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CN113541205B - Collaborative optimization method and device for low-carbon CSP system based on cluster learning - Google Patents

Collaborative optimization method and device for low-carbon CSP system based on cluster learning Download PDF

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CN113541205B
CN113541205B CN202111071355.6A CN202111071355A CN113541205B CN 113541205 B CN113541205 B CN 113541205B CN 202111071355 A CN202111071355 A CN 202111071355A CN 113541205 B CN113541205 B CN 113541205B
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吕天光
李竞
孙树敏
杨明
石访
赵浩然
李正烁
于芃
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Shandong University
Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
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Abstract

The invention discloses a low-carbon CSP system planning and operation collaborative optimization method and device based on cluster learning, which comprises the following steps: performing cluster grouping on CSP units in the system to obtain a plurality of CSP unit groups; constructing output power constraint, climbing constraint, minimum on-line time constraint, minimum off-line time constraint and instantaneous thermal power balance constraint of the CSP unit group by three continuous variables representing the on-line total capacity, the starting total capacity and the stopping total capacity of the CSP unit group, and further constructing a low-carbon CSP system planning and operation collaborative optimization model; acquiring the rated capacity of each unit in the low-carbon CSP system; and acquiring a capacity allocation scheme of each unit group according to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model. The variables in the constraints of the CSP unit group are continuous, the CSP unit group is a complete linear optimization model, the complexity of model calculation is reduced, and the CSP unit group is suitable for analyzing the long-term planning problem of a large-scale power system.

Description

基于集群学习的低碳CSP系统协同优化方法及装置Collaborative optimization method and device for low-carbon CSP system based on cluster learning

技术领域technical field

本发明涉及电力系统规划技术领域，尤其涉及基于集群学习的低碳CSP系统协同优化方法及装置。The invention relates to the technical field of power system planning, in particular to a method and device for collaborative optimization of a low-carbon CSP system based on cluster learning.

背景技术Background technique

本部分的陈述仅仅是提供了与本发明相关的背景技术信息，不必然构成在先技术。The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

为进一步提高电力系统中可再生能源的渗透率，太阳能光热发电（Concentratingsolar power, CSP）机组越来越受到人们的广泛关注，因为其不仅能够利用可再生能源发电，还可以有效提高系统的运行灵活性。在本发明中，将包含CSP机组等大量可再生能源发电机组的电力系统称之为低碳CSP系统。在对CSP系统进行长期规划问题的年运行成本分析计算时，关于CSP机组建立的模型为混合整数线性规划模型，在对该混合整数线性规划模型进行求解计算时，计算速度较慢。目前，已有一些研究集中在降低规划模型的计算复杂度，以用于长期规划问题的分析，例如通过缩减复杂场景和简化约束条件等方法来减少长期规划问题的计算负担；或从建模的角度出发，采用聚类技术对机组组合公式中相同或相似的机组进行分组，以降低计算复杂度，但是，这些方法均没有改变模型的混合整数性质，导致对模型进行求解时，计算速度仍然较慢。In order to further improve the penetration rate of renewable energy in the power system, solar thermal power generation (Concentrating solar power, CSP) units have attracted more and more attention, because they can not only use renewable energy to generate electricity, but also can effectively improve the operation of the system. flexibility. In the present invention, a power system including a large number of renewable energy generating units such as CSP units is referred to as a low-carbon CSP system. In the analysis and calculation of the annual operating cost of the long-term planning problem of the CSP system, the model established for the CSP unit is a mixed integer linear programming model, and the calculation speed is slow when solving the mixed integer linear programming model. At present, some studies have focused on reducing the computational complexity of planning models for the analysis of long-term planning problems, such as reducing the computational burden of long-term planning problems by reducing complex scenarios and simplifying constraints; or from modeling From the perspective of clustering technology, the same or similar units in the unit combination formula are grouped to reduce the computational complexity. However, these methods do not change the mixed integer properties of the model, resulting in the calculation speed of the model is still relatively high. slow.

发明内容SUMMARY OF THE INVENTION

本发明为了解决上述问题，提出了基于集群学习的低碳CSP系统协同优化方法及装置，首先对系统中的CSP机组进行组群划分，进而通过表示CSP机组组群的在线总容量、启动总容量和关停总容量的三个连续变量构建CSP机组组群的各项约束，进而构建低碳CSP系统规划与运行协同优化模型，由于构建的CSP机组组群的各项约束中不存在表示单个机组开关状态的二元变量，使得构建的CSP机组组群的各项约束均为完全的线性优化模型，有效降低了低碳CSP系统规划与运行协同优化模型的计算复杂度，在保证模型计算结果精度的同时，可以显著提高计算效率，适用于大规模电力系统长期规划问题的分析，解决了传统优化模型中包含表示单个机组开关状态的二元变量，模型复杂度高，计算效率低的问题。In order to solve the above problems, the present invention proposes a collaborative optimization method and device for a low-carbon CSP system based on cluster learning. First, the CSP units in the system are divided into groups, and then the online total capacity and startup total capacity of the CSP unit group are represented by the group. and the three continuous variables of the total shutdown capacity to construct the constraints of the CSP unit group, and then construct the low-carbon CSP system planning and operation collaborative optimization model, because the constraints of the constructed CSP unit group do not exist to represent a single unit. The binary variable of the switching state makes the constraints of the constructed CSP unit group a complete linear optimization model, which effectively reduces the computational complexity of the collaborative optimization model for low-carbon CSP system planning and operation, while ensuring the accuracy of the model calculation results. At the same time, it can significantly improve the computing efficiency, and is suitable for the analysis of long-term planning problems of large-scale power systems.

为实现上述目的，本发明采用如下技术方案：To achieve the above object, the present invention adopts the following technical solutions:

第一方面，提出了基于集群学习的低碳CSP系统协同优化方法，包括：In the first aspect, a collaborative optimization method for low-carbon CSP systems based on cluster learning is proposed, including:

对低碳CSP系统中的CSP机组进行集群分组，获得多个CSP机组组群；Group the CSP units in the low-carbon CSP system into clusters to obtain multiple CSP unit groups;

通过表示CSP机组组群的在线总容量、启动总容量和关停总容量的三个连续变量，构建CSP机组组群的输出功率约束、爬坡约束、最小在线时间约束、最小离线时间约束和瞬时热功率平衡约束，进而构建低碳CSP系统规划与运行协同优化模型；Construct the output power constraints, ramp constraints, minimum online time constraints, minimum offline time constraints and instantaneous Thermal power balance constraints, and then build a low-carbon CSP system planning and operation collaborative optimization model;

获取低碳CSP系统中各机组的额定容量；Obtain the rated capacity of each unit in the low-carbon CSP system;

根据各机组的额定容量及构建的低碳CSP系统规划与运行协同优化模型，获取各机组组群的容量配置方案。According to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model, the capacity allocation scheme of each unit group is obtained.

第二方面，提出了基于集群学习的低碳CSP系统协同优化装置，包括：In the second aspect, a collaborative optimization device for low-carbon CSP systems based on cluster learning is proposed, including:

组群划分模块，用于对低碳CSP系统中的CSP机组进行集群分组，获得多个CSP机组组群；The group division module is used to group the CSP units in the low-carbon CSP system into clusters to obtain multiple CSP unit groups;

模型构建模块，用于通过表示CSP机组组群的在线总容量、启动总容量和关停总容量的三个连续变量，构建CSP机组组群的输出功率约束、爬坡约束、最小在线时间约束、最小离线时间约束和瞬时热功率平衡约束，进而构建低碳CSP系统规划与运行协同优化模型；The model building module is used to construct the output power constraints, ramp constraints, minimum online time constraints, Minimum offline time constraints and instantaneous thermal power balance constraints, and then build a low-carbon CSP system planning and operation collaborative optimization model;

参数获取模块，用于获取低碳CSP系统中各机组的额定容量；The parameter acquisition module is used to acquire the rated capacity of each unit in the low-carbon CSP system;

容量配置方案获取模块，用于根据各机组的额定容量及构建的低碳CSP系统规划与运行协同优化模型，获取各机组组群的容量配置方案。The capacity configuration plan acquisition module is used to obtain the capacity configuration plan of each unit group according to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model.

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

本发明在进行低碳CSP系统规划与运行协同优化时，将相邻地理区域内具有相似运行特性的机组进行了集群分组，从而优化机组组群的群体行为，而不是单个机组的行为，并引入表示CSP机组组群的在线总容量、启动总容量和关停总容量的三个连续变量来构建CSP机组组群的各项约束，进而构建低碳CSP系统规划与运行协同优化模型，由于构建的CSP机组组群的各项约束中均不存在表示单个机组开关状态的二元变量，使得构建的CSP机组组群的各项约束为完全的线性优化模型，有效降低了低碳CSP系统规划与运行协同优化模型的计算复杂度，从而克服了传统优化模型中包含表示单个机组开关状态的二元变量，模型计算复杂度较高的问题，提高了计算速度，适用于大规模电力系统长期规划问题的分析。In the process of coordinated optimization of low-carbon CSP system planning and operation, the present invention groups units with similar operating characteristics in adjacent geographical areas into clusters, so as to optimize the group behavior of the unit group, rather than the behavior of a single unit, and introduces The three continuous variables representing the online total capacity, the total start-up capacity and the total shutdown capacity of the CSP unit group are used to construct the constraints of the CSP unit group, and then construct the low-carbon CSP system planning and operation collaborative optimization model. There is no binary variable representing the switching state of a single unit in the constraints of the CSP unit group, so that the constraints of the constructed CSP unit group are a complete linear optimization model, which effectively reduces the planning and operation of the low-carbon CSP system. The computational complexity of the collaborative optimization model overcomes the traditional optimization model that contains binary variables representing the switching state of a single unit, and the computational complexity of the model is high, which improves the computational speed and is suitable for long-term planning of large-scale power systems analyze.

本发明附加方面的优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本发明的实践了解到。Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will become apparent from the description which follows, or may be learned by practice of the invention.

附图说明Description of drawings

构成本申请的一部分的说明书附图用来提供对本申请的进一步理解，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的不当限定。The accompanying drawings that form a part of the present application are used to provide further understanding of the present application, and the schematic embodiments and descriptions of the present application are used to explain the present application and do not constitute improper limitations on the present application.

图1为实施例1公开方法的流程图；1 is a flow chart of the method disclosed in Embodiment 1;

图2为CSP机组的结构示意图；Fig. 2 is the structural representation of CSP unit;

图3为CSP机组组群在线总容量的可能值的示意图。Figure 3 is a schematic diagram of possible values for the total online capacity of a CSP unit group.

具体实施方式：Detailed ways:

下面结合附图与实施例对本发明作进一步说明。The present invention will be further described below with reference to the accompanying drawings and embodiments.

应该指出，以下详细说明都是例示性的，旨在对本申请提供进一步的说明。除非另有指明，本文使用的所有技术和科学术语具有与本申请所属技术领域的普通技术人员通常理解的相同含义。It should be noted that the following detailed description is exemplary and intended to provide further explanation of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

需要注意的是，这里所使用的术语仅是为了描述具体实施方式，而非意图限制根据本申请的示例性实施方式。如在这里所使用的，除非上下文另外明确指出，否则单数形式也意图包括复数形式，此外，还应当理解的是，当在本说明书中使用术语“包含”和/或“包括”时，其指明存在特征、步骤、操作、器件、组件和/或它们的组合。It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it is to be understood that when the terms "comprising" and/or "including" are used in this specification, it indicates that There are features, steps, operations, devices, components and/or combinations thereof.

实施例1Example 1

在该实施例中，公开了基于集群学习的低碳CSP系统协同优化方法，包括：In this embodiment, a collaborative optimization method for a low-carbon CSP system based on cluster learning is disclosed, including:

对低碳CSP系统中的CSP机组进行集群分组，获得多个CSP机组组群；Group the CSP units in the low-carbon CSP system into clusters to obtain multiple CSP unit groups;

通过表示CSP机组组群的在线总容量、启动总容量和关停总容量的三个连续变量，构建CSP机组组群的输出功率约束、爬坡约束、最小在线时间约束、最小离线时间约束和瞬时热功率平衡约束，进而构建低碳CSP系统规划与运行协同优化模型；Construct the output power constraints, ramp constraints, minimum online time constraints, minimum offline time constraints and instantaneous Thermal power balance constraints, and then build a low-carbon CSP system planning and operation collaborative optimization model;

获取低碳CSP系统中各机组的额定容量；Obtain the rated capacity of each unit in the low-carbon CSP system;

根据各机组的额定容量及构建的低碳CSP系统规划与运行协同优化模型，获取各机组组群的容量配置方案。According to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model, the capacity allocation scheme of each unit group is obtained.

