Robust safe economic dispatching method considering super-resolution wind power uncertainty

Abstract

The invention relates to the technical field of economic dispatching of an electric power system, in particular to a robust safe and economic dispatching method considering super-resolution wind power uncertainty, and aims at electric power unbalance and line power flow out-of-limit risks caused by unpredictable nonlinear fluctuation of wind power under super-resolution at moments. A wind power output model which finely considers the super-resolution fluctuation uncertainty is provided based on the wind power predicted value of each moment in the main time scale and the interval variation range of the wind power fluctuation speed in the secondary time scale. And establishing a robust safe and economic dispatching model considering the super-resolution wind power uncertainty by introducing sub-time scale operation feasibility constraints. In order to solve the problem that a model is difficult to directly solve due to a secondary time scale operation constraint containing random variables, on the basis of constraint mechanism analysis, the problem is equivalently transformed into a plurality of deterministic constraints which are expressed by a severe scene and are easy to solve, and rapid solving of the model is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power system economic dispatch, and in particular to a robust and safe economic dispatch method considering super-resolution wind power uncertainty. Background Art

[0002] Wind power output has unpredictable randomness and time-varying volatility. As the supply of flexible resources in new power systems becomes increasingly tight, its large-scale grid connection will trigger frequency and tidal safety risks in the power system. Improper handling will not only affect the full absorption of wind power, but also threaten the safe and stable operation of the power grid.

[0003] To effectively address the adverse effects of wind power output uncertainty on dispatch and improve the power system's ability to accommodate renewable energy generation, scholars at home and abroad have conducted extensive research. Based on how to address wind power uncertainty, uncertain dispatch methods such as stochastic optimization, robust optimization, a combination of stochastic and robust optimization, and artificial intelligence-based methods have been proposed. However, these methods all discretize the study period into several time points at a certain time granularity (e.g., 15 minutes) and then use probabilistic, fuzzy, or interval uncertainty theory to fine-tune the uncertainty at each time point. However, the time-varying transition of wind power output and line flows at super-resolution between time points is approximated in a regular, linear manner, without considering the various irregular and nonlinear fluctuations in wind power output between moments. Given the increasing scarcity of flexibility resources, these methods may not be able to effectively address these unexpected uncertainties, leading to the risk of grid frequency and line power exceeding limits.

[0004] As wind power penetration continues to rise, the safety risks caused by irregular fluctuations in wind power output at different times are gaining increasing attention. In response, many researchers have considered the impact of uncertainty in net load fluctuations on dispatch strategies from the perspectives of system ramping capability and power flow safety. This has effectively improved the control performance of the power grid. However, they have not considered the time smoothing effect of the average wind power output fluctuation rate increasing and decreasing over time. This has resulted in a somewhat crude depiction of the time-dependent fluctuations of uncertainty using time-dependent super-resolution, which has affected the effectiveness of dispatch strategies. Summary of the Invention

[0005] The purpose of the present invention is to overcome the defects of the existing technology and propose a robust and safe economic dispatch method that takes into account the uncertainty of super-resolution wind power. It can automatically identify the potential ramping demand and tidal safety risks of the system under super-resolution, adaptively adjust the base point power and participation factor of the AGC unit, and effectively improve the robustness of the economic dispatch strategy.

[0006] To achieve the above objectives, the present invention adopts the following specific technical solutions:

[0007] The present invention provides a robust, safe and economic dispatch method that considers super-resolution wind power uncertainty, comprising the following steps:

[0008] S1. Constructing a wind power output model that carefully considers super-resolution uncertainty:

[0009] Obtain the power value of each wind farm at the ultra-short-term forecast scheduling moment, the actual output value of each wind farm at the current moment, the maximum upward and downward climbing speeds of each wind farm under super-resolution, and calculate the output variation range of each wind farm under super-resolution;

[0010] A finer sub-time scale is introduced on the basis of the main time scale. Based on the wind power forecast value at each moment of the main time scale and the interval variation range of the wind power fluctuation speed at the sub-time scale, a combination of multiple time granularities at the sub-time scale is adopted to finely characterize the uncertainty of the wind power fluctuation speed between adjacent moments of the main time scale under super-resolution.

[0011] S2. Construct a robust and secure economic dispatch model considering the uncertainty of super-resolution wind power output:

[0012] The objective function is to minimize the sum of the power generation cost of conventional thermal power units and the cost of the upward and downward reserve reserved by AGC units to cope with the uncertainty of wind power during the period. The constraints include primary time scale constraints and secondary time scale constraints.

[0013] The primary timescale constraints include power balance constraints for power generation loads, upper and lower limits for unit output power, unit output power ramping constraints, and line transmission power constraints. The secondary timescale constraints include upper and lower limits for AGC unit output power, AGC unit ramping constraints, AGC unit participation factor constraints, AGC unit standby constraints, and line power transmission capacity constraints.

[0014] S3. Solve the robust and secure economic dispatch model considering the uncertainty of super-resolution wind power output.

