WO2017049428A1 - 一种基于备用对象代价性能比的旋转备用容量优化方法 - Google Patents

一种基于备用对象代价性能比的旋转备用容量优化方法 Download PDF

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WO2017049428A1
WO2017049428A1 PCT/CN2015/090087 CN2015090087W WO2017049428A1 WO 2017049428 A1 WO2017049428 A1 WO 2017049428A1 CN 2015090087 W CN2015090087 W CN 2015090087W WO 2017049428 A1 WO2017049428 A1 WO 2017049428A1
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boundary line
screening
standby
power
objects
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PCT/CN2015/090087
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French (fr)
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薛禹胜
蔡斌
薛峰
吴俊�
李威
宋晓芳
徐泰山
谢东亮
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国电南瑞科技股份有限公司
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Priority to CN201580000629.9A priority Critical patent/CN105518723B/zh
Priority to PCT/CN2015/090087 priority patent/WO2017049428A1/zh
Publication of WO2017049428A1 publication Critical patent/WO2017049428A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

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  • the invention belongs to the technical field of power system automation, and more particularly relates to a method for optimizing spare capacity in power system scheduling.
  • the deterministic method uses a certain ratio of the system's maximum load and/or the maximum single unit capacity to arrange the rotating reserve capacity.
  • the method is simple in calculation and is widely used in the actual power system operation, but it is easy to cause waste or shortage of spare.
  • the probabilistic method requires that the probability of power outage is lower than the reliability index such as the probability of a given loss of load or the expected value of the power shortage. However, this method does not distinguish the degree of loss of the event, and it is easy to ignore the small probability and high risk event.
  • the risk method takes into account the losses caused by passive power outages in the optimization objective function to obtain the solution that minimizes the sum of control costs and risks. After correcting the risk definition to the product of the probability of occurrence of events and the cost of active control to avoid passive power outages, the problem of difficult calculation of passive power outage losses is solved.
  • the decoupling iteration is used to coordinate the optimization of the total reserve demand on the generation side backup capacity and the demand side reserve capacity. The distribution between them, but requires a lot of calculations.
  • the existing research usually only sorts by "capacity price” or by "capacity price and electricity price multiplied by the sum of event probabilities”.
  • the scenario of ample events after the access of renewable energy power generation is more complicated and varied.
  • the proportion of various expenses in the total cost varies from scene to scenario. Therefore, the above simple sorting method will not truly reflect the target object in the target event. Alternate value under the scene.
  • the object of the present invention is to overcome the shortcomings of the prior art and overcome the shortcomings of the existing spare capacity optimization method that cannot fully reflect the true standby value of the standby object, the decoupling iterative optimization calculation amount is large, and the risk optimal solution cannot be obtained.
  • the present invention defines the cost performance ratio of the standby object as “the ratio of the total cost under the target scene set to the available spare power available”, thereby truly reflecting the standby value of the standby object, and according to This selects the spare capacity to be configured under the scene set, realizes the risk optimal configuration scheme, and effectively balances the adequacy and economy of the spare capacity configuration.
  • the invention can uniformly optimize the backup capacity on the power generation side and the standby capacity on the demand side, thereby effectively improving the calculation speed.
  • the present invention is implemented by the following technical solutions, including the following steps:
  • the initial dividing line is denoted as l 1 , l 2 ,..., l m , wherein the vertical axis zero tick mark is the first boundary line l 1 , and the maximum power deficiency value scale is the mth initial dividing line l m ,
  • the m-1 power deficit areas determined by each initial boundary line and its adjacent initial boundary line are denoted as a 1 , a 2 , ..., a m-1 ;
  • the j-th post-screening boundary is selected as the post-screening boundary line L j for the current optimization
  • Total capacity after 7) is determined on the basis of the selected object set on standby in the standby target objects is not selected spare new candidate and scale values L j after the current dividing line optimized screened for the magnitude relationship, if the new If the total capacity of the candidate object that is not selected in the candidate object is still not greater than the scale value of the filtered boundary line L j for the current optimization, return to step 4); otherwise, proceed to step 8);
  • step 12 judging the magnitude relationship between the total capacity of all selected spare objects in the selected spare object set and the scale value of the post-screening boundary line L j for which the current optimization is targeted, if the former is smaller than the latter, return to step 11);
  • the scheme exists as an alternative solution for the power shortage below the screening boundary line L j for the current optimization. If k ⁇ K, let k increase by 1 and return to step 9), otherwise it is considered that all of the standby objects are to be checked. After the check objects have been checked, go to step 13);
  • the numerator in equation (1) is the total cost of the standby object under the target scene set, and the denominator is the effective backup power provided under the target scene set,
  • Q r is the capacity of the standby object
  • T c is the contract period duration, ⁇ t
  • ⁇ m is the probability of the event sequence m
  • p cap is the capacity price of the standby object
  • M is the event sequence set
  • Q m,e (t) is the standby object of the power generation state at time t of the event sequence m
  • the total capacity, p e (t) is the price at which the standby object is in the power generation state at time t.