进一步的，构建的低碳CSP系统规划与运行协同优化模型以系统总成本最小为目标，以系统的功率平衡约束、备用约束、低碳政策约束、CSP机组组群的输出功率约束、爬坡约束、最小在线时间约束、最小离线时间约束、瞬时热功率平衡约束、CSP机组中储热模块的充放电平衡约束和荷电状态约束为约束条件。Further, the constructed low-carbon CSP system planning and operation collaborative optimization model aims to minimize the total system cost, and takes the system power balance constraints, reserve constraints, low-carbon policy constraints, output power constraints of CSP unit groups, and ramp constraints. , the minimum online time constraint, the minimum offline time constraint, the instantaneous thermal power balance constraint, the charge-discharge balance constraint of the heat storage module in the CSP unit, and the state-of-charge constraint are the constraints.

进一步的，系统总成本包括投资成本、固定运维成本和可变运行成本。Further, the total system cost includes investment cost, fixed operation and maintenance cost and variable operation cost.

进一步的，构建的CSP机组组群的输出功率约束为：t时刻各组群的输出功率不小于该组群的最小输出功率，不大于该组群的最大输出功率；其中，CSP机组组群的最小输出功率和最大输出功率分别通过该组群的最小输出功率与该组群在线总容量的比值、该组群的最大输出功率与该组群在线总容量的比值以及该组群的在线总容量获得。Further, the output power constraint of the constructed CSP unit group is: the output power of each group at time t is not less than the minimum output power of the group, and not greater than the maximum output power of the group; The minimum output power and the maximum output power are respectively calculated by the ratio of the minimum output power of the group to the total online capacity of the group, the ratio of the maximum output power of the group to the total online capacity of the group, and the total online capacity of the group get.

进一步的，表示CSP机组组群的在线总容量、启动总容量和关停总容量的三个连续变量之间的关系为：CSP机组组群在t时刻的在线总容量与t-1时刻的在线总容量之差等于CSP机组组群在t时刻的启动总容量与关停总容量之差。Further, the relationship between the three continuous variables representing the total online capacity, total start-up capacity and total shutdown capacity of the CSP unit group is: the total online capacity of the CSP unit group at time t and the online total capacity at time t-1. The difference between the total capacity is equal to the difference between the total start-up capacity and the total shut-down capacity of the CSP unit group at time t.

进一步的，CSP机组组群的在线总容量不小于0，且不大于组群内所有CSP机组的额定容量之和。Further, the total online capacity of the CSP unit group is not less than 0, and is not greater than the sum of the rated capacities of all CSP units in the group.

进一步的，低碳CSP系统中的机组包括火力发电机组、风力发电机组、太阳能光伏发电机组和CSP机组。Further, the units in the low-carbon CSP system include thermal generating units, wind generating units, solar photovoltaic generating units and CSP generating units.

对本实施例公开的基于集群学习的低碳CSP系统协同优化方法进行详细说明。The collaborative optimization method for a low-carbon CSP system based on cluster learning disclosed in this embodiment is described in detail.

传统的对CSP系统进行长期规划时，以CSP机组的输出功率约束、爬坡约束、最小在线时间约束、最小离线时间约束、瞬时热功率平衡约束、CSP机组中储热模块的充放电平衡约束、荷电状态约束等为约束条件构建了传统优化模型。In the traditional long-term planning of the CSP system, the output power constraints, ramp constraints, minimum online time constraints, minimum offline time constraints, instantaneous thermal power balance constraints, charge and discharge balance constraints of heat storage modules in CSP units, State-of-charge constraints, etc., build traditional optimization models for constraints.

其中，CSP机组的输出功率约束如式（1）所示：Among them, the output power constraint of the CSP unit is shown in formula (1):

Figure 129148DEST_PATH_IMAGE001
（1）
Figure 129148DEST_PATH_IMAGE001
(1)

其中，I i,t 表示CSP机组i在时刻t的开关状态，

Figure 276096DEST_PATH_IMAGE002
，
Figure 694308DEST_PATH_IMAGE003
表示CSP机组i在时刻t的输出功率，
Figure 672628DEST_PATH_IMAGE004
和
Figure 534405DEST_PATH_IMAGE005
分别表示CSP机组i的最小输出功率和最大输出功率。
Figure 168648DEST_PATH_IMAGE004
和
Figure 265917DEST_PATH_IMAGE005
具体表示分别如式（2）、（3）所示：Among them, I i,t represents the switching state of CSP unit i at time t,
Figure 276096DEST_PATH_IMAGE002
,
Figure 694308DEST_PATH_IMAGE003
represents the output power of CSP unit i at time t,
Figure 672628DEST_PATH_IMAGE004
and
Figure 534405DEST_PATH_IMAGE005
represent the minimum output power and maximum output power of CSP unit i, respectively.
Figure 168648DEST_PATH_IMAGE004
and
Figure 265917DEST_PATH_IMAGE005
The specific expressions are shown in formulas (2) and (3) respectively:

Figure 724843DEST_PATH_IMAGE006
（2）
Figure 724843DEST_PATH_IMAGE006
(2)

Figure 882155DEST_PATH_IMAGE007
（3）
Figure 882155DEST_PATH_IMAGE007
(3)

其中，P i,n 表示CSP机组i的额定容量，

Figure 3694DEST_PATH_IMAGE008
和
Figure 780020DEST_PATH_IMAGE009
分别表示CSP机组i的最小输出功率、最大输出功率与机组额定容量的比值。Among them, P i,n represents the rated capacity of CSP unit i,
Figure 3694DEST_PATH_IMAGE008
and
Figure 780020DEST_PATH_IMAGE009
Respectively represent the ratio of the minimum output power and maximum output power of CSP unit i to the rated capacity of the unit.

CSP机组的爬坡约束如式（4）所示：The climbing constraint of the CSP unit is shown in formula (4):

Figure 467354DEST_PATH_IMAGE010
（4）
Figure 467354DEST_PATH_IMAGE010
(4)

其中，

Figure 998829DEST_PATH_IMAGE011
表示CSP机组i在t时刻的输出功率，
Figure 529037DEST_PATH_IMAGE012
表示CSP机组i在
Figure 233687DEST_PATH_IMAGE014
时刻的输出功率，
Figure 978790DEST_PATH_IMAGE015
表示CSP机组向下爬坡限制，
Figure 618849DEST_PATH_IMAGE016
表示CSP机组向上爬坡限制。in,
Figure 998829DEST_PATH_IMAGE011
represents the output power of CSP unit i at time t,
Figure 529037DEST_PATH_IMAGE012
Indicates that CSP unit i is in
Figure 233687DEST_PATH_IMAGE014
output power at time,
Figure 978790DEST_PATH_IMAGE015
Indicates the CSP unit downhill limit,
Figure 618849DEST_PATH_IMAGE016
Indicates the CSP unit uphill limit.

CSP机组的最小在线时间约束和最小离线时间约束分别如式（5）、（6）所示：The minimum online time constraints and the minimum offline time constraints of CSP units are shown in equations (5) and (6), respectively:

Figure 246140DEST_PATH_IMAGE017
（5）
Figure 246140DEST_PATH_IMAGE017
(5)

Figure 426585DEST_PATH_IMAGE018
（6）
Figure 426585DEST_PATH_IMAGE018
(6)

其中，I i,t 表示CSP机组i在t时刻的开关状态，I i,t-1 表示CSP机组i在t-1时刻的开关状态，T on 表示CSP机组的最小在线时间，T off 表示CSP机组的最小离线时间。Among them, I i,t represents the switching state of CSP unit i at time t, I i,t-1 represents the switching state of CSP unit i at time t -1, T on represents the minimum online time of the CSP unit, and T off represents the CSP unit The minimum offline time of the unit.

CSP机组的瞬时热功率平衡约束，如式（7）所示：The instantaneous thermal power balance constraint of the CSP unit is shown in equation (7):

Figure 980189DEST_PATH_IMAGE019
（7）
Figure 980189DEST_PATH_IMAGE019
(7)

其中，

Figure 650205DEST_PATH_IMAGE020
表示CSP机组在t时刻的输出功率，
Figure 968053DEST_PATH_IMAGE021
表示CSP机组在t时刻的充电功率，
Figure 889873DEST_PATH_IMAGE022
表示CSP机组在t时刻的放电功率，
Figure 406305DEST_PATH_IMAGE023
表示CSP机组中功率模块的效率系数，
Figure 450484DEST_PATH_IMAGE024
表示CSP机组在t时刻可用的太阳能热功率。in,
Figure 650205DEST_PATH_IMAGE020
represents the output power of the CSP unit at time t,
Figure 968053DEST_PATH_IMAGE021
represents the charging power of the CSP unit at time t,
Figure 889873DEST_PATH_IMAGE022
represents the discharge power of the CSP unit at time t,
Figure 406305DEST_PATH_IMAGE023
represents the efficiency coefficient of the power module in the CSP unit,
Figure 450484DEST_PATH_IMAGE024
Represents the solar thermal power available to the CSP unit at time t.

CSP机组中储热模块的充放电平衡约束，如式（8）所示：The charge-discharge balance constraint of the heat storage module in the CSP unit is shown in equation (8):

Figure 442580DEST_PATH_IMAGE025
（8）
Figure 442580DEST_PATH_IMAGE025
(8)

其中，

Figure 27145DEST_PATH_IMAGE026
表示CSP机组中储热模块的效率系数，E t 表示CSP机组中储热模块在t时刻的荷电状态，E t-1 表示CSP机组中储热模块在t-1时刻的荷电状态。in,
Figure 27145DEST_PATH_IMAGE026
represents the efficiency coefficient of the heat storage module in the CSP unit, E t represents the state of charge of the heat storage module in the CSP unit at time t, and E t-1 represents the state of charge of the heat storage module in the CSP unit at time t -1.

CSP机组中储热模块的荷电状态约束，如式（9）所示：The state-of-charge constraints of the heat storage module in the CSP unit are shown in formula (9):

Figure 601346DEST_PATH_IMAGE027
（9）
Figure 601346DEST_PATH_IMAGE027
(9)

其中，E min和E max分别表示CSP机组中储热模块荷电状态的下限值和上限值。Among them, E min and E max represent the lower limit value and the upper limit value of the state of charge of the heat storage module in the CSP unit, respectively.

可知，传统优化模型的约束条件包括式（1）-（9），含有表示单个机组开关状态的变量，由于表示单个机组开关状态的变量存在0、1两种取值，故该表示单个机组开关状态的变量为二元变量，由于传统优化模型中存在表示单个机组开关状态的二元变量，使得传统优化模型为混合整数线性规划模型，在对传统优化模型进行求解时，计算复杂度较高，计算效率较低，对实现电力系统长期规划问题的快速计算带来困难。It can be seen that the constraints of the traditional optimization model include equations (1)-(9), which contain variables representing the switch state of a single unit. Since the variables representing the switch state of a single unit have two values of 0 and 1, this represents the switch state of a single unit. The variable of state is a binary variable. Since there are binary variables representing the switching state of a single unit in the traditional optimization model, the traditional optimization model is a mixed integer linear programming model. When solving the traditional optimization model, the computational complexity is high. The calculation efficiency is low, which brings difficulties to the fast calculation of the long-term planning problem of the power system.

