A multi-window coordinated power system capacity reserve optimization configuration method and device

Abstract

The present invention discloses a method and device for optimizing the configuration of power system capacity reserve with multi-window coordination. The method comprises: executing multiple rounds of optimization steps, wherein the multiple rounds of optimization steps comprise: launching multiple rounds of optimization, windowing the dispatching cycle in one round of optimization, and locating the first window; executing the optimization sorting step; after the sorting is completed, when any output scene to be regulated is lower than the output reduction limit in the window, evaluating the deep peak regulation cost; the window is moved back, and the optimization sorting step is returned to execute, and the control measures in the new window are re-optimized in turn until the last window is optimized and the control scheme of this round of optimization is obtained; after the processing between multiple rounds is completed, the next round of optimization is entered, and the optimization steps of multiple rounds are returned. The present invention effectively improves the accuracy of the evaluation of the cost performance of the reserve measures, seeks a better method for optimizing the configuration of the reserve, and is innovative and practical.

Description

Technical Field

[0001] The present invention relates to a multi-window coordinated power system capacity reserve optimization configuration method and device, belonging to the technical field of power system operation and dispatching. Background Art

[0002] Building a new power system with new energy as the main body, the system is becoming increasingly complex and unstable under the high penetration rate of new energy. Conventional power sources must not only follow the load changes, but also balance the output fluctuations of new energy. Insufficient startup or regulation rate of coal-fired power will lead to the risk of power outages, and excessive startup will result in the waste of wind and solar resources, which brings huge challenges to the balance and coordination of the power system and the safe and stable operation.

[0003] The reserve of traditional power systems mainly responds to the demand on the unilateral load side. The load fluctuations are highly regular and the random disturbances are small. The corresponding reserve optimization configuration is relatively extensive but can still maintain the safe and economic operation of the power grid. After the grid connection of new energy, the system has changed from unilateral load fluctuations to bilateral fluctuations on the power supply side and the load side. The fluctuations are less regular and the random disturbances are large. The traditional reserve strategy system lacks the means of risk management and risk decision-making. In order to coordinate the prevention and control of wind and solar power abandonment and power outage risks, the existing reserve system explores the value of starting and stopping thermal power units in the medium-term operation planning of the power grid, and optimizes the increase and decrease of reserve measures to minimize the risk cost. However, there are still problems such as the coordination of window operations and the low accuracy of the cost-effectiveness evaluation of measures. Therefore, this patent proposes a multi-window coordinated power system capacity reserve optimization configuration method. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a multi-window coordinated power system capacity reserve optimization configuration method and device, effectively improve the accuracy of cost-effectiveness evaluation of reserve measures, seek a better reserve optimization configuration method, and be innovative and practical.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] In a first aspect, the present invention provides a multi-window coordinated power system capacity reserve optimization configuration method, comprising:

[0007] Performing multiple rounds of optimization steps, the multiple rounds of optimization steps comprising: launching multiple rounds of optimization, windowing the scheduling period in one round of optimization, and locating a first window;

[0008] Executing a preferred sorting step, the preferred sorting step comprising: in the first round of optimization, according to the expected output line to be regulated, using the empirical sorting method to complete the preferred sorting of the backup control measures; if it is not the first round of optimization, using the CPR priority method to complete the preferred sorting of the backup control measures;

[0009] After the sorting is completed, when any output scenario to be regulated is lower than the lower output limit within the window, the deep peak regulation cost is evaluated;

[0010] The window moves back and returns to the optimization sorting step, and the control measures in the new window are re-optimized in turn until the last window is optimized and the control plan for this round of optimization is obtained;

[0011] After completing the comparison between multiple rounds of processing, enter the next round of optimization and return to the optimization steps of multiple rounds.

[0012] Furthermore, the expected output line to be regulated is a sequence composed of mathematical expectation values ​​of the output to be regulated, and the output to be regulated is defined as the difference between the load and the output of new energy. A group of output sequences to be regulated that may appear within the regulation cycle is called a "output scenario to be regulated", and the expected output line to be regulated is a sequence composed of the probability-weighted average values ​​of multiple "output scenarios to be regulated" at each moment.

