CN115833203A - A grid peak regulation method based on multi-time interval optimal power flow and energy storage battery - Google Patents

Abstract

The invention discloses a power grid peak regulation method based on multi-time interval optimal power flow and energy storage batteries. Since the role of energy storage in alleviating the overload of the transformer is two-fold: one is that it must provide sufficient power generation when overloaded, that is, discharge capacity. The second is to have sufficient reserve capacity to accommodate possible peak load forecast errors. Based on this basic idea, the technical solution adopted by the present invention is as follows: taking into account the constraints of generator ramping and power generation, combined with the constraints of state of charge and power of energy storage power stations, an optimal power flow formula based on multi-periods is established, and some After the generator is decommissioned, the energy storage power station participates in the energy storage battery operation strategy of peak regulation to alleviate the overload pressure of the transformer. The invention constructs an optimal power flow model based on multi-periods, proposes an operation strategy for energy storage batteries to participate in peak regulation, realizes the peak regulation effect on the power grid, and alleviates the overload pressure of main transformers and lines when coal-fired units are decommissioned.

Description

A grid peak regulation method based on multi-time interval optimal power flow and energy storage battery

technical field

The invention belongs to the technical field of regulation and control of energy storage power stations in electric power systems, and relates to a power grid peak regulation method based on multi-time interval optimal power flow and energy storage batteries.

Background technique

The power balance between power generation and load is an important basis for the safe operation of the power system. However, due to the gradual retirement of coal-fired thermal power units in the power grid and the rapid increase in the installed capacity of new energy sources, the grid power supply technology and power structure have changed rapidly. In the power system dispatching process, the peak shaving problem faces several difficulties: First, the rapid development of new energy sources further increases the peak-to-valley difference of the power grid. Second, the large increase in external electricity has weakened the peak-shaving capability of the power grid. Third, the decommissioning of coal-fired thermal power units increases the risk of transformer overload.

Energy storage is a device in the power system that performs active processing and realizes energy time and space transfer, which can effectively improve the power structure and regulation capabilities. Energy storage systems are widely recognized as an effective way to provide significant flexibility in the planning, operation, and control of modern power systems. In recent years, the cost reduction of electrochemical energy storage batteries has significantly promoted the grid-scale application of energy storage power plants.

At present, energy storage technology, as an important part of smart grid technology, has attracted widespread attention due to its advantages such as fast response, flexible power capacity configuration and independent of site selection, high cycle life, and environmental friendliness. The most potential application of battery energy storage is to use its energy bidirectional flow characteristics to realize the adjustment of the load peak-valley difference.

The application of battery energy storage system in the research of peak shaving and valley filling has achieved the purpose of load transfer and smoothing load fluctuation from different research angles, and can better deal with many difficulties faced by peak shaving. Therefore, with the development of energy storage technology, rational use of energy storage systems to achieve the optimal effect of peak shaving and valley filling will be a key research issue.

Contents of the invention

The technical problem to be solved by the present invention is to overcome the defects existing in the prior art, and provide a power grid peak regulation method based on multi-time interval optimal power flow and energy storage batteries. Since the role of energy storage in alleviating the overload of the transformer is two-fold: one is that it must provide sufficient power generation when overloaded, that is, discharge capacity. The second is to have sufficient reserve capacity to accommodate possible peak load forecast errors. Based on this basic idea, the present invention takes into account the constraints of generator ramping and power generation, combined with the constraints of state of charge and power of energy storage power stations, establishes an optimal power flow formula based on multiple time intervals, and proposes that after some generators are decommissioned, The energy storage power station participates in the peak load regulation to alleviate the overload pressure of the transformer and the energy storage battery operation strategy. The peak-shaving effect of the power grid is realized and the effectiveness of the method is verified by the load peak-to-valley difference rate index.

