WO2022193422A1 - 一种综合能源虚拟电厂多设备选址方法 - Google Patents
一种综合能源虚拟电厂多设备选址方法 Download PDFInfo
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- the invention relates to the field of virtual power plants, in particular to a multi-equipment site selection method for an integrated energy virtual power plant.
- the virtual power plant integrates distributed energy sources such as gas-fired units, intermittent renewable energy generating units, and controllable loads for unified management through advanced communication technology and software systems. Due to the inherently good properties of these distributed energy sources, virtual power plants can obtain economic or environmental benefits through rational dispatching of them by the power system. At the same time, in order to better realize the complementary coordination among energies in the comprehensive energy network and improve the energy utilization efficiency, the gas boilers, cogeneration equipment and refrigeration equipment in the virtual power plant couple the electric load, heating load and cooling load in the area. , which strengthens the connection between multiple energy sources, so that the virtual power plant gradually develops from a single electric power to an integrated energy virtual power plant.
- distributed energy sources such as gas-fired units, intermittent renewable energy generating units, and controllable loads for unified management through advanced communication technology and software systems. Due to the inherently good properties of these distributed energy sources, virtual power plants can obtain economic or environmental benefits through rational dispatching of them by the power system.
- the present invention proposes a multi-equipment site selection method for an integrated energy virtual power plant.
- a multi-equipment site selection method for an integrated energy virtual power plant comprising:
- Step 1 Construct a calculation method for calculating the entropy of the comprehensive energy flow distribution through the power flow distribution in the distribution network and the flow distribution in the heat distribution network to reflect the energy distribution balance in the energy network.
- Step 2 Under the condition that the capacity of each equipment is known, with the goal of maximizing the entropy index of the comprehensive energy flow distribution, a multi-equipment site selection optimization planning model of the comprehensive energy virtual power plant is established;
- Step 3 Determine the installation position of each device of the integrated energy virtual power plant in the energy network, and determine the operation status of each device.
- the multi-equipment site selection optimization planning model of the virtual power plant uses equipment site selection status and equipment operation status as decision variables.
- the calculation method is:
- E t represents the entropy of the comprehensive energy flow distribution of the distribution network and the heat distribution network at time t; Represents the energy flow distribution entropy in the distribution network at time t; Represents the energy flow distribution entropy in the heat distribution network at time t.
- Nl represents the total number of branches in the distribution network; l represents the branch serial number; ⁇ P l,t represents the active power transmitted on branch l at time t; Represents the active power transmitted in the entire distribution network at time t;
- Np represents the total number of pipelines in the heat distribution network
- p represents the branch serial number
- ⁇ m p,t represents the flow transmitted on the pipeline p at time t
- It represents the flow transmitted in the entire heat distribution network at time t.
- the typical daily load in the planning period is selected, and the final planning scheme is determined according to the state of the typical daily load.
- the multi-equipment site selection optimization planning model of the integrated energy virtual power plant includes equipment operation constraints, energy storage operation constraints, electric load demand constraints, heat load demand constraints, equipment location state constraints, equivalent load state constraints, and support constraints.
- the objective function of the multi-equipment site selection optimization planning model of the integrated energy virtual power plant described in the step 2 is:
- a computer-readable storage medium stores instructions, and when the instructions are executed, any one of the above-mentioned address selection methods is implemented.
- FIG. 1 is a flowchart of the site selection method of the present application.