[0013] Furthermore, the use of the empirical ranking method to complete the optimal ranking of the backup control measures includes:

[0014] For non-rotational level measures in the control measure set, filter the measure set available when the window is reached;

[0015] Reset the selection status of available measures in the current window;

[0016] Arrange the control measures in descending order according to their dispatch order. If the dispatch order is the same, sort them in descending order according to their maximum output.

[0017] Cover the expected output line to be regulated with the sorted measure set; update the status of the selected measure set until the output line to be regulated is covered;

[0018] Complete the sorting.

[0019] Furthermore, the deep peak regulation assessment includes the assessment of the total risk cost of deep regulation and the deep regulation benefits of each deep peak regulation measure. When the deep peak regulation measures cannot meet the demand, the reference abandoned wind and solar power costs are used for estimation. The deep regulation cost formula is as follows:

[0020] R dn =R dn +prob s price dn.i ·P dn.i.s.t ·Δt

[0021] Where, prob s is the probability of scene s appearing, price dn,i is the unit deep adjustment cost, P ab.s.tis the deep tuning capacity in scene s and time step t, and Δt is the time scale span.

[0022] Furthermore, the CPR, or cost-performance ratio, is defined as the ratio of the total process cost after a certain control measure is put into use to the alternative cost of removing the control measure. The formula is as follows:

[0023]

[0024] In the formula, C cap.i To control the capacity cost of i, C st.i To control the start-stop cost of i, R i To control the risk electricity cost of i, ΔR i is the alternative cost of removing the control object i.

[0025] Furthermore, the risk electricity cost of the control measures includes upward and downward risk costs:

[0026] The formula for increasing risk cost is as follows:

[0027]

[0028] In the formula, price up.i To control the risk cost of i units, μ up,i is the expected load rate of control measure i, P max.i is the maximum output of control i, T is the window duration;

[0029] The formula for downward risk cost is as follows:

[0030]

[0031] Where P min.i is the minimum output of control measure i, P min is the minimum output sum of all selected measures in the previous round, R dn represents the average total cost of downward adjustment risk in the previous round, I dn.i It represents the benefit obtained by controlling object i from participating in deep adjustment.

[0032] Furthermore, the replacement cost formula after removing the control object is as follows:

[0033]

[0034] The parameters in the formula are consistent with those described above.

[0035] Furthermore, the expected load rate in the risk electricity cost is defined as the expected value of the filling ratio of the output scene to be regulated within a certain output range, and the formula is as follows:

[0036]

[0037] Where P represents the power level, h represents the output range span, and ΔP(P,h,s,t) represents the increased capacity of scenario s in the interval (P,h) at time step t.

[0038] Furthermore, the use of the CPR priority method to complete the optimal sorting of backup control measures includes:

[0039] When the first window in the subsequent round is used, the start and stop states of the same window in the previous round are used to control the start and stop states. When the window is greater than 1, the start and stop states of the previous window are copied.

[0040] Consider adding measures: Calculate the CPR of all available measures that have not been selected at the specified power level. If no measures have been added and there are no measures with a CPR less than 1, then proceed to reducing measures; if measures have been added and there are no measures with a CPR less than 1, complete the sorting; otherwise proceed to the next step;

[0041] Select the measure with the smallest CPR and update its status, then return to consider adding measures;

[0042] Consider reducing measures: Calculate the CPR of each selected measure at the new power level, sort all CPRs from large to small, and if the CPR of the first measure is less than 1, the sorting is completed; otherwise, proceed to the next step;

[0043] Update the CPR maximum measure status to unselected and return to the reduction measure;

[0044] Complete the sorting.

