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CN112215641A - Control method and system for intelligent building type virtual power plant to participate in energy frequency modulation - Google Patents

Control method and system for intelligent building type virtual power plant to participate in energy frequency modulationDownload PDF

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CN112215641A
CN112215641ACN202011077665.4ACN202011077665ACN112215641ACN 112215641 ACN112215641 ACN 112215641ACN 202011077665 ACN202011077665 ACN 202011077665ACN 112215641 ACN112215641 ACN 112215641A
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virtual power
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frequency modulation
energy
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杜洋
郭灵瑜
杨心刚
苏磊
刘琦
孙沛
梁伟朋
曹博源
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State Grid Shanghai Electric Power Co Ltd
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Abstract

The invention relates to a control method and a system for an intelligent building type virtual power plant to participate in energy frequency modulation, wherein the intelligent building type virtual power plant comprises photovoltaic and air conditioner load virtual energy storage, and the method comprises the following steps: establishing a random planning model with the minimum daily operation cost as a target; different scenes and probabilities of the day-ahead market price and the predicted photovoltaic output are obtained by adopting a time-aligned Markov chain; solving the stochastic programming model based on the day-ahead market price, different scenes and the probability thereof; and controlling the running state of the intelligent building type virtual power plant according to the solved result. Compared with the prior art, the invention has the advantages of high stability, high regulation and control flexibility and the like.

Description

Translated fromChinese
智能楼宇型虚拟电厂参与能量调频的控制方法及系统Control method and system for intelligent building-type virtual power plant to participate in energy frequency regulation

技术领域technical field

本发明涉及一种电网控制方法，尤其是涉及一种智能楼宇型虚拟电厂参与能 量调频的控制方法及控制系统。The invention relates to a power grid control method, in particular to a control method and a control system for an intelligent building type virtual power plant to participate in energy frequency regulation.

背景技术Background technique

在分布式可再生能源与负荷控制技术大力发展的背景下，虚拟电厂成为需求侧资源参与电力市场调节的主要媒介。虚拟电厂通过对分布式资源的有效聚合，有效 减轻电网调度负担，实现了电力系统的多方共赢。智能楼宇作为一种包含分布式电 源与可控负荷的特殊虚拟电厂，具有较高研究价值。目前针对虚拟电厂尤其是楼宇 型虚拟电厂同时参与能量-调频市场的研究还比较欠缺。Under the background of vigorous development of distributed renewable energy and load control technology, virtual power plants have become the main medium for demand-side resources to participate in power market regulation. Through the effective aggregation of distributed resources, virtual power plants can effectively reduce the burden of grid dispatching and achieve a win-win situation for the power system. As a special virtual power plant including distributed power and controllable load, intelligent building has high research value. At present, there is still a lack of research on the fact that virtual power plants, especially building-type virtual power plants, participate in the energy-frequency regulation market at the same time.

发明内容SUMMARY OF THE INVENTION

本发明的目的就是为了克服上述现有技术存在的缺陷而提供一种稳定性高、调控灵活性高的智能楼宇型虚拟电厂参与能量调频的控制方法及控制系统。The purpose of the present invention is to provide a control method and control system for participating in energy frequency regulation of an intelligent building-type virtual power plant with high stability and high regulation flexibility in order to overcome the above-mentioned defects in the prior art.

本发明的目的可以通过以下技术方案来实现：The object of the present invention can be realized through the following technical solutions:

一种智能楼宇型虚拟电厂参与能量调频的控制方法，所述智能楼宇型虚拟电厂包括光伏和空调负荷虚拟储能，该方法包括以下步骤：A control method for an intelligent building-type virtual power plant to participate in energy frequency regulation, wherein the intelligent building-type virtual power plant includes photovoltaic and air-conditioning load virtual energy storage, and the method includes the following steps:

建立以日运行成本最小为目标的随机规划模型；Establish a stochastic programming model with the goal of minimizing the daily operating cost;

采用时齐马尔可夫链获得日前市场价格与预测光伏出力的不同场景及其概率；Different scenarios and their probabilities of obtaining the market price a day ago and predicting the PV output by using the time-aligned Markov chain;

基于所述日前市场价格、不同场景及其概率对所述随机规划模型进行求解；solving the stochastic programming model based on the day-ahead market price, different scenarios and their probabilities;

以求解结果对智能楼宇型虚拟电厂的运行状态进行控制。The operation state of the intelligent building-type virtual power plant is controlled by the solution result.

