CN115313519A - Power distribution network energy storage optimal configuration method, device, equipment and storage medium - Google Patents

Abstract

The invention discloses a power distribution network energy storage optimization configuration method, a device, equipment and a storage medium, wherein an upper-layer configuration decision model taking the minimum annual comprehensive cost of a power distribution network as a target function and a lower-layer operation optimization model taking the minimum risk cost with insufficient flexibility as the target function are constructed, a plurality of initial energy storage configuration schemes are input into the lower-layer operation optimization model, the initial energy storage configuration schemes are solved, the corresponding first risk cost with insufficient flexibility is input into the upper-layer configuration decision model, the initial energy storage configuration schemes are subjected to iterative updating based on a particle swarm optimization until a population optimal solution is obtained, and the optimal energy storage configuration scheme is output. According to the technical scheme, the risk cost with insufficient flexibility is considered, the flexibility resources of the power distribution network are fully called, the minimum comprehensive cost is taken as a target function, the effect of improving the flexibility of the power distribution network by energy storage is maximized, the capability of the power distribution network for dealing with wind and light load output fluctuation is improved, and the economical efficiency and the flexibility of the operation of the power distribution network can be guaranteed.

Description

A distribution network energy storage optimization configuration method, device, equipment and storage medium

technical field

The invention relates to the technical field of distribution networks, in particular to a distribution network energy storage optimization configuration method, device, equipment and storage medium.

Background technique

Due to the influence of natural environmental factors such as wind speed, light and temperature, the output of wind power photovoltaic power generation presents characteristics such as volatility and uncertainty, which have a great impact on the safe and stable operation of the power system, such as: abandoning wind Issues such as light abandonment, voltage instability, and unbalanced power flow distribution.

At present, there are many methods in the academic circles to study the uncertain fluctuation of wind power and load, but all of them have their limitations; the probabilistic scenario method needs a large amount of historical data to analyze the distribution of random variables, and with the increase of the number of input scenarios , the complexity of the model and the amount of calculation will also increase; the robust optimization method optimizes it based on the worst case of uncertain parameters, but the results are usually too conservative, which makes the investment and operation costs of the system too high, which is not economical. The requirement of stability; interval optimization avoids the over-conservatism of robust optimization, but the selection of the range of interval variables will have a great impact on the nature of the results; using the normal distribution method or using the confidence interval to define the upper and lower limits of wind and solar output, the two intervals are not The side information is taken into consideration, resulting in incomplete evaluation results.

At present, scholars at home and abroad have done some research on the economy and flexibility of the optimal configuration of distributed energy storage systems, but most of the optimization objectives are relatively single or based on multi-objective optimization, and based on the comprehensive evaluation of economy and reliability. There are fewer optimization models for indicators.

Contents of the invention

The technical problem to be solved by the present invention is to provide a distribution network energy storage optimization configuration method, device, equipment and storage medium, and to ensure the economy of distribution network operation through the constructed distribution network energy storage double-layer optimal configuration model with flexibility.

In order to solve the above technical problems, the present invention provides a distribution network energy storage optimization configuration method, including:

Taking the minimum annual comprehensive cost of the distribution network as the first objective function, constructing an upper-level configuration decision-making model, and setting a first constraint condition for the upper-level configuration decision-making model;

Taking the minimum risk cost of insufficiency of flexibility as the second objective function, constructing a lower-level operation optimization model, and setting a second constraint condition on the lower-level operation optimization model;

Set the population size of the particle swarm, set the randomly generated initial energy storage configuration scheme as a single particle in the particle swarm, and obtain multiple initial energy storage configuration schemes;

Input the multiple initial energy storage configuration schemes into the lower-level operation optimization model, so that the lower-level operation optimization model solves each initial energy storage configuration scheme, and outputs the first energy storage configuration scheme corresponding to each initial energy storage configuration scheme. - the risk cost of insufficient flexibility;

Input the first risk cost of insufficiency of flexibility into the upper-level configuration decision-making model, iteratively update the multiple initial energy storage configuration schemes based on the particle swarm optimization algorithm, until the population optimal solution is obtained, and output the optimal energy storage Configuration.

In a possible implementation, after obtaining multiple initial energy storage configuration schemes, it also includes:

Obtain distribution network parameters and wind power generation parameters, photovoltaic power generation parameters and load parameters on each typical day;

Predicting the wind power generation parameters, the photovoltaic power generation parameters and the load parameters to obtain a wind power output prediction value, a photovoltaic power generation prediction value and a load output prediction value;

A distribution network scenario is generated based on the network parameters of the distribution network, the predicted wind power output, the predicted photovoltaic output, the predicted load output and the initial energy storage configuration scheme.

In a possible implementation, the upper-level configuration decision-making model is constructed with the minimum annual comprehensive cost of the distribution network as the first objective function, which specifically includes:

The first objective function is to minimize the annual comprehensive cost of the distribution network, where the annual comprehensive cost includes the equivalent annual value investment cost of energy storage, the annual operating cost of the distribution network, the annual flexible resource call cost of the distribution network, and the distribution network cost. The risk cost of grid annual insufficiency;

The first objective function is as follows:

min C _total ＝C _ess +C _net +C _fs +C _risk ;

In the formula, C _total is the annual comprehensive cost of the distribution network; C _ess is the equivalent annual investment cost of energy storage; C _net is the annual operating cost of the distribution network; C _fs is the annual flexible resource mobilization cost of the distribution network; C _risk is the annual inflexibility risk cost of distribution network.

In a possible implementation manner, setting a first constraint condition on the upper-layer configuration decision model specifically includes:

The first constraints include energy storage rated power constraints, rated capacity constraints, and energy storage installation location constraints;

Wherein, the energy storage rated power constraints are as follows:

分别为储能额定功率的上下限In the formula,

are the upper and lower limits of the energy storage rated power

The energy storage rated power constraints are as follows:

分别为储能额定容量的上下限；In the formula,

are the upper and lower limits of the rated energy storage capacity;

The energy storage installation location constraints are as follows:

1≤L _ess,j ≤N _node ;

In the formula, L _ess,j is the installation position of the jth energy storage; N _node is the number of distribution network nodes.

In a possible implementation manner, the second objective function is to minimize the risk cost of insufficiency of flexibility, wherein the second objective function is as follows:

为配电网第d个典型日下的灵活性不足风险成本。In the formula,

is the inflexibility risk cost of the distribution network on the dth typical day.

In a possible implementation manner, the second constraint condition is set on the lower-level operation optimization model, which specifically includes:

The second constraints include energy storage constraints, upper-level main network constraints, and demand response constraints;

Among them, the energy storage constraints are as follows:

S _SOC,j, min≤S _SOC,j (t)≤S _SOC,j,max ;

S _SOC,j (0)=S _SOC,j (24);

为第j个储能在时刻t的充电、放电功率，S_SOC,j,max、 S_SOC,j,min分别为第j个储能的荷电状态的上下限；In the formula, S _SOC,j (t) is the state of charge of the jth energy storage at time t; η is the charging efficiency;

is the charging and discharging power of the jth energy storage at time t, S _SOC,j,max and S _SOC,j,min are the upper and lower limits of the state of charge of the jth energy storage respectively;

The upper-level main network constraints are as follows:

为t时段的主网供电量；In the formula,

is the power supply of the main network during the t period;

The demand response constraints are as follows:

In the formula, P′ _DR,j (t) is the load value when the transferable load of node j at time t does not participate in demand response; P _DR,j,max and P _DR,j,min are the load values of node j at time t The upper and lower limits of transferable load participation in demand response.

