CN105023058A

CN105023058A - Power distribution network intelligent soft switch operation optimization method with simultaneous consideration of switch motion

Info

Publication number: CN105023058A
Application number: CN201510395612.XA
Authority: CN
Inventors: 王成山; 宋关羽; 李鹏; 冀浩然; 张小天
Original assignee: Tianjin University; Hainan Power Grid Co Ltd
Current assignee: Tianjin University; Hainan Power Grid Co Ltd
Priority date: 2015-07-07
Filing date: 2015-07-07
Publication date: 2015-11-04
Anticipated expiration: 2035-07-07
Also published as: CN105023058B

Abstract

An intelligent soft-switching operation optimization method for distribution network considering switching action at the same time: according to the distribution system input line parameters, load level and network topology connection relationship, system operating voltage level and branch current limit, distributed power access location, Type, capacity and parameters, intelligent soft switch access position, capacity and parameters, load and distributed power supply operating characteristic prediction curves in the operation optimization cycle, system reference voltage and reference power initial value; establish distribution network tie switch and intelligent soft switch The timing optimization model of cooperative operation; according to the standard form of cone optimization, the objective function and nonlinear constraints in the timing optimization model of distribution network tie switch and intelligent soft switch cooperative operation are transformed into cone model; the objective function is linearized respectively, and nonlinear The linear constraints are transformed into linear constraints, second-order cone constraints or rotating cone constraints, and the obtained mathematical model is solved by CPLEX solver. The present invention avoids tedious iterations and a large number of tests.

Description

An intelligent soft switching operation optimization method for distribution network considering switching action at the same time

技术领域technical field

本发明涉及一种配电网运行的时序优化方法。特别是涉及一种同时考虑开关动作的配电网智能软开关运行优化方法。The invention relates to a timing optimization method for distribution network operation. In particular, it relates to a distribution network intelligent soft switching operation optimization method that considers switching action at the same time.

背景技术Background technique

对能源和环境的高度关注使得配电网的发展面临着新的压力和挑战，这些压力和挑战同时也是推动传统配电网向智能配电网发展的机遇。在智能配电网中，可控设备日益增多，网络结构和运行方式更加灵活多变，高级配电自动化技术、先进的信息通信技术得以广泛应用，分布式电源、储能、需求侧资源等开始参与配电网的运行与优化。智能配电网的发展和新能源发电技术的广泛应用正在推动着配用电模式和运行管理机制的深刻变革。The high attention to energy and environment makes the development of distribution network face new pressures and challenges. These pressures and challenges are also opportunities to promote the development of traditional distribution network to smart distribution network. In the smart distribution network, the number of controllable devices is increasing, the network structure and operation mode are more flexible and changeable, advanced distribution automation technology and advanced information communication technology are widely used, distributed power supply, energy storage, demand-side resources, etc. Participate in the operation and optimization of distribution network. The development of smart distribution network and the wide application of new energy power generation technology are promoting profound changes in power distribution mode and operation management mechanism.

智能软开关(Soft Normally Open Point，SNOP)装置就是在上述背景下衍生出的取代传统联络开关的一种新型智能配电装置。与开关操作相比，SNOP的功率控制更加安全、可靠，甚至可以实现实时优化，能够有效应对分布式电源和负荷带来的随机性和波动性。但是，SNOP的实现主要基于全控型电力电子装置，这些装置本身的成本较高，短期内配电网中的联络开关不可能完全被SNOP取代。这就使得配电网的运行优化需要从整体上考虑联络开关和SNOP并存的情况，其运行优化模型将是一个需要同时求解离散量(开关状态)和连续量(SNOP传输功率)的混合整数非线性规划问题。The intelligent soft switch (Soft Normally Open Point, SNOP) device is a new type of intelligent power distribution device derived from the above background to replace the traditional contact switch. Compared with switching operation, the power control of SNOP is safer and more reliable, and can even achieve real-time optimization, which can effectively deal with the randomness and volatility brought by distributed power sources and loads. However, the realization of SNOP is mainly based on full-control power electronic devices. The cost of these devices is relatively high, and it is impossible for the tie switches in the distribution network to be completely replaced by SNOP in the short term. This makes the operation optimization of the distribution network need to consider the coexistence of tie switches and SNOPs as a whole, and its operation optimization model will be a mixed integer non-linear equation that needs to solve both discrete quantities (switch states) and continuous quantities (SNOP transmission power). linear programming problem.

在智能配电网中，广泛接入的分布式电源加剧了系统运行的不确定性，考虑到开关损耗以及冲击电流等因素，联络开关不可能频繁开断，传统的网络重构很难做到配电网的实时调整，而SNOP则可以实时改变传输功率，调整运行状态，以应对分布式电源接入后带来的一系列电压越限、线路过载等问题，因此需要从时间序列的角度对配电网优化问题进行建模，并需要综合考虑联络开关与SNOP的协调优化问题。考虑其时序特征后，配电网运行优化过程中，开关动作的合理规划就成为一个亟待解决的问题。同时，考虑开关动作费用的SNOP运行的时序优化问题会随着时间断面数的增多求解维数急剧增加，成为大规模混合整数非线性规划问题，导致其求解变得更加困难，甚至不可行。In the smart distribution network, widely connected distributed power sources aggravate the uncertainty of system operation. Considering factors such as switching loss and inrush current, it is impossible for the contact switch to be disconnected frequently. Traditional network reconstruction is difficult to achieve. Real-time adjustment of the distribution network, while SNOP can change the transmission power in real time and adjust the operating status to deal with a series of problems such as voltage limit and line overload caused by the access of distributed power sources. Therefore, it is necessary to analyze from the perspective of time series The distribution network optimization problem is modeled, and the coordination optimization problem of tie switch and SNOP needs to be considered comprehensively. After considering its timing characteristics, the reasonable planning of switching actions becomes an urgent problem to be solved in the process of distribution network operation optimization. At the same time, the timing optimization problem of SNOP operation considering the switching action cost will increase sharply with the increase of the number of time sections, and become a large-scale mixed integer nonlinear programming problem, making its solution more difficult or even infeasible.

对于求解这类大规模混合整数非线性规划问题，目前还很难找到一种快速、有效的求解方法。对于该问题的求解目前已经提出和发展了多种优化方法，主要有包括：1)传统数学优化方法，其中包括解析法、连续消去法等；2)启发式算法，其中包括灵敏度分析法、专家系统等；3)随机优化方法，其中包括遗传算法、粒子群算法等。For solving this kind of large-scale mixed integer nonlinear programming problem, it is still difficult to find a fast and effective solution method. A variety of optimization methods have been proposed and developed to solve this problem, mainly including: 1) traditional mathematical optimization methods, including analytical methods, continuous elimination methods, etc.; 2) heuristic algorithms, including sensitivity analysis methods, expert optimization methods, etc. system, etc.; 3) stochastic optimization methods, including genetic algorithm, particle swarm algorithm, etc.

