WO2024060344A1 - 数据-物理融合驱动的柔性配电系统自适应电压控制方法 - Google Patents
数据-物理融合驱动的柔性配电系统自适应电压控制方法 Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Definitions
- the invention relates to an adaptive voltage control method for a flexible power distribution system.
- it relates to an adaptive voltage control method for flexible distribution systems driven by data-physical fusion.
- data-driven optimization control methods face two types of problems: 1) The data model generated by the data-driven method has problems with unclear physical meaning and poor interpretability. When the pseudo-Jacobian matrix or the initial value of the controlled strategy is unreasonable, below, it will affect the rationality of the results and have an impact on the flexible power distribution system. 2) There is uneven distribution of measurement and resources in actual flexible power distribution systems, making it difficult to establish data-driven models in some areas due to lack of measurement data, or insufficient control capabilities due to limited control resources in areas with complete measurement. These two types of problems make the practical application of data-driven control methods challenging.
- the optimal control algorithm for flexible distribution systems based on physical models establishes models based on prior information and does not rely on system operating data. It has the advantages of globality and interpretability.
- data-physical fusion-driven methods can be considered to build fusion-driven models to overcome various problems faced by data-driven models and obtain better application results.
- the respective advantages of data and physical models can be combined to adopt a data-physical fusion-driven flexible distribution system voltage control model to adapt to the operational optimization needs of flexible distribution systems in different operating scenarios.
- the technical problem to be solved by the present invention is to overcome the shortcomings of the existing technology and provide a data-physical fusion driver that can obtain a distributed power supply operation control strategy in the absence of accurate parameters and uneven spatial distribution of measurement devices.
- the technical solution adopted by the present invention is: an adaptive voltage control method for a flexible power distribution system driven by data-physical fusion, which includes the following steps:
- the selected flexible power distribution system is divided into a complete measurement area r and an incomplete measurement area s, and input basic parameter information, including: line parameter information, load, distribution
- step 2) Initialize the physical guidance coefficient ⁇ [t 0 ], the boundary information ⁇ r [t 0 ] of the complete measurement area r, and the boundary active power and reactive power requirements and Boundary information ⁇ s [t 0 ] of the incomplete measurement area s, and boundary active and reactive power requirements and Based on the basic parameter information input in step 1), calculate the initial value of the pseudo-Jacobian matrix of the complete measurement area r based on the physical model Initial value of distributed power supply operation strategy x p [t 0 ];
- the incomplete area s measuring time t- ⁇ t receives the boundary information ⁇ r [t- ⁇ t] of the adjacent complete measurement area r, as well as the boundary active power and reactive power requirements. and Based on the basic parameter information described above, a physical model-driven voltage control model for the incomplete measurement area s is established, including: taking the minimum node voltage deviation in this area, the minimum boundary information deviation in adjacent areas, and the minimum boundary power demand deviation as the objective function, Consider flexible distribution system operation constraints and distributed power supply capacity constraints;
- the complete measurement area r obtains the voltage measurement information of each measurement node, and receives the boundary information ⁇ s [t- ⁇ t] of the adjacent incomplete measurement area s, as well as the boundary active and reactive power requirements and Update the physical guidance coefficient ⁇ [t], and establish a data-driven adaptive voltage control model for the complete measurement region, including: minimizing the deviation of the measured node voltage in this region, minimizing the deviation of the boundary information of adjacent regions, and minimizing the deviation of the boundary power demand.
- the objective function considers distributed power supply capacity constraints;
- step 6) Solve the data-driven adaptive voltage control model for the complete measurement area r described in step 5) to obtain the distributed power supply operation control strategy in the complete measurement area r and issue it for execution.
- the boundary information ⁇ r [t], and boundary active and reactive power requirements Transmit to incomplete measurement area s;
- the adaptive voltage control method of the flexible power distribution system driven by data-physical fusion of the present invention comprehensively considers the unknowability of the line parameters of the flexible power distribution system, the uncertainty of the output of the distributed power supply, and the uneven spatial distribution of the measuring devices.