本实施例为了解决利用传统优化模型进行电力系统长期规划时，计算复杂度高、计算效率低的问题，对传统优化模型进行改进，首先对系统内的CSP机组进行组群划分，获得多个CSP机组组群，进而通过引入表示CSP机组组群在线总容量、启动总容量和关停总容量的三个连续变量来构建CSP机组组群的各项约束，在构建了CSP机组组群各项约束的基础上，构建低碳CSP系统规划与运行协同优化模型，通过对低碳CSP系统规划与运行协同优化模型进行求解，获得各机组组群的容量配置方案，由于构建的CSP机组组群的各项约束中的变量均为连续变量，不存在表示单个机组开关状态的二元变量，为完全的线性优化模型，从而有效降低了低碳CSP系统规划与运行协同优化模型的复杂度，提高了模型计算的效率。In this embodiment, in order to solve the problems of high computational complexity and low computational efficiency when using the traditional optimization model for long-term planning of the power system, the traditional optimization model is improved. First, the CSP units in the system are grouped to obtain multiple CSP units. The unit group, and then the constraints of the CSP unit group are constructed by introducing three continuous variables that represent the total online capacity, total start-up capacity and total shutdown capacity of the CSP unit group. On the basis of , construct a low-carbon CSP system planning and operation collaborative optimization model, and obtain the capacity allocation scheme of each unit group by solving the low-carbon CSP system planning and operation collaborative optimization model. The variables in the term constraints are all continuous variables, and there is no binary variable representing the switching state of a single unit. It is a complete linear optimization model, which effectively reduces the complexity of the low-carbon CSP system planning and operation collaborative optimization model and improves the model. computational efficiency.

如图1所示，本实施例公开的基于集群学习的低碳CSP系统协同优化方法，包括：As shown in FIG. 1 , the collaborative optimization method for a low-carbon CSP system based on cluster learning disclosed in this embodiment includes:

S1：对低碳CSP系统中的CSP机组进行集群分组，获得多个CSP机组组群。S1: Group the CSP units in the low-carbon CSP system into clusters to obtain multiple CSP unit groups.

低碳CSP系统中的机组包括火力发电机组、风力发电机组、太阳能光伏发电机组和CSP机组。The units in the low carbon CSP system include thermal power units, wind turbines, solar photovoltaic generators and CSP units.

为了提高低碳CSP系统规划与运行协同优化的速度，降低计算的复杂度，在构建低碳CSP系统规划与运行协同优化模型时，首先对CSP机组进行了集群分组，从而将优化问题从优化单个机组的行为转化到优化群体行为上来，在具体实施时，将相邻地理区域内具有相似运行特性的CSP机组进行集群分组，从而获得多个CSP机组组群。In order to improve the speed of collaborative optimization of low-carbon CSP system planning and operation and reduce the computational complexity, when building a low-carbon CSP system planning and operation collaborative optimization model, CSP units are firstly clustered, so that the optimization problem can be changed from optimizing a single The behavior of the unit is transformed into optimizing group behavior. In the specific implementation, CSP units with similar operating characteristics in adjacent geographical areas are clustered and grouped, so as to obtain multiple CSP unit groups.

S2：通过表示CSP机组组群的在线总容量、启动总容量和关停总容量的三个连续变量，构建CSP机组组群的各项约束，根据CSP机组组群的各项约束构建低碳CSP系统规划与运行协同优化模型。S2: Through three continuous variables representing the total online capacity, total startup capacity and total shutdown capacity of the CSP unit group, construct the constraints of the CSP unit group, and construct the low-carbon CSP according to the constraints of the CSP unit group A collaborative optimization model for system planning and operation.

其中，CSP机组组群的各项约束包括CSP机组组群的输出功率约束、爬坡约束、最小在线时间约束、最小离线时间约束、瞬时热功率平衡约束、CSP机组中储热模块的充放电平衡约束和荷电状态约束。Among them, the constraints of the CSP unit group include the output power constraint of the CSP unit group, the ramp constraint, the minimum online time constraint, the minimum offline time constraint, the instantaneous thermal power balance constraint, and the charge-discharge balance of the heat storage module in the CSP unit. constraints and state-of-charge constraints.

构建的低碳CSP系统规划与运行协同优化模型具体为：以系统总成本最小为目标，以系统的功率平衡约束、备用约束、低碳政策约束、CSP机组组群的输出功率约束、爬坡约束、最小在线时间约束、最小离线时间约束、瞬时热功率平衡约束、CSP机组中储热模块的充放电平衡约束和荷电状态约束为约束条件。The constructed low-carbon CSP system planning and operation collaborative optimization model is specifically: aiming at the minimum total system cost, taking the system power balance constraints, reserve constraints, low-carbon policy constraints, CSP unit group output power constraints, and ramp constraints , the minimum online time constraint, the minimum offline time constraint, the instantaneous thermal power balance constraint, the charge-discharge balance constraint of the heat storage module in the CSP unit, and the state-of-charge constraint are the constraints.

在具体实施时，为了确定各机组容量配置的最佳组合，以实现受环境约束条件下低碳CSP系统的精细可靠能源供应，构建了低碳CSP系统规划与运行协同优化模型。In the specific implementation, in order to determine the optimal combination of the capacity configuration of each unit to realize the refined and reliable energy supply of the low-carbon CSP system under the condition of environmental constraints, a collaborative optimization model of the planning and operation of the low-carbon CSP system is constructed.

低碳CSP系统规划与运行协同优化模型包括两个层面的决策变量，在规划层面，决策变量包括发电技术的类型、以及在特定年份应该投资多少新的发电容量，即在这一层面需要合理设计系统的设备配置，包括设备类型的选择及投资容量的确定，以减少投资成本。在运行层面，决策变量包括在每个特定的时间，每种发电技术应该投入和调度多少可用容量，即在这一层面合理安排各个发电设备的出力，以实现系统的经济可靠运行。The low-carbon CSP system planning and operation collaborative optimization model includes decision variables at two levels. At the planning level, the decision variables include the type of power generation technology and how much new power generation capacity should be invested in a specific year, that is, a reasonable design is required at this level. Equipment configuration of the system, including the selection of equipment types and the determination of investment capacity, to reduce investment costs. At the operation level, the decision variables include how much available capacity should be invested and dispatched by each power generation technology at each specific time, that is, at this level, the output of each power generation equipment should be reasonably arranged to achieve economical and reliable operation of the system.

故构建的低碳CSP系统规划与运行协同优化模型以系统的总成本最小为目标，其中的总成本包括投资成本、固定运维成本和可变运行成本，其目标函数如式（10）所示：Therefore, the constructed low-carbon CSP system planning and operation collaborative optimization model aims to minimize the total cost of the system, in which the total cost includes investment cost, fixed operation and maintenance cost and variable operation cost, and its objective function is shown in formula (10). :

Figure 754110DEST_PATH_IMAGE028
（10）
Figure 754110DEST_PATH_IMAGE028
(10)

其中，in,

Figure 577709DEST_PATH_IMAGE029
（11）
Figure 577709DEST_PATH_IMAGE029
(11)

Figure 169228DEST_PATH_IMAGE030
（12）
Figure 169228DEST_PATH_IMAGE030
(12)

Figure 286350DEST_PATH_IMAGE031
（13）
Figure 286350DEST_PATH_IMAGE031
(13)

式中，C表示总成本；C i 表示投资成本，a th-m 、a w 、a s 、a c-j 分别表示火力发电机组、风力发电机组、太阳能光伏发电机组和CSP机组的投资成本，I th-m 、I w 、I s 、I c-j 分别表示火力发电机组、风力发电机组、太阳能光伏发电机组和CSP机组的新增容量；C f 表示固定运维成本，f th-m 、f w 、f s 、f c-j 分别表示火力发电机组、风力发电机组、太阳能光伏发电机组和CSP机组的固定运维成本，

Figure 469070DEST_PATH_IMAGE032
、
Figure 248807DEST_PATH_IMAGE033
、
Figure 316120DEST_PATH_IMAGE034
、
Figure 599334DEST_PATH_IMAGE035
分别表示火力发电机组、风力发电机组、太阳能光伏发电机组和CSP机组的总容量；C v 表示可变运行成本，由启动成本和燃料成本组成，其中c th-m 和SD th-m 分别表示第m类火力发电机组的燃料成本和启动成本，
Figure 218534DEST_PATH_IMAGE036
表示第m类火力发电机组在t时刻的输出功率，
Figure 406939DEST_PATH_IMAGE037
表示第m类火力发电机组在t时刻的启动容量，M表示火力发电机组的类别，J表示CSP机组的类别，T表示时间段，
Figure 74681DEST_PATH_IMAGE038
表示时间间隔。where C represents the total cost; C i represents the investment cost, a th-m , a w , a s , and a cj represent the investment costs of thermal power generating units, wind power generating units, solar photovoltaic generating units and CSP units, respectively, I th -m , I w , Is s , and I cj represent the newly added capacity of thermal power generating units, wind generating units, solar photovoltaic generating units and CSP units, respectively; C f represents the fixed operation and maintenance cost, f th-m , f w , f s and f cj represent the fixed operation and maintenance costs of thermal power generating units, wind power generating units, solar photovoltaic generating units and CSP units, respectively,
Figure 469070DEST_PATH_IMAGE032
,
Figure 248807DEST_PATH_IMAGE033
,
Figure 316120DEST_PATH_IMAGE034
,
Figure 599334DEST_PATH_IMAGE035
represent the total capacity of thermal power generating units, wind generating units, solar photovoltaic generating units and CSP units, respectively; C v represents the variable operating cost, consisting of startup cost and fuel cost, where c th-m and SD th-m represent the first Fuel cost and start-up cost of class m thermal power generating units,
Figure 218534DEST_PATH_IMAGE036
represents the output power of the m-th thermal power generating unit at time t,
Figure 406939DEST_PATH_IMAGE037
represents the start-up capacity of the m-th thermal power generating unit at time t, M represents the category of thermal power generating units, J represents the category of CSP units, T represents the time period,
Figure 74681DEST_PATH_IMAGE038
represents the time interval.

CSP机组通常由聚光集热模块、储热模块、发电模块三部分组成，如图2所示。聚光集热模块通过反光镜将太阳能汇聚到太阳能收集装置，进而加热其中的导热工质，从而将太阳能转换为热能；导热工质流入储热模块进行热交换可以实现热存储或热释放；发电模块可以通过汽轮发电机将热能转换为机械能最后转换为电能。A CSP unit usually consists of three parts: a concentrating heat collecting module, a heat storage module, and a power generation module, as shown in Figure 2. The concentrating heat collecting module collects the solar energy to the solar energy collecting device through the reflector, and then heats the thermal conductive medium therein, thereby converting the solar energy into heat energy; the thermal conductive medium flows into the heat storage module for heat exchange, which can realize heat storage or heat release; The module can convert thermal energy into mechanical energy and finally into electrical energy through a steam turbine generator.

为了提高低碳CSP系统规划与运行协同优化的速度，降低计算的复杂度，在构建低碳CSP系统规划与运行协同优化模型时，对CSP机组进行了集群分组，从而将优化问题从优化单个机组的行为转化到优化群体行为上来，并引入表示CSP机组组群的在线总容量、启动总容量和关停总容量的三个连续变量来构建低碳CSP系统规划与运行协同优化模型的约束条件。In order to improve the speed of collaborative optimization of low-carbon CSP system planning and operation and reduce the computational complexity, when building a collaborative optimization model for low-carbon CSP system planning and operation, CSP units are grouped into clusters, so that the optimization problem can be changed from optimizing a single unit The behavior of CSP is transformed into the optimization group behavior, and three continuous variables representing the total online capacity, total start-up capacity and total shutdown capacity of the CSP unit group are introduced to construct the constraints of the collaborative optimization model of low-carbon CSP system planning and operation.

构建的低碳CSP系统规划与运行协同优化模型的约束条件包括：系统功率平衡约束、备用约束、低碳政策约束、CSP机组组群的输出功率约束、爬坡约束、最小在线时间约束、最小离线时间约束、瞬时热功率平衡约束、CSP机组中储热模块的充放电平衡约束和荷电状态约束等。The constraints of the constructed low-carbon CSP system planning and operation collaborative optimization model include: system power balance constraints, reserve constraints, low-carbon policy constraints, output power constraints of CSP unit groups, ramp constraints, minimum online time constraints, and minimum offline constraints Time constraints, instantaneous thermal power balance constraints, charge-discharge balance constraints and state-of-charge constraints of heat storage modules in CSP units, etc.