虽然上述方法或技术都有一定的应用，但也都存在着明显的不足，如传统数学优化方法虽然理论上可进行全局寻优，但在实际应用时不可避免地存在“维数灾”问题，计算时间往往呈现爆炸式激增；启发式算法在时间复杂度方面要求有一个多项式时间界，计算速度快，但得到的最优解或者缺乏数学意义上的最优性或者只是局部最优解；虽然随机优化方法所搜寻的最终解与初始解无关，但对于不同规模的配电网需要重新设置其控制参数、种群数量、迭代次数等，从而来保证以较大的几率找到全局最优解。启发式和随机方法多适用于求解整数规划问题，但对于考虑联络开关和SNOP协同运行的配电网时序优化问题，数学本质上是大规模混合整数非线性规划问题，所以传统数学优化方法、启发式算法对于求解这类问题上，速度或精度多不能同时满足要求。因此，需要一种准确、快速求解上述优化问题的模型与算法。Although the above methods or technologies have certain applications, they also have obvious shortcomings. For example, although the traditional mathematical optimization method can theoretically perform global optimization, it inevitably has the problem of "curse of dimensionality" in practical application. Computing time often shows an explosive surge; heuristic algorithms require a polynomial time bound in terms of time complexity, and the calculation speed is fast, but the optimal solution obtained either lacks the optimality in the mathematical sense or is only a local optimal solution; although The final solution searched by the stochastic optimization method has nothing to do with the initial solution, but for distribution networks of different scales, it is necessary to reset its control parameters, number of populations, number of iterations, etc., so as to ensure that the global optimal solution can be found with a greater probability. Heuristic and stochastic methods are mostly suitable for solving integer programming problems, but for distribution network timing optimization problems considering the coordinated operation of tie switches and SNOPs, mathematics is essentially a large-scale mixed integer nonlinear programming problem, so traditional mathematical optimization methods, heuristics For solving such problems, the formula algorithm cannot meet the requirements of speed or accuracy at the same time. Therefore, there is a need for an accurate and fast model and algorithm for solving the above optimization problems.

发明内容Contents of the invention

本发明所要解决的技术问题是，提供一种能够综合考虑开关动作费用等效损耗、SNOP运行损耗及网络损耗等配电网运行损耗，确定合理的开关动作时序及SNOP运行的同时考虑开关动作的配电网智能软开关运行优化方法。The technical problem to be solved by the present invention is to provide a system that can comprehensively consider the equivalent loss of switching action cost, SNOP operating loss, network loss and other distribution network operating losses, determine a reasonable switching action sequence and consider switching actions while SNOP is running. Operation optimization method of intelligent soft switching in distribution network.

本发明所采用的技术方案是：一种同时考虑开关动作的配电网智能软开关运行优化方法，包括如下步骤：The technical solution adopted in the present invention is: a distribution network intelligent soft switch operation optimization method considering switching action at the same time, including the following steps:

1)根据选定的配电系统，输入线路参数、负荷水平和网络拓扑连接关系，系统运行电压水平和支路电流限制，分布式电源接入位置、类型和容量及参数，智能软开关接入位置和容量及参数，运行优化周期内负荷及分布式电源运行特性预测曲线，以及系统基准电压和基准功率初值；1) According to the selected power distribution system, input line parameters, load level and network topology connection relationship, system operating voltage level and branch current limit, distributed power access location, type, capacity and parameters, intelligent soft switch access Location and capacity and parameters, load and distributed power supply operating characteristic prediction curves within the operation optimization period, and initial values of system reference voltage and reference power;

2)依据步骤1)提供的配电系统结构及参数，同时考虑网络损耗、网络重构的开关动作费用等效损耗及智能软开关的运行损耗，建立配电网联络开关和智能软开关协同运行的时序优化模型，包括：选取根节点为平衡节点，设定配电系统运行损耗最小为目标函数，分别考虑网络拓扑约束、系统潮流约束、系统运行约束、智能软开关运行约束；2) Based on the structure and parameters of the power distribution system provided in step 1), the network loss, the equivalent loss of the switching action cost of network reconstruction and the operation loss of the intelligent soft switch are considered at the same time, and the coordinated operation of the distribution network tie switch and the intelligent soft switch is established. The timing optimization model of , including: selecting the root node as the balance node, setting the minimum operating loss of the distribution system as the objective function, and considering network topology constraints, system power flow constraints, system operation constraints, and intelligent soft switch operation constraints;

3)根据锥优化的标准形式对步骤2)所述的配电网联络开关和智能软开关协同运行的时序优化模型中的目标函数和非线性约束进行锥模型转化；3) According to the standard form of cone optimization, the objective function and nonlinear constraints in the timing optimization model of the distribution network tie switch and intelligent soft switch cooperating described in step 2) are converted into cone model;

4)经过步骤3)的转化，分别将目标函数线性化，非线性约束转化为线性约束、二阶锥约束或旋转锥约束，将得到的数学模型采用CPLEX求解器进行求解；4) After the transformation in step 3), the objective function is linearized respectively, and the nonlinear constraint is converted into a linear constraint, a second-order cone constraint or a rotating cone constraint, and the obtained mathematical model is solved by a CPLEX solver;

5)输出步骤4)的求解结果，包括开关状态、智能软开关最优传输功率值、网络潮流结果以及目标函数值。5) Output the solution result of step 4), including the switch state, the optimal transmission power value of the intelligent soft switch, the network power flow result and the objective function value.

步骤2)所述的配电系统运行损耗最小为目标函数，表示为The minimum operating loss of the power distribution system described in step 2) is the objective function, expressed as

min f＝E_S，loss+E_L，loss+E_SNOP，loss min f = E _{S, loss} + E _{L, loss} + E _{SNOP, loss}

式中，开关动作费用等效损耗E_S，loss、网络损耗E_L，loss与智能软开关的运行损耗E_SNOP，loss分别用下式表示In the formula, the switching action cost equivalent loss E _{S, loss} , the network loss E _{L, loss} and the operating loss E _{SNOP, loss} of the intelligent soft switch are respectively expressed by the following formula

${E E.}_{S S,, l l o o s the s s the s} = = {C C}_{S S} {Σ Σ}_{t t = = 11}^{{N N}_{T T}} {Σ Σ}_{i i = = 11}^{{N N}_{N N}} {Σ Σ}_{j j &Element; &Element; Ω Ω ((i i))} | | {α α}_{i i j j} ((t t)) - - {α α}_{i i j j} ((t t - - 11)) | |$

${E E.}_{L L,, l l o o s the s s the s} = = {Σ Σ}_{t t = = 11}^{{N N}_{T T}} {Σ Σ}_{i i = = 11}^{{N N}_{N N}} {Σ Σ}_{j j &Element; &Element; Ω Ω ((i i))} {r r}_{i i j j} {I I}_{i i j j}^{22} ((t t)) Δ Δ t t$

${E E.}_{S S N N O o P P,, l l o o s the s s the s} = = {Σ Σ}_{t t = = 11}^{{N N}_{T T}} {Σ Σ}_{m m = = 11}^{{N N}_{S S N N O o P P}} (({A A}_{m m,, 11} | | {P P}_{m m,, 11} ((t t)) | | + + {A A}_{m m,, 22} | | {P P}_{m m,, 22} ((t t)) | |)) Δ Δ t t$

式中，C_S为开关动作费用等效转换系数；Δt为优化计算的时段间隔；N_T为优化计算的时段数，N_N为系统中的节点总数，N_SNOP为系统中接入智能软开关的个数；Ω(i)为节点i的相邻节点的集合；α_ij(t)为t时段支路ij的开关状态；r_ij为支路ij的电阻，I_ij(t)为t时段节点i流向节点j的电流幅值；P_m，1(t)和P_m，2(t)为t时段第m个智能软开关的两个换流器的有功输出功率，A_m，1和A_m，2为第m个智能软开关的两个换流器的有功损耗系数。In the formula, C _S is the equivalent conversion coefficient of the switching action cost; Δt is the period interval for optimal calculation; _NT is the number of time periods for optimal calculation, N _N is the total number of nodes in the system, and N _SNOP is the smart soft switch connected to the system Ω(i) is the set of adjacent nodes of node i; α _ij (t) is the switch state of branch ij in period t; r _ij is the resistance of branch ij, I _ij (t) is the period of t The amplitude of the current flowing from node i to node j; P _m,1 (t) and P _m,2 (t) are the active output power of the two converters of the mth intelligent soft switch in the t period, A _m,1 and A _m,2 is the active power loss coefficient of the two converters of the mth intelligent soft switch.

