CN117691675A - A flexible interconnection planning method for low-voltage distribution network considering power supply capacity - Google Patents

Abstract

The invention relates to a flexible interconnection planning method of a low-voltage distribution network considering power supply capacity, which comprises the following steps: step 1, considering a flexible interconnection planning frame of a low-voltage power distribution network with improved power supply capacity; step 2, constructing a flexible interconnection planning model of the low-voltage power distribution network by considering the lifting of power supply capacity; and 3, performing multi-body uncertainty fine modeling. The flexible interconnection planning method for the low-voltage distribution network considering the power supply capacity can effectively solve the three problems of uneven load of a distribution transformer of the low-voltage distribution network, insufficient new energy consumption capacity and low voltage at the tail end of the distribution network, and effectively improve the power supply capacity of the low-voltage distribution network.

Description

Translated fromChinese

一种考虑供电能力的低压配电网柔性互联规划方法A flexible interconnection planning method for low-voltage distribution network considering power supply capacity

技术领域Technical field

本发明属于低压配电网柔性互联规划技术领域，具体涉及一种考虑供电能力的低压配电网柔性互联规划方法。The invention belongs to the technical field of low-voltage distribution network flexible interconnection planning, and specifically relates to a low-voltage distribution network flexible interconnection planning method that considers power supply capacity.

背景技术Background technique

现阶段低压配电网供电能力主要受配电变压器负载率、新能源消纳能力以及配电网供电电压三大要素的影响。在配电变压器负载率方面常表现为空间与时间的二维差异化，这可能导致在负荷高峰期，部分配电网中配电变压器处于重载状态时某些配电变压器却长时间处于轻载状态。现有技术存在以下两点不足：At this stage, the power supply capacity of low-voltage distribution network is mainly affected by three major factors: distribution transformer load rate, new energy consumption capacity and distribution network supply voltage. The load factor of distribution transformers often shows two-dimensional differences in space and time, which may lead to the fact that during the peak load period, some distribution transformers in some distribution networks are under heavy load while some distribution transformers are under light load for a long time. loading status. The existing technology has the following two shortcomings:

(1)现有技术通过配置或规划配电网资源解决配电变压器负载不均、新能源消纳能力不足或低压配电网末端低电压问题，但均只考虑了单一问题，而现阶段直流充电站、储能以及光伏的装机容量逐年提高，可能导致上述各类问题频繁同时出现，配电网柔性互联则可将直流充电站、光储电站等直流源荷储设施配置于直流配电网中，在减少换流装置投资与换流损耗、提升配电网对于直流源荷储设施承载能力的同时，提升低压配电网供电能力。(1) Existing technologies solve the problem of uneven load of distribution transformers, insufficient new energy accommodation capacity or low voltage at the end of low-voltage distribution network by allocating or planning distribution network resources, but only consider a single problem. At present, DC The installed capacity of charging stations, energy storage and photovoltaics is increasing year by year, which may cause the above-mentioned problems to occur frequently at the same time. The flexible interconnection of the distribution network can configure DC source-load storage facilities such as DC charging stations and photovoltaic power storage stations in the DC distribution network. Among them, while reducing the investment in converter devices and commutation losses, and improving the carrying capacity of the distribution network for DC source and load storage facilities, it also improves the power supply capacity of the low-voltage distribution network.

(2)传统单主体规划运行只需要考虑单一主体的不确定性，因而只需采用同一种方法对不确定性进行建模。而考虑到多主体规划运行模型，一方面各规划主体均包含独立的不确定性因素，另一方面各不确定性因素又具有各自的特征，此时需针对各主体特性采用不同的不确定性方法进行不确定性的建模。(2) The traditional single-agent planning operation only needs to consider the uncertainty of a single agent, so it only needs to use the same method to model the uncertainty. Considering the multi-agent planning operation model, on the one hand, each planning subject contains independent uncertainty factors, and on the other hand, each uncertainty factor has its own characteristics. At this time, different uncertainties need to be used according to the characteristics of each subject. method to model uncertainty.

因此，在配电网规划中利用配电网柔性互联技术提升配电网供电能力具有很重要的研究意义。Therefore, it is of great research significance to use distribution network flexible interconnection technology to improve the power supply capacity of distribution network in distribution network planning.

发明内容Contents of the invention

本发明的目的在于克服现有技术的不足，提供一种考虑供电能力的低压配电网柔性互联规划方法，可实现利用配电网柔性互联技术提升配电网供电能力。The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a low-voltage distribution network flexible interconnection planning method that considers power supply capacity, so as to realize the use of distribution network flexible interconnection technology to improve the power supply capacity of the distribution network.

本发明解决其技术问题是通过以下技术方案实现的：The technical problems solved by the present invention are achieved through the following technical solutions:

一种考虑供电能力的低压配电网柔性互联规划方法，所述规划方法的步骤为：A low-voltage distribution network flexible interconnection planning method considering power supply capacity. The steps of the planning method are:

步骤1考虑供电能力提升的低压配电网柔性互联规划框架；Step 1: Planning framework for flexible interconnection of low-voltage distribution network considering the improvement of power supply capacity;

步骤2考虑供电能力提升的低压配电网柔性互联规划模型构建；Step 2: Construct a low-voltage distribution network flexible interconnection planning model considering the improvement of power supply capacity;

步骤3多主体不确定性精细化建模。Step 3: Refined modeling of multi-agent uncertainty.

而且，所述步骤1的考虑供电能力提升的低压配电网柔性互联规划框架的具体过程为：将现阶段影响低压配电网供电能力的主要场景抽象为A、B、C三类配电网，在低压配电网A中配置直流充电站，在低压配电网B中配置储能电站，在低压配电网C中配置光储电站，并采用分布式低压配电网柔性互联结构。Moreover, the specific process of the low-voltage distribution network flexible interconnection planning framework considering power supply capacity improvement in step 1 is: abstract the main scenarios that affect the low-voltage distribution network power supply capacity at the current stage into three types of distribution networks: A, B, and C. , configure a DC charging station in low-voltage distribution network A, configure an energy storage power station in low-voltage distribution network B, configure a photovoltaic power storage station in low-voltage distribution network C, and adopt a distributed low-voltage distribution network flexible interconnection structure.

而且，所述步骤2的考虑供电能力提升的低压配电网柔性互联规划模型构建具体过程为：Moreover, the specific process of constructing the low-voltage distribution network flexible interconnection planning model considering the improvement of power supply capacity in step 2 is:

1)光储联合电站模型构建：1) Construction of photovoltaic and storage combined power station model:

A.目标函数：对光储联合电站而言，目标函数为：A. Objective function: For a combined photovoltaic and storage power station, the objective function is:

其中，CPVSS rev为电站中光伏机组及其配套的储能设备带来的收益，CBESSrev为独立的储能设备带来的收益，CPV invest为光伏机组及其配套的储能设备的投资成本，CBESS invest为独立的储能设备的投资成本；Among them, CPVSS rev is the income brought by photovoltaic units and supporting energy storage equipment in the power station, CBESSrev is the income brought by independent energy storage equipment, CPV invest is the investment cost of photovoltaic units and supporting energy storage equipment, CBESS invest is the investment cost of independent energy storage equipment;