电力系统运行的首要任务就是保证系统的安全稳定运行，所以本实施例公开的低碳CSP系统规划与运行协同优化模型必须满足电力系统的功率平衡约束，即火力发电机组、风力发电机组、太阳能光伏发电机组和CSP机组的发电量之和，应始终等于本区域的电力需求与传输到区域外的功率之和，如式（14）所示：The primary task of power system operation is to ensure the safe and stable operation of the system. Therefore, the low-carbon CSP system planning and operation collaborative optimization model disclosed in this embodiment must meet the power balance constraints of the power system, that is, thermal power generating units, wind power generating units, and solar photovoltaics. The sum of the power generation of the generator set and the CSP set should always be equal to the sum of the power demand in this area and the power transmitted to the outside of the area, as shown in formula (14):

Figure 274718DEST_PATH_IMAGE039
（14）
Figure 274718DEST_PATH_IMAGE039
(14)

其中，D t 表示本区域在t小时的电力需求，

Figure 205765DEST_PATH_IMAGE040
表示本区域在t小时传输到区域外的功率值，
Figure 428936DEST_PATH_IMAGE041
分别表示火力发电机组、风力发电机组、太阳能光伏发电机组和CSP机组组群在t小时的输出功率。Among them, D t represents the electricity demand of the region at hour t,
Figure 205765DEST_PATH_IMAGE040
represents the power value transmitted from this area to outside the area at hour t,
Figure 428936DEST_PATH_IMAGE041
Respectively represent the output power of thermal power generating units, wind generating units, solar photovoltaic generating units and CSP unit groups at hour t.

对于火力发电机组，火力发电机组的每小时输出功率不应超过总装机容量，如式(15)所示：For thermal power generating units, the hourly output power of thermal power generating units should not exceed the total installed capacity, as shown in formula (15):

Figure 962685DEST_PATH_IMAGE042
（15）
Figure 962685DEST_PATH_IMAGE042
(15)

其中，

Figure 908907DEST_PATH_IMAGE036
和
Figure 807593DEST_PATH_IMAGE043
分别表示第m类火力发电机组在t小时的输出功率和在线容量，
Figure 580377DEST_PATH_IMAGE044
和
Figure 793183DEST_PATH_IMAGE045
分别表示第m类火力发电机组的总装机容量、现有容量和新增容量。in,
Figure 908907DEST_PATH_IMAGE036
and
Figure 807593DEST_PATH_IMAGE043
respectively represent the output power and online capacity of the m-th type thermal power generating unit at hour t,
Figure 580377DEST_PATH_IMAGE044
and
Figure 793183DEST_PATH_IMAGE045
Respectively represent the total installed capacity, existing capacity and newly added capacity of the m-th thermal power generating units.

对于风力发电机组、太阳能光伏发电机组和CSP机组，其每小时输出功率将受到现有容量、新建容量和不断变化的容量因子的共同限制，分别如式(16)、(17)、（18）所示：For wind turbines, solar photovoltaics and CSPs, the hourly output power will be limited by the existing capacity, the new capacity and the changing capacity factor, respectively, as shown in equations (16), (17), (18) shown:

Figure 905496DEST_PATH_IMAGE046
（16）
Figure 905496DEST_PATH_IMAGE046
(16)

Figure 302979DEST_PATH_IMAGE047
（17）
Figure 302979DEST_PATH_IMAGE047
(17)

Figure 422114DEST_PATH_IMAGE048
（18）
Figure 422114DEST_PATH_IMAGE048
(18)

其中，

Figure 500928DEST_PATH_IMAGE049
分别表示风力发电机组、太阳能光伏发电机组和CSP机组组群在t小时的输出功率，
Figure 795643DEST_PATH_IMAGE050
分别表示风力发电机组、太阳能光伏发电机组和CSP机组组群在t小时的小时容量因子，
Figure 708236DEST_PATH_IMAGE051
分别表示风力发电机组、太阳能光伏发电机组和CSP机组组群的总容量，
Figure 658874DEST_PATH_IMAGE052
分别表示风力发电机组、太阳能光伏发电机组和CSP机组组群的现有容量，
Figure 338117DEST_PATH_IMAGE053
分别表示风力发电机组、太阳能光伏发电机组和CSP机组组群的新增容量。in,
Figure 500928DEST_PATH_IMAGE049
respectively represent the output power of wind turbine, solar photovoltaic and CSP group at hour t,
Figure 795643DEST_PATH_IMAGE050
are the hourly capacity factors of wind turbines, solar photovoltaics and CSP groups at hour t, respectively,
Figure 708236DEST_PATH_IMAGE051
represent the total capacity of wind turbine, solar photovoltaic and CSP group, respectively,
Figure 658874DEST_PATH_IMAGE052
represent the existing capacities of wind turbines, solar photovoltaics and CSP groups, respectively,
Figure 338117DEST_PATH_IMAGE053
Represent the newly added capacity of wind turbine, solar photovoltaic and CSP group, respectively.

为了保证电力系统的安全可靠运行，在进行电源规划时往往需要留有一定的裕度来应对系统突发状况。本实施例考虑到风能和太阳能输出功率的预测误差，将风力发电、光伏发电的不确定性转化为系统的备用容量，从而保证电力系统的经济稳定运行。考虑到风光发电的随机性与波动性，构建系统的备用约束如式(19)所示：In order to ensure the safe and reliable operation of the power system, it is often necessary to leave a certain margin in power planning to deal with system emergencies. In this embodiment, the prediction error of the output power of wind energy and solar energy is considered, and the uncertainty of wind power generation and photovoltaic power generation is converted into the reserve capacity of the system, thereby ensuring the economical and stable operation of the power system. Considering the randomness and volatility of wind and solar power generation, the backup constraints of the construction system are shown in Equation (19):

Figure 847858DEST_PATH_IMAGE054
（19）
Figure 847858DEST_PATH_IMAGE054
(19)

其中，

Figure 259248DEST_PATH_IMAGE055
表示第m类火力发电机组在时间t时的最大输出比，
Figure 493920DEST_PATH_IMAGE056
表示在时间t时与电力需求相关的备用要求，它等于该地区最大火力发电机组的装机容量或由于预测误差导致的预期负荷偏差，
Figure 586641DEST_PATH_IMAGE057
、
Figure 528052DEST_PATH_IMAGE058
分别表示风力发电机组、太阳能光伏发电机组和CSP机组输出功率的预测误差。in,
Figure 259248DEST_PATH_IMAGE055
represents the maximum output ratio of the m-th thermal power generating unit at time t,
Figure 493920DEST_PATH_IMAGE056
represents the reserve requirement related to power demand at time t, which is equal to the installed capacity of the largest thermal power generating unit in the area or the expected load deviation due to forecast errors,
Figure 586641DEST_PATH_IMAGE057
,
Figure 528052DEST_PATH_IMAGE058
are the prediction errors of the output power of wind turbines, solar photovoltaic generators and CSP units, respectively.

可再生能源投资组合标准（Renewable Portfolio Standard, RPS）要求供电商必须拥有一个最低的可再生能源比例，本实施例拟采用RPS标准实现低碳政策约束，如式(20)所示：The Renewable Portfolio Standard (RPS) requires power suppliers to have a minimum renewable energy ratio. In this example, the RPS standard is proposed to implement low-carbon policy constraints, as shown in formula (20):

Figure 438239DEST_PATH_IMAGE059
（20）
Figure 438239DEST_PATH_IMAGE059
(20)

其中，r表示可再生能源发电量在总发电量中所占的比例。Among them, r represents the proportion of renewable energy power generation in the total power generation.

对通过分别表示CSP机组组群的在线总容量、启动总容量和关停总容量的三个连续变量构建CSP机组组群的各项约束进行详细说明。The constraints for constructing a CSP unit group by three continuous variables representing the total online capacity, total start-up capacity and total shutdown capacity of the CSP unit group are described in detail.

CSP机组组群的各项约束包括CSP机组组群的输出功率约束、爬坡约束、最小在线时间约束、最小离线时间约束、瞬时热功率平衡约束、CSP机组中储热模块的充放电平衡约束和荷电状态约束。The constraints of the CSP unit group include the output power constraint of the CSP unit group, the ramp constraint, the minimum online time constraint, the minimum offline time constraint, the instantaneous thermal power balance constraint, the charge-discharge balance constraint of the heat storage module in the CSP unit, and the State of charge constraints.

为了使构建的CSP机组组群的各项约束中所有的变量都是连续的，不存在表示CSP机组开关状态的二元变量，从而使构建的CSP机组组群的各项约束为完全的线性优化模型，降低低碳CSP系统规划与运行协同优化模型的复杂度，提高模型的计算效率，首先在传统优化模型中引入了分别表示CSP机组组群的在线总容量、启动总容量和关停总容量的整数变量

Figure 19262DEST_PATH_IMAGE060
、
Figure 977991DEST_PATH_IMAGE061
、
Figure 101805DEST_PATH_IMAGE062
，以模拟该组群内所有机组的群体行为，如式（21）、（22）、（23）所示：In order to make all the variables in the constraints of the constructed CSP unit group continuous, there is no binary variable representing the switching state of the CSP unit, so that the constraints of the constructed CSP unit group are completely linear optimization The model reduces the complexity of the collaborative optimization model for low-carbon CSP system planning and operation, and improves the computational efficiency of the model. First, the traditional optimization model introduces the total online capacity, total start-up capacity and total shutdown capacity of CSP unit groups respectively. integer variable of
Figure 19262DEST_PATH_IMAGE060
,
Figure 977991DEST_PATH_IMAGE061
,
Figure 101805DEST_PATH_IMAGE062
, to simulate the group behavior of all units in the group, as shown in equations (21), (22), (23):

Figure 120577DEST_PATH_IMAGE063
（21）
Figure 120577DEST_PATH_IMAGE063
(twenty one)

Figure 674049DEST_PATH_IMAGE064
（22）
Figure 674049DEST_PATH_IMAGE064
(twenty two)

Figure 498785DEST_PATH_IMAGE065
（23）
Figure 498785DEST_PATH_IMAGE065
(twenty three)

其中，

Figure 414789DEST_PATH_IMAGE066
表示CSP机组组群j在t时刻的在线总容量，即在t时刻j组群内正在运行的CSP机组的额定容量之和，I i,t 表示CSP机组的开关状态，当机组正在运行时，I i,t =1，否则，I i,t =0；
Figure 27298DEST_PATH_IMAGE067
表示CSP机组组群j在t时刻的启动总容量，即在t时刻j组群内启动的CSP机组的额定容量之和，u i,t 表示CSP机组的启动状态，当机组启动时，u i,t =1，否则，u i,t =0；
Figure 723858DEST_PATH_IMAGE068
表示CSP机组组群j在t时刻的关停总容量，即在t时刻j组群内关停的CSP机组的额定容量之和，d i,t 表示CSP机组的关停状态，当机组关停时，d i,t =1，否则，d i,t =0；P i,n 表示CSP机组i的额定容量，I为组群内机组数。需要注意的是，当通过
Figure 24390DEST_PATH_IMAGE066
、
Figure 467004DEST_PATH_IMAGE069
、
Figure 889895DEST_PATH_IMAGE068
构建CSP机组组群的各种约束条件时，
Figure 745855DEST_PATH_IMAGE066
、
Figure 37028DEST_PATH_IMAGE070
、
Figure 724361DEST_PATH_IMAGE068
均为间接控制变量，具有整数特征，均取离散值，使得构建的CSP机组组群的各种约束中依然存在整数变量，当通过该约束条件构建优化模型进行电力系统规划时，
Figure 255837DEST_PATH_IMAGE066
的可能值由I i,t 的不同组合决定。例如，在包含10个CSP机组的一组中，如果每个CSP机组的额定容量都不相同，则
Figure 536777DEST_PATH_IMAGE066
的可能值最多有1024种，若通过假设组群内所有机组的额定容量均相同来减少
Figure 179111DEST_PATH_IMAGE066
的可能值，并不能改变构建的CSP机组组群的各种约束的混合整数性质，使得通过该CSP机组组群的各种约束构建的优化模型依然具有较高的复杂度，计算效率较低。in,
Figure 414789DEST_PATH_IMAGE066
Represents the total online capacity of CSP unit group j at time t, that is, the sum of the rated capacities of CSP units running in group j at time t, I i,t represents the switching state of CSP units, when the unit is running, I i,t =1, otherwise, I i,t =0;
Figure 27298DEST_PATH_IMAGE067
Represents the total startup capacity of CSP unit group j at time t, that is, the sum of the rated capacities of CSP units started in group j at time t, ui , t represents the startup state of CSP units, when the unit starts, ui i ,t =1, otherwise, ui ,t =0;
Figure 723858DEST_PATH_IMAGE068
Represents the total shutdown capacity of CSP unit group j at time t, that is, the sum of the rated capacity of CSP units shut down in group j at time t, d i,t represents the shutdown state of CSP units, when the unit is shut down When , d i,t =1, otherwise, d i,t =0; P i,n represents the rated capacity of CSP unit i, and I is the number of units in the group. It should be noted that when passing
Figure 24390DEST_PATH_IMAGE066
,
Figure 467004DEST_PATH_IMAGE069
,
Figure 889895DEST_PATH_IMAGE068
When constructing the various constraints of the CSP unit group,
Figure 745855DEST_PATH_IMAGE066
,
Figure 37028DEST_PATH_IMAGE070
,
Figure 724361DEST_PATH_IMAGE068
are indirect control variables, have integer characteristics, and take discrete values, so that there are still integer variables in the various constraints of the constructed CSP unit group.
Figure 255837DEST_PATH_IMAGE066
The possible values of is determined by different combinations of I i,t . For example, in a group of 10 CSP units, if each CSP unit has a different rated capacity, then
Figure 536777DEST_PATH_IMAGE066
There are at most 1024 possible values for
Figure 179111DEST_PATH_IMAGE066
The possible value of , does not change the mixed integer properties of the various constraints of the constructed CSP unit group, so that the optimization model constructed by the various constraints of the CSP unit group still has high complexity and low computational efficiency.