For this reason, the technical scheme adopted by the present invention is as follows: a method for peak regulation of a power grid based on multi-time interval optimal power flow and energy storage batteries, the method comprising the following steps:

S1: Autoregressive models using time series analysis to predict load and wind/solar data;

S2: Obtain a set of forward-looking time intervals based on control requirements, establish a DC power flow for each time interval based on the forecast data of the autoregressive model, consider the ramping constraints of the generator and the constraints of the generated power, and combine the constraints of the state of charge of the energy storage And power constraints, as well as transmission line and transformer capacity constraints, integrated generator and energy storage battery output, with the minimum number of time intervals when the transformer load rate exceeds the threshold as the objective function, an overall multi-time interval optimal power flow model is established;

S3: Solve the established overall multi-time interval optimal power flow model to obtain the power output of the energy storage battery pack at each time period, thereby obtaining the operation strategy for the energy storage battery to participate in peak regulation, and using the load peak-to-valley difference rate as an indicator to determine the The peak-shaving effect of the load fluctuation range of the model is evaluated and analyzed.

Further, the step S1 is specifically: use current disturbances and past observations of limited items to predict the current value of the model, and use the predicted value to establish a forward-looking optimization form;

的未来时段预测数据

表示为：Use historical load or wind/solar data as historical data samples

Forecast data for the future period of

Expressed as:

为自回归系数，其中i＝0,1,2,3,…,p，p表示所选历史数据时刻数；k表示需预测的未来时刻；自回归系数通过历史数据构建线性方程组结合预测误差最小得到，X表示负荷或风力/太阳能，ε_t为一个白噪点。In the formula,

is the autoregressive coefficient, where i=0,1,2,3,...,p, p represents the number of selected historical data moments; k represents the future time to be predicted; the autoregressive coefficient constructs a linear equation system through historical data combined with prediction errors The minimum is obtained, X represents load or wind/solar energy, ε _t is a white noise point.

Further, the step S2 is specifically: consider a set of forward-looking time intervals S _T ={p+1,p+2,...,T ₀ }, where each time interval t establishes a DC power flow, as shown in formula (2 ) as shown:

和

分别表示时间区间t下发电机、储能电池组和负载的功率；B^t和θ^t分别表示系统的导纳矩阵和节点电压相角在时间区间t下的虚部；In the formula, A _G and A _ESS respectively represent the connection matrix of the generator and the energy storage battery pack;

and

respectively represent the power of the generator, energy storage battery pack and load in the time interval t; B ^t and θ ^t respectively represent the admittance matrix of the system and the imaginary part of the node voltage phase angle in the time interval t;

First, a set of variables is used to track the state of charge of the energy storage battery pack; the relationship between the variable E ^t and the charging and discharging power of the energy storage battery is shown in formula (3);

The power balance constraint of the system is shown in formula (4);

表示时间区间t下发电机i的功率；

和

分别表示时间区间t下的风力/太阳能出力和波动量；

表示时间区间t下的线损功率；

表示时间区间t下在j位置的负载功率，

分别表示时间区间t下储能电池组在j位置的放电和充电功率；where Ng represents the number of generators;

Indicates the power of generator i in the time interval t;

and

Respectively represent the wind/solar output and fluctuations in the time interval t;

Indicates the line loss power under the time interval t;

Indicates the load power at position j under time interval t,

Respectively represent the discharge and charge power of the energy storage battery pack at position j under the time interval t;

The unit output constraints of the generator are as follows:

和P_G分别表示发电机出力的上下限；In the formula,

and _PG respectively represent the upper and lower limits of generator output;

Consider the climbing constraint for the generator, as shown in formula (6):

分别表示发电机爬坡的下限和上限；In the formula, R _G and

Respectively represent the lower limit and upper limit of generator ramp;

The power constraints of the energy storage battery pack include energy storage battery operating constraints and energy state constraints, where the operating constraints are shown in the following formula:

和

分别表示时间区间t下储能电池组的放电和充电功率；

和

分别表示储能电池组的额定放电和充电功率；

和

分别表示时间区间t储能电池组的放电和充电状态，且皆为0-1变量，根据实际考虑的时间区间内发电机/储能电池的具体状态确定这两个参数的值；In the formula,

and

Respectively represent the discharge and charge power of the energy storage battery pack under the time interval t;

and

respectively represent the rated discharge and charge power of the energy storage battery pack;

and

Respectively represent the discharge and charge states of the energy storage battery pack in the time interval t, and both are 0-1 variables. The values of these two parameters are determined according to the specific state of the generator/energy storage battery in the actually considered time interval;