其中，csell,t PVSS为t时刻光储联合电站中光储系统(光伏机组及其配套的储能设备)向电网公司售电的售电电价，Among them, csell,t PVSS is the electricity sales price of the solar energy storage system (photovoltaic unit and its supporting energy storage equipment) in the solar energy storage combined power station to the power grid company at time t,

cbuy,t PVSS为t时刻光储联合电站中光储系统(光伏机组及其配套的储能设备)从电网公司购电的购电电价，cbuy,t PVSS is the power purchase price of the photovoltaic and storage system (photovoltaic unit and its supporting energy storage equipment) in the combined photovoltaic and storage power station from the power grid company at time t,

csell,t BESS为t时刻光储联合电站中独立的储能设备向电网公司售电的售电电价，csell,t BESS is the electricity sales price of the independent energy storage equipment in the solar-storage combined power station to the power grid company at time t,

cbuy,t BESS为t时刻光储联合电站中独立的储能设备从电网公司购电的购电电价，cbuy,t BESS is the power purchase price of the independent energy storage equipment in the solar-storage combined power station from the power grid company at time t,

Pt PVSS为t时刻光储系统的实时出力，Pt PVSS is the real-time output of the optical storage system at time t,

Pt,input PVESS为t时刻配套的储能设备的输入功率，Pt,input PVESS is the input power of the energy storage equipment at time t,

η_PVESS为配套的储能设备的运行损耗折算系数，η_PVESS is the operating loss conversion coefficient of the supporting energy storage equipment,

Pt,output BESS和Pt,input BESS为t时刻独立的储能设备的输出与输入功率，Pt,output BESS and Pt,input BESS are the output and input power of the independent energy storage device at time t,

η_BESS为独立的储能设备的运行损耗折算系数，η_BESS is the operating loss conversion coefficient of independent energy storage equipment,

α_PV为光伏机组的投资折算系数，βi PV为型号为i的光伏机组的单位投资成本，α_PV is the investment conversion coefficient of the photovoltaic unit, βi PV is the unit investment cost of the photovoltaic unit of model i,

Si PV为型号为i的光伏机组的投资数量，Si PV is the investment quantity of photovoltaic units of model i,

α_PVESS为配套的储能设备的投资折算系数，_αPVESS is the investment conversion coefficient of supporting energy storage equipment,

βi PVESS为型号为i的配套的储能机组的单位投资成本，βi PVESS is the unit investment cost of the supporting energy storage unit of model i,

Si PVESS为型号为i的配套的储能机组的投资数量，Si PVESS is the investment quantity of supporting energy storage units of model i,

α_BESS为独立的储能设备的投资折算系数，α_BESS is the investment conversion coefficient of independent energy storage equipment,

βi BESS为型号为i的独立的储能机组的单位投资成本，βi BESS is the unit investment cost of an independent energy storage unit of type i,

Si BESS为型号为i的独立的储能机组的投资数量。Si BESS is the investment quantity of independent energy storage units of model i.

B.约束条件：B. Constraints:

a.投资约束a. Investment constraints

其中，Smax PV与Smin PV分别为光伏机组投资数量的上下限，Among them, Smax PV and Smin PV are the upper and lower limits of the investment quantity of photovoltaic units respectively.

Smax PVESS与Smin PVESS分别为配套的储能机组投资数量的上下限，Smax PVESS and Smin PVESS are respectively the upper and lower limits of the investment quantity of supporting energy storage units.

Smax BESS与Smin BESS分别为独立的储能机组投资数量的上下限、值得注意的是当光伏机组及其配套的储能电站的投资数量为0时，该电站将成为储能电站；而当储能机组的投资容量为0时，该电站将成为光伏电站、由此，该模型可对光伏电站、储能电站以及光储电站三类设备进行规划；Smax BESS and Smin BESS are the upper and lower limits of the investment quantity of independent energy storage units respectively. It is worth noting that when the investment quantity of photovoltaic units and their supporting energy storage power stations is 0, the power station will become an energy storage power station; When the investment capacity of the energy generating unit is 0, the power station will become a photovoltaic power station. Therefore, this model can plan three types of equipment: photovoltaic power station, energy storage power station and photovoltaic power storage station;

b.储能运行约束b. Energy storage operation constraints

考虑到光储电站中配套的储能电站与独立的储能电站具有相同的运行机理，因此这里以独立的储能电站为例进行阐述。Considering that the supporting energy storage power station in the optical storage power station has the same operating mechanism as the independent energy storage power station, the independent energy storage power station is used as an example to explain here.

储能充放电约束：Energy storage charge and discharge constraints:

其中Pmax,output BESS和Pmax,input BESS为储能机组的输出与输入功率的上限值；Among them, Pmax, output BESS and Pmax, input BESS are the upper limits of the output and input power of the energy storage unit;

储能的SOC约束：SOC constraints for energy storage:

其中Ct SOC为t时刻储能的SOC水平，Where Ct SOC is the SOC level of energy storage at time t,

Cmax SOC与Cmin SOC分别为储能SOC水平的上下限，Cmax SOC and Cmin SOC are the upper and lower limits of energy storage SOC levels respectively.

Cinitial SOC与Cend SOC分别为一个调度周期的开始与结束阶段储能的SOC水平；Cinitial SOC and Cend SOC are the SOC levels of energy storage at the beginning and end of a dispatch period respectively;

c.光储系统出力约束：c. Optical storage system output constraints:

光储电站中光伏机组及其配套的储能设备组成的光储系统的出力约束Output constraints of the photovoltaic storage system composed of photovoltaic units and supporting energy storage equipment in photovoltaic power storage power stations

该约束限制在光伏机组出力时，在各时段h内光储系统具有恒定的运行状态(以恒定速率增加出力、以恒定速率减少出力、以恒定功率平稳出力；This constraint is that when the photovoltaic unit outputs power, the photovoltaic storage system has a constant operating state in each period h (increasing output at a constant rate, decreasing output at a constant rate, and stably outputting at constant power;

2)直流充电站模型构建：2) DC charging station model construction:

A.直流充电站目标函数：对直流充电站(DC Charging Station，DCCS)而言，目标函数为：A. DC charging station objective function: For DC Charging Station (DCCS), the objective function is:

其中，CDCCS rev为直流充电站提供充电服务的收益，Among them, CDCCS rev provides revenue from charging services for DC charging stations,

CDCCS invest为直流充电站的投资成本；CDCCS invest is the investment cost of DC charging stations;

其中，csell,t DCCS为直流充电站发布的充电电价，Among them, csell,t DCCS is the charging electricity price issued by the DC charging station,

cbuy,t DCCS为电网公司对直流充电站的售电电价，cbuy,t DCCS is the power sales price of DC charging stations by the power grid company.

ηDCCS为直流充电站的运行损耗折算系数，ηDCCS is the operating loss conversion coefficient of the DC charging station,

Pt DCCS为t时刻充电站的实时出力，Pt DCCS is the real-time output of the charging station at time t,

α_DCCS为直流充电桩的投资折算系数，βi DCCS为型号为i的直流充电桩的单位投资成本，α_DCCS is the investment conversion coefficient of DC charging pile, βi DCCS is the unit investment cost of DC charging pile of model i,

Si DCCS为型号为i的直流充电桩的投资数量：Si DCCS is the investment quantity of DC charging piles of model i:

B.直流充电站约束条件B. DC charging station constraints

其中，Smax DCCS与Smin DCCS分别为直流充电桩投资数量的上下限；Among them, Smax DCCS and Smin DCCS are the upper and lower limits of the investment number of DC charging piles respectively;

3)电网公司模型构建：3) Power grid company model construction:

A.电网公司目标函数：对电网公司而言，目标函数如下：A. Power grid company objective function: For power grid companies, the objective function is as follows:

其中，CPVSS rep为配置光储电站给电网公司带来的效益，Among them, CPVSS rep is the benefit brought by configuring optical storage power stations to the power grid company,

CDCCS rep为配置直流充电站给电网公司带来的效益，CDCCS rep is the benefit brought by configuring DC charging stations to the power grid company.

