CN112668751B - A method and device for establishing an optimal scheduling model for a unit - Google Patents

Abstract

The invention discloses a method and a device for establishing an optimal dispatch model of a unit, belonging to the field of dispatching of new energy units. The establishment method includes: constructing a random energy output fluctuation sub-interval based on an interval segmented robust optimization method; using historical random energy The predicted output data and actual output data of the station are used to set the segmented robust optimization parameters of each sub-interval; using the segmented robust optimization parameters of each sub-interval, the objective function is to minimize the combustion cost and start-stop cost of thermal power units, combined with constraints , to establish a unit optimal scheduling model. The present invention can better balance the robustness and economy of the operation of the power system while considering the random energy fluctuations in the sub-intervals. At the same time, the present invention uses the historical sample probability information to set the sub-interval segmented robust optimization parameters. The method only needs to set the segment coefficient to determine the maximum and minimum number of uncertain variables in each sub-interval, so as to make The optimization results are more objective.

Description

A method and device for establishing an optimal scheduling model for a unit

technical field

The invention belongs to the field of dispatching of new energy generating units, and more particularly relates to a method and device for establishing an optimal dispatching model for generating units.

Background technique

At present, large-scale new energy grid connection has brought great challenges to the operation and dispatch of the power system. To study the power system unit combination model and method that can cope with the uncertainty of new energy power generation, the large-scale grid connection of new energy power generation and the guarantee The safe and stable operation of the power system is of great significance. There are two main methods for dealing with the randomness of stochastic energy, one is the method based on random energy power prediction, and the other is the method based on the optimization problem with uncertain variables.

Stochastic programming is a method for solving optimization problems with uncertain variables. It considers that the change law of uncertain quantities obeys a certain probability distribution, and solves it by probabilistic modeling of constraints or objective functions. Conventional probability distribution functions are difficult to accurately describe the distribution characteristics of wind speed. The proposal of the scene generation method and the scene reduction method provides a new idea for the description and modeling of uncertain factors such as random energy in the power system. Although the scenario-based uncertainty modeling method can describe the uncertainty factors in the system, it is limited by the calculation scale, and it is difficult to clearly describe the degree of uncertainty expressed by the reduced scenarios.

Robust optimization is another mature theory for solving optimization problems with uncertain variables. The basic idea of solving optimization problems with uncertain variables in power systems is to give an uncertain set containing all possible values of uncertain variables, and then find a feasible solution for all possible values in the uncertain set. At present, the existing robust optimization can only consider the limit of the random energy fluctuation interval in solving the unit scheduling problem. Although the uncertainty parameter Γ _i can be used to adjust the robustness of the optimization result, the setting of the uncertainty parameter Γ _i is too high. Subjective, it cannot effectively take into account the robustness and economy of power system operation. Therefore, there is an urgent need for a robust optimization method that can simultaneously consider the limit and general conditions in the random energy fluctuation range, so as to more objectively dispatch units and take into account the robustness and economy of power system operation.

SUMMARY OF THE INVENTION

Aiming at the defects of the prior art, the purpose of the present invention is to provide a method and device for establishing an optimal dispatching model for units, which aims to solve the problem that in the current optimal dispatching of units considering the uncertainty of random energy, only the output fluctuation of random energy can be considered. In extreme cases, it is impossible to objectively adjust the robustness and economy of power system operation.

In order to achieve the above purpose, the present invention provides a method for establishing an optimal scheduling model for a unit, comprising the following steps:

S1: Based on the interval segmented robust optimization method, the output fluctuation interval of random energy is represented by segments, and a sub-interval of random energy output fluctuation is constructed;

S2: Use the historical predicted output and historical actual output data of random energy stations to set sub-interval robust optimization parameters;

S3: Use the segmented robust optimization parameters and dual variables of each sub-interval to establish linearization constraints, and take the minimum sum of the combustion cost and the start-stop cost of the thermal power unit as the objective function to establish the unit optimization scheduling model.

