CN104537258A

CN104537258A - Cone optimization modeling method for allowing distributed stored energy to participate in running adjustment of active power distribution network

Info

Publication number: CN104537258A
Application number: CN201510014153.6A
Authority: CN
Inventors: 赵金利; 于莹莹; 李鹏; 王成山; 孙充勃; 林盾; 邢峰
Original assignee: Tianjin University; Hainan Power Grid Co Ltd
Current assignee: Tianjin University; Hainan Power Grid Co Ltd
Priority date: 2015-01-12
Filing date: 2015-01-12
Publication date: 2015-04-22

Abstract

A cone optimization modeling method for allowing distributed stored energy to participate in running adjustment of an active power distribution network comprises the steps that (1), element parameters of the active power distribution network to be adjusted in an optimization mode are read; (2), according to the parameters provided by the step (1), a time sequence optimization model is established for allowing the distributed stored energy to participate in running adjustment of the active power distribution network, wherein an objective function with the minimum active loss is set, and the running constraint of the power distribution network and the running constraint of an energy storage system are each considered; (3), according to the standard form min {cTx/Ax=b, x<K} for second-order cone optimization, cone model conversion is performed on the time sequence optimization model in the step (2), wherein linearization is performed on the running constrain of the objective function and the running constraint of the active power distribution network, cone conversion is performed on the capacity constraint of stored energy inverters, and a non-linear rotating cone constraint is introduced. The complexity degree of an optimization model function relation is greatly lowered, and meanwhile the requirements for rapid convergence and optimal solving are met.

Description

Cone optimization modeling method for distributed energy storage participating in active distribution network operation regulation

技术领域technical field

本发明涉及一种有源配电网运行调度的建模方法。特别是涉及一种分布式储能参与有源配电网运行调节的锥优化建模方法。The invention relates to a modeling method for operation scheduling of an active distribution network. In particular, it involves a cone optimization modeling method for distributed energy storage to participate in the operation regulation of active distribution networks.

背景技术Background technique

传统电力系统的电能生产、输送、分配和消费具有极强的同时性，由于无法实现大规模电能的存储，电网的运行时刻处于即发即用状态，而发电机依靠自身的惯性所提供的电网调节能力也是极为有限的，因而导致了由负荷特性所产生的较大峰谷差的出现。在用电高峰时段，较重的负荷潮流从发电机输送到用户，系统中的线路、变压器等设备都处于较高的负载率，使得网损相应增加；在用电低谷时段，发电机处于轻载状态，降低了发电机本身的发电效率。近年来，随着电力需求的不断增加、化石能源的逐渐枯竭以及环境问题的日益恶化，以可再生能源利用为核心的分布式发电(distributed generation,DG)技术得到国际上的极大关注和发展。然而，以风力、光伏发电为代表的分布式电源在用于电能的转换和输出时具有明显的随机性和间歇性，给配电系统正常运行带来了更多不确定性的同时，也给传统配电网运行方式和调度方法提出了新的挑战。不含DG的配电网是“被动的”，接入用户所使用的电能由上一级输电网提供，当配电网接入DG产生双向潮流时，称该系统为“有源配电系统”。有源配电系统是具备组合控制各种分布式能源(distributed energy resource，DER，如DG、可控负荷、储能等)能力的复杂配电系统。The production, transmission, distribution and consumption of electric energy in the traditional power system have strong simultaneity. Since large-scale electric energy storage cannot be realized, the operation of the power grid is always in a state of ready-to-use, and the power grid provided by the generator relies on its own inertia. Adjustability is also extremely limited, resulting in large peak-to-valley differences resulting from the load characteristics. During the peak period of power consumption, the heavy load flow is transmitted from the generator to the user, and the lines, transformers and other equipment in the system are at a high load rate, which increases the network loss accordingly; The load state reduces the power generation efficiency of the generator itself. In recent years, with the increasing demand for electricity, the gradual depletion of fossil energy and the deteriorating environmental problems, the distributed generation (DG) technology with the use of renewable energy as the core has received great international attention and development. . However, the distributed power generation represented by wind power and photovoltaic power generation has obvious randomness and intermittency when used for the conversion and output of electric energy, which brings more uncertainty to the normal operation of the power distribution system and at the same time The traditional distribution network operation mode and dispatching method pose new challenges. The distribution network without DG is "passive", and the power used by the connected users is provided by the upper-level transmission network. When the distribution network is connected to DG to generate bidirectional power flow, the system is called "active distribution system". ". The active power distribution system is a complex power distribution system capable of combined control of various distributed energy resources (DER, such as DG, controllable load, energy storage, etc.).

大规模储能技术作为一种实现能量存储及功率双向调节的手段，目前已在配电系统层面获得应用，它为配电网的运行管理提供了新的发展思路。对配电网本身而言，大容量、高效储能装置的接入能够实现电能的规模化存储，给发电与负荷之间实时平衡的约束带来了颠覆性突破，进而可以有效缓解高峰负荷的用电需求，同时也可以减少系统中的备用容量，从而提高系统运行的经济性。而面向可再生能源等应用场景时，一方面储能系统能够有效抑制可再生能源发电系统出力的随机性和波动性，提高配电系统运行的安全性和可靠性；另一方面储能系统也可以提高配电系统对风机、光伏等分布式电源的接纳能力，降低因一次能源燃烧所产生的二氧化碳、二氧化硫等污染物的排放，从而获得巨大的环境效益。因此，如何利用储能系统参与配网潮流的运行调节和优化，充分发挥储能系统对于削峰填谷、平滑分布式电源出力以及改善系统运行状态的作用，是亟待解决的问题。Large-scale energy storage technology, as a means to realize energy storage and power two-way regulation, has been applied at the level of power distribution system, which provides a new development idea for the operation and management of distribution network. For the distribution network itself, the access of large-capacity and high-efficiency energy storage devices can realize large-scale storage of electric energy, which has brought a disruptive breakthrough to the constraints of real-time balance between power generation and load, and can effectively alleviate the peak load. At the same time, it can also reduce the reserve capacity in the system, thereby improving the economy of system operation. For application scenarios such as renewable energy, on the one hand, the energy storage system can effectively suppress the randomness and volatility of the output of the renewable energy power generation system, and improve the safety and reliability of the power distribution system; on the other hand, the energy storage system can also It can improve the acceptance capacity of the power distribution system for distributed power sources such as fans and photovoltaics, and reduce the emission of carbon dioxide, sulfur dioxide and other pollutants produced by the combustion of primary energy, thereby obtaining huge environmental benefits. Therefore, how to use the energy storage system to participate in the operation adjustment and optimization of the power flow of the distribution network, and give full play to the role of the energy storage system in shaving peaks and filling valleys, smoothing distributed power output, and improving system operation status is an urgent problem to be solved.

分布式储能参与有源配电网运行调节问题的本质为数学优化问题，对于这一问题虽然现有模型的优化目标不尽相同，但考虑的约束条件主要包括潮流约束、支路电流约束、电压水平约束等系统自身的运行约束以及分布式储能的运行约束，而这类问题的求解往往需要占用大量的计算时间，这是由于：首先，对于配电网的优化而言，其目标函数往往是非凸函数，并且优化模型中涉及大量的关于控制变量和状态变量的等式及不等式约束条件，同时变量之间又存在着复杂的函数关系，因此配电网的运行调节属于复杂的大规模非凸非线性优化问题；其次，对于分布式储能的优化，它具有明显的时序特性，其运行优化不再局限于单个的时间断面，而是扩展到具有时序特征的多个时间断面上，进而导致其决策变量维数随时序断面数迅疾增长，从而进一步增大了有源配电网运行优化问题的计算规模。上述两个因素共同导致了分布式储能参与有源配电网运行调节问题的复杂化和规模化。因此，急需一种准确、快速求解上述优化问题的计算模型与算法。The essence of the problem of distributed energy storage participating in the operation regulation of active distribution network is a mathematical optimization problem. Although the optimization objectives of existing models are different for this problem, the constraints considered mainly include power flow constraints, branch current constraints, The operating constraints of the system itself such as voltage level constraints and the operating constraints of distributed energy storage, and the solution of such problems often takes a lot of computing time. This is because: First, for the optimization of the distribution network, its objective function It is often a non-convex function, and the optimization model involves a large number of equations and inequality constraints on control variables and state variables. At the same time, there are complex functional relationships between variables, so the operation regulation of the distribution network is a complex large-scale Non-convex nonlinear optimization problem; secondly, for the optimization of distributed energy storage, it has obvious timing characteristics, and its operation optimization is no longer limited to a single time section, but extends to multiple time sections with timing characteristics, As a result, the dimension of its decision variables increases rapidly with the number of series sections, which further increases the calculation scale of the operation optimization problem of active distribution network. The above two factors together lead to the complexity and scale of distributed energy storage participating in the operation regulation of active distribution network. Therefore, there is an urgent need for a computational model and algorithm that can accurately and quickly solve 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，提供一种可快速、准确求解的分布式储能参与有源配电网运行调节的锥优化建模方法。The technical problem to be solved by the present invention is to provide a cone optimization modeling method for distributed energy storage participating in active distribution network operation regulation that can be quickly and accurately solved.