步骤2)所述的网络拓扑约束表示为Step 2) described network topology constraints are expressed as

α_ij(t)＝β_ij(t)+β_ji(t)α _ij (t)=β _ij (t)+β _ji (t)

${Σ Σ}_{j j &Element; &Element; Ω Ω ((i i))} {β β}_{i i j j} ((t t)) = = 11,, &ForAll; &ForAll; i i &Element; &Element; {N N}_{N N} \ \ {N N}_{S S}$

${Σ Σ}_{j j &Element; &Element; Ω Ω ((i i))} {β β}_{i i j j} ((t t)) = = 00,, &ForAll; &ForAll; i i &Element; &Element; {N N}_{S S}$

α_ij(t)∈{0，1}α _ij (t) ∈ {0, 1}

β_ij(t)∈{0，1}β _ij (t) ∈ {0, 1}

式中，N_S为系统中的源节点个数；β_ij(t)表示t时段节点i与节点j关系，节点j为节点i的母节点时为1，否则为0。In the formula, N _S is the number of source nodes in the system; β _ij (t) represents the relationship between node i and node j in period t, and is 1 when node j is the parent node of node i, otherwise it is 0.

步骤2)所述的系统潮流约束表示为The system power flow constraint described in step 2) is expressed as

${Σ Σ}_{j j &Element; &Element; Φ Φ ((i i))} (({P P}_{j j i i} ((t t)) - - {r r}_{j j i i} {I I}_{j j i i}^{22} ((t t)))) + + {P P}_{i i} ((t t)) = = {Σ Σ}_{k k &Element; &Element; Ψ Ψ ((i i))} {P P}_{i i k k} ((t t))$

${Σ Σ}_{j j &Element; &Element; Φ Φ ((i i))} (({Q Q}_{j j i i} ((t t)) - - {x x}_{j j i i} {I I}_{j j i i}^{22} ((t t)))) + + {Q Q}_{i i} ((t t)) = = {Σ Σ}_{k k &Element; &Element; Ψ Ψ ((i i))} {Q Q}_{i i k k} ((t t))$

P_i(t)＝P_DG，i(t)+P_SNOP，i(t)-P_LOAD，i(t)P _i (t) = P _{DG, i} (t) + P _{SNOP, i} (t) - P _{LOAD, i} (t)

Q_i(t)＝Q_DG，i(t)+Q_SNOP，i(t)-Q_LOAD，i(t)Q _i (t) = Q _{DG, i} (t) + Q _{SNOP, i} (t) - Q _{LOAD, i} (t)

${I I}_{i i j j}^{22} ((t t)) = = \frac{{P P}_{i i j j}^{22} ((t t)) + + {Q Q}_{i i j j}^{22} ((t t))}{{U u}_{i i}^{22} ((t t))}$

${U u}_{i i}^{22} ((t t)) - - {U u}_{j j}^{22} ((t t)) - - 22 (({r r}_{i i j j} {P P}_{i i j j} ((t t)) + + {x x}_{i i j j} {Q Q}_{i i j j} ((t t)))) + + (({r r}_{i i j j}^{22} + + {x x}_{i i j j}^{22})) {I I}_{i i j j}^{22} ((t t)) + + M m ((11 - - {α α}_{i i j j} ((t t)))) &GreaterEqual; &Greater Equal; 00$

${U u}_{i i}^{22} ((t t)) - - {U u}_{j j}^{22} ((t t)) - - 22 (({r r}_{i i j j} {P P}_{i i j j} ((t t)) + + {x x}_{i i j j} {Q Q}_{i i j j} ((t t)))) + + (({r r}_{i i j j}^{22} + + {x x}_{i i j j}^{22})) {I I}_{i i j j}^{22} ((t t)) - - M m ((11 - - {α α}_{i i j j} ((t t)))) \leq \leq 00$

式中，Φ(i)以节点i为末端节点的支路首端节点集合，Ψ(i)以节点i为首端节点的支路末端节点集合；U_i(t)为t时段节点i的电压幅值，x_ij为支路ij的电抗；P_ij(t)为t时段节点i流向节点j的有功功率，Q_ij(t)为t时段节点i流向节点j的无功功率；Ω(i)为节点i的相邻节点的集合；P_i(t)为t时段节点i上注入的有功功率之和，P_DG，i(t)、P_SNOP，i(t)、P_LOAD，i(t)分别为t时段节点i上分布式电源注入的有功功率、SNOP传输的有功功率、负荷消耗的有功功率，Q_i(t)为t时段节点i上注入的有功功率之和，Q_DG，i(t)、Q_SNOP，i(t)、Q_LOAD，i(t)分别为t时段节点i上分布式电源注入的无功功率、SNOP发出的无功功率、负荷消耗的无功功率；M为一个极大值。In the formula, Φ(i) is the head-end node set of the branch with node i as the end node, Ψ(i) is the end-node set of the branch with node i as the head-end node; U _i (t) is the voltage of node i during t amplitude, x _ij is the reactance of branch ij; P _ij (t) is the active power flowing from node i to node j during t period, Q _ij (t) is the reactive power flowing from node i to node j during t period; Ω(i ) is the set of adjacent nodes of node i; P _i (t) is the sum of active power injected on node i during t period, P _DG,i (t), P _SNOP,i (t), P _LOAD,i ( t) are respectively the active power injected by distributed power generation, the active power transmitted by SNOP, and the active power consumed by loads on node i in period t, Q _i (t) is the sum of the active power injected on node i in period t, _{QDG, i} (t), Q _{SNOP, i} (t), Q _{LOAD, i} (t) are the reactive power injected by the distributed power source on node i, the reactive power emitted by the SNOP, and the reactive power consumed by the load during the period t, respectively; M is a maximum value.

步骤2)所述的智能软开关运行约束表示为Step 2) the intelligent soft switch operating constraints described as

P_m，1(t)+P_m，2(t)+A_m，1|P_m，1(t)|+A_m，2|P_m，2(t)|＝0P _{m, 1} (t)+P _{m, 2} (t)+A _{m, 1} |P _{m, 1} (t)|+A _{m, 2} |P _{m, 2} (t)|＝0

$\sqrt{{P P}_{m m,, 11}^{22} ((t t)) + + {Q Q}_{m m,, 11}^{22} ((t t))} \leq \leq {S S}_{m m,, 11,, max max}$

$\sqrt{{P P}_{m m,, 22}^{22} ((t t)) + + {Q Q}_{m m,, 22}^{22} ((t t))} \leq \leq {S S}_{m m,, 22,, max max}$

-Q_m，1，max≤Q_m，1(t)≤Q_m，1，max -Q _{m, 1, max} ≤ Q _{m, 1} (t) ≤ Q _{m, 1, max}

-Q_m，2，max≤Q_m，2(t)≤Q_m，2，max -Q _{m, 2, max} ≤ Q _{m, 2} (t) ≤ Q _{m, 2, max}

式中，Q_m，1(t)和Q_m，2(t)为t时段第m个智能软开关的两个换流器输出的无功功率；S_m，1，max、S_m，2，max、Q_m，1，max、Q_m，2，max分别为第m个智能软开关两个换流器的接入容量和所能输出的无功功率上限。In the formula, Q _m,1 (t) and Q _m,2 (t) are the reactive power output by the two converters of the mth intelligent soft switch in the t period; S _m,1,max , S _{m,2 , max} , Q _{m, 1, max} , Q _{m, 2, max} are respectively the access capacity of the mth smart soft switch and the upper limit of the output reactive power of the two converters.