Preferably, step S3 specifically includes:

Establish an objective function that minimizes the sum of fuel cost and start-stop cost of thermal power units, and use sub-interval segmented robust optimization parameters to establish deterministic constraints;

Use piecewise linearization method to linearize the objective function;

By introducing dual variables, the positive spinning reserve constraint and the negative spinning reserve constraint are transformed and linearized to complete the optimal scheduling model of the unit.

Preferably, the sub-interval segmented robust optimization parameters include an upper limit and a lower limit of the number of deviation multiples of each sub-interval.

Preferably, the method for obtaining sub-interval segmented robust optimization parameters includes the following steps:

Compare the historical actual output of the random energy station with the historical predicted output to obtain the historical deviation multiple of the output of the random energy station;

Compare the number of deviation multiples in each time period in the sub-interval segment, and filter out the maximum and minimum values of the deviation multiples corresponding to each sub-interval;

Divide the maximum and minimum number of deviation multiples by the time period to obtain the upper and lower limits of the number of deviation multiples for each sub-interval. Preferably, the constraints include power balance constraints, upper and lower limits of unit output, unit ramp constraints, unit start-stop logic constraints, positive spinning reserve constraints and negative spinning reserve constraints.

Preferably, the output fluctuation interval of the random energy source is expressed as follows:

为第k个随机能源场站在第t时段的出力偏差，且满足条件：

为第k个随机能源场站在第t时段的实际出力；

为第k个随机能源场站在第t时段的预测出力；

M为区间分段鲁棒优化方法的分段系数；Q为m的编号集合；N_w为系统中随机能源场站的个数；T为总调度时段。in,

is the output deviation of the kth random energy station in the tth period, and it satisfies the conditions:

is the actual output of the kth random energy station in the tth period;

Output for the prediction of the kth random energy station in the tth period;

M is the segment coefficient of the interval segment robust optimization method; Q is the numbered set of m; N _w is the number of random energy stations in the system; T is the total dispatch period.

Preferably, the linearized objective function is:

δ _l,g,t ≤H _l,g -H _{l-1,g ,} l∈NLg ,

δ _{l,g,t ≥0} ,l∈NLg ,

为机组g的出力下限；

为机组g的出力上限；f_G为机组燃料成本；N_G系统中火电机组集合；a_g、b_g、c_g为火电机组二次、一次、常数成本系数；

为第g台火电机组在第t时刻的出力；u_g,t为第g台火电机组在第t时刻的运行状态。Among them, δ _{l, g, t} is an additional variable, representing the output power of unit g in the first section of time period t; NL _g is the segment number of the piecewise linearization of the fuel cost characteristic curve of unit g; A _g is the unit g in the The minimum fuel cost in the starting state; F _l,g is the slope of the fuel cost quadratic curve of the unit g in the first section; H _l,g is the segment point of the first section of the unit g;

is the lower output limit of unit g;

is the output upper limit of unit g; f _G is the fuel cost of the unit; the set of thermal power units in the _NG system; a _g , b _g , and c _g are the secondary, primary and constant cost coefficients of thermal power units;

is the output of the g-th thermal power unit at the t-th moment; u _g,t is the operating state of the g-th thermal power unit at the t-th moment.

Preferably, the positive spinning reserve constraint translated into a deterministic constraint is:

为对偶变量；

为t时段系统的正旋转备用容量需求；

为时段t全系统的负荷需求；u_g,t为第g台火电机组在第t时刻的运行状态；

为第k个随机能源场站在第t时段的出力偏差；

为第k个随机能源场站在第t时段的预测出力；

为机组g的出力上限；

和

分别为偏差倍数在各子区间分段的个数上限和下限。in,

is a dual variable;

is the positive spinning reserve capacity requirement of the system in period t;

is the load demand of the whole system in the period t; u _g,t is the operating state of the gth thermal power unit at the tth time;

is the output deviation of the kth random energy station in the tth period;

Output for the prediction of the kth random energy station in the tth period;

is the output upper limit of unit g;

and

are the upper and lower limits of the number of segments of the deviation multiple in each sub-interval, respectively.