[0015] Furthermore, in step S1, the minimum time granularity of the primary time scale is set to ΔT, and the minimum time granularity of the secondary time scale is set to Δt, and they satisfy:

[0016] Δt=ΔT / N;

[0017] Where: N is the number of particles that can be further subdivided under the particle size Δt;

[0018] By combining multiple time granularities at the secondary time scale, the uncertainty of wind power fluctuation speed between adjacent moments at the primary time scale can be precisely characterized at super-resolution:

[0019]

[0020] Where: represents the predicted power of wind farm h at the sub-time scale t; is the maximum upward and downward climbing speed observed at the wind farm h at the kΔt granularity; K is the maximum time granularity considered;

[0021] The predicted power of wind farm h at the beginning and end of a dispatch period in the main time scale is According to the wind power restraint relationship between different granularities, the possible upper and lower limits of wind power at each time in the sub-time scale are derived in reverse order starting from the first and last moments of the scheduling period:

[0022]

[0023] Where, are the upper and lower limits of the output power of wind farm h at time t, calculated from left to right along the time axis; are the upper and lower limits of the output power of wind farm h at time t calculated in reverse time axis order;

[0024] The intersection of the calculated upper and lower limits of wind power at each moment is the upper and lower limits of wind farm output power under multi-time granularity constraints:

[0025]

[0026] Where, are the upper and lower limits of the output power of wind farm h at time t under the sub-time scale;

[0027] The wind power output model that carefully considers super-resolution uncertainty can be expressed as follows:

[0028]

[0029] Furthermore, in step S2, the objective function is to minimize the sum of the power generation cost of the conventional thermal power unit and the cost of the upward and downward reserve reserved by the AGC unit to cope with the uncertainty of wind power during the period:

[0030]

[0031] Where: N G is the number of conventional thermal power units; N AGC is the number of AGC units in conventional units; c i is the unit power generation cost of unit i; is the power of unit i at the main time scale T1; and are the unit upward regulation cost and unit downward regulation cost of AGC unit m respectively; and are the maximum upward regulation capacity and downward regulation capacity reserved by AGC unit m during the dispatch period to cope with sub-timescale wind power fluctuations;

[0032] Due to the large time granularity of the main time scale, it is difficult to observe the power flow safety risks caused by the nonlinear fluctuation of wind power during the scheduling period, which is only observable under super-resolution. Therefore, the main time scale constraints only include the operation constraints of the system and units at the ΔT granularity.

[0033] Power balance constraints of power generation load:

[0034]

[0035] Where N W is the total number of wind farms, N D is the total load, is the predicted value of load d at the main time scale T1, The predicted output of wind farm h at the main time scale T1;

[0036] Upper and lower limit constraints of unit output power:

[0037]

[0038] Where, are the maximum and minimum outputs allowed for unit i respectively;

[0039] Unit output power ramp constraint:

[0040]

[0041] Where, are the upward and downward adjustment rates of the maximum output power of unit i, respectively;

[0042] Line transmission power constraints:

[0043]

[0044]

[0045] Where, f l,max is the maximum allowable transmission power of line l; A l-i 、A l-h 、A l-d are the transfer power factors of the unit, wind farm and load node to line l respectively;

[0046] Assuming that the base power of conventional units, the predicted power of wind power and the load power change linearly in [T0, T1], at any time t in [T0, T1], the base power P of the unit is i,t, wind power forecast and load power P d,t It can be expressed as follows:

[0047]

[0048] AGC unit output power upper and lower limit constraints:

[0049]

[0050] Where, represents the output of AGC unit m when the wind power deviates from the predicted power at the sub-time scale t;

[0051] AGC unit climbing constraints:

[0052]

[0053] AGC unit participation factor constraints:

[0054]

[0055] Where, α m,T is the participation factor of the AGC unit in the period [T0, T1]; represents the random output of wind farm h at the sub-time scale t;

[0056] AGC unit standby constraints:

[0057]

[0058] Where, and They represent the upward regulation capacity and downward regulation capacity reserved by the ACG unit at the sub-time scale t, respectively;

[0059] Line power transmission capacity constraints:

[0060]

[0061] Where p l,t is the base state power of line l, that is, the power flowing through line l when the power of each wind farm is the predicted power; is the transfer power factor of wind farm h to line l taking into account the power adjustment of AGC units.

[0062] Furthermore, in step S3, the robust security economic dispatch model considering the uncertainty of super-resolution wind power output is solved as follows:

[0063] Considering the fluctuation range of wind power, the upper and lower limit constraints of the AGC unit output power are equivalently transformed:

[0064]

[0065] Equivalent transformation of AGC unit climbing constraint:

[0066]

[0067] When t=1, it can be simplified to:

[0068]

[0069] The most demanding scenario considering the fluctuation range of wind power in the sub-time scale is:

[0070]

[0071] When t=2,…,N-1, it can be transformed into:

[0072]

[0073] The most demanding scenarios for wind power in sub-time scales are and This can be further simplified to:

[0074]

[0075] When t=N,

[0076]

[0077] and For the most demanding scenario that needs to be met, it can be equivalently transformed into the following constraints:

[0078]

[0079] Simplified AGC unit reserve constraints:

[0080]

[0081] Equivalent transformation of line power transmission capacity constraint:

[0082] because Available in The value can be taken arbitrarily, so the line power transmission capacity constraint must satisfy the following formula:

[0083]

[0084] Where, is an auxiliary variable.

