CN111400918A - Power grid new energy consumption capability evaluation and calculation method, device and system based on multi-scene generation technology - Google Patents

Power grid new energy consumption capability evaluation and calculation method, device and system based on multi-scene generation technology Download PDF

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CN111400918A
CN111400918A CN202010197840.7A CN202010197840A CN111400918A CN 111400918 A CN111400918 A CN 111400918A CN 202010197840 A CN202010197840 A CN 202010197840A CN 111400918 A CN111400918 A CN 111400918A
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涂杉杉
李利利
江长明
陈之栩
孙田
史普鑫
涂孟夫
张丙金
丁恰
昌力
杨鹏程
曹益奇
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NARI Group Corp
North China Grid Co Ltd
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
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North China Grid Co Ltd
Nari Technology Co Ltd
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Abstract

The invention discloses a power grid new energy consumption capability evaluation calculation method, a device and a system based on a multi-scene generation technology, which comprises the steps of determining the power grid range and the calculation period of power grid new energy consumption capability evaluation calculation to be carried out and other calculation boundaries; generating a large number of combined scenes containing multiple new energy power plants by freely arranging and combining the fields; merging similar scenes by adopting a scene reduction technology of backward reduction, reducing the number of combined scenes and generating a typical combined scene; merging analysis is carried out at similar time intervals, and the number of time intervals entering optimization is reduced; aiming at each typical combination scene, establishing a SCUC dimension reduction model after time interval merging, and solving to obtain a unit combination result; and establishing a full-time SCED model based on the unit combination result and solving, finally obtaining a new energy consumption result under each typical combination scene, and completing the evaluation and calculation of the new energy consumption capability of the power grid based on the multi-scene generation technology. The invention improves the validity and the referential property of the digestion capability evaluation result on the premise of ensuring the safety and the stability of the system.

Description

基于多场景生成技术的电网新能源消纳能力评估计算方法、 装置及系统Calculation method, device and system for evaluating new energy consumption capacity of power grid based on multi-scenario generation technology

技术领域technical field

本发明属于电力系统调度自动化技术领域,涉及一种电网新能源消纳能力评估计算方法,具体涉及一种基于多场景生成技术的电网新能源消纳能力评估计算方法。The invention belongs to the technical field of power system dispatching automation, and relates to a calculation method for evaluating new energy consumption capacity of a power grid, in particular to a calculation method for evaluating new energy consumption capacity of a power grid based on a multi-scenario generation technology.

背景技术Background technique

新能源在电力系统的渗透率逐步升高,使系统中调度资源愈发丰富、运行方式日趋复杂,而传统配电网仍然存在新能源消纳能力不足等问题,严重制约了新能源的高度渗透,不利于能源结构的优化调整。并且,新能源并网对电力系统调节能力提出了更高要求,然而常规电网系统通常以火电为主,柔性调节资源不足,导致电力系统的调节能力有限,不足以应对新能源出力的不确定性。为保证电力系统的安全稳定运行,在新能源出力高峰时段,系统将被迫弃风、弃光,极大地降低了新能源的利用率。The penetration rate of new energy in the power system is gradually increasing, making the dispatching resources in the system more and more abundant and the operation mode increasingly complex. However, the traditional distribution network still has problems such as insufficient new energy consumption capacity, which seriously restricts the high penetration of new energy. , which is not conducive to the optimization and adjustment of the energy structure. In addition, the integration of new energy into the grid places higher requirements on the power system's adjustment capability. However, conventional power grid systems are usually dominated by thermal power, and flexible adjustment resources are insufficient, resulting in limited adjustment capabilities of the power system and insufficient to cope with the uncertainty of new energy output. . In order to ensure the safe and stable operation of the power system, during the peak period of new energy output, the system will be forced to abandon wind and light, which greatly reduces the utilization rate of new energy.

新能源接入电网后,机组优化调度的首要问题是如何建立可靠的含新能源安全约束机组组合(Security Constraint Unit Commitment,SCUC)问题数学模型。SCUC模型的目标函数在很长一段时间内是使调度周期内总运行费用最小,其中主要包括常规机组运行成本及启停成本。随着电力市场的发展,基于不同的出发点,SCUC模型目标函数形式又有了新的变化。由于新能源利用率降低,目标函数可能要求系统能够接纳的新能源并网容量尽量高。约束条件方面,根据能源种类、目标函数的不同,约束条件形式也发生了相应的变化。After the new energy is connected to the power grid, the primary problem of the optimal scheduling of units is how to establish a reliable mathematical model of the Security Constraint Unit Commitment (SCUC) problem with new energy. The objective function of the SCUC model is to minimize the total operating cost in the scheduling period for a long period of time, which mainly includes the operating cost of conventional units and the cost of starting and stopping. With the development of the electricity market, based on different starting points, the form of the objective function of the SCUC model has undergone new changes. Due to the reduced utilization rate of new energy, the objective function may require that the grid-connected capacity of new energy that the system can accept is as high as possible. In terms of constraints, the form of constraints has also changed correspondingly according to different energy types and objective functions.

SCUC问题本身是一个多维、非线性、多时段耦合的混合整数规划问题,而新能源本身具有间歇性、波动性等不确定性,其大规模并网使得SCUC问题的求解更趋复杂。传统新能源消纳能力评估是基于预测的单场景进行计算,而实际电网存在多个新能源场站且其预测出力具有不确定性,仅仅考虑新能源单一预测场景的消纳能力评估已经不能满足电网多元化发展的需求,会显著影响优化结果。The SCUC problem itself is a multi-dimensional, nonlinear, multi-period coupled mixed integer programming problem, and the new energy itself has uncertainties such as intermittent and volatility, and its large-scale grid connection makes the solution of the SCUC problem more complicated. The traditional new energy absorptive capacity assessment is calculated based on a single forecasted scenario, but the actual power grid has multiple new energy stations and their predicted output is uncertain, and the consumption capacity assessment only considering a single forecasted scenario of new energy is no longer sufficient. The demand for diversified development of the power grid will significantly affect the optimization results.

随着数学优化算法的发展以及商用求解器计算性能的巨大进步,混合整数规划法(MIP)成为求解优化调度问题的主要方法。MIP的优点是:①全局最优;②直接测算解的最优性;③更加灵活和精确的建模能力。基于MIP算法的优化模型在电力调度领域得到越来越广泛的应用。With the development of mathematical optimization algorithms and the great progress in the computational performance of commercial solvers, Mixed Integer Programming (MIP) has become the main method for solving optimization scheduling problems. The advantages of MIP are: (1) global optimality; (2) direct measurement of the optimality of the solution; (3) more flexible and accurate modeling capabilities. The optimization model based on MIP algorithm is more and more widely used in the field of power dispatching.

目前基于多场景生成技术的电网新能源消纳能力评估算法的思路主要是利用场景生成技术对新能源预测不确定性进行建模,以系统可以接纳的新能源最大出力为目标,根据需求将所有场景、所有变量和所有约束统一建立为标准数学优化模型,直接调用优化求解器,对标准化模型进行大规模混合整数规划计算。At present, the idea of the new energy consumption capacity evaluation algorithm of the power grid based on the multi-scenario generation technology is mainly to use the scenario generation technology to model the uncertainty of the new energy forecast, aiming at the maximum output of the new energy that can be accepted by the system. The scene, all variables and all constraints are unifiedly established as a standard mathematical optimization model, and the optimization solver is directly invoked to perform large-scale mixed integer programming calculations on the standardized model.

新能源预测的随机误差是制约基于多场景生成技术的电网新能源消纳能力评估计算模型能否获得最优决策方案的关键因素,如何在原有的调度模式中合理考虑并刻画多个新能源的组合场景是含有大规模新能源的优化运行调度研究中需要关注的问题;另一方面,传统的基于多场景生成技术的电网新能源消纳能力评估计算模型没有考虑到SCUC问题,使得优化结果不能为电网的安全运行提供保障。The random error of new energy forecast is a key factor restricting whether the calculation model for evaluating the power grid's new energy consumption capacity based on multi-scenario generation technology can obtain the optimal decision-making scheme. Combination scenarios are issues that need to be paid attention to in the research of optimal operation and scheduling with large-scale new energy sources; on the other hand, the traditional calculation model for evaluating the power grid's new energy consumption capacity based on multi-scenario generation technology does not consider the SCUC problem, which makes the optimization results impossible. Provide protection for the safe operation of the power grid.

发明内容SUMMARY OF THE INVENTION

针对上述问题,本发明提出一种基于多场景生成技术的电网新能源消纳能力评估计算方法,能够确保系统安全稳定的前提下,提高消纳能力评估结果的有效性和可参考性。In view of the above problems, the present invention proposes a new energy consumption capacity evaluation calculation method of a power grid based on a multi-scenario generation technology, which can improve the validity and reference of the consumption capacity assessment results under the premise of ensuring the safety and stability of the system.

为了实现上述技术目的,达到上述技术效果,本发明通过以下技术方案实现:In order to realize the above-mentioned technical purpose and achieve the above-mentioned technical effect, the present invention is realized through the following technical solutions:

第一方面,本发明提供了一种基于多场景生成技术的电网新能源消纳能力评估计算方法,包括:In a first aspect, the present invention provides a calculation method for evaluating the new energy consumption capacity of a power grid based on a multi-scenario generation technology, including:

确定需要开展的电网新能源消纳能力评估计算的电网范围和计算周期,以及其他计算边界;Determine the power grid range and calculation period and other calculation boundaries that need to be carried out for the assessment and calculation of the new energy consumption capacity of the power grid;

通过场间自由排列组合,生成含多新能源发电厂的大量组合场景;Through the free arrangement and combination of fields, a large number of combined scenes with multiple new energy power plants are generated;

采用后向缩减法的场景削减技术,归并相近场景,降低组合场景数目,生成典型组合场景;The scene reduction technology of the backward reduction method is adopted to merge similar scenes, reduce the number of combined scenes, and generate typical combined scenes;

对相近时段进行归并,减少进入优化的时段数目,缩小优化模型的计算时段规模;Merge similar time periods, reduce the number of time periods entering the optimization, and reduce the size of the calculation time period of the optimization model;

针对各个典型组合场景,建立时段归并后的SCUC降维模型,并求解获得机组组合结果;For each typical combination scenario, establish the SCUC dimensionality reduction model after time period merge, and solve it to obtain the unit combination result;

基于机组组合结果建立全时段SCED模型并求解,最终获得各个典型组合场景下的新能源消纳结果,完成基于多场景生成技术的电网新能源消纳能力评估计算。Based on the unit combination results, a full-time SCED model is established and solved, and finally the new energy consumption results under each typical combination scenario are obtained, and the evaluation and calculation of the new energy consumption capacity of the power grid based on the multi-scenario generation technology is completed.