本发明所采用的技术方案是：一种分布式储能参与有源配电网运行调节的锥优化建模方法，包括如下步骤：The technical solution adopted in the present invention is: a cone optimization modeling method for distributed energy storage to participate in the operation regulation of active distribution network, including the following steps:

1)根据待优化调节的有源配电网，读取有源配电网中的基本元件参数，网络拓扑连接关系，分布式电源接入位置、类型和容量，分布式储能接入位置、逆变器额定功率、荷电状态的初值和运行限值，负荷及分布式电源运行特性预测曲线，系统基准电压以及基准功率；1) According to the active distribution network to be optimized and adjusted, read the basic component parameters in the active distribution network, network topology connection relationship, distributed power access location, type and capacity, distributed energy storage access location, Inverter rated power, initial value of state of charge and operating limit, load and distributed power supply operating characteristic prediction curve, system reference voltage and reference power;

2)依据步骤1)提供的有源配电网参数建立分布式储能参与有源配电网运行调节问题的时序优化模型，包括：选取根节点为平衡节点，设定有源配电网有功损耗最小为目标函数，以及分别考虑有源配电网潮流约束，运行电压水平约束，支路电流约束，储能逆变器容量约束，储能逆变器充放电功率约束，储能荷电状态连续变化约束，储能荷电状态运行约束，优化周期始末储能荷电状态相等约束的条件；2) Based on the parameters of the active distribution network provided in step 1), a time-sequence optimization model for distributed energy storage participating in the operation regulation of the active distribution network is established, including: selecting the root node as the balance node, and setting the active power of the active distribution network The minimum loss is the objective function, and the active distribution network power flow constraints, operating voltage level constraints, branch current constraints, energy storage inverter capacity constraints, energy storage inverter charge and discharge power constraints, and energy storage state of charge are considered respectively. Continuous change constraints, energy storage state of charge operation constraints, conditions for equal constraints of energy storage state of charge at the beginning and end of the optimization cycle;

3)根据二阶锥优化的标准形式min{c^Tx|Ax＝b,x∈Κ}，对步骤2)所述的分布式储能参与有源配电网运行调节问题的时序优化模型进行锥模型转化，其中，c、A、b为常量，K为有限个非空尖凸锥的笛卡尔乘积，用旋转锥来表示，所述的锥模型转化包括：对有源配电网有功损耗最小的目标函数、有源配电网潮流约束、运行电压水平约束、支路电流约束进行线性化，对储能逆变器容量约束进行锥转化，以及引入非线性旋转锥约束，从而得到转化后的有源配电网有功损耗最小的目标函数、转化后的有源配电网潮流约束、转化后的运行电压水平约束、转化后的支路电流约束和转化后的储能逆变器容量约束；3) According to the standard form of second-order cone optimization min{c ^T x|Ax=b, x∈Κ}, the timing optimization model of the distributed energy storage participating in the operation regulation problem of the active distribution network described in step 2) is carried out Cone model conversion, where c, A, b are constants, K is the Cartesian product of finite non-empty pointed convex cones, and the rotating cone to represent that the cone model conversion includes: linearizing the objective function of the active power loss of the active distribution network, the power flow constraints of the active distribution network, the operating voltage level constraints, and the branch current constraints, and the energy storage inverter Cone transformation based on transformer capacity constraints, and the introduction of nonlinear rotating cone constraints, so as to obtain the objective function of the minimum active power loss of the transformed active distribution network, the transformed active distribution network power flow constraints, and the transformed operating voltage level constraints , the transformed branch current constraint and the transformed energy storage inverter capacity constraint;

由步骤2)中给出的均为线性约束条件的储能逆变器充放电功率约束、储能荷电状态连续变化约束、储能荷电状态运行约束和分布式储能优化周期始末荷电状态相等约束，所述的转化后的有源配电网有功损耗最小的目标函数、转化后的有源配电网潮流约束、转化后的运行电压水平约束、转化后的支路电流约束和转化后的储能逆变器容量约束，以及所引入的非线性旋转锥约束共同构成分布式储能参与有源配电网运行调节的锥优化模型；From the energy storage inverter charging and discharging power constraints given in step 2), the energy storage state of charge continuous change constraint, the energy storage state of charge operation constraint and the distributed energy storage optimization cycle start and end charging State equality constraints, the objective function of the minimum active power loss of the transformed active distribution network, the transformed active distribution network power flow constraints, the transformed operating voltage level constraints, the transformed branch current constraints, and the transformed The latter energy storage inverter capacity constraint and the introduced nonlinear rotating cone constraint together constitute a cone optimization model for distributed energy storage to participate in the operation regulation of active distribution network;

4)利用锥优化计算软件对步骤3)得到的锥优化模型进行优化求解。4) Use cone optimization calculation software to optimize and solve the cone optimization model obtained in step 3).

步骤2)所述的分布式储能参与有源配电网运行调节问题的时序优化模型具体是：The timing optimization model of the distributed energy storage participating in the operation regulation problem of the active distribution network described in step 2) is specifically:

(1)所述的有源配电网有功损耗最小的目标函数表示为：(1) The objective function of the minimum active power loss of the active distribution network is expressed as:

$min min {Σ Σ}_{t t = = 00}^{T T} {Σ Σ}_{i i = = 11}^{n no} {P P}_{i i} ((t t)) Δt Δt - - - - - - ((11))$

式中，T为运行优化周期，Δt为运行优化周期内的计算步长，n为系统节点数；P_i(t)为t时刻节点i处注入的有功功率之和，用下述公式给出的有源配电网潮流约束中有功潮流的等式约束表示；In the formula, T is the operation optimization period, Δt is the calculation step size in the operation optimization period, n is the number of system nodes; P _i (t) is the sum of active power injected at node i at time t, which is given by the following formula The equation constraint representation of the active power flow in the active distribution network power flow constraint;

(2)所述的有源配电网潮流约束表示为：(2) The power flow constraint of the active distribution network is expressed as:

$\{\begin{matrix} {P P}_{i i} ((t t)) = = {G G}_{ii i} {V V}_{i i} {((t t))}^{22} + + \underset{j j &Element; &Element; Ω Ω ((i i))}{Σ Σ} {V V}_{i i} ((t t)) {V V}_{j j} ((t t)) [[{G G}_{ij ij} cos cos {θ θ}_{ij ij} ((t t)) + + {B B}_{ij ij} sin sin {θ θ}_{ij ij} ((t t))]] \\ = = {P P}_{i i}^{DG DG} ((t t)) + + {P P}_{i i}^{ESS ESS} ((t t)) + + {P P}_{i i}^{LD LD} ((t t)),, i i = = 22,, . . . . . .,, n no \\ {Q Q}_{i i} ((t t)) = = - - {B B}_{ii i} {V V}_{i i} {((t t))}^{22} - - \underset{j j &Element; &Element; Ω Ω ((i i))}{Σ Σ} {V V}_{i i} ((t t)) {V V}_{j j} ((t t)) [[{B B}_{ij ij} cos cos {θ θ}_{ij ij} ((t t)) - - {G G}_{ij ij} sin sin {θ θ}_{ij ij} ((t t))]] \\ = = {Q Q}_{i i}^{DG DG} ((t t)) + + {Q Q}_{i i}^{ESS ESS} ((t t)) + + {Q Q}_{i i}^{LD LD} ((t t)),, i i = = 22,, . . . . . .,, n no \end{matrix} - - - - - - ((22))$

