CN113193547A - Day-ahead-day-day coordinated scheduling method and system for power system considering new energy and load interval uncertainty - Google Patents

Abstract

The invention provides a day-ahead and day-in cooperative scheduling method and system for an electric power system, which take new energy and load interval uncertainty into account, wherein the day-ahead and day-in cooperative scheduling method comprises the steps of acquiring electric power system data and new energy and load day-ahead prediction data; constructing a mathematical model of a scheduling interval optimization problem in the day ahead; determining a day-ahead scheduling scheme and boundary conditions of scheduling in a day; acquiring new energy and load rolling prediction data in the day; and constructing a mathematical model of the in-day scheduling problem and solving the in-day scheduling scheme based on the boundary condition of the in-day scheduling scheme and the model of the number of new energy and load in-day intervals. According to the day-ahead and day-in cooperative scheduling method of the power system, a mathematical model of the interval problem of day-ahead and day-in cooperative scheduling of the power system is comprehensively constructed; and by applying an interval optimization theory, the uncertain objective function and the constraint function are converted into a deterministic problem to be solved, and compared with an opportunity constraint planning method, the method has the advantages of low requirement on input data information, good decision flexibility and high calculation speed.

Description

Day-ahead-intraday coordinated scheduling method and system for power system considering new energy and load interval uncertainty

technical field

The invention relates to the technical field of smart grids, in particular to power system scheduling technology, and in particular to a day-to-day coordinated scheduling method for a power system that takes into account new energy and load interval uncertainty.

Background technique

In recent years, the proportion of new energy represented by wind power and photovoltaics in the power system has been increasing. Vigorously developing new energy is an inevitable requirement for my country's energy transformation and carbon emission goals. Due to the randomness, volatility and gaps of new energy Due to the characteristics of sex, its prediction will inevitably have certain errors. On the load side, electricity demand is affected by various factors such as weather, time, electricity price, economic development stage and consumer psychology, and there is also a large forecast error. Therefore, the operation scenarios of modern power systems have strong uncertainties. How to scientifically dispatch the uncertain power system and improve the economy of the dispatch scheme on the basis of controlling the risk of the dispatch scheme is an urgent problem to be solved in the power system.

At present, regarding the existing technologies of uncertain power system dispatch, the scenario method and the probability method are often used. Among them, the scene method needs to sample and generate scene sets, and perform a large number of calculations on the basis of the scene sets. This method is simple and easy, but the calculation amount is large. The probabilistic method is also known as the chance-constrained programming method. This method converts the constraint inequality into a deterministic inequality solution with a certain degree of confidence according to the probability distribution function of input uncertain variables such as new energy or load power. Small.

Both of the above methods need to know the exact probability distribution function of the input variables, but this is often difficult for practical systems. In practice, due to the lack of sufficient historical data or the weak regularity of the variable itself, it is often difficult to know its probability distribution exactly.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a method that takes into account the new energy and load interval in consideration of the technical defects of the prior art that the probability distribution function of new energy and load uncertainty variables needs to be known, the amount of calculation is large, and the scheduling plan is not precise enough. The deterministic day-to-day coordinated scheduling method and system of the power system, based on the day-to-day and intra-day forecast data of new energy and load, uses the principle of interval optimization to coordinate conventional generators, rapid start-stop groups, new energy, flexible loads and storage. It can control resources, ensure sufficient safety reserve while achieving system power balance, balance the operating cost and safety of the power system, and make scientific power dispatching decisions.

According to the first aspect of the purpose of the present invention, a day-to-day coordinated scheduling method for a power system that takes into account the uncertainty of new energy sources and load intervals is proposed, which includes the following steps:

Step 1. Obtain power system data and day-ahead forecast data for new energy and load;

Step 2. Construct a mathematical model of the scheduling interval optimization problem in the day-ahead;

Step 3. Determine the day-ahead scheduling scheme and the boundary conditions of intra-day scheduling;

Step 4. Obtain the daily rolling forecast data of new energy and load; and

Step 5. Based on the boundary conditions of the intra-day scheduling scheme determined in step 3, and the new energy and load intra-day interval number model obtained in step 4, construct a mathematical model of the intra-day scheduling problem and solve the intra-day scheduling scheme;

Among them, the power system data includes the maximum and minimum output power of conventional generator sets and quick start and stop groups, the start and stop costs of the units, the operating cost factor, the power on the slope, the minimum start and stop time, and three types of flexible loads A, B, and C. The number of grades of P _ila , _Pilb and _Pilc and the maximum load reduction capacity of each grade, cost coefficient, elastic coefficient of demand response and maximum cumulative interruption time. Among them, Class A flexible loads need to be notified to the user 24 hours in advance, and Class B flexible loads The time for the load to inform the user in advance is 15min-2h, and the time for Class C flexible load to inform the user in advance is 5-15min;

The day-ahead forecast data of new energy and load includes the hourly forecast values of the output power P _wt and P _pv of the wind farm and photovoltaic power station in the next 24 hours and the fluctuation interval of the day-ahead forecast error, and the system load P _l per hour in the next 24 hours. The forecast value of , and the fluctuation range of the forecast error for the previous day.

According to the second aspect of the purpose of the present invention, a day-to-day coordinated dispatching system for a power system that takes into account the uncertainty of new energy sources and load intervals is also proposed, including:

one or more processors;

A memory storing instructions operable that, when executed by the one or more processors, cause the one or more processors to perform operations that include performing the aforementioned co-scheduling processes.

Compared with the prior art, the present invention has the following advantages and beneficial effects:

1. The day-to-day coordinated scheduling method of the power system that considers the uncertainty of new energy and load proposed by the present invention takes advantage of the feature that the prediction error of new energy and load decreases with the reduction of the time scale, and considers the flexibility of various units. And the multi-time scale characteristics of flexible load, comprehensive consideration of generator operating cost, penalty cost of abandoning wind or light, and various expenses required for flexible load to participate in power system dispatching, realize day-a-day coordinated dispatching of power system;

2. Under the condition that the probability distribution of the input data is not known, the interval optimization theory is applied to convert the uncertainty objective function into a deterministic function, and the interval inequality is transformed into a deterministic inequality under a certain interval possibility, so as to convert the uncertainty Deterministic problems are transformed into deterministic problem solving. Compared with chance-constrained programming methods, it has the advantages of lower requirements for input data information, good decision flexibility, and fast calculation speed;

3. Finally, a simulation check is carried out on the day-a-day-day collaborative scheduling scheme, which verifies that the day-a-day-day collaborative scheduling scheme proposed by the present invention can overcome the technical defect that the day-ahead scheduling scheme is not precise enough, and under the condition of reducing daily operating costs , which improves the security of the system and has better practicability.

It is to be understood that all combinations of the foregoing concepts, as well as additional concepts described in greater detail below, are considered to be part of the inventive subject matter of the present disclosure to the extent that such concepts are not contradictory. Additionally, all combinations of the claimed subject matter are considered to be part of the inventive subject matter of this disclosure.

The foregoing and other aspects, embodiments and features of the present teachings can be more fully understood from the following description when taken in conjunction with the accompanying drawings. Other additional aspects of the invention, such as features and/or benefits of the exemplary embodiments, will be apparent from the description below, or learned by practice of specific embodiments in accordance with the teachings of this invention.

Description of drawings

The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by the same reference numeral. For clarity, not every component is labeled in every figure. Embodiments of various aspects of the present invention will now be described by way of example and with reference to the accompanying drawings, wherein:

FIG. 1 is a flowchart of an embodiment of a day-to-day coordinated scheduling method for a power system that takes into account new energy and load interval uncertainty according to the present invention.