本发明的一种同时考虑开关动作的配电网智能软开关运行优化方法，立足于解决考虑开关动作费用的前提下多个时间断面联络开关与SNOP并存的配电网运行时序优化问题，其数学本质是大规模混合整数非线性规划问题(MINLP)，目前已有的方法均难以快速准确求解。本发明依据锥优化算法的基本原理，对优化模型的目标函数与约束条件进行了锥转化，将原问题转化为混合整数二阶锥规划问题(MISOCP)，大大降低了求解难度，便于使用求解工具进行求解。本发明所采用的锥优化方法可以对网络重构和SNOP运行优化问题进行统一描述，使得复杂的混合整数非线性规划的问题求解得以实现，避免了繁琐的迭代和大量的测试，在计算速度上有较大地提升。并且，因为锥所具有的优美的几何结构和特殊的处理方式，使其能够保证所求解问题的解的最优性，将其应用到SNOP运行的时序优化问题中，可以快速获得最优的系统运行方案。An intelligent soft switch operation optimization method for distribution network considering switch action at the same time in the present invention is based on solving the operation timing optimization problem of distribution network in which multiple time-section contact switches and SNOP coexist under the premise of considering the cost of switch action. The essence is a large-scale mixed integer nonlinear programming problem (MINLP), and the existing methods are difficult to solve quickly and accurately. Based on the basic principle of the cone optimization algorithm, the present invention performs cone transformation on the objective function and constraint conditions of the optimization model, transforms the original problem into a mixed integer second-order cone programming problem (MISOCP), greatly reduces the difficulty of solving, and is easy to use and solve tool to solve. The cone optimization method adopted in the present invention can uniformly describe the network reconfiguration and SNOP operation optimization problems, so that the solution of complex mixed integer nonlinear programming problems can be realized, tedious iterations and a large number of tests are avoided, and the calculation speed is improved. There is a big improvement. Moreover, because of the beautiful geometric structure and special processing method of the cone, it can guarantee the optimality of the solution of the problem to be solved. Applying it to the timing optimization problem of SNOP operation can quickly obtain the optimal system Run the scenario.

附图说明Description of drawings

图1是本发明同时考虑开关动作的配电网智能软开关运行优化方法的流程图；Fig. 1 is the flow chart of the distribution network intelligent soft switch operation optimization method considering switching action simultaneously in the present invention;

图2是IEEE 33节点算例以及分布式电源、SNOP接入位置图；Figure 2 is an IEEE 33 node calculation example and a diagram of distributed power supply and SNOP access location;

图3是分布式电源及负荷运行特性预测曲线；Figure 3 is the prediction curve of distributed power supply and load operation characteristics;

图4是开关动作费用转换系数为5kWh/个时各时间断面开关动作情况；Figure 4 shows the switching action of each time section when the switching cost conversion factor is 5kWh/piece;

图5a是不同开关动作费用转换系数对应SNOP传输有功功率变化情况；Figure 5a shows the change of SNOP transmission active power corresponding to different switching action cost conversion coefficients;

图5b是不同开关动作费用转换系数对应SNOP发出无功功率变化情况；Figure 5b shows the change of reactive power emitted by SNOP corresponding to different switching action cost conversion coefficients;

图6是不同开关动作费用转换系数对应节点18电压变化情况。FIG. 6 shows the voltage change of node 18 corresponding to different switching operation cost conversion factors.

具体实施方式Detailed ways

下面结合实施例和附图对本发明的一种同时考虑开关动作的配电网智能软开关运行优化方法做出详细说明。A method for optimizing the operation of distribution network intelligent soft switches while considering the switching action of the present invention will be described in detail below in conjunction with the embodiments and the accompanying drawings.

如图1所示，本发明的一种同时考虑开关动作的配电网智能软开关运行优化方法，包括如下步骤：As shown in Fig. 1, a kind of distribution network intelligent soft switch operation optimization method of the present invention that considers switching action at the same time, comprises the following steps:

1)根据选定的配电系统，输入线路参数、负荷水平和网络拓扑连接关系，系统运行电压水平和支路电流限制，分布式电源接入位置、类型和容量及参数，智能软开关(SNOP)接入位置和容量及参数，运行优化周期内负荷及分布式电源运行特性预测曲线，以及系统基准电压和基准功率等初值；1) According to the selected power distribution system, input line parameters, load level and network topology connection relationship, system operating voltage level and branch current limit, distributed power access location, type and capacity and parameters, intelligent soft switch (SNOP ) access location, capacity and parameters, load and distributed power supply operating characteristic prediction curves within the operation optimization period, and initial values such as system reference voltage and reference power;

2)依据步骤1)提供的配电系统结构及参数，同时考虑网络损耗、网络重构的开关动作费用等效损耗及智能软开关的运行损耗，建立配电网联络开关和智能软开关协同运行的时序优化模型，包括：选取根节点为平衡节点，设定配电系统运行损耗最小为目标函数，分别考虑网络拓扑约束、系统潮流约束、系统运行约束、智能软开关运行约束；其中：2) Based on the structure and parameters of the power distribution system provided in step 1), the network loss, the equivalent loss of the switching action cost of network reconstruction and the operation loss of the intelligent soft switch are considered at the same time, and the coordinated operation of the distribution network tie switch and the intelligent soft switch is established. The timing optimization model of , including: selecting the root node as the balance node, setting the minimum operating loss of the power distribution system as the objective function, and considering network topology constraints, system power flow constraints, system operation constraints, and intelligent soft-switching operation constraints; among them:

(1)所述的配电系统运行损耗最小为目标函数，表示为(1) The minimum operating loss of the power distribution system is the objective function, expressed as

min f＝E_S，loss+E_L，loss+E_SNOP，loss (1)min f = E _{S, loss} + E _{L, loss} + E _{SNOP, loss} (1)

${E E.}_{S S,, l l o o s the s s the s} = = {C C}_{S S} {Σ Σ}_{t t = = 11}^{{N N}_{T T}} {Σ Σ}_{i i = = 11}^{{N N}_{N N}} {Σ Σ}_{j j &Element; &Element; Ω Ω ((i i))} | | {α α}_{i i j j} ((t t)) - - {α α}_{i i j j} ((t t - - 11)) | | - - - - - - ((22))$

${E E.}_{L L,, l l o o s the s s the s} = = {Σ Σ}_{t t = = 11}^{{N N}_{T T}} {Σ Σ}_{i i = = 11}^{{N N}_{N N}} {Σ Σ}_{j j &Element; &Element; Ω Ω ((i i))} {r r}_{i i j j} {I I}_{i i j j}^{22} ((t t)) Δ Δ t t - - - - - - ((33))$

${E E.}_{S S N N O o P P,, l l o o s the s s the s} = = {Σ Σ}_{t t = = 11}^{{N N}_{T T}} {Σ Σ}_{m m = = 11}^{{N N}_{S S N N O o P P}} (({A A}_{m m,, 11} | | {P P}_{m m,, 11} ((t t)) | | + + {A A}_{m m,, 22} | | {P P}_{m m,, 22} ((t t)) | |)) Δ Δ t t - - - - - - ((44))$

(2)所述的网络拓扑约束表示为(2) The network topology constraints described in (2) are expressed as