In a possible implementation manner, the inflexibility risk cost includes increasing the inflexibility risk cost and reducing the inflexibility risk cost, wherein the inflexibility risk cost is as follows:

C _risk (t) = _fur (t) + f _dr (t);

为置信水平β下，通过条件风险价值CVaR计算得到的净负荷条件置信区间上下限， P_ub(t)、P_lb(t)为配电网的灵活性边界上下限，f_NL(z)为净负荷的概率密度函数、P_NL(t)为净负荷不确定功率。In the formula, f _ur (t) is the upward adjustment of the risk cost of insufficient flexibility, f _dr (t) is the downward adjustment of the risk cost of insufficient flexibility, cur is the loss coefficient of wind and solar _{curtailment; P ur (} t) is the situation of upward adjustment of insufficient flexibility The expected value of power difference under ; c _dr is the loss coefficient of load shedding; P _dr (t) is the expected value of power difference in the case of insufficient downward adjustment flexibility,

Under the confidence level β, the upper and lower limits of the net load condition confidence interval calculated by the conditional value at risk CVaR, P _ub (t), P _lb (t) are the upper and lower limits of the flexibility boundary of the distribution network, f _NL (z) is The probability density function of the net load, P _NL (t), is the uncertain power of the net load.

The present invention also provides a distribution network energy storage optimization configuration device, including: an upper layer configuration decision model building module, a lower layer operation optimization model building module, an initial energy storage configuration scheme acquisition module, a risk cost solution module for insufficient flexibility, and an optimal Energy storage configuration scheme output module;

Wherein, the upper-level configuration decision-making model construction module is used to construct the upper-level configuration decision-making model with the minimum annual comprehensive cost of the distribution network as the first objective function, and set the first constraint condition for the upper-level configuration decision-making model;

The lower-level operation optimization model building module is used to construct a lower-level operation optimization model with the minimum risk cost of insufficiency of flexibility as the second objective function, and set a second constraint condition for the lower-level operation optimization model;

The initial energy storage configuration scheme acquisition module is used to set the population size of the particle swarm, and set the randomly generated initial energy storage configuration scheme as a single particle in the particle swarm to obtain multiple initial energy storage configuration schemes;

The risk cost solution module for insufficient flexibility is used to input the multiple initial energy storage configuration schemes into the lower-level operation optimization model, so that the lower-level operation optimization model can solve each initial energy storage configuration scheme , output the first inflexibility risk cost corresponding to each initial energy storage configuration scheme;

The optimal energy storage configuration scheme output module inputs the first risk cost of insufficiency of flexibility into the upper configuration decision model, and iteratively updates the multiple initial energy storage configuration schemes based on the particle swarm optimization algorithm until it obtains Population optimal solution, output the optimal energy storage configuration scheme.

In a possible implementation, the initial energy storage configuration scheme acquisition module is also used to acquire distribution network parameters and wind power generation parameters, photovoltaic power generation parameters and load parameters on each typical day, Predict the photovoltaic power generation parameters and the load parameters to obtain the predicted value of wind power output, the predicted value of photovoltaic output and the predicted value of load output, based on the network parameters of the distribution network, the predicted value of wind power output, and the predicted value of photovoltaic output , the load output forecast value and the initial energy storage configuration scheme to generate a distribution network scenario.

In a possible implementation, the upper-level configuration decision-making model building module is used to construct the upper-level configuration decision-making model with the minimum annual comprehensive cost of the distribution network as the first objective function, specifically including:

The first objective function is to minimize the annual comprehensive cost of the distribution network, where the annual comprehensive cost includes the equivalent annual value investment cost of energy storage, the annual operating cost of the distribution network, the annual flexible resource call cost of the distribution network, and the distribution network cost. The risk cost of grid annual insufficiency;

The first objective function is as follows:

min C _total ＝C _ess +C _net +C _fs +C _risk ;

In the formula, C _total is the annual comprehensive cost of the distribution network; C _ess is the equivalent annual investment cost of energy storage; C _net is the annual operating cost of the distribution network; C _fs is the annual flexible resource mobilization cost of the distribution network; C _risk is the annual inflexibility risk cost of distribution network.

In a possible implementation manner, the upper-layer configuration decision-making model building module is configured to set a first constraint condition on the upper-layer configuration decision-making model, specifically including:

The first constraints include energy storage rated power constraints, rated capacity constraints, and energy storage installation location constraints;

Wherein, the energy storage rated power constraints are as follows:

分别为储能额定功率的上下限In the formula,

are the upper and lower limits of the energy storage rated power

The energy storage rated power constraints are as follows:

分别为储能额定容量的上下限；In the formula,

are the upper and lower limits of the rated energy storage capacity;

The energy storage installation location constraints are as follows:

1≤L _ess,j ≤N _node ;

In the formula, L _ess,j is the installation position of the jth energy storage; N _node is the number of distribution network nodes.

In a possible implementation manner, the lower layer operates an optimization model building block, which is used to minimize the risk cost of insufficiency of flexibility as the second objective function, wherein the second objective function is as follows:

为配电网第d个典型日下的灵活性不足风险成本。In the formula,

is the inflexibility risk cost of the distribution network on the dth typical day.

In a possible implementation manner, the lower-level operation optimization model construction module is configured to set a second constraint condition on the lower-level operation optimization model, specifically including:

The second constraints include energy storage constraints, upper-level main network constraints, and demand response constraints;

Among them, the energy storage constraints are as follows:

S _SOC,j, min≤S _SOC,j (t)≤S _SOC,j,max ;

S _SOC,j (0)=S _SOC,j (24);

为第j个储能在时刻t的充电、放电功率，S_SOC,j,max、 S_SOC,j,min分别为第j个储能的荷电状态的上下限；In the formula, S _SOC,j (t) is the state of charge of the jth energy storage at time t; η is the charging efficiency;

is the charging and discharging power of the jth energy storage at time t, S _SOC,j,max and S _SOC,j,min are the upper and lower limits of the state of charge of the jth energy storage respectively;

The upper-level main network constraints are as follows:

为t时段的主网供电量；In the formula,

is the power supply of the main network during the t period;

The demand response constraints are as follows:

In the formula, P′ _DR,j (t) is the load value when the transferable load of node j at time t does not participate in demand response; P _DR,j,max and P _DR,j,min are the load values of node j at time t The upper and lower limits of transferable load participation in demand response.