  • the beneficial effects of the present invention are as follows: 1) The present invention reflects the real standby value of the standby object under the target scene set by the cost performance ratio index, and can preferentially consider the standby object with the lowest cost performance ratio; 2) the present invention can reserve the standby capacity on the power generation side. Uniform optimization with demand side spare capacity, without decoupling-iteration between the two, thus avoiding a huge amount of calculation; 3) The present invention considers that the cost performance ratio of the standby object will vary with the power shortage of the target scene set.
  • a plurality of post-screening demarcation lines are set according to a certain threshold value along the vertical axis (power) increasing direction, and the spare capacity configuration is sequentially performed for the power shortage between the adjacent post-screening demarcation lines; 4)
  • the present invention considers the performance ratio at the cost In the process of selecting the alternate objects one by one to fill the power shortage under the boundary of the target screening, it may be that the total capacity after adding a candidate to be selected exceeds the boundary after the target screening, so that the cost performance ratio is overestimated, so A special calibration subprocess is processed and the results of the process are used as an alternative solution. Therefore, not only the optimization effect is improved, but also the calculation speed is improved compared to the decoupling-iteration method.
  • Figure 1 is a flow chart of the method of the present invention.
  • FIG. 2 is a diagram of a power shortage scenario set according to an embodiment of the present invention.
  • FIG. 3 is a division diagram of an initial boundary line according to an embodiment of the present invention.
  • FIG. 4 is a diagram showing a boundary line after screening according to an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of a target power shortage scenario after selecting the genset 3 according to an embodiment of the present invention.
  • the simulation example is set as follows: the research duration is 8 hours, the average electricity price is 300 yuan/MWh, and the threshold value ⁇ is 200 MWh.
  • the parameters of the standby object are shown in Table 1, Table 2, and the power shortage scenario set is shown in Figure 2.
  • Step 1 in Figure 1 describes setting the initial boundary line in the power deficiency scene set graph represented by the time (horizontal axis) and power (vertical axis) represented by the two-dimensional Cartesian axis: starting from the zero-scale line on the vertical axis, Increasing the direction along the vertical axis, the expected value of the power shortage of the target scene set in the two adjacent unit scale ranges is constant or decreasing, because the cost performance ratio of the standby object is equal to "the total cost of the standby object to the target scene set" and "alternate” The ratio of the effective expected power that the object can provide, the cost performance ratio will increase with the expected value of the power shortage, and the difference between the different standby objects will be different, thus affecting the relative ordering between the alternate objects, so it needs to be along the vertical axis.
  • step 2 determines the initial boundary line, and in step 2, the initial boundary line is selected according to the calculation accuracy and the calculation speed requirement. ).
  • the expected values of the two power-deficient regions in the range of three adjacent unit scales in the entire optimization period are sequentially compared. If it is decreased, one initial score is set in the middle scale.
  • Boundary line up to the maximum power shortage in the scene set, the initial dividing line is recorded as l 1 , l 2 ,..., l m (the zero-tick line is the first dividing line, and the maximum power deficiency value is the m-th dividing line)
  • the m-1 power deficit regions determined by each boundary line are denoted as a 1 , a 2 , ..., a m-1 .
  • the initial boundary line in this example is shown in Figure 3.
  • Step 2 in Figure 1 screens the initial boundary line according to a given threshold: for the initial boundary line l i (the initial value of i is 2, the zero-tick line does not need to be screened), the power deficit area a i-1 and the power deficit are calculated.
  • the initial dividing line l 2 , l 3 is removed, the initial dividing line l 4 is retained, the screening process is calculated as shown in Table 3, and the post-screening dividing line is shown in FIG. 4 .
  • Step 3 in Figure 1 is for the n post-screening boundary lines, denoted as L 1 , L 2 ,..., L n (n ⁇ m), and the j-th screening boundary is selected as the screening for the current optimization. After the boundary line L j .
  • Step 4 in Figure 1 is based on the selected set of spare objects (when optimized for the post-screening boundary line L 2 , the set is an empty set), according to the unified cost performance ratio calculation formula, for the current post-screening boundary line, The cost performance ratio of all candidate standby objects under the target power deficiencies scene set.
  • the cost performance ratio is calculated as shown in the following equation (1):
  • the numerator in equation (1) is the total cost of the standby object under the target scene set, and the denominator is the effective backup power provided under the target scene set,
  • Q r is the capacity of the standby object
  • T c is the contract period duration, ⁇ t
  • ⁇ m is the probability of the event sequence m
  • p cap is the capacity price of the standby object
  • M is the event sequence set
  • Q m,e (t) is the standby object of the power generation state at time t of the event sequence m
  • the total capacity, p e (t) is the price at which the standby object is in the power generation state at time t.
  • the cost performance ratio calculation for selecting the first standby object under the post-screening boundary line L 2 is shown in Table 4.
  • Generator set 3 50 385 Generator set 4 50 474 Generator set 5 75 452 Generator set 6 100 452 Interruptible load 1 50 902 Interruptible load 2 50 1201
  • Step 5 in Figure 1 selects the standby object with the lowest cost performance ratio, adds the selected spare object set, and updates the power shortage of each target scene.