进一步地，所述随机规划模型的目标函数为：Further, the objective function of the stochastic programming model is:

Figure BDA0002717804570000011
Figure BDA0002717804570000011

其中，I为随机规划选取的总场景数；ωi为场景i的概率；

Figure BDA0002717804570000012
为场景i下虚拟 电厂参与能量和调频市场的总交易成本；
Figure BDA0002717804570000013
为场景i下虚拟电厂未能响应市场调 度信号的惩罚成本；fPV和fVES分别为光伏和虚拟储能因参与虚拟电厂而产生的预期 调控成本。Among them, I is the total number of scenes selected by random planning; ωi is the probability of scene i;
Figure BDA0002717804570000012
is the total transaction cost of the virtual power plant participating in the energy and frequency regulation market under scenario i;
Figure BDA0002717804570000013
is the penalty cost of the virtual power plant failing to respond to the market dispatch signal in scenario i; fPV and fVES are the expected regulation costs of photovoltaic and virtual energy storage due to participating in the virtual power plant, respectively.

进一步地，所述场景i下虚拟电厂参与能量和调频市场的总交易成本

Figure BDA0002717804570000021
的计 算公式为：Further, the total transaction cost of the virtual power plant participating in the energy and frequency regulation market under the scenario i
Figure BDA0002717804570000021
The calculation formula is:

Figure BDA0002717804570000022
Figure BDA0002717804570000022

其中，

Figure BDA0002717804570000023
为场景i中能量市场在t时段的价格；
Figure BDA0002717804570000024
分别为场景i中t时段 上调频和下调频市场的容量价格；
Figure BDA0002717804570000025
为t时段虚拟电厂在能量市场的竞标量；
Figure BDA0002717804570000026
Figure BDA0002717804570000027
分别为t时段虚拟电厂在调频市场竞标的上、下调容量；
Figure BDA0002717804570000028
分别为场景i 中t时段虚拟电厂在上、下调频市场的中标概率。in,
Figure BDA0002717804570000023
is the price of the energy market in scenario i at time period t;
Figure BDA0002717804570000024
are the capacity prices of the frequency-up and frequency-down markets in the t period of scenario i, respectively;
Figure BDA0002717804570000025
is the bidding volume of virtual power plants in the energy market in period t;
Figure BDA0002717804570000026
Figure BDA0002717804570000027
are the capacity increase and decrease of the virtual power plant in the frequency regulation market bidding in the t period;
Figure BDA0002717804570000028
are the bidding probabilities of the virtual power plant in the up and down frequency market in the t period in scenario i, respectively.

进一步地，所述场景i下虚拟电厂未能响应市场调度信号的惩罚成本

Figure BDA0002717804570000029
的计 算公式为：Further, the penalty cost of the virtual power plant failing to respond to the market dispatch signal in the scenario i
Figure BDA0002717804570000029
The calculation formula is:

Figure BDA00027178045700000210
Figure BDA00027178045700000210

其中，

Figure BDA00027178045700000211
为虚拟电厂参与能量市场的预期响应惩罚成本，
Figure BDA00027178045700000212
为虚拟电厂参与 上调频市场的调用量不足惩罚成本，
Figure BDA00027178045700000213
为虚拟电厂参与下调频市场的预期惩罚成 本。in,
Figure BDA00027178045700000211
Penalty costs for the expected response of virtual power plants to participate in the energy market,
Figure BDA00027178045700000212
Penalize the cost for the insufficient call volume of the virtual power plant to participate in the frequency regulation market,
Figure BDA00027178045700000213
Expected penalty costs for virtual power plants to participate in the frequency reduction market.

进一步地，所述光伏因参与虚拟电厂而产生的预期调控成本fPV的计算公式为：Further, the calculation formula of the expected control costfPV of the photovoltaics due to participating in the virtual power plant is:

Figure BDA00027178045700000214
Figure BDA00027178045700000214

其中，

Figure BDA00027178045700000215
分别为光伏提供调频服务和供能的边际成本；
Figure BDA00027178045700000216
和
Figure BDA00027178045700000217
分别为虚拟电厂决策的光伏参与调频与能量市场的容量。in,
Figure BDA00027178045700000215
The marginal cost of providing frequency regulation services and energy supply for photovoltaics, respectively;
Figure BDA00027178045700000216
and
Figure BDA00027178045700000217
The PV participation frequency regulation and the capacity of the energy market, which are decided for the virtual power plant, respectively.

进一步地，所述虚拟储能因参与虚拟电厂而产生的预期调控成本fVES的计算公 式为：Further, the calculation formula of the expected regulation cost fVES generated by the virtual energy storage due to participating in the virtual power plant is:

Figure BDA00027178045700000218
Figure BDA00027178045700000218

其中，

Figure BDA00027178045700000219
为虚拟储能提供调频服务的边际成本；
Figure BDA00027178045700000220
和
Figure BDA00027178045700000221
为虚拟电厂决策 的光伏参与调频市场的容量。in,
Figure BDA00027178045700000219
The marginal cost of providing frequency regulation services for virtual energy storage;
Figure BDA00027178045700000220
and
Figure BDA00027178045700000221
The capacity of PV participating in the frequency regulation market for virtual power plant decisions.