可选地,所述组合场景的生成过程包括:Optionally, the generating process of the combined scene includes:

假设某电网的新能源场站总数为Nw,每个新能源场站的预测出力场景有Np种,各个预测出力场景的发生概率为prij(i=1,2,…,Nw;j=1,2,…,Np);Assuming that the total number of new energy stations in a power grid is N w , there are N p types of predicted output scenarios for each new energy station, and the probability of occurrence of each predicted output scene is pr ij (i=1,2,...,N w ; j=1,2,...,N p );

将所有新能源场站的出力场景进行排列组合,得到最终的新能源出力场景数Na

Figure BDA0002418266390000021
Arrange and combine the output scenarios of all new energy stations to obtain the final number of new energy output scenarios Na ,
Figure BDA0002418266390000021

所述组合场景发生概率即为对应出力场景发生概率的乘积。The probability of occurrence of the combined scene is the product of the probability of occurrence of the corresponding output scene.

可选地,所述典型组合场景的生成方法包括:Optionally, the method for generating the typical combined scene includes:

将被删除场景集合J初始化为空,需要删除的场景个数为K,第k次迭代被删除的场景是lkInitialize the deleted scene set J to be empty, the number of scenes to be deleted is K, and the scene to be deleted in the kth iteration is l k ;

重复进行以下步骤,直至迭代结束:Repeat the following steps until the end of the iteration:

计算坎托罗维奇距离,使l取场景lk时下式取得最小值,所述坎托罗维奇距离的计算公式为:Calculate the Kantorovich distance, so that l takes the scene l k to obtain the minimum value of the following formula. The calculation formula of the Kantorovich distance is:

Figure BDA0002418266390000031
Figure BDA0002418266390000031

式中:J是被删除场景集合;pi是场景i的概率;ξi对应场景序列i;T是场景时间尺度的分段数;cTij)表示场景序列ξi与场景序列ξj的距离,

Figure BDA0002418266390000032
where J is the set of deleted scenes; pi is the probability of scene i ; ξ i corresponds to scene sequence i; T is the number of segments in the scene time scale; c Tij ) represents the difference between scene sequence ξ i and the distance of the scene sequence ξ j ,
Figure BDA0002418266390000032

删除场景lk,令Jk=Jk-1∪{lk},并将场景lk的概率累加到距离其最近的场景上;Delete scene l k , let J k =J k-1 ∪{l k }, and accumulate the probability of scene l k to the scene closest to it;

若k<K,则令k=k+1。If k<K, then let k=k+1.

可选地,所述对相近时段进行归并,包括以下步骤:Optionally, the merging of similar time periods includes the following steps:

设Lt为时段t的系统负荷,则相邻时段t和t+1的系统负荷变化率为:Let L t be the system load of time period t, then the system load change rate of adjacent time periods t and t+1 is:

Figure BDA0002418266390000033
Figure BDA0002418266390000033

对全部时段进行系统负荷变化率的计算,找到最小变化率ΔLt所对应的时段,将时段t和t+1归并为一个新的时段;Calculate the system load change rate for all time periods, find the time period corresponding to the minimum change rate ΔL t , and combine time periods t and t+1 into a new time period;

根据系统负荷的变化率,重复进行时段归并,直至最小变化率ΔLt大于设定的阈值,或者归并后剩下的时段数目达到预设数目,归并过程结束。According to the change rate of the system load, the time period merge is repeated until the minimum change rate ΔL t is greater than the set threshold, or the number of time periods remaining after the merge reaches the preset number, and the merge process ends.

可选地,所述SCUC降维模型的目标函数为新能源机组总发电量的最大化,表示为:Optionally, the objective function of the SCUC dimensionality reduction model is the maximization of the total power generation of new energy generating units, which is expressed as:

Figure BDA0002418266390000034
Figure BDA0002418266390000034

式中,NW为新能源机组集合;T为时段归并后所包含的时段集合;Pw,t为新能源机组w在t时段的电力最大接纳能力;In the formula, N W is the set of new energy units; T is the set of time periods included after the time period is merged; P w,t is the maximum power receiving capacity of the new energy unit w in time period t;

所述SCUC降维模型的约束条件包括负荷平衡约束、常规机组出力上下限约束、机组最小开停机时间约束、新能源机组出力上下限约束、电网安全约束、系统备用容量约束:The constraints of the SCUC dimensionality reduction model include load balance constraints, upper and lower output limits of conventional units, minimum start-up and shutdown time constraints of units, upper and lower output limits of new energy units, power grid security constraints, and system reserve capacity constraints:

Figure BDA0002418266390000035
Figure BDA0002418266390000035

Pi,minui,t≤Pi,t≤Pi,maxui,t P i,min u i,t ≤P i,t ≤P i,max u i,t

Figure BDA0002418266390000036
Figure BDA0002418266390000036

Figure BDA0002418266390000041
Figure BDA0002418266390000041

yi,t-zi,t=ui,t-ui,t-1 y i,t -z i,t =ui ,t -u i,t-1

yi,t+zi,t≤1y i,t +z i,t ≤1

Pw,t≤P0,w,t P w,t ≤P 0,w,t

Figure BDA0002418266390000042
Figure BDA0002418266390000042

Figure BDA0002418266390000043
Figure BDA0002418266390000043

Figure BDA0002418266390000044
Figure BDA0002418266390000044

式中,Ni为火电机组总个数;Nw为新能源机组总个数;Nl为对外联络线总个数;Pi,t为常规机组i在时段t的有功出力;Pw,t为新能源机组w在时段t的有功出力;Pl,t为联络线l在时段t的有功出力;Lt为系统在时段t的负荷值;ui,t为机组i在t时段的启停状态;Pi,min为机组i的功率下限,Pi,max为机组i的功率上限;UTi和DTi分别为机组i的最小开机时间和最小停机时间;yi,t为机组i在时段t是否有停机到开机状态变化的标志;zi,t为机组i在时段t是否有开机到停机状态变化的标志;P0,w,t为新能源机组w在时段t的预测出力;Lij表示支路ij的潮流上限;M为电网计算节点集合;li,t为节点负荷功率;Si,j,t为节点i的注入功率对支路ij的灵敏度;Rt,u为系统在时段t的正备用容量下限;Rt,d为系统在时段t的负备用容量下限。In the formula, N i is the total number of thermal power units; N w is the total number of new energy units; N l is the total number of external connection lines; P i,t is the active power output of conventional unit i in time period t; P w, t is the active power output of the new energy unit w in the period t; P l,t is the active power output of the tie line l in the period t; L t is the load value of the system in the period t; u i,t is the unit i in the period t. Start-stop state; P i,min is the lower power limit of unit i, P i,max is the upper power limit of unit i; UT i and DT i are the minimum startup time and minimum shutdown time of unit i respectively; y i,t is the unit i, t Whether i has a sign that the state changes from shutdown to startup in time period t; zi ,t is whether the unit i has a sign of change from startup to shutdown state in time period t; P 0,w,t is the prediction of new energy unit w in time period t output; Li ij represents the upper limit of the power flow of branch ij; M is the set of grid computing nodes; li ,t is the node load power; S i,j,t is the sensitivity of the injected power of node i to branch ij; R t, u is the lower limit of the positive reserve capacity of the system in the time period t; R t,d is the lower limit of the negative reserve capacity of the system in the time period t.

可选地,所述SCED模型的目标函数为新能源机组总发电量的最大化,表示为:Optionally, the objective function of the SCED model is the maximization of the total power generation of new energy generating units, which is expressed as:

Figure BDA0002418266390000045
Figure BDA0002418266390000045

式中,T为全部时段集合;Nw为新能源机组总个数;Pw,t为新能源机组w在时段t的有功出力;In the formula, T is the set of all time periods; N w is the total number of new energy units; P w,t is the active power output of new energy units w in time period t;

约束条件包括负荷平衡约束、常规机组出力上下限约束、新能源机组出力上下限约束、电网安全约束、系统备用容量约束:The constraints include load balance constraints, upper and lower output constraints of conventional units, upper and lower output constraints of new energy units, power grid security constraints, and system reserve capacity constraints:

Figure BDA0002418266390000046
Figure BDA0002418266390000046

Pi,minui,t≤Pi,t≤Pi,maxui,t P i,min u i,t ≤P i,t ≤P i,max u i,t

Pw,t≤P0,w,t P w,t ≤P 0,w,t

Figure BDA0002418266390000047
Figure BDA0002418266390000047

Figure BDA0002418266390000048
Figure BDA0002418266390000048

Figure BDA0002418266390000051
Figure BDA0002418266390000051

式中,Ni为火电机组总个数;Nw为新能源机组总个数;Nl为对外联络线总个数;Pi,t为常规机组i在时段t的有功出力;Pw,t为新能源机组w在时段t的有功出力;Pl,t为联络线l在时段t的有功出力;Lt为系统在时段t的负荷值;ui,t为机组i在t时段的启停状态;Pi,min为机组i的功率下限,Pi,max为机组i的功率上限;UTi和DTi分别为机组i的最小开机时间和最小停机时间;P0,w,t为新能源机组w在时段t的预测出力;Lij表示支路ij的潮流上限;M为电网计算节点集合;li,t为节点负荷功率;Si,j,t为节点i的注入功率对支路ij的灵敏度;Rt,u为系统在时段t的正备用容量下限;Rt,d为系统在时段t的负备用容量下限。In the formula, N i is the total number of thermal power units; N w is the total number of new energy units; N l is the total number of external connection lines; P i,t is the active power output of conventional unit i in time period t; P w, t is the active power output of the new energy unit w in the period t; P l,t is the active power output of the tie line l in the period t; L t is the load value of the system in the period t; u i,t is the unit i in the period t. Start-stop state; P i,min is the lower power limit of unit i, P i,max is the upper power limit of unit i; UT i and DT i are the minimum startup time and minimum shutdown time of unit i respectively; P 0,w,t is the predicted output of the new energy unit w in the time period t; Li ij represents the upper limit of the power flow of the branch ij; M is the set of grid computing nodes; li ,t is the node load power; S i,j,t is the injected power of the node i Sensitivity to branch ij; R t,u is the lower limit of positive reserve capacity of the system in time period t; R t,d is the lower limit of negative reserve capacity of the system in time period t.