式中，Ω(i)为节点i的相邻节点的集合；V_i(t)、V_j(t)和θ_ij(t)分别为t时刻节点i、j的电压幅值和相角差；G_ii、B_ii、G_ij和B_ij分别为节点导纳矩阵中的自电导、自电纳、互电导和互电纳；Q_i(t)为t时刻节点i处注入的无功功率之和；分别为t时刻节点i处分布式电源、负荷以及分布式储能注入的有功功率和无功功率；In the formula, Ω(i) is the set of adjacent nodes of node i; V _i (t), V _j (t) and θ _ij (t) are the voltage amplitude and phase angle difference of nodes i and j at time t respectively ; G _ii , B _ii , G _ij and B _ij are the self-conductance, self-inductance, mutual conductance and mutual susceptance in the node admittance matrix respectively; Q _i (t) is the reactive power injected at node i at time t Sum; are the active power and reactive power injected by distributed power sources, loads and distributed energy storage at node i at time t, respectively;

(3)所述的运行电压水平约束表示为：(3) The operating voltage level constraint is expressed as:

V_imin≤V_i(t)≤V_imax,i＝1,…,n (3)V _imin ≤ _{V i} (t) ≤ V _imax , i=1,...,n (3)

式中，V_imax和V_imin分别为节点i电压幅值的上下限；In the formula, V _imax and V _imin are the upper and lower limits of the voltage amplitude of node i respectively;

(4)所述的支路电流约束表示为：(4) The branch current constraint is expressed as:

$\begin{matrix} {I I}_{ij ij} {((t t))}^{22} = = (({G G}_{ij ij}^{22} + + {B B}_{ij ij}^{22})) [[{V V}_{i i} {((t t))}^{22} + + {V V}_{j j} {((t t))}^{22} - - 22 {V V}_{i i} ((t t)) {V V}_{j j} ((t t)) cos cos {θ θ}_{ij ij} ((t t))]] \leq \leq {I I}_{ij ij max max}^{22} \\ i i = = 11,, . . . . . .,, n no,, j j &Element; &Element; Ω Ω ((i i)) \end{matrix} - - - - - - ((44))$

式中，I_ij(t)是t时刻支路ij的电流幅值，I_ijmax是支路ij的电流幅值上限；In the formula, I _ij (t) is the current amplitude of branch ij at time t, and I _ijmax is the upper limit of current amplitude of branch ij;

(5)所述的储能逆变器容量约束表示为：(5) The energy storage inverter capacity constraint is expressed as:

$\sqrt{{P P}_{k k}^{ESS ESS} {((t t))}^{22} + + {Q Q}_{k k}^{ESS ESS} {((t t))}^{22}} \leq \leq {S S}_{k k max max}^{ESS ESS},, k k &Element; &Element; {Ω Ω}_{ESS ESS} - - - - - - ((55))$

式中，Ω_ESS为分布式储能系统的集合；分别为t时刻第k个储能逆变器输出的有功功率和无功功率；为第k个储能逆变器的额定容量；In the formula, Ω _ESS is a collection of distributed energy storage systems; are the active power and reactive power output by the kth energy storage inverter at time t, respectively; is the rated capacity of the kth energy storage inverter;

(6)所述的储能逆变器充放电功率约束表示为：(6) The charging and discharging power constraint of the energy storage inverter is expressed as:

$\{\begin{matrix} - - {P P}_{k k max max}^{ESS ESS} \leq \leq {P P}_{k k}^{ESS ESS} ((t t)) \leq \leq {P P}_{k k max max}^{ESS ESS},, k k &Element; &Element; {Ω Ω}_{ESS ESS} \\ - - {Q Q}_{k k max max}^{ESS ESS} \leq \leq {Q Q}_{k k}^{ESS ESS} ((t t)) \leq \leq {Q Q}_{k k max max}^{ESS ESS},, k k &Element; &Element; {Ω Ω}_{ESS ESS} \end{matrix} - - - - - - ((66))$

式中，分别为第k个储能逆变器有功功率和无功功率充放电上限；In the formula, Respectively, the active power and reactive power charging and discharging upper limits of the kth energy storage inverter;

(7)所述的储能荷电状态连续变化约束可表示为：(7) The continuous change constraint of the energy storage state of charge can be expressed as:

${E E.}_{k k}^{ESS ESS} ((t t + + Δt Δt)) - - {E E.}_{k k}^{ESS ESS} ((t t)) = = {P P}_{k k}^{ESS ESS} ((t t)) Δt Δt,, k k &Element; &Element; {Ω Ω}_{ESS ESS} - - - - - - ((77))$

式中，为t时刻第k个分布式储能的荷电状态；In the formula, is the state of charge of the kth distributed energy storage at time t;

(8)所述的储能荷电状态运行约束表示为：(8) The operating constraints of the energy storage state of charge are expressed as:

${E E.}_{k k min min}^{ESS ESS} \leq \leq {E E.}_{k k}^{ESS ESS} ((t t)) \leq \leq {E E.}_{k k max max}^{ESS ESS},, k k &Element; &Element; {Ω Ω}_{ESS ESS} - - - - - - ((88))$

式中，分别为第k个分布式储能荷电状态的运行限值；In the formula, are the operating limits of the state of charge of the kth distributed energy storage;

(9)所述的分布式储能优化周期始末荷电状态相等约束表示为：(9) The state of charge equality constraint at the beginning and end of the distributed energy storage optimization cycle is expressed as:

${E E.}_{k k}^{ESS ESS} ((00)) = = {E E.}_{k k}^{ESS ESS} ((T T)),, k k &Element; &Element; {Ω Ω}_{ESS ESS} - - - - - - ((99))$

式中，分别为第k个分布式储能优化周期始末时刻的荷电状态。In the formula, are the state of charge at the beginning and end of the kth distributed energy storage optimization cycle, respectively.

步骤2)建立分布式储能参与有源配电网运行调节问题的时序优化模型，不仅从单个时间断面考虑了充放电功率和荷电状态运行约束，而且考虑了相邻时间断面间荷电状态变化的连续性和时序关系，以及优化周期始末荷电状态相等的运行要求。Step 2) Establish a time-sequence optimization model for distributed energy storage to participate in the operation regulation of active distribution networks, not only considering the charging and discharging power and state-of-charge operation constraints from a single time section, but also considering the state-of-charge between adjacent time sections The continuity and timing relationship of the changes, and the operational requirement of equal SOC at the beginning and end of the optimization cycle.

步骤3)所述的锥模型转化，具体是：Step 3) described cone model conversion, specifically:

首先，通过变量替换的方式对步骤2)给出的有源配电网有功损耗最小的目标函数进行线性化，即利用 $\{\begin{matrix} X_{i} (t) = V_{i} {(t)}^{2} / \sqrt{2} \\ Y_{ij} (t) = V_{i} (t) V_{j} (t) \cos θ_{ij} \\ Z_{ij} (t) = V_{i} (t) V_{j} (t) \sin θ_{ij} \end{matrix}$ 将目标函数中的V_i(t)、V_j(t)、θ_ij(t)乘积和三角函数的非线性形式进行替换，得到转化后的有源配电网有功损耗最小的目标函数：First, the objective function of the minimum active power loss in the active distribution network given in step 2) is linearized by variable substitution, that is, using $\{\begin{matrix} x_{i} (t) = V_{i} {(t)}^{2} / \sqrt{2} \\ Y_{ij} (t) = V_{i} (t) V_{j} (t) \cos θ_{ij} \\ Z_{ij} (t) = V_{i} (t) V_{j} (t) \sin θ_{ij} \end{matrix}$ Replace the product of V _i (t), V _j (t), θ _ij (t) and the nonlinear form of the trigonometric function in the objective function to obtain the objective function of the minimum active power loss of the transformed active distribution network:

$min min {Σ Σ}_{t t = = 00}^{T T} {Σ Σ}_{i i = = 11}^{n no} {{\sqrt{22} {G G}_{ii i} {X x}_{i i} ((t t)) + + \underset{j j &Element; &Element; Ω Ω ((i i))}{Σ Σ} [[{G G}_{ij ij} {Y Y}_{ij ij} ((t t)) + + {B B}_{ij ij} {Z Z}_{ij ij} ((t t))]]}} Δt Δt - - - - - - ((1010));;$