In a possible implementation manner, the risk cost of insufficient flexibility in the construction module of the lower-level operation optimization model includes an upward adjustment risk cost of insufficient flexibility and a downward adjustment risk cost of insufficient flexibility, wherein the risk cost of insufficient flexibility is, As follows:

C _risk (t) = _fur (t) + f _dr (t);

为置信水平β下，通过条件风险价值CVaR计算得到的净负荷条件置信区间上下限， P_ub(t)、P_lb(t)为配电网的灵活性边界上下限，f_NL(z)为净负荷的概率密度函数、P_NL(t)为净负荷不确定功率。In the formula, f _ur (t) is the upward adjustment of the risk cost of insufficient flexibility, f _dr (t) is the downward adjustment of the risk cost of insufficient flexibility, cur is the loss coefficient of wind and solar _{curtailment; P ur (} t) is the situation of upward adjustment of insufficient flexibility The expected value of power difference under ; c _dr is the loss coefficient of load shedding; P _dr (t) is the expected value of power difference in the case of insufficient downward adjustment flexibility,

Under the confidence level β, the upper and lower limits of the net load condition confidence interval calculated by the conditional value at risk CVaR, P _ub (t), P _lb (t) are the upper and lower limits of the flexibility boundary of the distribution network, f _NL (z) is The probability density function of the net load, P _NL (t), is the uncertain power of the net load.

The present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, any of the above-mentioned A method for optimizing distribution network energy storage configuration.

The present invention also provides a computer-readable storage medium, the computer-readable storage medium includes a stored computer program, wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned The distribution network energy storage optimization configuration method described in the item.

An embodiment of the present invention provides a distribution network energy storage optimization configuration method, device, equipment, and storage medium. Compared with the prior art, it has the following beneficial effects:

By constructing an upper-level configuration decision-making model with the minimum annual comprehensive cost of the distribution network as the objective function, and a lower-level operation optimization model with the minimum flexibility risk cost as the objective function, based on inputting multiple initial energy storage configuration schemes into the lower-level operation In the optimization model, the corresponding first inflexibility risk cost is solved and input into the upper-level configuration decision-making model, and multiple initial energy storage configuration schemes are iteratively updated based on the particle swarm optimization algorithm until the optimal solution of the population is obtained, and the optimal solution is output. Optimal energy storage configuration scheme. Compared with the existing technology, the technical solution of the present invention considers the risk cost of insufficient flexibility, fully invokes the flexible resources of the distribution network, and takes the minimum comprehensive cost as the objective function to maximize the role of energy storage in improving the flexibility of the distribution network , so that the ability of the distribution network to cope with the fluctuation of wind and solar load output can be improved, and the economy and flexibility of the operation of the distribution network can be guaranteed.

Description of drawings

Fig. 1 is a schematic flowchart of an embodiment of a distribution network energy storage optimization configuration method provided by the present invention;

Fig. 2 is a schematic structural diagram of an embodiment of a distribution network energy storage optimization configuration device provided by the present invention;

Fig. 3 is a schematic diagram of comparing traditional confidence intervals of net loads and conditional confidence intervals of net loads according to an embodiment of the present invention;

Figure 4 is a schematic diagram of the risk of insufficient flexibility in an embodiment provided by the present invention;

Fig. 5 is a schematic diagram of an IEEE33J node model of an embodiment provided by the present invention;

Fig. 6 is a schematic diagram of an energy storage configuration scheme and a comprehensive cost analysis result according to an embodiment of the present invention.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

Example 1

Referring to Fig. 1, Fig. 1 is a schematic flow chart of an embodiment of a distribution network energy storage optimization configuration method provided by the present invention. As shown in Fig. 1, the method includes steps 101 to 105, specifically as follows:

Step 101: Taking the minimum annual comprehensive cost of the distribution network as the first objective function, construct an upper-layer configuration decision-making model, and set a first constraint condition for the upper-layer configuration decision-making model.

In an embodiment, the upper-level configuration decision model mainly considers the configuration decision of the energy storage, and the decision variables include the rated power, rated capacity, and installation location of the energy storage.

In one embodiment, the upper-level configuration decision-making model uses the minimum annual comprehensive cost of the distribution network as the first objective function, wherein the annual comprehensive cost includes the equivalent annual investment cost of energy storage, the annual operating cost of the distribution network, and the distribution network. The resource mobilization cost of network annual flexibility and the risk cost of insufficient annual flexibility of distribution network.

Specifically, the first objective function is as follows:

min C _total ＝C _ess +C _net +C _fs +C _risk ;

In the formula, C _total is the annual comprehensive cost of the distribution network; C _ess is the equivalent annual investment cost of energy storage; C _net is the annual operating cost of the distribution network; C _fs is the annual flexible resource mobilization cost of the distribution network; C _risk is the annual inflexibility risk cost of distribution network.

For the equivalent annual value investment cost of energy storage, it is as follows:

分别为第t个储能的额定功率、额定容量。In the formula, γ _j is the equivalent annual value coefficient of the tth energy storage; Y _ess,j is the operating life of the tth energy storage; r is the discount rate, N _ess is the quantity of energy storage; c _ep and c _ee are respectively Investment cost per unit power and unit capacity of energy storage;

are the rated power and rated capacity of the tth energy storage, respectively.

The annual operating cost of the distribution network is as follows:

分别为对应的第d个典型日内时刻t的网损电量和主网购电量。In the formula, c _loss and c _grid are unit network loss cost and unit main grid electricity purchase cost respectively; T _D is the running time of each typical day; D is the number of selected typical days; t is the running time in a day;

Respectively, the network loss electricity and the main network purchase electricity at the corresponding d-th typical day time t.

For the distribution network annual flexible resource call cost, it is as follows:

为第d个典型日内时刻t第个储能的充放电功率；

为第d个典型日内时刻t的有载调压变压器分接头档位；P′_DR,j(t)、P_DR,j(t)分别为节点j在时刻t的可转移负荷参与需求响应前后的负荷值。In the formula, c _cd , c _oltc , and c _DR are the unit electricity cost of energy storage charging and discharging, the unit cost of OLTC gear adjustment, and the unit electricity cost of demand response;

is the charging and discharging power of the tth energy storage at the dth typical day time;

is the on-load tap changer tap position of the dth typical day at time t; P′ _DR,j (t) and P _DR,j (t) are the transferable loads of node j at time t before and after participating in demand response load value.

For the distribution network annual inflexibility risk cost, it is as follows:

为第d个典型日内时刻t的灵活性不足风险成本。In the formula:

is the risk cost of insufficiency of flexibility at the dth typical intraday time t.

In an embodiment, the first constraint conditions of the upper-level configuration decision-making model include energy storage rated power constraints, rated capacity constraints, and energy storage installation location constraints.

Wherein, the energy storage rated power constraints are as follows:

分别为储能额定功率的上下限。In the formula,

are the upper and lower limits of the rated power of the energy storage, respectively.

The energy storage rated power constraints are as follows:

分别为储能额定容量的上下限。In the formula,

are the upper and lower limits of the energy storage rated capacity, respectively.

The energy storage installation location constraints are as follows:

1≤L _ess,j ≤N _node ;

In the formula, L _ess,j is the installation position of the jth energy storage; N _node is the number of distribution network nodes.