Based on the above-mentioned method for establishing an optimal dispatching model for a unit, the present invention provides a corresponding device for establishing an optimal dispatching model for a unit, including: a sub-interval construction module, a parameter setting module and a model construction module connected in sequence;

The sub-interval building module is used to represent the output fluctuation interval of random energy in segments based on the interval segment robust optimization method, and construct a random energy output fluctuation sub-interval;

The parameter setting module is used to set the segmented robust optimization parameters of each sub-interval by using the historical predicted output data of the random energy station and the historical actual output data of the random energy station;

The model building module is used to establish the linearization constraints by using the segmented robust optimization parameters and dual variables of each sub-interval, and establish the optimal scheduling model of the unit with the minimum sum of the combustion cost and the start-stop cost of the thermal power unit as the objective function.

Preferably, the model establishment module includes an objective function establishment unit, a constraint condition processor, a linearization processor and a converter; the objective function establishment unit is connected with the linearization processor; the converter is connected with the constraint condition processor;

The objective function establishment module is used to establish the objective function that minimizes the sum of the fuel cost and the start-stop cost of the thermal power unit;

The constraint condition processor is used to use each sub-interval segmented robust optimization parameters to establish deterministic constraints;

The linearization processor is used to linearize the objective function using a piecewise linearization method;

The converter is used to linearize the positive spinning reserve constraint and the negative spinning reserve constraint by introducing dual variables to establish the optimal scheduling model of the unit.

Preferably, the sub-interval segmented robust optimization parameters include an upper limit and a lower limit of the number of deviation multiples of each sub-interval.

Preferably, the constraints include power balance constraints, upper and lower limits of unit output, unit ramp constraints, unit start-stop logic constraints, positive spinning reserve constraints and negative spinning reserve constraints.

Preferably, the output fluctuation interval of the random energy source is expressed as follows:

为第k个随机能源场站在第t时段的出力偏差，且满足条件：

为第k个随机能源场站在第t时段的实际出力；

为第k个随机能源场站在第t时段的预测出力；

in,

is the output deviation of the kth random energy station in the tth period, and it satisfies the conditions:

is the actual output of the kth random energy station in the tth period;

Output for the prediction of the kth random energy station in the tth period;

Preferably, the linearized objective function is:

δ _l,g,t ≤H _l,g -H _{l-1,g ,} l∈NLg ,

δ _{l,g,t ≥0} ,l∈NLg ,

为机组g的出力下限；

为机组g的出力上限；f_G为机组燃料成本；N_G系统中火电机组集合；a_g、b_g、c_g为火电机组二次、一次、常数成本系数；

为第g台火电机组在第t时刻的出力；u_g,t为第g台火电机组在第t时刻的运行状态。Among them, δ _{l, g, t} is an additional variable, representing the output power of unit g in the first section of time period t; NL _g is the segment number of the piecewise linearization of the fuel cost characteristic curve of unit g; A _g is the unit g in the The minimum fuel cost in the starting state; F _l,g is the slope of the fuel cost quadratic curve of the unit g in the first section; H _l,g is the segment point of the first section of the unit g;

is the lower output limit of unit g;

is the output upper limit of unit g; f _G is the fuel cost of the unit; the set of thermal power units in the _NG system; a _g , b _g , and c _g are the secondary, primary and constant cost coefficients of thermal power units;

is the output of the g-th thermal power unit at the t-th moment; u _g,t is the operating state of the g-th thermal power unit at the t-th moment.

Preferably, the positive spinning reserve constraint translated into a deterministic constraint is:

为对偶变量；

为t时段系统的正旋转备用容量需求；

为时段t全系统的负荷需求；u_g,t为第g台火电机组在第t时刻的运行状态；

为第k个随机能源场站在第t时段的出力偏差；

为第k个随机能源场站在第t时段的预测出力；

为机组g的出力上限；

和

分别为偏差倍数在各子区间分段的个数上限和下限。in,

is a dual variable;

is the positive spinning reserve capacity requirement of the system in period t;

is the load demand of the whole system in the period t; u _g,t is the operating state of the gth thermal power unit at the tth time;

is the output deviation of the kth random energy station in the tth period;

Output for the prediction of the kth random energy station in the tth period;

is the output upper limit of unit g;

and

are the upper and lower limits of the number of segments of the deviation multiple in each sub-interval, respectively.