C_invest为电网公司的投资总成本，C_invest is the total investment cost of the power grid company,

CVSC inout为VSC自身向电网中注入/吸收无功功率的成本，CVSC inout is the cost of VSC itself injecting/absorbing reactive power into the grid,

Cdifferent loss为进行低压配电网柔性互联规划前后电网中网损变化量；Cdifferent loss is the change in network loss in the power grid before and after low-voltage distribution network flexible interconnection planning;

其中，ct pp为电网公司发布的分时电价，Among them, ct pp is the time-of-use electricity price issued by the power grid company,

α_VSC为VSC机组的投资折算系数，α_VSC is the investment conversion coefficient of the VSC unit,

βi VSC为型号为i的VSC机组的投资成本，βi VSC is the investment cost of VSC unit model i,

xi VSC为型号为i的VSC机组的0-1投资变量，xi VSC is the 0-1 investment variable of the VSC unit with model i,

α_Line为低压配电网间联络线的投资折算系数，α_Line is the investment conversion coefficient of tie lines between low-voltage distribution networks,

βi Line为型号为i的低压配电网间联络线的投资成本，βi Line is the investment cost of the tie line between low-voltage distribution networks of type i,

βi Line为型号为i的低压配电网间联络线的0-1投资变量，βi Line is the 0-1 investment variable of the tie line between low-voltage distribution networks of type i,

γVSC inout为VSC自身向电网中注入/吸收无功功率的单位成本，γVSC inout is the unit cost of VSC itself injecting/absorbing reactive power into the power grid,

QVSC t,inout为t时刻VSC自身向电网中注入/吸收的实时无功功率；QVSC t,inout is the real-time reactive power injected/absorbed by VSC itself into the power grid at time t;

B.电网公司约束条件B. Grid company constraints

a.电网公司潮流约束：本方法采用Distflow模型描述配电网潮流；a. Power grid company power flow constraints: This method uses the Distflow model to describe the distribution network power flow;

b.电网公司安全约束：b. Power grid company safety constraints:

其中，m为交流节点集合，Among them, m is the set of communication nodes,

n为直流节点集合，n is the DC node set,

Umax AC与Umin AC、Umax DC与Umin DC分别为交流节点与直流节点的电压上下限，Umax AC and Umin AC, Umax DC and Umin DC are the upper and lower voltage limits of the AC node and DC node respectively.

Imax AC与Imin AC、Imax DC与Imin DC分别为流经交流节点与直流节点的电流上下限，Imax AC and Imin AC, Imax DC and Imin DC are the upper and lower limits of the current flowing through the AC node and DC node respectively.

P_t,trans为低压配电网配变在t时刻的实时功率，P_t,trans is the real-time power of the low-voltage distribution network distribution transformer at time t,

ν_trans为配变安全运行折算系数，ν_trans is the conversion factor for safe operation of the distribution transformer,

S_trans为低压配电网配变的额定容量。S_trans is the rated capacity of the low-voltage distribution network distribution transformer.

c.电网公司投资约束c. Investment constraints of power grid companies

其中，Si VSC为型号为i的VSC机组容量，Among them, Si VSC is the capacity of the VSC unit with model i,

Smax VSC与Smin VSC分别为VSC投资容量的上下限；Smax VSC and Smin VSC are the upper and lower limits of VSC investment capacity respectively;

d.VSC运行约束d.VSC operating constraints

其中，Pt,j VSC,AC与Pt,j VSC,DC分别代表t时刻下节点j处由VSC的交流端传递有功功率和直流端注入的有功功率，Among them, Pt,j VSC,AC and Pt,j VSC,DC respectively represent the active power transmitted by the AC terminal of VSC and the active power injected by the DC terminal at node j at time t.

η_VSC为VSC的转换效率，η_VSC is the conversion efficiency of VSC,

Pmax VSC,AC和Pmin VSC,AC分别为VSC传递的有功上下限，Pmax VSC,AC and Pmin VSC,AC are the upper and lower limits of active power transmitted by VSC respectively.

Qmax VSC,AC和Qmin VSC,AC分别为VSC发出无功功率的上下限。Qmax VSC,AC and Qmin VSC,AC are the upper and lower limits of the reactive power generated by VSC respectively.

而且，所述步骤3的多主体不确定性的精细化建模：Moreover, the refined modeling of multi-agent uncertainty in step 3:

1)多主体不确定性的表征：1) Characterization of multi-agent uncertainty:

对于光伏出力的不确定性与电动汽车充电负荷的不确定性，本方法采用盒式不确定性集合进行表征，对于电网负荷的不确定性则通过建立基于Wasserstein距离的不确定集合进行表征，具体有：For the uncertainty of photovoltaic output and the uncertainty of electric vehicle charging load, this method uses a box-type uncertainty set to characterize, and for the uncertainty of the grid load, it is characterized by establishing an uncertainty set based on Wasserstein distance. Specifically have:

其中，γ_PV、γ_EV、Ψ_load分别为光伏出力、电动汽车充电负荷以及电网负荷的实际值，PV、EV分别为光伏出力和电动汽车充电负荷的预测值，Among them, γ_PV , γ_EV and Ψ_load are the actual values of photovoltaic output, electric vehicle charging load and grid load respectively, PV and EV are the predicted values of photovoltaic output and electric vehicle charging load respectively.

γ_PV、γ_EV分别为光伏出力和电动汽车充电负荷的不确定系数，γ_PV and γ_EV are the uncertainty coefficients of photovoltaic output and electric vehicle charging load respectively,

W(P₀，P)为电网负荷的场景概率基准集合，W(P₀ , P) is the scenario probability benchmark set of power grid load,

P₀与电网负荷的场景概率集合P间的Wasserstein距离，The Wasserstein distance between P₀ and the grid load scenario probability set P,

e为以场景概率基准集合P₀为球心的Wasserstein球的半径；e is the radius of the Wasserstein sphere with the scene probability benchmark set P₀ as the center;

2)多主体不确定性的精细化建模：2) Refined modeling of multi-agent uncertainty:

考虑到极端天气概率不断增加会对光储联合电站带来一定的风险因此光储联合电站采用的风险规避策略模型可表示为：Considering that the increasing probability of extreme weather will bring certain risks to solar-storage combined power plants, the risk avoidance strategy model adopted by solar-storage combined power plants can be expressed as:

其中C_PVSS为光储联合电站的预期收益值，σ_PV为光储联合电站的期望收益偏差系数；Among them, C_PVSS is the expected revenue value of the combined photovoltaic and storage power station, and σ_PV is the expected revenue deviation coefficient of the combined photovoltaic and storage power station;

电动汽车保有量的增加会带来更高的电动汽车充电需求，进而为直流充电站带来更高的收益，因此直流充电站采用的投机套利策略可表示为：The increase in the number of electric vehicles will bring higher electric vehicle charging demand, which will in turn bring higher profits to DC charging stations. Therefore, the speculative arbitrage strategy adopted by DC charging stations can be expressed as:

其中CDCCS为直流充电站的预期收益值，σEV为直流充电站的期望收益偏差系数；Among them, CDCCS is the expected revenue value of the DC charging station, and σEV is the expected revenue deviation coefficient of the DC charging station;

考虑到电网负荷的年增长率较为固定且电网公司有较为详尽的历史数据，因此采用基于Wasserstein距离的分布鲁棒方法加以处理；Considering that the annual growth rate of the power grid load is relatively fixed and the power grid company has relatively detailed historical data, a distributed robust method based on Wasserstein distance is used to process it;

本发明的优点和有益效果为：The advantages and beneficial effects of the present invention are:

1.本发明的考虑供电能力的低压配电网柔性互联规划方法，能够有效解决低压配电网配电变压器负载不均、新能源消纳能力不足以及配电网末端低电压三大问题，有效提升低压配电网供电能力。1. The low-voltage distribution network flexible interconnection planning method of the present invention considering the power supply capacity can effectively solve the three major problems of uneven load of distribution transformers in the low-voltage distribution network, insufficient new energy accommodation capacity, and low voltage at the end of the distribution network. Effectively Improve the power supply capacity of low-voltage distribution network.