Step 102 : taking the minimum risk cost of insufficiency of flexibility as the second objective function, constructing a lower-level operation optimization model, and setting a second constraint condition for the lower-level operation optimization model.

In one embodiment, the lower-level operation optimization model is the operation optimization layer, which mainly considers calling various flexible resources to optimize the operation of the distribution network, and the upper-level configuration decision-making model provides the energy storage configuration plan, and the decision variable is the operation plan.

In one embodiment, the objective function of the lower-layer operation optimization model is to minimize the risk cost of insufficiency of flexibility.

为配电网第d个典型日下的灵活性不足风险成本。In the formula,

is the inflexibility risk cost of the distribution network on the dth typical day.

In one embodiment, the lower layer operation optimization module includes power flow constraints, security constraints, OLTC constraints, energy storage constraints, and demand response constraints.

Specifically, the second constraints include energy storage constraints, upper-level main network constraints, and demand response constraints;

Among them, the energy storage constraints are as follows:

S _SOC,j, min≤S _SOC,j (t)≤S _SOC,j,max ;

S _SOC,j (0)=S _SOC,j (24);

为第j个储能在时刻t的充电、放电功率，S_SOC,j,max、 S_SOC,j,min分别为第j个储能的荷电状态的上下限。In the formula, S _SOC,j (t) is the state of charge of the jth energy storage at time t; η is the charging efficiency;

is the charging and discharging power of the jth energy storage at time t, and S _SOC,j,max and S _SOC,j,min are the upper and lower limits of the state of charge of the jth energy storage respectively.

The upper-level main network constraints are as follows:

为t时段的主网供电量。In the formula,

is the power supply of the main network during the t period.

The demand response constraints are as follows:

In the formula, P′ _DR,j (t) is the load value when the transferable load of node j at time t does not participate in demand response; P _DR,j,max and P _DR,j,min are the load values of node j at time t The upper and lower limits of transferable load participation in demand response.

Specifically, the second constraint conditions also include common constraints such as power flow constraints, security constraints, and OLTC constraints.

Step 103: Set the population size of the particle swarm, set the randomly generated initial energy storage configuration scheme as a single particle in the particle swarm, and obtain multiple initial energy storage configuration schemes.

In one embodiment, the particle swarm of the upper-level configuration decision model is initialized. Specifically, the population size, number of iterations, convergence conditions, etc. of the particle swarm are set; initial particle swarm data is generated, and each particle contains an initial energy storage configuration The scheme includes the rated energy storage capacity, rated power, and installation location. The initial energy storage scheme is the energy storage data randomly generated by the computer that satisfies the energy storage constraints of the upper-level configuration decision-making model.

In one embodiment, the network parameters of the distribution network and the wind power generation parameters, photovoltaic power generation parameters and load parameters of each typical day are obtained; the wind power generation parameters, the photovoltaic power generation parameters and the load parameters are predicted to obtain the wind power output Predicted value, predicted value of photovoltaic output and predicted value of load output; based on the distribution network parameters, the predicted value of wind power output, the predicted value of photovoltaic output, the predicted value of load output and the initial energy storage configuration scheme , to generate a distribution network scenario.

In one embodiment, multiple different distribution network scenarios can be obtained based on different initial energy storage configuration schemes randomly generated; the upper layer configuration decision model calculates the annual comprehensive cost of the distribution network based on different distribution network scenarios.

Step 104: Input the multiple initial energy storage configuration schemes into the lower-level operation optimization model, so that the lower-level operation optimization model solves each initial energy storage configuration scheme, and outputs each initial energy storage configuration scheme The corresponding first inflexibility risk cost.

In one embodiment, the upper-level configuration decision-making model inputs multiple initial energy storage configuration schemes into the lower-level operation optimization model, and the lower-level operation optimization model is solved by using MATLAB to call the Yamlip+Gurobi solver.

Specifically, based on the distribution network scenario corresponding to the initial energy storage configuration scheme, the net load prediction in the distribution network scenario is calculated based on the predicted wind power output, photovoltaic output prediction value and load output prediction value in the distribution network scenario value.

光伏发电出力功率

负荷功率

的实际功率围绕预测值按照一定数学规律分布，可视为服从正态分布。According to the central limit theorem in probability theory, when a certain quantitative index is affected by many independent random factors, and the influence of each factor is very small, the index can be regarded as subject to a normal distribution. Existing research shows that the output power of wind power

Photovoltaic power generation output power

load power

The actual power of is distributed around the predicted value according to certain mathematical laws, which can be regarded as obeying the normal distribution.

f_NL(z)为净负荷的概率密度函数，其计算过程如下所示：Due to the additivity of the normal distribution, the independent random variables of the normal distribution still satisfy the normal distribution after a linear random combination. Wind and wind loads are independent of each other, so additivity can be used to add P _Load (t), -P _WT (t), -P _PV (t) to obtain the net load in each scheduling period t that obeys normal distribution Uncertain power

f _NL (z) is the probability density function of the net load, and its calculation process is as follows:

In the formula, μ _NL is the expectation of net load prediction value; μ _L,k , μ _WT,i , μ _PV,j are respectively the expectation of load and wind power output prediction value; k, i, j are the sequence numbers of load and wind power respectively ; b, n, m are the number of loads and scenery respectively; σ _NL is the standard deviation of net load forecast value; σ _L,k , σ _WT,i , σ _PV,j are the standards of load, wind power and photovoltaic output forecast value respectively Poor; z is net load prediction value.

Specifically, a net load condition confidence interval is also set for the net load prediction value.

Value at Risk (VaR) refers to the maximum loss that may occur in a certain investment portfolio under a certain confidence level; conditional value at risk (CVaR) refers to the conditional mean value exceeding the risk value under a certain confidence level; it can reflect The average level of loss overcomes the insufficiency of value at risk in estimating the tail distribution of net loads and is more reliable than VaR. Therefore, in this embodiment, the traditional confidence interval optimization method is improved by using conditional value at risk, and the concept of net load power conditional confidence interval is proposed, which can reflect the tail risk and has superior mathematical characteristics.

The net load fluctuation can be defined by the upper limit of net load power P ^up (t) and the lower limit of net load power P ^low (t). The net load is described by VaR and CVaR respectively as follows:

Traditional confidence interval of net load based on VaR calculation:

为置信水平β下，通过VaR计算得到的净负荷置信区间上下限；φ^up(α)、φ^low(α)为t时刻功率不越过阈值α的概率。In the formula,

Under the confidence level β, the upper and lower limits of the net load confidence interval calculated by VaR; φ ^up (α), φ ^low (α) is the probability that the power does not exceed the threshold α at time t.

Confidence intervals for payload conditions calculated based on CVaR:

为置信水平β下，通过条件风险价值CVaR 计算得到的净负荷条件置信区间上下限。In the formula,

Under the confidence level β, the upper and lower limits of the net load conditional confidence interval calculated by the conditional value at risk CVaR.