α_ij(t)＝β_ij(t)+β_ji(t) (5)α _ij (t) = β _ij (t) + β _ji (t) (5)

${Σ Σ}_{j j &Element; &Element; Ω Ω ((i i))} {β β}_{i i j j} ((t t)) = = 11,, &ForAll; &ForAll; i i &Element; &Element; {N N}_{N N} \ \ {N N}_{S S} - - - - - - ((66))$

${Σ Σ}_{j j &Element; &Element; Ω Ω ((i i))} {β β}_{i i j j} ((t t)) = = 00,, &ForAll; &ForAll; i i &Element; &Element; {N N}_{S S} - - - - - - ((77))$

α_ij(t)∈{0，1} (8)α _ij (t) ∈ {0, 1} (8)

β_ij(t)∈{0，1} (9)β _ij (t) ∈ {0, 1} (9)

(3)所述的系统潮流约束表示为(3) The system power flow constraint described in (3) is expressed as

${Σ Σ}_{j j &Element; &Element; Φ Φ ((i i))} (({P P}_{j j i i} ((t t)) - - {r r}_{j j i i} {I I}_{j j i i}^{22} ((t t)))) + + {P P}_{i i} ((t t)) = = {Σ Σ}_{k k &Element; &Element; Ψ Ψ ((i i))} {P P}_{i i k k} ((t t)) - - - - - - ((1010))$

${Σ Σ}_{j j &Element; &Element; Φ Φ ((i i))} (({Q Q}_{j j i i} ((t t)) - - {x x}_{j j i i} {I I}_{j j i i}^{22} ((t t)))) + + {Q Q}_{i i} ((t t)) = = {Σ Σ}_{k k &Element; &Element; Ψ Ψ ((i i))} {Q Q}_{i i k k} ((t t)) - - - - - - ((1111))$

P_i(t)＝P_DG，i(t)+P_SNOP，i(t)-P_LOAD，i(t) (12)P _i (t) = P _{DG, i} (t) + P _{SNOP, i} (t) - P _{LOAD, i} (t) (12)

Q_i(t)＝Q_DG，i(t)+Q_SNOP，i(t)-Q_LOAD，i(t) (13)Q _i (t) = Q _{DG, i} (t) + Q _{SNOP, i} (t) - Q _{LOAD, i} (t) (13)

${I I}_{i i j j}^{22} ((t t)) = = \frac{{P P}_{i i j j}^{22} ((t t)) + + {Q Q}_{i i j j}^{22} ((t t))}{{U u}_{i i}^{22} ((t t))} - - - - - - ((1414))$

${U u}_{i i}^{22} ((t t)) - - {U u}_{j j}^{22} ((t t)) - - 22 (({r r}_{i i j j} {P P}_{i i j j} ((t t)) + + {x x}_{i i j j} {Q Q}_{i i j j} ((t t)))) + + (({r r}_{i i j j}^{22} + + {x x}_{i i j j}^{22})) {I I}_{i i j j}^{22} ((t t)) + + M m ((11 - - {α α}_{i i j j} ((t t)))) &GreaterEqual; &Greater Equal; 00 - - - - - - ((1515))$

${U u}_{i i}^{22} ((t t)) - - {U u}_{j j}^{22} ((t t)) - - 22 (({r r}_{i i j j} {P P}_{i i j j} ((t t)) + + {x x}_{i i j j} {Q Q}_{i i j j} ((t t)))) + + (({r r}_{i i j j}^{22} + + {x x}_{i i j j}^{22})) {I I}_{i i j j}^{22} ((t t)) - - M m ((11 - - {α α}_{i i j j} ((t t)))) \leq \leq 00 - - - - - - ((1616))$

式中，Φ(i)以节点i为末端节点的支路首端节点集合，Ψ(i)以节点i为首端节点的支路末端节点集合；U_i(t)为t时段节点i的电压幅值，x_ij为支路ij的电抗；P_ij(t)为t时段节点i流向节点j的有功功率，Q_ij(t)为t时段节点i流向节点j的无功功率；Ω(i)为节点i的相邻节点的集合；P_i(t)为t时段节点i上注入的有功功率之和，P_DG，i(t)、P_SNOP，i(t)、P_LOAD，i(t)分别为t时段节点i上分布式电源注入的有功功率、SNOP传输的有功功率、负荷消耗的有功功率，Q_i(t)为t时段节点i上注入的有功功率之和，Q_DG，i(t)、Q_SNOP，i(t)、Q_LOAD，i(t)分别为t时段节点i上分布式电源注入的无功功率、智能软开关发出的无功功率、负荷消耗的无功功率；M为一个极大值。In the formula, Φ(i) is the head-end node set of the branch with node i as the end node, Ψ(i) is the end-node set of the branch with node i as the head-end node; U _i (t) is the voltage of node i during t amplitude, x _ij is the reactance of branch ij; P _ij (t) is the active power flowing from node i to node j during t period, Q _ij (t) is the reactive power flowing from node i to node j during t period; Ω(i ) is the set of adjacent nodes of node i; P _i (t) is the sum of active power injected on node i during t period, P _DG,i (t), P _SNOP,i (t), P _LOAD,i ( t) are respectively the active power injected by distributed power generation, the active power transmitted by SNOP, and the active power consumed by loads on node i in period t, Q _i (t) is the sum of the active power injected on node i in period t, _{QDG, i} (t), Q _{SNOP, i} (t), Q _{LOAD, i} (t) are respectively the reactive power injected by the distributed power source on node i during the t period, the reactive power emitted by the intelligent soft switch, and the reactive power consumed by the load Power; M is a maximum value.

(4)所述的系统运行约束表示为(4) The system operation constraints described in (4) are expressed as

${U u}_{i i,, min min}^{22} \leq \leq {U u}_{i i}^{22} ((t t)) \leq \leq {U u}_{i i,, max max}^{22} - - - - - - ((1717))$

${I I}_{i i j j}^{22} ((t t)) \leq \leq {I I}_{i i j j,, max max}^{22} - - - - - - ((1818))$

-Mα_ij(t)≤P_ii(t)≤Mα_ij(t) (19)-Mα _ij (t)≤P _ii (t)≤Mα _ij (t) (19)

-Mα_ij(t)≤Q_ij(t)≤Mα_ij(t) (20)-Mα _ij (t)≤Q _ij (t)≤Mα _ij (t) (20)

$00 \leq \leq {I I}_{i i j j}^{22} ((t t)) \leq \leq {Mα Mα}_{i i j j} ((t t)) - - - - - - ((21 twenty one))$

式中，U_i，min和U_i，max分别为节点i的最小允许电压值和最大允许电压值；I_ij，max为该支路的最大允许电流值。In the formula, U _{i, min} and U _{i, max} are the minimum allowable voltage value and maximum allowable voltage value of node i respectively; I _{ij, max} is the maximum allowable current value of the branch.