其次，对步骤2)给出的同样含有V_i(t)、V_j(t)、θ_ij(t)变量的约束条件：有源配电网潮流约束、运行电压水平约束和支路电流约束进行相应的变换，分别得到转化后的有源配电网潮流约束、转化后的运行电压水平约束和转化后的支路电流约束：Secondly, the constraints given in step 2) also contain V _i (t), V _j (t), and θ _ij (t) variables: active distribution network power flow constraints, operating voltage level constraints and branch current constraints Carry out corresponding transformations to obtain the transformed power flow constraints of the active distribution network, the transformed operating voltage level constraints, and the transformed branch current constraints:

$\{\begin{matrix} {P P}_{i i} ((t t)) = = \sqrt{22} {G G}_{ii i} {X x}_{i i} ((t t)) + + \underset{j j &Element; &Element; Ω Ω ((i i))}{Σ Σ} [[{G G}_{ij ij} {Y Y}_{ij ij} ((t t)) + + {B B}_{ij ij} {Z Z}_{ij ij} ((t t))]] \\ = = {P P}_{i i}^{DG DG} ((t t)) + + {P P}_{i i}^{ESS ESS} ((t t)) + + {P P}_{i i}^{LD LD} ((t t)),, i i = = 22,, . . . . . .,, n no \\ {Q Q}_{i i} ((t t)) = = - - {\sqrt{22} B B}_{ii i} {X x}_{i i} ((t t)) - - \underset{j j &Element; &Element; Ω Ω ((i i))}{Σ Σ} [[{B B}_{ij ij} {Y Y}_{ij ij} ((t t)) - - {G G}_{ij ij} {Z Z}_{ij ij} ((t t))]] \\ = = {Q Q}_{i i}^{DG DG} ((t t)) + + {Q Q}_{i i}^{ESS ESS} ((t t)) + + {Q Q}_{i i}^{LD LD} ((t t)),, i i = = 22,, . . . . . .,, n no \end{matrix} - - - - - - ((1111))$

$\frac{{V V}_{i i min min}^{22}}{\sqrt{22}} \leq \leq {X x}_{i i} ((t t)) \leq \leq \frac{{V V}_{i i max max}^{22}}{\sqrt{22}},, i i = = 11,, . . . . . .,, n no - - - - - - ((1212))$

$\begin{matrix} {I I}_{ij ij} {((t t))}^{22} = = (({G G}_{ij ij}^{22} + + {B B}_{ij ij}^{22})) [[\sqrt{22} {X x}_{i i} ((t t)) + + \sqrt{22} {X x}_{j j} ((t t)) - - {22 Y Y}_{ij ij} ((t t))]] \leq \leq {I I}_{ij ij max max}^{22} \\ i i = = 11,, . . . . . .,, n no,, j j &Element; &Element; Ω Ω ((i i)) \end{matrix} - - - - - - ((1313))$

然后，对步骤2)给出的非线性约束条件储能逆变器容量约束进行形式变换，使之满足旋转锥K的约束要求，得到转化后的储能逆变器容量约束：Then, transform the form of the energy storage inverter capacity constraint given in step 2) so that it meets the constraint requirements of the rotating cone K, and obtain the transformed energy storage inverter capacity constraint:

$22 \frac{{S S}_{i i max max}^{ESS ESS}}{\sqrt{22}} \frac{{S S}_{i i max max}^{ESS ESS}}{\sqrt{22}} &GreaterEqual; &Greater Equal; {P P}_{i i}^{ESS ESS} {((t t))}^{22} + + {Q Q}_{i i}^{ESS ESS} {((t t))}^{22},, i i &Element; &Element; {Ω Ω}_{ESS ESS} - - - - - - ((1414)),,$

步骤3)所述的非线性旋转锥约束：Step 3) The nonlinear rotating cone constraint:

2X_i(t)X_j(t)≥Y_ij(t)²+Z_ij(t)²,i＝1,…,n,j∈Ω(i) (15)。2X _i (t)X _j (t)≥Y _ij (t) ² +Z _ij (t) ² , i=1,...,n, j∈Ω(i) (15).

步骤3)通过目标函数的线性化、约束条件的线性化以及旋转锥约束条件的引入，将以V_i(t)、θ_ij(t)和为决策变量的数学模型进行等价转化，形成了以X_i(t)、Y_ij(t)、Z_ij(t)、为决策变量的分布式储能参与有源配电网运行调节的锥优化模型，使得原函数关系复杂的非线性优化问题转化为二阶锥优化问题进行求解。Step 3) Through the linearization of the objective function, the linearization of the constraints and the introduction of the rotating cone constraints, the V _i (t), θ _ij (t) and Equivalent transformation is carried out for the mathematical model of the decision variable, forming a model with X _i (t), Y _ij (t), Z _ij (t), The cone optimization model in which the distributed energy storage as the decision variable participates in the operation regulation of the active distribution network transforms the nonlinear optimization problem with complex original function relationship into a second-order cone optimization problem for solution.

本发明的分布式储能参与有源配电网运行调节的锥优化建模方法，极大地简化了优化模型函数关系的复杂程度，同时兼具优美的锥几何结构，可以保证优化问题的快速、准确求解。本发明在分布式储能优化建模方面，充分考虑了储能单元及其逆变器的运行边界，不仅从单个时间断面考虑了逆变器的快速控制特性，而且从时序上将各时间断面进行统一建模，从而使得分布式储能的运行优化在单个时间断面、相邻时间断面之间以及整个优化周期上形成一个有机整体。本发明所采用的锥优化方法可以对潮流问题和分布式储能运行优化问题进行统一描述，使得复杂的非线性优化问题和高维非线性方程组的同步求解得以实现，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，将其应用到分布式储能参与配电网运行调节的优化问题中，可以获得最优的系统运行方案。可见，锥优化方法能够同时满足快速收敛和准确求解的要求。The cone optimization modeling method for distributed energy storage participating in the operation regulation of active distribution network of the present invention greatly simplifies the complexity of the optimization model function relationship, and at the same time has a beautiful cone geometry structure, which can ensure the fast and efficient optimization of the optimization problem. Solve exactly. In terms of distributed energy storage optimization modeling, the present invention fully considers the operation boundary of the energy storage unit and its inverter, not only considers the fast control characteristics of the inverter from a single time section, but also integrates each time section from the time sequence Unified modeling is carried out, so that the operation optimization of distributed energy storage forms an organic whole in a single time section, between adjacent time sections, and in the entire optimization cycle. The cone optimization method adopted in the present invention can uniformly describe the power flow problem and the distributed energy storage operation optimization problem, so that the simultaneous solution of complex nonlinear optimization problems and high-dimensional nonlinear equations can be realized, and tedious iterations and a large number of The test has greatly improved the calculation speed. Moreover, because of the beautiful geometric structure and special processing method of the cone, it can ensure the optimality of the solution of the problem to be solved, and it is applied to the optimization problem of distributed energy storage participating in the operation regulation of the distribution network. The optimal system operation scheme can be obtained. It can be seen that the cone optimization method can meet the requirements of fast convergence and accurate solution at the same time.

附图说明Description of drawings

图1是IEEE 33节点算例以及分布式电源、分布式储能接入位置图；Figure 1 is an IEEE 33 node calculation example and a diagram of the access location of distributed power and distributed energy storage;

图2是本发明的一种分布式储能参与有源配电网运行调节的锥优化建模方法流程图；Fig. 2 is a flow chart of a cone optimization modeling method in which distributed energy storage participates in active distribution network operation regulation according to the present invention;

图3是负荷及分布式电源运行特性预测曲线；Figure 3 is the load and distributed power supply operating characteristic prediction curve;

图4a是分布式储能充放电功率优化结果；Figure 4a is the optimization result of distributed energy storage charging and discharging power;

图4b是分布式储能荷电状态变化结果；Figure 4b is the result of the state of charge change of the distributed energy storage;

图5a是不同模型下的16节点处分布式储能优化方案对比图；Figure 5a is a comparison diagram of distributed energy storage optimization schemes at 16 nodes under different models;

图5b是不同模型下的32节点处分布式储能优化方案对比图。Figure 5b is a comparison diagram of distributed energy storage optimization schemes at 32 nodes under different models.

具体实施方式Detailed ways

下面结合实施例和附图对本发明的分布式储能参与有源配电网运行调节的锥优化建模方法做出详细说明。The cone optimization modeling method for distributed energy storage participating in active distribution network operation regulation of the present invention will be described in detail below with reference to the embodiments and drawings.

