Wavelet-based wind power time-sharing balance loss calculation method, terminal and medium

Abstract

The application discloses a wind power time-sharing balance loss calculation method based on volatility, a terminal and a medium, relates to the technical field of wind power balance technology, and solves the problem of how to find the real value of wind power and quantitatively evaluate the real value in real time in the prior art. Technical scheme points are as follows: total balance loss of wind power in a scheduling period is solved by comparing system total operation loss difference of optimization scheduling results based on a prediction scenario and an equivalent scenario; an index, i.e., hourly relative volatility, for quantitatively describing time sequence evolution characteristics of wind power relative to load fluctuation power is obtained; a corresponding mathematical model and algorithm are established; local fluctuation influence factors are obtained based on the hourly relative volatility; total balance loss of wind power in the scheduling period is allocated time by time; wind power time-sharing balance loss is obtained; and the purpose of quantitatively evaluating wind power in real time according to the real value of wind power is achieved.

Description

Technical Field

[0001] This invention relates to the field of wind power balancing technology, and more specifically, to a method, terminal and medium for calculating wind power time-sharing balancing losses based on volatility. Background Technology

[0002] Wind power development has been rapid, and the situation of wind curtailment has improved significantly year by year. The more proactive goal at this stage should be to further improve the power trading mechanism and price incentives, based on enhancing wind power absorption capacity, to promote the sustainable development of both wind power and the power grid. The key to achieving this goal lies in clarifying the time-of-use losses caused by wind power grid connection on system operation, which places higher demands on identifying wind power losses on short timescales. Therefore, how to discover the true value of wind power and conduct real-time quantitative assessment is an urgent problem to be solved. Summary of the Invention

[0003] The purpose of this invention is to provide a method, terminal, and medium for calculating wind power time-sharing balance loss based on volatility. The method uses a dynamic scheduling model to obtain the total wind power balance loss and, based on the local fluctuation characteristics of wind power relative to load in each target time period of the scheduling cycle, distributes the total wind power balance loss to each target time period to obtain the wind power time-sharing balance loss, thereby achieving the goal of real-time quantitative evaluation of the true value of wind power.

[0004] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0005] A method for calculating time-of-use wind power balance losses based on volatility, comprising the following steps:

[0006] Construct a dynamic scheduling model;

[0007] Based on the dynamic scheduling model, the total wind power balance loss during the scheduling period is obtained;

[0008] Extract the characteristic quantity of the local fluctuation degree of wind power relative load in each target time period within the scheduling cycle;

[0009] Based on the aforementioned characteristic quantities, the total wind power balance loss is allocated to each target time period to obtain the wind power time-sharing balance loss.

[0010] Furthermore, the construction process of the dynamic scheduling model is as follows:

[0011] Obtain the power generation loss of thermal power units, start-up and shutdown loss of thermal power units, operation and maintenance loss of wind power, and reserved spinning reserve loss of thermal power units under the conditions of load and system safe operation constraints.

[0012] After solving the power generation loss, start-up and shutdown loss, wind power operation and maintenance loss, and reserved spinning reserve loss of the thermal power unit using the cplex solver, a dynamic scheduling model is obtained.

[0013] Furthermore, the process for obtaining the total wind power balance loss within the scheduling period is as follows:

[0014] The dynamic scheduling model obtains the total system balance loss of the wind power predicted output scenario when the unit output constraint in the constraint conditions meets the first condition.

[0015] The dynamic scheduling model obtains the total system balance loss of the wind power equivalent output scenario when the unit output constraint in the constraint conditions meets the second condition.

[0016] The difference between the total system balance loss of the predicted wind power output scenario and the total system balance loss of the equivalent wind power output scenario is calculated to obtain the total wind power balance loss within the scheduling period.

[0017] Furthermore, the extraction process of the characteristic quantity of the degree of local fluctuation of wind power relative to load is as follows:

[0018] Extract the continuous fluctuation state of wind power relative load power from the real-time triangular coefficient diagram of the wind power relative load fluctuation power sequence for the target period;

[0019] The suboptimal solution of the continuous fluctuation state quantity of wind power relative to load power is determined by the grey relational analysis method, and the characteristic quantity of the local fluctuation degree of wind power relative to load is obtained.