  • the genset 3 in Table 4 is selected (cost performance ratio is 385 yuan / MWh), and the power shortage curve of the target scene is as shown in FIG. It should be emphasized that during the iterative process, the cost performance ratio of the alternate objects in Table 4 will change, and the selected standby objects will also change.
  • Step 6 in Figure 1 determines the relationship between the total capacity of all the standby objects in the selected standby object set and the power shortage value represented by the post-screening boundary line L j . If the former is smaller than the latter, the process proceeds to step 7 to determine whether it needs to enter. Check the sub-process, otherwise, go to step 14 and save as an alternative solution.
  • step 7 After selecting the generator set 3 after the screening boundary line L 2 , the total capacity of the selected standby object (50 MW) is smaller than the value of the post-screening boundary line L 2 (200 MW), then proceeds to step 7; Step 1 until the generator set 3, generator set 5, generator set 1, generator set 2, and generator set 4 are sequentially selected according to the cost performance ratio, the total capacity of the selected standby object (225 MW) is greater than the value of the post-screening boundary line L 2 Then proceed to step 14.
  • Step 7 in Figure 1 is to determine whether the optimization process needs to enter the calibration sub-process or continue the main optimization process. Judging the relationship between the total capacity of the unselected standby object and the power shortage value represented by the post-screening boundary line L j on the basis of the last selected standby object, if the total capacity of any unselected standby object is added, It is not greater than the power shortage value represented by the boundary line L j after the screening, that is, the calibration sub-process is not required, and the process returns to step 4; if there is a newly added unselected standby object, the total capacity is greater than the power represented by the post-screening boundary line L j If the value is missing, go to step 8.
  • step 4 when selecting the generator 3 (total capacity of 50MW selected, a new spare capacity after the chief object is the maximum is not selected only 150MW, the filter is still less than the boundary value L of 200MW 2), the process proceeds to step 4;
  • the generator set 3 and the generator set 5 are selected (the total capacity is 125 MW, if the total capacity after the new generator set 6 is 225 MW, which is greater than the value of the post-screening boundary line L 2 of 200 MW)
  • the process proceeds to step 8 for checking.
  • Step 8 in Figure 1 uses the unselected spare object as the set of standby objects to be checked under the current step (recorded as CRC 1 , CRC 2 , ..., CRC K , K is the number of standby objects to be checked) , save the current optimization section.
  • the genset 3 and the genset 5 are selected (the total capacity is 125 MW, and if the total capacity of the genset 6 is 225 MW, which is greater than the value of L 2 of 200 MW), the calibration is required at this time.
  • the object is the generator set 6. Let the initial value of the serial number k of the selected candidate to be checked be 1
  • Step 9 in Figure 1 removes the last selected spare object from the saved optimization section and updates the power shortage of each target scene.
  • the standby object to be removed is the genset 5 (selected standby object) The last one).
  • step 110 of FIG alternate set of objects to be checked to be checked k-th backup objects to be selected as the CRC check k backup objects (initial value is 1 k), for checking the subsequent process.
  • the selected standby object to be checked is the generator set 6.
  • Step 11 in Figure 1 updates the power vacancy of each target scenario, calculates the cost performance ratio of all unselected standby objects, and selects the smallest one; in this embodiment, after selecting genset 6 (all selected spare objects have a capacity of 150 MW) For the remaining power vacancy scenario, calculate the cost performance of the unselected standby object as shown in Table 5.
  • the returning step 11 when the genset 6 is checked, after the genset 1 is selected for the remaining power shortage (all the selected spare objects have a capacity of 175 MW, which is less than the value of the post-screening boundary line L 2 of 200 MW), the returning step 11; When genset 1 and genset 2 are selected in turn (all selected spare objects have a capacity of 200 MW, which is equal to the value of the post-screening boundary line L 2 of 200 MW), then return to step 9.
  • Step 13 in Figure 1 is to restore the optimized section saved in step 8, and return to step 4.
  • Step 14 in Figure 1 is an alternative solution to save the current scheme as a power deficit below the post-screening boundary line L j .
  • Step 15 in Fig. 1 is to select the optimal one of all the alternative solutions: for all the alternative solutions under the post-screening boundary line L j , select the alternate object combination with the smallest total cost as the post-screening boundary line L j Optimize the solution (that is, all the alternate objects that need to be selected after optimization) and update the selected alternate object set to the optimized solution.
  • all alternative solutions under the post-screening boundary line L 2 are as shown in Table 6 below.
  • Step 16 in Figure 1 is to determine if all of the post-screening boundaries have been optimized. If L j is not L n , let j increase by 1, and return to step 3), otherwise the optimization ends.
  • the present invention defines the cost performance ratio of the standby object as “the total cost under the target power deficiency curve scene set and the ratio of the available power available”, and the cost performance ratio.
  • the indicator reflects the real standby value of the standby object under the target scene set, and can prioritize the standby object with the lowest cost performance ratio, and optimize the standby object to be configured accordingly, realize the risk optimal configuration scheme, and effectively coordinate the configuration of the standby object. Adequacy and economy.