[0085] The present invention can achieve the following technical effects:

[0086] The robust, safe, and economic dispatch method proposed in this paper considers super-resolution wind power uncertainty. Taking into account the temporal smoothing effect, it uses multiple time granularities to construct a refined model of wind power output uncertainty at super-resolution. This model can better economically track wind power fluctuations. By introducing sub-timescale operational feasibility constraints, a robust, safe, and real-time economic dispatch model that considers super-resolution wind power uncertainty is constructed. This model can effectively address power imbalances and line flow over-limit risks caused by nonlinear fluctuations in wind power. By converting the difficult-to-solve robust, safe, and economic dispatch model directly into an easily solvable deterministic linear programming model through equivalent transformation, the robust, safe, and economic dispatch strategy can be rapidly solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 2 is a flow chart of a robust, secure and economic dispatch method considering super-resolution wind power uncertainty according to an embodiment of the present invention;

[0088] Figure 2 is a diagram of the relationship between primary and secondary time scales provided according to an embodiment of the present invention;

[0089] Figure 3 2. It is a schematic diagram of derivation of sub-time scale wind power fluctuation range provided by an embodiment of the present invention;

[0090] Figure 4 is an improved 6-node system wiring diagram provided according to an embodiment of the present invention;

[0091] Figure 5 is a thermal power unit parameter data diagram provided according to an embodiment of the present invention;

[0092] Figure 6 is a graph of wind farm related parameter data provided according to an embodiment of the present invention;

[0093] Figure 7 is a load data diagram provided according to an embodiment of the present invention;

[0094] Figure 8 is a branch parameter data diagram provided according to an embodiment of the present invention;

[0095] Figure 9 This is a data diagram of system operation under different schemes based on the multi-time granularity characterization of wind power fluctuation speed and the impact of the dispatch strategy provided by an embodiment of the present invention;

[0096] Figure 10 is a comparison chart of operating costs under different participation factor configurations provided by an embodiment of the present invention;

[0097] Figure 11This is a diagram of the power flow exceeding limit process of line 1 provided in an embodiment of the present invention;

[0098] Figure 12 1 is an operating cost data diagram corresponding to two solutions of the impact of super-resolution wind power uncertainty on power flow safety provided by an embodiment of the present invention;

[0099] Figure 13 This is a data diagram of the operating results of the robust scheduling method provided according to an embodiment of the present invention compared with different robust methods. DETAILED DESCRIPTION

[0100] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the following description, identical modules are denoted by identical reference numerals. In the case of identical reference numerals, their names and functions are also identical. Therefore, their detailed description will not be repeated.

[0101] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not constitute a limitation of the present invention.

[0102] The embodiment of the present invention provides a robust, safe and economic dispatch method considering the uncertainty of super-resolution wind power. The overall process is as follows: Figure 1 The specific steps are as follows:

[0103] S1. Construct a wind power output model that carefully considers super-resolution uncertainty.

[0104] Obtain the power value of each wind farm at the ultra-short-term forecast scheduling moment, the actual output value of each wind farm at the current moment, the maximum upward and downward climbing speeds of each wind farm under super-resolution, and calculate the output variation range of each wind farm under super-resolution;

[0105] A more refined secondary time scale is introduced on the basis of the primary time scale. Based on the wind power prediction value at each moment of the primary time scale and the interval variation range of the wind power fluctuation speed at the secondary time scale, a combination of multiple time granularities at the secondary time scale is adopted to finely characterize the uncertainty of the wind power fluctuation speed between adjacent moments of the primary time scale under super-resolution.

[0106] Based on the main time scale, a finer sub-time scale is introduced, and the uncertainty of wind power fluctuation speed between adjacent moments in the main time scale is finely characterized based on the combination of multiple time granularities in the sub-time scale, which improves the observability of wind power output uncertainty in super-time resolution. The specific steps of model construction are as follows:

[0107] Based on the main time scale, a more refined secondary time scale is introduced. The relationship between the main and secondary time scales is as follows: Figure 2As shown, the minimum time granularity of the primary time scale is set to ΔT, and the minimum time granularity of the secondary time scale is set to Δt, and they satisfy:

[0108] Δt=ΔT / N; (1)

[0109] Where: N is the number of particles that can be further subdivided under the particle size Δt.