The energy constraints are shown in the following formula;

和

分别表示储能电池组的充电效率和放电效率；τ表示1到T之间的任意一个点；E^Max表示储能电池组的最大容量；E₀表示储能电池组在所选时间周期中初始时刻的能量状态，时间周期由T个时间区间构成；T表示所选时间区间总数；In the formula,

and

Respectively represent the charging efficiency and discharge efficiency of the energy storage battery pack; τ represents any point between 1 and T; E ^Max represents the maximum capacity of the energy storage battery pack; E ₀ represents the initial energy storage battery pack in the selected time period The energy state at any moment, the time period is composed of T time intervals; T represents the total number of selected time intervals;

Equation (10) indicates that the energy state of the energy storage battery pack must not be negative at any time within the selected time period, nor exceed its energy capacity limit; Equation (11) indicates that after a time period, the energy storage battery pack The state should be balanced, that is, the total charge and discharge of the energy storage battery pack should be equal within a period of time;

Transmission line and transformer capacity constraints: Consistent with the form of power flow from one node to another, as shown in the following equation:

和P_L分别表示传输线功率的上下限；In the formula, T ^t represents the line admittance matrix; in the formula,

and _PL respectively represent the upper and lower limits of the transmission line power;

Note that both T ^t in Equation (12) and B ^t in Equation (2) become time-varying due to the observation of line switching actions in the current time span; t in Equation (2)-Equation (12) represents the tth time interval;

The objective function of the established model is to minimize the number of time intervals in which the transformer load rate exceeds the threshold in the selected time interval, considering the output of the energy storage battery pack; the threshold is time-varying due to the influence of power consumption, and is used to reflect the Energy cost difference in different time intervals; the objective function is shown in formula (13);

表示变压器负载率是否过载的系数，其中t＝p+1,p+2,…,T；φ_Z表示变压器负载率超阈值总数；

表示t时间区间下的变压器负载率阈值；In the formula,

The coefficient indicating whether the transformer load rate is overloaded, where t=p+1,p+2,...,T; φ _Z indicates the total number of transformer load rates exceeding the threshold;

Indicates the transformer load rate threshold under the t time interval;

The following overall multi-time interval optimal power flow model is established:

Furthermore, the overall multi-time interval optimal power flow model solves the power output of the energy storage battery group in each time interval, and then gives the operation strategy of the energy storage battery group based on the output situation;

In the time interval of the peak load, the connected energy storage battery pack is discharged as a power source, and the electric energy is input to the system to reduce the peak load. The electric energy thus prepares for another peak shaving in the subsequent peak load time interval.

Furthermore, when there are load and renewable energy forecast data obtained through the autoregressive model, the overall multi-time interval optimal power flow model is solved once at each preset time interval, and by increasing the scheduling of energy storage battery packs, the realization of The best peaking effect.

Further, in the step S3, the load peak-to-valley difference rate index is constructed, and the load peak-to-valley difference rate index is used to evaluate and analyze the load fluctuation range of the established model; the details are as follows:

Load peak-to-valley difference rate: describes the fluctuation range of the grid load within one or more sampling periods, the smaller the value, the smaller the load fluctuation range; its expression is as follows:

和

分别表示采样周期内的负荷最大值和最小值。In the formula, β represents the load peak-to-valley difference rate; i represents the number of sampling cycles;

and

represent the maximum and minimum values of the load in the sampling period, respectively.

The beneficial effects that the present invention has are as follows:

Based on the basic idea that the energy storage battery plays a dual role in alleviating the overload of the transformer, the present invention constructs an optimal power flow model based on multiple time intervals, and proposes an operation strategy for the energy storage battery to participate in peak regulation, that is, during the peak load period, The connected energy storage battery is discharged as a power supply to input suitable electric energy for the system, and when the load is low, the energy storage battery is used as a load to absorb appropriate electric energy from the system, realizing the peak-shaving effect on the power grid, and alleviating the decommissioning of coal-fired units When the main transformer and line overload pressure.