  • the invention can uniformly optimize the backup capacity on the power generation side and the standby capacity on the demand side, thereby effectively improving the calculation speed. Therefore, the present invention not only improves the optimization effect but also increases the calculation speed.

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Abstract

一种基于备用对象代价性能比的旋转备用容量优化方法，属于电力系统自动化技术领域。该方法根据旋转备用对象（下称备用对象）在目标功率缺额曲线场景集（下称目标场景集）下的总代价及其可提供的有效备用电量计算其代价性能比，并据此优化该场景集下需配置的备用对象。该方法能够综合考虑备用对象的代价与性能，相比按照备用对象的"容量价格"或者按照"容量价格与电量价格乘以事件概率之和"进行优化配置时能够取得更佳的优化效果。同时，该方法能够直接实现发需两侧的统一优化，避免了发需两侧间解耦－迭代过程的巨大计算量。

Description

一种基于备用对象代价性能比的旋转备用容量优化方法 技术领域
本发明属于电力系统自动化技术领域，更准确地说本发明涉及一种在电力系统调度中进行备用容量优化的方法。
背景技术
近年来，气候变化、环境污染和化石能源枯竭等因素，促使风电和光伏发电等可再生能源发电的装机容量保持了高速增长的态势。然而，大规模间歇性可再生能源发电的接入也给电力系统的备用容量配置带来新的挑战，成为限制电网接纳间歇性可再生能源发电的主要障碍之一。对电力系统的备用容量配置方式进行改进是提高电网接纳间歇性可再生能源发电的能力，以及改善电力系统运行可靠性与经济性最有效的手段之一。
从备用容量的配置方法来看，可分为确定性、概率性和风险性方法。确定性方法采用系统最大负荷的某一比例和/或最大单台机组容量大小来安排旋转备用容量。该方法计算简单，在实际电力系统运行中被普遍采用，但容易造成备用的浪费或不足。概率性方法要求停电概率低于给定的失负荷概率或电量不足期望值等可靠性指标，但该方法并不区分事件的损失程度，容易忽视小概率高风险事件。风险性方法在优化目标函数中计入被动停电引起的损失，以获得控制成本与风险之和最小的方案。在将风险定义修正为事件发生概率与避免被动停电所付出的主动控制成本的乘积之后，解决了被动停电损失难以计算的问题。
从参与控制的备用对象种类来看，可分为发电侧备用容量和以可中断负荷为代表的需求侧备用容量，两者有着不同的物理-经济特性，存在互补效应。一般通过解耦迭代来协调优化总备用需求量在发电侧备用容量与需求侧备用容量 之间的分配，但需要很大的计算量。
从备用对象的优化选取方法来看，现有研究中通常仅按“容量价格”或按“容量价格与电量价格乘以事件概率之和”进行排序。然而，可再生能源发电接入后的充裕性事件场景更为复杂多变，各类费用在总费用中的占比因场景而异，故以上简单的排序方法将无法真实反映备用对象在目标事件场景下的备用价值。
因此，亟待提出能真实反映发电侧备用容量与需求侧备用容量的真实代价性能比，在保证优化效果的同时能避免解耦迭代方法所需的巨大计算量的风险性备用容量优化方法。
发明内容
本发明目的是：针对现有技术中的不足，克服现有备用容量优化方法不能全面反映备用对象的真实备用价值、解耦迭代优化计算量巨大、且无法获得风险最优解的缺点，提供一种基于备用对象代价性能比、计算量小、且能获得风险最优解的备用优化方法。
本发明根据备用对象的物理-经济特性，将备用对象的代价性能比定义为“目标场景集下的总代价与其可提供的有效备用电量的比值”，从而真实反映备用对象的备用价值，并据此选取该场景集下需配置的备用容量，实现风险最优的配置方案，有效地协调的备用容量配置的充裕性与经济性。本发明能够对发电侧备用容量与需求侧备用容量进行统一优化，有效地提高了计算速度。
具体地说，本发明是采用以下技术方案实现的，包括如下步骤：
1)设置初始分界线：在由时间为刻度的横轴与功率为刻度的纵轴组成的二维直角坐标轴表示的功率缺额场景集图中，从纵轴零刻度线开始、沿纵轴增加 方向，依次比较整个优化时段内相邻三个单位刻度范围内的两个功率缺额区的期望值，若下降则在中间的刻度设置1条初始分界线，直至场景集的功率缺额最大值；
将初始分界线记为l1，l2，...，lm，其中纵轴零刻度线为第1条分界线l1，最大功率缺额值刻度为第m条初始分界线lm，将各初始分界线与其相邻的初始分界线确定的m-1个功率缺额区记为a1，a2，...，am-1；