Fig. 2 is a system structure diagram of an IEEE10 machine 39 node calculation example according to an embodiment of the present invention, wherein "WT" represents a wind farm, and "FG" represents a fast start-stop group.

Fig. 3 is a schematic diagram of a daily load curve according to an embodiment of the present invention, wherein "°" represents the forecast value for the day before, "*" represents the forecast value for the day, "△" represents the lower bound of the load forecast value interval, and "▽" represents the load forecast value interval Upper Bound.

Fig. 4 is a schematic diagram of a daily wind power curve according to an embodiment of the present invention, wherein "°" represents a day-ahead forecast value, "*" represents an intra-day forecast value, "△" represents the lower bound of the wind power forecast value interval, and "▽" represents a wind power forecast value upper bound of the interval.

FIG. 5 is a comparison diagram of the daily total power generation of conventional generator sets of day-ahead scheduling and day-ahead-day coordinated scheduling according to an embodiment of the present invention.

FIG. 6 is a comparison diagram of abandoned air volume between day-ahead scheduling and day-a-day coordinated scheduling according to an embodiment of the present invention. .

FIG. 7 is a schematic diagram of system power balance of day-to-day coordinated scheduling according to an embodiment of the present invention.

FIG. 8 is a schematic diagram of the possibility of establishment of system power balance and positive and negative reserve power constraints of day-to-day coordinated scheduling according to an embodiment of the present invention.

Detailed ways

In order to better understand the technical content of the present invention, specific embodiments are given and described below in conjunction with the accompanying drawings.

Aspects of the invention are described in this disclosure with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the present disclosure are not necessarily intended to include all aspects of the invention. It should be understood that the various concepts and embodiments described above, as well as those described in greater detail below, can be implemented in any of a number of ways, as the concepts and embodiments disclosed herein do not limited to any implementation. Additionally, some aspects of the present disclosure may be used alone or in any suitable combination with other aspects of the present disclosure.

With reference to the drawings, according to an exemplary embodiment of the present invention, a day-to-day coordinated scheduling method for a power system that takes into account the uncertainty of new energy and load interval is disclosed. FIG. 1 shows the present invention taking into account the uncertainty of new energy and load interval. The flow chart of the day-a-day-intraday coordinated dispatching method of the electric power system includes the following steps: Step 1. Obtain the power system data and the day-ahead forecast data of new energy and load; Step 2. Build a mathematical model of the day-ahead scheduling interval optimization problem; Step 3. Determine The day-ahead scheduling scheme and the boundary conditions of the intra-day scheduling; step 4, obtaining the new energy and load intraday rolling forecast data; and step 5, based on the boundary conditions of the intra-day scheduling scheme determined in step 3, and the new energy and load intraday interval obtained in step 4 Mathematical model, construct a mathematical model of intraday scheduling problem and solve intraday scheduling scheme.

The specific implementation of the above steps will be described in more detail below with reference to the accompanying drawings.

Step 1. Obtain power system data and day-ahead forecast data for new energy and load

Among them, the power system data includes the maximum and minimum output power of conventional generator sets and quick start and stop groups, the start and stop costs of the units, the operating cost factor, the power on the slope, the minimum start and stop time, and three types of flexible loads A, B, and C. The number of grades of P _ila , _Pilb and _Pilc and the maximum load reduction capacity of each grade, cost coefficient, elastic coefficient of demand response and maximum cumulative interruption time. Among them, Class A flexible loads need to be notified to the user 24 hours in advance, and Class B flexible loads The time for the load to inform the user in advance is 15min-2h, and the time for Class C flexible load to inform the user in advance is 5-15min;

The day-ahead forecast data of new energy and load includes the hourly forecast values of the output power P _wt and P _pv of the wind farm and photovoltaic power station in the next 24 hours and the fluctuation interval of the day-ahead forecast error, and the system load P _l per hour in the next 24 hours. The forecast value of , and the fluctuation range of the forecast error for the previous day.

Step 2. Establish a mathematical model for the optimization problem of day-ahead scheduling interval

其日前预测误差

位于区间

内，上标“+”表示区间数上界，上标“-”表示区间数下界，由此可以推出新能源发电功率P_N1的上界为

下界为

P_N1的日前区间数模型为

由此可建立风力及光伏发电的日前区间数模型

Assuming that the forecast value of new energy power generation is

its day-ahead forecast error

in the interval

Inside, the superscript "+" represents the upper bound of the interval number, and the superscript "-" represents the lower bound of the interval number. From this, it can be deduced that the upper bound of the new energy power generation power P _N1 is

The lower bound is

The day-ahead interval number model of P _N1 is

From this, a day-ahead interval number model for wind and photovoltaic power generation can be established.

其日前预测误差

位于区间

内，由此可以推出负荷功率P_l1的上界为

下界为

则负荷功率的日前区间数模型为

Assuming that the load day forecast is

its day-ahead forecast error

in the interval

From this, it can be deduced that the upper bound of the load power P _l1 is

The lower bound is

Then the day-ahead interval number model of load power is

Generally, the time scale of day-ahead scheduling is 1 hour, and the time-scale of intra-day scheduling is 15 minutes. In order to make the connection between day-ahead and intra-day scheduling better, the present invention takes the time scale of day-ahead scheduling as 15 minutes, and the predicted value of new energy and load per hour is Linear interpolation is performed as the day-ahead forecast value every 15 minutes, and day-ahead scheduling is performed on this basis.

Therefore, the objective function of the day-ahead scheduling optimization problem of the power system is described as:

min f ₁ =f ₁₁ +f ₁₂ +f ₁₃ +f ₁₄ (1)

为常规发电机组的运行费用，右边第1项为常规机组启停成本，第2项为发电成本。

为常规发电机台数,T₁为调度周期，由于日前调度时间尺度取为15分钟，故T₁＝96，即96个调度周期。

is the operating cost of the conventional generator set, the first item on the right is the start and stop cost of the conventional generator set, and the second item is the power generation cost.

is the number of conventional generators, and T ₁ is the scheduling period. Since the day-ahead scheduling time scale is taken as 15 minutes, T ₁ =96, that is, 96 scheduling periods.

分别为j时刻第i台常规发电机的启动控制0-1变量、冷启动控制0-1变量，

为“1”分别表示j时刻第i台常规发电机接受启动或冷启动指令，

为“0”则表示j时刻无启动或冷启动指令。

are the starting control 0-1 variable and the cold starting control 0-1 variable of the ith conventional generator at time j, respectively,

"1" means that the ith conventional generator at time j receives the start or cold start command, respectively.

If it is "0", it means that there is no start or cold start command at time j.

为0-1变量，为“1”表示j时刻第i台常规发电机处于开机状态，为“0”则表示处于停机状态。C _hot,i and C _cold,i are the hot start cost and cold start cost of the ith conventional generator, respectively,

It is a 0-1 variable, "1" indicates that the ith conventional generator at time j is in the starting state, and "0" indicates that it is in a shutdown state.

为第i台常规发电机的发电成本系数，

为j时刻第i台常规发电机的功率。

is the power generation cost coefficient of the ith conventional generator,

is the power of the ith conventional generator at time j.

为快速启停机组运行成本。

Running costs for quick start and stop groups.

为快速启动发电机台数；

为第i台快速启停机组的运行成本系数，

为j时刻第i台快速启停机组的功率。in,

is the number of quick-start generators;

is the operating cost coefficient of the i-th rapid start-up and stop group,

is the power of the i-th fast start-stop group at time j.