第二方面,本发明提供了一种基于多场景生成技术的电网新能源消纳能力评估计算装置,包括:In a second aspect, the present invention provides a computing device for evaluating new energy consumption capacity of a power grid based on a multi-scenario generation technology, including:

确定单元,确定需要开展的电网新能源消纳能力评估计算的电网范围和计算周期,以及其他计算边界;Determine the unit, determine the power grid range and calculation period for the calculation of the new energy consumption capacity evaluation calculation of the power grid, and other calculation boundaries;

第一生成单元,用于通过场间自由排列组合,生成含多新能源发电厂的大量组合场景;The first generation unit is used to generate a large number of combined scenarios including multiple new energy power plants through free arrangement and combination between fields;

第二生成单元,用于采用后向缩减法的场景削减技术,归并相近场景,降低组合场景数目,生成典型组合场景;The second generation unit is used for the scene reduction technology of the backward reduction method to merge similar scenes, reduce the number of combined scenes, and generate typical combined scenes;

归并单元,用于对相近时段进行归并,减少进入优化的时段数目,缩小优化模型的计算时段规模;The merging unit is used to merge similar time periods, reduce the number of time periods entering the optimization, and reduce the scale of the calculation time period of the optimization model;

第一求解单元,用于针对各个典型组合场景,建立时段归并后的SCUC降维模型,并求解获得机组组合结果;The first solving unit is used for establishing the SCUC dimensionality reduction model after time merging for each typical combination scenario, and solving to obtain the unit combination result;

第二求解单元,用于基于机组组合结果建立全时段SCED模型并求解,最终获得各个典型组合场景下的新能源消纳结果,完成基于多场景生成技术的电网新能源消纳能力评估计算。The second solving unit is used to establish and solve the full-time SCED model based on the unit combination results, and finally obtain the new energy consumption results under each typical combination scenario, and complete the evaluation calculation of the new energy consumption capacity of the power grid based on the multi-scenario generation technology.

可选地,所述SCUC降维模型的目标函数为新能源机组总发电量的最大化,表示为:Optionally, the objective function of the SCUC dimensionality reduction model is the maximization of the total power generation of new energy generating units, which is expressed as:

Figure BDA0002418266390000052
Figure BDA0002418266390000052

式中,NW为新能源机组集合;T为时段归并后所包含的时段集合;Pw,t为新能源机组w在t时段的电力最大接纳能力;In the formula, N W is the set of new energy units; T is the set of time periods included after the time period is merged; P w,t is the maximum power receiving capacity of the new energy unit w in time period t;

所述SCUC降维模型的约束条件包括负荷平衡约束、常规机组出力上下限约束、机组最小开停机时间约束、新能源机组出力上下限约束、电网安全约束、系统备用容量约束:The constraints of the SCUC dimensionality reduction model include load balance constraints, upper and lower output limits of conventional units, minimum start-up and shutdown time constraints of units, upper and lower output limits of new energy units, power grid security constraints, and system reserve capacity constraints:

Figure BDA0002418266390000061
Figure BDA0002418266390000061

Pi,minui,t≤Pi,t≤Pi,maxui,t P i,min u i,t ≤P i,t ≤P i,max u i,t

Figure BDA0002418266390000062
Figure BDA0002418266390000062

Figure BDA0002418266390000063
Figure BDA0002418266390000063

yi,t-zi,t=ui,t-ui,t-1 y i,t -z i,t =ui ,t -u i,t-1

yi,t+zi,t≤1y i,t +z i,t ≤1

Pw,t≤P0,w,t P w,t ≤P 0,w,t

Figure BDA0002418266390000064
Figure BDA0002418266390000064

Figure BDA0002418266390000065
Figure BDA0002418266390000065

Figure BDA0002418266390000066
Figure BDA0002418266390000066

式中,Ni为火电机组总个数;Nw为新能源机组总个数;Nl为对外联络线总个数;Pi,t为常规机组i在时段t的有功出力;Pw,t为新能源机组w在时段t的有功出力;Pl,t为联络线l在时段t的有功出力;Lt为系统在时段t的负荷值;ui,t为机组i在t时段的启停状态;Pi,min为机组i的功率下限,Pi,max为机组i的功率上限;UTi和DTi分别为机组i的最小开机时间和最小停机时间;yi,t为机组i在时段t是否有停机到开机状态变化的标志;zi,t为机组i在时段t是否有开机到停机状态变化的标志;P0,w,t为新能源机组w在时段t的预测出力;Lij表示支路ij的潮流上限;M为电网计算节点集合;li,t为节点负荷功率;Si,j,t为节点i的注入功率对支路ij的灵敏度;Rt,u为系统在时段t的正备用容量下限;Rt,d为系统在时段t的负备用容量下限。In the formula, N i is the total number of thermal power units; N w is the total number of new energy units; N l is the total number of external connection lines; P i,t is the active power output of conventional unit i in time period t; P w, t is the active power output of the new energy unit w in the period t; P l,t is the active power output of the tie line l in the period t; L t is the load value of the system in the period t; u i,t is the unit i in the period t. Start-stop state; P i,min is the lower power limit of unit i, P i,max is the upper power limit of unit i; UT i and DT i are the minimum startup time and minimum shutdown time of unit i respectively; y i,t is the unit i, t Whether i has a sign that the state changes from shutdown to startup in time period t; zi ,t is whether the unit i has a sign of change from startup to shutdown state in time period t; P 0,w,t is the prediction of new energy unit w in time period t output; Li ij represents the upper limit of the power flow of branch ij; M is the set of grid computing nodes; li ,t is the node load power; S i,j,t is the sensitivity of the injected power of node i to branch ij; R t, u is the lower limit of the positive reserve capacity of the system in the time period t; R t,d is the lower limit of the negative reserve capacity of the system in the time period t.

可选地,所述SCED模型的目标函数为新能源机组总发电量的最大化,表示为:Optionally, the objective function of the SCED model is the maximization of the total power generation of new energy generating units, which is expressed as:

Figure BDA0002418266390000067
Figure BDA0002418266390000067

式中,T为全部时段集合;Nw为新能源机组总个数;Pw,t为新能源机组w在时段t的有功出力;In the formula, T is the set of all time periods; N w is the total number of new energy units; P w,t is the active power output of new energy units w in time period t;

约束条件包括负荷平衡约束、常规机组出力上下限约束、新能源机组出力上下限约束、电网安全约束、系统备用容量约束:The constraints include load balance constraints, upper and lower output constraints of conventional units, upper and lower output constraints of new energy units, power grid security constraints, and system reserve capacity constraints:

Figure BDA0002418266390000068
Figure BDA0002418266390000068

Pi,minui,t≤Pi,t≤Pi,maxui,t P i,min u i,t ≤P i,t ≤P i,max u i,t

Pw,t≤P0,w,t P w,t ≤P 0,w,t

Figure BDA0002418266390000071
Figure BDA0002418266390000071

Figure BDA0002418266390000072
Figure BDA0002418266390000072

Figure BDA0002418266390000073
Figure BDA0002418266390000073

式中,Ni为火电机组总个数;Nw为新能源机组总个数;Nl为对外联络线总个数;Pi,t为常规机组i在时段t的有功出力;Pw,t为新能源机组w在时段t的有功出力;Pl,t为联络线l在时段t的有功出力;Lt为系统在时段t的负荷值;ui,t为机组i在t时段的启停状态;Pi,min为机组i的功率下限,Pi,max为机组i的功率上限;UTi和DTi分别为机组i的最小开机时间和最小停机时间;P0,w,t为新能源机组w在时段t的预测出力;Lij表示支路ij的潮流上限;M为电网计算节点集合;li,t为节点负荷功率;Si,j,t为节点i的注入功率对支路ij的灵敏度;Rt,u为系统在时段t的正备用容量下限;Rt,d为系统在时段t的负备用容量下限。In the formula, N i is the total number of thermal power units; N w is the total number of new energy units; N l is the total number of external connection lines; P i,t is the active power output of conventional unit i in time period t; P w, t is the active power output of the new energy unit w in the period t; P l,t is the active power output of the tie line l in the period t; L t is the load value of the system in the period t; u i,t is the unit i in the period t. Start-stop state; P i,min is the lower power limit of unit i, P i,max is the upper power limit of unit i; UT i and DT i are the minimum startup time and minimum shutdown time of unit i respectively; P 0,w,t is the predicted output of the new energy unit w in the time period t; Li ij represents the upper limit of the power flow of the branch ij; M is the set of grid computing nodes; li ,t is the node load power; S i,j,t is the injected power of the node i Sensitivity to branch ij; R t,u is the lower limit of positive reserve capacity of the system in time period t; R t,d is the lower limit of negative reserve capacity of the system in time period t.

第三方面,本发明提供了一种基于多场景生成技术的电网新能源消纳能力评估计算系统,包括存储介质和处理器;In a third aspect, the present invention provides a computing system for evaluating new energy consumption capacity of a power grid based on a multi-scenario generation technology, including a storage medium and a processor;

所述存储介质用于存储指令;the storage medium is used for storing instructions;

所述处理器用于根据所述指令进行操作以执行根据第一方面中任一项所述方法的步骤。The processor is adapted to operate in accordance with the instructions to perform the steps of the method according to any of the first aspects.

与现有技术相比,本发明的有益效果:Compared with the prior art, the beneficial effects of the present invention:

本发明首先确定需要开展基于多场景生成技术的电网新能源消纳能力评估计算的电网范围和计算周期,确定计算边界;通过场间自由排列组合,生成含多新能源发电厂的大量组合场景;采用后向缩减法的场景削减技术,生成典型组合场景;然后开展相近时段归并分析,减少进入SCUC计算的时段数目;针对各个典型组合场景,建立时段归并后的SCUC降维模型,快速求解获得机组组合结果;基于机组组合结果建立全时段SCED模型并求解;最终获得各个典型组合场景下的新能源消纳结果,能够确保系统安全稳定的前提下,提高消纳能力评估结果的有效性和可参考性。The invention firstly determines the power grid range and calculation period that need to carry out the evaluation and calculation of the new energy consumption capacity of the power grid based on the multi-scenario generation technology, and determines the calculation boundary; through the free arrangement and combination between the fields, a large number of combined scenarios containing multiple new energy power plants are generated; The scene reduction technology of the backward reduction method is used to generate typical combined scenes; then the merge analysis of similar periods is carried out to reduce the number of periods entered into SCUC calculation; for each typical combined scene, a SCUC dimensionality reduction model after period merge is established to quickly solve and obtain the unit Combination results; based on the unit combination results, a full-time SCED model is established and solved; the new energy consumption results under each typical combination scenario are finally obtained, which can improve the validity and reference of the assessment results of the consumption capacity under the premise of ensuring the safety and stability of the system sex.