进一步地，所述随机规划模型的约束条件包括光伏在能量市场和调频辅助服务市场的容量约束、空调负荷虚拟储能特性下可贡献的上下调频容量约束、虚拟电厂 的总调频容量约束、虚拟电厂参与能量市场的预期响应偏差约束和虚拟电厂参与调 频市场无法满足的最大调频容量偏差约束。Further, the constraints of the stochastic programming model include the capacity constraints of photovoltaics in the energy market and the frequency regulation auxiliary service market, the up and down frequency regulation capacity constraints that can be contributed under the virtual energy storage characteristics of air-conditioning loads, the total frequency regulation capacity constraints of virtual power plants, and virtual power plants. The expected response deviation constraint of participating in the energy market and the maximum frequency regulation capacity deviation constraint that the virtual power plant participates in the frequency regulation market cannot satisfy.

本发明还提供一种智能楼宇型虚拟电厂参与能量调频的控制系统，所述智能楼宇型虚拟电厂包括光伏和空调负荷虚拟储能，该系统包括：The present invention also provides a control system for an intelligent building-type virtual power plant to participate in energy frequency regulation. The intelligent building-type virtual power plant includes photovoltaic and air-conditioning load virtual energy storage, and the system includes:

模型构建模块，用于建立以日运行成本最小为目标的随机规划模型；The model building module is used to build a stochastic programming model with the goal of minimizing the daily operating cost;

数据准备模块，用于采用时齐马尔可夫链获得日前市场价格与预测光伏出力的不同场景及其概率；The data preparation module is used to obtain different scenarios and their probabilities of the market price and predicted photovoltaic output using the time-aligned Markov chain;

求解模块，用于基于所述日前市场价格、不同场景及其概率对所述随机规划模 型进行求解；a solving module, for solving the stochastic programming model based on the market price a day before, different scenarios and their probabilities;

控制模块，以求解结果对智能楼宇型虚拟电厂的运行状态进行控制。The control module controls the operation state of the intelligent building-type virtual power plant with the solution result.

本发明还提供一种计算机设备，包括：The present invention also provides a computer device, comprising:

处理器；processor;

存储处理器可执行指令的存储器；a memory that stores processor-executable instructions;

其中，所述处理器耦合于所述存储器，用于读取所述存储器存储的程序指令， 并作为响应，执行以上所述方法的步骤。The processor is coupled to the memory for reading program instructions stored in the memory, and in response, performing the steps of the above-described method.

本发明还提供一种计算机可读介质，其上存储有计算机程序，所述计算机程序 被处理器执行时实现以上所述方法的步骤。The present invention also provides a computer-readable medium on which a computer program is stored, the computer program implementing the steps of the above-described method when executed by a processor.

与现有技术相比，本发明具有如下有益效果：Compared with the prior art, the present invention has the following beneficial effects:

本发明将虚拟电厂同时参与能量-调频市场的容量控制，有效提高电力网络稳 定性，而且相比于单独参与能量市场具有明显的经济优势，而空调负荷虚拟储能的 参与为虚拟电厂提供了更多的调控灵活性。The present invention simultaneously participates in the capacity control of the energy-frequency regulation market by the virtual power plant, thereby effectively improving the stability of the power network, and has obvious economic advantages compared to participating in the energy market alone. A lot of control flexibility.

附图说明Description of drawings

图1为本发明方法的流程示意图；Fig. 1 is the schematic flow chart of the method of the present invention;

图2为虚拟电厂负荷基线与虚拟储能充放电容量图；Figure 2 is a diagram of the virtual power plant load baseline and the virtual energy storage charging and discharging capacity;

图3为虚拟电厂参与调频市场的各时段竞标容量图；Figure 3 shows the bidding capacity diagram of virtual power plants participating in the frequency regulation market in each time period;

图4为两种策略下虚拟电厂的能量市场竞标量对比图。Figure 4 is a comparison chart of the bidding volume in the energy market of virtual power plants under the two strategies.

具体实施方式Detailed ways

下面结合附图和具体实施例对本发明进行详细说明。本实施例以本发明技术方案为前提进行实施，给出了详细的实施方式和具体的操作过程，但本发明的保护范 围不限于下述的实施例。The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and provides a detailed implementation manner and a specific operation process, but the protection scope of the present invention is not limited to the following examples.

实施例1Example 1

本实施例提供一种智能楼宇型虚拟电厂参与能量调频的控制方法，所述智能楼宇型虚拟电厂包括光伏和空调负荷虚拟储能，如图1所示，该方法包括以下步骤：This embodiment provides a control method for an intelligent building-type virtual power plant to participate in energy frequency regulation. The intelligent building-type virtual power plant includes photovoltaic and air-conditioning load virtual energy storage. As shown in FIG. 1 , the method includes the following steps:

步骤1：建立以日运行成本最小为目标的随机规划模型。Step 1: Establish a stochastic programming model with the goal of minimizing the daily operating cost.