为快速启停机组的启动控制0-1变量，

为“1”分别表示j时刻第i台快速启停机组接受启动指令，

为第i台快速启停机组的启动成本。

To control the 0-1 variable for the start-up of the fast start-stop group,

"1" means that the i-th fast start-stop group accepts the start-up command at time j, respectively.

It is the start-up cost of the i-th rapid start-up and stop group.

为弃风惩罚费用，其中

为j时刻削减的风电功率，C_w为弃风惩罚因子。

It is the penalty fee for wind abandonment, of which

is the wind power cut at time j, and C _w is the wind curtailment penalty factor.

为A、B、C三类柔性负荷的调控费用，ns为柔性负荷的分档数，其中

分别为j时刻A、B、C三类柔性负荷s档的调控功率，由于柔性负荷响应具有弹性，故

为区间数，

分别为A、B、C三类柔性负荷以s档参与调控时的代价因子。

is the regulation cost of three types of flexible loads A, B and C, ns is the number of grades of flexible loads, where

are the regulated powers of the three types of flexible loads A, B, and C at time j at the s-gear respectively. Since the response of the flexible load is elastic, so

is the interval number,

are the cost factors when three types of flexible loads, A, B, and C, participate in the regulation in the s-level.

Wherein, the present invention determines the constraints of the day-ahead scheduling optimization problem of the power system including:

① Active power balance equation

In formula (2), the left side is the sum of conventional generator sets, quick start and stop units and wind power generation, and the right side is the total system load minus the reduction of the three types of flexible loads. The active power balance equation is an interval equation;

②Constraints on conventional generator output and ramping power

分别为第i台常规发电机的最小和最大功率，

分别表示第i台常规发电机向下和向上爬坡功率的极限值；In formula (3)

are the minimum and maximum power of the ith conventional generator, respectively,

Respectively represent the limit value of the i-th conventional generator's downward and upward climbing power;

③Constraints on the minimum start and stop time of conventional generators

分别为第i台常规发电机的最小开机时间和最小停机时间；In formulas (5) and (6)

are the minimum startup time and minimum shutdown time of the ith conventional generator, respectively;

④Constraints on cold and hot start of conventional generator sets

为第i台常规发电机的冷启动时间；Equations (7) and (8) are the constraints on the cold start and hot start of the ith conventional generator at time j,

is the cold start time of the ith conventional generator;

⑤Maximum and minimum power and climbing power constraints of quick start and stop groups

分别为第i台快速启停机组的最小和最大功率，

为0-1变量，为“1”表示j时刻第i台快速机组处于开机状态，为“0”则表示处于停机状态；式(10)是快速机组向下、向上爬坡功率约束，

分别表示第i台快速启停机组向下和向上爬坡功率的极限值；Equation (9) is the minimum and maximum power constraints of the fast-start generator,

are the minimum and maximum power of the i-th fast start-stop group, respectively,

It is a variable of 0-1, "1" means that the ith fast unit is in the starting state at time j, and "0" means it is in a shutdown state; Equation (10) is the power constraint of the fast unit climbing down and up,

Respectively represent the limit value of the down and up climbing power of the i-th fast start-stop group;

⑥The minimum start-up time of the fast unit and the minimum shutdown time constraint

最小停机时间

的约束，

为截止j时刻第i台快速机组的连续运行时间，

为截止j时刻第i台快速机组的连续停机时间；Equation (11) and Equation (12) are the minimum start-up time of the i-th fast unit at time j, respectively.

Minimum downtime

constraints,

is the continuous running time of the i-th fast unit up to time j,

is the continuous shutdown time of the i-th fast unit up to time j;

⑦Abandoned wind constraint

分别为日前调度中j时刻削减的风功率及风功率波动区间下界；In formula (13)

are the wind power cut at time j in the previous scheduling and the lower bound of the wind power fluctuation interval, respectively;

⑧Flexible load restraint

为A、B、C三类柔性负荷s档的调控功率最大值，

及

分别为A、B、C三类柔性负荷s档响应系数的弹性波动区间；Among them, P _ila,s,j , P _ilb,s,j , P _ilc,s,j are the three types of flexible loads A, B, and C at the time of j time.

is the maximum regulated power of the s-gear of the three types of flexible loads A, B, and C,

and

are the elastic fluctuation intervals of the s-gear response coefficients of the three types of flexible loads A, B, and C, respectively;

⑨ Positive and negative backup power constraints

为j时刻常规机组和快速机组所能提供的正备用功率，r为备用系数，

为j时刻常规机组和快速机组所能提供的负备用功率，

为负荷向上、风速向下波动时的波动区间，

为负荷向下、风速向上波动时的波动区间。in

is the positive standby power provided by conventional units and fast units at time j, r is the standby coefficient,

is the negative standby power that conventional units and fast units can provide at time j,

is the fluctuation interval when the load fluctuates upward and the wind speed fluctuates downward,

It is the fluctuation range when the load is down and the wind speed is fluctuated up.

Therefore, the aforementioned formulas (1)-(20) together constitute the mathematical model of the day-ahead scheduling interval optimization problem.

Step 3. Determine the day-ahead scheduling scheme and the boundary conditions for intra-day scheduling

其中：The objective function f ₁ of the day-ahead scheduling interval optimization problem of the power system described by equation (1) is an interval function, and its upper and lower bounds are set as

in:

目标函数半径为

将区间目标函数转换为

β₁为加权系数；Let the mean of the interval objective function be

The radius of the objective function is

Convert the interval objective function to

β ₁ is the weighting coefficient;

Then, the interval inequalities described by equations (2), (17) and (19) in the day-ahead scheduling interval optimization problem of the power system are constrained under the preset interval probability and converted into deterministic inequalities, with power balance and reserve constraints. The interval possibilities for the establishment of the equation are ζ ₁₁ , ζ ₁₂ and ζ ₁₃ respectively, then according to the interval possibility theory, equations (2), (17) and (19) can be transformed into:

Therefore, the day-ahead scheduling interval optimization problem can be transformed into the following deterministic problem:

min F ₁ (24)

Its constraints include formulas (3)-(16), formulas (18), (20) and formulas (21)-(23);

Then, the mixed integer linear programming method is used to solve the above deterministic problem, and the optimization scheme of the day-ahead scheduling interval is obtained. From this, the boundary conditions of the intra-day scheduling problem can be determined, that is, the startup state of the conventional unit remains unchanged, and the control amount of the class A flexible load It remains unchanged, while the start-stop status of the rapid start-stop group and the flexible loads of B and C flexible loads need to be adjusted in the intraday scheduling.

Step 4. Obtain daily rolling forecast data of new energy and load

其日内预测误差

位于区间

内，由此可以推出新能源发电功率P_N2的上界为

下界为

P_N2的日内区间数模型为

由此可分别建立风力及光伏发电的日内区间数模型

The ultra-short-term forecast of wind power, photovoltaic and load power in the next 2 hours is carried out every 15 minutes, the time scale is 15 minutes, and the forecast data is obtained from the first scheduling period k=1. As mentioned above, the scheduling period is 96. Assuming that the intraday forecast of new energy power generation is

its intraday forecast error

in the interval

From this, it can be deduced that the upper bound of the new energy power generation power P _N2 is

The lower bound is

The intraday interval number model of P _N2 is

From this, the intra-day interval number models of wind power and photovoltaic power generation can be established respectively.