As shown in Figure 3, Figure 3 is a schematic diagram of the comparison between the traditional confidence interval of the net load and the confidence interval of the net load condition; as can be seen from Figure 3, the range of the traditional confidence interval of the net load is small, and only the quantile corresponding to the confidence level is considered point, the estimation of the distribution at both ends is not sufficient, and there are certain restrictions; the probability of occurrence of the part other than the quantile point corresponding to the confidence level is low, but its value is large, which is reflected in the large fluctuation of the corresponding net load. Once it happens, it will have a great impact on the distribution network, so it cannot be ignored directly. Compared with the traditional confidence interval of net load, the improved net load condition confidence interval takes into account the tail risk expectations at both ends, so the range is larger, more in line with the actual needs of the distribution network, and avoids severe power distribution caused by excessive net load fluctuations. The occurrence of network accidents has both economy and reliability.

Specifically, since the objective function of the lower-layer operation optimization model is to minimize the risk cost of insufficiency, the risk cost of insufficiency includes increasing the risk cost of insufficiency in flexibility and reducing the risk cost of insufficiency in flexibility. When the load power is lower than the lower limit of the flexibility boundary of the distribution network, the expected value of the loss caused by the wind and solar curtailment due to insufficient flexibility in the upward adjustment. The risk cost of insufficiency of downward adjustment flexibility refers to the expected value of the loss caused by load shedding due to insufficient downward adjustment flexibility when the net load power is higher than the upper limit of the flexibility boundary of the distribution network; as shown in Figure 4, Figure 4 is the flexibility Insufficient risk diagram, the upper limit of net load exceeds the upper limit of flexibility, that is, the flexibility of downward adjustment is insufficient, resulting in the risk cost of load shedding; the lower limit of net load exceeds the lower limit of flexibility, that is, the flexibility of upward adjustment is insufficient, resulting in the risk cost of curtailment of wind and solar.

Therefore, before calculating the risk cost of insufficient flexibility, it is also necessary to set the flexibility boundary of the distribution network. The upper and lower limits of the flexibility boundary of the distribution network refer to the upper and lower limits of the net load fluctuation of the distribution network that can be allowed through the coordinated scheduling of flexible resources when the distribution network is operating safely and stably; by comprehensively considering the distribution network equipment and lines Constraints and call strategies for flexible resources, superimpose the maximum and minimum net loads of each node respectively, and then obtain the flexibility boundary of the distribution network, as follows:

为配电网在t时段的灵活性边界上下限；

分别表示t时段j节点的有功负荷；

分别表示t时段j节点的光伏风力发电的有功功率；C为上层配置决策模型的约束条件集合。In the formula,

is the upper and lower limits of the flexibility boundary of the distribution network in the period t;

Respectively represent the active load of node j in period t;

Respectively represent the active power of photovoltaic wind power generation of node j in period t; C is the constraint condition set of the upper configuration decision model.

In one embodiment, based on the upper and lower limits of the flexibility boundary, the risk cost of insufficiency of flexibility is calculated, as follows:

C _risk (t) = _fur (t) + f _dr (t);

为置信水平β下，通过条件风险价值CVaR计算得到的净负荷条件置信区间上下限， P_ub(t)、P_lb(t)为配电网的灵活性边界上下限，f_NL(z)为净负荷的概率密度函数、P_NL(t)为净负荷不确定功率。In the formula, f _ur (t) is the upward adjustment of the risk cost of insufficient flexibility, f _dr (t) is the downward adjustment of the risk cost of insufficient flexibility, cur is the loss coefficient of wind and solar _{curtailment; P ur (} t) is the situation of upward adjustment of insufficient flexibility The expected value of power difference under ; c _dr is the loss coefficient of load shedding; P _dr (t) is the expected value of power difference in the case of insufficient downward adjustment flexibility,

Under the confidence level β, the upper and lower limits of the net load condition confidence interval calculated by the conditional value at risk CVaR, P _ub (t), P _lb (t) are the upper and lower limits of the flexibility boundary of the distribution network, f _NL (z) is The probability density function of the net load, P _NL (t), is the uncertain power of the net load.

Step 105: Input the first risk cost of insufficiency of flexibility into the upper-level configuration decision-making model, iteratively update the multiple initial energy storage configuration schemes based on the particle swarm optimization algorithm, until the population optimal solution is obtained, and output the optimal solution Optimal energy storage configuration scheme.

In one embodiment, after the lower-level operation optimization model calculates the first risk cost of insufficient flexibility, it is re-inputted into the upper-level configuration decision-making model, and based on the particle swarm optimization algorithm, the fitness value corresponding to each initial energy storage configuration scheme is solved, That is, the comprehensive cost of the distribution network, to obtain the optimal solution of the population, update the particle velocity and position of each particle in the particle swarm, and perform flexibility boundary processing; at the same time, based on the iterative processing set in step 103, it is judged whether the current maximum The number of iterations, if yes, based on the optimal solution of the population, the most output optimal energy storage configuration scheme, if not, then repeat step 104 and step 105, so that the upper and lower models are iteratively optimized until the maximum number of iterations is reached, and the best energy storage configuration is output The optimal energy storage configuration scheme ensures the flexible operation of the system at the minimum economic cost and improves the renewable energy consumption capacity.

The technical scheme provided by this embodiment is illustrated by way of example:

Apply the technical solution of this application to the IEEE33 node model, set a series of flexible resources, and build the distribution network simulation model used. As shown in Figure 5, Figure 5 is a schematic diagram of the IEEE33J node model. In this power distribution system, nodes 3 and 17 are respectively connected to distributed wind power stations with installed capacity of 500kW and 1000kW, and nodes 16 and 30 are respectively connected to For distributed photovoltaic power stations with a capacity of 500kW and 1000kW, set the wind and wind power generation data and load data of each typical day of the distribution network, and set demand response at nodes 5, 15, and 31, mainly considering transferable loads.

The unit capacity investment cost of energy storage is 2,500 yuan/kWh, the unit power investment cost is 1,000 yuan/kW, the energy storage unit charge and discharge cost is 0.03 yuan/kWh, and the on-load tap changer adjustment cost is 1.5 yuan/time. Set the rated capacity of the energy storage to [500, 1000] kWh, and the rated power to [100, 300] kW. Set the maximum number of iterations of the particle swarm algorithm to 50, and the number of particles to 20.

Based on the above parameters, set the energy storage configuration scheme as follows:

Option 1: The energy storage system is not considered, only flexible resources such as on-load tap changer, load response and static var compensator are considered, and the gurobi solver of the lower-level operation optimization model is used, and the minimum operation cost is regarded as the objective function , including network loss cost and main network power purchase cost. Solve the objective function and analyze the comprehensive cost situation of the scheme.

Option 2: Consider the energy storage system, without considering the uncertainty of the flexibility demand of the distribution network, that is, without considering the risk of insufficient flexibility. Using the particle swarm algorithm of the upper-level configuration decision-making model, the energy storage investment cost and distribution network operation cost are minimized as the objective function to optimize the configuration of energy storage. Solve the objective function and analyze the comprehensive cost situation of the scheme.