[0045] In a second aspect, the present invention provides a multi-window coordinated power system capacity reserve optimization configuration device, including a processor and a storage medium;

[0046] The storage medium is used to store instructions;

[0047] The processor is used to operate according to the instructions to execute the steps of any of the methods described above.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention considers multiple rounds of iterative optimization on the basis of scheduling cycle windowing, adopts the empirical sorting method in the first round of optimization, and adopts the CPR sorting method in the non-first round of optimization, leaving an interface for the coordination of backup at multiple time and space scales; at the same time, the deep peak-shaving cost is considered when evaluating the cost-effectiveness of the measures, and the cost-performance ratio is defined as the ratio of the control cost to the alternative cost, which effectively improves the accuracy of the cost-effectiveness evaluation of the backup measures and seeks a better backup optimization configuration method, which is innovative and practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a multi-window collaborative power system capacity reserve optimization configuration method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0052] Example 1

[0053] This embodiment introduces a multi-window coordinated power system capacity reserve optimization configuration method, including:

[0054] Performing multiple rounds of optimization steps, the multiple rounds of optimization steps comprising: launching multiple rounds of optimization, windowing the scheduling period in one round of optimization, and locating a first window;

[0055] Executing a preferred sorting step, the preferred sorting step comprising: in the first round of optimization, according to the expected output line to be regulated, using the empirical sorting method to complete the preferred sorting of the backup control measures; if it is not the first round of optimization, using the CPR priority method to complete the preferred sorting of the backup control measures;

[0056] After the sorting is completed, when any output scenario to be regulated is lower than the lower output limit within the window, the deep peak regulation cost is evaluated;

[0057] The window moves back and returns to the optimization sorting step, and the control measures in the new window are re-optimized in turn until the last window is optimized and the control plan for this round of optimization is obtained;

[0058] After completing multiple rounds of processing, enter the next round of optimization and return to the optimization steps of multiple rounds.

[0059] like Figure 1 As shown, the multi-window coordinated power system capacity reserve optimization configuration method provided in this embodiment specifically involves the following steps in its application process:

[0060] Step 1: The optimization is carried out in multiple rounds. In a round of optimization, the scheduling period is windowed and the first window is located.

[0061] Each scheduling window should be of equal length; the window size should be determined by referring to the start and stop time constant of the control measure; the start and stop status of the control measure only changes when the scheduling window is switched;

[0062] Step 2: In the first round of optimization, since the initial decision has not yet been made, the expected output line to be regulated should be generated first; "expected output line to be regulated" refers to a sequence composed of the mathematical expectation values ​​of the output to be regulated, and the output to be regulated is defined as the difference between the load and the output of new energy. A set of output sequences to be regulated that may appear in the regulation cycle is called "output scenario to be regulated", and the expected output line to be regulated is a sequence composed of the probability weighted average values ​​of multiple "output scenarios to be regulated" at each moment; according to the expected output line to be regulated, the empirical sorting method is used to complete the optimal sorting of the backup control measures; for the non-rotating level measures in the control measure set, the available measure set when reaching the window is screened; reset the selection status of the available measures in the current window; arrange them from small to large according to the scheduling order of the control measures, and when the scheduling order is the same, sort them from large to small according to the maximum output; cover the expected output line to be regulated with the sorted measure set; select the first measure, update its status to unselected and remove it from the available measure set until there are no redundant uninvested available measures after covering the output line to be regulated; update the status of the final available measure set and complete the sorting;

[0063] If it is not the first round of optimization, the update includes but is not limited to the control set of other regions, and the CPR priority method is used. The CPR indicator calculation includes:

[0064] (1) Cost-performance ratio

[0065]

[0066] In the formula, C cap.i To control the capacity cost of i, C st.i To control the start-stop cost of i, R i To control the risk electricity cost of i, ΔR i is the alternative cost of removing the control object i;

[0067] (2) Increase risk cost

[0068]

[0069] Where, prob s is the probability of scene s appearing, price up.i To control the unit i, the risk cost is increased, ΔP up.i (s, t) is the up-regulation capacity of control i in scene s and time step t, Δt is the time scale span; μ i is the expected load rate of control measure i, P max.i is the maximum output of control measure i, T is the window duration;

[0070] (3) Lowering risk costs

[0071]

[0072] Where P min.i is the minimum output of control measure i, P min is the minimum output sum of all selected measures in the previous round, R dn represents the total cost of the previous round of downward adjustment risk, I dn.i represents the benefits obtained by controlling i to participate in deep adjustment;