2.本发明的考虑供电能力的低压配电网柔性互联规划方法，首先构建影响低压配电网供电能力的主要场景，根据各场景现状及需求确定规划对象，建立考虑供电能力提升的低压配电网柔性互联规划框架。其次，建立了考虑供电能力提升的低压配电网柔性互联规划模型，其中光储联合电站和直流充电站以第三方投资商的获利最大为目标，电网公司则以进行低压配电网柔性互联规划的获利最大为目标。2. The low-voltage distribution network flexible interconnection planning method considering power supply capacity of the present invention first constructs the main scenarios that affect the power supply capacity of the low-voltage distribution network, determines the planning objects according to the current situation and needs of each scenario, and establishes a low-voltage distribution network that considers the improvement of power supply capacity. Network flexible interconnection planning framework. Secondly, a low-voltage distribution network flexible interconnection planning model considering the improvement of power supply capacity was established. Among them, the combined photovoltaic and storage power stations and DC charging stations aimed at maximizing profits for third-party investors, while the power grid company aimed at flexible interconnection of the low-voltage distribution network. The goal of planning is to maximize profits.

3.本发明的考虑供电能力的低压配电网柔性互联规划方法，能够有效处理同时考虑的光储电站、直流充电站以及电网公司的多主体规划运行问题；在多主体不确定性的精细化建模的参数选取方面，IGDT方法的期望收益偏差系数选取对规划结构影响较大，实际中应根据相关历史数据信息并结合当地实际需求确定，基于Wasserstein距离的分布鲁棒方法的Wasserstein球半径越大，候选概率分布也就越恶劣，电网公司规划收益也就越低；在方法对比方面，相较于其它方法，本文采用的IGDT+分布鲁棒方法能够兼顾规划的经济性与保守性。3. The low-voltage distribution network flexible interconnection planning method of the present invention considering power supply capacity can effectively handle the multi-agent planning and operation problems of optical storage power stations, DC charging stations and power grid companies that are simultaneously considered; in the refinement of multi-agent uncertainties In terms of parameter selection for modeling, the selection of the expected return deviation coefficient of the IGDT method has a greater impact on the planning structure. In practice, it should be determined based on relevant historical data information and combined with local actual needs. The Wasserstein sphere radius of the distributed robust method based on Wasserstein distance is more The larger the candidate probability distribution is, the worse the candidate probability distribution will be, and the lower the planning benefits of the power grid company will be. In terms of method comparison, compared with other methods, the IGDT+ distribution robust method used in this article can take into account both the economy and conservatism of planning.

附图说明Description of the drawings

图1为本发明光储联合电站结构示意图。Figure 1 is a schematic structural diagram of the combined optical and storage power station of the present invention.

具体实施方式Detailed ways

下面通过具体实施例对本发明作进一步详述，以下实施例只是描述性的，不是限定性的，不能以此限定本发明的保护范围。The present invention will be further described in detail below through specific examples. The following examples are only descriptive, not restrictive, and cannot be used to limit the scope of the present invention.

一种考虑供电能力的低压配电网柔性互联规划方法，该规划方法的步骤为：A flexible interconnection planning method for low-voltage distribution networks considering power supply capacity. The steps of the planning method are:

步骤1、考虑供电能力提升的低压配电网柔性互联规划框架，具体过程为；Step 1. Develop a flexible interconnection planning framework for low-voltage distribution networks considering the improvement of power supply capacity. The specific process is as follows;

将现阶段影响低压配电网供电能力的主要场景抽象为A、B、C三类配电网，各配电网的详细情况如下：The main scenarios that affect the power supply capacity of low-voltage distribution networks at this stage are abstracted into three types of distribution networks: A, B, and C. The details of each distribution network are as follows:

1)低压配电网A中需要新建大型直流充电站，但低压配电网配变A的负载较重，致使低压配电网A面临严峻的保供压力，此外，由于土地资源紧张，低压配电网内没有新建储能电站的条件；1) A large-scale DC charging station needs to be built in low-voltage distribution network A, but the load of low-voltage distribution network distribution transformer A is relatively heavy, causing low-voltage distribution network A to face severe pressure to ensure supply. In addition, due to the shortage of land resources, low-voltage distribution network There are no conditions for new energy storage power stations within the power grid;

2)低压配电网B中分布式光伏的渗透率处于较高水平，但受限于自身消纳水平，弃光现象较为严重，此外，低压配电网B同样有着较高的储能电站的建设潜力；2) The penetration rate of distributed photovoltaics in low-voltage distribution network B is at a high level, but due to its own consumption level, the phenomenon of light abandonment is more serious. In addition, low-voltage distribution network B also has high energy storage power stations. construction potential;

3)低压配电网C负荷主要为农业负荷与居民负荷，低压配电网配变C的负载较轻且有着较高的分布式光伏与储能电站的建设潜力，但在农业灌溉期间由于负荷较为集中，配电网末端低电压时常发生；3) The load of low-voltage distribution network C is mainly agricultural load and residential load. The load of low-voltage distribution network distribution transformer C is lighter and has high potential for the construction of distributed photovoltaic and energy storage power stations. However, due to the load during agricultural irrigation, It is relatively concentrated, and low voltage at the end of the distribution network often occurs;

在低压配电网A中配置直流充电站，在低压配电网B中配置储能电站，在低压配电网C中配置光储电站，并采用分布式低压配电网柔性互联结构。A DC charging station is configured in low-voltage distribution network A, an energy storage power station is configured in low-voltage distribution network B, and a photovoltaic power storage station is configured in low-voltage distribution network C, and a distributed low-voltage distribution network flexible interconnection structure is used.

步骤2、考虑供电能力提升的低压配电网柔性互联规划模型构建；上述步骤2的考虑供电能力提升的低压配电网柔性互联规划模型构建具体过程为：Step 2. Construct a low-voltage distribution network flexible interconnection planning model considering the improvement of power supply capacity. The specific process of constructing the low-voltage distribution network flexible interconnection planning model considering the improvement of power supply capacity in step 2 above is:

1)光储联合电站模型构建：1) Construction of photovoltaic and storage combined power station model:

本方法所规划的光储联合电站(Photovoltaic Storage Union Power Station，PVSS)由光伏机组与储能设备组成，其中储能设备又由与光伏机组配套的储能设备和独立的储能设备组成，与光伏机组配套的储能设备指与光伏机组配套并配合平抑光伏出力波动的储能，在光伏机组出力阶段，与光伏机组配套的储能设备根据光伏机组实时出力调整自身出力，从而平抑光伏出力的波动性；在光伏机组不出力阶段，与光伏机组配套的储能设备则在满足相关约束的前提下自由决策充放电功率以获取更高的利润，改善投资的经济性；独立的储能设备则指在每个调度周期内在满足相关约束的前提下自由决策充放电功率，根据电网公司发布的分时电价采取“低储高放”策略获取更高利润的储能，可有效改善光储联合电站的经济性；The Photovoltaic Storage Union Power Station (PVSS) planned by this method consists of photovoltaic units and energy storage equipment. The energy storage equipment is composed of energy storage equipment matched with the photovoltaic units and independent energy storage equipment. The energy storage equipment supporting the photovoltaic unit refers to the energy storage that is matched with the photovoltaic unit and cooperates with the fluctuation of photovoltaic output. During the output stage of the photovoltaic unit, the energy storage equipment matched with the photovoltaic unit adjusts its own output according to the real-time output of the photovoltaic unit, thereby smoothing the fluctuation of photovoltaic output. Volatility; when the photovoltaic unit is not producing power, the energy storage equipment supporting the photovoltaic unit can freely decide the charging and discharging power under the premise of meeting relevant constraints to obtain higher profits and improve the economics of investment; independent energy storage equipment It refers to the free decision-making of charging and discharging power under the premise of meeting relevant constraints in each dispatch cycle. According to the time-of-use electricity price released by the power grid company, the "low storage, high discharge" strategy is adopted to obtain higher-profit energy storage, which can effectively improve the combined photovoltaic and storage power stations. economy;

A.目标函数：对光储联合电站而言，目标函数为：A. Objective function: For a combined photovoltaic and storage power station, the objective function is:

其中，CPVSS rev为电站中光伏机组及其配套的储能设备带来的收益，CBESSrev为独立的储能设备带来的收益，CPV invest为光伏机组及其配套的储能设备的投资成本，CBESS invest为独立的储能设备的投资成本；Among them, CPVSS rev is the income brought by photovoltaic units and supporting energy storage equipment in the power station, CBESSrev is the income brought by independent energy storage equipment, CPV invest is the investment cost of photovoltaic units and supporting energy storage equipment, CBESS invest is the investment cost of independent energy storage equipment;

其中，csell,t PVSS为t时刻光储联合电站中光储系统(光伏机组及其配套的储能设备)向电网公司售电的售电电价，Among them, csell,t PVSS is the electricity sales price of the solar energy storage system (photovoltaic unit and its supporting energy storage equipment) in the solar energy storage combined power station to the power grid company at time t,

cbuy,t PVSS为t时刻光储联合电站中光储系统(光伏机组及其配套的储能设备)从电网公司购电的购电电价，cbuy,t PVSS is the power purchase price of the photovoltaic and storage system (photovoltaic unit and its supporting energy storage equipment) in the combined photovoltaic and storage power station from the power grid company at time t,

csell,t BESS为t时刻光储联合电站中独立的储能设备向电网公司售电的售电电价，csell,t BESS is the electricity sales price of the independent energy storage equipment in the solar-storage combined power station to the power grid company at time t,

cbuy,t BESS为t时刻光储联合电站中独立的储能设备从电网公司购电的购电电价，cbuy,t BESS is the power purchase price of the independent energy storage equipment in the solar-storage combined power station from the power grid company at time t,

Pt PVSS为t时刻光储系统的实时出力，Pt PVSS is the real-time output of the optical storage system at time t,

Pt,input PVESS为t时刻配套的储能设备的输入功率，Pt,input PVESS is the input power of the energy storage equipment at time t,

η_PVESS为配套的储能设备的运行损耗折算系数，η_PVESS is the operating loss conversion coefficient of the supporting energy storage equipment,

Pt,output BESS和Pt,input BESS为t时刻独立的储能设备的输出与输入功率，Pt,output BESS and Pt,input BESS are the output and input power of the independent energy storage device at time t,

η_BESS为独立的储能设备的运行损耗折算系数，η_BESS is the operating loss conversion coefficient of independent energy storage equipment,

α_PV为光伏机组的投资折算系数，βi PV为型号为i的光伏机组的单位投资成本，α_PV is the investment conversion coefficient of the photovoltaic unit, βi PV is the unit investment cost of the photovoltaic unit of model i,

Si PV为型号为i的光伏机组的投资数量，Si PV is the investment quantity of photovoltaic units of model i,

α_PVESS为配套的储能设备的投资折算系数，_αPVESS is the investment conversion coefficient of supporting energy storage equipment,

βi PVESS为型号为i的配套的储能机组的单位投资成本，βi PVESS is the unit investment cost of the supporting energy storage unit of model i,

Si PVESS为型号为i的配套的储能机组的投资数量，Si PVESS is the investment quantity of supporting energy storage units of model i,

α_BESS为独立的储能设备的投资折算系数，α_BESS is the investment conversion coefficient of independent energy storage equipment,

βi BESS为型号为i的独立的储能机组的单位投资成本，βi BESS is the unit investment cost of an independent energy storage unit of type i,

Si BESS为型号为i的独立的储能机组的投资数量。Si BESS is the investment quantity of independent energy storage units of model i.

B.约束条件：B. Constraints:

a.投资约束a. Investment constraints

其中，Smax PV与Smin PV分别为光伏机组投资数量的上下限，Among them, Smax PV and Smin PV are the upper and lower limits of the investment quantity of photovoltaic units respectively.

Smax PVESS与Smin PVESS分别为配套的储能机组投资数量的上下限，Smax PVESS and Smin PVESS are respectively the upper and lower limits of the investment quantity of supporting energy storage units.

Smax BESS与Smin BESS分别为独立的储能机组投资数量的上下限、值得注意的是当光伏机组及其配套的储能电站的投资数量为0时，该电站将成为储能电站；而当储能机组的投资容量为0时，该电站将成为光伏电站、由此，该模型可对光伏电站、储能电站以及光储电站三类设备进行规划；Smax BESS and Smin BESS are the upper and lower limits of the investment quantity of independent energy storage units respectively. It is worth noting that when the investment quantity of photovoltaic units and their supporting energy storage power stations is 0, the power station will become an energy storage power station; When the investment capacity of the energy generating unit is 0, the power station will become a photovoltaic power station. Therefore, this model can plan three types of equipment: photovoltaic power station, energy storage power station and photovoltaic power storage station;

b.储能运行约束b. Energy storage operation constraints

考虑到光储电站中配套的储能电站与独立的储能电站具有相同的运行机理，因此这里以独立的储能电站为例进行阐述。Considering that the supporting energy storage power station in the optical storage power station has the same operating mechanism as the independent energy storage power station, the independent energy storage power station is used as an example to explain here.

储能充放电约束：Energy storage charge and discharge constraints:

其中Pmax,output BESS和Pmax,input BESS为储能机组的输出与输入功率的上限值；Among them, Pmax, output BESS and Pmax, input BESS are the upper limits of the output and input power of the energy storage unit;

储能的SOC约束：SOC constraints for energy storage:

其中Ct SOC为t时刻储能的SOC水平，Where Ct SOC is the SOC level of energy storage at time t,

Cmax SOC与Cmin SOC分别为储能SOC水平的上下限，Cmax SOC and Cmin SOC are the upper and lower limits of energy storage SOC levels respectively.