- This invention considers using a data-physical fusion drive method to build a fusion drive model, effectively improving the rationality and interpretability of data-driven control effects, mitigating the impact on the power distribution system caused by excessive changes in control strategies, and achieving inter-regional objective functions Mutual aid, fully utilizing the voltage regulation potential of distributed power converters, effectively improving the voltage over-limit situation of the active power distribution system, completing the complementary support of regulatory resources between regions, and improving the flexible and efficient operation level of the active power distribution system. .
- Figure 1 is a flow chart of the adaptive voltage control method of a flexible power distribution system driven by data-physical fusion of the present invention
- Figure 2 is the topology structure of the flexible power distribution system used in the present invention.
- Figure 3 is the prediction curve of distributed power output and load changes
- Figure 4 is the voltage comparison at node 18
- Figure 5 is the reactive power output strategy of the distributed power supply at node 18;
- Figure 6 shows the 24-hour voltage control effect of Scenario 1 and Scenario 2;
- Figure 7 shows the 24-hour voltage control effect of scenario two and scenario three.
- the adaptive voltage control method of flexible power distribution system driven by data-physical fusion of the present invention includes the following steps:
- the selected flexible power distribution system is divided into a complete measurement area r and an incomplete measurement area s, and input basic parameter information, including: line parameter information, load, distribution
- step 2) Initialize the physical guidance coefficient ⁇ [t 0 ], the boundary information ⁇ r [t 0 ] of the complete measurement area r, and the boundary active power and reactive power requirements and Boundary information ⁇ s [t 0 ] of the incomplete measurement area s, and boundary active and reactive power requirements and Based on the basic parameter information input in step 1), calculate the initial value of the pseudo-Jacobian matrix of the complete measurement area r based on the physical model Initial value of distributed power supply operation strategy x p [t 0 ];
- x p [t 0 ] represents the initial value of the controlled equipment strategy of the flexible distribution system.
- x p represents the control variable
- y represents the power flow variable matrix
- g represents the objective function
- m(x p , y) represents the inequality constraints in the flexible distribution system operation constraints, including system safety constraints and distributed power supply operation constraints
- n(x p , y) represents the equality constraints in the operation constraints of the flexible distribution system, including power flow constraints
- x max and x min are the upper and lower limits of the control variables
- y max and y min are the upper and lower limits of the power flow variables.
- the incomplete area s measuring time t- ⁇ t receives the boundary information ⁇ r [t- ⁇ t] of the adjacent complete measurement area r, as well as the boundary active power and reactive power requirements. and Based on the basic parameter information described above, a physical model-driven voltage control model for the incomplete measurement area s is established, including: taking the minimum node voltage deviation in this area, the minimum boundary information deviation in adjacent areas, and the minimum boundary power demand deviation as the objective function,
- the flexible distribution system operation constraints and distributed power supply capacity constraints where,
- the boundary information ⁇ r [t- ⁇ t] includes the boundary tie line transmission power, the boundary tie line current information, and the boundary voltage information of the adjacent measurement complete area r, expressed as:
- the voltage control model of the incomplete measurement area s driven by the physical model is expressed as:
- f represents the objective function
- f 1 represents the node voltage deviation in the incomplete measurement area s
- f 2 represents the iteration error penalty term of the boundary value between the incomplete measurement area s and the adjacent area
- f 3 represents the incomplete measurement.
- x s represents the operation strategy of the controlled equipment measuring the incomplete area s
- x′ s represents the operation strategy of the controlled object measuring the incomplete area s
- N s is the number of internal nodes in the incomplete measurement area s
- v s,i [t] represents the quantity at time t Measure the voltage value of node i in incomplete area s
- y represents the power flow variable matrix
- ⁇ s [t] represents the boundary information calculated through the physical model of the incomplete measurement area s at
- the boundary information ⁇ s [t] includes the boundary tie line transmission power, boundary tie line current information, and boundary voltage information calculated from the incomplete measurement area s, which is expressed as:
- the complete measurement area r obtains the voltage measurement information of each measurement node, and receives the boundary information ⁇ s [t- ⁇ t] of the adjacent incomplete measurement area s, as well as the boundary active and reactive power requirements. and Update the physical guidance coefficient ⁇ [t], and establish a data-driven adaptive voltage control model for the complete measurement region, including: minimizing the deviation of the measured node voltage in this region, minimizing the deviation of the boundary information of adjacent regions, and minimizing the deviation of the boundary power demand.