The method for establishing an optimal scheduling model of a unit disclosed in the present invention can be stored in a computer-readable storage medium, and when a computer program is executed by a processor, the method for establishing an optimal scheduling model for a unit provided by the present invention can be implemented.

Through the above technical solutions conceived by the present invention, compared with the prior art, the following beneficial effects can be achieved:

The invention provides a method for establishing an optimal dispatching model of a unit. The method is based on an interval segmented robust optimization method. The output fluctuation interval of random energy is represented in segments to construct a random energy output fluctuation sub-interval; at the same time, the historical random energy field is used. According to the predicted output and actual output of the station, set the sub-interval robust optimization parameters, and use the sub-interval sub-interval robust optimization parameters to establish positive spinning reserve and negative spinning reserve constraints based on the sub-intervals of random energy station output fluctuations. In the positive spinning reserve constraint and the negative spinning reserve constraint, the random energy station output fluctuation sub-interval is used to replace the random energy station output, which realizes the transformation of the uncertain spinning reserve constraint into the deterministic spinning reserve constraint. In addition, the dual variable is introduced based on the dual transformation, and the nonlinear part of the deterministic spinning reserve constraint is converted into a linear part, which realizes the transformation from the deterministic spinning reserve constraint with nonlinear part to the deterministic linear spinning reserve constraint. Compared with the existing uncertain spinning reserve constraint, the deterministic linear spinning reserve constraint can avoid the waste of fuel cost of thermal power units caused by excessive spinning reserve, and effectively improve the operating economy of the system; in addition, the deterministic linear spinning reserve constraint can avoid System load loss and abandoned scenery caused by insufficient spinning reserve effectively improve the robustness of system operation. Therefore, the present invention can better balance the robustness and economy of the operation of the power system while considering the random energy fluctuations in the sub-intervals.

Based on the interval segmented robust optimization method, the invention expresses the output fluctuation interval of random energy in segments, covers the extreme and general situations of random energy fluctuations, and makes up for the limit robust optimization and Seng-CheolKang robust optimization only. The insufficiency of being able to account for extreme fluctuations in stochastic energy.

The present invention adopts the historical sample probability information for sub-interval segment robust optimization parameter setting method, the method only needs to set segment coefficients to determine the maximum and minimum number of uncertain variables in each sub-interval, avoiding the traditional The interval piecewise robust optimization has the disadvantage that too many parameters need to be set, and makes the optimization results more objective.

Description of drawings

Fig. 1 is the flow chart of the establishment method of the unit optimization scheduling model provided by the present invention;

Fig. 2 is the schematic diagram of the unit output comparison of SCK-RO and MBU-RO in the scheme one provided by the embodiment;

Fig. 3 is the schematic diagram of the system rotation standby of SCK-RO and MBU-RO in scheme one provided by the embodiment;

4 is a schematic diagram showing the comparison of system operating costs of CRO, SCK-RO and MBU-RO in scheme one provided by the embodiment;

Fig. 5 (a) is the average abandoned air volume comparison diagram of CRO, SCK-RO and MBU-RO in the scheme one provided by the embodiment;

Fig. 5(b) is a comparison diagram of the average number of abandoned winds of CRO, SCK-RO and MBU-RO in the scheme one provided by the embodiment;

Fig. 5 (c) is the average adjustment amount comparison diagram of CRO, SCK-RO and MBU-RO in the scheme one provided by the embodiment;

Fig. 6 is the system operating cost comparison schematic diagram of SCK-RO and MBU-RO in scheme 2 provided by the embodiment;

Figure 7 (a) the comparison diagram of the average abandoned air volume of CRO, SCK-RO and MBU-RO in the second scheme provided by the embodiment;

Fig. 7(b) is a comparison chart of the average number of wind abandonment times of CRO, SCK-RO and MBU-RO in scheme 2 provided by the embodiment;

Figure 7(c) is a comparison diagram of the average adjustment amount of CRO, SCK-RO and MBU-RO in the second solution provided by the embodiment.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

As shown in Figure 1, the present invention proposes a method for establishing an optimal scheduling model of a unit, including the following steps:

S1: Based on the interval segmented robust optimization method, the output fluctuation interval of random energy is represented by segments, and a sub-interval of random energy output fluctuation is constructed;