本发明利用组合场景削减、相近时段归并以及无效启停变量辨识技术,降低了进入SCUC优化计算的时段规模、变量维度,缩小了机组组合的状态组合空间,也减少了SCUC-SCED优化求解的循环次数,有效提升了整个消纳能力评估的求解效率;The invention utilizes combination scene reduction, similar time period merging and invalid start-stop variable identification technology to reduce the time period scale and variable dimension of entering SCUC optimization calculation, reduce the state combination space of unit combination, and reduce the cycle of SCUC-SCED optimization solution. times, effectively improving the solution efficiency of the entire absorptive capacity evaluation;

本发明基于多个新能源组合场景生成技术进行机组组合降维出清和全时段经济调度计算,获得不同组合场景、不同发生概率下的计算结果,更为详细地反映了新能源的消纳情况;The invention performs unit combination dimension reduction and clearing and full-time economic dispatch calculation based on multiple new energy combination scenario generation technologies, obtains calculation results under different combination scenarios and different occurrence probabilities, and reflects the consumption of new energy in more detail;

本发明基于安全约束机组组合进行基于多场景生成技术的电网新能源消纳能力评估计算,保证新能源消纳结果满足各类电网运行约束,模型计算结果稳定,为解决新能源消纳和电网安全等实际问题提供了一种有效思路,具有一定的实用价值。The invention performs the evaluation and calculation of the new energy consumption capacity of the power grid based on the multi-scenario generation technology based on the combination of the safety constraints, ensuring that the new energy consumption results meet various power grid operation constraints, and the model calculation results are stable. It provides an effective way of thinking and has certain practical value.

附图说明Description of drawings

为了使本发明的内容更容易被清楚地理解,下面根据具体实施例并结合附图,对本发明作进一步详细的说明,其中:In order to make the content of the present invention easier to be understood clearly, the present invention will be described in further detail below according to specific embodiments and in conjunction with the accompanying drawings, wherein:

图1为本发明一种实施例的基于多场景生成技术的电网新能源消纳能力评估计算方法流程示意图。FIG. 1 is a schematic flowchart of a calculation method for evaluating new energy consumption capacity of a power grid based on a multi-scenario generation technology according to an embodiment of the present invention.

具体实施方式Detailed ways

为了使本发明的目的、技术方案及优点更加清楚明白,以下结合实施例,对本发明进行进一步详细说明。应当理解,此处所描述的具体实施例仅仅用以解释本发明,并不用于限定本发明的保护范围。In order 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. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the protection scope of the present invention.

下面结合附图对本发明的应用原理作详细的描述。The application principle of the present invention will be described in detail below with reference to the accompanying drawings.

在电网新能源消纳能力评估计算过程中,需要采用安全约束机组组合技术,优化月度744时段的机组组合与新能源出力结果。优化过程既要求精细化考虑机组运行约束、电网安全约束、负荷平衡约束等电网运行中的各种安全约束限制,又要求新能源出力场景尽量能够描述调度运行实际情况,并且计算过程能够在运行人员能够接受的时间内结束,使得计算精度和计算时间满足工程实际要求。In the process of evaluating and calculating the new energy consumption capacity of the power grid, it is necessary to adopt the technology of safety constraint unit combination to optimize the unit combination and new energy output results in the monthly 744 period. The optimization process not only requires careful consideration of various security constraints in power grid operation, such as unit operation constraints, power grid security constraints, and load balance constraints, but also requires that the new energy output scenario can describe the actual situation of dispatching operations as much as possible, and the calculation process can be used by operators. It can be finished within an acceptable time, so that the calculation accuracy and calculation time can meet the actual requirements of the project.

实施例1Example 1

本发明提出一种基于多场景生成技术的电网新能源消纳能力评估计算方法,对新能源的多种概率场景进行预处理,考虑到电力系统结构特点,通过对多个新能源的组合场景生成处理,归并相似计算时段和组合场景,识别无效的整型变量,降低组合场景数量,缩小混合整数规划的优化范围和新能源消纳能力评估的迭代次数,提升求解效率和有效性。下面通过具体实例来详细说明本发明提供的方法。The invention proposes a new energy consumption capacity evaluation calculation method of the power grid based on the multi-scenario generation technology, which preprocesses various probability scenarios of the new energy, and takes into account the structural characteristics of the power system. Processing, merging similar calculation periods and combined scenarios, identifying invalid integer variables, reducing the number of combined scenarios, narrowing the optimization range of mixed integer programming and the iteration times of new energy absorption capacity evaluation, improving solution efficiency and effectiveness. The method provided by the present invention will be described in detail below through specific examples.

具体地,所述基于多场景生成技术的电网新能源消纳能力评估计算方法包括以下步骤:Specifically, the method for evaluating and calculating the new energy consumption capacity of the power grid based on the multi-scenario generation technology includes the following steps:

步骤1)考虑某区域电力系统的新能源消纳能力评估,该电网包括190台需要开展状态组合计算的发电机组,计算过程需要考虑29个输电断面,新能源分区为12个,每个分区包含3种概率预测场景,新能源消纳能力评估以1小时作为一个计算时段,计算时段数目为744个。首先进行数据准备,获取电网内的发电机组出力上下限、最小开停机时间等参数,获取电网的拓扑结构以及输电断面的组成设备、输电限值等参数信息。同时获取各类计划数据,包括负荷预测、检修计划、备用需求、新能源预测出力及其概率等信息,以确定电网新能源消纳能力评估计算的边界。Step 1) Consider the assessment of the new energy consumption capacity of the power system in a certain area. The power grid includes 190 generator sets that need to be calculated by state combination. The calculation process needs to consider 29 transmission sections, and there are 12 new energy partitions. Each partition contains For 3 probability prediction scenarios, the new energy consumption capacity evaluation takes 1 hour as a calculation period, and the number of calculation periods is 744. Firstly, data preparation is performed to obtain parameters such as the upper and lower output limits of the generator sets in the power grid, the minimum start and stop time, and other parameters such as the topology structure of the power grid, the components of the transmission section, and the transmission limit. At the same time, various planning data are obtained, including load forecast, maintenance plan, standby demand, new energy forecast output and its probability, etc., to determine the boundary of the power grid new energy consumption capacity evaluation calculation.

步骤2)根据各个新能源不同概率下的预测出力,通过场间自由排列组合,生成含多新能源发电厂的大量组合场景,并计算各个组合场景发生的概率;Step 2) According to the predicted output under different probabilities of each new energy source, a large number of combined scenarios containing multiple new energy power plants are generated through free arrangement and combination between fields, and the probability of occurrence of each combined scenario is calculated;

在本发明实施例的一种具体实施方式中,所述组合场景生成的具体过程包括以下步骤:In a specific implementation of the embodiment of the present invention, the specific process of generating the combined scene includes the following steps:

假设某电网的新能源场站总数为Nw,每个新能源场站的预测出力场景有Np种,各个预测出力场景的发生概率为prij(i=1,2,…,Nw;j=1,2,…,Np);Assuming that the total number of new energy stations in a power grid is N w , there are N p types of predicted output scenarios for each new energy station, and the probability of occurrence of each predicted output scene is pr ij (i=1,2,...,N w ; j=1,2,...,N p );

将所有新能源场站的出力场景进行排列组合,可以得到最终的新能源出力场景数Na如下式:By arranging and combining the output scenarios of all new energy stations, the final number N a of new energy output scenarios can be obtained as follows:

Figure BDA0002418266390000091
Figure BDA0002418266390000091

所述组合场景发生概率即为对应出力场景发生概率的乘积。The probability of occurrence of the combined scene is the product of the probability of occurrence of the corresponding output scene.

步骤3)采用后向缩减法的场景削减技术,归并相近场景,降低组合场景数目,生成典型组合场景;Step 3) adopt the scene reduction technology of the backward reduction method, merge similar scenes, reduce the number of combined scenes, and generate typical combined scenes;

从新能源出力场景反映新能源出力不确定性的角度来看,相似的场景所能提供的不确定性信息也是相近的,但同时也会增加不必要的计算量,影响计算效率。因此,需要在生成的组合场景集基础上进行场景缩减,去掉一部分概率较低的场景、合并相似的场景。场景缩减实质上是一种以牺牲计算精度为代价提高计算效率的方法,因此,进行场景缩减时应最大程度地保证新能源出力场景的有效性。From the perspective of new energy output scenarios reflecting the uncertainty of new energy output, similar scenarios can provide similar uncertainty information, but at the same time, it will also increase the amount of unnecessary calculation and affect the calculation efficiency. Therefore, it is necessary to perform scene reduction on the basis of the generated combined scene set, remove some scenes with low probability, and merge similar scenes. Scenario reduction is essentially a method to improve computing efficiency at the expense of computing accuracy. Therefore, the effectiveness of new energy output scenarios should be guaranteed to the greatest extent during scenario reduction.

场景缩减的基本原则是:使缩减后的场景集合与缩减前的场景集合概率距离最小。概率距离是一种权衡各个场景距离及场景概率大小的方式,它使得缩减前的场景与缩减后的场景所能表达的信息最接近,即使缩减过程引起的精度损失最低。此优化模型的概率距离采用坎托罗维奇距离Dk描述,Dk表达式如下:The basic principle of scene reduction is to minimize the probability distance between the reduced scene set and the scene set before reduction. Probabilistic distance is a way of weighing the distance of each scene and the probability of the scene, which makes the information expressed by the scene before reduction and the scene after reduction the closest, even if the precision loss caused by the reduction process is the lowest. The probabilistic distance of this optimization model is described by the Kantorovich distance D k , which is expressed as follows :

Figure BDA0002418266390000092
Figure BDA0002418266390000092

式中:J是被删除场景集合;pi是场景i的概率;ξi对应场景序列i;T是场景时间尺度的分段数;cTij)表示场景序列ξi与场景序列ξj的距离,即:where J is the set of deleted scenes; pi is the probability of scene i ; ξ i corresponds to scene sequence i; T is the number of segments in the scene time scale; c Tij ) represents the difference between scene sequence ξ i and The distance of the scene sequence ξ j , namely:

Figure BDA0002418266390000093
Figure BDA0002418266390000093

后向缩减法主要分为以下几步:The backward reduction method is mainly divided into the following steps:

Step1:将被删除场景集合J初始化为空,需要删除的场景个数为K,第k次迭代被删除的场景是lkStep1: Initialize the deleted scene set J to be empty, the number of scenes to be deleted is K, and the deleted scene of the k-th iteration is l k ;

Step2:计算坎托罗维奇距离,使l取场景lk时下式取得最小值;Step2: Calculate the Kantorovich distance, so that the following formula obtains the minimum value when l takes the scene l k ;

Step3:删除场景lk,令Jk=Jk-1∪{lk},并将场景lk的概率累加到距离其最近的场景上;Step3: delete scene l k , let J k =J k-1 ∪{l k }, and accumulate the probability of scene l k to the scene closest to it;

Step4:若k<K,则令k=k+1,返回Step2,否则迭代结束。Step4: If k<K, set k=k+1, and return to Step2, otherwise the iteration ends.