- the objective function considers distributed power supply capacity constraints; where,
- the updated physical guidance coefficient ⁇ [t] is:
- ⁇ [t] and ⁇ [t- ⁇ t] represent the physical guidance coefficients at time t and t- ⁇ t respectively
- Y r [t] represents the voltage measurement value of node r in the complete measurement area at time t
- ⁇ represents the voltage.
- the data-driven adaptive voltage control model of measurement complete region r is expressed as:
- J represents the objective function
- J 1 represents the sum of the node voltage deviation and the change amplitude of the controlled variable in the complete measurement area r
- J 2 represents the iteration error penalty term of the boundary value between the complete measurement area r and the adjacent area
- J 3 represents the deviation of the measured complete area r boundary power and the adjacent area boundary power demand
- ′ r [t] represents the operation strategy of the controlled object in the complete measurement area r at time t
- the amount of change in the operating strategy and Respectively represent the active and reactive power
- ⁇ X′ r [t - ⁇ t] Represents the weight coefficient; Represents the initial value of the pseudo-Jacobian matrix; (give explanations for all letters)
- step 6) Solve the data-driven adaptive voltage control model for the complete measurement area r described in step 5) to obtain the distributed power supply operation control strategy in the complete measurement area r and issue it for execution.
- the boundary information ⁇ r [t], and boundary active and reactive power requirements Transmit to incomplete measurement area s;
- the distribution network includes 33 nodes.
- the topological connection situation is shown in Figure 2.
- the wind turbine system with a capacity of 500kVA is connected at system nodes 13, 15, 16, 29 and 30 respectively.
- At node 11 12, 17, 18, 20, 21, 22, 23, 24, 25, 32 and 33 are connected to photovoltaic systems with a capacity of 100kWp.
- the distributed power output fluctuation curve is shown in Figure 3-4.
- the system voltage is 12.66kV
- the reference power is 1MVA
- the system active and reactive loads are 3715kW and 2300kvar respectively.
- the flexible distribution system voltage control method driven by data-physical fusion is used for optimization. After the above steps, the distributed power output strategy can be obtained. In order to verify the effectiveness of this method, the following three control scenarios are used for comparison with the flexible power distribution system:
- Scenario 1 Without controlling the distributed energy storage system, the initial operating status of the flexible power distribution system is obtained;
- Scenario 2 Flexible power distribution system voltage control method driven by data-physical fusion.
- Scenario 3 Using data-driven voltage control method for flexible power distribution system.
- the computer hardware environment for performing optimization calculations is Intel(R) Core(TM) CPU i5-10210U, with a main frequency of 1.6GHz and a memory of 16GB; the software environment is the Windows 10 operating system.
- the calculation example topology used in the embodiment of the present invention is shown in Figure 2.
- Table 1 shows the comparison of all-day optimization control effects in the three scenarios.
- the predicted curve changes of distributed power output and load information are shown in Figure 3.
- the voltage comparison at node 18 is shown in Figure 4.
- the reactive power output strategy of the distributed power supply at node 18 is shown in Figure 5.
- the 24-hour voltage control effects of Scenario 1 and Scenario 2 are shown in Figure 6.
- the 24-hour voltage control effects of Scenario 2 and Scenario 3 are shown in Figure 7.
- scenario two can effectively adjust the voltage level of the flexible distribution system in this embodiment and effectively reduce the global voltage deviation. Its voltage deviation index is reduced by 64.13% compared with scenario one. Compared with the data-driven method in scenario three, the data-physics fusion-driven method in scenario two can improve the global optimization effect, and its voltage deviation index is reduced by 43.10% compared to scenario three.