- a multi-equipment site selection decision method for a comprehensive energy virtual power plant considering the comprehensive energy flow distribution entropy of the present invention includes the following steps:
- P ESS,t represents the charging and discharging power of the energy storage at time t, if the value is positive, it means charging, if the value is negative, it means discharging; Represents the upper limit of the charging and discharging power of the energy storage; SOC t is the storage capacity of the energy storage at time t, SOC min is the minimum value of the storage capacity of the energy storage, and SOC max is the maximum value of the storage capacity of the energy storage;
- P ele,t represents the purchased power at time t
- P i,t represents the electrical load power of node i of the distribution network at time t
- Ni is the total number of load nodes in the distribution network
- ⁇ represents the comprehensive loss rate of the distribution network
- H heat,t represents the power purchase at time t
- H CHP,t represents the heat output of the CHP unit at time t
- H j,t represents the thermal load power of node j of the heat distribution network at time t
- N j is the load in the heat distribution network
- the total number of nodes ⁇ represents the comprehensive loss rate of the distribution network
- ⁇ represents the electricity-heat proportional coefficient of the CHP unit
- ⁇ GT,i , ⁇ ESS,i , ⁇ GB,i , ⁇ CHP,n-ij are the installation position state variables of the four types of equipment, and they are all binary variables. Taking ⁇ GT,i as an example, if the value If it is 1, it means that the GT equipment is installed at the position of node i; it should be noted that n is the serial number of the distribution network-distribution heat network coupling node, and i and j in n-ij indicate that the coupling node is in the distribution network, Node serial number in the heat distribution network;
- H′ j,t H j,t - ⁇ GB,i H GB,t - ⁇ CHP,n-ij H CHP,t (20)
- P′ i,t represents the equivalent load of node i of the distribution network at time t after equipment installation is considered
- H′ j,t represents the equivalent load of node j of distribution heating network at time t after equipment installation is considered;
- P l is the maximum transmission capacity of line l; represents the power transmission distribution factor of the electrical load at the node i to the line l, which is a pre-approved value;