所述随机规划模型的目标函数为：The objective function of the stochastic programming model is:

Figure BDA0002717804570000041
Figure BDA0002717804570000041

其中，I为随机规划选取的总场景数；ωi为场景i的概率；

Figure BDA0002717804570000042
为场景i下虚拟 电厂参与能量和调频市场的总交易成本；
Figure BDA0002717804570000043
为场景i下虚拟电厂未能响应市场调 度信号的惩罚成本；fPV和fVES分别为光伏和虚拟储能因参与虚拟电厂而产生的预期 调控成本。各项成本的具体表述如下：Among them, I is the total number of scenes selected by random planning; ωi is the probability of scene i;
Figure BDA0002717804570000042
is the total transaction cost of the virtual power plant participating in the energy and frequency regulation market under scenario i;
Figure BDA0002717804570000043
is the penalty cost of the virtual power plant failing to respond to the market dispatch signal in scenario i; fPV and fVES are the expected regulation costs of photovoltaic and virtual energy storage due to participating in the virtual power plant, respectively. The specific expressions of each cost are as follows:

场景i下虚拟电厂参与能量和调频市场的总交易成本

Figure BDA0002717804570000044
的计算公式为：The total transaction cost of virtual power plants participating in the energy and frequency regulation market under scenario i
Figure BDA0002717804570000044
The calculation formula is:

Figure BDA0002717804570000045
Figure BDA0002717804570000045

其中，

Figure BDA0002717804570000046
为场景i中能量市场在t时段的价格；
Figure BDA0002717804570000047
分别为场景i中t时段 上调频和下调频市场的容量价格；
Figure BDA0002717804570000048
为t时段虚拟电厂在能量市场的竞标量；
Figure BDA0002717804570000049
Figure BDA00027178045700000410
分别为t时段虚拟电厂在调频市场竞标的上、下调容量；
Figure BDA00027178045700000411
分别为场景i 中t时段虚拟电厂在上、下调频市场的中标概率。in,
Figure BDA0002717804570000046
is the price of the energy market in scenario i at time period t;
Figure BDA0002717804570000047
are the capacity prices of the frequency-up and frequency-down markets in the t period of scenario i, respectively;
Figure BDA0002717804570000048
is the bidding volume of virtual power plants in the energy market in period t;
Figure BDA0002717804570000049
Figure BDA00027178045700000410
are the capacity increase and decrease of the virtual power plant in the frequency regulation market bidding in the t period;
Figure BDA00027178045700000411
are the bidding probabilities of the virtual power plant in the up and down frequency market in the t period in scenario i, respectively.

所述场景i下虚拟电厂未能响应市场调度信号的惩罚成本

Figure BDA00027178045700000412
的计算公式为：Penalty cost of the virtual power plant failing to respond to market dispatch signals in the scenario i
Figure BDA00027178045700000412
The calculation formula is:

Figure BDA00027178045700000413
Figure BDA00027178045700000413

其中，

Figure BDA00027178045700000414
为虚拟电厂参与能量市场的预期响应惩罚成本，
Figure BDA00027178045700000415
为虚拟电厂参与 上调频市场的调用量不足惩罚成本，
Figure BDA00027178045700000416
为虚拟电厂参与下调频市场的预期惩罚成 本。in,
Figure BDA00027178045700000414
Penalty costs for the expected response of virtual power plants to participate in the energy market,
Figure BDA00027178045700000415
Penalize the cost for the insufficient call volume of the virtual power plant to participate in the frequency regulation market,
Figure BDA00027178045700000416
Expected penalty costs for virtual power plants to participate in the frequency reduction market.

所述光伏因参与虚拟电厂而产生的预期调控成本fPV的计算公式为：The formula for calculating the expected regulation costfPV of the photovoltaics due to participating in the virtual power plant is:

Figure BDA00027178045700000417
Figure BDA00027178045700000417

其中，

Figure BDA00027178045700000418
分别为光伏提供调频服务和供能的边际成本；
Figure BDA00027178045700000419
和
Figure BDA00027178045700000420
分 别为虚拟电厂决策的光伏参与调频与能量市场的容量。in,
Figure BDA00027178045700000418
The marginal cost of providing frequency regulation services and energy supply for photovoltaics, respectively;
Figure BDA00027178045700000419
and
Figure BDA00027178045700000420
The PV participation frequency regulation and the capacity of the energy market, which are decided for the virtual power plant, respectively.

所述虚拟储能因参与虚拟电厂而产生的预期调控成本fVES的计算公式为：The calculation formula of the expected regulation cost fVES of the virtual energy storage due to participating in the virtual power plant is:

Figure BDA00027178045700000421
Figure BDA00027178045700000421

其中，

Figure BDA00027178045700000422
为虚拟储能提供调频服务的边际成本；
Figure BDA00027178045700000423
和
Figure BDA00027178045700000424
为虚拟电厂决策 的光伏参与调频市场的容量。in,
Figure BDA00027178045700000422
The marginal cost of providing frequency regulation services for virtual energy storage;
Figure BDA00027178045700000423
and
Figure BDA00027178045700000424
The capacity of PV participating in the frequency regulation market for virtual power plant decisions.