Description of drawings

Fig. 1 is a scene graph that the present invention faces;

Fig. 2 is a flow chart of the energy storage participating in the peak-shaving operation strategy based on the multi-period optimal power flow proposed by the present invention;

Fig. 3 is a result diagram of main transformer power, energy storage battery power and state of charge before and after adopting the proposed operation strategy of energy storage participating in peak regulation in the application example of the present invention.

Detailed ways

The present invention will be further elaborated and illustrated below in conjunction with the accompanying drawings and specific embodiments.

In the following description, a lot of specific details are set forth in order to fully understand the present invention, but the present invention can also be implemented in other ways different from those described here, and those skilled in the art can do it without departing from the meaning of the present invention. By analogy, the present invention is therefore not limited to the specific examples disclosed below.

In a preferred embodiment of the present invention, according to the actual typical substation structure of Zhejiang Power Grid, the situation shown in Figure 1 is used as a scenario for testing the proposed operation strategy. According to the scenario shown in Figure 1: the 500kV backbone network is referred to as power supply G1 for short, and supplies power to the 220kV network through the transformer connecting B1 and B2. The meshed 220kV network is simply referred to as a ring with three buses B3-B4, and one of the coal-fired generators, G4, will be decommissioned. It can be seen from the power flow diagram that the generator can provide power for partial loads, thereby reducing the load condition of the transformer T1. However, when it is decommissioned, all the loads of the 220kV grid must be provided by transformer T1, resulting in a significant increase in the power flow from B1 to B2, while causing T1 to be overloaded, and the stress on T1 and the line will increase under load peaks.

To alleviate this transformer and line overload situation, distributed generation and demand response are effective alternatives. However, system operators may not have direct access to these schemes due to their distributed nature or privacy concerns. Therefore, energy storage peak shaving is considered to be an ideal technology to solve this problem. In order to verify that the peak-shaving operation strategy of the energy storage battery proposed in the present invention can effectively alleviate the identified transformer overload, according to the actual situation, a coal-fired thermal power generator with a generator capacity of 200MW to be decommissioned is assumed to be installed at the original site of the decommissioned generator. 100MW/100MWh energy storage power station, which is more convenient to install in practice, because the facility construction and service lines can be directly reused.

The present invention provides a power grid peak regulation method based on multi-time interval optimal power flow and energy storage batteries. As shown in Figure 2, the method includes the following steps: first, a certain amount of historical data needs to be collected, and according to the linear constraints and the minimum error The objective is to obtain the p autoregressive coefficients of the autoregressive model; then obtain the forecast data at the selected time according to the autoregressive model; In terms of output, during the iteration process, it is judged whether the iteration is over by comparing the load peak-valley difference indicators of the two results before and after, and finally an appropriate energy storage battery operation strategy is obtained. Specific steps are as follows:

S1: Load and wind/solar data forecasted by autoregressive models using time series analysis. Specifically:

The autoregressive model of the time series analysis method is used to predict the regional load and wind/solar energy data, and the independent variable series can be used for prediction. The prediction principle is to use the current disturbance and the past observation value of the limited items to predict the current value of the model. Using the predicted values to establish a forward-looking optimization form, the mathematical representation of the autoregressive model is:

为自回归系数，其中i＝1,2,3,…,p；p表示所选历史数据时刻数；k表示需预测的未来时刻，x_t为一个时间序列，其中t＝p+1,p+2,…T₀；ε_t为一个白噪点；通过一定数量历史数据构建线性方程组结合预测误差最小可得到各自回归系数，假设有n组历史数据{a_1,1,a_1,2,…,a_1,p+1,a_2,1,…a_n,p+1}(n>p)，则根据上式可构建如下线性方程组：In the formula,

is the autoregressive coefficient, where i=1,2,3,...,p; p represents the number of selected historical data moments; k represents the future time to be predicted, x _t is a time series, where t=p+1,p +2,...T ₀ ; ε _t is a white noise point; through a certain amount of historical data to construct a linear equation system combined with the minimum prediction error, the respective regression coefficients can be obtained, assuming that there are n sets of historical data {a _1,1 ,a _1,2 , …,a _1,p+1 ,a _2,1 ,…a _n,p+1 }(n>p), then the following linear equations can be constructed according to the above formula:

...