Solution 3: Consider the risk of insufficient flexibility of the distribution network, and fully utilize various flexible resources of the distribution network such as energy storage. Using the energy storage double-layer optimal configuration model constructed in this embodiment, that is, the upper-level configuration decision-making model and the lower-level operation optimization model, the minimum comprehensive cost is taken as the objective function to optimize energy storage configuration, solve the objective function, and analyze the comprehensiveness of the scheme. cost situation.

By solving the above energy storage configuration scheme, the configuration scheme and comprehensive cost analysis are obtained, as shown in Figure 6, which is a schematic diagram of the energy storage configuration scheme and comprehensive cost analysis results. Based on the solution results, we can get:

Comparing scheme 1 and scheme 2, scheme 2 is compared with scheme 1 in the configuration of energy storage in the distribution network. From the analysis of the comprehensive cost results, it can be seen that the configuration cost of energy storage in Scheme 2 is one more than that of Scheme 1, but in the operation cost, the power purchase cost and the network loss cost have decreased, and even the configuration cost of Scheme 2 is equivalent to the operation cost. Plus, the operating cost of Option 1 is 568,700 yuan lower. This shows that energy storage has good economic benefits in terms of shaving peaks and filling valleys. In terms of the risk of insufficient flexibility of the two schemes, compared with scheme 1, the risk cost of insufficient flexibility in scheme 2 has been greatly improved, reducing 7.9192 million yuan, and only 376,900 yuan has been increased in the cost of flexible resource deployment. Comprehensive The cost dropped by 8.111 million yuan.

Comparing scheme 2 and scheme 3, scheme 3 considers the uncertainty of distribution network flexibility demand compared with scheme 2, and converts it into the risk cost of insufficient flexibility, which is included in the objective function for optimization solution. And the situation that the net load confidence interval curve of Scheme 3 crosses the flexibility boundary is somewhat improved compared with Scheme 2. From the analysis of the comprehensive cost results, it can be seen that the installation location of the energy storage in Scheme 3 has changed compared with Scheme 2, and the rated power and rated capacity have been improved, so the cost of energy storage configuration is 645,400 yuan higher. However, its operating cost and the risk cost of insufficient flexibility have all decreased. The operating cost has decreased by 387,400 yuan, the risk cost of insufficient flexibility has decreased by 2,286,200 yuan, and only the cost of invoking flexible resources has increased by 73,600 yuan, and the overall cost has decreased. 1.9546 million yuan.

Option 3 considers the risk cost of insufficient flexibility, takes the minimum comprehensive cost as the objective function, and maximizes the role of energy storage in improving the flexibility of the distribution network, so that the distribution network’s ability to cope with fluctuations in wind and solar load output is improved, and the flexibility of each time period Risk of inadequacy has also improved. In the context of the increasing proportion of wind and solar resources connected to the distribution network, the flexible planning model constructed in this embodiment is more in line with the actual needs of the distribution network, and can better ensure the security, stability and economic benefits of the distribution network.

To sum up, the present invention provides a distribution network energy storage optimization configuration method, which improves the traditional confidence interval calculation method by using the conditional value-at-risk theory, and proposes a net load power conditional confidence interval, which has good mathematical characteristics; and By calculating the flexibility boundary and the risk cost of insufficient flexibility, it is possible to quantify the economic loss caused by the insufficient flexibility of the distribution network. With the construction of a distribution network energy storage double-layer optimal configuration model, the risk cost of insufficient flexibility is considered, and the distribution network can be fully utilized. Power grid flexibility resources, with the minimum annual comprehensive cost as the objective function, maximize the role of energy storage in improving the flexibility of the distribution network, so that the distribution network's ability to cope with fluctuations in wind and solar load output can be improved, and the economical operation of the distribution network can be guaranteed. Sex and flexibility.

Example 2

Referring to Fig. 2, Fig. 2 is a schematic structural diagram of an embodiment of a distribution network energy storage optimization configuration device provided by the present invention. As shown in Fig. 2, the device includes an upper layer configuration decision model building module 201, a lower layer operation optimization The model construction module 202, the initial energy storage configuration scheme acquisition module 203, the flexibility insufficiency risk cost solution module 204, and the optimal energy storage configuration scheme output module 205 are as follows:

The upper-level configuration decision-making model construction module 201 is used to construct an upper-level configuration decision-making model with the minimum annual comprehensive cost of the distribution network as the first objective function, and set a first constraint condition for the upper-level configuration decision-making model.

The lower-level operation optimization model construction module 202 is used to construct a lower-level operation optimization model with the minimum risk cost of insufficiency of flexibility as the second objective function, and set a second constraint condition for the lower-level operation optimization model.

The initial energy storage configuration acquisition module 203 is used to set the population size of the particle swarm, and set the randomly generated initial energy storage configuration as a single particle in the particle swarm to obtain multiple initial energy storage configurations.

The risk cost solution module 204 for lack of flexibility is configured to input the multiple initial energy storage configuration schemes into the lower-level operation optimization model, so that the lower-level operation optimization model performs Solve and output the first risk cost of insufficiency of flexibility corresponding to each initial energy storage configuration scheme.

The optimal energy storage configuration scheme output module 205 inputs the first risk cost of insufficiency of flexibility into the upper configuration decision model, and iteratively updates the multiple initial energy storage configuration schemes based on the particle swarm optimization algorithm until The population optimal solution is obtained, and the optimal energy storage configuration scheme is output.

In one embodiment, the initial energy storage configuration plan acquisition module 203 is also used to acquire distribution network parameters and wind power generation parameters, photovoltaic power generation parameters and load parameters on each typical day, Parameters and the load parameters are predicted to obtain the predicted value of wind power output, the predicted value of photovoltaic output and the predicted value of load output, based on the network parameters of the distribution network, the predicted value of wind power output, the predicted value of photovoltaic output, the The load output forecast value and the initial energy storage configuration scheme are used to generate a distribution network scenario.

In one embodiment, the upper-level configuration decision-making model construction module 201 is used to construct the upper-level configuration decision-making model with the minimum annual comprehensive cost of the distribution network as the first objective function, specifically including:

The first objective function is to minimize the annual comprehensive cost of the distribution network, where the annual comprehensive cost includes the equivalent annual value investment cost of energy storage, the annual operating cost of the distribution network, the annual flexible resource call cost of the distribution network, and the distribution network cost. The risk cost of grid annual insufficiency;

The first objective function is as follows:

min C _total ＝C _ess +C _net +C _fs +C _risk ;

In the formula, C _total is the annual comprehensive cost of the distribution network; C _ess is the equivalent annual investment cost of energy storage; C _net is the annual operating cost of the distribution network; C _fs is the annual flexible resource mobilization cost of the distribution network; C _risk is the annual inflexibility risk cost of distribution network.

In an embodiment, the upper-layer configuration decision model building module 201 is configured to set a first constraint condition on the upper-layer configuration decision model, specifically including:

The first constraints include energy storage rated power constraints, rated capacity constraints, and energy storage installation location constraints;

Wherein, the energy storage rated power constraints are as follows:

分别为储能额定功率的上下限。In the formula,

are the upper and lower limits of the rated power of the energy storage, respectively.