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Abstract
一种数据-物理融合驱动的柔性配电系统自适应电压控制方法,包括:根据量测装置的空间分布情况,将选定的柔性配电系统分为量测完备区域和量测不完备区域,并输入基本参数信息;初始化物理引导系数、量测完备区域的边界信息以及边界有功功率和无功功率需求、量测不完备区域s的边界信息以及边界有功、无功功率需求;计算量测完备区域r的伪雅可比矩阵初值、分布式电源运行策略初值;依据基本参数信息建立物理模型驱动的量测不完备区域电压控制模型;求解模型;建立数据驱动的量测完备区域自适应电压控制模型;求解数据驱动的量测完备区域自适应电压控制模型。本发明有效改善有源配电系统的电压越限情况,完成区域间调控资源的互补支撑。
Description
本发明涉及一种柔性配电系统自适应电压控制方法。特别是涉及一种数据-物理融合驱动的柔性配电系统自适应电压控制方法。
大规模分布式能源等多元化供电设备接入柔性配电系统,使得柔性配电系统结构形态发生演变,运行状态变化频繁,对柔性配电系统运行控制方法提出了更高的要求。随着配电系统信息化和数字化水平的提高,柔性配电系统积累了海量多源异构运行数据和历史信息。包括统计分析方法、人工智能方法、迭代学习方法在内的数据驱动方法在配电系统运行优化问题中已经得到了广泛应用。数据驱动方法基于运行数据建立数据模型,构建特征与研究问题间的关联关系,且不依赖于精确的物理模型参数,形式简单,在复杂场景下求解速度快、适应性强等优势。
然而,数据驱动的优化控制方法面临两类问题:1)数据驱动方法生成的数据模型,存在物理意义不明确、可解释性差的问题,在伪雅可比矩阵或受控策略初值不合理的情况下,会影响结果的合理性、对柔性配电系统造成冲击。2)实际柔性配电系统中存在量测和资源分布不均的情况,使得部分区域因缺少量测数据导致数据驱动模型难以建立,或因量测完备区域调控资源有限而导致调控能力不足。这两类问题使数据驱动控制方法的实际应用面临挑战。
基于物理模型的柔性配电系统优化控制算法基于先验信息建立模型,不依赖于系统运行数据,具有全局性、可解释性等优势。当可以获得部分柔性配电系统物理模型或近似模型时,可以考虑采用数据-物理融合驱动的方式,构建融合驱动模型,以克服数据驱动模型面临的各类问题,获得更好的应用效果。针对柔性配电系统自适应控制问题,可以结合数据、物理模型各自优势,采用数据-物理融合驱动的柔性配电系统电压控制模型,以适应柔性配电系统不同运行场景下运行优化需求。
发明内容
本发明所要解决的技术问题是,为了克服现的技术的不足,提供一种在缺乏精确参数和量测装置空间分布不均的情况下,能够得到分布式电源运行控制策略的数据-物理融合驱动的柔性配电系统自适应电压控制方法。
本发明所采用的技术方案是:一种数据-物理融合驱动的柔性配电系统自适应电压控制方法,包括如下步骤:
1)根据量测装置的空间分布情况,将选定的柔性配电系统分为量测完备区域r和量测不完备区域s,并输入基本参数信息,包括:线路参数信息,负荷、分布式电源的位置和参数,节点电压参考值,设置起始时刻为t=t
0+Δt,优化控制总时长为T,控制步长Δt;
2)初始化物理引导系数σ[t
0]、量测完备区域r的边界信息ω
r[t
0],以及边界有功功率和无功功率需求
和
量测不完备区域s的边界信息ω
s[t
0],以及边界有功、无功功率 需求
和
依据步骤1)输入的基本参数信息,基于物理模型计算量测完备区域r的伪雅可比矩阵初值
分布式电源运行策略初值x
p[t
0];
3)量测t-Δt时刻的不完备区域s接收相邻量测完备区域r的边界信息ω
r[t-Δt],以及边界有功功率和无功功率需求
和
