higher the market concentration. When the market is in a complete monopoly, HHI = 10000; when the HHI value is less than 500, it can be considered that competition is relatively sufficient.

[0217] For example, suppose there are 4 companies in a market, and their market shares are as follows:

[0218] Company 1: Market share s1 = 0.3 (i.e. 30%)

[0219] Company 2: Market share s2 = 0.25 (i.e. 25%)

[0220] Company 3: Market share s3 = 0.2 (i.e. 20%)

[0221] Company 4: Market share s4 = 0.25 (i.e. 25%)

[0222] Calculate the squared market share for each firm:

[0223] Company 1: (100 × 0.3)² = 900

[0224] Company 2: (100 × 0.25)² = 625

[0225] Company 3: (100 × 0.2)2 = 400

[0226] Company 4: (100 × 0.25)² = 625

[0227] Sum: 900+625+400+625=2550, that is, the HHI index is 2550.

[0228] 2) Top-m share indicator

[0229] The Top-m share indicator refers to the market share held by the m largest generators in a market. m = 4 is typically used. A larger Top-4 share indicates a higher degree of market concentration; a Top-4 share greater than 65% indicates an oligopolistic market.

[0230] Furthermore, it is important to explain the construction of an entropy-weighted fuzzy hierarchical analysis evaluation model for scenario feasibility verification. Appropriate indicators are selected and normalized at the indicator and sub-indicator levels. The entropy-weighted fuzzy hierarchical analysis method is then used to construct a fuzzy matrix and calculate subjective weights, while also verifying the feasibility of the data. The specific steps are as follows.

[0231] (1) Normalization

[0232] Since the value ranges of various indicators in the evaluation system for feasibility verification of typical power grid scenarios vary, they need to be normalized to ensure the rigor of the evaluation. The ideal point approximation method is used to transform the values ​​of various indicators to the interval [0,100], as shown in the following formula:

[0233]

[0234] Where: I is a specific indicator; I + , I - are the optimal and worst values ​​of the indicator respectively; I norm is the transformed value of the indicator.

[0235] In an optional embodiment, the optimal value and the worst value may be determined based on historical data.

[0236] In another alternative embodiment, the optimal value and the worst value may be determined according to industry standards or specifications.

[0237] For example, suppose we have an indicator called "N-1 pass rate" whose value range is [0%, 100%]. We have the following N-1 pass rates for the following three scenarios:

[0238] Scenario 1: 90%; Scenario 2: 80%; Scenario 3: 70%, assuming optimal value I + =100%, worst value I - =0%.

[0239] Then, by substituting formula (27) into the calculation, we can obtain that the pass rate of scenario 1 is 90%, scenario 2: 80%; scenario 3: 70%.

[0240] (2) Entropy weight-fuzzy analytic hierarchy process

[0241] To address the issues of subjective weighting methods being susceptible to expert bias and objective weighting methods being overly dependent on raw data, the EW-FAHP method was selected to integrate multidimensional operational mode evaluation indicators. A comprehensive evaluation score (a threshold for the comprehensive evaluation score is set based on historical data, industry standards, or expert experience; this threshold can be set based on actual conditions and is not limited in this method) represents the performance of the random scenario across multiple dimensions, including economic efficiency, safety, and policy adaptability. A random scenario is considered to have passed feasibility verification when the comprehensive score exceeds the threshold. The weight calculation model is divided into two independent calculation models: subjective and objective. The specific implementation steps are as follows.

[0242] 1) Establish the subjective weight judgment matrix Z = (z sh ) n×n ,in:

[0243]

[0244] Where: z sh is the importance of indicator s relative to indicator h (experts evaluate indicators based on scheduling experience, score them according to different focus points and compare them pairwise), s = 1, 2, …, n, z sh =0.5 means that the two indicators are equally important, and z sh The larger the value, the more important the indicator s is compared to the indicator h; n is the number of indicators.

[0245] It should be noted that Z ss =0.5 means the same indicator has the same importance when compared with itself; Z sh +Z hs =1 means that if indicator s is more important than indicator h, then indicator h will appear less important relative to indicator s.