Figure BDA00027178045700000425
的计算公式中等式右边的三项惩罚成本具体获取公式如下：
Figure BDA00027178045700000425
The calculation formula of the three penalty costs on the right side of the formula is as follows:

Figure BDA00027178045700000426
Figure BDA00027178045700000426

Figure BDA0002717804570000051
Figure BDA0002717804570000051

Figure BDA0002717804570000052
Figure BDA0002717804570000052

式(6)中，

Figure BDA0002717804570000053
分别为实际需电量高于、低于能量市场中标量的惩罚单价； 式(7)中，
Figure BDA0002717804570000054
为场景i中上调频容量在t时段的调用率；
Figure BDA0002717804570000055
为实际调用容量 不满足时的惩罚价格均值(假设实际调用容量在中标量范围内均匀分布)；式(8) 中各符号含义与式(7)类似，分别对应上调频的各参数。In formula (6),
Figure BDA0002717804570000053
are the penalty unit price of the actual electricity demand higher than and lower than the scalar quantity in the energy market, respectively; In formula (7),
Figure BDA0002717804570000054
is the calling rate of the up-frequency capacity in the scenario i in the period t;
Figure BDA0002717804570000055
is the mean value of the penalty price when the actual calling capacity is not satisfied (assuming that the actual calling capacity is evenly distributed within the range of the mid-scalar); the meanings of the symbols in equation (8) are similar to those in equation (7), corresponding to the parameters of frequency up-regulation respectively.

随机规划模型的约束条件包括光伏在能量市场和调频辅助服务市场的容量约束、空调负荷虚拟储能特性下可贡献的上下调频容量约束、虚拟电厂的总调频容量 约束、虚拟电厂参与能量市场的预期响应偏差约束和虚拟电厂参与调频市场无法满 足的最大调频容量偏差约束。The constraints of the stochastic programming model include the capacity constraints of photovoltaics in the energy market and the frequency regulation auxiliary service market, the up and down frequency regulation capacity constraints that can be contributed under the virtual energy storage characteristics of air-conditioning loads, the total frequency regulation capacity constraints of virtual power plants, and the expectation of virtual power plants participating in the energy market. The response deviation constraint and the maximum frequency regulation capacity deviation constraint that the virtual power plant participates in the frequency regulation market cannot satisfy.

考虑光伏出力的不确定性，引入辅助决策变量

Figure BDA0002717804570000056
分别为光伏出力预测的上下限，则光伏在能量市场和调频辅助服务市场的竞标量约束如下：Considering the uncertainty of photovoltaic output, introducing auxiliary decision variables
Figure BDA0002717804570000056
are the upper and lower limits of PV output forecast, respectively, the constraints on the bidding volume of PV in the energy market and the frequency regulation auxiliary service market are as follows:

Figure BDA0002717804570000057
Figure BDA0002717804570000057

Figure BDA0002717804570000058
Figure BDA0002717804570000058

Figure BDA0002717804570000059
Figure BDA0002717804570000059

空调负荷虚拟储能特性下可贡献的上、下调频容量取值范围为：The range of up and down frequency capacity that can be contributed under the virtual energy storage characteristics of air-conditioning load is as follows:

Figure BDA00027178045700000510
Figure BDA00027178045700000510

Figure BDA00027178045700000511
Figure BDA00027178045700000511

其中，αVES为虚拟储能调频信号的响应概率。Among them, αVES is the response probability of the virtual energy storage frequency modulation signal.

虚拟电厂的总调频容量为屋顶光伏和空调负荷虚拟储能提供的调频容量之和：The total frequency regulation capacity of the virtual power plant is the sum of the frequency regulation capacity provided by the rooftop photovoltaic and air-conditioning load virtual energy storage:

Figure BDA00027178045700000512
Figure BDA00027178045700000512

Figure BDA00027178045700000513
Figure BDA00027178045700000513

考虑调频市场的调用率，虚拟电厂参与能量市场的预期响应偏差

Figure BDA00027178045700000514
和
Figure BDA00027178045700000515
在 优化模型中可线性化表示为：Considering the call rate of the frequency regulation market, the expected response deviation of virtual power plants participating in the energy market
Figure BDA00027178045700000514
and
Figure BDA00027178045700000515
In the optimization model, the linearizability is expressed as:

Figure BDA00027178045700000516
Figure BDA00027178045700000516

Figure BDA00027178045700000517
Figure BDA00027178045700000517

其中，Lt为t时段虚拟电厂的负荷基值；PPV,i,t为场景i中光伏在t时段的实际功率。Among them, Lt is the load base value of the virtual power plant in the t period; PPV,i,t is the actual power of the photovoltaic in the scene i in the t period.