- m p is the maximum transmission flow of the pipeline p; Represents the heat load at node j to the flow transmission distribution factor of pipeline p, which is a pre-approved value;
- N l is the total number of distribution network lines
- N p is the total number of pipes in the heat distribution network
- step 2 Taking the optimization solution of the optimization model in step 2 as a reference, a decision-making scheme for multi-equipment location selection of an integrated energy virtual power plant is obtained.
- the present invention also provides a computer-readable storage medium storing computer instructions.
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Abstract
一种综合能源虚拟电厂多设备选址方法，属于虚拟电厂领域。一种综合能源虚拟电厂多设备选址方法，包括以下步骤：构建通过配电网中的潮流分布以及配热网中的流量分布计算综合能流分布熵的计算方法，以反映能源网络中能量分布均衡性。在各设备容量已知的条件下，以最大化综合能流分布熵指标为目标，建立综合能源虚拟电厂多设备选址优化规划模型；确定综合能源虚拟电厂的各设备在能源网络中的安装位置，同时确定各设备的运行状态。
Description
本发明涉及虚拟电厂领域，具体涉及一种综合能源虚拟电厂多设备选址方法。
虚拟电厂通过先进的通信技术和软件系统将燃气机组、间歇性可再生能源发电机组、可控负荷等分布式能源聚合在一起进行统一管理。由于这些分布式能源的固有优良性质，虚拟电厂可以通过电力系统对其的合理调度获得经济或环境收益。同时，为了更好地实现综合能源网络中能源间互补协调，提升能源利用效率，虚拟电厂中的燃气锅炉、热电联产设备、制冷设备将该区域内的电负荷、热负荷、冷负荷耦合起来，加强了多种能源之间的联络，从而使得虚拟电厂从单一的电能逐渐发展为综合能源虚拟电厂。
目前，针对虚拟电厂的研究内容，多集中于优化调度与交易竞价方面，而在虚拟电厂优化规划方面所做的工作相对较少。此外，现有的关于虚拟电厂优化规划的研究，涉及到设备选址问题大多以降低损耗为目标，即考虑的经济性问题。然而，虚拟电厂作为一种实际运行的物理系统，以单一经济性为目标的规划在一定程度上忽视了安全性的考虑。
现有的关于虚拟电厂优化规划的研究大多未考虑改善能源网络的运行状态，事实上，虚拟电厂存在于实际的能源网络中，整个能源网络中的能量分布越均匀，则代表系统越稳定。如何在虚拟电厂规划时考虑能源网络中的能量分布均衡性是虚拟电厂规划时一个值得思考的问题。
发明内容
针对现有技术的不足，本发明提出了一种综合能源虚拟电厂多设备选址方法。
本发明的目的可以通过以下技术方案实现：
一种综合能源虚拟电厂多设备选址方法，包括：
步骤1：构建通过配电网中的潮流分布以及配热网中的流量分布计算综合能流分布熵的计算方法，以反映能源网络中能量分布均衡性。
步骤2：在各设备容量已知的条件下，以最大化综合能流分布熵指标为目标，建立综合能源虚拟电厂多设备选址优化规划模型；
步骤3：确定综合能源虚拟电厂的各设备在能源网络中的安装位置，同时确定各设备的运行状态。
可选地，所述虚拟电厂多设备选址优化规划模型以设备选址状态、设备运行状态为决策变量。
可选地，所述计算方法为：
式中，E
t表示t时刻配电网、配热网综合能流分布熵；
表示t时刻配电网中的能流分布熵；
表示t时刻配热网中的能流分布熵。
式中，Nl表示配电网的总支路数量；l表示支路序号；ΔP
l,t表示t时刻支路l上传输的有功功率；
表示t时刻整个配电网中传输的有功功率；
式中，Np表示配热网的总管道数量；p表示支路序号；Δm
p,t表示t时刻管 道p上传输的流量；
表示t时刻整个配热网中传输的流量。
可选地，选取规划周期内典型日负荷，根据典型日负荷状态确定最终的规划方案。
可选地，所述综合能源虚拟电厂多设备选址优化规划模型包括设备运行约束、储能运行约束、电负荷需求约束、热负荷需求约束、设备选址状态约束、等效负荷状态约束、支路功率传输约束、管道流量传输约束、配电网功率传输总量约束中的一种或多种。
可选地，所述步骤2中所述的综合能源虚拟电厂多设备选址优化规划模型的目标函数为：
式中，
表示整个调度周期内综合能流分布熵的平均值。
一种计算机可读的存储介质，存储有指令，所述指令运行时实现上述的任一选址方法。
下面结合附图对本发明作进一步的说明。
图1为本申请的选址方法的流程图。
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其它实施例，都属于本发明保护的范围。
本发明的一种考虑综合能流分布熵的综合能源虚拟电厂多设备选址决策方法，包括以下步骤：
(1)针对能源网络中能量分布的均衡性问题，基于热力学领域反映系统无序和混乱状态的“熵”的概念，提出反映能源网络中能量分布均衡性的综合能流分布熵的指标计算方法。
(2)以改善能量分布的均衡性为目标，即最大化综合能流分布熵指标为目标，建立虚拟电厂多设备选址优化规划模型，该优化模型以设备选址状态、设备运行状态为决策变量，其目标函数为：