本实施例在引入

Figure 720950DEST_PATH_IMAGE066
、
Figure 111743DEST_PATH_IMAGE071
、
Figure 942295DEST_PATH_IMAGE068
这三个整数变量的基础上，通过连续变量
Figure 450637DEST_PATH_IMAGE072
、
Figure 987929DEST_PATH_IMAGE073
和
Figure 861207DEST_PATH_IMAGE074
来分别近似逼近整数变量
Figure 975793DEST_PATH_IMAGE066
、
Figure 225509DEST_PATH_IMAGE071
和
Figure 132154DEST_PATH_IMAGE075
，进而用连续变量
Figure 973071DEST_PATH_IMAGE076
、
Figure 512637DEST_PATH_IMAGE073
和
Figure 238147DEST_PATH_IMAGE074
来代替整数变量
Figure 609086DEST_PATH_IMAGE066
、
Figure 824167DEST_PATH_IMAGE077
和
Figure 816742DEST_PATH_IMAGE075
，构建最终的CSP机组组群的各项约束，使得最终构建的CSP机组组群的各项约束中所有的变量都是连续的，不包含传统优化模型中表示每个机组开关状态的3*I个二元变量，为完全的线性优化模型，大大减少决策变量的数量，降低了低碳CSP系统规划与运行协同优化模型的复杂度，加快了模型的计算速度。This embodiment introduces
Figure 720950DEST_PATH_IMAGE066
,
Figure 111743DEST_PATH_IMAGE071
,
Figure 942295DEST_PATH_IMAGE068
These three integer variables are based on continuous variables through
Figure 450637DEST_PATH_IMAGE072
,
Figure 987929DEST_PATH_IMAGE073
and
Figure 861207DEST_PATH_IMAGE074
to approximate integer variables separately
Figure 975793DEST_PATH_IMAGE066
,
Figure 225509DEST_PATH_IMAGE071
and
Figure 132154DEST_PATH_IMAGE075
, and then use continuous variables
Figure 973071DEST_PATH_IMAGE076
,
Figure 512637DEST_PATH_IMAGE073
and
Figure 238147DEST_PATH_IMAGE074
instead of integer variables
Figure 609086DEST_PATH_IMAGE066
,
Figure 824167DEST_PATH_IMAGE077
and
Figure 816742DEST_PATH_IMAGE075
, construct the constraints of the final CSP unit group, so that all the variables in the constraints of the final CSP unit group are continuous, excluding the 3* I representing the switching state of each unit in the traditional optimization model A binary variable is a complete linear optimization model, which greatly reduces the number of decision variables, reduces the complexity of the collaborative optimization model for low-carbon CSP system planning and operation, and accelerates the calculation speed of the model.

其中，连续变量

Figure 142681DEST_PATH_IMAGE076
表示CSP机组组群j在t时刻的在线总容量，即t时刻j组群内正在运行的CSP机组的额定容量之和，它满足式（24）：Among them, continuous variables
Figure 142681DEST_PATH_IMAGE076
Represents the total online capacity of CSP unit group j at time t, that is, the sum of the rated capacities of CSP units running in group j at time t, which satisfies Equation (24):

Figure 633705DEST_PATH_IMAGE078
（24）
Figure 633705DEST_PATH_IMAGE078
(twenty four)

其中，S j 表示CSP机组组群j的总容量，即为j组群内所有CSP机组的额定容量之和，由式（25）获得：Among them, S j represents the total capacity of CSP unit group j, that is, the sum of the rated capacities of all CSP units in group j, obtained from formula (25):

Figure 957370DEST_PATH_IMAGE079
（25）
Figure 957370DEST_PATH_IMAGE079
(25)

其中，

Figure 471528DEST_PATH_IMAGE080
表示j组群内CSP机组i的最大输出功率。in,
Figure 471528DEST_PATH_IMAGE080
Indicates the maximum output power of CSP unit i in group j.

连续变量

Figure 663475DEST_PATH_IMAGE081
表示CSP机组组群j在t时刻的启动总容量，即在t时刻j组群内启动的CSP机组的额定容量之和，连续变量
Figure 212268DEST_PATH_IMAGE074
表示CSP机组组群j在t时刻的关停总容量，即在t时刻j组群内关停的CSP机组的额定容量之和。continuous variable
Figure 663475DEST_PATH_IMAGE081
Represents the total start-up capacity of CSP unit group j at time t, that is, the sum of the rated capacities of CSP units started in group j at time t, continuous variable
Figure 212268DEST_PATH_IMAGE074
Indicates the total shutdown capacity of CSP unit group j at time t, that is, the sum of the rated capacity of the CSP units shut down in group j at time t.

连续变量

Figure 690523DEST_PATH_IMAGE076
、
Figure 754294DEST_PATH_IMAGE082
和
Figure 687615DEST_PATH_IMAGE074
之间的关系符合公式（26）：continuous variable
Figure 690523DEST_PATH_IMAGE076
,
Figure 754294DEST_PATH_IMAGE082
and
Figure 687615DEST_PATH_IMAGE074
The relationship between them conforms to formula (26):

Figure 28598DEST_PATH_IMAGE083
（26）
Figure 28598DEST_PATH_IMAGE083
(26)

基于连续决策变量，CSP机组组群的输出功率约束为式（27）：Based on continuous decision variables, the output power constraint of the CSP unit group is Eq. (27):

Figure 553120DEST_PATH_IMAGE084
（27）
Figure 553120DEST_PATH_IMAGE084
(27)

其中，P j,min 和P j,max 分别表示CSP机组组群j的最小输出功率和最大输出功率，分别由式（28）、（29）获得：Among them, P j,min and P j,max represent the minimum output power and maximum output power of CSP unit group j, respectively, which are obtained from equations (28) and (29), respectively:

Figure 41870DEST_PATH_IMAGE085
（28）
Figure 41870DEST_PATH_IMAGE085
(28)

Figure 467297DEST_PATH_IMAGE086
（29）
Figure 467297DEST_PATH_IMAGE086
(29)

其中，

Figure 521841DEST_PATH_IMAGE087
和
Figure 420527DEST_PATH_IMAGE088
分别表示CSP机组组群j的最小输出功率、最大输出功率与CSP机组组群j的在线总容量的比值。对于一组群具有相似运行特性的机组，
Figure 334256DEST_PATH_IMAGE089
和
Figure 406117DEST_PATH_IMAGE087
、
Figure 518430DEST_PATH_IMAGE090
和
Figure 40547DEST_PATH_IMAGE088
之间的差异相对较小，故取
Figure 238310DEST_PATH_IMAGE087
=
Figure 379441DEST_PATH_IMAGE089
，
Figure 18364DEST_PATH_IMAGE088
=
Figure 524432DEST_PATH_IMAGE091
。in,
Figure 521841DEST_PATH_IMAGE087
and
Figure 420527DEST_PATH_IMAGE088
respectively represent the ratio of the minimum output power and maximum output power of CSP unit group j to the total online capacity of CSP unit group j. For a group of units with similar operating characteristics,
Figure 334256DEST_PATH_IMAGE089
and
Figure 406117DEST_PATH_IMAGE087
,
Figure 518430DEST_PATH_IMAGE090
and
Figure 40547DEST_PATH_IMAGE088
The difference is relatively small, so take
Figure 238310DEST_PATH_IMAGE087
=
Figure 379441DEST_PATH_IMAGE089
,
Figure 18364DEST_PATH_IMAGE088
=
Figure 524432DEST_PATH_IMAGE091
.

基于连续决策变量，CSP机组组群的爬坡约束为式（30）、（31）：Based on continuous decision variables, the climbing constraints of the CSP unit group are expressed as equations (30) and (31):

Figure 271808DEST_PATH_IMAGE092
（30）
Figure 271808DEST_PATH_IMAGE092
(30)

Figure 888734DEST_PATH_IMAGE093
（31）
Figure 888734DEST_PATH_IMAGE093
(31)

其中，

Figure 398475DEST_PATH_IMAGE094
和
Figure 137761DEST_PATH_IMAGE095
分别表示向上爬坡率和向下爬坡率。in,
Figure 398475DEST_PATH_IMAGE094
and
Figure 137761DEST_PATH_IMAGE095
represent the upward and downward slope rates, respectively.

对式（30）、（31）进行补充，对CSP机组组群j在t时刻的输出功率进一步增加约束条件，如式（32）所示：Complement equations (30) and (31), and further increase constraints on the output power of CSP unit group j at time t, as shown in equation (32):

Figure 310117DEST_PATH_IMAGE096
（32）
Figure 310117DEST_PATH_IMAGE096
(32)

基于连续决策变量，CSP机组组群的最小在线时间约束、最小离线时间约束为式（33）、（34）、（35）和（36）：Based on continuous decision variables, the minimum online time constraints and the minimum offline time constraints of the CSP unit group are equations (33), (34), (35) and (36):

Figure 668417DEST_PATH_IMAGE097
（33）
Figure 668417DEST_PATH_IMAGE097
(33)

Figure 406566DEST_PATH_IMAGE098
（34）
Figure 406566DEST_PATH_IMAGE098
(34)

Figure 254436DEST_PATH_IMAGE099
（35）
Figure 254436DEST_PATH_IMAGE099
(35)

Figure 101038DEST_PATH_IMAGE100
（36）
Figure 101038DEST_PATH_IMAGE100
(36)

CSP机组组群的瞬时热功率平衡约束如式（37）所示：The instantaneous thermal power balance constraint of the CSP unit group is shown in equation (37):

Figure 59767DEST_PATH_IMAGE101
（37）
Figure 59767DEST_PATH_IMAGE101
(37)

由于传统优化模型中CSP机组储热模块的充放电平衡约束和储热模块的荷电状态约束中的决策变量均为连续变量，故低碳CSP系统规划与运行协同优化模型的CSP机组中储热模块的充放电平衡约束和荷电状态约束依然采用式（8）和（9）。Since the decision variables in the charge-discharge balance constraint of the heat storage module of the CSP unit and the state-of-charge constraint of the heat storage module in the traditional optimization model are both continuous variables, the heat storage in the CSP unit of the collaborative optimization model of low-carbon CSP system planning and operation The charge-discharge balance constraints and state-of-charge constraints of the module still use equations (8) and (9).