[0110] By combining multiple time granularities at the secondary time scale, the uncertainty of wind power fluctuation speed between adjacent moments at the primary time scale can be precisely characterized at super-resolution:

[0111]

[0112] Where: represents the predicted power of wind farm h at the sub-time scale t; is the maximum upward and downward climbing speed observed at the wind farm h at the kΔt granularity; K is the maximum time granularity considered.

[0113] The predicted power of wind farm h at the beginning and end of a dispatch period in the main time scale is According to the wind power restraint relationship between moments at different granularities, the sub-time scale wind power fluctuation range is derived as follows: Figure 3 As shown in FIG, the possible upper and lower limits of wind power at each moment of the secondary time scale are derived in sequence (inversely) starting from the first (last) moment of the scheduling period.

[0114]

[0115] Where, are the upper and lower limits of the output power of wind farm h at time t, calculated from left to right along the time axis; They are respectively the upper limit and lower limit of the output power of wind farm h at time t calculated in reverse time axis order.

[0116] The intersection of the upper and lower limits of wind power at each moment calculated by formula (3) is the upper and lower limits of wind farm output power under multi-time granularity constraints, namely:

[0117]

[0118] Where, are the upper and lower limits of the output power of wind farm h at time t under the sub-time scale.

[0119] According to the above, the wind power output model that carefully considers super-resolution uncertainty can be expressed as follows:

[0120]

[0121] S2. Construct a robust and secure economic dispatch model considering the uncertainty of super-resolution wind power output.

[0122] The objective function is to minimize the sum of the power generation cost of conventional thermal power units and the cost of the upward and downward reserve reserved by AGC units to cope with the uncertainty of wind power during the period. The constraints include primary time scale constraints and secondary time scale constraints.

[0123] The primary time scale constraints include power balance constraints of power generation loads, upper and lower limit constraints of unit output power, unit output power ramping constraints, and line transmission power constraints; the secondary time scale constraints include upper and lower limit constraints of AGC unit output power, AGC unit ramping constraints, AGC unit participation factor constraints, AGC unit standby constraints, and line power transmission capacity constraints.

[0124] The uncertainty fluctuation process of wind power as shown in Equation (5) within a dispatching period of the main time scale and the power flow safety risk under super-resolution that may be caused are carefully considered. By optimizing the base power of conventional units and the participation factor of AGC units, the ramp capacity and response capability are reasonably reserved to ensure the safe and economic operation of the power grid. The model construction includes the following steps:

[0125] Since wind power generation itself does not consume fuel, the present invention ignores the cost of wind power and only takes the sum of the power generation cost of conventional thermal power units and the cost of the upward and downward reserve reserved by AGC units to cope with the uncertainty of wind power during the period as the minimum. The following objective function is established:

[0126]

[0127] Where: N G is the number of conventional thermal power units; N AGC is the number of AGC units in conventional units; c i is the unit power generation cost of unit i; is the power of unit i at the main time scale T1; and are the unit upward regulation cost and unit downward regulation cost of AGC unit m respectively; and They are the maximum upward adjustment capacity and downward adjustment capacity reserved by AGC unit m during the dispatch period to cope with sub-timescale wind power fluctuations.

[0128] Main time constraints:

[0129] Due to the large time granularity of the main time scale, it is difficult to observe the power flow safety risks caused by the nonlinear fluctuations of wind power during the scheduling period, which are only observable at super-resolution. Therefore, the main time scale constraints only include the operation constraints of the system and units at the ΔT granularity, as follows;

[0130] 1) Power balance constraints of power generation load:

[0131]

[0132] Where N W is the total number of wind farms, N D is the total load, is the predicted value of load d at the main time scale T1, The predicted output of wind farm h at the main time scale T1.

[0133] 2) Upper and lower limit constraints of unit output power:

[0134]

[0135] Where, are the maximum and minimum outputs allowed for unit i respectively.

[0136] 3) Unit output power ramp constraints:

[0137]

[0138] Where, are the maximum upward and downward adjustment rates of the output power of unit i, respectively.

[0139] 4) Line transmission power constraints:

[0140]

[0141] Where, f l,max is the maximum allowable transmission power of line l; A l-i 、A l-h 、A l-d are the transfer power factors of the unit, wind farm and load node to line l respectively.

[0142] Secondary time constraints:

[0143] The purpose of considering the feasibility constraints of sub-time scale operation is to drive the main time scale decision to take into account the potential power imbalance and power flow safety risks under super-resolution. That is, in the decision of the base point power and participation factor of thermal power units in the main time scale, the ability of the sub-time scale to cope with wind power fluctuations is coordinated and taken into account.

[0144] Assuming that the base power of conventional units, the predicted power of wind power and the load power change linearly in [T0, T1], at any time t in [T0, T1], the base power P of the unit is i,t , wind power forecast and load power P d,t It can be expressed as follows:

[0145]

[0146] For any uncertain scenario within the wind power uncertainty set described by equation (5), considering the sub-time scale power balance and line flow constraints, each AGC unit should meet the following constraints:

[0147] 1) AGC unit output power upper and lower limit constraints:

[0148]

[0149] Where, It represents the output of AGC unit m when the wind power deviates from the predicted power at the sub-time scale t.