依据所述的基本参数信息,建立物理模型驱动的量测不完备区域s电压控制模型,包括:以本区域节点电压偏差最小、相邻区域边界信息偏差最小和边界功率需求偏差最小为目标函数,考虑柔性配电系统运行约束和分布式电源容量约束;
5)量测完备区域r获取各量测节点的电压量测信息,并接收相邻量测不完备区域s的边界信息ω
s[t-Δt],以及边界有功、无功功率需求
和
更新物理引导系数σ[t],建立数据驱动的量测完备区域r自适应电压控制模型,包括:以本区域量测节点电压偏差最小、相邻区域边界信息偏差最小和边界功率需求偏差最小为目标函数,考虑分布式电源容量约束;
7)更新控制时刻t=t+Δt,判断t是否大于优化时长T,若否则转到步骤3),若是,则结束。
本发明的数据-物理融合驱动的柔性配电系统自适应电压控制方法,综合考虑了柔性配电系统线路参数不可知性、分布式电源出力情况不确定性以及量测装置空间分布不均情况,通过建立基于数据驱动的分布式储能系统自适应预测电压控制策略,实现数据-物理融合驱动的柔性配电系统自适应电压控制。为配电网电压优化问题提供新的思路,有助于配电侧安全性和用户体验的提升。
本发明考虑采用数据-物理融合驱动的方式,构建融合驱动模型,有效改善数据驱动控制效果的合理性与可解释性,缓解控制策略变化过大造成对配电系统的冲击,实现区域间目标函数的互济,充分调用分布式电源换流器的电压调节潜力,有效改善有源配电系统的电压越限情况,完成区域间调控资源的互补支撑,提升有源配电系统的灵活高效运行水平。
图1是本发明数据-物理融合驱动的柔性配电系统自适应电压控制方法的流程图;
图2是本发明中采用的柔性配电系统拓扑结构;
图3是分布式电源出力、负荷变化的预测曲线;
图4是节点18处电压对比;
图5是节点18处分布式电源无功出力策略;
图6是场景一和场景二24小时电压控制效果;
图7是场景二和场景三24小时电压控制效果。
下面结合实施例和附图对本发明的数据-物理融合驱动的柔性配电系统自适应电压控制方法做出详细说明。
如图1所示,本发明的数据-物理融合驱动的柔性配电系统自适应电压控制方法,包括如下步骤:
1)根据量测装置的空间分布情况,将选定的柔性配电系统分为量测完备区域r和量测不完备区域s,并输入基本参数信息,包括:线路参数信息,负荷、分布式电源的位置和参数,节点电压参考值,设置起始时刻为t=t
0+Δt,优化控制总时长为T,控制步长Δt;
2)初始化物理引导系数σ[t
0]、量测完备区域r的边界信息ω
r[t
0],以及边界有功功率和无功功率需求
和
量测不完备区域s的边界信息ω
s[t
0],以及边界有功、无功功率需求
和
依据步骤1)输入的基本参数信息,基于物理模型计算量测完备区域r的伪雅可比矩阵初值
分布式电源运行策略初值x
p[t
0];其中,
式中,x
p表示通过物理模型计算得到的柔性配电系统受控设备策略;ζ表示典型场景,Λ表示柔性配电系统典型场景集合,N
Λ表示由历史数据生成的典型场景数量;
所述的基于物理模型计算量测完备区域r分布式电源运行策略初值x
p[t
0],如下:
x
p[t
0]=argmin(f) (2)
式中,x
p[t
0]表示柔性配电系统受控设备策略初始值,考虑柔性配电系统运行约束,通过基于物理模型的柔性配电系统运行优化控制模型求解:
式中,x
p表示控制变量;y表示潮流变量矩阵;g表示目标函数;m(x
p,y)表示柔性配电系统运行约束中的不等式约束,包括系统安全约束、分布式电源运行约束;n(x
p,y)表示柔性配电系统运行约束中的等式约束,包括潮流约束;式中x
max和x
min为控制变量的上下限,y
max和y
min为潮流变量的上下限。
3)量测t-Δt时刻的不完备区域s接收相邻量测完备区域r的边界信息ω
r[t-Δt],以及边界有功功率和无功功率需求
和