(5)所述的智能软开关运行约束表示为(5) The intelligent soft switch operation constraint described in is expressed as

P_m，1(t)+P_m，2(t)+A_m，1|P_m，1(t)|+A_m，2|P_m，2(t)|＝0 (22)P _{m, 1} (t)+P _{m, 2} (t)+A _{m, 1} |P _{m, 1} (t)|+A _{m, 2} |P _{m, 2} (t)|＝0 (22)

$\sqrt{{P P}_{m m,, 11}^{22} ((t t)) + + {Q Q}_{m m,, 11}^{22} ((t t))} \leq \leq {S S}_{m m,, 11,, max max} - - - - - - ((23 twenty three))$

$\sqrt{{P P}_{m m,, 22}^{22} ((t t)) + + {Q Q}_{m m,, 22}^{22} ((t t))} \leq \leq {S S}_{m m,, 22,, max max} - - - - - - ((24 twenty four))$

-Q_m，1，max≤Q_m，1(t)≤Q_m，1，max (25)-Q _m,1,max ≤Q _m,1 (t)≤Q _m,1,max (25)

-Q_m，2，max≤Q_m，2(t)≤Q_m，2，max (26)-Q _m,2,max ≤Q _m,2 (t)≤Q _m,2,max (26)

3)根据锥优化的标准形式对步骤2)所述的配电网联络开关和智能软开关协同运行的时序优化模型中的目标函数和非线性约束进行锥模型转化，具体转化方法如下：3) According to the standard form of cone optimization, the objective function and nonlinear constraints in the timing optimization model of the coordinated operation of distribution network tie switch and intelligent soft switch described in step 2) are transformed into cone model. The specific transformation method is as follows:

(1)目标函数开关动作费用等效损耗 $E_{S, l o s s} = C_{S} Σ_{t = 1}^{N_{T}} Σ_{i = 1}^{N_{N}} Σ_{j &Element; Ω (i)} | α_{i j} (t) - α_{i j} (t - 1) | - - - (2)$ 中含有绝对值项|α_ij(t)-α_ij(t-1)|，引入辅助变量(1) Equivalent loss of switching action cost of objective function ${E.}_{S, l o the s the s} = C_{S} Σ_{t = 1}^{N_{T}} Σ_{i = 1}^{N_{N}} Σ_{j &Element; Ω (i)} | α_{i j} (t) - α_{i j} (t - 1) | - - - (2)$ Contains the absolute value item |α _ij (t)-α _ij (t-1)|, introduces the auxiliary variable

M₀(t)＝|α_ii(t)-α_ij(t-1)|＝max{α_ij(t)-α_ij(t-1)，α_ij(t-1)-α_ij(t)}，并增加约束M ₀ (t)＝|α _ii (t)-α _ij (t-1)|＝max{α _ij (t)-α _ij (t-1), α _ij (t-1)-α _ij (t )}, and add constraints

M₀(t)≥0 (27)M ₀ (t)≥0 (27)

M₀(t)≥α_ij(t)-α_ij(t-1) (28)M ₀ (t)≥α _ij (t)-α _ij (t-1) (28)

M₀(t)≥α_ij(t-1)-α_ij(t) (29)M ₀ (t)≥α _ij (t-1)-α _ij (t) (29)

(2)目标函数网络损耗与约束条件(10)、(11)、(14)～(18)和(21)中的含有二次项和采用U_2，i(t)和I_2，ij(t)替换二次项和将其线性化。(2) Objective function network loss Contains quadratic terms in constraints (10), (11), (14)～(18) and (21) and Replace quadratic terms with U _2,i (t) and I _2,ij (t) and to linearize it.

(3)目标函数智能软开关的运行损耗(3) Operating loss of objective function intelligent soft switch

$E_{S N O P, l o s s} = Σ_{t = 1}^{N_{T}} Σ_{m = 1}^{N_{S N O P}} (A_{m, 1} | P_{m, 1} (t) | + A_{m, 2} | P_{m, 2} (t) |) Δ t - - - (4)$ 与 ${E.}_{S N o P, l o the s the s} = Σ_{t = 1}^{N_{T}} Σ_{m = 1}^{N_{S N o P}} (A_{m, 1} | P_{m, 1} (t) | + A_{m, 2} | P_{m, 2} (t) |) Δ t - - - (4)$ and

智能软开关运行约束条件P_m，1(t)+P_m，2(t)+A_m，1|P_m，1(t)|+A_m，2|P_m，2(t)|＝0(22)中含有绝对值项|P_m，1(t)|和|P_m，2(t)|，引入辅助变量M₁(t)＝|P_m，1(t)|＝max{P_m，1(t)，-P_m，1(t)}和M₂(t)＝|p_m，2(t)|＝max{P_m，2(t)，-P_m，2(t)}，并增加约束Intelligent soft switch operating constraints P _{m, 1} (t)+P _{m, 2} (t)+A _{m, 1} |P _{m, 1} (t)|+A _{m, 2} |P _{m, 2} (t)|= 0(22) contains absolute value items |P _{m, 1} (t)| and |P _{m, 2} (t)|, and introduces auxiliary variable M ₁ (t)=|P _{m, 1} (t)|=max{ P _{m, 1} (t), -P _{m, 1} (t)} and M ₂ (t) = | p _{m, 2} (t) | = max{P _{m, 2} (t), -P _{m, 2} ( t)}, and add constraints

M₁(t)≥0 (29)M ₁ (t)≥0 (29)

M₂(t)≥0 (30)M ₂ (t)≥0 (30)

M₁(t)≥P_m，1(t) (31)M ₁ (t)≥P _m,1 (t) (31)

M₁(t)≥-P_m，1(t) (32)M ₁ (t)≥-P _m,1 (t) (32)

M₂(t)≥P_m，2(t) (33)M ₂ (t) ≥ P _m,2 (t) (33)

M₂(t)≥-P_m，2(t) (34)M ₂ (t)≥-P _m,2 (t) (34)

(4)系统潮流约束为经上述步骤替换后非线性约束，将其松驰为二阶锥约束(4) System power flow constraints To replace the nonlinear constraint after the above steps, relax it into a second-order cone constraint

||[2P_ij(t) 2Q_ij(t) I_2，ij(t)-U_2，i(t)]^T||₂≤I_2，ij(t)-U_2，i(t) (35)||[2P _ij (t) 2Q _ij (t) I _{2, ij} (t)-U _{2, i} (t)] ^T || ₂ ≤ I _{2, ij} (t)-U _{2, i} (t) ( 35)

(5)智能软开关运行约束中 $\sqrt{P_{m, 1}^{2} (t) + Q_{m, 1}^{2} (t)} \leq S_{m, 1, \max} - - - (23)$ 和(5) The intelligent soft switch is under operation constraints $\sqrt{P_{m, 1}^{2} (t) + Q_{m, 1}^{2} (t)} \leq S_{m, 1, \max} - - - (twenty three)$ and

为非线性约束，将其转换为旋转锥约束 is a nonlinear constraint, convert it to a rotating cone constraint

${P P}_{m m,, 11}^{22} ((t t)) + + {Q Q}_{m m,, 11}^{22} ((t t)) \leq \leq 22 \frac{{s the s}_{m m,, 11,, max max}}{\sqrt{22}} \frac{{s the s}_{m m,, 11,, max max}}{\sqrt{22}} - - - - - - ((3636))$

${P P}_{m m,, 22}^{22} ((t t)) + + {Q Q}_{m m,, 22}^{22} ((t t)) \leq \leq 22 \frac{{s the s}_{m m,, 22,, max max}}{\sqrt{22}} \frac{{s the s}_{m m,, 22,, max max}}{\sqrt{22}} - - - - - - ((3737))$

本发明基于锥优化算法实现了开关状态、智能软开关最优传输功率和潮流的同时求解。建立了考虑开关动作费用的配电网联络开关和智能软开关协同运行的时序优化问题的数学模型，不仅从单个时间断面考虑了开关动作和智能软开关运行约束，而且考虑了相邻时间断面间开关变化的连续性和时序关系。Based on the cone optimization algorithm, the invention realizes the simultaneous solution of the switch state, the optimal transmission power of the intelligent soft switch and the power flow. A mathematical model for the timing optimization problem of the coordinated operation of the distribution network tie switch and the intelligent soft switch considering the switching action cost is established, not only considering the switching action and the operating constraints of the intelligent soft switch from a single time section, but also considering the The continuity and timing relationship of switching changes.