步骤4)根据系统负荷在计算周期内不同时段的变化趋势,对相近计算时段进行归并,减少进入优化的时段数目,缩小优化模型的计算时段规模;根据机组的可优化状态,辨识安全约束机组组合决策中的必开必停机组、缓冲机组;Step 4) According to the change trend of the system load in different time periods in the calculation cycle, merge the similar calculation time periods, reduce the number of time periods entering the optimization, and reduce the scale of the calculation time period of the optimization model; According to the optimizable state of the unit, identify the combination of safety-constrained units The must-open and must-stop group and buffer group in decision-making;

时段归并的具体过程为:设Lt为时段t的系统负荷,则相邻时段t和t+1的系统负荷变化率为:The specific process of time period merging is: Let L t be the system load of time period t, then the system load change rate of adjacent time periods t and t+1 is:

Figure BDA0002418266390000101
Figure BDA0002418266390000101

对全部时段进行系统负荷变化率的计算,找到最小变化率ΔLt所对应的时段,将时段t和t+1归并为一个新的时段。根据系统负荷的变化率,重复进行时段归并,直至最小变化率ΔLt大于设定的阈值,或者归并后剩下的时段数目达到预设数目,归并过程结束。Calculate the system load change rate for all time periods, find the time period corresponding to the minimum change rate ΔL t , and combine time periods t and t+1 into a new time period. According to the change rate of the system load, the time period merge is repeated until the minimum change rate ΔL t is greater than the set threshold, or the number of time periods remaining after the merge reaches the preset number, and the merge process ends.

安全约束机组组合决策中的必开必停机组为电网运行中的必开发电机组或者必停发电机组,不需要在安全约束机组组合中进行机组状态的优化决策;缓冲机组为无法提前确定开停机状态的发电机组,需要在安全约束机组组合中进行机组状态的优化决策。根据所提供的机组状态数据辨识缓冲机组与必开必停机组,以减少整型变量数量,缩小混合整数规划的优化范围,提升求解效率。The must-start and must-stop units in the decision-making of the safety-constrained unit combination are those that must be developed or must be stopped during the operation of the power grid, and there is no need to make an optimization decision on the unit state in the security-constrained unit combination; the buffer unit cannot be determined in advance. For the generator set in the state, it is necessary to make the optimal decision of the state of the generator set in the combination of safety constraints. According to the provided unit state data, the buffer unit and the must-start must-stop unit are identified to reduce the number of integer variables, narrow the optimization range of mixed integer programming, and improve the solution efficiency.

步骤5)针对某个典型组合场景,将缓冲机组的开停机状态作为组合决策变量,考虑有效断面的输电限值约束,建立时段归并后的SCUC降维模型,采用混合整数规划算法,快速求解获得缓冲机组的启停状态结果。Step 5) For a typical combination scenario, the on-off state of the buffer unit is used as the combination decision-making variable, considering the transmission limit constraints of the effective section, and the SCUC dimension reduction model after time period merge is established, and the mixed integer programming algorithm is used to quickly solve the problem. The result of the start-stop status of the buffer unit.

月度新能源消纳能力评估计算优化各时段火电机组开停与出力、以及新能源机组出力计划,以满足负荷曲线需求。以区域内的新能源机组为具体的评估对象,基于各新能源机组的发电功率总加,获得研究区域内的新能源机组总出力,优化目标是在满足各种约束的条件下,最大化新能源机组的发电量。SCUC降维模型的目标函数可以表示为:Monthly evaluation and calculation of new energy consumption capacity optimizes the start-up, shutdown and output of thermal power units at each time period, as well as the output plan of new energy units to meet the demand of the load curve. Taking the new energy units in the area as the specific evaluation object, based on the total power generation of each new energy unit, the total output of the new energy units in the study area is obtained. The amount of electricity produced by the power unit. The objective function of the SCUC dimensionality reduction model can be expressed as:

Figure BDA0002418266390000111
Figure BDA0002418266390000111

式中,NW为新能源机组集合;T为时段归并后所包含的时段集合;Pw,t为新能源机组w在t时段的电力最大接纳能力。In the formula, N W is the set of new energy units; T is the set of time periods included after the time period is merged; P w,t is the maximum power receiving capacity of the new energy unit w in time period t.

约束条件包括负荷平衡约束、常规机组出力上下限约束、机组最小开停机时间约束、新能源机组出力上下限约束、电网安全约束、系统备用容量约束:Constraints include load balance constraints, upper and lower output constraints of conventional units, minimum start-up and shutdown time constraints of units, upper and lower output constraints of new energy units, power grid security constraints, and system reserve capacity constraints:

Figure BDA0002418266390000112
Figure BDA0002418266390000112

Pi,minui,t≤Pi,t≤Pi,maxui,t P i,min u i,t ≤P i,t ≤P i,max u i,t

Figure BDA0002418266390000113
Figure BDA0002418266390000113

Figure BDA0002418266390000114
Figure BDA0002418266390000114

yi,t-zi,t=ui,t-ui,t-1 y i,t -z i,t =ui ,t -u i,t-1

yi,t+zi,t≤1y i,t +z i,t ≤1

Pw,t≤P0,w,t P w,t ≤P 0,w,t

Figure BDA0002418266390000115
Figure BDA0002418266390000115

Figure BDA0002418266390000116
Figure BDA0002418266390000116

Figure BDA0002418266390000117
Figure BDA0002418266390000117

式中,Ni为火电机组总个数;Nw为新能源机组总个数;Nl为对外联络线总个数;Pi,t为常规机组i在时段t的有功出力;Pw,t为新能源机组w在时段t的有功出力;Pl,t为联络线l在时段t的有功出力;Lt为系统在时段t的负荷值;ui,t为机组i在t时段的启停状态;Pi,min为机组i的功率下限,Pi,max为机组i的功率上限;UTi和DTi分别为机组i的最小开机时间和最小停机时间;yi,t为机组i在时段t是否有停机到开机状态变化的标志;zi,t为机组i在时段t是否有开机到停机状态变化的标志;P0,w,t为新能源机组w在时段t的预测出力;Lij表示支路ij的潮流上限;M为电网计算节点集合;li,t为节点负荷功率;Si,j,t为节点i的注入功率对支路ij的灵敏度;Rt,u为系统在时段t的正备用容量下限;Rt,d为系统在时段t的负备用容量下限。In the formula, N i is the total number of thermal power units; N w is the total number of new energy units; N l is the total number of external connection lines; P i,t is the active power output of conventional unit i in time period t; P w, t is the active power output of the new energy unit w in the period t; P l,t is the active power output of the tie line l in the period t; L t is the load value of the system in the period t; u i,t is the unit i in the period t. Start-stop state; P i,min is the lower power limit of unit i, P i,max is the upper power limit of unit i; UT i and DT i are the minimum startup time and minimum shutdown time of unit i respectively; y i,t is the unit i, t Whether i has a sign that the state changes from shutdown to startup in time period t; zi ,t is whether the unit i has a sign of change from startup to shutdown state in time period t; P 0,w,t is the prediction of new energy unit w in time period t output; Li ij represents the upper limit of the power flow of branch ij; M is the set of grid computing nodes; li ,t is the node load power; S i,j,t is the sensitivity of the injected power of node i to branch ij; R t, u is the lower limit of the positive reserve capacity of the system in the time period t; R t,d is the lower limit of the negative reserve capacity of the system in the time period t.

步骤6)以缓冲机组的启停状态结果,以及必开必停机组的启停状态为基础,建立考虑全部计算时段的SCED模型,采用线性规划算法进行求解;若存在部分时段的系统平衡约束无法满足,则将该时段加入计算时段集合,进入步骤5),否则进入步骤7)。Step 6) On the basis of the start-stop state results of the buffer unit and the start-stop state of the must-open must-stop group, establish a SCED model that considers the entire calculation period, and use a linear programming algorithm to solve it; If satisfied, add the period to the calculation period set, and go to step 5), otherwise, go to step 7).

全时段SCED模型的目标函数为新能源机组总发电量的最大化,可以表示为:The objective function of the full-time SCED model is to maximize the total power generation of new energy units, which can be expressed as:

Figure BDA0002418266390000121
Figure BDA0002418266390000121

式中,T为全部时段集合。In the formula, T is the set of all time periods.