[0020] Furthermore, the extraction of the continuous fluctuation state quantity of wind power relative to load power specifically involves:

[0021] Input the wind power relative load fluctuation power sequence at a preset time scale for each target time period;

[0022] Starting from the first data point of the wind power relative load fluctuation power sequence, find the set of power maxima and minima of the sequence in sequence to form a new wind power relative load fluctuation power sequence.

[0023] In the new wind power relative load fluctuation power sequence, the line connecting two adjacent data points intersects with the vertical and horizontal directions to form a power triangle;

[0024] Calculate the amplitude U and phase angle θ of each power triangle in the new wind power relative load fluctuation power sequence to obtain the continuous fluctuation state quantity of wind power relative load power.

[0025] Furthermore, the new wind power relative load fluctuation power sequence does not contain non-extreme points.

[0026] Furthermore, the process of obtaining the characteristic quantity of the degree of local fluctuation of wind power relative to load is as follows:

[0027] Using the continuous fluctuation state of the relative wind power load during the target period as the event set, and the amplitude and phase angle as the strategy set, a situation set is constructed.

[0028] Based on the decision-making objectives, determine the sample values ​​of the effects of the situation concentration strategy;

[0029] Based on the grey relational degree between the effect sample value and the optimal effect sample value, the suboptimal solution of the continuous fluctuation state quantity of wind power relative to load power is obtained.

[0030] Furthermore, the process of obtaining the time-sharing loss of wind power is as follows:

[0031] After normalizing and summing the characteristic quantities of the local fluctuation of wind power relative load in each target period, the relative fluctuation rate of the target period is obtained.

[0032] Based on the relative volatility of the target period, determine the local volatility influencing factors of the relative volatility of the target period;

[0033] Based on the local fluctuation impact factor, the total wind power balance loss is allocated to each target time period to obtain the wind power time-sharing balance loss.

[0034] An electronic device includes: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the method.

[0035] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The total wind power balance loss is obtained by using a dynamic scheduling model. Based on the characteristics of the local fluctuation of wind power relative to load in each target period of the scheduling cycle, the total wind power balance loss is allocated to each target period to obtain the time-sharing balance loss of wind power, so as to achieve the purpose of real-time quantitative evaluation of wind power according to its true value. Attached Figure Description

[0038] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are part of this application, do not constitute a limitation on the embodiments of the invention. In the drawings:

[0039] Figure 1 This is a schematic diagram of the wind power time-sharing balance loss calculation method based on volatility in this embodiment;

[0040] Figure 2This is a schematic diagram of the real-time trigonometric coefficients of the wind power relative load fluctuation power sequence at a 5-minute time scale in the wind power time-sharing balance loss calculation method based on volatility in this embodiment.

[0041] Figure 3 This is a schematic diagram of the wind power equivalent alternative power source structure used in the example in this embodiment;

[0042] Figure 4 This is the improved IEEE 30-node system topology used in the example in this embodiment;

[0043] Figure 5 This is a schematic diagram of wind power output scenarios with different grid-connected power volumes but consistent fluctuation trends used in the examples in this embodiment;

[0044] Figure 6 This is a schematic diagram of typical daily wind power, load forecast power, and hourly relative volatility used in the calculation examples in this embodiment;

[0045] Figure 7 This is a schematic diagram showing the comparison curves of wind power time-sharing balance loss under different penetration rates obtained from the calculation examples in this embodiment;

[0046] Figure 8 This is a schematic diagram of wind power output scenarios with different fluctuation levels used in the examples in this embodiment;

[0047] Figure 9 for Figure 7 Schematic diagram of wind power output, load forecast power and hourly relative volatility under different scenarios, where (a) represents wind power scenario 2-1; (b) represents wind power scenario 2-2; (c) represents wind power scenario 2-3; (d) represents wind power scenario 2-4;

[0048] Figure 10 This is a schematic diagram showing the power output fluctuation of each wind farm obtained from the calculation examples in this embodiment. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0050] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0051] Example: A method for calculating time-of-use wind power balancing losses based on volatility, along with the terminal and medium.

[0052] like Figure 1 As shown, a method for calculating wind power time-of-use balancing losses based on volatility, a terminal, and a medium include:

[0053] A method for calculating time-of-use wind power balance losses based on volatility, comprising the following steps:

[0054] Construct a dynamic scheduling model;

[0055] The construction process of the dynamic scheduling model is as follows: Obtain the power generation losses, start-up and shutdown losses, wind power operation and maintenance losses, and reserved spinning reserve losses of thermal power units under the constraints of load and system safe operation; solve for these losses using a CPLEX solver to obtain the dynamic scheduling model.