[0246] It should be further explained that the matrix is ​​constructed through the subjective judgment of experts to provide data support for the subsequent subjective weight calculation.

[0247] For example, suppose we have the following four indicators:

[0248] Security (S1); Economy (S2); Policy (S3); Fairness (S4).

[0249] Experts compare these indicators pairwise based on their experience and construct the judgment matrix Z as follows:

[0250]

[0251] That is, Z 12 =0.6 means safety (S1) is more important than economy (S2); Z 21=0.4 means that economy (S2) is less important than safety (S1); the diagonal element Z ss = 0.5 means that the same indicator has the same importance when compared with itself; the matrix satisfies the symmetry, that is, Z sh =Z hs .

[0252] 2) Calculate the subjective weight and check the consistency. The subjective weight Ψ is calculated using the following formula: s :

[0253]

[0254] Where, It represents the sum of all elements in the sth row of the judgment matrix, that is, the sum of the importance of indicator s relative to all other indicators; is a correction item used to adjust the deviation in the calculation; -1 is an adjustment item used to ensure the rationality of the calculation results.

[0255] It should be noted that the subjective weight reflects the experts' subjective judgment on the relative importance of each indicator.

[0256] Exemplarily, the subjective weight is calculated according to the above judgment matrix Z, where the number of indicators n=4:

[0257] Security

[0258] Economical

[0259] Policy

[0260] Fairness

[0261] 3) Based on the previously calculated values ​​for security, economy, policy, and fairness, calculate the objective weight of each indicator, as shown in the following formula:

[0262]

[0263] Where: s=1,2,…,n; p sl is the normalized data of the sth indicator of sample l; R sl is the sth indicator data of sample l; e s is the entropy value of the sth indicator (i.e. the specific value of each indicator calculated previously); ω s is the weight of the sth indicator of sample l; m is the number of data samples.

[0264] For example, suppose we have the following data, which contains three samples and three indicators:

[0265] Sample No. <![CDATA[Indicator 1 (R 1l )]]> <![CDATA[Indicator 2 (R 2l )]]> <![CDATA[Indicator 3 (R 1l )]]> 1 10 20 30 2 20 30 40 2 30 40 50

[0266] First normalize each indicator and calculate p Sl :

[0267] Indicator 1:

[0268] P 12 =0.3333, P 13 =0.5;

[0269] P 21 =0.2222, P 22 =0.3333, P 23 =0.4444;

[0270] P 31 =0.25, P 32 =0.3333, P 33 =0.4167;

[0271] Calculate the entropy value e s :

[0272] e2=1.4649;e3=1.3919.

[0273] Calculate objective weight:

[0274]

[0275] ω2≈0.312, ω3≈0.264.

[0276] That is, the objective weight index 1 is 0.424, the index 2 is 0.312, and the index 3 is 0.264.

[0277] 4) Calculate the comprehensive weight W s , as shown below:

[0278]

[0279] It should be noted that the calculated subjective weights are combined with the objective weights to form a comprehensive weight, which is used for the final evaluation index fusion. This combination of subjective and objective methods can reduce the bias caused by a single method and improve the reliability of the evaluation results.

[0280] It should be further explained that the calculation process of the comprehensive evaluation score is to combine the subjective and objective weights, perform weighted summation on each indicator, and obtain the final comprehensive evaluation score.

[0281] For example, assuming that the comprehensive weight W1 is 0.412, W2 is 0.285, W3 is 0.223, and W4 is 0.08, the normalized index values ​​are 80, 90, 70, and 60, respectively.

[0282] Then, the comprehensive evaluation score S = 0.412×0.8+0.285×90+0.223×70+0.08×60=79.02.

[0283] Assume that the threshold is 80, then the scenario is considered to have failed the feasibility check.

[0284] S3. Generate a set of random scenarios based on time series modeling of new energy forecast errors, and verify the feasibility of the random scenarios in multiple dimensions through the pre-clearing model.