最小为目标，通过求解上述线性方程组即可得到各自回归系数。by

The minimum is the goal, and the respective regression coefficients can be obtained by solving the above linear equations.

于是未来时段预测数据

可表示为：According to formula (1), a certain historical load or wind/solar data is taken as a historical data sample

Therefore, the forecast data for the future period

Can be expressed as:

where X represents load or wind/solar power.

S2: Considering a set of forward-looking time intervals, based on the forecast data of the autoregressive model, a DC power flow can be established for each time interval, and the objective function is to establish the overall multi-time interval optimal power flow model with the minimum number of transformer load rates exceeding the threshold. Specifically: consider a set of forward-looking time intervals S _T ={p+1,p+2,...,t,...,T ₀ }, each time interval t can establish a DC power flow, as shown in formula (3).

和

分别表示时间区间t下发电机、储能电池组和负载的功率；B^t和θ^t分别表示系统的导纳矩阵和节点电压相角在时间区间t下的虚部。In the formula, A _G and A _ESS respectively represent the connection matrix of the generator and the energy storage battery pack;

and

Respectively represent the power of generator, energy storage battery pack and load in the time interval t; B ^t and θ ^t represent the admittance matrix of the system and the imaginary part of the node voltage phase angle in the time interval t, respectively.

First, a set of variables E ^t is used to track the state of charge (SOC) of the energy storage battery pack. The relationship between it and the charging and discharging power of the energy storage battery is shown in formula (4).

表示t时刻储能电池的充放电功率。In the formula,

Indicates the charging and discharging power of the energy storage battery at time t.

For the selected system, the power balance constraint is one of the most important constraints, as shown in Equation (5).

和

分别表示t时刻的风力/太阳能出力和波动量；

表示t时刻的线损功率；

表示t时刻在j位置的负荷功率。In the formula, Ng represents the number of coal-fired units;

and

Respectively represent the wind/solar output and fluctuations at time t;

Indicates the line loss power at time t;

Indicates the load power at position j at time t.

The unit output constraints of the generator are as follows:

和P_G分别表示发电机出力的上下限，特别的，当发电机离线时上下限都应取0。In the formula,

and _PG respectively represent the upper and lower limits of the generator output, especially, when the generator is offline, both the upper and lower limits should be set to 0.

In addition, the ramp constraint is considered for the generator, as shown in Equation (7). However, it is not considered for the energy storage battery pack, because the energy storage battery pack has a sufficient charge and discharge adjustment rate under the operating time interval in minutes.

分别表示发电机爬坡的下限和上限。In the formula, R _G and

Respectively represent the lower limit and upper limit of generator ramp.

Battery energy storage operating constraints:

和

分别表示t时刻处电池储能电站的放电和充电功率；

和

分别表示储能电池的额定放电和充电功率；

和

分别表示t时刻处电池储能电站的放电和充电状态，且皆为0-1变量，可根据实际考虑的时间区间内储能电池的具体状态确定这两个参数的值：充电状态时

取1，

取0；放电状态时

取0，

取1；特别的，若储能电池为离线状态，则两个参数皆设为0。In the formula,

and

respectively represent the discharge and charge power of the battery energy storage power station at time t;

and

respectively represent the rated discharge and charge power of the energy storage battery;

and

represent the discharge and charge states of the battery energy storage power station at time t, respectively, and both are 0-1 variables. The values of these two parameters can be determined according to the specific state of the energy storage battery in the actually considered time interval:

take 1,

Take 0; in discharge state

take 0,

Take 1; in particular, if the energy storage battery is offline, both parameters are set to 0.