依据所述的基本参数信息,建立物理模型驱动的量测不完备区域s电压控制模型,包括:以本区域节点电压偏差最小、相邻区域边界信息偏差最小和边界功率需求偏差最小为目标函数,考虑柔性配电系统运行约束和分布式电源容量约束;其中,
所述的边界信息ω
r[t-Δt]包括边界联络线传输功率、边界联络线电流信息、相邻量测完备区域r的边界电压信息,表示为:
式中,
和
分别表示t-Δt时刻量测完备区域r边界传输联络线l上的有功、无功功率的量测值;
表示t-Δt时刻量测完备区域r的边界节点n的电压量测值;
表示t-Δt时刻量测完备区域r边界联络线l上的电流量测值。
考虑柔性配电系统运行约束,所述的物理模型驱动的量测不完备区域s电压控制模型表示为:
式中,f表示目标函数;f
1表示量测不完备区域s区内节点电压偏差;f
2表示量测不完备区域s与相邻区域边界值的迭代误差惩罚项;f
3表示量测不完备区域s边界功率与相邻区域边界需求的偏差;x
s表示量测不完备区域s的受控设备运行策略;x′
s表示量测不完备区域s的受控对象的运行策略;
和
分别表示t时刻量测完备区域r作为边界信息向相邻区域发送的边界有功、无功功率需求;N
s为量测不完备区域s内部节点数;v
s,i[t]表示t时刻量测不完备区域s节点i的电压值;
表示量测不完备区域s的节点电压参考值;y表示潮流变量矩阵;ω
s[t]表示t时刻通过量测不完备区域s物理模型计算得到的边界信息;Ω
s表示量测不完备区域s内部节点集合;
和
分别表示t时刻和t-Δt时刻量测不完备区域s的辅助变量;ω
s[t-Δt]表示t-Δt时刻量测不完备区域s计算得到的边界状态信息,m(x′
s,y)表示柔性配电系统运行约束中的不等式约束,包括系统安全约束、分布式电源运行约束;n(x′
s,y)表示柔性配电系统运行约束中的等式约束,包括潮流约束。
所述的边界信息ω
s[t]包括量测不完备区域s计算得到的边界联络线传输功率、边界联络线电流信息、边界电压信息,表示为:
5)量测完备区域r获取各量测节点的电压量测信息,并接收相邻量测不完备区域s的边界信息ω
s[t-Δt],以及边界有功、无功功率需求
和
更新物理引导系数σ[t],建立数据驱动的量测完备区域r自适应电压控制模型,包括:以本区域量测节点电压偏差最小、相邻区域边界信息偏差最小和边界功率需求偏差最小为目标函数,考虑分布式电源容量约束;其中,
所述的更新物理引导系数σ[t]为:
式中,σ[t]和σ[t-Δt]分别表示t时刻和t-Δt时刻的物理引导系数,Y
r[t]表示t时刻量测完备区域r节点电压量测值,γ表示电压引导范围。
所述的数据驱动的量测完备区域r自适应电压控制模型表示为:
式中,J表示目标函数,J
1表示量测完备区域r区内节点电压偏差与受控变量变化幅度之和;J
2表示量测完备区域r与相邻区域边界值的迭代误差惩罚项;J
3表示量测完备区域r边界功率与相邻区域边界功率需求的偏差;
表示量测完备区域r节点电压参考值;X
r[t]为t时刻量测完备区域r受控设备运行策略;Y
r[t]表示t时刻量测完备区域r节点电压量测值;X′
r[t]表示t时刻量测完备区域r受控对象的运行策略;ΔX′
r[t]=X′
r[t]-X′
r[t-Δt],表示t时刻量测完备区域r受控对象的运行策略变化量;ΔX′
r[t-Δt]=X′
r[t-Δt]-X′
r[t-2Δt],表示t-Δt时刻量测完备区域r受控对象的运行策略变化量;
和
分别表示t时刻量测完备区域r作为边界信息向相邻区域发送的有功、无功功率需求;
和
分别表示t时刻和t-Δt时刻 量测完备区域r的辅助变量;
表示t时刻量测完备区域r伪雅可比矩阵的估计值,估计方法如下:
所述的考虑分布式电源容量约束表示如下:
式中,X
r[t]为t时刻量测完备区域r受控设备运行策略;P