The energy storage rated power constraints are as follows:

分别为储能额定容量的上下限；In the formula,

are the upper and lower limits of the rated energy storage capacity;

The energy storage installation location constraints are as follows:

1≤L _ess,j ≤N _node ;

In the formula, L _ess,j is the installation position of the jth energy storage; N _node is the number of distribution network nodes.

In one embodiment, the lower-level operation optimization model construction module 202 is configured to take the minimum risk cost of insufficiency of flexibility as the second objective function, wherein the second objective function is as follows:

为配电网第d个典型日下的灵活性不足风险成本。In the formula,

is the inflexibility risk cost of the distribution network on the dth typical day.

In an embodiment, the lower-level operation optimization model building module 202 is configured to set a second constraint condition on the lower-level operation optimization model, specifically including:

The second constraints include energy storage constraints, upper-level main network constraints, and demand response constraints;

Among them, the energy storage constraints are as follows:

S _SOC,j, min≤S _SOC,j (t)≤S _SOC,j,max ;

S _SOC,j (0)=S _SOC,j (24);

为第j个储能在时刻t的充电、放电功率，S_SOC,j,max、 S_SOC,j,min分别为第j个储能的荷电状态的上下限；In the formula, S _SOC,j (t) is the state of charge of the jth energy storage at time t; η is the charging efficiency;

is the charging and discharging power of the jth energy storage at time t, S _SOC,j,max and S _SOC,j,min are the upper and lower limits of the state of charge of the jth energy storage respectively;

The upper-level main network constraints are as follows:

为t时段的主网供电量；In the formula,

is the power supply of the main network during the t period;

The demand response constraints are as follows:

In the formula, P′ _DR,j (t) is the load value when the transferable load of node j at time t does not participate in demand response; P _DR,j,max and P _DR,j,min are the load values of node j at time t The upper and lower limits of transferable load participation in demand response.

In one embodiment, the risk cost of insufficient flexibility in the lower-level operation optimization model construction module 202 includes upward adjustment of risk cost of insufficient flexibility and downward adjustment of risk cost of insufficient flexibility, wherein the risk cost of insufficient flexibility is as follows :

C _risk (t) = _fur (t) + f _dr (t);

为置信水平β下，通过条件风险价值CVaR计算得到的净负荷条件置信区间上下限， P_ub(t)、P_lb(t)为配电网的灵活性边界上下限，f_NL(z)为净负荷的概率密度函数、P_NL(t)为净负荷不确定功率。In the formula, f _ur (t) is the upward adjustment of the risk cost of insufficient flexibility, f _dr (t) is the downward adjustment of the risk cost of insufficient flexibility, cur is the loss coefficient of wind and solar _{curtailment; P ur (} t) is the situation of upward adjustment of insufficient flexibility The expected value of power difference under ; c _dr is the loss coefficient of load shedding; P _dr (t) is the expected value of power difference in the case of insufficient downward adjustment flexibility,

Under the confidence level β, the upper and lower limits of the net load condition confidence interval calculated by the conditional value at risk CVaR, P _ub (t), P _lb (t) are the upper and lower limits of the flexibility boundary of the distribution network, f _NL (z) is The probability density function of the net load, P _NL (t), is the uncertain power of the net load.

Those skilled in the art can clearly understand that for the convenience and brevity of the description, the specific working process of the device described above can refer to the corresponding process in the foregoing method embodiment, and details are not repeated here.

It should be noted that the above-mentioned embodiments of the distribution network energy storage optimization configuration device are only schematic, wherein the modules described as separate components may or may not be physically separated, and the components shown as modules may be Or it may not be a physical unit, that is, it may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

On the basis of the above embodiments of the distribution network energy storage optimal configuration method, another embodiment of the present invention provides a distribution network energy storage optimal configuration terminal device, the distribution network energy storage optimal configuration terminal device, including A processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the distribution network energy storage optimization of any embodiment of the present invention is realized. configuration method.

Exemplarily, in this embodiment, the computer program can be divided into one or more modules, and the one or more modules are stored in the memory and executed by the processor to complete this invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the distribution network energy storage optimization configuration terminal device.

The distribution network energy storage optimization configuration terminal device may be computing devices such as desktop computers, notebooks, palmtop computers, and cloud servers. The distribution network energy storage optimal configuration terminal equipment may include, but not limited to, a processor and a memory.

The so-called processor can be a central processing unit (Central Processing Unit, CPU), and can also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf Programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., the processor is the control center of the distribution network energy storage optimization configuration terminal equipment, and uses various interfaces and lines to connect the entire Distribution network energy storage optimally configures various parts of terminal equipment.

The memory can be used to store the computer programs and/or modules, and the processor implements the configuration by running or executing the computer programs and/or modules stored in the memory and calling the data stored in the memory. Grid energy storage optimizes the configuration of various functions of terminal equipment. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function, etc.; the data storage area may store data created according to the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card , a flash memory card (Flash Card), at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

On the basis of the above-mentioned embodiments of the distribution network energy storage optimization configuration method, another embodiment of the present invention provides a storage medium, the storage medium includes a stored computer program, wherein, when the computer program is running, Control the device where the storage medium is located to execute the distribution network energy storage optimization configuration method in any embodiment of the present invention.

In this embodiment, the above-mentioned storage medium is a computer-readable storage medium, and the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, etc. . The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-OnlyMemory), Random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, computer-readable media Excludes electrical carrier signals and telecommunication signals.

To sum up, the present invention provides a distribution network energy storage optimization configuration method, device, equipment and storage medium, by constructing an upper-level configuration decision-making model with the minimum annual comprehensive cost of the distribution network as the objective function, and with insufficient flexibility The lower-level operation optimization model with the minimum risk cost as the objective function is based on inputting multiple initial energy storage configuration schemes into the lower-level operation optimization model, solving and inputting the corresponding first inflexibility risk cost into the upper-level configuration decision-making model, Based on the particle swarm algorithm, multiple initial energy storage configuration schemes are iteratively updated until the optimal solution of the population is obtained, and the optimal energy storage configuration scheme is output. The technical solution of the present invention considers the risk cost of insufficient flexibility, fully utilizes the flexible resources of the distribution network, takes the minimum comprehensive cost as the objective function, and maximizes the role of energy storage in improving the flexibility of the distribution network, so that the distribution network can cope with wind and wind The ability to improve the load output fluctuation can ensure the economy and flexibility of the distribution network operation.

The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, some improvements and replacements can also be made, these improvements and replacements It should also be regarded as the protection scope of the present invention.

Claims (10)

1. A power distribution network energy storage optimal configuration method is characterized by comprising the following steps:

constructing an upper-layer configuration decision model by taking the minimum annual comprehensive cost of the power distribution network as a first objective function, and setting a first constraint condition for the upper-layer configuration decision model;

constructing a lower-layer operation optimization model by taking the minimum risk cost with insufficient flexibility as a second objective function, and setting a second constraint condition for the lower-layer operation optimization model;

setting the population scale of the particle swarm, and setting the randomly generated initial energy storage configuration scheme as a single particle in the particle swarm to obtain a plurality of initial energy storage configuration schemes;

respectively inputting the plurality of initial energy storage configuration schemes into the lower-layer operation optimization model, so that the lower-layer operation optimization model solves each initial energy storage configuration scheme, and outputting a first insufficient flexibility risk cost corresponding to each initial energy storage configuration scheme;

inputting the first insufficient-flexibility risk cost into the upper-layer configuration decision model, iteratively updating the plurality of initial energy storage configuration schemes based on a particle swarm algorithm until a population optimal solution is obtained, and outputting an optimal energy storage configuration scheme.