式中，
表示整个调度周期内综合能流分布熵的平均值；
其包含的约束条件为：
(21)设备运行约束：
(22)储能运行约束：
SOC
min≤SOC
t≤SOC
max (9)
其中，P
ESS,t表示t时刻储能的充放电功率，若该值为正，表示充电，若该值为负，表示放电；
表示储能的充放电功率上限；SOC
t为储能t时刻的存储容量，SOC
min为储能存储容量最小值，SOC
max为储能存储容量最大值；
(23)电负荷需求约束：
其中，P
ele,t表示t时刻购电功率；P
i,t表示t时刻配电网节点i的电负荷功率；N
i为配电网内负荷节点总数；α表示配电网综合损耗率；
(24)热负荷需求约束：
H
CHP,t＝ηP
CHP,t (12)
其中，H
heat,t表示t时刻购电功率；H
CHP,t表示t时刻CHP机组的热出力；H
j,t表示t时刻配热网节点j的热负荷功率；N
j为配热网内负荷节点总数；β表示配电网综合损耗率；η表示CHP机组的电-热比例系数；
(25)设备选址状态约束：
其中，γ
GT,i、γ
ESS,i、γ
GB,i、γ
CHP,n-ij分别四种设备的安装位置状态变量，且均为二进制变量，以γ
GT,i为例，若该值为1，则代表GT设备安装在节点i的位置上；需要注意的是，n为配电网-配热网耦合节点的序号，n-ij中的i、j表示耦合节点在配电网、配热网中的节点序号；
(25)等效负荷状态约束：
对于独立配电网节点i有：
P′
i,t＝P
i,t-γ
GT,iP
WT,t-γ
ESS,iP
ESS,t (17)
对于耦合节点i有：
P′
i,t＝P
i,t-γ
GT,iP
WT,t-γ
ESS,iP
ESS,t-γ
CHP,n-ijP
CHP,t (18)
对于独立配热网节点j有：
H′
j,t＝H
j,t-γ
GB,iH
GB,t (19)
对于耦合节点j有：
H′
j,t＝H
j,t-γ
GB,iH
GB,t-γ
CHP,n-ijH
CHP,t (20)
其中，P′
i,t表示考虑设备安装后t时刻配电网节点i的等效负荷；H′
j,t表示考虑设备安装后t时刻配热网节点j的等效负荷；
(26)支路功率传输约束：
其中，P
l为线路l的最大传输容量；
表示节点i处的电负荷对线路l的功率传输分布因子，为事先核定值；
(27)管道流量传输约束：
其中，m
p为管道p的最大传输流量；
表示节点j处的热负荷对管道p的流量传输分布因子，为事先核定值；
(28)配电网功率传输总量约束：
其中，N
l为配电网线路总数；
(29)配热网流量传输总量约束：
其中，N
p为配热网管道总数；
(3)以步骤2中的优化模型的优化解为参考，得到综合能源虚拟电厂多设备选址决策方案。
另外，本发明还提出了一种计算机可读的存储介质，存储有计算机指令。
综上所述，本发明的上述示例中，首先基于热力学领域反映系统无序和混乱状态的“熵”的概念，提出反映能源网络中能量分布均衡性的综合能流分布熵的指标计算方法，通过配电网中的潮流分布以及配热网中的流量分布计算综合能流分布熵。其次，在各设备容量已知的条件下，以最大化综合能流分布熵指标为目标，建立虚拟电厂多设备选址优化规划模型，从而确定综合能源虚拟电厂多设备选址优化规划模型以设备选址状态、设备运行状态。
以上显示和描述了本发明的基本原理、主要特征和本发明的优点。本行业的技术人员应该了解，本发明不受上述实施例的限制，上述实施例和说明书中描述的只是说明本发明的原理，在不脱离本发明精神和范围的前提下，本发明还会有各种变化和改进，这些变化和改进都落入要求保护的本发明范围内。
Claims (7)
- 一种综合能源虚拟电厂多设备选址方法，其特征在于，包括：步骤1：构建通过配电网中的潮流分布以及配热网中的流量分布计算综合能流分布熵的计算方法，以反映能源网络中能量分布均衡性；步骤2：在各设备容量已知的条件下，以最大化综合能流分布熵指标为目标，建立综合能源虚拟电厂多设备选址优化规划模型；步骤3：确定综合能源虚拟电厂的各设备在能源网络中的安装位置，同时确定各设备的运行状态。
- 根据权利要求1所述的综合能源虚拟电厂多设备选址方法，其特征在于，所述综合能源虚拟电厂多设备选址优化规划模型以设备选址状态、设备运行状态为决策变量。
- 根据权利要求1所述的综合能源虚拟电厂多设备选址方法，其特征在于，所述计算方法为：
式中，E t表示t时刻配电网、配热网综合能流分布熵；表示t时刻配电网中的能流分布熵；
表示t时刻配热网中的能流分布熵。
式中，Nl表示配电网的总支路数量；l表示支路序号；ΔP l,t表示t时刻支路l上传输的有功功率；表示t时刻整个配电网中传输的有功功率；
式中，Np表示配热网的总管道数量；p表示支路序号；Δm p,t表示t时刻管道p上传输的流量；表示t时刻整个配热网中传输的流量。
- 根据权利要求1所述的综合能源虚拟电厂多设备选址方法，其特征在于， 选取规划周期内典型日负荷，根据典型日负荷状态确定最终的规划方案。
- 根据权利要求1所述的综合能源虚拟电厂多设备选址方法，其特征在于，所述综合能源虚拟电厂多设备选址优化规划模型包括设备运行约束、储能运行约束、电负荷需求约束、热负荷需求约束、设备选址状态约束、等效负荷状态约束、支路功率传输约束、管道流量传输约束、配电网功率传输总量约束中的一种或多种。
- 根据权利要求1所述的综合能源虚拟电厂多设备选址方法，其特征在于，所述步骤2中所述的综合能源虚拟电厂多设备选址优化规划模型模型的目标函数为：
式中，表示整个调度周期内综合能流分布熵的平均值。
- 一种计算机可读的存储介质，存储有指令，其特征在于，所述指令运行时实现权利要求1～6中任一所述的选址方法。
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