[0150] 2) AGC unit climbing constraints:

[0151]

[0152] 3) AGC unit participation factor constraints:

[0153] During the dispatch process, the unbalanced power caused by wind power fluctuations is distributed among the AGC units according to the participation factor. The actual output of the AGC units must meet the following constraints:

[0154]

[0155] Where, α m,T is the participation factor of the AGC unit in the period [T0, T1]; represents the random output of wind farm h at the sub-time scale t.

[0156] 4) AGC unit standby constraints:

[0157] Due to physical limitations, the regulation capacity that each AGC unit can provide is limited at this time scale, namely:

[0158]

[0159] Where, and They represent the upward adjustment capacity and downward adjustment capacity reserved by the ACG unit at the sub-time scale t.

[0160] 5) Line power transmission capacity constraints:

[0161]

[0162] Where p l,t is the base state power of line l, that is, the power flowing through line l when the power of each wind farm is the predicted power; is the transfer power factor of wind farm h to line l taking into account the power adjustment of AGC units.

[0163] The model composed of equations (5) to (27) is a robust and secure economic dispatch model that carefully considers the uncertainty of super-resolution wind power output. It uses the multi-time granularity combination shown in equation (5) to finely describe the uncertainty of wind power under super-resolution. It can avoid the excessively aggressive decision-making caused by only considering the primary time scale due to excessive time granularity and ignoring the potential ramping risk of the secondary time scale. It can also avoid the excessively harsh description of wind power fluctuation scenarios and overly conservative decision-making results caused by ignoring the time smoothing effect of wind power when only considering the minimum time granularity of the secondary time scale. At the same time, the AGC unit in this model can adaptively configure the participation factor according to the potential ramping demand under super-resolution (Equation (16)) and the power flow security constraint (Equations (24) to (27)), thereby improving the robustness and security of the economic dispatch strategy.

[0164] S3. Solve the robust and secure economic dispatch model considering the uncertainty of super-resolution wind power output.

[0165] For the robust and secure real-time economic dispatch model that carefully considers the uncertainty of super-resolution wind power output, it can be seen from the analysis that the random variables contained in the sub-time scale constraints are This makes the model difficult to solve directly. To this end, this section performs an equivalent transformation on the constraints containing random variables, converting it from a robust optimization problem containing random variables to a deterministic optimization problem described by several severe scenarios.

[0166] 1) Equivalent transformation of upper and lower limit constraints of AGC unit output power.

[0167] Considering the fluctuation range of wind power, formula (15) can be equivalent to the following form:

[0168]

[0169] 2) Equivalent transformation of AGC unit climbing constraints.

[0170] Substituting formula (17) into formula (16), we can obtain:

[0171]

[0172] When t=1, equation (30) can be simplified to:

[0173]

[0174] Considering the fluctuation range of wind power in the sub-time scale, the most demanding scenario corresponding to Equation (31) is:

[0175]

[0176] When t = 2,…,N-1, Equation (30) can be further transformed into:

[0177]

[0178] The wind power in the sub-time scale for the most demanding scenario of formula (33) is and Therefore, formula (33) can be further simplified to:

[0179]

[0180] When t=N, equation (30) can be transformed into:

[0181]

[0182] and This is the most demanding scenario that Equation (35) needs to satisfy. Equation (35) can be equivalently transformed into the following constraints:

[0183]

[0184] 3) Simplified AGC unit standby constraints.

[0185] Among the AGC unit reserve constraints described by equations (19) to (23), only equation (24) contains random variables. By performing an equivalent transformation on equation (21) according to the most demanding wind power fluctuation scenario, we can obtain:

[0186]

[0187] 4) Equivalent transformation of line power transmission capacity constraints.

[0188] For formula (24) and formula (25) containing random variables, since Available in The value can be taken arbitrarily, so the line power transmission capacity constraint must satisfy the following formula:

[0189]

[0190] From formula (27), we can see that The expression contains the variable α to be determined m,T, its positive or negative value cannot be determined, so it is impossible to directly find the corresponding expression for the most severe scenario of Equation (38) and Equation (39). The robust linearization method is used to convert it into an equivalent analytical expression of Equation (40) and Equation (41).

[0191]

[0192]

[0193] Where, are newly introduced auxiliary variables.

[0194] When the coefficient matrix When, due to Heng is established, at this time The value of Therefore, the part of the second polynomial in equation (40) related to the wind farm h can be expressed as Corresponding to When the maximum value of the left side term of the first equation of equation (43) is obtained, the constraint of equation (38) is guaranteed to hold. hour, at this time The value of is 0. Therefore, the part of the second polynomial in the first equation in equation (40) related to the wind farm h can be expressed as for When the maximum value of the left side term of the first equation of equation (40) is reached, the constraint of equation (38) can also be guaranteed to hold. Therefore, the power flow constraint of the transformed equation (40) can be calculated based on The positive and negative values of are adaptively selected within the wind power disturbance range, so that the left-hand side of Equation (38) can always reach the maximum value, ensuring the robustness of the result. Similarly, the validity of the constraint in Equation (42) can also be verified by the same proof method.