[0056] Specifically, the dynamic economic optimization scheduling model that takes wind power into account is constructed. The dynamic economic optimization scheduling model optimizes the start-up, shutdown and spinning reserve configuration of thermal power and thermal power units while meeting the load and system safety operation constraints, so as to minimize the total system operating loss during the scheduling cycle. The model is then optimized and solved using a cplex solver.

[0057] The dynamic economic optimization scheduling model includes the power generation losses of thermal power units, the start-up and shutdown losses of thermal power units, the operation and maintenance losses of wind power, and the losses of the reserved spinning reserve of thermal power units.

[0058] Thermal power unit power generation loss C G The key to modeling is to consider the unit valve point effect and reflect it as a sinusoidal correction component in the conventional unit cost function.

[0059]

[0060] C Gi,t (P Gi,t ) = a i P 2 Gi,t +b i P Gi,t +c i (2)

[0061]

[0062] In the formula, T represents a scheduling cycle; N G Indicates the number of thermal power units; U i,t U represents the state of the i-th thermal power unit at time t. i,tA value of 1 indicates that the unit is in operation, while a value of 0 indicates that the unit is in shutdown; P Gi,t C represents the planned output of the i-th thermal power unit at time t; Gi,t (P Gi,t ) represents the energy consumption of the i-th thermal power unit at time t, where a i b i and c i E represents the coal consumption coefficient of a thermal power unit. i,t (P Gi,t ) represents the additional energy consumption of the i-th thermal power unit due to the valve point effect, where d i and e i It is the coal consumption coefficient of thermal power units that takes into account the valve point effect; It is the minimum output of unit i.

[0063] Taking into account the power generation losses C of the aforementioned thermal power units G The scheduling problem of functions is a non-convex programming model that is difficult to solve. Therefore, the power generation loss C of thermal power units is also a factor. G A piecewise linear approximation is performed, expressed by the following formula:

[0064]

[0065]

[0066] In the formula, L i K represents the total number of linear segments in the loss function of unit i; i,t,j 0 and B i,t,j Let P be the slope and intercept of the j-th linear segment of the loss function of unit i at time t; Gi,t,j Z represents the output sub-variable of unit i at time t corresponding to the j-th linear segment; i,t,j To be with P Gi,t,j The corresponding binary variable.

[0067] Secondly, consider the start-up and shutdown losses C of thermal power units. SU And wind power operation and maintenance losses C W .

[0068]

[0069]

[0070]

[0071] In the formula, This represents the starting loss of thermal power unit i; Y represents the downtime loss of thermal power unit i. i,t and Z i,t It is a 0-1 variable. When Y i,tA value of 1 indicates that the i-th unit is in the start-up process during the t-th scheduling cycle; otherwise, a value of 0 indicates that the i-th unit is in the start-up process. i,t A value of 1 indicates that the i-th generating unit is in a shutdown process during the t-th scheduling cycle; otherwise, it is 0. W P represents the unit operation and maintenance loss of a wind farm. Wj,t N represents the planned output of wind farm j at time t; W This represents the number of wind farms.

[0072] Finally, there is the rotational reserve loss C for thermal power units. SR :

[0073]

[0074] In the formula, ρ i UP ρ i DOWN These are the standby prices for thermal power unit i at time t, above and below. These are the reserved spare capacities for vertical rotation.

[0075] In summary, the dynamic scheduling model is as follows:

[0076] minTOC=min(C G +C SU +C SR +C W (10)

[0077] The system's safe operation constraints include:

[0078] (1) System active power balance constraints:

[0079]

[0080] In the formula, P L,t This represents the total predicted load power at time t.

[0081] (2) Unit output constraints:

[0082]

[0083]

[0084] In the formula, This represents the upper and lower limits of the output of thermal power unit i.

[0085] (3) Climbing constraints of thermal power units:

[0086]

[0087] In the formula, ΔP i UPand ΔP i DOWN These represent the limits of the upward and downward ramp power per unit time for thermal power unit i, respectively; Δt is the dispatch time interval.

[0088] (4) Minimum start-stop time constraint:

[0089]

[0090] In the formula, T i off and T i on These represent the minimum continuous shutdown and minimum continuous start-up times for thermal power unit i, respectively.