Cinitial SOC与Cend SOC分别为一个调度周期的开始与结束阶段储能的SOC水平；Cinitial SOC and Cend SOC are the SOC levels of energy storage at the beginning and end of a dispatch period respectively;

c.光储系统出力约束：c. Optical storage system output constraints:

光储电站中光伏机组及其配套的储能设备组成的光储系统的出力约束Output constraints of the photovoltaic storage system composed of photovoltaic units and supporting energy storage equipment in photovoltaic power storage power stations

该约束限制在光伏机组出力时，在各时段h内光储系统具有恒定的运行状态(以恒定速率增加出力、以恒定速率减少出力、以恒定功率平稳出力；This constraint is that when the photovoltaic unit outputs power, the photovoltaic storage system has a constant operating state in each period h (increasing output at a constant rate, decreasing output at a constant rate, and stably outputting at constant power;

2)直流充电站模型构建：2) DC charging station model construction:

A.直流充电站目标函数：对直流充电站(DC Charging Station，DCCS)而言，目标函数为：A. DC charging station objective function: For DC Charging Station (DCCS), the objective function is:

其中，CDCCS rev为直流充电站提供充电服务的收益，Among them, CDCCS rev provides revenue from charging services for DC charging stations,

CDCCS invest为直流充电站的投资成本；CDCCS invest is the investment cost of DC charging stations;

其中，csell,t DCCS为直流充电站发布的充电电价，Among them, csell,t DCCS is the charging electricity price issued by the DC charging station,

cbuy,t DCCS为电网公司对直流充电站的售电电价，cbuy,t DCCS is the power sales price of DC charging stations by the power grid company.

ηDCCS为直流充电站的运行损耗折算系数，ηDCCS is the operating loss conversion coefficient of the DC charging station,

Pt DCCS为t时刻充电站的实时出力，Pt DCCS is the real-time output of the charging station at time t,

α_DCCS为直流充电桩的投资折算系数，βi DCCS为型号为i的直流充电桩的单位投资成本，α_DCCS is the investment conversion coefficient of DC charging pile, βi DCCS is the unit investment cost of DC charging pile of model i,

Si DCCS为型号为i的直流充电桩的投资数量：Si DCCS is the investment quantity of DC charging piles of model i:

B.直流充电站约束条件B. DC charging station constraints

其中，Smax DCCS与Smin DCCS分别为直流充电桩投资数量的上下限；Among them, Smax DCCS and Smin DCCS are the upper and lower limits of the investment number of DC charging piles respectively;

3)电网公司模型构建：3) Power grid company model construction:

A.电网公司目标函数：对电网公司而言，目标函数如下：A. Power grid company objective function: For power grid companies, the objective function is as follows:

其中，CPVSS rep为配置光储电站给电网公司带来的效益，Among them, CPVSS rep is the benefit brought by configuring optical storage power stations to the power grid company,

CDCCS rep为配置直流充电站给电网公司带来的效益，CDCCS rep is the benefit brought by configuring DC charging stations to the power grid company.

C_invest为电网公司的投资总成本，C_invest is the total investment cost of the power grid company,

CVSC inout为VSC自身向电网中注入/吸收无功功率的成本，Cdifferent loss为进行低压配电网柔性互联规划前后电网中网损变化量；CVSC inout is the cost of VSC injecting/absorbing reactive power into the power grid, and Cdifferent loss is the change in network loss in the power grid before and after low-voltage distribution network flexible interconnection planning;

其中，ct pp为电网公司发布的分时电价，Among them, ct pp is the time-of-use electricity price issued by the power grid company,

α_VSC为VSC机组的投资折算系数，α_VSC is the investment conversion coefficient of the VSC unit,

βi VSC为型号为i的VSC机组的投资成本，βi VSC is the investment cost of VSC unit model i,

xi VSC为型号为i的VSC机组的0-1投资变量，xi VSC is the 0-1 investment variable of the VSC unit with model i,

α_Line为低压配电网间联络线的投资折算系数，α_Line is the investment conversion coefficient of tie lines between low-voltage distribution networks,

βi Line为型号为i的低压配电网间联络线的投资成本，βi Line is the investment cost of the tie line between low-voltage distribution networks of type i,

βi Line为型号为i的低压配电网间联络线的0-1投资变量，βi Line is the 0-1 investment variable of the tie line between low-voltage distribution networks of type i,

γVSC inout为VSC自身向电网中注入/吸收无功功率的单位成本，QVSC t,inout为t时刻VSC自身向电网中注入/吸收的实时无功功率；B.电网公司约束条件γVSC inout is the unit cost of VSC injecting/absorbing reactive power into the power grid, QVSC t,inout is the real-time reactive power that VSC injects/absorbs into the power grid at time t; B. Grid company constraints

a.电网公司潮流约束：本方法采用Distflow模型描述配电网潮流；a. Power grid company power flow constraints: This method uses the Distflow model to describe the distribution network power flow;

b.电网公司安全约束：b. Power grid company safety constraints:

其中，m为交流节点集合，Among them, m is the set of communication nodes,

n为直流节点集合，n is the DC node set,

Umax AC与Umin AC、Umax DC与Umin DC分别为交流节点与直流节点的电压上下限，Umax AC and Umin AC, Umax DC and Umin DC are the upper and lower voltage limits of the AC node and DC node respectively.

Imax AC与Imin AC、Imax DC与Imin DC分别为流经交流节点与直流节点的电流上下限，Imax AC and Imin AC, Imax DC and Imin DC are the upper and lower limits of the current flowing through the AC node and DC node respectively.

P_t,trans为低压配电网配变在t时刻的实时功率，P_t,trans is the real-time power of the low-voltage distribution network distribution transformer at time t,

ν_trans为配变安全运行折算系数，ν_trans is the conversion factor for safe operation of the distribution transformer,

S_trans为低压配电网配变的额定容量。S_trans is the rated capacity of the low-voltage distribution network distribution transformer.

c.电网公司投资约束c. Investment constraints of power grid companies

其中，Si VSC为型号为i的VSC机组容量，Among them, Si VSC is the capacity of the VSC unit with model i,

Smax VSC与Smin VSC分别为VSC投资容量的上下限；Smax VSC and Smin VSC are the upper and lower limits of VSC investment capacity respectively;

d.VSC运行约束d.VSC operating constraints

其中，Pt,j VSC,AC与Pt,j VSC,DC分别代表t时刻下节点j处由VSC的交流端传递有功功率和直流端注入的有功功率，Among them, Pt,j VSC,AC and Pt,j VSC,DC respectively represent the active power transmitted by the AC terminal of VSC and the active power injected by the DC terminal at node j at time t.

η_VSC为VSC的转换效率，η_VSC is the conversion efficiency of VSC,

Pmax VSC,AC和Pmin VSC,AC分别为VSC传递的有功上下限，Pmax VSC,AC and Pmin VSC,AC are the upper and lower limits of active power transmitted by VSC respectively.

Qmax VSC,AC和Qmin VSC,AC分别为VSC发出无功功率的上下限。Qmax VSC,AC and Qmin VSC,AC are the upper and lower limits of the reactive power generated by VSC respectively.

步骤3、多主体不确定性精细化建模，上述步骤3的多主体不确定性的精细化建模：Step 3. Refined modeling of multi-agent uncertainty. Refined modeling of multi-agent uncertainty in step 3 above:

1)多主体不确定性的表征：1) Characterization of multi-agent uncertainty:

对于光伏出力的不确定性与电动汽车充电负荷的不确定性，本方法采用盒式不确定性集合进行表征，对于电网负荷的不确定性则通过建立基于Wasserstein距离的不确定集合进行表征，具体有：For the uncertainty of photovoltaic output and the uncertainty of electric vehicle charging load, this method uses a box-type uncertainty set to characterize, and for the uncertainty of the grid load, it is characterized by establishing an uncertainty set based on Wasserstein distance. Specifically have:

其中，γ_PV、γ_EV、Ψ_load分别为光伏出力、电动汽车充电负荷以及电网负荷的实际值，PV、EV分别为光伏出力和电动汽车充电负荷的预测值，Among them, γ_PV , γ_EV and Ψ_load are the actual values of photovoltaic output, electric vehicle charging load and grid load respectively, PV and EV are the predicted values of photovoltaic output and electric vehicle charging load respectively.