[0195] Through the above analysis, it can be seen that the line power transmission capacity constraint formula (38) and formula (39) can be transformed from a nonlinear form containing random variables to a deterministic linear constraint form based on severe scenarios in formula (40) and formula (41). Moreover, formula (40) and formula (41) are completely equivalent to formula (38) and formula (39), which can ensure the accuracy of the transformed constraints.

[0196] It can be seen that through constraint equivalent transformation, the robust and secure real-time economic dispatch model that carefully considers the uncertainty of super-resolution wind power output can be transformed into an equivalent deterministic linear optimization problem consisting of Equations (6)-(14), (19)-(20), (22)-(23), (26)-(29), (32), (34), (36)-(37), and (40)-(41), which can be directly solved by calling the CPLEX solver.

[0197] The feasibility of the solution in the embodiment is verified below with reference to specific examples.

[0198] This example is based on a modified 6-node system with the following system topology: Figure 4 As shown in the figure, the example includes 3 thermal power units and 1 wind farm. The thermal power unit data can be found in Figure 5 , where G1 and G2 are AGC units, and G3 is non-AGC unit; wind farm data can be found in Figure 6 ; Load data see Figure 7 ;Branch parameters see Figure 8 .

[0199] 1) Analysis of the impact of wind power fluctuation speed characterization on scheduling strategy based on multiple time granularities.

[0200] In order to verify the impact of the wind power fluctuation speed uncertainty characterization method based on multiple time granularities proposed in the embodiment of the present invention on the scheduling strategy, the following three schemes are set up for comparative analysis.

[0201] Solution 1: Use the traditional economic dispatch model that only considers the main time scale, that is, the model composed of equations (9) to (14).

[0202] Solution 2: The model proposed in the embodiment does not consider the time smoothing effect of wind power fluctuations, that is, it is assumed that the maximum average fluctuation speed of wind power at each time granularity is equal to the maximum average fluctuation speed at the minimum time granularity of the sub-time scale, that is, it is assumed that

[0203] Solution 3: The model proposed in the embodiment takes into account the time smoothing effect of wind power fluctuation speed under multi-granularity, that is, assuming

[0204] Figure 9 The total system cost, regulation capacity and operating base point of the AGC unit corresponding to the three schemes are given.

[0205] Depend on Figure 9It can be seen that since Scheme 1 only considers the main time scale constraints, there is no need to reserve backup capacity to deal with the uncertainty of wind power output at the sub-time scale. Therefore, its total cost is the lowest, and the reserved upward and downward adjustment capacities are both 0. It will be difficult to deal with the situation where the wind farm output deviates from the predicted scenario under super-resolution, which is not conducive to maintaining safe and stable operation of the system; compared with Scheme 1, Schemes 2 and 3 take into account the nonlinear fluctuations of wind power at a smaller time granularity at the sub-time scale. The system needs to configure more backup, which causes the operating base point of the unit to deviate from the optimal economic operating position, and the total system operation cost is also increased accordingly.

[0206] A comparative analysis of Schemes 2 and 3 shows that, because Scheme 2 fails to account for the time-smoothing effect of wind power fluctuations, the sub-timescale wind power fluctuations depicted are more dramatic, placing more stringent requirements on the ramping capability of the units. This forces the system to reserve more spare capacity and increases the total cost of Scheme 2. Scheme 3, which accounts for the time-smoothing effect of wind power fluctuations, depicts relatively gentle wind power fluctuations. This results in an increase in the operating base point of the more economical unit G1 and a corresponding decrease in the operating base point of the less economical unit G2, thereby improving the economic efficiency of system operation.

[0207] It can be seen that the use of multiple time granularities to finely characterize the uncertainty of wind power ramping speed under super-time resolution can not only avoid the risk of ignoring the sharp fluctuations of wind power due to excessive granularity, but also avoid the increase in system operating costs due to the excessively harsh characterization of wind power fluctuation scenarios by only considering the minimum time granularity of the sub-time scale.

[0208] 2) The impact of AGC unit participation factor configuration on the model.

[0209] The participation factor reflects the contribution of each AGC unit in smoothing wind power fluctuations and influences the decision-making results. To compare the impact of the participation factor setting methods on the decision-making results, the adaptive configuration method proposed in the embodiment is referred to as Method 1; in Method 2, the participation factor of each AGC unit is set proportional to the unit capacity; in Method 3, the participation factor of each AGC unit is set proportional to the unit response speed. In Method 4, the participation factor of each AGC unit is set based on the unit's economic indicators; and in Method 5, the participation factor of each AGC unit is set proportionally.