约束条件包括负荷平衡约束、常规机组出力上下限约束、新能源机组出力上下限约束、电网安全约束、系统备用容量约束:The constraints include load balance constraints, upper and lower output constraints of conventional units, upper and lower output constraints of new energy units, power grid security constraints, and system reserve capacity constraints:

Figure BDA0002418266390000122
Figure BDA0002418266390000122

Pi,minui,t≤Pi,t≤Pi,maxui,t P i,min u i,t ≤P i,t ≤P i,max u i,t

Pw,t≤P0,w,t P w,t ≤P 0,w,t

Figure BDA0002418266390000123
Figure BDA0002418266390000123

Figure BDA0002418266390000124
Figure BDA0002418266390000124

Figure BDA0002418266390000125
Figure BDA0002418266390000125

式中,Ni为火电机组总个数;Nw为新能源机组总个数;Nl为对外联络线总个数;Pi,t为常规机组i在时段t的有功出力;Pw,t为新能源机组w在时段t的有功出力;Pl,t为联络线l在时段t的有功出力;Lt为系统在时段t的负荷值;ui,t为机组i在t时段的启停状态;Pi,min为机组i的功率下限,Pi,max为机组i的功率上限;UTi和DTi分别为机组i的最小开机时间和最小停机时间;P0,w,t为新能源机组w在时段t的预测出力;Lij表示支路ij的潮流上限;M为电网计算节点集合;li,t为节点负荷功率;Si,j,t为节点i的注入功率对支路ij的灵敏度;Rt,u为系统在时段t的正备用容量下限;Rt,d为系统在时段t的负备用容量下限。In the formula, N i is the total number of thermal power units; N w is the total number of new energy units; N l is the total number of external connection lines; P i,t is the active power output of conventional unit i in time period t; P w, t is the active power output of the new energy unit w in the period t; P l,t is the active power output of the tie line l in the period t; L t is the load value of the system in the period t; u i,t is the unit i in the period t. Start-stop state; P i,min is the lower power limit of unit i, P i,max is the upper power limit of unit i; UT i and DT i are the minimum startup time and minimum shutdown time of unit i respectively; P 0,w,t is the predicted output of the new energy unit w in the time period t; Li ij represents the upper limit of the power flow of the branch ij; M is the set of grid computing nodes; li ,t is the node load power; S i,j,t is the injected power of the node i Sensitivity to branch ij; R t,u is the lower limit of positive reserve capacity of the system in time period t; R t,d is the lower limit of negative reserve capacity of the system in time period t.

步骤7)若遍历完所有典型组合场景,则进入步骤8),否则根据下一个典型组合场景,进入步骤5);Step 7) If traversing all typical combination scenes, then enter step 8), otherwise according to the next typical combination scene, enter step 5);

步骤8)生成各个典型组合场景下的各个新能源最大出力及其发生概率,以及系统新能源总出力,电网新能源消纳能力评估计算结束。Step 8) Generate the maximum output of each new energy and its occurrence probability under each typical combination scenario, and the total output of the new energy of the system, and the calculation of the new energy consumption capacity evaluation calculation of the power grid is completed.

为验证所提模型的有效性,设基于极限场景法的优化模型为方案1,文章所提及的基于多场景生成技术的优化模型为方案2。该电网包括190台需要开展状态组合计算的发电机组,计算时段数目为744个,总的发电机组状态离散变量数目为141360(190*744)个,输电断面约束条件数目为21576(29*744)个。相近时段归并后的优化模型计算时段数目为93个,识别无效的整型变量,降维后的优化模型的机组状态离散变量数目降为15252个。,平均总的计算时间在30分钟以内。In order to verify the effectiveness of the proposed model, the optimization model based on the extreme scene method is set as scheme 1, and the optimization model based on multi-scenario generation technology mentioned in the article is set as scheme 2. The power grid includes 190 generator sets that need to carry out state combination calculation, the number of calculation periods is 744, the total number of generator set state discrete variables is 141360 (190*744), and the number of transmission section constraints is 21576 (29*744) indivual. The number of calculation periods of the optimization model after the merging of similar periods is 93, and the integer variables that are invalid are identified. The number of discrete variables of the unit state of the optimization model after dimensionality reduction is reduced to 15252. , the average total computation time is within 30 minutes.

方案1基于极限场景法分别取每个新能源分区的最大概率和最小概率场景作为2种组合场景,目标函数平均值为1847121;方案2基于多场景生成技术的电网新能源消纳能力评估技术,总的新能源组合场景数目为312个,削减后降为5个,目标函数平均值为1865524。方案1的目标函数平均值较低,是因为它考虑的各个分区概率最低的场景一起发生的概率非常小,导致优化结果过于保守,影响到优化结果的客观性,而场景组合再缩减所生成的组合场景更为合理,能代表典型预测场景,所以本文所提优化方法更符合调度运行实际,具有一定的参考意义;并且场景缩减和时段合并使得求解效率显著提升,满足实际应用要求。Scheme 1 takes the maximum probability and minimum probability scenarios of each new energy partition as two combined scenarios based on the extreme scenario method, and the average value of the objective function is 1847121; The total number of new energy combination scenarios is 312, which is reduced to 5 after the reduction, and the average value of the objective function is 1,865,524. The average value of the objective function of scheme 1 is low, because the probability of occurrence of the scenarios with the lowest probability of each partition is very small, resulting in too conservative optimization results, which affects the objectivity of the optimization results. Combination scenarios are more reasonable and can represent typical prediction scenarios. Therefore, the optimization method proposed in this paper is more in line with the actual scheduling operation and has certain reference significance. Moreover, the reduction of scenarios and the combination of time periods significantly improve the solution efficiency and meet the requirements of practical applications.

本发明的方法在实际电网数据下开展的基于多场景生成技术的电网新能源消纳能力评估计算方法的研究和尝试。该方法通过新能源的多概率场景组合预处理,减少组合场景数量,通过时段归并缩小混合整数规划的优化范围,快速获得满足计算要求的机组组合与有功出力结果,并将安全约束机组组合技术应用于电网新能源消纳能力评估计算之中,考虑到新能源消纳与网络安全的关系,进而得出更为可靠的优化结果。该方法计算速度可以满足实际应用的需要,有效地解决了传统的新能源消纳只根据单一预测场景进行评估的弊病,增强了新能源优化调度的安全性和合理性,具有广泛的推广前景。The method of the present invention is a research and attempt of a calculation method for evaluating the new energy consumption capacity of a power grid based on a multi-scenario generation technology under the actual power grid data. This method reduces the number of combined scenarios through multi-probability scenario combination preprocessing of new energy, narrows the optimization range of mixed integer programming through time period merging, quickly obtains unit combinations and active power output results that meet computing requirements, and applies the safety constraint unit combination technology. In the evaluation and calculation of the new energy consumption capacity of the power grid, considering the relationship between new energy consumption and network security, a more reliable optimization result can be obtained. The calculation speed of this method can meet the needs of practical applications, effectively solve the disadvantage of traditional new energy consumption that is only evaluated according to a single forecast scenario, enhance the safety and rationality of new energy optimal scheduling, and has broad prospects for promotion.

实施例2Example 2

基于与实施例1相同的发明构思,本发明实施例中提供了一种基于多场景生成技术的电网新能源消纳能力评估计算装置,包括:Based on the same inventive concept as Embodiment 1, an embodiment of the present invention provides a computing device for evaluating new energy consumption capacity of a power grid based on a multi-scenario generation technology, including:

确定单元,确定需要开展的电网新能源消纳能力评估计算的电网范围和计算周期,以及其他计算边界;Determine the unit, determine the power grid range and calculation period for the calculation of the new energy consumption capacity evaluation calculation of the power grid, and other calculation boundaries;

第一生成单元,用于通过场间自由排列组合,生成含多新能源发电厂的大量组合场景;The first generation unit is used to generate a large number of combined scenarios including multiple new energy power plants through free arrangement and combination between fields;

第二生成单元,用于采用后向缩减法的场景削减技术,归并相近场景,降低组合场景数目,生成典型组合场景;The second generation unit is used for the scene reduction technology of the backward reduction method to merge similar scenes, reduce the number of combined scenes, and generate typical combined scenes;

归并单元,用于对相近时段进行归并,减少进入优化的时段数目,缩小优化模型的计算时段规模;The merging unit is used to merge similar time periods, reduce the number of time periods entering the optimization, and reduce the scale of the calculation time period of the optimization model;

第一求解单元,用于针对各个典型组合场景,建立时段归并后的SCUC降维模型,并求解获得机组组合结果;The first solving unit is used for establishing the SCUC dimensionality reduction model after time merging for each typical combination scenario, and solving to obtain the unit combination result;

第二求解单元,用于基于机组组合结果建立全时段SCED模型并求解,最终获得各个典型组合场景下的新能源消纳结果,完成基于多场景生成技术的电网新能源消纳能力评估计算。The second solving unit is used to establish and solve the full-time SCED model based on the unit combination results, and finally obtain the new energy consumption results under each typical combination scenario, and complete the evaluation calculation of the new energy consumption capacity of the power grid based on the multi-scenario generation technology.

在本发明实施例的一种具体实施例中,所述组合场景的生成过程包括:In a specific embodiment of the embodiment of the present invention, the generating process of the combined scene includes:

假设某电网的新能源场站总数为Nw,每个新能源场站的预测出力场景有Np种,各个预测出力场景的发生概率为prij(i=1,2,…,Nw;j=1,2,…,Np);Assuming that the total number of new energy stations in a power grid is N w , there are N p types of predicted output scenarios for each new energy station, and the probability of occurrence of each predicted output scene is pr ij (i=1,2,...,N w ; j=1,2,...,N p );

将所有新能源场站的出力场景进行排列组合,得到最终的新能源出力场景数Na

Figure BDA0002418266390000141
Arrange and combine the output scenarios of all new energy stations to obtain the final number of new energy output scenarios Na ,
Figure BDA0002418266390000141

所述组合场景发生概率即为对应出力场景发生概率的乘积。The probability of occurrence of the combined scene is the product of the probability of occurrence of the corresponding output scene.

在本发明实施例的一种具体实施方式中,所述典型组合场景的生成方法包括:In a specific implementation of the embodiment of the present invention, the method for generating the typical combined scene includes:

将被删除场景集合J初始化为空,需要删除的场景个数为K,第k次迭代被删除的场景是lkInitialize the deleted scene set J to be empty, the number of scenes to be deleted is K, and the scene to be deleted in the kth iteration is l k ;

重复进行以下步骤,直至迭代结束:Repeat the following steps until the end of the iteration:

计算坎托罗维奇距离,使l取场景lk时下式取得最小值,所述坎托罗维奇距离的计算公式为:Calculate the Kantorovich distance, so that l takes the scene l k to obtain the minimum value of the following formula. The calculation formula of the Kantorovich distance is:

Figure BDA0002418266390000142
Figure BDA0002418266390000142

式中:J是被删除场景集合;pi是场景i的概率;ξi对应场景序列i;T是场景时间尺度的分段数;cTij)表示场景序列ξi与场景序列ξj的距离,

Figure BDA0002418266390000143
In the formula: J is the set of deleted scenes; pi is the probability of scene i ; ξ i corresponds to scene sequence i; T is the number of segments of the scene time scale; c Tij ) represents scene sequence ξi and scene the distance of the sequence ξ j ,
Figure BDA0002418266390000143

删除场景lk,令Jk=Jk-1∪{lk},并将场景lk的概率累加到距离其最近的场景上;Delete scene l k , let J k =J k-1 ∪{l k }, and accumulate the probability of scene l k to the scene closest to it;

若k<K,则令k=k+1。If k<K, then let k=k+1.