[0285] Furthermore, time series modeling includes the use of kernel density estimation and Markov chain (MCMC) methods to generate simulated series of forecast errors;

[0286] Random scenarios are generated by superimposing the prediction error simulation sequence onto the predicted output sequence.

[0287] Furthermore, time series modeling includes the use of kernel density estimation and Markov chain methods to generate simulated sequences of forecast errors, including:

[0288] Divide the prediction bins and use Bayesian optimization to determine the predicted output range. A prediction bin is formed by first combining the predicted output and actual output data at the same moment in the historical data into data pairs, and then calculating the prediction error corresponding to each data pair. The data pairs are then arranged from smallest to largest according to the predicted output value and divided into N groups, each group forming a prediction bin.

[0289] It should be noted that Bayesian optimization is a model-based sequential optimization method. Its main idea is to infer the region where the most likely optimal value of the function is located by establishing a posterior probability model of the objective function in each iteration. Then, sampling is performed in this region to obtain new function values ​​and update the posterior probability model. This method proposes a prediction box partitioning method based on Bayesian optimization. The objective function is the average of the determination coefficients of all prediction boxes. The hyperparameter that needs to be adjusted is the predicted output range covered by each prediction box, as shown in Equation (33):

[0290] z * =argmaxg(z) (33);

[0291] Where z is the hyperparameter vector, g(z) is the objective function, and z * is the optimization result of the hyperparameters.

[0292] The main components of the Bayesian optimization framework include a probabilistic surrogate model and an acquisition function. The probabilistic surrogate model is an estimation model for the objective function. It predicts the value of the objective function at unknown points based on existing sampling points and gradually approximates the true objective function through dynamic updates during the optimization process. Commonly used probabilistic surrogate models include Gaussian processes (GP) and random forests (RF). Due to its superior computational efficiency, the random forest method is selected as the model surrogate.

[0293] The acquisition function determines which points are selected for sampling in each iteration to update the surrogate model and continue optimizing the objective function. Commonly used methods include Probability of Improvement (PI), Expected Improvement (EI), and Upper Confidence Bound (UCB). This method uses EI as the acquisition function. Based on the existing surrogate model, it calculates the expected improvement in the objective function I(x) at each possible sampling point x, E[[I(x)], and then selects the point with the highest E[[I(x)] as the next sampling point.

[0294] Fit the error probability distribution within each prediction box based on kernel density estimation;

[0295] It should be noted that the prediction error calculation formula is as follows:

[0296]

[0297] Where, E i is the prediction error at time i, P Pi is the actual output at time i, P Mi For the prediction of time i, C i is the startup capacity at time i.

[0298] It should be further explained that when fitting the prediction errors within the prediction box, since the probability distribution of the prediction errors corresponding to different prediction outputs is different, the same distribution function cannot be used to fit all the prediction errors. This method uses the kernel density estimation method to fit the prediction errors in different prediction boxes separately. Assume that e1, e2, ..., e n Let f(e) be a random sample of prediction errors within a prediction box, and let the probability density function of the random variable E be f(e), then the kernel density estimate of f(e) can be expressed as:

[0299]

[0300] Where n is the sample size, h is the bandwidth, is the kernel function, e k is the kth error sample.

[0301] In kernel density estimation, kernel function and bandwidth are hyperparameters that need to be selected. Different kernel functions have little effect on the accuracy of the model, but the selection of bandwidth will greatly affect the final fitting result. This method selects the commonly used Gaussian kernel function as the probability density estimation model of the prediction error within the prediction box. The kernel density estimation of f(e) can be rewritten as:

[0302]

[0303] Refer to the optimal bandwidth calculation method, the optimal bandwidth h best The calculation formula is:

[0304] h best =1.06σn -1 / 5 (37);

[0305] Where σ is the standard deviation of the sample variable.

[0306] Furthermore, based on the new energy output prediction sequence, the prediction box corresponding to each moment is obtained, and then the probability distribution of the prediction error at each moment is obtained.

[0307] The state transition matrix of the Markov chain is used to generate a time-dependent simulation sequence of prediction errors.