故构建的低碳CSP系统规划与运行协同优化模型中CSP机组组群的输出功率约束、爬坡约束、最小在线时间约束、最小离线时间约束、瞬时热功率平衡约束、CSP机组中储热模块的充放电平衡约束和荷电状态约束包括式（8）、（9）和（24）-（37），其中所有的变量都是连续的，不涉及表示CSP机组开关状态的二元变量，为完全的线性优化模型，降低了低碳CSP系统规划与运行协同优化模型的复杂度，通过该低碳CSP系统规划与运行协同优化模型，进行低碳含CSP系统的长期规划时，在保持计算结果精度的同时，可以显著提高计算效率，解决了传统优化模型中包含表示CSP机组开关状态的二元变量，模型复杂度高，计算效率低的问题。Therefore, the output power constraints, ramp constraints, minimum online time constraints, minimum offline time constraints, instantaneous thermal power balance constraints, and heat storage modules in CSP units in the constructed low-carbon CSP system planning and operation collaborative optimization model. The charge-discharge balance constraints and state-of-charge constraints include equations (8), (9), and (24)-(37), in which all variables are continuous and do not involve binary variables representing the switching state of the CSP unit, which are completely The low-carbon CSP system planning and operation collaborative optimization model reduces the complexity of the low-carbon CSP system planning and operation collaborative optimization model. Through the low-carbon CSP system planning and operation collaborative optimization model, the long-term planning of the low-carbon CSP-containing system can be carried out while maintaining the accuracy of the calculation results. At the same time, it can significantly improve the calculation efficiency, and solve the problems of high model complexity and low calculation efficiency that the traditional optimization model contains binary variables representing the switching state of the CSP unit.

S3：获取低碳CSP系统中各机组的额定容量。S3: Obtain the rated capacity of each unit in the low-carbon CSP system.

S4：根据各机组的额定容量及构建好的低碳CSP系统规划与运行协同优化模型，获取各机组组群的容量配置方案。S4: According to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model, obtain the capacity allocation plan of each unit group.

为进一步说明本发明所提出方法的可行性和有效性，进行举例说明。例如，对于一个包含了5台CSP机组，其中单个机组的额定容量分别为250，300，330，350，350MW的CSP机组组群来说，若采用传统优化模型，则每一台CSP机组存在在线（I i,t =1）和不在线（I i,t =0）两种状态，那么表示整个机组组群（即5台CSP机组）在线总容量的整数变量

Figure 183581DEST_PATH_IMAGE102
可以取0，250，300，330，350，550，580，600，630，650，680，700，880，900，930，950，980，1000，1030，1230，1250，1280，1330，1580二十四种可能值，如图3中“
Figure 608877DEST_PATH_IMAGE103
”所示；若采用传统的聚类方法进行简化，则假设每一台CSP机组的额定容量均为316MW（取平均值），那么表示整个机组组群（即5台CSP机组）在线总容量的整数变量
Figure 755824DEST_PATH_IMAGE102
可以取0，316，632，948，1264，1580六种可能值，如图3中“
Figure 580561DEST_PATH_IMAGE105
”所示，因此这样虽然减少了决策变量
Figure 496564DEST_PATH_IMAGE102
的可能值，但没有改变构建的约束条件的混合整数性质，导致对模型进行求解时计算速度仍然较慢。本发明所提出的基于集群学习的低碳CSP系统协同优化方法，引入了表示整个机组组群（即5台CSP机组）在线总容量的连续变量
Figure 374653DEST_PATH_IMAGE076
，由于此时整个机组组群的总容量，即5台CSP机组额定容量之和为S j =1580MW，所以
Figure 540055DEST_PATH_IMAGE076
可以取0至1580MW之间的任何值，如图3中“
Figure 840586DEST_PATH_IMAGE107
”所示，此时若优化得到的在线总容量的最佳值为750MW，其实际值应为880MW，两者相差130MW，仅占总容量S j 的8.2%，其最大差异不会大于组内最大机组的额定容量，且差异会随着组内机组数量的增加而减小，因此该方法可以在保持计算结果精度的同时，显著降低计算复杂度。In order to further illustrate the feasibility and effectiveness of the method proposed by the present invention, an example is given. For example, for a group of CSP units including 5 CSP units, where the rated capacity of a single unit is 250, 300, 330, 350, and 350 MW, if the traditional optimization model is used, each CSP unit is online. ( I i,t =1) and offline ( I i,t =0) two states, then the integer variable representing the total online capacity of the entire unit group (that is, 5 CSP units)
Figure 183581DEST_PATH_IMAGE102
Can take 0, 250, 300, 330, 350, 550, 580, 600, 630, 650, 680, 700, 880, 900, 930, 950, 980, 1000, 1030, 1230, 1250, 1280, 1330, 1580 two Fourteen possible values, as shown in Figure 3"
Figure 608877DEST_PATH_IMAGE103
” shown; if the traditional clustering method is used for simplification, it is assumed that the rated capacity of each CSP unit is 316MW (average value), then it represents the total online capacity of the entire unit group (that is, 5 CSP units). integer variable
Figure 755824DEST_PATH_IMAGE102
You can take 0, 316, 632, 948, 1264, 1580 six possible values, as shown in Figure 3 "
Figure 580561DEST_PATH_IMAGE105
”, so although this reduces the decision variables
Figure 496564DEST_PATH_IMAGE102
possible values of , but without changing the mixed-integer nature of the constructed constraints, resulting in still slower computations when solving the model. The collaborative optimization method of low-carbon CSP system based on cluster learning proposed by the present invention introduces a continuous variable representing the total online capacity of the entire unit group (ie, 5 CSP units).
Figure 374653DEST_PATH_IMAGE076
, since the total capacity of the entire unit group at this time, that is, the sum of the rated capacity of the 5 CSP units is S j = 1580MW, so
Figure 540055DEST_PATH_IMAGE076
Can take any value between 0 and 1580MW, as shown in Figure 3"
Figure 840586DEST_PATH_IMAGE107
”, at this time, if the optimal value of the total online capacity obtained by optimization is 750MW, the actual value should be 880MW, the difference between the two is 130MW, which only accounts for 8.2% of the total capacity S j , and the maximum difference will not be greater than that within the group. The rated capacity of the largest unit, and the difference will decrease with the increase of the number of units in the group, so this method can significantly reduce the computational complexity while maintaining the accuracy of the calculation results.

故本实施例公开的基于集群学习的低碳CSP系统协同优化方法，通过引入表示CSP机组组群的在线总容量、启动总容量和关停总容量的三个连续变量，建立了更详细的低碳CSP系统规划与运行协同优化模型，且建立的低碳CSP系统规划与运行协同优化模型中CSP机组组群的各项约束不含表示单个机组开关状态的二元变量，为完全的线性优化模型，降低了模型求解的复杂度，提高了计算的效率，更适用于大规模电力系统长时间尺度的多样化复杂场景的规划及分析。Therefore, the collaborative optimization method for low-carbon CSP systems based on cluster learning disclosed in this embodiment establishes a more detailed low-carbon CSP system by introducing three continuous variables representing the total online capacity, total startup capacity and total shutdown capacity of the CSP unit group. The carbon CSP system planning and operation collaborative optimization model, and the constraints of the CSP unit group in the established low-carbon CSP system planning and operation collaborative optimization model do not contain binary variables representing the switching state of a single unit, which is a complete linear optimization model , which reduces the complexity of the model solution and improves the calculation efficiency, and is more suitable for the planning and analysis of diversified and complex scenarios of large-scale power systems on a long-term scale.

实施例2Example 2

在该实施例中，公开了基于集群学习的低碳CSP系统协同优化装置，包括：In this embodiment, a low-carbon CSP system collaborative optimization device based on cluster learning is disclosed, including:

组群划分模块，用于对低碳CSP系统中的CSP机组进行集群分组，获得多个CSP机组组群；The group division module is used to group the CSP units in the low-carbon CSP system into clusters to obtain multiple CSP unit groups;

模型构建模块，用于通过表示CSP机组组群的在线总容量、启动总容量和关停总容量的三个连续变量，构建CSP机组组群的输出功率约束、爬坡约束、最小在线时间约束、最小离线时间约束和瞬时热功率平衡约束，进而构建低碳CSP系统规划与运行协同优化模型；The model building module is used to construct the output power constraints, ramp constraints, minimum online time constraints, Minimum offline time constraints and instantaneous thermal power balance constraints, and then build a low-carbon CSP system planning and operation collaborative optimization model;

参数获取模块，用于获取低碳CSP系统中各机组的额定容量；The parameter acquisition module is used to acquire the rated capacity of each unit in the low-carbon CSP system;

容量配置方案获取模块，用于根据各机组的额定容量及构建的低碳CSP系统规划与运行协同优化模型，获取各机组组群的容量配置方案。The capacity configuration plan acquisition module is used to obtain the capacity configuration plan of each unit group according to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model.

需要说明的是，上述各模块的具体实现方式已经在实施例1中进行了详细的说明，不再赘述。It should be noted that the specific implementation manners of the above-mentioned modules have been described in detail in Embodiment 1, and are not repeated here.

最后应当说明的是：以上实施例仅用以说明本发明的技术方案而非对其限制，尽管参照上述实施例对本发明进行了详细的说明，所属领域的普通技术人员应当理解：依然可以对本发明的具体实施方式进行修改或者等同替换，而未脱离本发明精神和范围的任何修改或者等同替换，其均应涵盖在本发明的权利要求保护范围之内。Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the present invention can still be Modifications or equivalent replacements are made to the specific embodiments of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention shall be included within the protection scope of the claims of the present invention.

Claims (2)