In addition, the energy state constraints of the energy storage battery are as follows:

和

分别表示储能电池的充电效率和放电效率；E^Max表示储能电池的最大容量；E₀表示储能电池在所选时间周期中初始时刻的能量状态；T表示所选时间区间总数。τ表示1到T之间的任意一个点；时间周期由T个时间区间构成In the formula,

and

Respectively represent the charging efficiency and discharge efficiency of the energy storage battery; E ^Max represents the maximum capacity of the energy storage battery; E ₀ represents the energy state of the energy storage battery at the initial moment in the selected time period; T represents the total number of selected time intervals. τ represents any point between 1 and T; the time period consists of T time intervals

Equation (11) indicates that the energy state of the energy storage battery must not be negative at any time within the selected time period, nor exceed its energy capacity limit; Equation (12) indicates that after a time period, the energy state of the energy storage battery should be Maintain balance, that is, the total charge and discharge of the energy storage battery should be equal within a period of time.

The transmission line and transformer capacity constraints are consistent with the form of power flow from one node to another, as shown in Equation (13).

和P_L分别表示传输线功率的上下限。注意T^t和B^t都会因观察到当前时间跨度下有线路切换动作而变成时变的。where T ^t represents the line admittance matrix,

and _PL represent the upper and lower limits of the transmission line power, respectively. Note that both T ^t and B ^t will become time-varying due to the observed line switching action in the current time span.

The t in formula (3) - formula (13) all represent the tth time interval.

The objective function of the established model is to minimize the number of transformer load rates exceeding the threshold within the selected time interval, considering the output of the energy storage battery. The threshold value is time-varying due to the influence of power consumption, which can reflect the energy cost difference in different time intervals of the day. The objective function is shown in formula (14).

表示变压器负载率是否过载的系数，其中t＝p+1,p+2,…,T；φ_Z表示变压器负载率超阈值总数；

表示t时间区间下的变压器负载率阈值。In the formula,

The coefficient indicating whether the transformer load rate is overloaded, where t=p+1,p+2,...,T; φ _Z indicates the total number of transformer load rates exceeding the threshold;

Indicates the transformer load rate threshold in the t time interval.

In summary, the following overall multi-time interval optimal power flow model can be established:

When there are load and renewable energy forecast data obtained through the autoregressive model, the established model (15) is solved every 15 minutes. Therefore, by increasing the scheduling of energy storage battery packs, the best peak shaving effect can be achieved while satisfying various constraints, including alleviating the overload pressure of the transformer.

S3: Solve the established overall multi-time interval optimal power flow model, propose an operation strategy for energy storage batteries to participate in peak regulation, and evaluate and analyze the peak regulation effect of the model with the load peak-to-valley difference rate index.

After obtaining the power output of the energy storage battery pack at each time period based on the above-mentioned overall multi-time interval optimal power flow model, the operation strategy of the energy storage battery during the peak load period can be proposed based on this, that is, in the time interval of the peak load, The connected energy storage battery is used as a power supply to discharge the appropriate electric energy for the system to reduce the peak load. In the time interval of the low load, the energy storage battery is used as a load to absorb the appropriate electric energy from the system to provide for the subsequent load peak time interval. Prepare for peak shaving again.

In order to evaluate and analyze the peak-shaving effect of the proposed model, a load peak-to-valley difference rate index is constructed. details as follows:

Load peak-to-valley difference rate: describes the fluctuation range of the grid load within one or more sampling periods, the smaller the value, the smaller the load fluctuation range. Its expression is as follows:

和

分别表示采样周期内的负荷最大值和最小值。In the formula, β represents the load peak-to-valley difference rate; i represents the number of sampling cycles;

and

represent the maximum and minimum values of the load in the sampling period, respectively.