[0308] It should be noted that the discrete state of the prediction error is defined, and the state transfer matrix P of the prediction error and the cumulative state transfer matrix P are calculated based on the historical new energy prediction error data. cum ; If the maximum value in the historical data of the forecast error is E max , the number of states is M, then the definition falls within the range The state of the prediction error between is j (j=1,2,...,M). The Markov chain assumes that the probability of state transition at a certain moment depends only on its previous state. If the prediction error sequence state is represented as X1,X2,...,X i-1 ,X i ,..., then the state X at time i i The conditional probability of the state depends only on the state X at time i-1 i-1 , which has nothing to do with the state before time i-1, that is:

[0309] P(X i |X1,X2,...,X i-1 )=P(X i |Xi-1 ) (38);

[0310] Where P(·|·) is the conditional transition probability.

[0311] The Markov chain uses a state transition matrix and a cumulative state transition matrix to describe the changing patterns of prediction errors at different moments. The state transition matrix P describes the probability of prediction error state transitions between adjacent moments. All prediction error state transition probabilities can be derived from historical data statistics. The expression for matrix P is as follows:

[0312]

[0313] In the formula, element p ab Represents the probability of the prediction error transferring from state a to state b.

[0314] Cumulative state transfer matrix P cum The elements in are obtained by summing the elements in the state transfer matrix P, and the calculation formula is as follows:

[0315]

[0316] Furthermore, generating a prediction error simulation sequence specifically includes:

[0317] Step 1, i=1, the generation of the prediction error simulation sequence begins. The probability distribution function of the prediction error at the initial moment is sampled to obtain the value of the prediction error simulation sequence at the initial moment;

[0318] Step 2: Find the prediction error E at this moment i The corresponding state j, according to the cumulative state transfer matrix P of the prediction error cum , sample and obtain the state j′ of the prediction error simulation sequence at the next moment;

[0319] Step 3: Sample the probability distribution of the prediction error at the next moment and determine whether the state corresponding to the sampling result is consistent with the state obtained in step 2. If so, use the sampling result as the value of the prediction error simulation sequence at the next moment. If not, resample.

[0320] Step 4: i = i + 1. If i reaches the preset number of time periods T, the generation of the forecast error simulation sequence ends, otherwise go to step 3.

[0321] It should be further explained that the prediction error simulation sequence is superimposed with the predicted output sequence to obtain the new energy power sequence scenario, and the pre-clearing model constructed above is used to verify the feasibility of the random scenario from multiple dimensions such as economy, safety and policy adaptability.

[0322] S4. If the feasibility verification is passed, the provincial power grid boundary clearing data is determined to be feasible; if not, the non-market unit and hydropower output plan is adjusted.

[0323] In summary, the beneficial effect of the provincial power grid clearing boundary data verification method of the present invention is to comprehensively and meticulously check the rationality and feasibility of the provincial power grid clearing boundary data by sequentially implementing manual simple verification, typical scenario verification and new energy random scenario verification. Manual simple verification serves as the basis for quickly screening out solutions that obviously do not meet system requirements; on this basis, typical scenario verification, by constructing typical scenarios covering a variety of uncertainty factors, uses pre-clearing models to deeply evaluate the applicability of boundary data in different situations from the dimensions of economy, security, policy, etc., thereby making up for the limitations of manual verification; new energy random scenario verification further focuses on the random fluctuation characteristics of new energy output, and generates a large number of random scenarios close to reality with the help of kernel density estimation and Markov chain, so that boundary data can more accurately respond to the uncertainty of new energy, thereby ensuring that the final boundary data can achieve economic efficiency and effective absorption of new energy while ensuring the safe and stable operation of the system, thereby improving the operational efficiency and reliability of the entire power market.

[0324] Example 2 is the second embodiment of the present invention, which provides a provincial power grid clearing boundary data verification system, including a verification module for manually verifying the provincial power grid operation plan, including power verification and peak load verification;

[0325] The first verification module is used to construct a set of typical scenarios based on the forecast data and perform multi-dimensional feasibility verification on all typical scenarios through the pre-clearing model;

[0326] The second verification module is used to generate a set of random scenarios based on the time series modeling of new energy forecast errors, and to perform multi-dimensional feasibility verification on the random scenarios through the pre-clearing model;

[0327] The determination module is used to determine whether the provincial power grid clearing boundary data is feasible if the feasibility verification is passed; if not, the non-market unit and hydropower output plan is adjusted.