虚拟电厂参与调频市场无法满足的最大调频容量偏差可表示为如下约束：The maximum frequency regulation capacity deviation that cannot be satisfied by the participation of the virtual power plant in the frequency regulation market can be expressed as the following constraints:

Figure BDA0002717804570000061
Figure BDA0002717804570000061

Figure BDA0002717804570000062
Figure BDA0002717804570000062

值得注意的是，由于负荷预测的精度高于光伏出力预测的精度，该方法将负荷 预测的误差纳入光伏预测误差，不再单独考量。It is worth noting that since the accuracy of load forecasting is higher than that of PV output forecasting, this method incorporates the error of load forecasting into the PV forecasting error and no longer considers it separately.

步骤2：采用时齐马尔可夫链(time-homogeneous Markov chain)获得日前市 场价格与预测光伏出力的不同场景及其概率。Step 2: Use a time-homogeneous Markov chain to obtain different scenarios and their probabilities of day-ahead market prices and predicted PV output.

步骤3：基于所述日前市场价格、不同场景及其概率对所述随机规划模型进行 求解。Step 3: Solve the stochastic programming model based on the day-ahead market price, different scenarios and their probabilities.

步骤4：以求解结果对智能楼宇型虚拟电厂的运行状态进行控制。Step 4: Control the operation state of the intelligent building-type virtual power plant with the solution result.

本实施例以某现代化商业区为例评估智能楼宇型虚拟电厂同时参与能量-调频市场竞标、实现能源控制的有效性。夏季某日商业区的负荷基线以及根据室外温度 及空调功率计算得到的各时段空调负荷虚拟储能充、放电容量如图2所示。虚拟储 能的单位调频容量成本为0.0015USD/kW，调频响应率为80％。屋顶光伏的安装容 量为4MW，发电边际成本为0.005USD/kWh，参与调频成本为0.002USD/kW。虚 拟电厂参与调频辅助服务市场的中标概率为0.6，调频容量最终被调用的概率为0.7。In this embodiment, a modern business district is taken as an example to evaluate the effectiveness of the intelligent building-type virtual power plant participating in the bidding in the energy-frequency regulation market at the same time and realizing energy control. Figure 2 shows the load baseline of the commercial area on a certain day in summer and the virtual energy storage charging and discharging capacity of the air-conditioning load in each period calculated according to the outdoor temperature and air-conditioning power. The unit frequency regulation capacity cost of virtual energy storage is 0.0015USD/kW, and the frequency regulation response rate is 80%. The installed capacity of rooftop photovoltaics is 4MW, the marginal cost of power generation is 0.005USD/kWh, and the cost of participating in frequency regulation is 0.002USD/kW. The probability of winning the bid for the virtual power plant to participate in the frequency regulation ancillary service market is 0.6, and the probability that the frequency regulation capacity is finally called is 0.7.

利用上述数据求解得到的智能楼宇型虚拟电厂参与上、下调频市场的竞标容量及其组成关系如图3所示。虚拟电厂同时参与能量-调频市场(Case 1)与只参与能 量市场(Case 2)时的能量市场竞标量对比由图4给出。Figure 3 shows the bidding capacity and composition relationship of intelligent building-type virtual power plants participating in the up- and down-frequency market by using the above data. Figure 4 shows the comparison of the bidding volume in the energy market when the virtual power plant simultaneously participates in the energy-frequency regulation market (Case 1) and when it only participates in the energy market (Case 2).

由图3可以看出，由于光伏出力和空调负荷在时间上的相关性，虚拟电厂参与 调频辅助服务市场的竞标时段主要集中在白天。同时，空调负荷虚拟储能提供的上、 下调频容量相差不大，而光伏在同一时段则会选择性的提供一种调频容量。此外， 光伏全天提供的下调频总容量明显比上调频总容量大，这主要是由于一般情况下光 伏提供下调频时因弃光而产生的机会成本比光伏提供上调频而无法获取能量市场 收益的机会成本小，即便调频市场在出清时会考虑因不能参与能量市场而造成的损 失。As can be seen from Figure 3, due to the time correlation between photovoltaic output and air-conditioning load, the bidding period for virtual power plants to participate in the frequency regulation auxiliary service market is mainly concentrated in the daytime. At the same time, there is little difference between the up- and down-frequency capacity provided by the virtual energy storage of the air-conditioning load, while the photovoltaic can selectively provide a frequency-modulation capacity at the same time period. In addition, the total capacity of frequency regulation provided by photovoltaics throughout the day is obviously larger than the total capacity of frequency regulation. This is mainly due to the fact that under normal circumstances, the opportunity cost caused by abandoning light when photovoltaics provide frequency regulation is higher than that of photovoltaics providing frequency regulation and cannot obtain energy market benefits. The opportunity cost is small, even if the FM market will take into account the loss caused by not being able to participate in the energy market when clearing.