[0091] (5) Rotational spare constraint:

[0092]

[0093]

[0094] In the formula, and These represent the upward and downward reserve requirements for system load balancing and wind power fluctuations at time t, respectively.

[0095] (6) Active power flow constraints of the line:

[0096]

[0097] In the formula, N is the total number of nodes in the system; G li This represents the change in active power flow on the l-th line caused by the injection of a unit of active power at node i. Let a and b represent the elements in the i-th row and j-th column of the node association matrix for thermal power units and wind farms, respectively. If unit j is connected to node i, then a ij =1, conversely, a kj =0, k≠i; This represents the maximum active power transmission capacity of the l-th line.

[0098] Based on the dynamic scheduling model, the total wind power balance loss during the scheduling period is obtained;

[0099] The process of obtaining the total wind power balance loss during the scheduling period is as follows:

[0100] The dynamic scheduling model obtains the total system balance loss of the wind power predicted output scenario when the unit output constraint in the constraint conditions meets the first condition.

[0101] The dynamic scheduling model obtains the total system balance loss of the wind power equivalent output scenario when the unit output constraint in the constraint conditions meets the second condition.

[0102] The difference between the total system balance loss of the predicted wind power output scenario and the total system balance loss of the equivalent wind power output scenario is calculated to obtain the total wind power balance loss within the scheduling period.

[0103] Specifically, when quantifying wind power balance loss, it is assumed that wind power should also bear the responsibility of tracking load. Therefore, an equivalent scenario is constructed to ensure that the fluctuation of the wind power output curve is consistent with the load. At this time, the system no longer needs to make additional adjustments to the conventional units to balance the impact of its fluctuations, so no balance loss is generated. The difference between the total system balance loss of the wind power predicted output scenario and the total system balance loss of the wind power equivalent output scenario is calculated according to the dynamic scheduling model, and this difference is defined as the total wind power balance loss within the scheduling period. Using the above dynamic scheduling model, the above equation (13) satisfies At that time, the total system balance loss (TOC) for the wind power predicted output scenario within the scheduling period is obtained. pred The above equation (13) satisfies At that time, the total system balance loss TOC for the equivalent wind power output scenario is obtained. equ Then, the total wind power balance loss BC within a scheduling cycle can be calculated using equation (19). tot .

[0104] BC tot =TOC pred -TOC equ (19)

[0105] Extract the characteristic quantity of the local fluctuation degree of wind power relative load in each target time period within the scheduling cycle;

[0106] The extraction process of the local fluctuation degree feature of wind power relative load is as follows: extract the continuous fluctuation state quantity of wind power relative load power in the real-time triangular coefficient diagram of the wind power relative load fluctuation power sequence of the target period; determine the suboptimal solution of the continuous fluctuation state quantity of wind power relative load power through the grey relational analysis method, and obtain the local fluctuation degree feature quantity of wind power relative load.

[0107] The extraction of the continuous fluctuation state quantity of wind power relative to load power specifically involves: inputting a wind power relative load fluctuation power sequence at a preset time scale for each target time period; starting from the first data point of the wind power relative load fluctuation power sequence, sequentially finding the set of power maxima and minima of the sequence to form a new wind power relative load fluctuation power sequence; forming a power triangle by intersecting the vertical and horizontal directions of the line connecting two adjacent data points in the new wind power relative load fluctuation power sequence; calculating the amplitude U and phase angle θ of each power triangle in the new wind power relative load fluctuation power sequence to obtain the continuous fluctuation state quantity of wind power relative load power. The new wind power relative load fluctuation power sequence does not contain non-extreme points.

[0108] Specifically: Figure 2 This is a real-time trigonometric coefficient diagram of the wind power relative load fluctuation power sequence on a 5-minute time scale. The diagram marks individual power extreme points, non-extreme points, and the amplitude U and phase angle θ of the power triangle. The amplitude U and phase angle θ of different power triangles can be used to quantitatively describe the continuous fluctuation state of wind power relative load power during this period.

[0109] step:

[0110] a. Input the wind power relative load fluctuation power sequence P on a 5-minute timescale every hour. W-L (1),P W-L (2),…,P W-L (k),…P W-L (12).