[0210] Figure 10The operating costs of the system under five different participation factor configuration methods are given. Analysis shows that as the participation factor of unit G1 (G2) continues to decrease (increase), the total cost of the system shows an upward trend. This is because the unit power generation cost and regulation cost of unit G1 are both lower than those of unit G2, and unit G1 has better climbing performance. Increasing the participation factor of unit G1 makes it easier for the system to track wind power fluctuations. It can be seen that the configuration method of the participation factor will directly affect the scheduling results. Configuring the unit participation factor according to the method proposed in the embodiment can better achieve the goal of tracking wind power fluctuations in an economical manner.

[0211] 3) The impact of super-resolution wind power uncertainty on power flow safety.

[0212] To illustrate the impact of super-resolution wind power uncertainty on network security and verify the model's ability to cope with power flow exceeding the limit caused by super-resolution wind power uncertainty, the following two schemes are designed for analysis:

[0213] Scheme 1: Taking into account the uncertainty of super-resolution wind power, but only considering the power transmission constraints of the main time scale line, without considering the power transmission constraints of the sub-time scale super-resolution line.

[0214] Solution 2: The model proposed in the embodiment, that is, considering the sub-time scale line power transmission constraint on the basis of Solution 1.

[0215] like Figure 11 As shown in Figure 1, since Scheme 1 only considers the power flow safety constraints of the primary time scale and does not consider the safety risks caused by the nonlinear fluctuation of line power under super-resolution, for a certain wind power fluctuation scenario described by Equation (8), the power flow of line 1 does not exceed the limit at t = 0 min and t = 15 min when the primary time scale is observable, but exceeds the limit at t∈[4,14] on the secondary time scale.

[0216] Figure 12 The corresponding operating costs for two schemes are presented. Analysis shows that the operating cost of Scheme 2 is USD 12,659, USD 30 higher than that of Scheme 1. This is because Scheme 2 accounts for the impact of nonlinear fluctuations in super-resolution wind power output on network security. To avoid the over-limit power flow on Line 1 under super-resolution, as in Scheme 1, the economic allocation of unit power in Scheme 1 must be changed. Therefore, when the impact of nonlinear fluctuations in super-resolution wind power on power flow security is not considered, the results are often overly optimistic, failing to ensure that the power flow on the line within the time period remains within the safe operating range, thus endangering the safe and stable operation of the system.

[0217] 4) Comparison of robust scheduling methods.

[0218] Figure 13A comparison of the proposed method in the example with other robust dispatch methods is presented. It can be seen that Method 1 only considers the fluctuation range of wind power output at each moment in the sub-timescale, without considering the constraints of super-resolution wind power fluctuation speed and power flow safety constraints. The resulting economic dispatch strategy has an operating cost of 13,108 USD. Compared to Method 1, Method 2 considers the restraining effect of sub-timescale wind power fluctuations, eliminating fluctuation scenarios that are impossible in Method 1 due to wind power output fluctuation speed constraints. This provides a more refined characterization of wind power output uncertainty under super-resolution. The corresponding operating cost is reduced from 13,269 USD to 12,629 USD, a reduction of 640 USD. Building on Method 2, the method proposed in the example further considers sub-timescale power flow safety constraints. Although the system operating cost is slightly increased, it can avoid the possibility of power flow exceeding the limit at the sub-timescale. Furthermore, the additional 30 USD in the proposed method compared to Method 2 reflects the economic cost of improving the robustness and safety of the economic dispatch strategy.

[0219] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0220] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

[0221] The above specific embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made based on the technical concept of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A robust and secure economic dispatch method considering super-resolution wind power uncertainty, characterized by: The steps include: S1. Constructing a wind power output model that carefully considers super-resolution uncertainty: Obtain the power value of each wind farm at the ultra-short-term forecast scheduling moment, the actual output value of each wind farm at the current moment, the maximum upward and downward climbing speeds of each wind farm under super-resolution, and calculate the output variation range of each wind farm under super-resolution; A finer sub-time scale is introduced on the basis of the main time scale. Based on the wind power forecast value at each moment of the main time scale and the interval variation range of the wind power fluctuation speed at the sub-time scale, a combination of multiple time granularities at the sub-time scale is adopted to finely characterize the uncertainty of the wind power fluctuation speed between adjacent moments of the main time scale under super-resolution. S2. Construct a robust and secure economic dispatch model considering the uncertainty of super-resolution wind power output: The objective function is to minimize the sum of the power generation cost of conventional thermal power units and the cost of the upward and downward reserve reserved by AGC units to cope with the uncertainty of wind power during the period. The constraints include primary time scale constraints and secondary time scale constraints. The primary timescale constraints include power balance constraints for power generation loads, upper and lower limits for unit output power, unit output power ramping constraints, and line transmission power constraints. The secondary timescale constraints include upper and lower limits for AGC unit output power, AGC unit ramping constraints, AGC unit participation factor constraints, AGC unit standby constraints, and line power transmission capacity constraints. S3. Solve the robust and secure economic dispatch model considering the uncertainty of super-resolution wind power output.