2)确定筛选后分界线：将纵轴零刻度线记为第1条筛选后分界线L1，并针对由第2条初始分界线l2到第m-1条初始分界线lm-1的各初始分界线li，其中2≤i≤m-1，计算功率缺额区ai-1与功率缺额区ai的单位刻度功率内缺额期望值Ei-1和Ei，若两者差值大于预先设置的阈值σ，则保留初始分界线li并将其记为筛选后分界线，否则将初始分界线li移除使功率缺额区ai-1并入ai；最后将第m条初始分界线lm记为最后一条筛选后分界线；记获得的筛选后分界线为L1，L2，...，Ln；令选定的当前优化所针对的筛选后分界线的序号值j的初始值为2；并将已选备用对象集为初始化为空集；
3)针对n条筛选后分界线L1，L2，...，Ln，将第j条筛选后分界线选定为当前优化所针对的筛选后分界线Lj；
4)在已选备用对象集的基础上，根据代价性能比计算公式，求取目标场景集下发电侧与需求侧所有待选备用对象的代价性能比；
5)从所有待选备用对象中选取代价性能比最小的备用对象，加入已选备用对象集，并更新各目标场景的功率缺额；
6)判断已选备用对象集中所有备用对象的总容量与当前优化所针对的筛选后分界线Lj的刻度值的大小关系，若前者小于后者，则进入步骤7)，否则进入步骤14)；
7)判断在已选备用对象集的基础上新增待选备用对象中未选中的备用对象后的总容量与当前优化所针对的筛选后分界线Lj的刻度值的大小关系，若新增任 意一个待选备用对象中未选中备用对象后的总容量仍不大于当前优化所针对的筛选后分界线Lj的刻度值，则回到步骤4)；否则进入步骤8)；
8)将步骤7)中在已选备用对象集的基础上新增后的总容量大于当前优化所针对的筛选后分界线Lj的刻度值的待选备用对象中未选中的备用对象作为待校核备用对象集，记为CRC1，CRC2，...，CRCK，K为待校核备用对象的个数，并保存当前寻优断面；令选中的待校核备用对象的序号值k的初始值为1；
9)从保存的寻优断面中移除已选备用对象集中的最后一个选取备用对象，更新各目标场景的功率缺额；
10)将待校核备用对象集中第k个待校核备用对象CRCk作为选中的待校核备用对象；
11)更新各目标场景的功率缺额，计算待选备用对象中所有未选中的备用对象的代价性能比并选取最小者；
12)判断已选备用对象集中所有已选备用对象的总容量与当前优化所针对的筛选后分界线Lj的刻度值的大小关系，若前者小于后者，回到步骤11)；否则将该方案存为当前优化所针对的筛选后分界线Lj以下功率缺额的备选解待用，如果k＜K，则令k增1并回到步骤9)，否则认为待校核备用对象中所有待校核备用对象均已校核完毕，进入步骤13)；
13)恢复步骤8)中保存的寻优断面，返回步骤4)；
14)将该方案存为当前优化所针对的筛选后分界线Lj以下功率缺额的备选解待用；
15)针对当前优化所针对的筛选后分界线Lj下所有备选解，选取总代价最小的备用对象组合，作为当前优化所针对的筛选后分界线Lj下的优化解，并将已选备用对象集更新为该优化解；
16)如Lj不为Ln，则令j增1，回到步骤3)，否则寻优结束。
上述技术方案的进一步特征在于，所述步骤4)中代价性能比计算公式如下 式(1)：
Figure PCTCN2015090087-appb-000001
式(1)中分子为备用对象在目标场景集下的总代价，分母为在目标场景集下所提供的有效备用电量，其中Qr为备用对象的容量大小；Tc为合约期时长，Δt为优化步长；ρm为事件序列m的概率，pcap为备用对象的容量价格；M为事件序列集合；Qm，e(t)为事件序列m下t时刻处于发电状态的备用对象的总容量，pe(t)为t时刻备用对象处于发电状态的价格。
本发明的有益效果如下：1)本发明通过代价性能比指标反映备用对象在目标场景集下的真实备用价值，能够优先考虑代价性能比最小的备用对象；2)本发明能够对发电侧备用容量与需求侧备用容量进行统一优化，无需进行两者的解耦-迭代，从而避免了巨大的计算量；3)本发明考虑到备用对象的代价性能比会随目标场景集中功率缺额的变化而变化，故沿纵轴(功率)增加方向按某一阈值设置若干条筛选后分界线，依次针对相邻筛选后分界线间的功率缺额进行备用容量配置；4)本发明考虑到在按照代价性能比逐个选取备用对象来填补目标筛选后分界线下功率缺额的过程中，可能出现新增某待选备用对象后的总容量超过目标筛选后分界线从而代价性能比被高估的情况，故设置了专门的校核子过程进行处理，并将该过程的结果作为一个备选解。因此，不但提高了优化效果，且相比解耦-迭代的方式提高了计算速度。
附图说明
图1为本发明方法的流程图。
图2为本发明实施例的功率缺额场景集图。
图3为本发明实施例的初始分界线的划分图。
图4为本发明实施例的筛选后分界线图。
图5为本发明实施例的选取发电机组3后目标功率缺额场景图。
具体实施方式
下面参照附图对本发明作进一步详细描述。将本发明方法应用于某电力系统，仿真算例设置如下：研究时长为8小时、平均电价为300元/MWh、阈值σ为200MWh。备用对象参数见表1、表2，功率缺额场景集参见图2