1. The low-carbon CSP system collaborative optimization method based on cluster learning is characterized by comprising the following steps:
performing cluster grouping on CSP units in the low-carbon CSP system to obtain a plurality of CSP unit groups;
constructing output power constraint, climbing constraint, minimum on-line time constraint, minimum off-line time constraint and instantaneous thermal power balance constraint of the CSP unit group by three continuous variables representing the on-line total capacity, the starting total capacity and the stopping total capacity of the CSP unit group, and further constructing a low-carbon CSP system planning and operation collaborative optimization model;
acquiring the rated capacity of each unit in the low-carbon CSP system;
acquiring a capacity allocation scheme of each unit group according to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model;
the low-carbon CSP system planning and operation collaborative optimization model is constructed by taking the minimum total cost of the system as a target, wherein the total cost comprises investment cost, fixed operation and maintenance cost and variable operation cost, and the target function is as follows:
Figure 905751DEST_PATH_IMAGE001
；
wherein,
Figure 913022DEST_PATH_IMAGE002
；
Figure 907522DEST_PATH_IMAGE003
；
Figure 143594DEST_PATH_IMAGE004
；
in the formula,Crepresents the total cost;C i which represents the cost of the investment,a th-m 、a w 、a s 、a c-j respectively represents the investment costs of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set,I th-m 、I w 、I s 、I c-j respectively representing the newly increased capacity of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set;C f the fixed operation and maintenance cost is shown,f th-m 、f w 、f s 、f c-j respectively represents the fixed operation and maintenance costs of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set,
Figure 172730DEST_PATH_IMAGE005
、
Figure 85322DEST_PATH_IMAGE006
、
Figure 832698DEST_PATH_IMAGE007
、
Figure 370996DEST_PATH_IMAGE008
respectively representing the total capacity of the thermal generator set, the wind generator set, the solar photovoltaic generator set and the CSP set;C v represents a variable operating cost, consisting of a start-up cost and a fuel cost, wherein c th-m And SD th-m Respectively representing the fuel cost and the starting cost of the mth type thermal generator set,
Figure 254638DEST_PATH_IMAGE009
the output power of the mth type thermal generator set at the time t is shown,
Figure 603711DEST_PATH_IMAGE010
representing the starting capacity of the mth type thermal generator set at the time t,Mthe category of the thermal generator set is represented,Jthe class of the CSP unit is represented,Twhich represents a time period of time,
Figure 572804DEST_PATH_IMAGE011
represents a time interval; the low-carbon CSP system planning and operation collaborative optimization model must meet the power balance constraint of the power system, namely the sum of the generated energy of the thermal generator set, the wind generator set, the solar photovoltaic generator set and the CSP unit should always be equal to the sum of the power demand of the region and the power transmitted outside the region, as follows:
Figure 416258DEST_PATH_IMAGE012
；
wherein,D t indicating the power demand of the region at t hours,
Figure 154406DEST_PATH_IMAGE013
indicating the power value that the region transmits outside the region in t hours,
Figure 408801DEST_PATH_IMAGE014
respectively representing the output power of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set group in t hours;
for a thermal power generating unit, the hourly output power of the thermal power generating unit should not exceed the total installed capacity, as follows:
Figure 255404DEST_PATH_IMAGE015
；
wherein,
Figure 10870DEST_PATH_IMAGE016
and
Figure 744471DEST_PATH_IMAGE017
respectively representing the output power and the online capacity of the mth type thermal generator set in t hours,
Figure 559980DEST_PATH_IMAGE018
and
Figure 129764DEST_PATH_IMAGE019
respectively representing the total installed capacity, the existing capacity and the newly added capacity of the mth type of thermal generator set;
for a wind generating set, a solar photovoltaic generating set and a CSP set, the hourly output power is limited by the existing capacity, the newly-built capacity and the continuously-changed capacity factor, which are respectively as follows:
Figure 423342DEST_PATH_IMAGE020
；
Figure 11449DEST_PATH_IMAGE021
；
Figure 997860DEST_PATH_IMAGE022
；
wherein,
Figure 553475DEST_PATH_IMAGE023
respectively represents the output power of the wind generating set, the solar photovoltaic generating set and the CSP set group in t hours,
Figure 916323DEST_PATH_IMAGE024
respectively represents the hourly capacity factors of the wind generating set, the solar photovoltaic generating set and the CSP set group in t hours,
Figure 358937DEST_PATH_IMAGE025
respectively represents the total capacity of the wind generating set, the solar photovoltaic generating set and the CSP set group,
Figure 250670DEST_PATH_IMAGE026
respectively represents the existing capacities of a wind generating set, a solar photovoltaic generating set and a CSP set group,
Figure 795046DEST_PATH_IMAGE027
respectively representing the newly added capacities of the wind generating set, the solar photovoltaic generating set and the CSP set group;
considering the randomness and the volatility of the wind-solar power generation, the standby constraint for constructing the system is as follows:
Figure 696006DEST_PATH_IMAGE028
；
wherein,
Figure 258705DEST_PATH_IMAGE029
represents the maximum output ratio of the mth type thermal generator set at the time t,
Figure 586918DEST_PATH_IMAGE030
represents a backup requirement related to the power demand at time t, which is equal to the installed capacity of the largest thermal generator set in the area or the expected load deviation due to prediction error,
Figure 851546DEST_PATH_IMAGE031
、
Figure 697143DEST_PATH_IMAGE032
respectively representing the prediction errors of the output power of the wind generating set, the solar photovoltaic generating set and the CSP set;
the renewable energy investment portfolio standard requires that a power supplier must have a lowest renewable energy proportion, and adopts the RPS standard to realize low-carbon policy constraint:
Figure 973403DEST_PATH_IMAGE033
；
wherein,rrepresenting the proportion of the renewable energy power generation in the total power generation;
integer variables respectively representing the on-line total capacity, the starting total capacity and the shutdown total capacity of the CSP unit group are introduced into a traditional optimization model
Figure 472518DEST_PATH_IMAGE034
、
Figure 725907DEST_PATH_IMAGE035
、
Figure 703090DEST_PATH_IMAGE036
To simulate the group behavior of all units in the group, as follows:
Figure 240382DEST_PATH_IMAGE037
；
Figure 910397DEST_PATH_IMAGE038
；
Figure 149618DEST_PATH_IMAGE039
；
wherein,
Figure 930492DEST_PATH_IMAGE040
the online total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units which are running in the CSP unit group at the time t,I i,t indicating the switching state of the CSP unit and, when the unit is operating,I i,t =1, and otherwise,I i,t =0；
Figure 322290DEST_PATH_IMAGE041
the starting total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units started in the CSP unit group at the time t,u i,t indicating the start-up status of the CSP unit and, when the unit is started,u i,t =1, and otherwise,u i,t =0；
Figure 163207DEST_PATH_IMAGE042
representing the shutdown total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units shutdown in the CSP unit group j at the time t,d i,t indicating the shutdown state of the CSP unit, when the unit is shut down,d i,t =1, and otherwise,d i,t =0；P i,n represents the rated capacity of the CSP unit i,Ithe number of groups in the group; when passing through
Figure 391188DEST_PATH_IMAGE043
、
Figure 975754DEST_PATH_IMAGE044
、
Figure 956479DEST_PATH_IMAGE045
In constructing the various constraints of a CSP fleet,
Figure 968297DEST_PATH_IMAGE043
、
Figure 916531DEST_PATH_IMAGE044
、
Figure 304787DEST_PATH_IMAGE045
all indirect control variables have integer characteristics, and all discrete values are taken, so that the integer variables still exist in various constraints of the constructed CSP unit group; in the introduction of
Figure 405598DEST_PATH_IMAGE043
、
Figure 588318DEST_PATH_IMAGE046
、
Figure 790891DEST_PATH_IMAGE047
Based on the three integer variables, by continuous variables
Figure 717259DEST_PATH_IMAGE048
、
Figure 672576DEST_PATH_IMAGE049
And
Figure 291777DEST_PATH_IMAGE050
to approximate respectively to integer variables
Figure 214602DEST_PATH_IMAGE043
、
Figure 679081DEST_PATH_IMAGE044
And
Figure 613539DEST_PATH_IMAGE045
and further using continuous variables
Figure 279007DEST_PATH_IMAGE051
、
Figure 298916DEST_PATH_IMAGE052
And
Figure 470483DEST_PATH_IMAGE053
to replace integer variables
Figure 525026DEST_PATH_IMAGE043
、
Figure 95816DEST_PATH_IMAGE044
And
Figure 868600DEST_PATH_IMAGE054
and constructing each constraint of the final CSP unit group, so that all variables in each constraint of the finally constructed CSP unit group are continuous and do not contain 3 x representing the on-off state of each unit in the traditional optimization modelIA binary variable which is a complete linear optimization model;
wherein the continuous variable
Figure 65095DEST_PATH_IMAGE055
The online total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units running in the CSP unit group at the time t j, satisfies the following conditions:
Figure 708566DEST_PATH_IMAGE056
；
wherein,S j the total capacity of the CSP unit group j is represented, namely the sum of the rated capacities of all CSP units in the group j is obtained according to the following formula:
Figure 981415DEST_PATH_IMAGE057
；
in the formula,
Figure 710337DEST_PATH_IMAGE058
representing the maximum output power of the CSP unit i in the j group;
continuous variable
Figure 211988DEST_PATH_IMAGE059
Representing the total starting capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units started in the group at the time t and a continuous variable
Figure 241124DEST_PATH_IMAGE060
Representing the shutdown total capacity of the CSP unit group j at the time t, namely the sum of the rated capacities of the CSP units shutdown in the CSP unit group j at the time t;
continuous variable
Figure 153716DEST_PATH_IMAGE055
、
Figure 901092DEST_PATH_IMAGE059
And
Figure 439390DEST_PATH_IMAGE060
the relationship between them follows the formula:
Figure 323032DEST_PATH_IMAGE061
；
based on the continuous decision variables, the output power constraint of the CSP unit group is as follows:
Figure 406526DEST_PATH_IMAGE062
；
wherein,P j,min andP j,max respectively representing the minimum output power and the maximum output power of the CSP unit group j, and respectively obtaining the minimum output power and the maximum output power through the following formulas:
Figure 110040DEST_PATH_IMAGE063
；
Figure 327394DEST_PATH_IMAGE064
；
wherein,
Figure 691642DEST_PATH_IMAGE065
and
Figure 336250DEST_PATH_IMAGE066
respectively representing the ratios of the minimum output power and the maximum output power of the CSP unit group j to the on-line total capacity of the CSP unit group j, for a group of units with similar operating characteristics,
Figure 668005DEST_PATH_IMAGE067
and
Figure 423471DEST_PATH_IMAGE068
、
Figure 406340DEST_PATH_IMAGE069
and
Figure 956270DEST_PATH_IMAGE070
the difference between them is relatively small, so take
Figure 775321DEST_PATH_IMAGE071
=
Figure 68899DEST_PATH_IMAGE072
，
Figure 407739DEST_PATH_IMAGE073
=
Figure 394150DEST_PATH_IMAGE074
；
Based on the continuous decision variables, the climbing constraint of the CSP unit group is as follows:
Figure 700497DEST_PATH_IMAGE075
；
Figure 797766DEST_PATH_IMAGE076
；
wherein,
Figure 489648DEST_PATH_IMAGE077
and
Figure 381380DEST_PATH_IMAGE078
respectively representing the upward climbing rate and the downward climbing rate;
and further adding a constraint condition to the output power of the CSP unit group j at the time t, wherein the constraint condition is as follows:
Figure 34078DEST_PATH_IMAGE079
；
based on the continuous decision variables, the minimum online time constraint and the minimum offline time constraint of the CSP unit group are as follows:
Figure 810405DEST_PATH_IMAGE080
；
Figure 497738DEST_PATH_IMAGE081
；
Figure 452050DEST_PATH_IMAGE082
；
Figure 592044DEST_PATH_IMAGE083
；
the instantaneous thermal power balance constraints of a CSP unit group are as follows:
Figure 906482DEST_PATH_IMAGE084
；
wherein,
Figure 448322DEST_PATH_IMAGE085
the efficiency coefficient of the power module in the CSP unit is shown,
Figure 72070DEST_PATH_IMAGE086
representing the charging power of the CSP unit at the time t,
Figure 699360DEST_PATH_IMAGE087
showing the discharge power of the CSP unit at the time t,
Figure 817489DEST_PATH_IMAGE088
the available solar thermal power of the CSP unit at the moment t is represented;
because the decision variables in the charge-discharge balance constraint of the heat storage module of the CSP unit and the charge state constraint of the heat storage module in the traditional optimization model are continuous variables, the charge-discharge balance constraint and the charge state constraint of the heat storage module in the CSP unit of the low-carbon CSP system planning and operation collaborative optimization model still adopt the following formulas:
and (3) charge-discharge balance constraint of the heat storage module in the CSP unit:
Figure 213835DEST_PATH_IMAGE089
；
wherein,
Figure 509950DEST_PATH_IMAGE090
the efficiency coefficient of the heat storage module in the CSP unit is shown,E t showing the charge state of the heat storage module in the CSP unit at the time t,E t-1 indicating the heat storage module in CSP unitt-state of charge at time 1;
and (3) charge state constraint of a heat storage module in the CSP unit:
Figure 358957DEST_PATH_IMAGE091
；
wherein,E minandE maxrespectively representing CSP unitsAnd the lower limit value and the upper limit value of the state of charge of the intermediate heat storage module.
2. Low carbon CSP system collaborative optimization device based on cluster learning, its characterized in that includes:
the group division module is used for carrying out cluster grouping on the CSP units in the low-carbon CSP system to obtain a plurality of CSP unit groups;