Specifically:

The random energy output can be expressed as:

为第k个随机能源场站在第t时段的实际出力；

为第k个随机能源场站在第t时段的预测出力；

为

相对于

的偏差倍数；

d^-M为负偏差百分比；d^M为正偏差百分比；

的区间形式表示如下：Among them, N _w is the number of random energy stations; T is the total dispatch period;

is the actual output of the kth random energy station in the tth period;

Output for the prediction of the kth random energy station in the tth period;

for

relative to

the deviation multiple;

d- ^M is the negative deviation percentage; d ^M is the positive deviation percentage;

The interval form of is as follows:

相对于

的偏差百分比区间；基于区间分段鲁棒优化方法，[d^-M,d^M]可表示为如下多个子区间形式：where [d - ^M ,d ^M ] is called

relative to

The deviation percentage interval of ; based on the interval piecewise robust optimization method, [d ^-M ,d ^M ] can be expressed as the following multiple sub-interval forms:

d ^-M <…<d ^m-1 <d ^m <…<d ^M

Among them, M is the segment coefficient of the interval piecewise robust optimization method, m is the number of each subinterval, and there is m∈{Q|-M,…,-1,0,1,…,M}, and Q is the numbered set of m;

对于

未发生偏差；基于以上，

的子区间形式可以表示为：When m=-M, the subinterval is a single number d- ^M ; when m∈{-M+1,...,-1,0,1,...,M}, the subinterval is (d ^m-1 , d ^m ]; in particular, when m=0, d ⁰ =0, i.e.

for

No deviation occurred; based on the above,

The subinterval form of can be expressed as:

当

时，

相对于

未发生偏差；当

时，

相对于

发生偏差，因此，

可简化表示为：in,

when

hour,

relative to

No deviation occurs; when

hour,

relative to

deviation occurs, therefore,

It can be simplified as:

为第k个随机能源场站在第t时段的出力偏差上限，且满足下面的条件：in,

is the upper limit of the output deviation of the kth random energy station in the tth period, and meets the following conditions:

The above completes the output fluctuation interval segmentation of random energy, and constructs the sub-interval of random energy output;

S2: Set sub-interval segment robust optimization parameters:

属于子区间(1+d^m-1,1+d^m]的偏差倍数的个数下限，定义u_m代表

属于子区间(1+d^m-1,1+d^m]的偏差倍数的个数上限，并且有0<l_m<u_m<N_WT；基于

的历史数据

即可确定每个子区间(1+d^m-1,1+d^m]的l_m与u_m值；假设

是

的历史样本，

是

的历史样本，d是样本天的编号，D是样本天的集合，确定l_m与u_m的具体步骤如下：The segmentation coefficient M divides [d - ^M , _d ^M ] into 2M+1 sub-intervals, and defines lm to represent

The lower limit of the number of deviation multiples belonging to the sub-interval (1+d ^m-1 , 1+d ^m ], which defines _um to represent

The upper limit of the number of deviation multiples belonging to the subinterval (1+d ^m-1 , 1+d ^m ], and 0<l _m <u _m <N _W T; based on

historical data of

The lm and _um values of each subinterval (1+d ^m-1 , 1+ _d ^m ] can be determined; suppose

Yes

historical samples,

Yes

The historical samples of , _{d is the number of the sample day, D is the set of sample days, the specific steps to determine lm and um are} as follows:

与

的值采用下列计算

的历史值

S2.1: Based on

and

The value of is calculated using the following

historical value of

的个数N^d,m，并筛选出各子区间对应的偏差倍数个数的最大值

和最小值

和

代表了每个子区间(1+d^{m -1},1+d^m]在时段中

的个数上限与个数下限；一般时段以一天作为一个时段；S2.2: Calculate each time period in each sub-interval (1+d ^m-1 ,1+d ^m ]

N ^d,m , and filter out the maximum number of deviation multiples corresponding to each sub-interval

and minimum

and

represents each subinterval (1+d ^{m -1} ,1+d ^m ] in the period

The upper limit and lower limit of the number of ; the general period takes one day as a period;