γ_PV、γ_EV分别为光伏出力和电动汽车充电负荷的不确定系数，γ_PV and γ_EV are the uncertainty coefficients of photovoltaic output and electric vehicle charging load respectively,

W(P₀，P)为电网负荷的场景概率基准集合，W(P₀ , P) is the scenario probability benchmark set of power grid load,

P₀与电网负荷的场景概率集合P间的Wasserstein距离，The Wasserstein distance between P₀ and the grid load scenario probability set P,

e为以场景概率基准集合P₀为球心的Wasserstein球的半径。e is the radius of the Wasserstein sphere with the scene probability benchmark set P₀ as the center.

2)多主体不确定性的精细化建模：2) Refined modeling of multi-agent uncertainty:

对于光储联合电站而言，直接根据历史光伏出力数据分析不确定性可能会因为极端天气出现概率水平较低而导致结果过于乐观。而随着电动汽车保有量的不断提高，对于直流充电站而言，直接由相关历史数据分析不确定性可能会因为电动汽车直流充电的需求水平较低而导致结果过于保守。而采用IGDT方法，通过对期望收益偏差系数的调节即可实现历史数据的乐观性和保守性控制，从而解决上述问题。IGDT方法可分为投机套利策略模型和风险规避策略模型两类。考虑到极端天气概率不断增加会对光储联合电站带来一定的风险因此光储联合电站采用的风险规避策略模型可表示为：For combined photovoltaic and storage power plants, analyzing uncertainty directly based on historical photovoltaic output data may lead to overly optimistic results due to the low probability of extreme weather. As the number of electric vehicles continues to increase, for DC charging stations, direct analysis of uncertainty based on relevant historical data may lead to overly conservative results due to the low demand level for DC charging of electric vehicles. Using the IGDT method, optimism and conservative control of historical data can be achieved by adjusting the expected return deviation coefficient, thus solving the above problems. The IGDT method can be divided into two categories: speculative arbitrage strategy model and risk aversion strategy model. Considering that the increasing probability of extreme weather will bring certain risks to solar-storage combined power plants, the risk avoidance strategy model adopted by solar-storage combined power plants can be expressed as:

其中C_PVSS为光储联合电站的预期收益值，σ_PV为光储联合电站的期望收益偏差系数；Among them, C_PVSS is the expected revenue value of the combined photovoltaic and storage power station, and σ_PV is the expected revenue deviation coefficient of the combined photovoltaic and storage power station;

电动汽车保有量的增加会带来更高的电动汽车充电需求，进而为直流充电站带来更高的收益，因此直流充电站采用的投机套利策略可表示为：The increase in the number of electric vehicles will bring higher electric vehicle charging demand, which will in turn bring higher profits to DC charging stations. Therefore, the speculative arbitrage strategy adopted by DC charging stations can be expressed as:

其中CDCCS为直流充电站的预期收益值，σEV为直流充电站的期望收益偏差系数；Among them, CDCCS is the expected revenue value of the DC charging station, and σEV is the expected revenue deviation coefficient of the DC charging station;

由此，通过改变期望收益偏差系数σPV与σEV，即可改变投资商的运行投资策略，进而对冲因历史数据过于保守/乐观而对运行投资策略造成的影响，对于电网负荷的不确定性，考虑到电网负荷的年增长率较为固定且电网公司有较为详尽的历史数据，因此本方法采用基于Wasserstein距离的分布鲁棒方法加以处理；Therefore, by changing the expected return deviation coefficients σPV and σEV, the investor's operation investment strategy can be changed, and then the impact on the operation investment strategy caused by too conservative/optimistic historical data can be hedged. For the uncertainty of the power grid load, consider The annual growth rate of the grid load is relatively fixed and the grid company has relatively detailed historical data, so this method uses a distributed robust method based on Wasserstein distance to process it;

尽管为说明目的公开的本发明的实施例和附图，但是本领域的技术人员可以理解，在不脱离本发明及所附权利要求的精神和范围内，各种替换、变化和修改都是可能的，因此本发明的范围不局限于实施例和附图所公开的内容。Although the embodiments and drawings of the present invention have been disclosed for illustrative purposes, those skilled in the art will understand that various substitutions, changes and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. , therefore the scope of the present invention is not limited to the contents disclosed in the embodiments and drawings.

Claims (4)

1. A flexible interconnection planning method of a low-voltage distribution network considering power supply capacity is characterized by comprising the following steps of: the planning method comprises the following steps:

step 1, considering a flexible interconnection planning frame of a low-voltage power distribution network with improved power supply capacity;

step 2, constructing a flexible interconnection planning model of the low-voltage power distribution network by considering the lifting of power supply capacity;

and 3, performing multi-body uncertainty fine modeling.

2. The flexible interconnection planning method for a low-voltage distribution network considering power supply capacity according to claim 1, wherein the method comprises the following steps: the specific process of the low-voltage power distribution network flexible interconnection planning framework considering the power supply capacity improvement in the step 1 is as follows: the main scene affecting the power supply capacity of the low-voltage distribution network at the present stage is abstracted into A, B, C three types of distribution networks, a direct-current charging station is configured in the low-voltage distribution network A, an energy storage power station is configured in the low-voltage distribution network B, an optical storage power station is configured in the low-voltage distribution network C, and a distributed low-voltage distribution network flexible interconnection structure is adopted.

3. The flexible interconnection planning method for a low-voltage distribution network considering power supply capacity according to claim 1, wherein the method comprises the following steps: the specific process of constructing the flexible interconnection planning model of the low-voltage power distribution network considering the lifting of the power supply capacity in the step 2 is as follows:

1) Building an optical storage combined power station model:

A. objective function: for an optical storage combined power station, the objective function is:

the CPVSS rev is the benefit brought by the photovoltaic unit and the matched energy storage equipment in the power station, the CBESSrev is the benefit brought by the independent energy storage equipment, the CPV invent is the investment cost of the photovoltaic unit and the matched energy storage equipment, and the CBESS invent is the investment cost of the independent energy storage equipment;

wherein csell, t PVSS is the electricity price of electricity sold to the power grid company by the light storage system (photovoltaic unit and the energy storage equipment matched with the photovoltaic unit) in the light storage combined power station at t moment,

cbuy, t PVSS is the electricity purchasing price of electricity purchasing from a power grid company by an optical storage system (a photovoltaic unit and energy storage equipment matched with the photovoltaic unit) in the optical storage combined power station at t moment,

csell, t BESS is the electricity price of the independent energy storage equipment in the light-storage combined power station at the moment t for selling electricity to the power grid company,

cbuy, t BESS is the electricity purchasing price of the independent energy storage equipment in the light storage combined power station at the moment t for purchasing electricity from the power grid company,

pt PVSS is the real-time output of the optical storage system at time t,

pt, input PVESS is the input power of the energy storage device matched at time t,

η_PVESS the coefficient is converted for the operation loss of the matched energy storage equipment,

pt, output pass and Pt, input pass being the output and input power of the energy storage device independent at time t,

η_BESS the coefficients are converted for the operational losses of the individual energy storage devices,

α_PV the investment conversion coefficient of the photovoltaic unit is that the beta iPV is the unit investment cost of the photovoltaic unit with the model of i,

si PV is the investment quantity of the photovoltaic unit with the model i,

α_PVESS is an investment conversion coefficient of the matched energy storage equipment,

beta iPVESS is the unit investment cost of the matched energy storage unit with the model of i,

the SiPVESS is the investment quantity of the matched energy storage unit with the model i,