2. The robust, secure and economic dispatch method considering super-resolution wind power uncertainty according to claim 1 is characterized in that: In step S1, the minimum time granularity of the primary time scale is set to ΔT, and the minimum time granularity of the secondary time scale is set to Δt, and they satisfy: Δt=ΔT / N; Where: N is the number of particles that can be further subdivided under the particle size Δt; By combining multiple time granularities at the secondary time scale, the uncertainty of wind power fluctuation speed between adjacent moments at the primary time scale can be precisely characterized at super-resolution: Where: represents the predicted power of wind farm h at the sub-time scale t; is the maximum upward and downward climbing speed observed at the wind farm h at the kΔt granularity; K is the maximum time granularity considered; The predicted power of wind farm h at the beginning and end of a dispatch period in the main time scale is According to the wind power restraint relationship between different granularities, the possible upper and lower limits of wind power at each time in the sub-time scale are derived in reverse order starting from the first and last moments of the scheduling period: Where, are the upper and lower limits of the output power of wind farm h at time t, calculated from left to right along the time axis; are the upper and lower limits of the output power of wind farm h at time t calculated in reverse time axis order; The intersection of the calculated upper and lower limits of wind power at each moment is the upper and lower limits of wind farm output power under multi-time granularity constraints: Where, are the upper and lower limits of the output power of wind farm h at time t under the sub-time scale; The wind power output model that carefully considers super-resolution uncertainty can be expressed as follows:

3. The robust, safe and economic dispatch method considering super-resolution wind power uncertainty according to claim 2 is characterized in that: In step S2, the objective function is to minimize the sum of the power generation cost of conventional thermal power units and the cost of increasing and decreasing reserve reserves reserved by AGC units to cope with the uncertainty of wind power during the period: Where: N G is the number of conventional thermal power units; N AGC is the number of AGC units in conventional units; c i is the unit power generation cost of unit i; is the power of unit i at the main time scale T1; and are the unit upward regulation cost and unit downward regulation cost of AGC unit m respectively; and are the maximum upward regulation capacity and downward regulation capacity reserved by AGC unit m during the dispatch period to cope with sub-timescale wind power fluctuations; Due to the large time granularity of the main time scale, it is difficult to observe the power flow safety risks caused by the nonlinear fluctuation of wind power during the scheduling period, which is only observable under super-resolution. Therefore, the main time scale constraints only include the operation constraints of the system and units at the ΔT granularity. Power balance constraints of power generation load: Where N W is the total number of wind farms, N D is the total load, is the predicted value of load d at the main time scale T1, The predicted output of wind farm h at the main time scale T1; Upper and lower limit constraints of unit output power: Where, are the maximum and minimum outputs allowed for unit i respectively; Unit output power ramp constraint: Where, are the upward and downward adjustment rates of the maximum output power of unit i, respectively; Line transmission power constraints: Where, f l,max is the maximum allowable transmission power of line l; A l-i 、A l-h 、A l-d are the transfer power factors of the unit, wind farm and load node to line l respectively; Assuming that the base power of conventional units, the predicted power of wind power and the load power change linearly in [T0, T1], then at any time t in [T0, T1], the base power P of the unit is i,t , wind power forecast and load power P d,t It can be expressed as follows: AGC unit output power upper and lower limit constraints: Where, represents the output of AGC unit m when the wind power deviates from the predicted power at the sub-time scale t; AGC unit climbing constraints: AGC unit participation factor constraints: Where, α m,T is the participation factor of the AGC unit in the period [T0, T1]; represents the random output of wind farm h at the sub-time scale t; AGC unit standby constraints: Where, and They represent the upward regulation capacity and downward regulation capacity reserved by the ACG unit at the sub-time scale t, respectively; Line power transmission capacity constraints: Where p l,t is the base state power of line l, that is, the power flowing through line l when the power of each wind farm is the predicted power; is the transfer power factor of wind farm h to line l taking into account the power adjustment of AGC units.

4. The robust, secure and economic dispatch method considering super-resolution wind power uncertainty according to claim 3 is characterized in that: In step S3, the robust security economic dispatch model considering the uncertainty of super-resolution wind power output is solved as follows: Considering the fluctuation range of wind power, the upper and lower limit constraints of the AGC unit output power are equivalently transformed: Equivalent transformation of AGC unit climbing constraint: When t=1, it can be simplified to: The most demanding scenario considering the fluctuation range of wind power in the sub-time scale is: When t=2,…,N-1, it can be transformed into: The most demanding scenarios for wind power in sub-time scales are and This can be further simplified to: When t=N, and For the most demanding scenario that needs to be met, it can be equivalently transformed into the following constraints: Simplified AGC unit reserve constraints: Equivalent transformation of line power transmission capacity constraint: because Available in The value can be taken arbitrarily, so the line power transmission capacity constraint must satisfy the following formula: Where, is an auxiliary variable.

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