本发明的分布式储能参与有源配电网运行调节的锥优化建模方法，用于含分布式电源及分布式储能的配电系统运行优化研究中，可以采用MOSEK、CPLEX等锥优化软件进行模拟实现。本发明采用MOSEK软件，以图1所示的IEEE 33节点测试系统为实施例。The cone optimization modeling method of the distributed energy storage participating in the operation regulation of the active distribution network of the present invention is used in the research on the operation optimization of the distribution system including distributed power supply and distributed energy storage, and the cone optimization such as MOSEK and CPLEX can be used software for simulation. The present invention adopts MOSEK software, is embodiment with the IEEE 33 node test system shown in Fig. 1.

本发明的分布式储能参与有源配电网运行调节的锥优化建模方法，如图2所示，包括如下步骤：The cone optimization modeling method of distributed energy storage participating in the operation regulation of active distribution network of the present invention, as shown in Figure 2, includes the following steps:

1)根据待优化调节的有源配电网，读取有源配电网中的基本元件参数，网络拓扑连接关系，分布式电源接入位置、类型和容量，分布式储能接入位置、逆变器额定功率、荷电状态(state of charge,SOC)的初值和运行限值，负荷及分布式电源运行特性预测曲线，系统基准电压以及基准功率等；1) According to the active distribution network to be optimized and adjusted, read the basic component parameters in the active distribution network, network topology connection relationship, distributed power access location, type and capacity, distributed energy storage access location, Inverter rated power, initial value and operating limit of state of charge (SOC), load and distributed power supply operating characteristic prediction curve, system reference voltage and reference power, etc.;

对于本实施例，首先读取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；其次，设定4台550kVA风电机组的接入位置为节点13、18、31、33，2台600kVA光伏系统的接入位置为节点15、30；再次，设定2台分布式储能系统的接入位置为节点16、32，二者逆变器的额定容量均为500kVA，额定功率均为400kW，额定储能量分别为1600kWh和800kWh，荷电状态初值分别为50.0％和12.5％，荷电状态运行限值分别为6.25％/87.5％和6.25％/95.0％；然后，以天为单位，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100MVA。For this embodiment, first read the impedance value of the line element in the IEEE 33 node system, the active power and reactive power of the load element, and the network topology connection relationship; secondly, set the access position of the four 550kVA wind turbines as node 13 , 18, 31, 33, the connection positions of two 600kVA photovoltaic systems are nodes 15 and 30; again, set the connection positions of two distributed energy storage systems as nodes 16 and 32, and the rated The capacity is 500kVA, the rated power is 400kW, the rated storage energy is 1600kWh and 800kWh respectively, the initial value of the state of charge is 50.0% and 12.5%, and the operating limit of the state of charge is 6.25%/87.5% and 6.25%/ 95.0%; then, use the load forecasting method to simulate the load and the daily operation curves of wind power and photovoltaics in units of days, as shown in Figure 3; finally, set the system’s reference voltage to 12.66kV and reference power to 100MVA.

2)依据步骤1)提供的有源配电网参数建立分布式储能参与有源配电网运行调节问题的时序优化模型，包括：选取根节点为平衡节点，设定有源配电网有功损耗最小为目标函数，以及分别考虑有源配电网潮流约束，运行电压水平约束，支路电流约束，储能逆变器容量约束，储能逆变器充放电功率约束，储能荷电状态(SOC)连续变化约束，储能荷电状态运行约束，优化周期始末储能荷电状态相等约束等的条件；2) Based on the parameters of the active distribution network provided in step 1), a time-sequence optimization model for distributed energy storage participating in the operation regulation of the active distribution network is established, including: selecting the root node as the balance node, and setting the active power of the active distribution network The minimum loss is the objective function, and the active distribution network power flow constraints, operating voltage level constraints, branch current constraints, energy storage inverter capacity constraints, energy storage inverter charge and discharge power constraints, and energy storage state of charge are considered respectively. (SOC) continuous change constraints, energy storage state of charge operation constraints, conditions such as energy storage state of charge equal constraints at the beginning and end of the optimization cycle;

所述的分布式储能参与有源配电网运行调节问题的时序优化模型具体是：The timing optimization model of the distributed energy storage participating in the operation regulation problem of the active distribution network is specifically:

(1)所述的有源配电网有功损耗最小的目标函数可表示为：(1) The objective function of the minimum active power loss of the active distribution network can be expressed as:

式中，T为运行优化周期，Δt为运行优化周期内的计算步长，n为系统节点数；P_i(t)为t时刻节点i处注入的有功功率之和，可用下述公式(2)给出的有源配电网潮流约束中有功潮流的等式约束表示；In the formula, T is the operation optimization period, Δt is the calculation step size in the operation optimization period, n is the number of system nodes; P _i (t) is the sum of active power injected at node i at time t, and the following formula (2 ) given by the equation constraint representation of the active power flow in the active distribution network power flow constraint;

(2)所述的有源配电网潮流约束可表示为：(2) The power flow constraint of the active distribution network can be expressed as:

(3)所述的运行电压水平约束可表示为：(3) The operating voltage level constraints described in (3) can be expressed as:

(4)所述的支路电流约束可表示为：(4) The branch current constraint can be expressed as:

(5)所述的储能逆变器容量约束可表示为：(5) The energy storage inverter capacity constraints can be expressed as:

(6)所述的储能逆变器充放电功率约束可表示为：(6) The charging and discharging power constraints of the energy storage inverter can be expressed as:

$\{\begin{matrix} - P_{k \max}^{ESS} \leq P_{k}^{ESS} (t) \leq P_{k \max}^{ESS}, k &Element; Ω_{ESS} \\ - Q_{k \max}^{ESS} \leq Q_{k}^{ESS} (t) \leq Q_{k \max}^{ESS}, k &Element; Ω_{ESS} \end{matrix} - - - (6)$ 式中，分别为第k个储能逆变器有功功率和无功功率充放电上限； $\{\begin{matrix} - P_{k \max}^{ESS} \leq P_{k}^{ESS} (t) \leq P_{k \max}^{ESS}, k &Element; Ω_{ESS} \\ - Q_{k \max}^{ESS} \leq Q_{k}^{ESS} (t) \leq Q_{k \max}^{ESS}, k &Element; Ω_{ESS} \end{matrix} - - - (6)$ In the formula, Respectively, the active power and reactive power charging and discharging upper limits of the kth energy storage inverter;

(8)所述的储能荷电状态运行约束可表示为：(8) The operating constraints of the energy storage state of charge can be expressed as:

(9)所述的分布式储能优化周期始末荷电状态相等约束可表示为：(9) The state of charge equality constraint at the beginning and end of the distributed energy storage optimization cycle can be expressed as:

建立分布式储能参与有源配电网运行调节问题的时序优化模型，不仅从单个时间断面考虑了充放电功率和荷电状态运行约束，而且考虑了相邻时间断面间荷电状态变化的连续性和时序关系，以及优化周期始末荷电状态相等的运行要求。Establishing a time-sequence optimization model for distributed energy storage to participate in the operation regulation of active distribution networks not only considers the charging and discharging power and state-of-charge operation constraints from a single time section, but also considers the continuous state-of-charge changes between adjacent time sections. The relationship between sex and timing, and the operation requirement that the state of charge at the beginning and end of the optimization cycle is equal.