α_BESS to be the investment conversion coefficient of the independent energy storage equipment,

the beta iBESS is the unit investment cost of the independent energy storage unit with the model i,

SiBESS is the investment quantity of the independent energy storage unit with the model i;

B. constraint conditions:

a. investment constraints

Wherein Smax PV and Smin PV are respectively the upper limit and the lower limit of the investment quantity of the photovoltaic unit,

smax PVESS and Smin PVESS are respectively the upper limit and the lower limit of the investment quantity of the matched energy storage unit,

smax BESS and Smin BESS are the upper limit and the lower limit of the investment quantity of independent energy storage units respectively, and notably, when the investment quantity of a photovoltaic unit and an energy storage power station matched with the photovoltaic unit is 0, the power station becomes an energy storage power station; when the investment capacity of the energy storage unit is 0, the power station becomes a photovoltaic power station, so that the model can plan three types of equipment, namely the photovoltaic power station, the energy storage power station and the optical storage power station;

b. energy storage operation constraint

Considering that the matched energy storage power station and the independent energy storage power station in the optical energy storage power station have the same operation mechanism, the independent energy storage power station is taken as an example for explanation;

energy storage charge-discharge constraint:

wherein Pmax, output BESS and Pmax, input BESS are the upper limit value of the output and input power of the energy storage unit;

SOC constraint of stored energy:

wherein Ct SOC is the SOC level of the stored energy at time t,

cmax SOC and Cmin SOC are upper and lower limits of the stored energy SOC level respectively,

the Cinitial SOC and the Cend SOC are the SOC levels of energy storage at the beginning and ending phases of a scheduling period respectively;

c. output constraint of the optical storage system:

output constraint of photovoltaic unit in photovoltaic power station and photovoltaic storage system formed by matched energy storage equipment

The constraint limits that the photovoltaic system has a constant operating state (increasing the output at a constant rate, decreasing the output at a constant rate, stabilizing the output at a constant power;

2) Constructing a direct current charging station model:

A. direct current charging station objective function: for a direct current charging station (DC Charging Station, DCCS), the objective function is:

wherein the CDCCS rev provides benefits of charging services for the DC charging station,

CDCCS invent is the investment cost of the direct current charging station;

wherein csell, t DCCS is the charge price issued by the direct current charging station,

cbuy, t DCCS is the selling price of electricity of the grid company to the dc charging station,

ηdccs is the operational loss conversion coefficient of the dc charging station,

pt DCCS is the real-time output of the charging station at time t,

α_DCCS the investment conversion coefficient of the direct current charging pile is that the beta iDCCS is the unit investment cost of the direct current charging pile with the model of i,

the SiDCCS is the investment quantity of the direct current charging pile with the model i:

B. DC charging station constraints

The Smax DCCS and the Smin DCCS are respectively the upper limit and the lower limit of the investment quantity of the direct current charging pile;

3) Building a power grid company model:

A. grid company objective function: for the grid company, the objective function is as follows:

wherein CPVSS rep is the benefit brought to the power grid company by configuring the optical storage station,

the CDCCS rep provides benefits to the grid company for configuring dc charging stations,

C_invest is the total cost of investment for the grid company,

CVSC inout is the cost of the VSC itself to inject/absorb reactive power into the grid,

cdifferent loss is the network loss variable quantity in the power network before and after flexible interconnection planning of the low-voltage power distribution network;

wherein ct pp is the time-of-use electricity price issued by the power grid company,

α_VSC is an investment conversion coefficient of the VSC unit,

the beta iscc is the investment cost of the VSC unit of model i,

the xiVSC is the investment variable 0-1 of the VSC unit of model i,

α_Line the investment conversion coefficient of the interconnecting line between the low-voltage distribution networks,

βiLine is the investment cost of the inter-low-voltage distribution network tie Line with the model i,

βiLine is a 0-1 investment variable of a tie Line between low-voltage distribution networks with the model of i,

gamma VSC inout is the unit cost of the VSC itself to inject/absorb reactive power into the grid,

QVSC t, inout is real-time reactive power injected/absorbed by the VSC itself into the power grid at time t;

B. grid company constraints

a. Grid company tide constraints: the method adopts a Distflow model to describe the power flow of the power distribution network;

b. grid company security constraints:

wherein m is an alternating current node set,

n is a set of direct current nodes,

umax AC and Umin AC, umax DC and Umin DC are respectively the upper and lower voltage limits of alternating current nodes and direct current nodes,

imax AC and Imax AC, imax DC and Imin DC are upper and lower limits of current flowing through the AC node and the DC node respectively,

P_t,trans real-time power of the low-voltage distribution network at the time t is distributed,

ν_trans for the safe operation of the distribution transformer, the conversion coefficient,

S_trans rated capacity of the distribution transformer for the low-voltage distribution network;

c. investment constraints for grid companies

Wherein SiVSC is the capacity of the VSC unit with the model of i,

smax VSC and Smin VSC are the upper and lower limits of the VSC investment capacity respectively;

VSC operation constraints

Wherein Pt, j VSC, AC and Pt, j VSC, DC respectively represent active power transmitted by an alternating current end and active power injected by a direct current end of the VSC at a node j at a moment t,

η_VSC for the conversion efficiency of the VSC,

pmax VSC, AC and Pmin VSC, AC being the upper and lower active limits of VSC delivery respectively,

qmax VSC, AC and Qmin VSC, AC is the upper and lower limit of the reactive power emitted by VSC respectively.

4. The flexible interconnection planning method for a low-voltage distribution network considering power supply capacity according to claim 1, wherein the method comprises the following steps: and 3, performing fine modeling on the multi-body uncertainty in the step:

1) Characterization of multi-subject uncertainty:

for uncertainty of photovoltaic output and uncertainty of charging load of an electric automobile, the method adopts a box type uncertainty set for representation, and for uncertainty of power grid load, the uncertainty is represented by establishing an uncertainty set based on Wasserstein distance, and specifically comprises the following steps:

wherein, gamma_PV 、γ_EV 、Ψ_load The actual values of the photovoltaic output, the charging load of the electric automobile and the power grid load are respectively, the PV and the EV are respectively the predicted values of the photovoltaic output and the charging load of the electric automobile,

γ_PV 、γ_EV the uncertainty coefficients of the photovoltaic output and the charging load of the electric automobile are respectively,

W(P₀ p) is a scene probability reference set of grid loads,

P₀ the wasperstein distance from the scene probability set P of grid load,

e is a scene probability reference set P₀ Radius of Wasserstein sphere which is the center of sphere;

2) Fine modeling of multi-subject uncertainty:

considering that the increasing of extreme weather probability brings a certain risk to the light storage combined power station, a risk avoidance strategy model adopted by the light storage combined power station can be expressed as follows:

wherein C is_PVSS For the expected profit value, sigma, of the combined light and storage power station_PV The expected gain deviation coefficient of the optical storage combined power station;

the increase of the electric vehicle holding amount can bring about higher electric vehicle charging demand and higher benefit for the direct current charging station, so that the speculative arbitrage strategy adopted by the direct current charging station can be expressed as follows:

wherein CDCCS is the expected profit value of the DC charging station and sigma EV is the expected profit deviation coefficient of the DC charging station;

considering that the annual growth rate of the power grid load is fixed and the power grid company has more detailed historical data, the method is processed by adopting a distributed robust method based on Wasserstein distance;