从图4中容易得知，虚拟电厂采用同时参与能量、调频市场的竞标策略时，在 午间光伏出力较大时段会选择从能量市场购入高于实际需求的电量，以满足提供调 频容量的需要，说明在拥有相同资源的情况下虚拟电厂参与调频辅助服务市场的积 极性更高，从而可进一步扩大自身的经济效益。但如果虚拟电厂参与调频市场的实 际响应指标较差，导致其在调频市场中的容量中标率降低，则会出现完全不同的结 果。It is easy to know from Figure 4 that when the virtual power plant adopts the bidding strategy of participating in the energy and frequency regulation markets at the same time, it will choose to purchase electricity from the energy market that is higher than the actual demand during the noon period when the photovoltaic output is large to meet the needs of providing frequency regulation capacity. , indicating that the virtual power plant is more motivated to participate in the frequency regulation auxiliary service market under the condition of the same resources, which can further expand its own economic benefits. However, if the actual response indicators of virtual power plants participating in the frequency regulation market are poor, resulting in a lower rate of winning bids for their capacity in the frequency regulation market, completely different results will appear.

以上分析对比了两种情况下虚拟电厂参与能量市场时的不同行为。为了进一步说明智能楼宇型虚拟电厂在考虑空调负荷虚拟储能时同时参与能量、调频市场的策 略优势，本实施例对比了三种情况下虚拟电厂的期望运行成本，如表1所示。其中， Case 1即为本发明提出的方案，考虑空调负荷虚拟储能，并同时参与能量-调频市 场；Case 2中虚拟电厂也同时参与能量-调频市场，但不考虑空调负荷虚拟储能； Case 3中虚拟电厂只参与能量市场，且不考虑虚拟储能。The above analysis compares the different behaviors of virtual power plants when they participate in the energy market in two cases. In order to further illustrate the strategic advantages of smart building-type virtual power plants that simultaneously participate in energy and frequency regulation markets when considering the virtual energy storage of air-conditioning loads, this example compares the expected operating costs of virtual power plants in three cases, as shown in Table 1. Among them,Case 1 is the solution proposed by the present invention, which considers the air-conditioning load virtual energy storage and participates in the energy-frequency regulation market at the same time; the virtual power plant inCase 2 also participates in the energy-frequency regulation market at the same time, but does not consider the air-conditioning load virtual energy storage; Case In 3, the virtual power plant only participates in the energy market, and does not consider virtual energy storage.

表1不同市场策略下虚拟电厂运行成本比较Table 1 Comparison of operating costs of virtual power plants under different market strategies

CaseCase112233Expected cost/USDExpected cost/USD1514.621514.622026.742026.744085.014085.01

从表中可以看出，采用本发明提出的虚拟电厂优化控制方法得到的预期运行成本比虚拟电厂只参与能量市场时降低了约63％，说明虚拟电厂利用资源的可调控 性参与调频辅助服务市场具有显著的经济效益。同时，利用空调负荷的虚拟储能特 性提供调频容量可进一步扩大虚拟电厂参与调频市场的经济优势。It can be seen from the table that the expected operating cost obtained by adopting the virtual power plant optimization control method proposed by the present invention is reduced by about 63% compared with that when the virtual power plant only participates in the energy market. Has significant economic benefits. At the same time, using the virtual energy storage characteristics of air-conditioning loads to provide frequency regulation capacity can further expand the economic advantages of virtual power plants participating in the frequency regulation market.

实施例2Example 2

本实施例提供一种智能楼宇型虚拟电厂参与能量调频的控制系统，所述智能楼宇型虚拟电厂包括光伏和空调负荷虚拟储能，该系统包括：模型构建模块，用于建 立以日运行成本最小为目标的随机规划模型；数据准备模块，用于采用时齐马尔可 夫链获得日前市场价格与预测光伏出力的不同场景及其概率；求解模块，用于基于 所述日前市场价格、不同场景及其概率对所述随机规划模型进行求解；控制模块， 以求解结果对智能楼宇型虚拟电厂的运行状态进行控制。This embodiment provides a control system for an intelligent building-type virtual power plant to participate in energy frequency regulation. The intelligent building-type virtual power plant includes photovoltaic and air-conditioning load virtual energy storage. The stochastic programming model as the target; the data preparation module is used to obtain different scenarios and their probabilities of the day-ahead market price and predicted photovoltaic output by using the time-aligned Markov chain; The probability is used to solve the stochastic programming model; the control module controls the operation state of the intelligent building-type virtual power plant based on the solution result.

其余同实施例1。The rest are the same as in Example 1.

以上详细描述了本发明的较佳具体实施例。应当理解，本领域的普通技术人员 无需创造性劳动就可以根据本发明的构思作出诸多修改和变化。因此，凡本技术领 域中技术人员依本发明的构思在现有技术的基础上通过逻辑分析、推理或者有限的 实验可以得到的技术方案，皆应在由权利要求书所确定的保护范围内。The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make many modifications and changes according to the concept of the present invention without creative efforts. Therefore, any technical solutions that can be obtained by those skilled in the art through logical analysis, reasoning or limited experiments on the basis of the prior art according to the concept of the present invention shall be within the protection scope determined by the claims.