在本发明实施例的一种具体实施方式中,所述对相近时段进行归并,包括以下步骤:In a specific implementation of the embodiment of the present invention, the merging of similar time periods includes the following steps:

设Lt为时段t的系统负荷,则相邻时段t和t+1的系统负荷变化率为:Let L t be the system load of time period t, then the system load change rate of adjacent time periods t and t+1 is:

Figure BDA0002418266390000144
Figure BDA0002418266390000144

对全部时段进行系统负荷变化率的计算,找到最小变化率ΔLt所对应的时段,将时段t和t+1归并为一个新的时段;Calculate the system load change rate for all time periods, find the time period corresponding to the minimum change rate ΔL t , and combine time periods t and t+1 into a new time period;

根据系统负荷的变化率,重复进行时段归并,直至最小变化率ΔLt大于设定的阈值,或者归并后剩下的时段数目达到预设数目,归并过程结束。According to the change rate of the system load, the time period merge is repeated until the minimum change rate ΔL t is greater than the set threshold, or the number of time periods remaining after the merge reaches the preset number, and the merge process ends.

在本发明实施例的一种具体实施方式中,所述SCUC降维模型的目标函数为新能源机组总发电量的最大化,表示为:In a specific implementation of the embodiment of the present invention, the objective function of the SCUC dimensionality reduction model is the maximization of the total power generation of new energy generating units, which is expressed as:

Figure BDA0002418266390000151
Figure BDA0002418266390000151

式中,NW为新能源机组集合;T为时段归并后所包含的时段集合;Pw,t为新能源机组w在t时段的电力最大接纳能力;In the formula, N W is the set of new energy units; T is the set of time periods included after the time period is merged; P w,t is the maximum power receiving capacity of the new energy unit w in time period t;

所述SCUC降维模型的约束条件包括负荷平衡约束、常规机组出力上下限约束、机组最小开停机时间约束、新能源机组出力上下限约束、电网安全约束、系统备用容量约束:The constraints of the SCUC dimensionality reduction model include load balance constraints, upper and lower output limits of conventional units, minimum start-up and shutdown time constraints of units, upper and lower output limits of new energy units, power grid security constraints, and system reserve capacity constraints:

Figure BDA0002418266390000152
Figure BDA0002418266390000152

Pi,minui,t≤Pi,t≤Pi,maxui,t P i,min u i,t ≤P i,t ≤P i,max u i,t

Figure BDA0002418266390000153
Figure BDA0002418266390000153

Figure BDA0002418266390000154
Figure BDA0002418266390000154

yi,t-zi,t=ui,t-ui,t-1 y i,t -z i,t =ui ,t -u i,t-1

yi,t+zi,t≤1y i,t +z i,t ≤1

Pw,t≤P0,w,t P w,t ≤P 0,w,t

Figure BDA0002418266390000155
Figure BDA0002418266390000155

Figure BDA0002418266390000156
Figure BDA0002418266390000156

Figure BDA0002418266390000157
Figure BDA0002418266390000157

式中,Ni为火电机组总个数;Nw为新能源机组总个数;Nl为对外联络线总个数;Pi,t为常规机组i在时段t的有功出力;Pw,t为新能源机组w在时段t的有功出力;Pl,t为联络线l在时段t的有功出力;Lt为系统在时段t的负荷值;ui,t为机组i在t时段的启停状态;Pi,min为机组i的功率下限,Pi,max为机组i的功率上限;UTi和DTi分别为机组i的最小开机时间和最小停机时间;yi,t为机组i在时段t是否有停机到开机状态变化的标志;zi,t为机组i在时段t是否有开机到停机状态变化的标志;P0,w,t为新能源机组w在时段t的预测出力;Lij表示支路ij的潮流上限;M为电网计算节点集合;li,t为节点负荷功率;Si,j,t为节点i的注入功率对支路ij的灵敏度;Rt,u为系统在时段t的正备用容量下限;Rt,d为系统在时段t的负备用容量下限。In the formula, N i is the total number of thermal power units; N w is the total number of new energy units; N l is the total number of external connection lines; P i,t is the active power output of conventional unit i in time period t; P w, t is the active power output of the new energy unit w in the period t; P l,t is the active power output of the tie line l in the period t; L t is the load value of the system in the period t; u i,t is the unit i in the period t. Start-stop state; P i,min is the lower power limit of unit i, P i,max is the upper power limit of unit i; UT i and DT i are the minimum startup time and minimum shutdown time of unit i respectively; y i,t is the unit i, t Whether i has a sign that the state changes from shutdown to startup in time period t; zi ,t is whether the unit i has a sign of change from startup to shutdown state in time period t; P 0,w,t is the prediction of new energy unit w in time period t output; Li ij represents the upper limit of the power flow of branch ij; M is the set of grid computing nodes; li ,t is the node load power; S i,j,t is the sensitivity of the injected power of node i to branch ij; R t, u is the lower limit of the positive reserve capacity of the system in the time period t; R t,d is the lower limit of the negative reserve capacity of the system in the time period t.

在本发明实施例的一种具体实施方式中,所述SCED模型的目标函数为新能源机组总发电量的最大化,表示为:In a specific implementation of the embodiment of the present invention, the objective function of the SCED model is the maximization of the total power generation of new energy generating units, which is expressed as:

Figure BDA0002418266390000161
Figure BDA0002418266390000161

式中,T为全部时段集合;Nw为新能源机组总个数;Pw,t为新能源机组w在时段t的有功出力;In the formula, T is the set of all time periods; N w is the total number of new energy units; P w,t is the active power output of new energy units w in time period t;

约束条件包括负荷平衡约束、常规机组出力上下限约束、新能源机组出力上下限约束、电网安全约束、系统备用容量约束:The constraints include load balance constraints, upper and lower output constraints of conventional units, upper and lower output constraints of new energy units, power grid security constraints, and system reserve capacity constraints:

Figure BDA0002418266390000162
Figure BDA0002418266390000162

Pi,minui,t≤Pi,t≤Pi,maxui,t P i,min u i,t ≤P i,t ≤P i,max u i,t

Pw,t≤P0,w,t P w,t ≤P 0,w,t

Figure BDA0002418266390000163
Figure BDA0002418266390000163

Figure BDA0002418266390000164
Figure BDA0002418266390000164

Figure BDA0002418266390000165
Figure BDA0002418266390000165

式中,Ni为火电机组总个数;Nw为新能源机组总个数;Nl为对外联络线总个数;Pi,t为常规机组i在时段t的有功出力;Pw,t为新能源机组w在时段t的有功出力;Pl,t为联络线l在时段t的有功出力;Lt为系统在时段t的负荷值;ui,t为机组i在t时段的启停状态;Pi,min为机组i的功率下限,Pi,max为机组i的功率上限;UTi和DTi分别为机组i的最小开机时间和最小停机时间;P0,w,t为新能源机组w在时段t的预测出力;Lij表示支路ij的潮流上限;M为电网计算节点集合;li,t为节点负荷功率;Si,j,t为节点i的注入功率对支路ij的灵敏度;Rt,u为系统在时段t的正备用容量下限;Rt,d为系统在时段t的负备用容量下限。In the formula, N i is the total number of thermal power units; N w is the total number of new energy units; N l is the total number of external connection lines; P i,t is the active power output of conventional unit i in time period t; P w, t is the active power output of the new energy unit w in the period t; P l,t is the active power output of the tie line l in the period t; L t is the load value of the system in the period t; u i,t is the unit i in the period t. Start-stop state; P i,min is the lower power limit of unit i, P i,max is the upper power limit of unit i; UT i and DT i are the minimum startup time and minimum shutdown time of unit i respectively; P 0,w,t is the predicted output of the new energy unit w in the time period t; Li ij represents the upper limit of the power flow of the branch ij; M is the set of grid computing nodes; li ,t is the node load power; S i,j,t is the injected power of the node i Sensitivity to branch ij; R t,u is the lower limit of positive reserve capacity of the system in time period t; R t,d is the lower limit of negative reserve capacity of the system in time period t.

实施例3Example 3

基于与实施例1相同的发明构思,本发明提供了一种基于多场景生成技术的电网新能源消纳能力评估计算系统,包括存储介质和处理器;Based on the same inventive concept as Embodiment 1, the present invention provides a computing system for evaluating new energy consumption capacity of a power grid based on a multi-scenario generation technology, including a storage medium and a processor;

所述存储介质用于存储指令;the storage medium is used for storing instructions;

所述处理器用于根据所述指令进行操作以执行根据实施例1中任一项所述方法的步骤。The processor is configured to operate in accordance with the instructions to perform the steps of the method according to any of Embodiment 1.

综上所述:In summary:

本发明在考虑调度运行实际并保证系统安全要求的前提下,根据新能源单厂预测出力及其概率,生成组合场景及其概率,并进行场景缩减,以保证多个新能源场景生成的有效性。基于此,调用成熟的混合整数线性规划算法软件计算各个组合场景下的SCUC模型,确定机组组合状态,进一步计算大规模新能源接入下系统的消纳能力,从而在确保系统安全稳定的前提下,提高消纳能力评估结果的有效性和可参考性。On the premise of considering the actual dispatching operation and ensuring system safety requirements, the present invention generates combined scenarios and their probabilities according to the predicted output of a single new energy plant and its probability, and reduces the scenarios to ensure the effectiveness of generating multiple new energy scenarios . Based on this, the mature mixed integer linear programming algorithm software is called to calculate the SCUC model in each combination scenario, determine the unit combination state, and further calculate the system's consumption capacity under the access of large-scale new energy, so as to ensure the safety and stability of the system. , to improve the validity and reference of the assessment results of absorptive capacity.

本领域内的技术人员应明白,本申请的实施例可提供为方法、系统、或计算机程序产品。因此,本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且,本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。As will be appreciated by those skilled in the art, the embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器,使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中,使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品,该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上,使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理,从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

以上结合附图对本发明的实施例进行了描述,但是本发明并不局限于上述的具体实施方式,上述的具体实施方式仅仅是示意性的,而不是限制性的,本领域的普通技术人员在本发明的启示下,在不脱离本发明宗旨和权利要求所保护的范围情况下,还可做出很多形式,这些均属于本发明的保护之内。The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are only illustrative rather than restrictive. Under the inspiration of the present invention, without departing from the scope of protection of the present invention and the claims, many forms can be made, which all belong to the protection of the present invention.