由步骤2)中给出的均为线性约束条件的储能逆变器充放电功率约束、储能荷电状态连续变化约束、储能荷电状态运行约束和分布式储能优化周期始末荷电状态相等约束，所述的转化后的有源配电网有功损耗最小的目标函数、转化后的有源配电网潮流约束、转化后的运行电压水平约束、转化后的支路电流约束和转化后的储能逆变器容量约束，以及所引入的非线性旋转锥约束共同构成分布式储能参与有源配电网运行调节的锥优化模型；The energy storage inverter charging and discharging power constraints given in step 2), the energy storage state of charge continuous change constraints, the energy storage state of charge operation constraints, and the distributed energy storage optimization cycle start and end charging are all given in step 2). State equality constraints, the objective function of the minimum active power loss of the transformed active distribution network, the transformed active distribution network power flow constraints, the transformed operating voltage level constraints, the transformed branch current constraints, and the transformed The latter energy storage inverter capacity constraint and the introduced nonlinear rotating cone constraint together constitute a cone optimization model for distributed energy storage to participate in the operation regulation of active distribution network;

首先，通过变量替换的方式对步骤2)公式(1)给出的有源配电网有功损耗最小的目标函数进行线性化，即利用 $\{\begin{matrix} X_{i} (t) = V_{i} {(t)}^{2} / \sqrt{2} \\ Y_{ij} (t) = V_{i} (t) V_{j} (t) \cos θ_{ij} \\ Z_{ij} (t) = V_{i} (t) V_{j} (t) \sin θ_{ij} \end{matrix}$ 将目标函数中的V_i(t)、V_j(t)、θ_ij(t)乘积和三角函数的非线性形式进行替换，得到转化后的有源配电网有功损耗最小的目标函数：First, linearize the objective function of the minimum active power loss in the active distribution network given by formula (1) in step 2) by variable substitution, that is, use $\{\begin{matrix} x_{i} (t) = V_{i} {(t)}^{2} / \sqrt{2} \\ Y_{ij} (t) = V_{i} (t) V_{j} (t) \cos θ_{ij} \\ Z_{ij} (t) = V_{i} (t) V_{j} (t) \sin θ_{ij} \end{matrix}$ Replace the product of V _i (t), V _j (t), θ _ij (t) and the nonlinear form of the trigonometric function in the objective function to obtain the objective function of the minimum active power loss of the transformed active distribution network:

其次，对步骤2)给出的同样含有V_i(t)、V_j(t)、θ_ij(t)变量的约束条件式(2)～(4)：有源配电网潮流约束、运行电压水平约束和支路电流约束进行相应的变换，分别得到转化后的有源配电网潮流约束、转化后的运行电压水平约束和转化后的支路电流约束，如式(11)～(13)所示：Secondly, for the constraint condition formulas (2)-(4) given in step 2) that also contain V _i (t), V _j (t), and θ _ij (t) variables: active distribution network power flow constraints, operation The voltage level constraints and branch current constraints are transformed accordingly, and the transformed active distribution network power flow constraints, transformed operating voltage level constraints, and transformed branch current constraints are respectively obtained, as shown in equations (11)-(13 ) as shown:

然后，对步骤2)给出的非线性约束条件储能逆变器容量约束公式(5)进行形式变换，使之满足旋转锥K的约束要求，得到转化后的储能逆变器容量约束，如式(14)所示：Then, transform the form of the energy storage inverter capacity constraint formula (5) given in step 2) to meet the constraint requirements of the rotating cone K, and obtain the transformed energy storage inverter capacity constraint, As shown in formula (14):

$22 \frac{{S S}_{i i max max}^{ESS ESS}}{\sqrt{22}} \frac{{S S}_{i i max max}^{ESS ESS}}{\sqrt{22}} &GreaterEqual; &Greater Equal; {P P}_{i i}^{ESS ESS} {((t t))}^{22} + + {Q Q}_{i i}^{ESS ESS} {((t t))}^{22},, i i &Element; &Element; {Ω Ω}_{ESS ESS} - - - - - - ((1414))$

而公式(6)～(9)为线性约束条件，满足二阶锥优化的标准形式，无需进行转化；However, formulas (6) to (9) are linear constraint conditions, which satisfy the standard form of second-order cone optimization and do not need to be transformed;

步骤3)所述的引入非线性旋转锥约束：Step 3) describes the introduction of nonlinear rotating cone constraints:

2X_i(t)X_j(t)≥Y_ij(t)²+Z_ij(t)²,i＝1,…,n,j∈Ω(i) (15)2X _i (t)X _j (t)≥Y _ij (t) ² +Z _ij (t) ² , i=1,...,n,j∈Ω(i) (15)

此时，上述式(6)～(13)中的目标函数和约束条件均为变量X_i(t)、Y_ij(t)、Z_ij(t)和的线性函数形式，式(14)、(15)为旋转锥约束形式，能够满足二阶锥优化的标准形式。At this time, the objective functions and constraints _{in the above formulas (6) to (13) are variables Xi (t), Y ij} ₍ t), Z _ij (t) and The linear function form of , the formulas (14) and (15) are the constrained form of the rotating cone, which can satisfy the standard form of the second-order cone optimization.

4)利用锥优化计算软件对步骤3)得到的锥优化模型进行优化求解，利用GAMS CONOPT求解器对步骤2)中的基本模型进行优化求解。4) Use the cone optimization calculation software to optimize and solve the cone optimization model obtained in step 3), and use the GAMS CONOPT solver to optimize and solve the basic model in step 2).

5)输出锥优化模型的优化结果，并将锥优化模型和基本模型的计算结果进行比较验证。5) Output the optimization results of the cone optimization model, and compare and verify the calculation results of the cone optimization model and the basic model.

分布式储能参与有源配电网运行调节的优化方案如图4所示；求解锥优化模型与基本模型的优化结果对比如图5(a)、5(b)所示。The optimization scheme of distributed energy storage participating in the operation regulation of active distribution network is shown in Figure 4; the comparison of the optimization results between the solution cone optimization model and the basic model is shown in Figure 5(a) and 5(b).

观察图4可知，分布式储能能够积极参与有源配电网电能供需平衡的调节。以节点32处的分布式储能为例，风电和光伏的接入使得系统功率流发生较大波动，在分布式电源出力较大/小、用电需求较小/较大时，储能系统向电网输送/吸收电能，并且在供求矛盾极大的2：30、16：30时刻达到了全周期最大的充放电功率。然而，图4的充放电状态和大小并不与图2所示的电能供求关系一一对应，这是由于分布式储能由于受限于其逆变器额定功率和SOC运行限值的约束，在参与配电网的长时间运行优化中，它并不仅仅以单个时间断面的电能需求为依托，而是立足于整个优化周期的运行工况，即首先在整体上实现电网发电和负荷用电的平衡，然后再着手于局部调节其充放电状态和大小，从而最大限度地发挥其对于全局能量管理的重要作用。Observing Figure 4, it can be seen that distributed energy storage can actively participate in the adjustment of the balance between supply and demand of power distribution network. Taking the distributed energy storage at node 32 as an example, the access of wind power and photovoltaics makes the power flow of the system fluctuate greatly. When the distributed power output is large/small and the power demand is small/large, the energy storage system It transmits/absorbs electric energy to the grid, and reaches the maximum charging and discharging power of the whole cycle at 2:30 and 16:30 when the contradiction between supply and demand is extremely great. However, the charging and discharging states and sizes in Figure 4 do not correspond to the power supply and demand relationship shown in Figure 2. This is because distributed energy storage is limited by its inverter rated power and SOC operating limits. In participating in the long-term operation optimization of the distribution network, it is not only based on the power demand of a single time section, but based on the operating conditions of the entire optimization cycle, that is, firstly realize the power generation and load consumption of the power grid as a whole , and then proceed to locally adjust its charge and discharge state and size, thereby maximizing its important role for global energy management.

由图5(a)、5(b)可知，锥优化模型的优化结果与原基本模型的优化结果是一致的，验证了锥优化建模方法合理性和正确性。此外，对求解两模型的计算效率进比较，结果显示锥优化模型的求解更加快速高效，如表1所示。It can be seen from Figure 5(a) and 5(b) that the optimization results of the cone optimization model are consistent with those of the original basic model, which verifies the rationality and correctness of the cone optimization modeling method. In addition, the calculation efficiency of solving the two models is compared, and the results show that the solution of the cone optimization model is faster and more efficient, as shown in Table 1.