表1 发电侧备用机组
机组名称 容量大小(MW) 旋转容量费(元/MWh) 电量费(元/MWh)
发电机组1 25 40 420
发电机组2 25 38 425
发电机组3 50 35 350
发电机组4 50 44 430
发电机组5 75 40 412
发电机组6 100 42 410
表2 需求侧可中断负荷
可中断负荷名称 容量大小(MW) 容量费(元/MWh) 赔偿倍率
可中断负荷1 50 2 3
可中断负荷2 50 1 4
图1中步骤1描述的是在由时间(横轴)与功率(纵轴)组成的以二维直角坐标轴表示的功率缺额场景集图中设置初始分界线：从纵轴零刻度线开始、沿纵轴增加方向，相邻两个单位刻度范围内目标场景集的功率缺额期望值呈不变或下降趋势，因备用对象的代价性能比等于“备用对象应对目标场景集的总代价”与“备用对象可提供的有效期望电量”的比值，其代价性能比会随功率 缺额期望值下降而增加，且不同备用对象的增加幅度存在差异，从而影响备用对象之间的相对排序，故需沿纵轴划定多条分界线，并依次针对相邻两条分界线所划定的范围进行优化(本步骤确定初始分界线，在步骤2中将视计算精度与计算速度的要求，对初始分界线进行筛选)。从纵轴零刻度线开始，沿纵轴增加方向，依次比较整个优化时段内相邻三个单位刻度范围内的两个功率缺额区的期望值，若下降，则在中间的刻度设置1条初始分界线，直至场景集中的功率缺额最大值，初始分界线记为l1，l2，...，lm(零刻度线为第1条分界线，最大功率缺额值刻度为第m条分界线)，各分界线确定的m-1个功率缺额区(由初始分界线lm与相邻的初始分界线lm确定)记为a1，a2，...，am-1。本算例中的初始分界线如图3。
图1中步骤2根据给定的阈值对初始分界线进行筛选：针对初始分界线li(i的初值为2，零刻度线无需筛选，)，计算功率缺额区ai-1与功率缺额区ai的单位刻度内功率缺额期望值Ei-1和Ei，若差值大于设置的阈值σ，则保留初始分界线li，记为筛选后分界线Ln(n初值为2)，令i＝i+1，n＝n+1，继续以上过程直至i＝m-1，否则，将初始分界线li移除，将功率缺额区ai-1并入ai，继续以上过程直至i＝m-1；令Ln＝lm，确定最后一条筛选后分界线。阈值的选取决定了筛选后分界线的数量和稀疏程度，阈值越小，筛选后分界线的数量越多，优化的效果越好，但计算量越大。算例中，初始分界线l2，l3被移除，初始分界线l4被保留，筛选过程的计算如表3，筛选后分界线如图4。
表3 初始分界线筛选过程
Figure PCTCN2015090087-appb-000002
Figure PCTCN2015090087-appb-000003
令选定的当前优化所针对的筛选后分界线的序号值j的初始值为2；并将已选备用对象集为初始化为空集。
图1中步骤3针对n条筛选后分界线，记为L1，L2，...，Ln(n≤m)，将第j条筛选后分界线选定为当前优化所针对的筛选后分界线Lj。
图1中步骤4在已选备用对象集基础上(针对筛选后分界线L2进行优化时，该集合为空集)，根据统一的代价性能比计算公式，针对当前筛选后分界线，求取所有待选备用对象在目标功率缺额场景集下的代价性能比。代价性能比计算公式如下式(1)：
Figure PCTCN2015090087-appb-000004
式(1)中分子为备用对象在目标场景集下的总代价，分母为在目标场景集下所提供的有效备用电量，其中Qr为备用对象的容量大小；Tc为合约期时长，Δt为优化步长；ρm为事件序列m的概率，pcap为备用对象的容量价格；M为事件序列集合；Qm，e(t)为事件序列m下t时刻处于发电状态的备用对象的总容量，pe(t)为t时刻备用对象处于发电状态的价格。
本实施例中，针对筛选后分界线L2下选取第一个备用对象的代价性能比计算见表4。
表4 筛选后分界线L2下备用对象的代价性能比
备用对象 容量大小(MW) 代价性能比(元/MWh)
发电机组1 25 460
发电机组2 25 463
发电机组3 50 385
发电机组4 50 474
发电机组5 75 452
发电机组6 100 452
可中断负荷1 50 902
可中断负荷2 50 1201
图1中步骤5选取代价性能比最小的备用对象，加入已选备用对象集，并更新各目标场景的功率缺额。在本实施例中，表4中的发电机组3被选中(代价性能比为385元/MWh)，此时目标场景的功率缺额曲线如图5所示。需要强调的是，迭代过程中，表4中备用对象的代价性能比都会发生变化，被选中的备用对象也都会发生变化。