与最小发生概率

若时段以一天为基准，则T＝24h；S2.3: Calculate the maximum probability of occurrence of each sub-interval (1+d ^m-1 , 1+d ^m ] within the time period

with minimum probability of occurrence

If the time period is based on one day, then T=24h;

S2.4: Calculate the lm and _um values of the deviation multiples of each sub-interval (1+d ^m-1 , 1+ _d ^m ];

It can be seen from the above steps that based on the historical data of random energy, the parameter setting of interval segment robust optimization is completed. It can be intuitively found that the values of lm and _um can be determined only by setting the segment coefficient _M ; _m and _um are determined by the historical probability information of random energy, which makes the result of robust optimization more objective;

S3: Based on the uncertainty of stochastic energy output based on the interval piecewise robust optimization method, an interval piecewise robust model is established;

Based on the uncertainty of stochastic energy output of the interval piecewise robust optimization method, the objective function of the established unit optimization model is to minimize the fuel cost and start-stop cost of thermal power units; the constraints of the objective function include power balance constraints, unit output Upper and lower limit constraints, unit climbing constraints, unit start-stop logic constraints, and positive and negative rotation reserve constraints;

The objective function is as follows:

f _UC =min(f _G +f _C )

系统中火电机组集合；a_g、b_g、c_g为火电机组二次、一次、常数成本系数；

为第g台火电机组在第t时刻的出力；u_g,t为第g台火电机组在第t时刻的运行状态；

为第g台火电机组的总开机次数；

第g台火电机组在第t时刻开机成本；

第g台火电机组的总关机次数；

第g台火电机组在第t时刻关机成本；In the above objective function, f _UC is the total cost of system operation; f _G is the fuel cost of the unit; f _C is the start and stop cost of the unit;

Set of thermal power units in the system; a _g , b _g , c _g are the secondary, primary and constant cost coefficients of thermal power units;

is the output of the g-th thermal power unit at the t-th moment; u _g,t is the operating state of the g-th thermal power unit at the t-th moment;

is the total startup times of the gth thermal power unit;

The startup cost of the gth thermal power unit at the tth moment;

The total shutdown times of the gth thermal power unit;

The shutdown cost of the gth thermal power unit at the tth time;

In order to reduce the difficulty of solving the optimal scheduling model of the unit, the piecewise linearization method is used to linearize the objective function, and the objective function is reduced from the quadratic objective function to the primary objective function;

δ _l,g,t ≤H _l,g -H _{l-1,g ,} l∈NLg ,

δ _{l,g,t ≥0} ,l∈NLg ,

为机组g的出力下限；

为机组g的出力上限；f_G为机组燃料成本；N_G系统中火电机组集合；a_g、b_g、c_g为火电机组二次、一次、常数成本系数；

为第g台火电机组在第t时刻的出力；u_g,t为第g台火电机组在第t时刻的运行状态。Among them, δ _{l, g, t} is an additional variable, representing the output power of unit g in the first section of time period t; NL _g is the segment number of the piecewise linearization of the fuel cost characteristic curve of unit g; A _g is the unit g in the The minimum fuel cost in the power-on state; F _l,g is the slope of the fuel cost quadratic curve of the unit g in the first section; H _l,g is the segment point of the first section of the unit g;

is the lower output limit of unit g;

is the output upper limit of unit g; f _G is the fuel cost of the unit; the set of thermal power units in the _NG system; a _g , b _g , and c _g are the secondary, primary and constant cost coefficients of thermal power units;

is the output of the g-th thermal power unit at the t-th moment; u _g,t is the operating state of the g-th thermal power unit at the t-th moment.