其日内预测误差

位于区间

内，由此可以推出负荷功率P_l2的上界为

下界为

则负荷功率的日内区间数模型为

Assume that the intraday forecast of load is

its intraday forecast error

in the interval

, it can be deduced that the upper bound of load power P _l2 is

The lower bound is

Then the intraday interval number model of load power is

Step 5: Establish a mathematical model of the intraday scheduling problem and solve the intraday scheduling scheme.

Based on the boundary conditions of the intraday dispatch scheme determined in step 3, and the intraday interval number model of new energy and load obtained in step 4, a mathematical model of intraday optimal dispatch problem is established; its objective function is:

f ₂ =f ₂₁ +f ₂₂ +f ₂₃ +f ₂₄ (25)

为常规发电机组发电成本，对于日内调度而言T₂＝8；

为快速启停机组运行成本；

is the power generation cost of conventional generator sets, for intraday dispatch, T ₂ =8;

To quickly start and stop group operating costs;

为弃风成本；

for the cost of wind abandonment;

Among them, the constraints for determining the intraday optimal scheduling problem include:

① Active power balance equation

②Abandoned wind restraint

③ Positive and negative backup power constraints

为负荷向上、风速向下波动时的波动区间，

为负荷向下、风速向上波动时的波动区间；in,

is the fluctuation interval when the load fluctuates upward and the wind speed fluctuates downward,

is the fluctuation interval when the load is downward and the wind speed is upward;

In addition, the constraints of the intraday optimal scheduling problem also include: conventional generator output and ramping power constraint inequalities (3)-(4), fast start-stop group constraint inequalities (9)-(12), flexible load constraint equations (15) )-(16);

其中：Among them, the objective function f ₂ of the power system intraday dispatch interval optimization problem described by equation (25) is an interval function, and its upper and lower bounds are set as

in:

目标函数半径为

将区间目标函数转换为

β₂为加权系数；Let the mean of the interval objective function be

The radius of the objective function is

Convert the interval objective function to

β ₂ is the weighting coefficient;

Assuming that the active power balance equation (26), the standby frequency constraint equation (28) and the equation (30) of the power system intraday dispatch interval optimization problem are established as ζ ₂₁ , ζ ₂₂ and ζ ₂₃ respectively, according to the interval possibility theory , formulas (26), (28) and (30) can be transformed into:

Therefore, the day-ahead scheduling interval optimization problem can be transformed into the following deterministic problem:

min F ₂ (35) and its constraints include equations (3)-(4), (9)-(12), (15)-(16), (27), (29), (31) and Formulas (32)-(34);

Substitute the power-on state maintenance of the conventional unit and the control amount of the A-type flexible load obtained in step 4 as boundary conditions into the calculation, apply the mixed integer linear programming method to solve the above deterministic problem, and obtain the intraday scheduling interval optimization plan;

From this, the day-to-day collaborative scheduling scheme at time k is generated. If k=96, output the collaborative scheduling scheme. If k<96, then k=k+1, and go to step 4 to continue processing until k reaches 96.

In a further preferred solution, after k reaches 96 to determine the coordinated scheduling solution, the economy and safety of the day-to-day coordinated scheduling solution are also checked.

Take the day-ahead optimal scheduling scheme obtained in step 3 as scheme A, and the day-ahead-day collaborative scheduling scheme obtained in step 5 as scheme B, and compare the operating costs and default probability of the two schemes.

发生负备用功率约束不等式(30)违约的场景个数为

则在一个调度日内发生正备用不足的概率Prob_u为：Assuming that the forecast errors of new energy and load are uniformly distributed in their respective intervals, the variables are independent of each other, and the correlation between scenarios at different times is not considered. Sampling is performed within the interval, and N _s =30000 different scenarios are generated at each moment to form a test sample set; set in the N _s scenarios at time j, the number of scenarios in which the positive standby power constraint inequality (28) is violated is

The number of scenarios in which the negative backup power constraint inequality (30) is violated is

Then the probability Prob _u of a shortage of positive reserves in a scheduling day is:

And the probability Prob _d of the occurrence of negative reserve shortage is:

Calculate the default probability and daily operating expenses of the two schemes A and B, and compare and verify their economy and safety.

2 is a structural diagram of an IEEE10 machine 39 node calculation example system according to an embodiment of the present invention. In this embodiment, the method of the present invention is applied to an IEEE10 machine 39 node calculation example system including a new energy source and a fast start and stop group, and the system is carried out. Day-ahead-day-day coordinated optimal scheduling, and the comprehensive performance of the day-ahead and day-day optimal scheduling scheme proposed by the present invention is analyzed.

Combined with the process shown in Figure 1, the implementation steps are as follows:

Step 1. Obtain power system data and day-ahead forecast data for new energy and load

Figure 2 shows the system structure of the IEEE10-machine 39-node calculation example including the new energy source and the fast start-stop group in this embodiment. On the basis of the standard example of IEEE10-machine 39-node system, wind farms and quick start-stop groups are installed, and the generator power is adjusted, so that the total power generation of the system remains unchanged before and after the modification.

及

运行费用参数

及

发电机的热启动费用C_hot,i和冷启动费用C_cold,i，发电机最小开机时间

最小停机时间

冷启动时间T_cold,i，爬坡功率

参数如表1所示。Maximum and minimum output power of each generator of a conventional generator set

and

Running Cost Parameters

and

The hot start cost C _hot,i and the cold start cost C _cold,i of the generator, the minimum start time of the generator

Minimum downtime

Cold start time T _cold,i , ramp power

The parameters are shown in Table 1.

及

运行成本系数

启动成本

发电机最小运行时间

最小停机时间

爬坡功率

参数如表2所示。Quick start and stop group maximum and minimum output power

and

running cost factor

start-up cost

Generator Minimum Running Time

Minimum downtime

Climbing power

The parameters are shown in Table 2.

Table 1. General generator parameters

Table 2. Fast start and stop generator parameters

The day-ahead forecast load curve of the system in the next 24 hours is shown in Figure 3, and its predicted value is shown in Table 3; the wind power curve in the next 24 hours is shown in Figure 4, and its predicted value is shown in Table 4. The wind abandonment penalty factor _Cw Take 100$/MW; the number of grades of three types of flexible loads A, B, and C, the maximum capacity of each grade, the cost coefficient, and the elastic coefficient of demand response are shown in Table 5.

Table 3. Day-ahead predictions of system load power in the next 24 hours

Table 4. Day-ahead forecast values of wind farm power in the next 24 hours

Table 5. Three types of flexible load parameters A, B and C

Step 2. Establish a mathematical model for the optimization problem of day-ahead scheduling interval

即

由此可以推出负荷功率P_l1的上界为

下界为

则负荷功率的日前区间数模型为

In this embodiment, the day-ahead forecasted load power P _l1 of the system every hour for the next 24 hours is shown in Table 3. It is assumed that the day-ahead prediction error

which is

From this, it can be deduced that the upper bound of load power P _l1 is

The lower bound is

Then the day-ahead interval number model of load power is

由此可以推出风电场输出功率的日前区间数模型为

The predicted value P _wt1 of wind farm power in the next 24 hours is shown in Table 5, assuming the prediction error

From this, it can be deduced that the day-ahead interval number model of the output power of the wind farm is:

The objective function of the day-ahead scheduling optimization problem of the power system is described as:

min f ₁ =f ₁₁ +f ₁₂ +f ₁₃ +f ₁₄ (1)