[0111] b. Starting from the first data point of the wind power relative load fluctuation power sequence, sequentially find the set of power maxima points in the sequence. and the set of minimum points

[0112] c. Ignore the non-extreme points in the wind power relative load fluctuation power sequence to form a new wind power relative load fluctuation power sequence P′. W-L In the new wind power relative load fluctuation power sequence, the line connecting two adjacent data points intersects with both the vertical and horizontal directions to form a power triangle, such as... Figure 1 As shown in the shaded area.

[0113] d. Calculate the new wind power relative load fluctuation power sequence P′ W-L The amplitude and phase angle of each power triangle are used to form the time-sharing triangular coefficients to quantitatively describe the continuous fluctuation state of wind power relative to load power.

[0114] The process of obtaining the characteristic quantity of the local fluctuation degree of wind power relative load is as follows: taking the continuous fluctuation state of wind power relative load in the target period as the event set, and the amplitude and phase angle as the countermeasure set, a situation set is constructed; based on the decision objective, the effect sample value of the situation set is determined; based on the effect sample value and the grey relational degree of the best effect sample value among the effect sample values, the suboptimal solution of the continuous fluctuation state quantity of wind power relative load power is obtained.

[0115] Specifically, in the relative wind power load sequence for each hour, if the relative wind power load fluctuates drastically during a certain period, the change process of the fluctuating power sequence during that period should exhibit characteristics of rapid change time and large fluctuation amplitude. That is, the trigonometric coefficients for that period should simultaneously satisfy both the conditions of maximum amplitude and maximum phase angle. However, these two conditions are difficult to satisfy simultaneously, such as... Figure 1 As shown, power triangle 1 has the maximum phase angle θ1, while power triangle 2 has the maximum amplitude U2. During this time period, the maximum phase angle and the maximum amplitude do not occur simultaneously. Therefore, the grey relational analysis method, a multi-attribute decision-making method, is used to seek suboptimal solutions for amplitude and phase angle.

[0116] Grey relational decision-making provides an important quantitative method for measuring the degree of correlation between different factors. This method can identify the effect evaluation vector corresponding to the ideal optimal solution. The optimal solution and its ranking are determined by the grey relational degree between the effect evaluation vectors of each solution and the effect evaluation vector of the optimal solution. Through grey relational analysis, time-partial triangular coefficients representing typical states of fluctuation in the relative load power sequence of wind power can be extracted at different time periods. The specific steps are as follows:

[0117] a. Based on the event set A = {a1, a2, ..., a...} n} and the game set B = {b1,b2,…,b m Construct the situation set S = {s} i,j =(a i ,b j )|a i ∈A,b j ∈B}. In this embodiment, the continuous fluctuation state of the relative wind power load in each hour is taken as the event set A={a1,a2,…,a}. 12 The magnitude and phase angle of the time-sharing triangular coefficients are used as the game set B = {U, θ}.

[0118] b. Determine the decision objective k, k = 1, 2, ..., s.

[0119] c. Seeking the situation s ij ∈S, the sample values ​​of the effect under target k

[0120] d. Determine the optimal sample value

[0121] e. Calculation and Grey relational degree γ ij ,Depend on Obtain the suboptimal effect vector u i1j1 And the suboptimal situation S1, that is, the suboptimal solution of amplitude and phase angle.

[0122] Based on the aforementioned characteristic quantities, the total wind power balance loss is allocated to each target time period to obtain the wind power time-sharing balance loss.

[0123] The process of obtaining the wind power time-sharing balance loss is as follows: after normalizing and adding the characteristic quantities of the local fluctuation degree of wind power relative load in each target time period with equal weight, the relative fluctuation rate of the target time period is obtained; based on the relative fluctuation rate of the target time period, the local fluctuation influence factor of the relative fluctuation rate of the target time period is determined; based on the local fluctuation influence factor, the total wind power balance loss is allocated to each target time period to obtain the wind power time-sharing balance loss.

[0124] ① Specifically: The amplitude U obtained hourly through grey relational decision optimization t and phase angle θ t As the time-division trigonometric coefficient, U′ is obtained by normalization using equation (20). t and θ′ t Then, by adding them with equal weights, we obtain the relative volatility λ of wind power relative to load fluctuation in the t-th hour. t W-L As shown in equation (21).

[0125]

[0126] In the formula, and These are the maximum and minimum time-sharing trigonometric coefficients in the t-th hour of the fluctuation power sequence, respectively.