表1 不同模型下求解效率比较结果Table 1 Comparison results of solution efficiency under different models

本发明的分布式储能参与有源配电网运行调节的锥优化建模方法，立足于多个时间断面上配电网运行的时序优化，建立了以最小化全网有功损耗为目标函数，考虑系统潮流约束，运行电压水平约束，支路电流约束，储能逆变器容量约束，储能逆变器充放电功率约束，储能SOC连续变化约束，储能SOC运行约束，优化周期始末储能SOC相等约束等条件的锥优化模型。在模型转化时，首先，根据二阶锥优化的标准形式(非空尖凸锥导入偏序下，线性等式、线性不等式约束条件下的线性目标函数的问题)，通过变量替换的方式将目标函数进行线性化，同时对配电系统自身的约束条件进行线性化；然后，针对储能逆变器容量约束的非线性形式进行变换，使之满足旋转锥约束形式的要求；最后，根据新变量的函数关系引入旋转锥约束，形成锥优化模型。其中，旋转锥约束保证了锥优化模型与原模型的一致性。与以往的分布式储能参与有源配电网运行调节的非凸非线性模型相比，锥优化模型的目标函数是线性的，并且其可行域是由线性等式、不等式以及非线性旋转锥约束构成，从而将搜索空间限制在封闭的凸锥范围内，使得搜索空间具有一定的光滑性、封闭性以及对称性，极大地简化了优化模型函数关系的复杂程度，同时兼具优美的锥几何结构，可以保证优化问题的快速、准确求解。The cone optimization modeling method of distributed energy storage participating in the operation regulation of active distribution network of the present invention is based on the timing optimization of distribution network operation on multiple time sections, and establishes the objective function of minimizing the active power loss of the entire network, Consider the system power flow constraints, operating voltage level constraints, branch current constraints, energy storage inverter capacity constraints, energy storage inverter charge and discharge power constraints, energy storage SOC continuous change constraints, energy storage SOC operation constraints, and optimize the storage at the beginning and end of the cycle. Cone optimization model capable of SOC equality constraints and other conditions. When transforming the model, firstly, according to the standard form of second-order cone optimization (non-hollow convex cone import partial order, the problem of linear equation and linear objective function under linear inequality constraints), the objective is replaced by variable substitution. The function is linearized, and the constraints of the power distribution system are linearized; then, the nonlinear form of the energy storage inverter capacity constraint is transformed to meet the requirements of the rotating cone constraint; finally, according to the new variable The function relationship of is introduced the rotating cone constraint to form the cone optimization model. Among them, the rotating cone constraint ensures the consistency between the cone optimization model and the original model. Compared with the previous non-convex nonlinear model in which distributed energy storage participates in the operation regulation of active distribution network, the objective function of the cone optimization model is linear, and its feasible region is composed of linear equations, inequalities and nonlinear rotating cone Constraint composition, so that the search space is limited to the closed convex cone range, so that the search space has a certain smoothness, closure and symmetry, which greatly simplifies the complexity of the optimization model function relationship, and at the same time has a beautiful cone geometry The structure can ensure the fast and accurate solution of the optimization problem.

在分布式储能优化建模方面，充分考虑了储能单元及其逆变器的运行边界。其中，逆变器主要用于充放电状态和功率的调换控制，其运行边界主要考虑了储能逆变器的额定容量约束，以及有功和无功功率充放电约束；储能单元主要用于电荷的存储，其运行边界主要考虑了SOC变化的时序关系、SOC的运行限值以及SOC变化的周期特性，即分别体现为SOC在时序上随充放电功率的连续变化、SOC满足限值要求以及优化周期始末SOC相等的约束条件。因此，本发明对于分布式储能优化建模不仅从单个时间断面考虑了逆变器的快速控制特性，而且从时序上将各时间断面进行统一建模，从而使得分布式储能的运行优化在单个时间断面、相邻时间断面之间以及整个优化周期上形成一个有机整体。In terms of distributed energy storage optimization modeling, the operating boundaries of energy storage units and their inverters are fully considered. Among them, the inverter is mainly used for the switching control of the charge and discharge state and power, and its operating boundary mainly considers the rated capacity constraints of the energy storage inverter, as well as the constraints of active and reactive power charge and discharge; the energy storage unit is mainly used for charge storage, its operating boundary mainly considers the timing relationship of SOC changes, the operating limit of SOC and the periodic characteristics of SOC changes, that is, the continuous change of SOC with the charge and discharge power in time sequence, SOC meeting the limit requirements and optimization The constraint condition that the SOC is equal at the beginning and end of the cycle. Therefore, the present invention not only considers the fast control characteristics of the inverter from a single time section for the distributed energy storage optimization modeling, but also uniformly models each time section from the time sequence, so that the operation optimization of the distributed energy storage is A single time section, adjacent time sections and the entire optimization cycle form an organic whole.

在计算效率方面，本发明所采用的锥优化方法可以对潮流问题和分布式储能运行优化问题进行统一描述，使得复杂的非线性优化问题和高维非线性方程组的同步求解得以实现，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；另一方面，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，将其应用到分布式储能参与配电网运行调节的优化问题中，可以获得最优的系统运行方案。可见，锥优化方法能够同时满足快速收敛和准确求解的要求。In terms of computational efficiency, the cone optimization method adopted in the present invention can uniformly describe the power flow problem and the distributed energy storage operation optimization problem, so that the simultaneous solution of complex nonlinear optimization problems and high-dimensional nonlinear equations can be realized, avoiding cumbersome The iterations and a large number of tests have greatly improved the calculation speed; on the other hand, because of the beautiful geometric structure and special processing methods of the cone, it can ensure the optimality of the solution of the problem to be solved. It is applied to the optimization problem of distributed energy storage participating in the operation regulation of distribution network, and the optimal system operation scheme can be obtained. It can be seen that the cone optimization method can meet the requirements of fast convergence and accurate solution at the same time.

Claims

1. distributed energy storage participates in a cone Optimization Modeling method for active power distribution network runing adjustment, it is characterized in that, comprises the steps:

1) according to the active power distribution network of adjustment to be optimized, read the primary element parameter in active power distribution network, network topology annexation, distributed power source on-position, type and capacity, the initial value of distributed energy storage on-position, inverter rated power, state-of-charge and operation limit value, load and distributed power source operation characteristic prediction curve, system reference voltage and reference power;

2) according to step 1) the active power distribution network parameter that provides sets up the timing optimization model that distributed energy storage participates in active power distribution network runing adjustment problem, comprise: choosing root node is balance node, the active loss of setting active power distribution network is minimum is objective function, and consider the constraint of active power distribution network trend respectively, operation voltage level retrains, branch current retrains, energy storage inverter capacity-constrained, energy storage inverter charge-discharge electric power retrains, energy storage charge state consecutive variations retrains, energy storage charge state runs constraint, the condition of optimization cycle energy storage charge state at whole story equated constraint,

3) according to the canonical form min{c that second order cone is optimized ^tx|Ax=b, x ∈ Κ }, to step 2) described in the distributed energy storage timing optimization model that participates in active power distribution network runing adjustment problem carry out Based On The Conic Model conversion, wherein, c, A, b are constant, and K is the cartesian product of limited non-NULL point convex cone, uses rotating cone represent, described Based On The Conic Model transforms and comprises: carry out linearization to the minimum objective function of active power distribution network active loss, the constraint of active power distribution network trend, operation voltage level constraint, branch current constraint, carry out cone to energy storage inverter capacity-constrained to transform, and introduce the constraint of non-linear rotating cone, thus obtain the minimum objective function of active power distribution network active loss after transforming, transform after the constraint of active power distribution network trend, transform after operation voltage level constraint, transform after branch current constraint and the energy storage inverter capacity-constrained after transforming;

By step 2) in provide be Linear Constraints energy storage inverter charge-discharge electric power constraint, energy storage charge state consecutive variations retrains, energy storage charge state runs constraint and distributed energy storage optimization cycle state-of-charge at whole story equated constraint, the objective function that active power distribution network active loss after described conversion is minimum, active power distribution network trend constraint after conversion, operation voltage level constraint after conversion, branch current constraint after conversion and the energy storage inverter capacity-constrained after transforming, and the common cone Optimized model forming distributed energy storage participation active power distribution network runing adjustment of the non-linear rotating cone constraint introduced,

4) cone is utilized to optimize software for calculation to step 3) the cone Optimized model that obtains is optimized and solves.