[0328] Example 3 is the third embodiment of the present invention, which differs from the first two embodiments in that:

[0329] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0330] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0331] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0332] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0333] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A provincial power grid clearing boundary data verification method, characterized by: include, Manual verification of provincial power grid operation plans, including power verification and peak load verification; Build a set of typical scenarios based on forecast data, and conduct multi-dimensional feasibility verification of all typical scenarios through pre-clearing models; Generate a set of random scenarios based on time series modeling of new energy forecast errors, and verify the feasibility of the random scenarios in multiple dimensions through the pre-clearing model; If the feasibility verification is passed, it is determined that it is feasible for the provincial power grid to clear boundary data.

2. The provincial power grid clearing boundary data verification method according to claim 1, characterized in that: The power verification is to verify whether the maximum power generation of the starting unit meets the load power in the province; The peak load calibration is performed by verifying whether the total system output at the load peak and valley moments meets the corresponding load requirements.

3. The provincial power grid clearing boundary data verification method according to claim 2, characterized in that: The typical scenario set includes a forecast deviation scenario and a plan adjustment scenario; The forecast deviation scenario is generated by adjusting the forecast values ​​of renewable energy output, load forecast, hydropower output and market quotation; The plan adjustment scenario is generated by adjusting the western power plan, non-market unit output and grid equipment status.

4. The provincial power grid clearing boundary data verification method according to claim 3, characterized in that: The construction of the pre-clearing model includes: Based on boundary data, combined with grid topology, unit quotation information, and grid safety thresholds, a safety-constrained unit combination model for provincial boundary scenarios is established. The establishment of a safety constraint unit combination model under a provincial boundary scenario includes: Construct the objective function of the safety-constrained unit commitment model under the prediction scenario; Construct the constraint function of the safety constraint unit commitment model under the provincial boundary scenario; The safety constraint unit combination model is solved by a solver to obtain the start-up and shutdown plan of each unit.

5. The provincial power grid clearing boundary data verification method according to claim 4, characterized in that: The time series modeling includes using kernel density estimation and Markov chain methods to generate a simulated sequence of prediction errors; The random scenario is generated by superimposing a prediction error simulation sequence onto a predicted output sequence.

6. The provincial power grid clearing boundary data verification method according to claim 5, characterized in that: The time series modeling includes using kernel density estimation and Markov chain methods to generate simulation sequences of prediction errors including: Divide the prediction box and use Bayesian optimization to determine the predicted output range; Fit the error probability distribution within each prediction box based on kernel density estimation; The state transition matrix of the Markov chain is used to generate a time-dependent simulation sequence of prediction errors.

7. The provincial power grid clearing boundary data verification method according to any one of claims 1 to 6, characterized in that: The indicators of the multi-dimensional feasibility verification include: Safety indicators: N-1 pass rate, line load rate and section load rate, power shortage and maximum power shortage, and spare capacity; Economic indicators: grid loss rate, power generation cost, average market electricity price, and extreme electricity price; Policy indicators: proportion of renewable energy power generation and proportion of renewable energy consumption; Fairness indicators: HHI index, Top-m share index.

8. A provincial power grid clearing boundary data verification system, characterized in that: include: Verification module, used to manually verify provincial power grid operation plans, including power verification and peak load verification; The first verification module is used to construct a set of typical scenarios based on the forecast data and perform multi-dimensional feasibility verification on all typical scenarios through the pre-clearing model; A second verification module is used to generate a set of random scenarios based on the time series modeling of new energy forecast errors, and to perform multi-dimensional feasibility verification on the random scenarios through the pre-clearing model; A determination module is used to determine whether the provincial power grid clearing boundary data is feasible if the feasibility verification is passed; If not passed, the non-market units and hydropower output plans will be adjusted.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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