[0127]

[0128] Based on the magnitude of the influence of the local fluctuation degree characteristic of wind power relative load (i.e., hourly relative fluctuation rate) extracted in each scheduling period on the macroscopic fluctuation state of wind power relative load in the complete scheduling cycle (i.e., local fluctuation influence factor), the total wind power balance loss (19) in the scheduling cycle is allocated time by time to obtain the wind power time-sharing balance loss (23).

[0129]

[0130]

[0131] In the formula, BC is a local volatility factor affecting hourly relative volatility. t Let t be the wind power time-sharing loss.

[0132] In summary, the wind power time-of-use balance loss calculation method based on volatility provided in this embodiment has the following advantages:

[0133] 1. A method is proposed to solve the total wind power balance loss during the scheduling cycle by comparing the differences in total system operating losses based on the optimized scheduling results of the predicted scenario and the equivalent scenario.

[0134] 2. An index for quantitatively describing the time-series evolution characteristics of wind power relative load fluctuation power, namely hourly relative volatility, was proposed, and a corresponding mathematical model and algorithm were established.

[0135] 3. Based on the hourly relative volatility, the local volatility impact factor is obtained and the total balance loss of the wind power dispatch cycle is allocated to each time period to obtain the wind power time-sharing balance loss.

[0136] This embodiment also provides an electronic device, including: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute instructions to implement the method.

[0137] This embodiment also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method.

[0138] Calculation example:

[0139] Adopting such Figure 3 The equivalent alternative power source of wind power shown in Figure 4 and the improved IEEE 30-bus system topology diagram shown in Figure 4 are analyzed using numerical examples.

[0140] 1) Analyze the impact of wind power penetration rate on time-of-use balance losses, using methods such as... Figure 5 The wind power output scenarios shown have different grid-connected power volumes but consistent fluctuation trends. Figure 6 The typical daily wind power, load forecast power, and hourly relative volatility shown are obtained as follows: Figure 7 The curves showing the time-sharing balance loss of wind power under different penetration rates are shown.

[0141] thus Figure 5 and Figure 6 It can be seen that this method more comprehensively and accurately reflects the regulation losses brought about by wind power grid connection. Under the premise that the hourly relative volatility of each wind power scenario is synchronized, both the total balance loss within the dispatch cycle and the time-sharing balance loss of each dispatch period increase with the increase of its grid-connected capacity, and the time-sharing balance loss of wind power is positively correlated with the relative volatility of that period.

[0142] Depend on Figure 7 It can be seen that the relative volatility of time periods 10, 14, 17, and 20 is relatively high, and the wind power balance loss of the corresponding time periods is also the peak value of their respective loss curves. During time periods with lower hourly relative volatility, the real-time wind power balance loss is also lower. This is because during time periods with higher relative volatility, the fluctuation of wind power relative to the load is more drastic, and the conventional units of the system will start and stop more frequently and adjust their output to balance the wind power fluctuations, incurring more additional operating losses. During time periods with lower hourly relative volatility, wind power can track the load better. Since wind power itself has energy value, wind power that changes in the same direction as the load can help conventional units to jointly regulate peak loads, thereby reducing the adjustment losses of conventional units in smoothing load fluctuations.

[0143] The simulation results show that the algorithm verifies the effectiveness of the model and analyzes the impact of permeability on time-sharing balance loss.

[0144] 2) Analysis of the impact of wind power fluctuation on time-of-use balancing losses

[0145] Four wind power scenarios were set up, with a consistent system wind power penetration rate across all four scenarios. However, the output fluctuations of each wind farm varied within the dispatch period. Figure 8 and Figure 9 As shown.

[0146] The hourly relative volatility and wind power time-of-use balance loss were calculated using the proposed method under four scenarios, as shown in the following results. Figure 10 As shown.

[0147] Therefore, it can be seen that the hourly relative volatility of each scheduling period changes with the relative volatility of wind power and load, and this change is positively correlated. Furthermore, the magnitude of the hourly relative volatility depends only on the distance between the wind power and load change rates at a given time period, and is unrelated to the power output. During periods where the wind power output and load curves are close to or even intersect, the reverse power change may result in a large hourly relative volatility; conversely, periods where the wind power output and load curves are far apart may result in a smaller hourly relative volatility due to synchronous power changes. Comparing the wind power time-sharing balance loss curves of the four wind power scenarios shows that, with the total grid-connected wind power capacity remaining the same, the lower the relative volatility of wind power to load, the smaller the wind power time-sharing balance loss, as seen in wind power scenarios 2-2 and 2-3. This reflects the reduced losses incurred by the system's conventional units in balancing wind power fluctuations through start-up, shutdown, and output regulation.