为常规发电机组的运行费用，右边第1项为常规机组启停成本，第2项为发电成本，

为常规发电机台数,T₁为调度周期，由于日前调度时间尺度取为15分钟，故T₁＝96，

分别为j时刻第i台常规发电机的启动控制0-1变量、冷启动控制0-1变量，

为“1”分别表示j时刻第i台常规发电机接受启动或冷启动指令，

为“0”则表示j时刻无启动或冷启动指令，C_hot,i、C_cold,i分别为第i台常规发电机的热启动费用和冷启动费用，

为0-1变量，为“1”表示j时刻第i台常规发电机处于开机状态，为“0”则表示处于停机状态，

为第i台常规发电机的发电成本系数，

为j时刻第i台常规发电机的功率；in,

is the operating cost of the conventional generator set, the first item on the right is the start and stop cost of the conventional generator set, the second item is the power generation cost,

is the number of conventional generators, and T ₁ is the scheduling period. Since the day-ahead scheduling time scale is taken as 15 minutes, T ₁ =96,

are the starting control 0-1 variable and the cold starting control 0-1 variable of the ith conventional generator at time j, respectively,

"1" means that the ith conventional generator at time j receives the start or cold start command, respectively.

If it is "0", it means that there is no start or cold start command at time j. C _hot,i and C _cold,i are the hot start cost and cold start cost of the ith conventional generator, respectively.

It is a 0-1 variable, "1" means that the ith conventional generator is in the starting state at time j, and "0" means it is in a shutdown state,

is the power generation cost coefficient of the ith conventional generator,

is the power of the ith conventional generator at time j;

为快速启停机组运行成本，

为快速启动发电机台数；

为第i台快速启停机组的运行成本系数，

为j时刻第i台快速启停机组的功率，

为快速启停机组的启动控制0-1变量，

为“1”分别表示j时刻第i台快速启停机组接受启动指令，

为第i台快速启停机组的启动成本；

为弃风惩罚费用，其中

为j时刻削减的风电功率，C_w为弃风惩罚因子；

为A、B、C三类柔性负荷的调控费用，ns为柔性负荷的分档数，其中

分别为j时刻A、B、C三类柔性负荷s档的调控功率，由于柔性负荷响应具有弹性，故

为区间数，

分别为A、B、C三类柔性负荷以s档参与调控时的代价因子；

In order to quickly start and stop the operating cost of the group,

is the number of quick-start generators;

is the operating cost coefficient of the i-th rapid start-up and stop group,

is the power of the i-th fast start-stop group at time j,

To control the 0-1 variable for the start-up of the fast start-stop group,

"1" means that the i-th fast start-stop group accepts the start-up command at time j, respectively.

is the start-up cost of the i-th rapid start-up and stop group;

It is the penalty fee for wind abandonment, of which

is the wind power cut at time j, and C _w is the wind curtailment penalty factor;

is the regulation cost of three types of flexible loads A, B and C, ns is the number of grades of flexible loads, where

are the regulated powers of the three types of flexible loads A, B, and C at time j at the s-gear respectively. Since the response of the flexible load is elastic, so

is the interval number,

are the cost factors when three types of flexible loads, A, B, and C, participate in regulation in s-level;

The constraints of the day-ahead scheduling optimization problem of the power system include:

① Active power balance equation

In formula (2), the left side is the sum of conventional generator sets, quick start and stop units and wind power generation, and the right side is the total system load minus the reduction of the three types of flexible loads. The active power balance equation is an interval equation;

②Constraints on conventional generator output and ramping power

分别为第i台常规发电机的最小和最大功率，

分别表示第i台常规发电机向下和向上爬坡功率的极限值；In formula (3)

are the minimum and maximum power of the ith conventional generator, respectively,

Respectively represent the limit value of the i-th conventional generator's downward and upward climbing power;

③Constraints on the minimum start and stop time of conventional generators

分别为第i台常规发电机的最小开机时间和最小停机时间；In formulas (5) and (6)

are the minimum startup time and minimum shutdown time of the ith conventional generator, respectively;

④Constraints on cold and hot start of conventional generator sets

为第i台常规发电机的冷启动时间；Equations (7) and (8) are the constraints on the cold start and hot start of the ith conventional generator at time j,

is the cold start time of the ith conventional generator;

⑤Maximum and minimum power and climbing power constraints of quick start and stop groups

分别为第i台快速启停机组的最小和最大功率，

为0-1变量，为“1”表示j时刻第i台快速机组处于开机状态，为“0”则表示处于停机状态；式(10)是快速机组向下、向上爬坡功率约束，

分别表示第i台快速启停机组向下和向上爬坡功率的极限值；Equation (9) is the minimum and maximum power constraints of the fast-start generator,

are the minimum and maximum power of the i-th fast start-stop group, respectively,

It is a variable of 0-1, "1" means that the ith fast unit is in the starting state at time j, and "0" means it is in a shutdown state; Equation (10) is the power constraint of the fast unit climbing down and up,

Respectively represent the limit value of the down and up climbing power of the i-th fast start-stop group;

⑥The minimum start-up time of the fast unit and the minimum shutdown time constraint

最小停机时间

的约束，

为截止j时刻第i台快速机组的连续运行时间，

为截止j时刻第i台快速机组的连续停机时间；Equation (11) and Equation (12) are the minimum start-up time of the i-th fast unit at time j, respectively.

Minimum downtime

constraints,

is the continuous running time of the i-th fast unit up to time j,

is the continuous shutdown time of the i-th fast unit up to time j;

⑦Abandoned wind constraint

分别为日前调度中j时刻削减的风功率及风功率波动区间下界；In formula (13)

are the wind power cut at time j in the previous scheduling and the lower bound of the wind power fluctuation interval, respectively;

⑧Flexible load restraint

为A、B、C三类柔性负荷s档的调控功率最大值，

及

分别为A、B、C三类柔性负荷s档响应系数的弹性波动区间；Among them, P _ila,s,j , P _ilb,s,j , P _ilc,s,j are the three types of flexible loads A, B, and C at the time of j time.

is the maximum regulated power of the s-gear of the three types of flexible loads A, B, and C,

and

are the elastic fluctuation intervals of the s-gear response coefficients of the three types of flexible loads A, B, and C, respectively;

⑨ Positive and negative backup power constraints

为j时刻常规机组和快速机组所能提供的正备用功率，r为备用系数，

为j时刻常规机组和快速机组所能提供的负备用功率，

为负荷向上、风速向下波动时的波动区间，

为负荷向下、风速向上波动时的波动区间；方程(1)-(20)共同构成了日前调度区间优化问题的数学模型。in

is the positive standby power provided by conventional units and fast units at time j, r is the standby coefficient,

is the negative standby power that conventional units and fast units can provide at time j,

is the fluctuation interval when the load fluctuates upward and the wind speed fluctuates downward,

is the fluctuation interval when the load is down and the wind speed fluctuates up; equations (1)-(20) together constitute the mathematical model of the optimization problem of the day-ahead scheduling interval.