Attached Figure 3 shows the transformer loading and energy storage charging and discharging operation on a typical summer peak day, with a time interval of 15 minutes. It can be observed that the energy storage battery can relieve the overload of the transformer by charging at non-load peak hours and releasing energy at load peak hours, and successfully achieve the purpose of peak regulation. In the iterative process, the peak-shaving effect of the proposed strategy can be guaranteed by comparing the load peak-to-valley difference index. At the same time, it can be found that this strategy has more flexible requirements for the capacity of energy storage power stations. When the capacity of the energy storage power station is small, you can choose the off-peak load to properly charge the energy storage power station, and then discharge it at an appropriate time to alleviate the transformer overload and peak regulation; when the capacity of the energy storage power station is large, in non-peak More energy can be charged during the peak load period, and better peak shaving can be achieved through this strategy.

The above descriptions are only preferred embodiments of one or more embodiments of this specification, and are not intended to limit one or more embodiments of this specification. Within the spirit and principles of one or more embodiments of this specification, Any modification, equivalent replacement, improvement, etc. should be included in the scope of protection of one or more embodiments of this specification.

Claims (6)

1. A power grid peak shaving method based on multi-time interval optimal power flow and an energy storage battery is characterized by comprising the following steps:

s1: predicting load and wind/solar data by using an autoregressive model of a time series analysis method;

s2: obtaining a group of forward looking time intervals based on control requirements, establishing a direct current power flow for each time interval based on prediction data of an autoregressive model, considering the climbing constraint and the power generation constraint of a generator, combining the charge state constraint and the power constraint of energy storage and the capacity constraint of a transmission line and a transformer, synthesizing the output of the generator and an energy storage battery, and establishing an integral multi-time interval optimal power flow model by taking the minimum number of time intervals when the load rate of the transformer exceeds a threshold as a target function;

s3: and solving the established integral multi-time interval optimal power flow model to obtain the power output condition of the energy storage battery pack at each time interval, thereby obtaining an operation strategy that the energy storage battery participates in peak shaving, and evaluating and analyzing the peak shaving effect of the load fluctuation range of the model by taking the load peak-valley difference rate as an index.

2. The method for peak load regulation of a power grid based on an optimal power flow and an energy storage battery in multiple time intervals as claimed in claim 1, wherein the step S1 specifically comprises: predicting a current value of the model by using current interference and a limited past observation value, and using the predicted value to establish a prospective optimization form;

using historical load or wind/solar data as historical data samples

In the futurePredicting data

Expressed as:

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

is an autoregressive coefficient, where i =0,1,2,3, …, p, p represents the selected historical data time instant; k represents the future time to be predicted; the autoregressive coefficient is obtained by constructing a linear equation set through historical data and combining the minimum prediction error, X represents load or wind power/solar energy, and epsilon _t Is a white noise point.

3. The method for peak load regulation of a power grid based on the optimal power flow and the energy storage battery in multiple time intervals as claimed in claim 2, wherein the step S2 specifically comprises: consider a set of look-ahead time intervals S _T ＝{p+1,p+2,…,T ₀ And creating a direct current flow in each time interval t, as shown in formula (2):

in the formula, A _G And A _ESS Respectively representing the connection matrixes of the generator and the energy storage battery pack;

and

respectively representing the power of the generator, the energy storage battery pack and the load in a time interval t; b is ^t And theta ^t Respectively representing the imaginary parts of the admittance matrix and the node voltage phase angle of the system in a time interval t;

firstly, tracking the charge state of an energy storage battery pack by using a group of variables; variable E ^t Charging and discharging power of energy storage battery

The relation of (A) is shown as a formula (3);

the power balance constraint of the system is shown as equation (4);

wherein Ng represents the number of generators;

representing the power of the generator i in a time interval t;

and

respectively representing wind power/solar power output and fluctuation quantity in a time interval t;

representing the line loss power in a time interval t;

representing the load power at position j for the time interval t,

respectively representing the discharging power and the charging power of the energy storage battery pack at the position j in the time interval t;

the unit output of the generator is constrained as follows:

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

and P _G Respectively representing the upper limit and the lower limit of the output of the generator;

the generator is considered to be climbing constrained, as shown in equation (6):

in the formula, R _G And

respectively representing the lower limit and the upper limit of the generator climbing slope;

the power constraints of the energy storage battery pack comprise energy storage battery operation constraints and energy state constraints, wherein the operation constraints are represented by the following formula:

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

and

respectively representing the discharging power and the charging power of the energy storage battery pack under the time interval t;

and

respectively representing rated discharge and charge power of the energy storage battery pack;

and

respectively representing the discharging state and the charging state of the energy storage battery pack in a time interval t, wherein the discharging state and the charging state are all variables of 0-1, and determining the values of the two parameters according to the specific state of the generator/the energy storage battery in the actually considered time interval;

the energy constraint is shown as follows;

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

and

respectively representing the charging efficiency and the discharging efficiency of the energy storage battery pack; τ represents any one point between 1 and T; e ^Max Representing the maximum capacity of the energy storage battery pack; e ₀ Representing the energy state of the energy storage battery pack at the initial moment in a selected time period, wherein the time period consists of T time intervals; t represents a selected time zoneThe total number of cells;

equation (10) indicates that the energy state of the energy storage battery pack must not be negative nor exceed its energy capacity limit at any time during the selected time period; equation (11) shows that the energy states of the energy storage battery pack should be balanced after a time period, that is, the total charge and the total discharge of the energy storage battery pack should be equal in a time period;

transmission line and transformer capacity constraints: consistent with the form of power flowing from one node to another, the following equation applies:

in the formula, T ^t Representing a line admittance matrix; in the formula (I), the compound is shown in the specification,

and P _L Respectively representing the upper limit and the lower limit of the transmission line power;

note that T in the formula (12) ^t And B in the formula (2) ^t Will become time-varying due to the observation of the line switching action at the current time span; formula (2) -formula (12) wherein t represents the t-th time interval;

the objective function of the established model is that the output of the energy storage battery pack is considered in a selected time interval, so that the number of time intervals when the load rate of the transformer exceeds a threshold value is minimum; the threshold value is influenced by the power utilization condition and is time-varying, and is used for reflecting the energy cost difference of different time intervals in one day; the objective function is shown in equation (13);

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

a coefficient representing whether the transformer load factor is overloaded or not, where T = p +1, p +2, …, T; phi is a _Z Representing a voltage transformationTotal number of machine load rate exceeding threshold;

representing a transformer load rate threshold value in a time interval t;

establishing an overall multi-time interval optimal power flow model as follows:

4. the power grid peak shaving method based on the multi-time interval optimal power flow and the energy storage battery as claimed in claim 3, characterized in that the overall multi-time interval optimal power flow model solves the power output condition of the energy storage battery pack in each time interval, and then gives the operation strategy of the energy storage battery pack based on the output condition;

in the time interval of the load peak, the accessed energy storage battery pack is discharged as a power supply to input electric energy for the system so as to reduce the load peak value, and in the time interval of the load valley, the energy storage battery is used as one type of load to absorb the electric energy from the system so as to prepare for the next peak regulation in the subsequent load peak time interval.

5. The power grid peak regulation method based on the optimal power flow in multiple time intervals and the energy storage battery as claimed in claim 1, wherein when prediction data of load and renewable energy sources are obtained through an autoregressive model, the optimal power flow model in multiple time intervals is solved once every preset time interval, and the optimal peak regulation effect is achieved by increasing the dispatching on the energy storage battery pack.

6. The method for peak shaving of power grid based on optimal power flow and energy storage battery in multiple time intervals as claimed in claim 1, wherein in the step S3, a load peak-to-valley difference rate index is constructed, and the peak shaving of the established model is subjected to estimation analysis of load fluctuation range by using the load peak-to-valley difference rate index; the method comprises the following specific steps:

load peak-to-valley difference rate: describing the fluctuation range of the power grid load in one or more sampling periods, wherein the smaller the numerical value of the fluctuation range, the smaller the fluctuation range of the load is; the expression is as follows:

in the formula, beta represents a load peak-valley difference rate; i represents the number of sampling cycles;

and

representing the load maximum and minimum values within the sampling period, respectively.

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