2. distributed energy storage according to claim 1 participates in the cone Optimization Modeling method of active power distribution network runing adjustment, it is characterized in that, step 2) described in distributed energy storage participate in the timing optimization model of active power distribution network runing adjustment problem specifically:

(1) the minimum objective function of the active power distribution network active loss described in is expressed as:

\min Σ_{t = 0}^{T} Σ_{i = 1}^{n} P_{i} (t) Δt - - - (1)

In formula, T is the running optimizatin cycle, and Δ t is the material calculation in the running optimizatin cycle, and n is system node number; P _it active power sum that () injects for t node i place, the equality constraint of the about intrafascicular effective power flow of the active power distribution network trend provided with following formula represents;

(2) the active power distribution network trend constraint representation described in is:

\{\begin{matrix} P_{i} (t) = G_{ii} V_{i} {(t)}^{2} + \underset{j &Element; Ω (i)}{Σ} V_{i} (t) V_{j} (t) [G_{ij} \cos θ_{ij} (t) + B_{ij} \sin θ_{ij} (t)] \\ = P_{i}^{DG} (t) + P_{i}^{ESS} (t) + P_{i}^{LD} (t), i = 2, . . ., n \\ Q_{i} (t) = - B_{ii} V_{i} {(t)}^{2} - \underset{j &Element; Ω (i)}{Σ} V_{i} (t) V_{j} (t) [R_{ij} \cos θ_{ij} (t) - G_{ij} \sin θ_{ij} (t)] \\ = Q_{i}^{DG} (t) + Q_{i}^{ESS} (t) + Q_{i}^{LD} (t), i = 2, . . ., n \end{matrix} - - - (2)

In formula, the set of the adjacent node that Ω (i) is node i; V _i(t), V _j(t) and θ _ijt () is respectively t node i, the voltage magnitude of j and phase angle difference; G _ii, B _ii, G _ijand B _ijbe respectively the self-conductance in bus admittance matrix, from susceptance, transconductance and mutual susceptance; Q _it reactive power sum that () injects for t node i place; be respectively active power and the reactive power of the injection of t node i place distributed power source, load and distributed energy storage;

(3) the operation voltage level constraint representation described in is:

V _imin≤V _i(t)≤V _imax,i＝1,…,n (3)

In formula, V _imaxand V _iminbe respectively the bound of node i voltage magnitude;

(4) the branch current constraint representation described in is:

L_{ij} {(t)}^{2} = (G_{ij}^{2} + B_{ij}^{2}) [V_{i} {(t)}^{2} + V_{j} {(t)}^{2} - {2 V}_{i} (t) V_{j} (t) \cos θ_{ij} (t)] \leq {L_{ij \max}}^{2}

(4)

i＝1,…,n,j∈Ω(i)

In formula, I _ijt () is the current amplitude of t branch road ij, I _ijmaxit is the current amplitude upper limit of branch road ij

(5) the energy storage inverter capacity-constrained described in is expressed as:

\sqrt{P_{k}^{ESS} {(t)}^{2} + Q_{k}^{ESS} {(t)}^{2}} \leq S_{k \max}^{ESS}, k &Element; Ω_{ESS} - - - (5)

In formula, Ω _eSSfor the set of distributed energy storage system; be respectively active power and the reactive power of the energy storage inverter output of t kth; for the rated capacity of a kth energy storage inverter;

(6) the energy storage inverter charge-discharge electric power constraint representation described in is:

\{\begin{matrix} - P_{k \max}^{ESS} \leq P_{k}^{ESS} (t) \leq P_{k \max}^{ESS}, k &Element; Ω_{ESS} \\ - Q_{k \max}^{ESS} \leq Q_{k}^{ESS} (t) \leq Q_{k \max}^{ESS}, k &Element; Ω_{ESS} \end{matrix} - - - (6)

In formula, be respectively a kth energy storage inverter active power and the reactive power discharge and recharge upper limit;

(7) the energy storage charge state consecutive variations constraint described in can be expressed as:

E_{k}^{ESS} (t + Δt) - E_{k}^{ESS} (t) = P_{k}^{ESS} (t) Δt, k &Element; Ω_{ESS} - - - (7)

In formula, for the state-of-charge of a t kth distributed energy storage;

(8) energy storage charge state described in runs constraint representation:

E_{k \min}^{ESS} \leq E_{k}^{ESS} (t) \leq E_{k \max}^{ESS}, k &Element; Ω_{ESS} - - - (8)

In formula, be respectively the operation limit value of a kth distributed energy storage state-of-charge;

(9) distributed energy storage optimization cycle state-of-charge at the whole story equated constraint described in is expressed as:

E_{k}^{ESS} (0) = E_{k}^{ESS} (T), k &Element; Ω_{ESS} - - - (9)

In formula, be respectively the state-of-charge in a kth distributed energy storage optimization cycle moment at the whole story.

3. distributed energy storage according to claim 1 participates in the cone Optimization Modeling method of active power distribution network runing adjustment, it is characterized in that, step 2) set up the timing optimization model that distributed energy storage participates in active power distribution network runing adjustment problem, not only consider charge-discharge electric power and state-of-charge operation constraint from discontinuity surface time single, and consider continuity and the sequential relationship of state-of-charge change between adjacent time section, and the service requirement that optimization cycle state-of-charge at the whole story is equal.

4. distributed energy storage according to claim 1 participates in the cone Optimization Modeling method of active power distribution network runing adjustment, it is characterized in that, step 3) described in Based On The Conic Model transform, specifically:

First, the mode of being replaced by variable is to step 2) the minimum objective function of the active power distribution network active loss that provides carries out linearization, namely utilizes

\{\begin{matrix} X_{i} (t) = V_{i} {(t)}^{2} / \sqrt{2}, \\ Y_{ij} (t) = V_{i} (t) V_{j} (t) \cos θ_{ij} \\ Z_{ij} (t) = V_{i} (t) V_{j} (t) \sin θ_{ij} \end{matrix}

By the V in objective function _i(t), V _j(t), θ _ijt the non-linear form of () sum of products trigonometric function is replaced, obtain the objective function that the active power distribution network active loss after transforming is minimum:

\min Σ_{t = 0}^{T} Σ_{i = 1}^{n} {\sqrt{2} G_{ii} X_{i} (t) + \underset{j &Element; Ω (i)}{Σ} [G_{ij} Y_{ij} (t) + B_{ij} Z_{ij} (t)]} Δt - - - (10);

Secondly, to step 2) provide same containing V _i(t), V _j(t), θ _ijthe constraint condition of (t) variable: active power distribution network trend retrains, operation voltage level retrains and branch current constraint converts accordingly, the branch current constraint after the operation voltage level obtained respectively after the active power distribution network trend constraint after transforming, conversion retrains and transforms:

\{\begin{matrix} P_{i} (t) = \sqrt{2} G_{ii} X_{i} (t) + \underset{j &Element; Ω (i)}{Σ} [G_{ij} Y_{ij} (t) + B_{ij} Z_{ij} (t)] \\ = P_{i}^{DG} (t) + P_{i}^{ESS} (t) + P_{i}^{LD} (t), i = 2, . . ., n \\ Q_{i} (t) = - {\sqrt{2} B}_{ii} X_{i} (t) - \underset{j &Element; Ω (i)}{Σ} [R_{ij} Y_{ij} (t) - G_{ij} Z_{ij} (t)] \\ = Q_{i}^{DG} (t) + Q_{i}^{ESS} (t) + Q_{i}^{LD} (t), i = 2, . . ., n \end{matrix} - - - (11)

\frac{V_{i \min}^{2}}{\sqrt{2}} \leq X_{i} (t) \leq \frac{V_{i \max}^{2}}{\sqrt{2}}, i = 1, . . ., n - - - (12)

L_{ij} {(t)}^{2} = (G_{ij}^{2} + B_{ij}^{2}) [\sqrt{2} X_{i} (t) + \sqrt{2} X_{j} (t) - {2 Y}_{ij} (t)] \leq {I_{ij \max}}^{2}

(13)

i＝1,…,n,j∈Ω(i)

Then, to step 2) the Nonlinear Constraints energy storage inverter capacity-constrained that provides carries out formal argument, and make it the constraint requirements meeting rotating cone K, obtain the energy storage inverter capacity-constrained after transforming:

2 \frac{S_{i \max}^{ESS}}{\sqrt{2}} \frac{S_{i \max}^{ESS}}{\sqrt{2}} &GreaterEqual; P_{i}^{ESS} {(t)}^{2} + Q_{i}^{ESS} {(t)}^{2}, i &Element; Ω_{ESS} - - - (14),

Step 3) described in the constraint of non-linear rotating cone:

2X _i(t)X _j(t)≥Y _ij(t) ²+Z _ij(t) ²,i＝1,…,n,j∈Ω(i) (15)。

5. distributed energy storage according to claim 1 participates in the cone Optimization Modeling method of active power distribution network runing adjustment, it is characterized in that, step 3) by the introducing of the linearization of objective function, the linearization of constraint condition and rotating cone constraint condition, will with V _i(t), θ _ij(t) and mathematical model for decision variable is carried out equivalence and is transformed, and defines with X _i(t), Y _ij(t), Z _ij(t), distributed energy storage for decision variable participates in the cone Optimized model of active power distribution network runing adjustment, makes the nonlinear optimal problem of original function relation complexity be converted into second order cone optimization problem and solves.