[0073] (4) Replacement cost

[0074]

[0075] The parameters in the formula are consistent with those described above;

[0076] (5) Expected load rate

[0077]

[0078] Where P represents the power level of the output to be regulated, h represents the height of the interval, and ΔP(P,h,s,t) represents the increased capacity of the output scenario s to be regulated in the interval (P,h) at time step t;

[0079] The CPR priority method specifically includes: in the first window, the control start and stop state adopts the corresponding state of the same window in the previous round; when the window is greater than 1, the start and stop state of the previous window is copied; consider adding measures: calculate the CPR of all unselected available rotation-level measures at the specified power level (the sum of the maximum outputs of all selected measures). If no measures have been added and there are no measures with a CPR less than 1, then switch to reducing measures; if measures have been added and there are no measures with a CPR less than 1, then the sorting is completed; otherwise, proceed to the next step; select the measure with the smallest CPR and return to considering adding measures; consider reducing measures: test each selected measure, and first calculate the CPR under the new power level (the difference between the sum of the maximum outputs of all selected measures and the maximum output of this measure). Sort all CPRs from large to small. If the CPR of the first measure is less than 1, the sorting is completed; otherwise, proceed to the next step; set the state of the measure with the largest CPR to unselected, and return to reducing measures; complete the sorting;

[0080] Step 3: After the sorting is completed, if any output scenario to be regulated is lower than the lower output limit within the window, the deep peak regulation cost should be calculated; the deep peak regulation cost assessment includes the assessment of the total risk cost of deep regulation and the deep regulation benefits of each deep peak regulation measure:

[0081] R dn =R dn +prob s price dn.i ·P dn.i.s.t ΔT

[0082] In the formula, price dn.i is the unit peak load regulation cost of deep peak load regulation measure i, P dn.i.s.t is the downward adjustment output limit of deep peak-shaving measure i in scenario s and time step t;

[0083] When deep peak load regulation measures cannot meet demand, the reference wind and solar curtailment cost is used to estimate:

[0084] Step 4: Move the window backward and return to step 2. Re-optimize and sort the control measures in the new window in turn until the last window is sorted to obtain the control plan for this round of optimization.

[0085] Step 5: After completing multiple rounds of processing, enter the next round of optimization and return to step 1.

[0086] Example 2

[0087] This embodiment provides a multi-window coordinated power system capacity reserve optimization configuration device, including a processor and a storage medium;

[0088] The storage medium is used to store instructions;

[0089] The processor is configured to operate according to the instructions to perform the steps of any of the following methods:

[0090] Performing multiple rounds of optimization steps, the multiple rounds of optimization steps comprising: launching multiple rounds of optimization, windowing the scheduling period in one round of optimization, and locating a first window;

[0091] Executing a preferred sorting step, the preferred sorting step comprising: in the first round of optimization, according to the expected output line to be regulated, using the empirical sorting method to complete the preferred sorting of the backup control measures; if it is not the first round of optimization, using the CPR priority method to complete the preferred sorting of the backup control measures;

[0092] After the sorting is completed, when any output scenario to be regulated is lower than the lower output limit within the window, the deep peak regulation cost is evaluated;

[0093] The window moves back and returns to the optimization sorting step, and the control measures in the new window are re-optimized in turn until the last window is optimized and the control plan for this round of optimization is obtained;

[0094] After completing multiple rounds of processing, enter the next round of optimization and return to the optimization steps of multiple rounds.