Step 3. Determine the day-ahead scheduling scheme and the boundary conditions for intra-day scheduling

其中：The objective function f ₁ of the day-ahead scheduling interval optimization problem of the power system described by equation (1) is an interval function, and its upper and lower bounds are set as

in:

目标函数半径为

将区间目标函数转换为

β₁为加权系数，此处取值β₁＝0.1；Let the mean of the interval objective function be

The radius of the objective function is

Convert the interval objective function to

β ₁ is a weighting coefficient, and the value here is β ₁ =0.1;

Secondly, the interval inequality constraints described by equations (2), (17) and (19) in the day-ahead scheduling interval optimization problem of the power system are converted into deterministic inequalities under a certain interval possibility, and there are power balance and standby constraint equations. The established interval possibilities are ζ ₁₁ =0.85, ζ ₁₂ =0.85 and ζ ₁₃ =0.85 respectively, then according to the interval possibility theory, equations (2), (17) and (19) can be transformed into:

Therefore, the day-ahead scheduling interval optimization problem can be transformed into the following deterministic problem:

min F ₁ (24)

Constraints include equations (3)-(16), (18), (20), and equations (21)-(23); the mixed integer linear programming method is used to solve the above deterministic problem, and the optimization scheme of the day-ahead scheduling interval is obtained, As scheme A; from this, the boundary conditions of the intraday scheduling problem can be determined, that is, the start-up state of the conventional unit remains unchanged, the control amount of the A-type flexible load remains unchanged, and the start-stop state of the rapid start-stop group and the start-up and stop states of B and C units remain unchanged. The flexible loads of the two types of flexible loads need to be adjusted in intraday scheduling.

Step 4. Obtain daily rolling forecast data of new energy and load

由此可以推出新能源发电功率P_N2的上界为

下界为

P_N2的日内区间数模型为

假设

由此可以推出负荷功率的日内区间数模型为

The ultra-short-term forecast of wind power and load power for the next 2 hours is carried out every 15 minutes, and the time scale is 15 minutes. The predicted wind power curve is shown in Figure 4, and the predicted value is shown in Table 7; in the ultra-short-term prediction, the prediction accuracy is more accurate, and the prediction error is smaller than that of the previous prediction;

From this, it can be deduced that the upper bound of the new energy power generation power P _N2 is

The lower bound is

The intraday interval number model of P _N2 is

Assumption

From this, it can be deduced that the intra-day interval number model of load power is:

Table 6. Intraday predicted values of system load power

Table 7. Intraday forecast of wind farm power

Step 5. Establish a mathematical model of the intraday scheduling problem and solve the intraday scheduling scheme

Based on the boundary conditions of the intraday dispatch scheme determined in step 3, and the intraday interval number model of new energy and load power obtained in step 4, a mathematical model of intraday optimal dispatch problem is established; its objective function is:

f ₂ =f ₂₁ +f ₂₂ +f ₂₃ +f ₂₄ (25)

为常规发电机组发电成本，对于日内调度而言T₂＝8；

为快速启停机组运行成本；

为弃风成本；

in,

is the power generation cost of conventional generator sets, for intraday dispatch, T ₂ =8;

To quickly start and stop group operating costs;

for the cost of wind abandonment;

The constraints of the intraday optimal scheduling problem include:

① Active power balance equation

②Abandoned wind restraint

③ Positive and negative backup power constraints

为负荷向上、风速向下波动时的波动区间，

为负荷向下、风速向上波动时的波动区间；in,

is the fluctuation interval when the load fluctuates upward and the wind speed fluctuates downward,

is the fluctuation interval when the load is downward and the wind speed is upward;

In addition, the constraints of the intraday optimal scheduling problem also include: conventional generator output and ramping power constraint inequalities (3)-(4), fast start-stop group constraint inequalities (9)-(12), flexible load constraint equations (15) )-(16);

其中：The objective function f ₂ of the interval optimization problem of intraday dispatching of the power system described by equation (25) is an interval function, and its upper and lower bounds are set as

in:

目标函数半径为

将区间目标函数转换为

β₂为加权系数，此处取β₂＝0.1；Let the mean of the interval objective function be

The radius of the objective function is

Convert the interval objective function to

β ₂ is a weighting coefficient, and β ₂ =0.1 is taken here;

Assuming that the active power balance equation (26), the standby constraint equation (28) and the equation (30) of the power system intraday dispatch interval optimization problem are established, the interval possibilities are ζ ₂₁ =0.95, ζ ₂₂ =0.99 and ζ ₂₃ =0.99, respectively. , according to the interval possibility theory, equations (26), (28) and (30) can be transformed into:

Therefore, the day-ahead scheduling interval optimization problem can be transformed into the following deterministic problem:

min F ₂ (35)

Constraints include equations (3)-(4), (9)-(12), (15)-(16), (27), (29), (31) and (32)-(34) ); Substitute the power-on state maintenance of the conventional unit and the control amount of the A-type flexible load obtained in step 4 into the calculation as boundary conditions, apply the mixed integer linear programming method to solve the above deterministic problem, and obtain the intraday scheduling interval optimization plan.

In this example, we carry out the check of economy and safety in the manner of the previous embodiment.

Table 8 shows the comprehensive performance comparison of the two schemes A and B. It can be seen from Table 8 that the system daily operating cost of scheme B is significantly reduced, and the mean value of the fluctuation interval and the fluctuation range are small, that is, the economy of scheme B is better; The probability of insufficient positive and negative reserves in scheme B is lower than that in scheme A, indicating that day-to-day coordinated scheduling can make full use of the multi-time scale characteristics of generator sets and flexible loads, and effectively suppress the power caused by the uncertainty of new energy power and load power. The unbalanced amount can take into account the needs of economy and safety.

Table 8. Comprehensive performance comparison of day-ahead scheduling and day-ahead-day collaborative scheduling schemes

A---day-ahead scheduling scheme; B---day-a-day collaborative scheduling scheme;

The comparison chart of the total power generation of conventional generators of scheme A and scheme B is shown in Figure 5, and the comparison chart of abandoned air volume is shown in Figure 6. From Figures 5 and 6, it can be seen that the total power generation and abandoned air volume of conventional generators of scheme B are less than The total power generation and abandoned air volume of scheme A shows that scheme B can consume more new energy, reduce energy waste and reduce operating costs, ease the peak regulation pressure of conventional generator sets, reduce their power generation costs, and improve system operation. economy.

The schematic diagram of the power balance of the scheme B system is shown in Figure 7. It can be seen from Figure 7 that the total generated power and total load power of the dispatched system fluctuate within a certain interval. During the entire dispatching period, the probability that the generated power is greater than the load power is greater than the preset value; under the scheduling scheme B, the interval probability of the system meeting the power balance and positive and negative reserve constraints at each moment is shown in Figure 8. It can be seen from Figure 8 that at night 0:00-5:00, due to the wind power It is sufficient during this time period, so the main problem of the system is that the reserve power for downward adjustment is insufficient, that is, the system is prone to the phenomenon of insufficient negative reserve. high, and the wind power output is small during the day, so the problem of insufficient backup power of the system is more prominent.

According to an embodiment of another aspect of the present invention, combined with the example shown in FIG. 1 , a day-to-day coordinated scheduling system for a power system is also proposed, for example, implemented in the form of a server or a server array, which includes:

one or more processors;

a memory storing instructions operable that, when executed by the one or more processors, cause the one or more processors to perform operations comprising executing the power system of any of the preceding embodiments- The implementation process of the intraday collaborative scheduling method, especially the specific implementation process of the embodiment in FIG. 1 .