图1中步骤6判断已选备用对象集中所有备用对象的总容量与筛选后分界线Lj所代表的功率缺额值的大小关系，若前者小于后者，则进入步骤7，判断其是否需进入校核子过程，否则，进入步骤14，存为一个备选解。在本实施例中，筛选后分界线L2下选取发电机组3后，已选备用对象总容量(50MW)小于筛选后分界线L2的值(200MW)，则进入步骤7；当继续按照图1所示步骤直到按代价性能比依次选取发电机组3、发电机组5、发电机组1、发电机组2、发电机组4后，已选备用对象总容量(225MW)大于筛选后分界线L2的值则进入步骤14。
图1中步骤7是为判断寻优流程是否需要进入校核子过程，还是继续主寻优流程。判断在最后一个选中备用对象基础上新增未选中备用对象后的总容量与筛选后分界线Lj所代表的功率缺额值的大小关系，若新增任一未选中备用对象后的总容量仍不大于筛选后分界线Lj所代表的功率缺额值，即无需进行校核子过程，回到步骤4；若存在新增未选中备用对象后的总容量大于筛选后分界线Lj所代表的功率缺额值，则进入步骤8。在本实施例中，当选取发电机组3后(已选总容量50MW，新增任一未选中备用对象后总容量最大只有150MW，仍小于筛选后分界线L2的值200MW)，则进入步骤4；当选取发电机组3、发电机组5后(已 选总容量125MW，若新增发电机组6后总容量为225MW，大于筛选后分界线L2的值200MW)则进入步骤8进行校核。
图1中步骤8将该部分未选中备用对象作为当前步下的待校核备用对象集(记为CRC1，CRC2，...，CRCK，K为待校核备用对象的个数)，保存当前寻优断面。在本实施例中，当选取发电机组3、发电机组5后(已选总容量125MW，若新增发电机组6后总容量为225MW，大于L2的值200MW)，此时需要校核的备用对象为发电机组6。令选中的待校核备用对象的序号值k的初始值为1
图1中步骤9从保存的寻优断面中移除已选中的最后一个备用对象，更新各目标场景的功率缺额。在本实施例中，在筛选后分界线L2下对发电机组6进行校核时(已选发电机组3、发电机组5)，需移除的备用对象为发电机组5(已选备用对象中最末一个)。
图1中步骤10将待校核备用对象集中第k个待校核备用对象CRCk作为选中的待校核备用对象(k初值为1)，用于后续的校核过程。在本实施例中，选中的待校核备用对象为发电机组6。
图1中步骤11更新各目标场景的功率缺额，计算所有未选中备用对象的代价性能比，并选取最小者；在本实施例中，选中发电机组6后(所有已选中备用对象容量大小为150MW)，针对余下功率缺额场景，计算未选中备用对象的代价性能比如表5所示。
表5 未选中备用对象的代价性能比
备用对象 容量大小(MW) 代价性能比(元/MWh)
发电机组1 25 483
发电机组2 25 485
发电机组4 50 515
发电机组5 75 524
可中断负荷1 50 904
可中断负荷2 50 1202
图1中步骤12是关于后续寻优应该进入步骤11或步骤9的一个判断。若所有已选备用对象总容量小于筛选后分界线Lj所代表的功率缺额值，则回到步骤11，继续针对CRCk的校核，按代价性能比从小到大选取备用对象；否则，将该方案存为筛选后分界线Lj以下功率缺额的备选解待用，令k＝k+1，回到步骤9，对下一个待校核备用对象进行校核，直到K个备用对象均校核完毕。在本实施例中，当对发电机组6进行校核时，针对余下功率缺额选中发电机组1后(所有已选中备用对象容量大小为175MW，小于筛选后分界线L2的值200MW)，返回步骤11；当依次选中发电机组1、发电机组2后(所有已选中备用对象容量大小为200MW，等于筛选后分界线L2的值200MW)则返回步骤9。
图1中步骤13是恢复步骤8中保存的寻优断面，返回步骤4。
图1中步骤14是将当前方案存为筛选后分界线Lj以下功率缺额的备选解待用。
图1中步骤15是从所有备选解中选取最优的一个解：针对筛选后分界线Lj下所有备选解，选取总代价最小的备用对象组合，作为筛选后分界线Lj下的优化解(即经优化后需选取的所有备用对象)，并将已选备用对象集更新为该优化解。在本实施例中，筛选后分界线L2下所有备选解如下表6所示。
表6 筛选后分界线L2下所有备选解
Figure PCTCN2015090087-appb-000005
Figure PCTCN2015090087-appb-000006
图1中步骤16是为了判断是否已经对所有筛选后分界线寻优完毕。如Lj不为Ln，则令j增1，回到步骤3)，否则寻优结束。在本实施例中，n＝3，当筛选后分界线L2校核完毕(j＜n)，则回到步骤3；当筛选后分界线L3校核完毕(j＝n)，则寻优结束。
总之，本发明根据功率缺额场景集及备用对象的特点，将备用对象的代价性能比定义为“目标功率缺额曲线场景集下的总代价及其可提供的有效电量的比值”，通过代价性能比指标反映备用对象在目标场景集下的真实备用价值，能够优先考虑代价性能比最小的备用对象，并据此优化需配置的备用对象，实现风险最优的配置方案，有效地协调备用对象配置的充裕性与经济性。本发明能够对发电侧备用容量与需求侧备用容量进行统一优化，有效地提高了计算速度。因此，本发明不但提高了优化效果，亦能提升计算速度。