2. The method for optimal configuration of energy storage for a power distribution network according to claim 1, wherein after obtaining a plurality of initial energy storage configuration schemes, the method further comprises:

acquiring power distribution network parameters and wind power generation parameters, photovoltaic power generation parameters and load parameters of each typical day;

predicting the wind power generation parameters, the photovoltaic power generation parameters and the load parameters to obtain a wind power output predicted value, a photovoltaic output predicted value and a load output predicted value;

and generating a power distribution network scene based on the power distribution network parameters, the wind power output predicted value, the photovoltaic output predicted value, the load output predicted value and the initial energy storage configuration scheme.

3. The method for optimal configuration of energy storage of the power distribution network according to claim 1, wherein a minimum annual integrated cost of the power distribution network is used as a first objective function to construct an upper-level configuration decision model, and the method specifically comprises the following steps:

the minimum annual comprehensive cost of the power distribution network is taken as a first objective function, wherein the annual comprehensive cost comprises the equal annual investment cost of energy storage, the annual operation cost of the power distribution network, the annual flexibility resource calling cost of the power distribution network and the annual flexibility shortage risk cost of the power distribution network;

the first objective function is as follows:

min C _total ＝C _ess +C _net +C _fs +C _risk ；

in the formula, C _total The annual comprehensive cost of the power distribution network; c _ess Equal annual investment cost for energy storage; c _net The annual operating cost of the power distribution network is calculated; c _fs The cost is called for annual flexible resources of the power distribution network; c _risk The annual flexibility of the distribution network is insufficient for risk cost.

4. The method according to claim 1, wherein setting a first constraint condition for the upper layer configuration decision model specifically includes:

the first constraint condition comprises an energy storage rated power constraint, a rated capacity constraint and an energy storage installation position constraint;

wherein the energy storage rated power constraint is as follows:

in the formula (I), the compound is shown in the specification,

upper and lower limits of the rated power of the stored energy

The energy storage rated power constraint is as follows:

in the formula (I), the compound is shown in the specification,

respectively an upper limit and a lower limit of the rated energy storage capacity;

the energy storage mounting position constraint is as follows:

1≤L _ess，j ≤N _node ；

in the formula, L _ess，j The jth installation location of stored energy; n is a radical of _node The number of the nodes of the power distribution network.

5. The method for optimally configuring energy storage of the power distribution network according to claim 1, wherein the risk cost with insufficient flexibility is minimized as a second objective function, wherein the second objective function is as follows:

in the formula (I), the compound is shown in the specification,

for flexibility of distribution network at d typical daySufficient risk cost.

6. The method for optimally configuring energy storage of the power distribution network according to claim 1, wherein setting a second constraint condition for the lower-layer operation optimization model specifically comprises:

the second constraint condition comprises energy storage constraint, superior main network constraint and demand response constraint;

wherein the energy storage constraint is as follows:

S _{SOC，j，min} ≤S _SOC，j (t)≤S _{SOC，j，max} ；

S _SOC，j (0)＝S _SOC，j (24)；

in the formula, S _SOC，j (t) is the state of charge of the jth stored energy at time t; eta is charging efficiency;

for the jth energy stored charging, discharging power at time t, S _{SOC，j，max} 、S _{SOC，j，min} Respectively the upper and lower limits of the j-th stored energy charge state;

the upper level main network constraint is as follows:

in the formula (I), the compound is shown in the specification,

the power supply amount of the main grid in the t period is determined;

the demand response constraint is as follows:

of formula (II) to (III)' _DR，j (t) is the load value of the node j at the time t when the transferable load does not participate in the demand response; p _DR，j，max 、P _DR，j，min And participating the upper limit and the lower limit of the demand response for the transferable load of the node j at the moment t.

7. The optimal configuration method for energy storage of power distribution network according to claim 5, wherein the insufficient flexibility risk cost includes an up-flexibility insufficient risk cost and a down-flexibility insufficient risk cost, wherein the insufficient flexibility risk cost is as follows:

C _risk (t)＝f _ur (t)+f _dr (t)；

in the formula, f _ur (t) risk cost of insufficient flexibility for upscaling, f _dr (t) Down-Regulation of insufficient flexibility Risk cost, c _ur The loss coefficient is wind-light electricity limiting; p _ur (t) is the expected value of the power difference under the condition of insufficient up-regulation flexibility; c. C _dr Loss factor for load shedding; p _dr (t) a desired value of the power difference in case of insufficient turndown flexibility,

the upper and lower limits of a confidence interval, P, of the net load condition obtained by calculating the condition risk value CVaR under the confidence level beta _ub (t)、P _lb (t) is the flexibility bound upper and lower limits, f, of the distribution network _NL (z) is the probability density function of the payload, P _NL (t) is the payload uncertainty power.

8. The utility model provides a distribution network energy storage optimal configuration device which characterized in that includes: the system comprises an upper-layer configuration decision model building module, a lower-layer operation optimization model building module, an initial energy storage configuration scheme obtaining module, an insufficient-flexibility risk cost solving module and an optimal energy storage configuration scheme output module;

the upper-layer configuration decision model building module is used for building an upper-layer configuration decision model by taking the minimum annual comprehensive cost of the power distribution network as a first objective function, and setting a first constraint condition for the upper-layer configuration decision model;

the lower-layer operation optimization model building module is used for building a lower-layer operation optimization model by taking the minimum risk cost due to insufficient flexibility as a second objective function, and setting a second constraint condition for the lower-layer operation optimization model;

the initial energy storage configuration scheme acquisition module is used for setting the population scale of the particle swarm, setting the randomly generated initial energy storage configuration scheme as a single particle in the particle swarm, and obtaining a plurality of initial energy storage configuration schemes;

the insufficient flexibility risk cost solving module is used for respectively inputting the plurality of initial energy storage configuration schemes into the lower-layer operation optimization model so that the lower-layer operation optimization model can solve each initial energy storage configuration scheme and output a first insufficient flexibility risk cost corresponding to each initial energy storage configuration scheme;

and the optimal energy storage configuration scheme output module inputs the first insufficient flexibility risk cost into the upper-layer configuration decision model, iteratively updates the initial energy storage configuration schemes based on a particle swarm algorithm until a population optimal solution is obtained, and outputs an optimal energy storage configuration scheme.

9. A terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the power distribution network energy storage optimization configuration method according to any one of claims 1 to 7.

10. A computer-readable storage medium, comprising a stored computer program, wherein when the computer program runs, the computer-readable storage medium controls a device to execute the power distribution network energy storage optimization configuration method according to any one of claims 1 to 7.

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