To sum up, the day-to-day coordinated scheduling method of the power system considering the uncertainty of the new energy and load interval of the present invention overcomes the need to know the probability distribution, The technical defects of the large amount of calculation and the inaccurate day-ahead scheduling plan are based on the day-ahead and intra-day forecast data of new energy and load, taking advantage of the characteristics that the prediction error of new energy and load decreases with the reduction of the time scale, considering various types of units. The multi-time scale characteristics of flexible load and flexible load, comprehensively considering the operating cost of generators, the penalty cost of new energy curtailment, curtailment of light, and the cost of flexible load participating in power system scheduling, the interval of day-to-day coordinated scheduling of the power system is constructed. Mathematical model of the problem; using interval optimization theory, the uncertain objective function and constraint function are transformed into deterministic problem solving. Compared with the chance-constrained programming method, it has lower requirements for input data information, good decision-making flexibility, and fast calculation speed. Finally, the simulation check is carried out on the day-a-day collaborative scheduling scheme, which verifies that the day-a-day collaborative scheduling scheme proposed by the present invention can better absorb new energy, reduce resource waste, and can be used in uncertain scenarios. Taking into account the economy and safety of system operation.

Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Those skilled in the art to which the present invention pertains can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention should be determined according to the claims.

Claims (9)

1. A day-ahead and day-in cooperative scheduling method for a power system considering uncertainty of new energy and load intervals is characterized by comprising the following steps:

step 1, acquiring power system data and new energy and load day-ahead prediction data;

step 2, constructing a mathematical model of a scheduling interval optimization problem in the day ahead;

step 3, determining a day-ahead scheduling scheme and boundary conditions of day-in scheduling;

step 4, acquiring new energy and load rolling prediction data in the day; and

step 5, constructing a mathematical model of the in-day scheduling problem and solving the in-day scheduling scheme based on the boundary conditions of the in-day scheduling scheme determined in the step 3 and the model of the new energy and load in-day intervals acquired in the step 4;

the power system data comprises maximum and minimum output power of a conventional generator set and a quick start-stop unit, start-stop cost of the generator set, an operation cost coefficient, climbing power, minimum start-up and stop time, and A, B, C three types of flexible loads P_ila、P_ilb、P_ilcThe grade number and the elastic coefficient of each grade capable of reducing the maximum capacity, the cost coefficient, the demand response and the maximum accumulated interruption time of the load, wherein the class A flexible load needs to be informed to the user 24h in advance, the class B flexible load needs to be informed to the user 15min-2h in advance, and the class C flexible load needs to be informed to the user 5-15min in advance;

the new energy and load day-ahead prediction data comprise the output power P of a wind power plant and a photovoltaic power plant 24 hours in the future_wt、P_pvThe predicted value per hour and the fluctuation interval of the prediction error before the day, and the system load P of 24 hours in the future_lThe predicted value of each hour and the fluctuation interval of the prediction error before the hour.

2. The method for day-ahead and day-inside collaborative scheduling of an electric power system considering uncertainty of a new energy and a load interval according to claim 1, wherein in the step 2, the process of constructing a mathematical model of a day-ahead scheduling interval optimization problem comprises:

the predicted value before the new energy power generation day is assumed to be

Its prediction error in the day ahead

Located in a section

The upper mark "+" represents the upper boundary of the interval number, and the upper mark "-" represents the lower boundary of the interval number, so that the new energy power generation power P is obtained_N1Has an upper bound of

Lower boundary is

P_N1The model of the number of the day-ahead intervals is

So as to respectively establish a model of the number of the day-ahead intervals of wind power generation and photovoltaic power generation

Assume a predicted value of the load before the day is

Its prediction error in the day ahead

Located in a section

From which the load power P is derived_l1Has an upper bound of

Lower boundary is

The number of the load power in the day-ahead interval model is

Taking the time scale of day-ahead scheduling as 15 minutes and the time scale of day-in-day scheduling as 15 minutes, carrying out linear interpolation on the predicted values of new energy and load every hour, taking the linear interpolation as the day-ahead predicted value every 15 minutes, and carrying out day-ahead scheduling on the basis;

thus, the objective function of the power system day-ahead scheduling optimization problem is described as:

min f₁＝f₁₁+f₁₂+f₁₃+f₁₄ (1)

wherein f is₁₁Representing the operating costs of a conventional generator set, f₁₂Representing the operating cost of the unit, f₁₃Represents a wind curtailment penalty charge, f₁₄The control cost is represented as A, B, C three types of flexible load;

meanwhile, determining the constraint conditions of the day-ahead scheduling optimization problem of the power system comprises the following steps:

active power balance;

restraining the output of a conventional generator and the climbing power;

a conventional generator minimum on-off time constraint;

the cold and hot start of the conventional generator set is restricted;

restraining the maximum and minimum power and the climbing power of the quick start-stop unit;

the minimum starting time and the minimum stopping time of the quick start-stop unit are restricted;

a flexible load constraint;

wind abandon restriction; and

positive and negative standby power constraints;

and (3) forming a mathematical model of the day-ahead scheduling interval optimization problem by the formula (1) and the nine constraint conditions.

3. The method of claim 2, wherein the expression of the objective function is a running cost f of a conventional generator set₁₁Quick start-stop unit operation cost f₁₂And represents a wind curtailment penalty cost f₁₃And A, B, C control cost f of flexible load₁₄The obtaining comprises the following steps:

1) operating costs f of conventional generator sets₁₁：

Representing the starting and stopping cost of a conventional unit;

expressed as the cost of electricity generation;

wherein,

is the number of conventional generators, T₁For scheduling periods, T₁＝96，

Respectively setting a starting control 0-1 variable and a cold starting control 0-1 variable of the ith conventional generator at the moment j;

a "1" indicates that the ith conventional generator receives a start or cold start command at time j,

if the value is 0, no starting or cold starting instruction is given at the moment j;

C_hot,i、C_cold,ithe hot start cost and the cold start cost of the ith conventional generator respectively,

the variable is 0-1, the 1 indicates that the ith conventional generator is in a starting state at the moment j, and the 0 indicates that the ith conventional generator is in a stopping state;

for the power generation cost coefficient of the ith conventional generator,

the power of the ith conventional generator at the moment j;

in order to quickly start and stop the running cost of the unit,

the number of the generators is rapidly started;

for the operation cost coefficient of the ith quick start-stop unit,

the power of the ith station for quickly starting and stopping the unit at the moment j,

the variable 0-1 is controlled for starting the quick start-stop unit,

the number of the start-up and stop units is 1, the ith quick start-up and stop unit receives the start instruction at the moment j,

starting cost of the ith quick start-stop unit is saved;

penalizing costs for wind curtailment, wherein

Wind power cut down for moment j, C_wA penalty factor for wind abandon;

the control expense for A, B, C three types of flexible loads, ns is the number of grades of the flexible loads, wherein

The regulation power of s gear of three flexible loads at the moment of j A, B, C respectively, and the response of the flexible loads has elasticity, so

The number of the intervals is the number of the intervals,

a, B, C, respectively, are cost factors when the flexible loads participate in regulation and control in the s-gear.