以上显示和描述了本发明的基本原理和主要特征和本发明的优点。本行业的技术人员应该了解,本发明不受上述实施例的限制,上述实施例和说明书中描述的只是说明本发明的原理,在不脱离本发明精神和范围的前提下,本发明还会有各种变化和改进,这些变化和改进都落入要求保护的本发明范围内。本发明要求保护范围由所附的权利要求书及其等效物界定。The basic principles and main features of the present invention and the advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above-mentioned embodiments, and the descriptions in the above-mentioned embodiments and the description are only to illustrate the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have Various changes and modifications fall within the scope of the claimed invention. The claimed scope of the present invention is defined by the appended claims and their equivalents.

Claims (10)

1. A power grid new energy consumption capability assessment and calculation method based on a multi-scenario generation technology is characterized by comprising the following steps:
determining a power grid range and a calculation period of power grid new energy consumption capability evaluation calculation to be carried out and other calculation boundaries;
generating a large number of combined scenes containing multiple new energy power plants by freely arranging and combining the fields;
merging similar scenes by adopting a scene reduction technology of backward reduction, reducing the number of combined scenes and generating a typical combined scene;
merging the similar time periods, reducing the number of time periods entering optimization, and reducing the scale of the calculation time period of the optimization model;
aiming at each typical combination scene, establishing a SCUC dimension reduction model after time interval merging, and solving to obtain a unit combination result;
and establishing a full-time SCED model based on the unit combination result and solving, finally obtaining a new energy consumption result under each typical combination scene, and completing the evaluation and calculation of the new energy consumption capability of the power grid based on the multi-scene generation technology.
2. The method for evaluating and calculating the new energy consumption capability of the power grid based on the multi-scenario generation technology according to claim 1, wherein: the generation process of the combined scene comprises the following steps:
the total number of new energy stations of a certain power grid is assumed to be NwThe predicted output scene of each new energy station has NpThe occurrence probability of each predicted contribution scene is prij(i=1,2,…,Nw;j=1,2,…,Np);
Arranging and combining the output scenes of all the new energy stations to obtain the final number N of the new energy output scenesa
Figure FDA0002418266380000011
The combined scene occurrence probability is the product of the corresponding contribution scene occurrence probabilities.
3. The method for evaluating and calculating the new energy consumption capability of the power grid based on the multi-scenario generation technology according to claim 1, wherein: the reduction method of the typical combination scene comprises the following steps:
initializing a deleted scene set J to be null, wherein the number of scenes needing to be deleted is K, and the number of scenes deleted in the K iteration is lk
The following steps are repeated until the iteration is finished:
calculating the distance of Kantorovzval to make l take the scene lkObtaining a minimum value by using a time formula, wherein the computing formula of the Kantouvyqi distance is as follows:
Figure FDA0002418266380000012
in the formula: j is the deleted scene set; p is a radical ofiIs the probability of scene i ξiCorresponding to a scene sequence i; t is the number of segments of the scene timescale; c. CTij) Representing a sequence of scenes ξiAnd scene sequence ξjThe distance of (a) to (b),
Figure FDA0002418266380000013
deleting scene lkLet Jk=Jk-1∪{lkAnd will scene lkThe probability of (c) is accumulated to the scene closest to it;
if K < K, let K be K + 1.
4. The method for evaluating and calculating the new energy consumption capability of the power grid based on the multi-scenario generation technology according to claim 1, wherein: the merging of the similar time periods comprises the following steps:
let LtFor the system load of time period t, the system load change rate of adjacent time periods t and t +1 is:
Figure FDA0002418266380000021
calculating the change rate of the system load in all time periods to find the minimum change rate delta LtMerging the time interval t and the time interval t +1 into a new time interval in the corresponding time interval;
according to the change rate of the system load, the time interval merging is repeated until the minimum change rate delta LtAnd if the number of the time intervals is larger than the set threshold value or the number of the time intervals left after the merging reaches the preset number, the merging process is ended.
5. The method for evaluating and calculating the new energy consumption capability of the power grid based on the multi-scenario generation technology according to claim 1, wherein: the objective function of the SCUC dimension reduction model is the maximization of the total power generation of the new energy unit, and is represented as follows:
Figure FDA0002418266380000022
in the formula, NWIs a new energy machine set; t is a time interval set contained after time interval merging; pw,tThe maximum power receiving capacity of the new energy source unit w in the time period t;
the constraint conditions of the SCUC dimension reduction model comprise load balance constraint, conventional unit output upper and lower limit constraint, unit minimum start-up and shut-down time constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure FDA0002418266380000023
Pi,minui,t≤Pi,t≤Pi,maxui,t
Figure FDA0002418266380000024
Figure FDA0002418266380000025
yi,t-zi,t=ui,t-ui,t-1
yi,t+zi,t≤1
Pw,t≤P0,w,t
Figure FDA0002418266380000026
Figure FDA0002418266380000027
Figure FDA0002418266380000031
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwThe total number of the new energy units; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t; u. ofi,tStarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxThe upper power limit of the unit i is set; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; y isi,tWhether the unit i has a sign of change from a shutdown state to a startup state in a time period t or not is marked; z is a radical ofi,tWhether the unit i has a sign of change from a starting state to a stopping state in a time period t or not is marked; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dThe negative spare capacity lower limit for the system at time t.
6. The method for evaluating and calculating the new energy consumption capability of the power grid based on the multi-scenario generation technology according to claim 1, wherein: the objective function of the SCED model is the maximization of the total power generation of the new energy unit, and is represented as follows:
Figure FDA0002418266380000032
in the formula, T is a set of all time periods; n is a radical ofwThe total number of the new energy units; pw,tThe active power output of the new energy unit w in the time period t is obtained;
the constraint conditions comprise load balance constraint, conventional unit output upper and lower limit constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure FDA0002418266380000033
Pi,minui,t≤Pi,t≤Pi,maxui,t
Pw,t≤P0,w,t
Figure FDA0002418266380000034
Figure FDA0002418266380000035
Figure FDA0002418266380000036
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwThe total number of the new energy units; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t;ui,tstarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxThe upper power limit of the unit i is set; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dThe negative spare capacity lower limit for the system at time t.
7. A power grid new energy consumption capability assessment and calculation device based on multi-scenario generation technology is characterized by comprising the following steps:
the determining unit is used for determining the power grid range and the calculation period of the power grid new energy consumption capability evaluation calculation to be carried out and other calculation boundaries;
the first generation unit is used for generating a large number of combination scenes containing multiple new energy power plants through free arrangement and combination among fields;
the second generation unit is used for merging similar scenes by adopting a scene reduction technology of backward reduction, reducing the number of combined scenes and generating a typical combined scene;
the merging unit is used for merging the similar time intervals, reducing the number of time intervals entering optimization and reducing the scale of the calculation time intervals of the optimization model;
the first solving unit is used for establishing a SCUC dimension reduction model after time interval merging aiming at each typical combination scene and solving to obtain a unit combination result;
and the second solving unit is used for establishing a full-time SCED model based on the unit combination result and solving the full-time SCED model to finally obtain a new energy consumption result under each typical combination scene, and finishing the evaluation and calculation of the new energy consumption capability of the power grid based on the multi-scene generation technology.
8. The device for evaluating and calculating the new energy consumption capability of the power grid based on the multi-scenario generation technology according to claim 7, wherein: the objective function of the SCUC dimension reduction model is the maximization of the total power generation of the new energy unit, and is represented as follows:
Figure FDA0002418266380000041
in the formula, NWIs a new energy machine set; t is a time interval set contained after time interval merging; pw,tThe maximum power receiving capacity of the new energy source unit w in the time period t;
the constraint conditions of the SCUC dimension reduction model comprise load balance constraint, conventional unit output upper and lower limit constraint, unit minimum start-up and shut-down time constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure FDA0002418266380000042
Pi,minui,t≤Pi,t≤Pi,maxui,t
Figure FDA0002418266380000043
Figure FDA0002418266380000044
yi,t-zi,t=ui,t-ui,t-1
yi,t+zi,t≤1
Pw,t≤P0,w,t
Figure FDA0002418266380000051
Figure FDA0002418266380000052
Figure FDA0002418266380000053
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwThe total number of the new energy units; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t; u. ofi,tStarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxThe upper power limit of the unit i is set; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; y isi,tWhether the unit i has a sign of change from a shutdown state to a startup state in a time period t or not is marked; z is a radical ofi,tWhether the unit i has a sign of change from a starting state to a stopping state in a time period t or not is marked; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dThe negative spare capacity lower limit for the system at time t.
9. The device for evaluating and calculating the new energy consumption capability of the power grid based on the multi-scenario generation technology according to claim 7, wherein: the objective function of the SCED model is the maximization of the total power generation of the new energy unit, and is represented as follows:
Figure FDA0002418266380000054
in the formula, T is a set of all time periods; n is a radical ofwThe total number of the new energy units; pw,tThe active power output of the new energy unit w in the time period t is obtained;
the constraint conditions comprise load balance constraint, conventional unit output upper and lower limit constraint, new energy unit output upper and lower limit constraint, power grid safety constraint and system reserve capacity constraint:
Figure FDA0002418266380000055
Pi,minui,t≤Pi,t≤Pi,maxui,t
Pw,t≤P0,w,t
Figure FDA0002418266380000056
Figure FDA0002418266380000057
Figure FDA0002418266380000058
in the formula, NiThe total number of the thermal power generating units; n is a radical ofwThe total number of the new energy units; n is a radical oflThe total number of the external connecting lines; pi,tThe active power output of the conventional unit i in the time period t is obtained; pw,tThe active power output of the new energy unit w in the time period t is obtained; pl,tL for the active power of the tie line l in the time period ttIs the load value of the system in the time period t; u. ofi,tStarting and stopping a unit i at a time t; pi,minLower power limit, P, of unit ii,maxThe upper power limit of the unit i is set; UT (unified device)iAnd DTiRespectively the minimum starting time and the minimum stopping time of the unit i; p0,w,tL predicted output for new energy unit w in time period tijRepresenting the upper current limit of branch ij; m is a power grid computing node set; li,tLoad power for the node; si,j,tSensitivity of injected power to branch ij for node i; rt,uA positive spare capacity lower limit for the system at time period t; rt,dFor negative standby of the system during time period tThe lower limit of the capacity.
10. A power grid new energy consumption capability evaluation and calculation system based on a multi-scenario generation technology is characterized by comprising a storage medium and a processor;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 6.
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