[0148] The simulation results show that the proposed method quantifies the impact of wind power fluctuations on balance losses, effectively solving the problem of hourly allocation of total wind power balance losses. This has practical value for establishing and improving the spot market wind power trading mechanism and price incentive methods.

[0149] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for calculating time-of-use wind power balance losses based on volatility, characterized in that, Including the following steps: Construct a dynamic scheduling model; Based on the dynamic scheduling model, the total wind power balance loss during the scheduling period is obtained; Extracting the characteristic quantity of the local fluctuation degree of wind power relative load for each target time period within the scheduling cycle; the specific process of extracting the characteristic quantity of the local fluctuation degree of wind power relative load is as follows: Extract the continuous fluctuation state of wind power relative load power from the real-time triangular coefficient diagram of the wind power relative load fluctuation power sequence for the target period; The suboptimal solution of the continuous fluctuation state quantity of wind power relative to load power is determined by the grey relational analysis method, and the characteristic quantity of the local fluctuation degree of wind power relative load is obtained. Based on the aforementioned characteristic quantities, the total wind power balance loss is allocated to each target time period to obtain the wind power time-sharing balance loss.

2. The method as described in claim 1, characterized in that, The construction process of the dynamic scheduling model is as follows: Obtain the power generation loss of thermal power units, start-up and shutdown loss of thermal power units, operation and maintenance loss of wind power, and reserved spinning reserve loss of thermal power units under the conditions of load and system safe operation constraints. use cplex After the solver solves for the power generation loss of the thermal power unit, the start-up and shutdown loss of the thermal power unit, the operation and maintenance loss of the wind power unit, and the reserved spinning reserve loss of the thermal power unit, a dynamic scheduling model is obtained.

3. The method as described in claim 2, characterized in that, The process of obtaining the total wind power balance loss during the scheduling period is as follows: The dynamic scheduling model obtains the total system balance loss of the wind power predicted output scenario when the unit output constraint in the constraint conditions meets the first condition. The dynamic scheduling model obtains the total system balance loss of the wind power equivalent output scenario when the unit output constraint in the constraint conditions meets the second condition. The difference between the total system balance loss of the predicted wind power output scenario and the total system balance loss of the equivalent wind power output scenario is calculated to obtain the total wind power balance loss within the scheduling period.

4. The method as described in claim 1, characterized in that, The extraction of the continuous fluctuation state quantity of wind power relative to load power is specifically as follows: Input the wind power relative load fluctuation power sequence at a preset time scale for each target time period; Starting from the first data point of the wind power relative load fluctuation power sequence, find the set of power maxima and minima of the sequence in sequence to form a new wind power relative load fluctuation power sequence. In the new wind power relative load fluctuation power sequence, the line connecting two adjacent data points intersects with the vertical and horizontal directions to form a power triangle; Calculate the amplitude of each power triangle in the new wind power relative load fluctuation power sequence. and phase angle This yields the continuous fluctuation state quantity of wind power relative to load power.

5. The method as described in claim 4, characterized in that, The new wind power relative load fluctuation power sequence does not contain non-extreme points.

6. The method as described in claim 4, characterized in that, The specific process for obtaining the characteristic quantity of the local fluctuation degree of wind power relative load is as follows: Using the continuous fluctuation state of the relative wind power load during the target period as the event set, and the amplitude and phase angle as the strategy set, a situation set is constructed. Based on the decision-making objectives, determine the sample values ​​of the effects of the situation concentration strategy; Based on the grey relational degree between the effect sample value and the optimal effect sample value, the suboptimal solution of the continuous fluctuation state quantity of wind power relative to load power is obtained.

7. The method as described in claim 1, characterized in that, The process of obtaining the time-sharing loss balance of wind power is as follows: After normalizing and summing the characteristic quantities of the local fluctuation of wind power relative load in each target period, the relative fluctuation rate of the target period is obtained. Based on the relative volatility of the target period, determine the local volatility influencing factors of the relative volatility of the target period; Based on the local fluctuation impact factor, the total wind power balance loss is allocated to each target time period to obtain the wind power time-sharing balance loss.

8. An electronic device, characterized in that, include: processor; Memory for storing instructions executable by the processor; The processor is configured to execute instructions to implement the method of any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-7.

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