下面给出具体实例：Specific examples are given below:

对于本实施例，首先输入如图2所示的IEEE 33节点系统中线路元件的阻抗值，负荷元件的有功功率、无功功率，网络拓扑连接关系，详细参数见表1和表2；然后设定5台风电机组的接入位置为节点10、16、17、30、33，接入容量分别为500kVA、300kVA、200kVA、200kVA、300kVA，3台光伏系统的接入位置为节点7、13、27，接入容量分别为500kVA、300kVA、400kVA，功率因数均为1.0；再次设定一组SNOP接入配电网，取代联络开关TS1，两个换流器的容量均为500kVA，无功功率输出上限均为200kVar；然后，以天为单位，以1小时为时间间隔，利用负荷预测方法来模拟负荷以及风电、光伏的日运行曲线，如图3所示；最后设置系统的基准电压为12.66kV、基准功率为1MVA，极大值M取9999。For the present embodiment, first input the impedance value of the line element in the IEEE 33 node system as shown in Figure 2, the active power of the load element, the reactive power, the network topology connection relationship, the detailed parameters are shown in Table 1 and Table 2; then set The access positions of 5 wind turbines are set as nodes 10, 16, 17, 30, and 33, and the access capacities are 500kVA, 300kVA, 200kVA, 200kVA, 300kVA respectively. The access positions of 3 photovoltaic systems are nodes 7, 13, 27. The connected capacity is 500kVA, 300kVA and 400kVA respectively, and the power factor is 1.0; set a group of SNOPs connected to the distribution network again to replace the tie switch TS1, the capacity of the two converters is 500kVA, and the reactive power The upper limit of the output is 200kVar; then, using the day as the unit and the time interval of 1 hour, use the load forecasting method to simulate the load and the daily operation curves of wind power and photovoltaics, as shown in Figure 3; finally set the system's reference voltage to 12.66 kV, the reference power is 1MVA, and the maximum value M is 9999.

表1 IEEE33节点算例负荷接入位置及功率Table 1 Load connection location and power of IEEE33 node example

表2 IEEE33节点算例线路参数Table 2 IEEE33 node calculation line parameters

本实施例以1小时为一个时间断面对联络开关和智能软开关并存的配电网进行时序优化，优化结果见表3，方案2的开关动作情况如图4所示。In this embodiment, the time sequence optimization of the distribution network where the tie switch and the intelligent soft switch coexist is carried out with 1 hour as a time section. The optimization results are shown in Table 3.

表3 不同开关费用方案优化结果Table 3 Optimization results of different switching cost schemes

执行优化计算的计算机硬件环境为Intel(R)Xeon(R)CPU E5-1620，主频为3.70GHz，内存为32GB；软件环境为Windows 7操作系统。The computer hardware environment for performing optimized calculations is Intel(R) Xeon(R) CPU E5-1620, the main frequency is 3.70GHz, and the memory is 32GB; the software environment is Windows 7 operating system.

优化方案考虑不同的开关动作费用转换系数，对联络开关与智能软开关并存的配电网运行进行时序优化，并考虑智能软开关传输功率过程中产生的损耗，智能软开关传输的有功功率与在节点22出发出的无功功率见图5。随着开关动作费用转换系数的增大，开关动作次数明显减少，有助于提高开关的使用寿命。另一方面，网络重构和智能软开关运行优化可以在一定程度上改善系统的运行电压水平，如图6所示，进一步改善电能质量、提高供电可靠性。The optimization scheme considers different switching cost conversion coefficients, and optimizes the time sequence of the distribution network operation where the tie switch and the intelligent soft switch coexist, and considers the loss generated during the power transmission process of the intelligent soft switch, and the active power transmitted by the intelligent soft switch. The reactive power emitted by node 22 is shown in Fig. 5 . With the increase of the switching cost conversion factor, the number of switching operations is significantly reduced, which helps to improve the service life of the switch. On the other hand, network reconfiguration and intelligent soft switching operation optimization can improve the operating voltage level of the system to a certain extent, as shown in Figure 6, further improving power quality and power supply reliability.

配电网联络开关和智能软开关协同运行的时序优化问题的数学本质是大规模混合整数非线性规划问题，目前已有的优化方法大多无法求解这一问题。本发明提出的一种同时考虑开关动作的配电网智能软开关运行优化方法，能够快速、准确的求解此类问题，并能保证解的最优性。针对方案二，同时采用一种基于模拟退火和锥优化的混合求解方法进行求解，并对解的最优性和计算性能进行比较，比较结果见表4。The mathematical essence of the timing optimization problem for the coordinated operation of distribution network tie switches and intelligent soft switches is a large-scale mixed integer nonlinear programming problem, and most of the existing optimization methods cannot solve this problem. The invention proposes an intelligent soft switch operation optimization method for a distribution network that simultaneously considers switch actions, can quickly and accurately solve such problems, and can ensure the optimality of the solution. For the second scheme, a hybrid solution method based on simulated annealing and cone optimization is used to solve the problem, and the optimality and calculation performance of the solution are compared. The comparison results are shown in Table 4.

表4 不同求解方法计算性能比较Table 4 Computational performance comparison of different solution methods

// 本发明方法The method of the invention 混合优化方法hybrid optimization method 目标函数值(元)Objective function value (element) 463.08463.08 485.31485.31 求解时间(s)Solution time (s) 27.1227.12 1429.681429.68

Claims

1. A distribution network intelligent soft switch operation optimization method considering switching action simultaneously, is characterized in that, comprises the steps:

1) According to the selected power distribution system, input line parameters, load level and network topology connection relationship, system operating voltage level and branch current limit, distributed power access location, type, capacity and parameters, intelligent soft switch access Location and capacity and parameters, load and distributed power supply operating characteristic prediction curves within the operation optimization period, and initial values of system reference voltage and reference power;

2) Based on the structure and parameters of the power distribution system provided in step 1), the network loss, the equivalent loss of the switching action cost of network reconstruction and the operation loss of the intelligent soft switch are considered at the same time, and the coordinated operation of the distribution network tie switch and the intelligent soft switch is established. The timing optimization model of , including: selecting the root node as the balance node, setting the minimum operating loss of the distribution system as the objective function, and considering network topology constraints, system power flow constraints, system operation constraints, and intelligent soft switch operation constraints;

3) According to the standard form of cone optimization, the objective function and nonlinear constraints in the timing optimization model of the distribution network tie switch and intelligent soft switch cooperating described in step 2) are converted into cone model;

4) After the transformation in step 3), the objective function is linearized respectively, and the nonlinear constraint is converted into a linear constraint, a second-order cone constraint or a rotating cone constraint, and the obtained mathematical model is solved by a CPLEX solver;

5) Output the solution result of step 4), including the switch state, the optimal transmission power value of the intelligent soft switch, the network power flow result and the objective function value.