虽然本发明已以较佳实施例公开如上，但实施例并不是用来限定本发明的。在不脱离本发明之精神和范围内，所做的任何等效变化或润饰，同样属于本发明之保护范围。因此本发明的保护范围应当以本申请的权利要求所界定的内容为标准。

Claims (2)

  1. 一种基于备用对象代价性能比的旋转备用容量优化方法，其特征在于，包括以下步骤：
    1)设置初始分界线：在由时间为刻度的横轴与功率为刻度的纵轴组成的二维直角坐标系中，针对功率缺额场景集，从纵轴零刻度线开始、沿纵轴增加方向，依次比较整个优化时段内相邻三个单位刻度范围内的两个功率缺额区的期望值，若下降则在中间的刻度设置1条初始分界线，直至场景集的功率缺额最大值；
    将初始分界线记为l1,l2,…,lm，其中纵轴零刻度线为第1条分界线l1，最大功率缺额值刻度为第m条初始分界线lm，将各初始分界线与其相邻的初始分界线确定的m-1个功率缺额区记为a1,a2,…,am-1；
    2)确定筛选后分界线：将纵轴零刻度线记为第1条筛选后分界线L1，并针对由第2条初始分界线l2到第m-1条初始分界线lm-1的各初始分界线li，其中2≤i≤m-1，计算功率缺额区ai-1与功率缺额区ai的单位刻度功率内缺额期望值Ei-1和Ei，若两者差值大于预先设置的阈值σ，则保留初始分界线li并将其记为筛选后分界线，否则将初始分界线li移除使功率缺额区ai-1并入ai；最后将第m条初始分界线lm记为最后一条筛选后分界线；记获得的筛选后分界线为L1,L2,…,Ln；令当前优化所针对的筛选后分界线的序号值j的初始值为2；并将已选备用对象集为初始化为空集；
    3)针对n条筛选后分界线L1,L2,…,Ln，将第j条筛选后分界线选定为当前优化所针对的筛选后分界线Lj；
    4)在已选备用对象集的基础上，根据代价性能比计算公式，求取目标场景集下发电侧与需求侧所有待选备用对象的代价性能比；
    5)从所有待选备用对象中选取代价性能比最小的备用对象，加入已选备用对象集，并更新各目标场景的功率缺额；
    6)判断已选备用对象集中所有备用对象的总容量与当前优化所针对的筛选 后分界线Lj的刻度值的大小关系，若前者小于后者，则进入步骤7)，否则进入步骤14)；
    7)判断在已选备用对象集的基础上新增待选备用对象中未选中的备用对象后的总容量与当前优化所针对的筛选后分界线Lj的刻度值的大小关系，若新增任意一个待选备用对象中未选中备用对象后的总容量仍不大于当前优化所针对的筛选后分界线Lj的刻度值，则回到步骤4)；否则进入步骤8)；
    8)将步骤7)中在已选备用对象集的基础上新增后的总容量大于当前优化所针对的筛选后分界线Lj的刻度值的待选备用对象中未选中的备用对象作为待校核备用对象集，记为CRC1,CRC2,…,CRCK，K为待校核备用对象的个数，并保存当前寻优断面；令选中的待校核备用对象的序号值k的初始值为1；
    9)从保存的寻优断面中移除已选备用对象集中的最后一个选取备用对象，更新各目标场景的功率缺额；
    10)将待校核备用对象集中第k个待校核备用对象CRCk作为选中的待校核备用对象；
    11)更新各目标场景的功率缺额，计算待选备用对象中所有未选中的备用对象的代价性能比并选取最小者；
    12)判断已选备用对象集中所有已选备用对象的总容量与当前优化所针对的筛选后分界线Lj的刻度值的大小关系，若前者小于后者，回到步骤11)；否则将该方案存为当前优化所针对的筛选后分界线Lj以下功率缺额的备选解待用，如果k<K，则令k增1并回到步骤9)，否则认为待校核备用对象中所有待校核备用对象均已校核完毕，进入步骤13)；
    13)恢复步骤8)中保存的寻优断面，返回步骤4)；
    14)将该方案存为当前优化所针对的筛选后分界线Lj以下功率缺额的备选解待用；
    15)针对当前优化所针对的筛选后分界线Lj下所有备选解，选取总代价最小 的备用对象组合，作为当前优化所针对的筛选后分界线Lj下的优化解，并将已选备用对象集更新为该优化解；
    16)如Lj不为Ln，则令j增1，回到步骤3)，否则寻优结束。
  2. 根据权利要求1所述的基于备用对象代价性能比的旋转备用容量优化方法，其特征在于，所述步骤4)中代价性能比计算公式如下式(1)：
    Figure PCTCN2015090087-appb-100001
    式(1)中分子为备用对象在目标场景集下的总代价，分母为在目标场景集下所提供的有效备用电量，其中Qr为备用对象的容量大小；Tc为合约期时长，Δt为优化步长；ρm为事件序列m的概率，pcap为备用对象的容量价格；M为事件序列集合；Qm,e(t)为事件序列m下t时刻处于发电状态的备用对象的总容量，pe(t)为t时刻备用对象处于发电状态的价格。
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