4. The electric power system day-ahead-day cooperative scheduling method considering uncertainty of new energy and load intervals according to claim 3, wherein the electric power system day-ahead scheduling optimization problem constraint condition comprises:

active power balance equation

In the formula (2), the left side is the sum of the conventional generator set, the quick start-stop unit and the wind power generation, the right side is the reduction of the total load of the system minus the 3 types of flexible loads, and the active power balance equation is an interval equation;

② restraining the output and climbing power of the conventional generator

In the formula (3)

The minimum and maximum power of the ith conventional generator respectively,

respectively representing the limit values of the downward climbing power and the upward climbing power of the ith conventional generator;

third, the minimum on-off time constraint of the conventional generator

In formulas (5) and (6)

Respectively the minimum starting time and the minimum shutdown time of the ith conventional generator;

fourthly, restraint of cold and hot start of conventional generator set

Equations (7) and (8) are the constraint of the cold start and the hot start of the ith conventional generator at the moment j,

the cold start time of the ith conventional generator is set;

fast start-stop unit maximum and minimum power and climbing power constraint

Equation (9) is the minimum, maximum power constraint for a fast start generator,

respectively the minimum power and the maximum power of the ith quick start-stop unit,

the variable is 0-1, the 1 indicates that the ith quick unit is in a starting state at the moment j, and the 0 indicates that the ith quick unit is in a stopping state; the formula (10) is the power constraint of the rapid unit for climbing downwards and upwards,

respectively representing the limit values of the downward climbing power and the upward climbing power of the ith quick start-stop unit;

sixthly, quickly starting and stopping unit minimum starting time and minimum stopping time constraint

The formula (11) and the formula (12) are respectively the minimum starting time of the ith quick unit at the moment j

Minimum down time

The constraint of (a) to (b),

to stop the continuous running time of the ith fast unit at time j,

stopping the continuous shutdown time of the ith quick unit at the moment j;

seventh, the wind is abandoned to restrain

In the formula (13)

Respectively reducing the wind power at the moment j in the day-ahead scheduling and the lower bound of the wind power fluctuation interval;

flexible load constraint

Wherein, P_ila,s,j、P_ilb,s,j、P_ilc,s,jIs j time AB, C the power is regulated and controlled by three kinds of flexible loads in s gear,

the maximum value of the regulated power of the s gear of A, B, C three types of flexible loads,

and

elastic fluctuation intervals of response coefficients of s-gear of A, B, C three types of flexible loads respectively;

ninthly positive and negative standby power constraints

Wherein,

the positive standby power which can be provided by the conventional unit and the quick unit at the moment j, r is a standby coefficient,

the negative standby power which can be provided by the conventional unit and the quick unit at the moment j,

is a fluctuation interval when the load fluctuates upwards and the wind speed fluctuates downwards,

the fluctuation interval is when the load is downward and the wind speed is upward.

5. The method of claim 4, wherein the determining the day-ahead scheduling scheme and the boundary conditions of day-ahead scheduling comprises:

first, the power system day-ahead scheduling interval optimization problem objective function f described by equation (1)₁For interval functions, the upper and lower bounds are set to f₁ ⁺、f₁ ^-Wherein:

setting the mean value of the interval objective function as

Radius of the objective function of

Converting an interval objective function to F₁＝(1-β₁)f₁ ^m+β₁f₁ ^w，β₁Is a weighting coefficient;

then, the interval inequality constraints determined by the formula (2), the formula (17) and the formula (19) in the power system day-ahead scheduling interval optimization problem are converted into deterministic inequalities under the preset interval possibility, and the interval possibility with work power balance and the establishment of a standby constraint equation is zeta₁₁、ζ₁₂And ζ₁₃Then, according to the interval probability principle, the equations (2), (17) and (19) are respectively converted into:

therefore, the day-ahead scheduling interval optimization problem is converted into the following deterministic problem:

min F₁ (24)

the constraints of formula (24) include formulas (3) - (16), formulas (18), (20), and formulas (21) - (23);

finally, solving the deterministic problem by using a mixed integer linear programming method to obtain a day-ahead scheduling interval optimization scheme, thereby determining boundary conditions of the scheduling problem in the day, namely: the starting state of the conventional unit is kept unchanged, the regulation and control quantity of the A-type flexible load is kept unchanged, and the starting and stopping state of the quick start-stop unit and the flexible loads of the B, C-type flexible loads need to be adjusted in daily scheduling.

6. The method of claim 5, wherein obtaining new energy and load intra-day rolling prediction data comprises obtaining new energy and load intra-day rolling prediction data

Carrying out 1-time ultra-short-term prediction on wind power, photovoltaic power and load power for 2 hours in the future every 15 minutes, wherein the time scale is 15 minutes;

the predicted value in the new energy power generation day is assumed to be

Error of prediction in day

Located in a section

In the method, the new energy power generation power P is obtained_N2Has an upper bound of

Lower boundary is

P_N2The model of the number of intervals in the day is

The model of the day-to-day interval digital model of wind power and photovoltaic power generation is established

Assume a predicted value of load in the day of

Error of prediction in day

Located in a section

From which the load power P is derived_l2Has an upper bound of

Lower boundary is

The number of intervals per day model of the load power is

7. The electric power system day-ahead-day cooperative scheduling method considering uncertainty of new energy and load intervals as claimed in claim 5, wherein the constructing a mathematical model of a day-ahead scheduling problem and solving a day-ahead scheduling scheme comprises:

establishing a mathematical model of the intraday optimization scheduling problem, wherein an objective function is as follows:

f₂＝f₂₁+f₂₂+f₂₃+f₂₄ (25)

for the cost of power generation of conventional generator sets, T₂＝8；

The running cost of the unit is quickly started and stopped;

cost for wind abandon;

determining the constraint condition of the optimization scheduling problem in the day, comprising the following steps:

active power balance;

wind abandon restriction;

positive and negative standby power constraints;

restraining the output of a conventional generator and the climbing power;

restraining the quick start-stop unit; and

flexible load restraint.

8. The electric power system day-ahead-day cooperative scheduling method taking into account uncertainty of new energy and load interval according to claim 7, wherein the constraint condition of the day-ahead optimization scheduling problem comprises:

active power balance equation

Wind abandon restriction

Positive and negative standby power constraints

Wherein,

is a fluctuation interval when the load fluctuates upwards and the wind speed fluctuates downwards,

the fluctuation interval is when the load fluctuates downwards and the wind speed fluctuates upwards;

conventional generator contribution and hill climb power constraints, i.e. constraints determined by said equations (3) - (4);

a fast start-stop train constraint, i.e. a constraint determined by said equations (9) - (12); and

a flexible load constraint equation, i.e., a constraint determined by the equations (15) - (16);

thus, the power system intra-day scheduling interval optimization problem objective function f described by equation (25)₂For the interval function, the upper and lower bounds are set to

Wherein:

setting the mean value of the interval objective function as

Radius of the objective function of

Converting an interval objective function to

β₂Is a weighting coefficient;

the interval possibility degrees of establishment of an active power balance equation (26), a standby power constraint equation (28) and a standby power constraint equation (30) of the scheduling interval optimization problem in the day of the power system are respectively zeta₂₁、ζ₂₂And ζ₂₃Equations (26), (28) and (30) are converted to:

then, according to the interval probability principle, converting the day-ahead scheduling interval optimization problem into the following deterministic problem:

min F₂ (35)

the constraints include formulae (3) to (4), formulae (9) to (12), formulae (15) to (16), formulae (27), (29), (31), and formulae (32) to (34);

substituting the startup state maintenance of the conventional unit and the regulation and control quantity of the A-type flexible load obtained in the step 4 into a boundary condition for calculation, and solving the deterministic problem by using a mixed integer linear programming method to obtain an intra-day scheduling interval optimization scheme;

and generating a day-ahead and day-inside cooperative scheduling scheme at the time k, further judging whether k reaches 96, if so, outputting the day-ahead and day-inside cooperative scheduling scheme, otherwise, making k equal to k +1, and returning to the step 4 for processing until k reaches 96.

9. A day-ahead-day cooperative scheduling system for a power system, which takes new energy and load interval uncertainty into account, is characterized by comprising:

one or more processors;

a memory storing instructions that are operable, when executed by the one or more processors, to cause the one or more processors to perform operations comprising performing a process of any one of claims 1-8.

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