2. a kind of distribution network intelligent soft switch operation optimization method considering switch action simultaneously according to claim 1, is characterized in that, step 2) described power distribution system operating loss minimum is an objective function, expressed as

min f = E _{S, loss} + E _{L, loss} + E _{SNOP, loss}

In the formula, the switching action cost equivalent loss E _{S, loss} , the network loss E _{L, loss} and the operating loss E _{SNOP, loss} of the intelligent soft switch are respectively expressed by the following formula

{E E.}_{S S,, l l o o s the s s the s} = = {C C}_{S S} {Σ Σ}_{t t = = 11}^{{N N}_{T T}} {Σ Σ}_{i i = = 11}^{{N N}_{N N}} {Σ Σ}_{j j &Element; &Element; Ω Ω ((i i))} | | {α α}_{i i j j} ((t t)) - - {α α}_{i i j j} ((t t - - 11)) | |

{E E.}_{L L,, l l o o s the s s the s} = = {Σ Σ}_{t t = = 11}^{{N N}_{T T}} {Σ Σ}_{i i = = 11}^{{N N}_{N N}} {Σ Σ}_{j j &Element; &Element; Ω Ω ((i i))} {r r}_{i i,, j j} {I I}_{i i j j}^{22} ((t t)) Δ Δ t t

{E E.}_{S S N N O o P P,, l l o o s the s s the s} = = {Σ Σ}_{t t = = 11}^{{N N}_{T T}} {Σ Σ}_{m m = = 11}^{{N N}_{S S N N O o P P}} (({A A}_{m m,, 11} | | {P P}_{m m,, 11} ((t t)) | | + + {A A}_{m m .2 .2} | | {P P}_{m m,, 22} ((t t)) | |)) Δ Δ t t

In the formula, C _S is the equivalent conversion coefficient of the switching action cost; Δt is the period interval for optimal calculation; _NT is the number of time periods for optimal calculation, N _N is the total number of nodes in the system, and N _SNOP is the smart soft switch connected to the system Ω(i) is the set of adjacent nodes of node i; α _ij (t) is the switch state of branch ij in period t; r _ij is the resistance of branch ij, I _ij (t) is the period of t The amplitude of the current flowing from node i to node j; P _m,1 (t) and P _m,2 (t) are the active output power of the two converters of the mth intelligent soft switch in the t period, A _m,1 and A _m,2 is the active power loss coefficient of the two converters of the mth intelligent soft switch.

3. a kind of distribution network intelligent soft switch operation optimization method considering switching action simultaneously according to claim 1, it is characterized in that, the network topology constraint described in step 2) is expressed as

α _ij (t)=β _ij (t)+β _ji (t)

{Σ Σ}_{j j &Element; &Element; Ω Ω ((i i))} {β β}_{i i j j} ((t t)) = = 11,, &ForAll; &ForAll; i i &Element; &Element; {N N}_{N N} \ \ {N N}_{S S}

{Σ Σ}_{j j &Element; &Element; Ω Ω ((i i))} {β β}_{i i j j} ((t t)) = = 00,, &ForAll; &ForAll; i i &Element; &Element; {N N}_{S S}

α _ij (t) ∈ {0, 1}

β _ij (t) ∈ {0, 1}

In the formula, N _S is the number of source nodes in the system; β _ij (t) represents the relationship between node i and node j in period t, and is 1 when node j is the parent node of node i, otherwise it is 0.

4. a kind of distribution network intelligent soft switching operation optimization method considering switching action simultaneously according to claim 1, is characterized in that, the system power flow constraint described in step 2) is expressed as

{Σ Σ}_{j j &Element; &Element; Φ Φ ((i i))} (({P P}_{j j i i} ((t t)) - - {r r}_{j j i i} {I I}_{j j i i}^{22} ((t t)))) + + {P P}_{i i} ((t t)) = = {Σ Σ}_{k k &Element; &Element; Ψ Ψ ((i i))} {P P}_{i i k k} ((t t))

{Σ Σ}_{j j &Element; &Element; Φ Φ ((i i))} (({Q Q}_{j j i i} ((t t)) - - {x x}_{. . i i i i} {I I}_{j j i i}^{22} ((t t)))) + + {Q Q}_{i i} ((t t)) = = {Σ Σ}_{k k &Element; &Element; Ψ Ψ ((i i))} {Q Q}_{i i k k} ((t t))

P _i (t) = P _{DG, i} (t) + P _{SNOP, i} (t) - P _{LOAD, i} (t)

Q _i (t) = Q _{DG, i} (t) + Q _{SNOP, i} (t) - Q _{LOAD, i} (t)

{I I}_{i i j j}^{22} ((t t)) = = \frac{{P P}_{i i j j}^{22} ((t t)) + + {Q Q}_{i i j j}^{22} ((t t))}{{U u}_{i i}^{22} ((t t))}

{U u}_{i i}^{22} ((t t)) - - {U u}_{j j}^{22} ((t t)) - - 22 (({r r}_{i i j j} {P P}_{i i j j} ((t t)) + + {x x}_{i i j j} {Q Q}_{i i j j} ((t t)))) + + (({r r}_{i i j j}^{22} + + {x x}_{i i j j}^{22})) {I I}_{i i j j}^{22} ((t t)) + + M m ((11 - - {α α}_{i i j j} ((t t)))) &GreaterEqual; &Greater Equal; 00

{U u}_{i i}^{22} ((t t)) - - {U u}_{j j}^{22} ((t t)) - - 22 (({r r}_{i i j j} {P P}_{i i j j} ((t t)) + + {x x}_{i i j j} {Q Q}_{i i j j} ((t t)))) + + (({r r}_{i i j j}^{22} + + {x x}_{i i j j}^{22})) {I I}_{i i j j}^{22} ((t t)) - - M m ((11 - - {α α}_{i i j j} ((t t)))) \leq \leq 00

In the formula, Φ(i) is the head-end node set of the branch with node i as the end node, Ψ(i) is the end-node set of the branch with node i as the head-end node; U _i (t) is the voltage of node i during t amplitude, x _ij is the reactance of branch ij; P _ij (t) is the active power flowing from node i to node j during t period, Q _ij (t) is the reactive power flowing from node i to node j during t period; Ω(i ) is the set of adjacent nodes of node i; P _i (t) is the sum of active power injected on node i during t period, P _DG,i (t), P _SNOP,i (t), P _LOAD,i ( t) are respectively the active power injected by distributed power generation, the active power transmitted by SNOP, and the active power consumed by loads on node i in period t, Q _i (t) is the sum of the active power injected on node i in period t, _{QDG, i} (t), Q _{SNOP, i} (t), Q _{LOAD, i} (t) are the reactive power injected by the distributed power source on node i, the reactive power emitted by the SNOP, and the reactive power consumed by the load during the period t, respectively; M is a maximum value.

5. a kind of distribution network intelligent soft switch operation optimization method considering switch action simultaneously according to claim 1, is characterized in that, the intelligent soft switch operation constraint described in step 2) is expressed as

P _{m, 1} (t)+P _{m, 2} (t)+A _{m, 1} |P _{m, 1} (t)|+A _{m, 2} |P _{m, 2} (t)|＝0

\sqrt{{P P}_{m m,, 11}^{22} ((t t)) + + {Q Q}_{m m,, 11}^{22} ((t t))} \leq \leq {S S}_{m m,, 11,, max max}

\sqrt{{P P}_{m m,, 22}^{22} ((t t)) + + {Q Q}_{m m,, 22}^{22} ((t t))} \leq \leq {S S}_{m m,, 22,, max max}

-Q _{m, 1, max} ≤ Q _{m, 1} (t) ≤ Q _{m, 1, max}

-Q _{m, 2, max} ≤ Q _{m, 2} (t) ≤ Q _{m, 2, max}

In the formula, Q _m,1 (t) and Q _m,2 (t) are the reactive power output by the two converters of the mth intelligent soft switch in the t period; S _m,1,max , S _{m,2 , max} , Q _{m, 1, max} , Q _{m, 2, max} are respectively the access capacity of the mth smart soft switch and the upper limit of the output reactive power of the two converters.