the model building module is used for building output power constraint, climbing constraint, minimum on-line time constraint, minimum off-line time constraint and instantaneous thermal power balance constraint of the CSP unit group through three continuous variables representing the on-line total capacity, the starting total capacity and the stopping total capacity of the CSP unit group, and further building a low-carbon CSP system planning and operation collaborative optimization model;
the parameter acquisition module is used for acquiring the rated capacity of each unit in the low-carbon CSP system;
the capacity allocation scheme acquisition module is used for acquiring the capacity allocation scheme of each unit group according to the rated capacity of each unit and the constructed low-carbon CSP system planning and operation collaborative optimization model;
the low-carbon CSP system planning and operation collaborative optimization model is constructed by taking the minimum total cost of the system as a target, wherein the total cost comprises investment cost, fixed operation and maintenance cost and variable operation cost, and the target function is as follows:
Figure 280777DEST_PATH_IMAGE092
；
wherein,
Figure 797208DEST_PATH_IMAGE093
；
Figure 762759DEST_PATH_IMAGE094
；
Figure 833484DEST_PATH_IMAGE095
；
in the formula,Crepresents the total cost;C i which represents the cost of the investment,a th-m 、a w 、a s 、a c-j respectively represents the investment costs of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set,I th-m 、I w 、I s 、I c-j respectively representing the newly increased capacity of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set;C f the fixed operation and maintenance cost is shown,f th-m 、f w 、f s 、f c-j respectively represents the fixed operation and maintenance costs of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set,
Figure 418049DEST_PATH_IMAGE096
、
Figure 664353DEST_PATH_IMAGE097
、
Figure 676172DEST_PATH_IMAGE098
、
Figure 391449DEST_PATH_IMAGE099
respectively representing the total capacity of the thermal generator set, the wind generator set, the solar photovoltaic generator set and the CSP set;C v represents a variable operating cost, consisting of a start-up cost and a fuel cost, wherein c th-m And SD th-m Respectively representing the fuel cost and the starting cost of the mth type thermal generator set,
Figure 514126DEST_PATH_IMAGE016
expressing the m-th class of fireThe output power of the generator set at the time t,
Figure 880516DEST_PATH_IMAGE100
representing the starting capacity of the mth type thermal generator set at the time t,Mthe category of the thermal generator set is represented,Jthe class of the CSP unit is represented,Twhich represents a time period of time,
Figure 797657DEST_PATH_IMAGE101
represents a time interval;
the low-carbon CSP system planning and operation collaborative optimization model must meet the power balance constraint of the power system, namely the sum of the generated energy of the thermal generator set, the wind generator set, the solar photovoltaic generator set and the CSP unit should always be equal to the sum of the power demand of the region and the power transmitted outside the region, as follows:
Figure 233186DEST_PATH_IMAGE102
；
wherein,D t indicating the power demand of the region at t hours,
Figure 425133DEST_PATH_IMAGE103
indicating the power value that the region transmits outside the region in t hours,
Figure 646030DEST_PATH_IMAGE104
respectively representing the output power of a thermal generator set, a wind generator set, a solar photovoltaic generator set and a CSP set group in t hours;
for a thermal power generating unit, the hourly output power of the thermal power generating unit should not exceed the total installed capacity, as follows:
Figure 734072DEST_PATH_IMAGE105
；
wherein,
Figure 158362DEST_PATH_IMAGE016
and
Figure 888421DEST_PATH_IMAGE106
respectively representing the output power and the online capacity of the mth type thermal generator set in t hours,
Figure 963824DEST_PATH_IMAGE107
and
Figure 488346DEST_PATH_IMAGE108
respectively representing the total installed capacity, the existing capacity and the newly added capacity of the mth type of thermal generator set;
for a wind generating set, a solar photovoltaic generating set and a CSP set, the hourly output power is limited by the existing capacity, the newly-built capacity and the continuously-changed capacity factor, which are respectively as follows:
Figure 898468DEST_PATH_IMAGE109
；
Figure 166638DEST_PATH_IMAGE110
；
Figure 221182DEST_PATH_IMAGE111
；
wherein,
Figure 791972DEST_PATH_IMAGE112
respectively represents the output power of the wind generating set, the solar photovoltaic generating set and the CSP set group in t hours,
Figure 564755DEST_PATH_IMAGE113
respectively represent a wind generating set, a solar photovoltaic generating set and a CSThe hourly capacity factor of the P unit group in t hours,
Figure 250997DEST_PATH_IMAGE114
respectively represents the total capacity of the wind generating set, the solar photovoltaic generating set and the CSP set group,
Figure 160047DEST_PATH_IMAGE115
respectively represents the existing capacities of a wind generating set, a solar photovoltaic generating set and a CSP set group,
Figure 167317DEST_PATH_IMAGE116
respectively representing the newly added capacities of the wind generating set, the solar photovoltaic generating set and the CSP set group;
considering the randomness and the volatility of the wind-solar power generation, the standby constraint for constructing the system is as follows:
Figure 896239DEST_PATH_IMAGE117
；
wherein,
Figure 896425DEST_PATH_IMAGE118
represents the maximum output ratio of the mth type thermal generator set at the time t,
Figure 659981DEST_PATH_IMAGE119
represents a backup requirement related to the power demand at time t, which is equal to the installed capacity of the largest thermal generator set in the area or the expected load deviation due to prediction error,
Figure 838153DEST_PATH_IMAGE120
、
Figure 319950DEST_PATH_IMAGE121
respectively representing the output power prediction errors of the wind generating set, the solar photovoltaic generating set and the CSP setA difference;
the renewable energy investment portfolio standard requires that a power supplier must have a lowest renewable energy proportion, and adopts the RPS standard to realize low-carbon policy constraint:
Figure 359712DEST_PATH_IMAGE122
；
wherein,rrepresenting the proportion of the renewable energy power generation in the total power generation;
integer variables respectively representing the on-line total capacity, the starting total capacity and the shutdown total capacity of the CSP unit group are introduced into a traditional optimization model
Figure 243354DEST_PATH_IMAGE123
、
Figure 451482DEST_PATH_IMAGE124
、
Figure 561520DEST_PATH_IMAGE125
To simulate the group behavior of all units in the group, as follows:
Figure 903509DEST_PATH_IMAGE126
；
Figure 641658DEST_PATH_IMAGE127
；
Figure 161632DEST_PATH_IMAGE128
；
wherein,
Figure 352442DEST_PATH_IMAGE043
indicating the total online capacity of the CSP unit group j at time t, i.e. the nominal capacity of the CSP units operating in the group at time tThe sum of the capacities is,I i,t indicating the switching state of the CSP unit and, when the unit is operating,I i,t =1, and otherwise,I i,t =0；
Figure 107908DEST_PATH_IMAGE046
the starting total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units started in the CSP unit group at the time t,u i,t indicating the start-up status of the CSP unit and, when the unit is started,u i,t =1, and otherwise,u i,t =0；
Figure 592241DEST_PATH_IMAGE129
representing the shutdown total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units shutdown in the CSP unit group j at the time t,d i,t indicating the shutdown state of the CSP unit, when the unit is shut down,d i,t =1, and otherwise,d i,t =0；P i,n represents the rated capacity of the CSP unit i,Ithe number of groups in the group; when passing through
Figure 142172DEST_PATH_IMAGE043
、
Figure 961223DEST_PATH_IMAGE044
、
Figure 254801DEST_PATH_IMAGE130
In constructing the various constraints of a CSP fleet,
Figure 92176DEST_PATH_IMAGE043
、
Figure 78586DEST_PATH_IMAGE044
、
Figure 119355DEST_PATH_IMAGE130
all indirect control variables have integer characteristics, and all discrete values are taken, so that the integer variables still exist in various constraints of the constructed CSP unit group; in the introduction of
Figure 216624DEST_PATH_IMAGE043
、
Figure 675549DEST_PATH_IMAGE046
、
Figure 832861DEST_PATH_IMAGE129
Based on the three integer variables, by continuous variables
Figure 219980DEST_PATH_IMAGE131
、
Figure 261885DEST_PATH_IMAGE132
And
Figure 683639DEST_PATH_IMAGE060
to approximate respectively to integer variables
Figure 136486DEST_PATH_IMAGE043
、
Figure 276481DEST_PATH_IMAGE044
And
Figure 590919DEST_PATH_IMAGE130
and further using continuous variables
Figure 132758DEST_PATH_IMAGE131
、
Figure 257971DEST_PATH_IMAGE133
And
Figure 619683DEST_PATH_IMAGE060
to replace integer variables
Figure 737811DEST_PATH_IMAGE043
、
Figure 134158DEST_PATH_IMAGE044
And
Figure 928807DEST_PATH_IMAGE130
and constructing each constraint of the final CSP unit group, so that all variables in each constraint of the finally constructed CSP unit group are continuous and do not contain 3 x representing the on-off state of each unit in the traditional optimization modelIA binary variable which is a complete linear optimization model;
wherein the continuous variable
Figure 43394DEST_PATH_IMAGE055
The online total capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units running in the CSP unit group at the time t j, satisfies the following conditions:
Figure 230793DEST_PATH_IMAGE134
；
wherein,S j the total capacity of the CSP unit group j is represented, namely the sum of the rated capacities of all CSP units in the group j is obtained according to the following formula:
Figure 216066DEST_PATH_IMAGE135
；
in the formula,
Figure 683082DEST_PATH_IMAGE136
representing the maximum output power of the CSP unit i in the j group;
continuous variable
Figure 284964DEST_PATH_IMAGE059
Representing the total starting capacity of the CSP unit group j at the time t, namely the sum of rated capacities of the CSP units started in the group at the time t and a continuous variable
Figure 744896DEST_PATH_IMAGE060
Representing the shutdown total capacity of the CSP unit group j at the time t, namely the sum of the rated capacities of the CSP units shutdown in the CSP unit group j at the time t;
continuous variable
Figure 709310DEST_PATH_IMAGE055
、
Figure 721128DEST_PATH_IMAGE059
And
Figure 436405DEST_PATH_IMAGE060
the relationship between them follows the formula:
Figure 559082DEST_PATH_IMAGE137
；
based on the continuous decision variables, the output power constraint of the CSP unit group is as follows:
Figure 518948DEST_PATH_IMAGE138
；
wherein,P j,min andP j,max respectively representing the minimum output power and the maximum output power of the CSP unit group j, and respectively obtaining the minimum output power and the maximum output power through the following formulas:
Figure 842613DEST_PATH_IMAGE139
；
Figure 153508DEST_PATH_IMAGE140
；
wherein,
Figure 204510DEST_PATH_IMAGE071
and
Figure 550041DEST_PATH_IMAGE073
respectively representing the ratios of the minimum output power and the maximum output power of the CSP unit group j to the on-line total capacity of the CSP unit group j, for a group of units with similar operating characteristics,
Figure 779028DEST_PATH_IMAGE072
and
Figure 311640DEST_PATH_IMAGE068
、
Figure 667798DEST_PATH_IMAGE074
and
Figure 867835DEST_PATH_IMAGE070
the difference between them is relatively small, so take
Figure 267723DEST_PATH_IMAGE071
=
Figure 818790DEST_PATH_IMAGE072
，
Figure 211594DEST_PATH_IMAGE073
=
Figure 559DEST_PATH_IMAGE074
；
Based on the continuous decision variables, the climbing constraint of the CSP unit group is as follows:
Figure 836928DEST_PATH_IMAGE141
；
Figure 609712DEST_PATH_IMAGE142
；
wherein,
Figure 53811DEST_PATH_IMAGE143
and
Figure 228440DEST_PATH_IMAGE144
respectively representing the upward climbing rate and the downward climbing rate;
and further adding a constraint condition to the output power of the CSP unit group j at the time t, wherein the constraint condition is as follows:
Figure 829186DEST_PATH_IMAGE145
；
based on the continuous decision variables, the minimum online time constraint and the minimum offline time constraint of the CSP unit group are as follows:
Figure 964632DEST_PATH_IMAGE146
；
Figure 574605DEST_PATH_IMAGE147
；
Figure 728375DEST_PATH_IMAGE148
；
Figure 31180DEST_PATH_IMAGE149
；
the instantaneous thermal power balance constraints of a CSP unit group are as follows:
Figure 388343DEST_PATH_IMAGE150
；
wherein,
Figure 536428DEST_PATH_IMAGE151
the efficiency coefficient of the power module in the CSP unit is shown,
Figure 46169DEST_PATH_IMAGE152
representing the charging power of the CSP unit at the time t,
Figure 519876DEST_PATH_IMAGE153
showing the discharge power of the CSP unit at the time t,
Figure 364335DEST_PATH_IMAGE154
the available solar thermal power of the CSP unit at the moment t is represented;
because the decision variables in the charge-discharge balance constraint of the heat storage module of the CSP unit and the charge state constraint of the heat storage module in the traditional optimization model are continuous variables, the charge-discharge balance constraint and the charge state constraint of the heat storage module in the CSP unit of the low-carbon CSP system planning and operation collaborative optimization model still adopt the following formulas:
and (3) charge-discharge balance constraint of the heat storage module in the CSP unit:
Figure 316110DEST_PATH_IMAGE155
；
wherein,
Figure 444472DEST_PATH_IMAGE156
the efficiency coefficient of the heat storage module in the CSP unit is shown,E t showing the charge state of the heat storage module in the CSP unit at the time t,E t-1 indicating the heat storage module in CSP unitt-state of charge at time 1;
and (3) charge state constraint of a heat storage module in the CSP unit:
Figure 823501DEST_PATH_IMAGE157
；
wherein,E minandE maxand respectively representing the lower limit value and the upper limit value of the charge state of the heat storage module in the CSP unit.
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