Claims (10)

1. A control method for an intelligent building type virtual power plant to participate in energy frequency modulation is characterized in that the intelligent building type virtual power plant comprises photovoltaic and air conditioner load virtual energy storage, and the method comprises the following steps:
establishing a random planning model with the minimum daily operation cost as a target;
different scenes and probabilities of the day-ahead market price and the predicted photovoltaic output are obtained by adopting a time-aligned Markov chain;
solving the stochastic programming model based on the day-ahead market price, different scenes and the probability thereof;
and controlling the running state of the intelligent building type virtual power plant according to the solved result.
2. The method for controlling intelligent building-type virtual power plant to participate in energy frequency modulation according to claim 1, wherein the objective function of the stochastic programming model is as follows:
Figure FDA0002717804560000011
wherein, I is the total scene number selected by random planning; omegaiProbability of scene i; f. ofimkParticipating in the total trading cost of the energy and frequency modulation market for the virtual power plant under the scene i; f. ofipenPunishment cost of the virtual power plant failing to respond to the market scheduling signal under the scene i; f. ofPVAnd fVESThe expected regulation and control costs of the photovoltaic and the virtual energy storage due to participation in the virtual power plant are respectively.
3. The method as claimed in claim 2, wherein the virtual power plant participates in energy frequency modulation under the scenario i, and the total transaction cost f of the virtual power plant participating in energy and frequency modulation market isimkThe calculation formula of (2) is as follows:
Figure FDA0002717804560000012
wherein,
Figure FDA0002717804560000013
the price of the energy market in the scene i in the time period t;
Figure FDA0002717804560000014
capacity prices of the frequency modulation market and the frequency modulation market at the time t in the scene i are respectively set; pteDelta T is the competitive bidding amount of the virtual power plant in the energy market at the time period T; ptru、PtrdRespectively competitive upper and lower capacity of the virtual power plant in the frequency modulation market in the t time period;
Figure FDA0002717804560000015
and respectively the bid winning probability of the virtual power plant in the upper and lower frequency modulation markets in the t period in the scene i.
4. The method as claimed in claim 2, wherein the penalty cost f of the virtual power plant failing to respond to the market scheduling signal under the scenario i isipenThe calculation formula of (2) is as follows:
fipen＝fipe+fipru+fiprd
wherein f isipePenalty cost, f, for virtual plant participation in expected response of energy marketipruPenalty cost, f, for virtual power plant participation in call volume shortage in frequency modulation marketiprdAnd (4) the expected penalty cost of the frequency-down market for the virtual power plant to participate in.
5. The method as claimed in claim 2, wherein the expected regulation cost f of the photovoltaic due to participation in the virtual power plantPVThe calculation formula of (2) is as follows:
Figure FDA0002717804560000021
wherein,
Figure FDA0002717804560000022
marginal costs of providing frequency modulation service and energy supply for the photovoltaic cells respectively;
Figure FDA0002717804560000023
and
Figure FDA0002717804560000024
and (4) capacity of photovoltaic participation frequency modulation and energy market decided for the virtual power plant respectively.
6. The method as claimed in claim 2, wherein the virtual energy storage has an expected regulation cost f due to participation in the virtual power plantVESThe calculation formula of (2) is as follows:
Figure FDA0002717804560000025
wherein,
Figure FDA0002717804560000026
marginal cost of providing frequency modulation service for virtual energy storage;
Figure FDA0002717804560000027
and
Figure FDA0002717804560000028
and (4) capacity of photovoltaic participating in frequency modulation market for virtual power plant decision.
7. The method as claimed in claim 1, wherein the constraint conditions of the stochastic programming model include capacity constraints of the energy market and the frequency modulation auxiliary service market, upper and lower frequency modulation capacity constraints that can be contributed by the photovoltaic in the virtual energy storage characteristic of the air conditioning load, total frequency modulation capacity constraints of the virtual power plant, expected response deviation constraints of the virtual power plant participating in the energy market, and maximum frequency modulation capacity deviation constraints that cannot be satisfied by the virtual power plant participating in the frequency modulation market.
8. The utility model provides a control system that intelligent building type virtual power plant participated in energy frequency modulation, a serial communication port, intelligent building type virtual power plant includes photovoltaic and the virtual energy storage of air conditioner load, and this system includes:
the model building module is used for building a random planning model with the minimum daily operation cost as a target;
the data preparation module is used for obtaining different scenes and probabilities of the day-ahead market price and the predicted photovoltaic output by adopting a time-aligned Markov chain;
the solving module is used for solving the stochastic programming model based on the day-ahead market price, different scenes and the probability of the scenes;
and the control module is used for controlling the running state of the intelligent building type virtual power plant according to the solving result.
9. A computer device, comprising:
a processor;
a memory storing processor-executable instructions;
wherein the processor is coupled to the memory for reading program instructions stored by the memory and, in response, performing the steps of the method of any of claims 1-7.
10. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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