r[t]表示t时刻量测完备区域r内分布式电源的有功出力,C
r[t]表示t时刻量测完备区域r内分布式电源的容量。
7)更新控制时刻t=t+Δt,判断t是否大于优化时长T,若否则转到步骤3),若是,则结束。
下面给出具体实例:
对于本发明的实施例,配电网包括33个节点,拓扑连接情况如图2所示,分别于系统节点13、15、16、29和30处接入容量为500kVA的风机系统,在节点11、12、17、18、20、21、22、23、24、25、32和33处接入容量为100kWp的光伏系统。分布式电源出力波动曲线如图3-4所示。系统电压12.66kV,基准功率为1MVA,系统有功及无功负荷分别为3715kW和2300kvar。控制步长Δt=0.5min,优化时长T=24h;配电网的电压参考值设定为1.0p.u.。权重系数λ、ρ、η、μ、δ取值均为1.0,电压引导范围γ=[0.97p.u.,1.03p.u.]。采用数据-物理融合驱动的柔性配电系统电压控制方法进行优化,经过上述步骤可以得到分布式电源出力策略。为验证该方法的有效性,针对柔性配电系统采用如下三种控制场景进行对比:
场景一:不对分布式储能系统进行控制,得到柔性配电系统初始运行状态;
场景二:采用数据-物理融合驱动的柔性配电系统电压控制方法。
场景三:采用数据驱动的柔性配电系统电压控制方法。
执行优化计算的计算机硬件环境为Intel(R)Core(TM)CPU i5-10210U,主频为1.6GHz,内存为16GB;软件环境为Windows10操作系统。
本发明实施例所采用的算例拓扑如图2所示。三种场景下全天优化控制效果对比如表1所示。分布式电源出力、负荷信息的预测曲线变化如图3所示。节点18处电压对比如图4所示。节点18处分布式电源无功出力策略如图5所示。场景一和场景二24小时电压控制效果如图6所示。场景二和场景三24小时电压控制效果如图7所示。
结合表1以及图4至图7可以看出,场景二能有效调节本实施例中柔性配电系统电压水平,有效减少全局电压偏差,其电压偏差指标相比场景一降低了64.13%。相比于场景三中数据驱动方法,采用场景二中的数据-物理融合驱动方法可以提高全局优化效果,其电压偏差指标相比场景三降低了43.10%。
表1全天优化控制效果对比
Claims (8)
- 一种数据-物理融合驱动的柔性配电系统自适应电压控制方法,其特征在于,包括如下步骤:1)根据量测装置的空间分布情况,将选定的柔性配电系统分为量测完备区域r和量测不完备区域s,并输入基本参数信息,包括:线路参数信息,负荷、分布式电源的位置和参数,节点电压参考值,设置起始时刻为t=t 0+Δt,优化控制总时长为T,控制步长Δt;2)初始化物理引导系数σ[t 0]、量测完备区域r的边界信息ω r[t 0],以及边界有功功率和无功功率需求 和 量测不完备区域s的边界信息ω s[t 0],以及边界有功、无功功率需求 和 依据步骤1)输入的基本参数信息,基于物理模型计算量测完备区域r的伪雅可比矩阵初值 分布式电源运行策略初值x p[t 0];3)量测t-Δt时刻的不完备区域s接收相邻量测完备区域t的边界信息ω r[t-Δt],以及边界有功功率和无功功率需求 和 依据所述的基本参数信息,建立物理模型驱动的量测不完备区域s电压控制模型,包括:以本区域节点电压偏差最小、相邻区域边界信息偏差最小和边界功率需求偏差最小为目标函数,考虑柔性配电系统运行约束和分布式电源容量约束;5)量测完备区域r获取各量测节点的电压量测信息,并接收相邻量测不完备区域s的边界信息ω s[t-Δt],以及边界有功、无功功率需求 和 更新物理引导系数σ[t],建立数据驱动的量测完备区域r自适应电压控制模型,包括:以本区域量测节点电压偏差最小、相邻区域边界信息偏差最小和边界功率需求偏差最小为目标函数,考虑分布式电源容量约束;7)更新控制时刻t=t+Δt,判断t是否大于优化时长T,若否则转到步骤3),若是,则结束。