[0095] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A multi-window coordinated power system capacity reserve optimization configuration method, characterized in that: include: Performing multiple rounds of optimization steps, the multiple rounds of optimization steps comprising: launching multiple rounds of optimization, windowing the scheduling period in one round of optimization, and locating a first window; Execute a preferred sorting step, the preferred sorting step includes: in the first round of optimization, according to the expected output line to be regulated, use the empirical sorting method to complete the preferred sorting of the backup control measures; if it is not the first round of optimization, use the CPR priority method to complete the preferred sorting of the backup control measures; the expected output line to be regulated is a sequence composed of mathematical expectation values ​​of the output to be regulated, the output to be regulated is defined as the difference between the load and the output of new energy, a group of output sequences to be regulated that may appear in the regulation cycle is called "output scenario to be regulated", and the expected output line to be regulated is a sequence composed of probability weighted average values ​​of multiple "output scenarios to be regulated" at each moment; The preferred sorting of backup control measures using the empirical sorting method includes: For non-rotational level measures in the control measure set, filter the measure set available when the window is reached; Reset the selection status of available measures in the current window; Arrange the control measures in descending order according to their dispatch order. If the dispatch order is the same, sort them in descending order according to their maximum output. Cover the expected output line to be regulated with the sorted measure set; update the status of the selected measure set until the output line to be regulated is covered; Complete the sorting; The CPR is the cost-performance ratio indicator, which is defined as the ratio of the total process cost after the implementation of a certain control measure to the alternative cost of removing the control measure. The formula is as follows: ； In the formula, For control The capacity cost, For control The start-stop cost, For control The risk electricity cost, To remove the control object The replacement cost; The CPR priority method is used to complete the optimal sorting of backup control measures, including: When the first window in the subsequent round is used, the start and stop states of the same window in the previous round are used to control the start and stop states. When the window is greater than 1, the start and stop states of the previous window are copied. Consider adding measures: Calculate the CPR of all available measures that have not been selected at the specified power level. If no measures have been added and there are no measures with a CPR less than 1, then proceed to reducing measures; if measures have been added and there are no measures with a CPR less than 1, complete the sorting; otherwise proceed to the next step; Select the measure with the smallest CPR and update its status, then return to consider adding measures; Consider reducing measures: Calculate the CPR of each selected measure at the new power level, sort all CPRs from large to small, and if the CPR of the first measure is less than 1, the sorting is completed; otherwise, proceed to the next step; Update the CPR maximum measure status to unselected and return to the reduction measure; Complete the sorting; After the sorting is completed, when any output scenario to be regulated is lower than the lower output limit within the window, the deep peak regulation cost is evaluated; The evaluation of deep peak regulation cost includes the evaluation of the total risk cost of deep regulation and the deep regulation benefits of each deep peak regulation measure. When the deep peak regulation measures cannot meet the demand, the reference abandoned wind and solar cost is used for estimation. The deep peak regulation cost formula is as follows: ； In the formula, For the scene s The probability of occurrence, is the unit deep adjustment cost, For deep peak load regulation i In the scene s Time Step t The lower output limit is the time scale span; The window moves back and returns to the optimization sorting step, and the control measures in the new window are re-optimized in turn until the last window is optimized and the control plan for this round of optimization is obtained; After completing the comparison between multiple rounds of processing, enter the next round of optimization and return to the optimization steps of multiple rounds.

2. The multi-window coordinated power system capacity reserve optimization configuration method according to claim 1 is characterized by: The risk electricity cost of the control measures themselves includes upward and downward risk costs: The formula for increasing risk cost is as follows: ； In the formula, For control The unit risk cost is increased by For control measures The expected load factor, For control The maximum output, is the window duration; The formula for downward risk cost is as follows: ； In the formula, For control measures The minimum output, is the minimum output sum of all selected measures in the previous round, represents the average total cost of downward risk in the previous rounds, Indicates the control object The benefits gained from participating in deep adjustment.

3. The multi-window coordinated power system capacity reserve optimization configuration method according to claim 2 is characterized by: The replacement cost formula after removing the control object is as follows: ； The parameters in the formula are consistent with those described above.

4. The multi-window coordinated power system capacity reserve optimization configuration method according to claim 3 is characterized by: The expected load rate in the risk electricity cost is defined as the expected value of the filling ratio of the output scene to be regulated within a certain output range, and the formula is as follows: ； In the formula, Indicates the power level, Indicates the output range span, Representation interval Interior scene exist The upward adjustment capacity of the time step.

5. A multi-window coordinated power system capacity reserve optimization configuration device, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 4.

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