- 根据权利要求1所述的数据-物理融合驱动的柔性配电系统自适应电压控制方法,其特征在于,步骤2)中,式中,x p表示通过物理模型计算得到的柔性配电系统受控设备策略;ζ表示典型场景,Λ表示柔性配电系统典型场景集合,N Λ表示由历史数据生成的典型场景数量;所述的基于物理模型计算量测完备区域r分布式电源运行策略初值x p[t 0],如下:x p[t 0]=argmin(f) (2)式中,x p[t 0]表示柔性配电系统受控设备策略初始值,考虑柔性配电系统运行约束,通过基于物理模型的柔性配电系统运行优化控制模型求解:式中,x p表示控制变量;y表示潮流变量矩阵;g表示目标函数;m(x p,y)表示柔性配电系统运行约束中的不等式约束,包括系统安全约束、分布式电源运行约束;n(x p,y)表示柔性配电系统运行约束中的等式约束,包括潮流约束;式中x max和x min为控制变量的上下限,y max和y min为潮流变量的上下限。
- 根据权利要求1所述的数据-物理融合驱动的柔性配电系统自适应电压控制方法,考虑柔性配电系统运行约束,其特征在于,步骤3)中所述的物理模型驱动的量测不完备区域s电压控制模型表示为:式中,f表示目标函数;f 1表示量测不完备区域s区内节点电压偏差;f 2表示量测不完备区域s与相邻区域边界值的迭代误差惩罚项;f 3表示量测不完备区域s边界功率与相邻区域边界需求的偏差;x s表示量测不完备区域s的受控设备运行策略;x′ s表示量测不完备区域s的受控对象的运行策略; 和 分别表示t时刻量测完备区域r作为边界信息向相邻区域发送的边界有功、无功功率需求;N s为量测不完备区域s内部节点数;v s,i[t]表示t时刻量测不完备区域s节点i的电压值; 表示量测不完备区域s的节点电压参考值;y表示潮流变量矩阵;ω s[t]表示t时刻通过量测不完备区域s物理模型计算得到的边界信息;Ω s表示量测不完备区域s内部节点集合; 和 分别表示t时刻和t-Δt时刻量测不完备区域s的辅助变量;ω s[t-Δt]表示t-Δt时刻量测不完备区域s计算得到的边界状态信息;m(x′ s,y)表示柔性配电系统运行约束中的不等式约束,包括系统安全约束、分布式电源运行约束;n(x′ s,y)表示柔性配电系统运行约束中的等式约束,包括潮流约束。
- 根据权利要求1所述的数据-物理融合驱动的柔性配电系统自适应电压控制方法,其特征在于,步骤5)中所述的数据驱动的量测完备区域r自适应电压控制模型表示为:J=min(J 1+J 2+J 3)式中,J表示目标函数,J 1表示量测完备区域r区内节点电压偏差与受控变量变化幅度之和;J 2表示量测完备区域r与相邻区域边界值的迭代误差惩罚项;J 3表示量测完备区域r边界功率与相邻区域边界功率需求的偏差; 表示量测完备区域r节点电压参考值;X r[t]为t时刻量测完备区域r受控设备运行策略;Y r[t]表示t时刻量测完备区域r节点电压量测值;X′ r[t]表示t时刻量测完备区域r受控对象的运行策略;ΔX′ r[t]=X′ r[t]-X′ r[t-Δt],表示t时刻量测完备区域r受控对象的运行策略变化量;ΔX′ r[t-Δt]=X′ r[t-Δt]-X′ r[t-2Δt],表示t-Δt时刻量测完备区域r受控对象的运行策略变化量; 和 分别表示t时刻量测完备区域r作为边界信息向相邻区域发送的有功、无功功率需求; 和 分别表示t时刻和t-Δt时刻量测完备区域r的辅助变量; 表示t时刻量测完备区域r伪雅可比矩阵的估计值,估计方法如下:
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