Restrictions:

Power Balance Constraints:

为时段t全系统的负荷需求；in,

is the load demand of the whole system in period t;

The power balance constraint requires that in the time period t, the sum of the total output of thermal power units and the total output of random energy in the system is equal to the system load demand;

Unit output upper and lower limit constraints:

为机组g的出力下限；

为机组g的出力上限；该约束条件要求火电机组的出力满足机组出力上下限；in,

is the lower output limit of unit g;

is the output upper limit of unit g; this constraint requires that the output of the thermal power unit meets the upper and lower output limits of the unit;

Crew climbing constraints:

为火电机组g的向下爬坡功率；

为火电机组g的向上爬坡功率；机组爬坡约束的物理含义为：机组的在前后时段的功率变化量满足机组自身的爬坡功率；in,

is the downhill power of the thermal power unit g;

is the upward climbing power of the thermal power unit g; the physical meaning of the unit's climbing constraint is: the power change of the unit in the front and rear periods meets the unit's own climbing power;

Unit start-stop logic constraints:

为机组g的连续运行时间；

为机组g的最小连续运行时间；

为机组g的连续停机时间；

为机组g的最小连续停机时间；机组启停逻辑约束的物理含义为：在

时，机组状态必开机；在

时，机组状态必关机；除此之外的情况不强制限制机组状态；in,

is the continuous running time of unit g;

is the minimum continuous running time of unit g;

is the continuous shutdown time of unit g;

is the minimum continuous shutdown time of unit g; the physical meaning of the unit start-stop logic constraint is:

When the unit is in state, it must be turned on; when

When the genset status is turned off; otherwise, the genset status is not forcibly limited;

Positive Spinning Alternate Constraints (Indeterminate Constraints):

为t时段系统的正旋转备用容量需求；正旋转备用约束中含有随机能源出力

正旋转备用约束表征了随机能源的不确定性，其物理含义是所有在线火电机组的出力上限与随机能源出力之和大于负荷与正旋转备用容量需求之和；in,

is the positive spinning reserve capacity requirement of the system in period t; the positive spinning reserve constraint includes random energy output

The positive spinning reserve constraint represents the uncertainty of random energy, and its physical meaning is that the sum of the output upper limit of all online thermal power units and the random energy output is greater than the sum of the load and the positive spinning reserve capacity demand;

Negative Spinning Spare Constraint (Indeterminate Constraint):

为t时段系统的负旋转备用容量需求；负旋转备用约束中含有随机能源出力

因此负旋转备用约束表征了随机能源的不确定性；负旋转备用约束的物理含义为：所有在线火电机组的出力下限与随机能源出力之和小于负荷与负旋转备用容量需求之差；in,

is the negative spinning reserve capacity requirement of the system in period t; the negative spinning reserve constraint contains random energy output

Therefore, the negative spinning reserve constraint represents the uncertainty of random energy; the physical meaning of the negative spinning reserve constraint is: the sum of the output lower limit of all online thermal power units and the output of random energy is less than the difference between the load and the negative spinning reserve capacity demand;

S4: Convert the positive spinning reserve constraints and negative spinning reserve constraints with random energy into deterministic constraints based on the interval piecewise robust optimization method;

带入

得到如下公式：Taking the positive spinning reserve constraint as an example, set the

bring in

The following formula is obtained:

取最小值时上述公式仍成立，从而将

等价为：The premise of interval segmented robust optimization is that the above formula can also be guaranteed under the worst fluctuations of random energy, so in

The above formula still holds when the minimum value is taken, so that the

Equivalent to:

中随机能源场站的个数满足该区间随机能源场站个数上下限；Among them, DEV _t is the maximum total deviation of random energy fluctuations; satisfying the constraints of formula (1) can realize each sub-interval

The number of random energy stations in the interval meets the upper and lower limits of the number of random energy stations in the interval;

分别代表公式(1)～(3)的对偶变量，获取正旋转备用约束的线性化形式：introduce

Represent the dual variables of formulas (1) to (3) respectively, and obtain the linearized form of the positive rotation reserve constraint:

获取负旋转备用约束的线性化形式：The same principle, introducing dual variables

Obtain the linearized form of the negative spinning reserve constraint:

After linearizing the positive and negative rotation reserve constraints, an interval piecewise robust model is obtained;

S5: Solve the unit optimal scheduling model for unit scheduling.

Based on the above-mentioned method for establishing an optimal dispatching model for a unit, the present invention provides a corresponding device for establishing an optimal dispatching model for a unit, including: a sub-interval construction module, a parameter setting module and a model construction module connected in sequence;

The sub-interval building module is used to represent the output fluctuation interval of random energy in segments based on the interval segment robust optimization method, and construct a random energy output fluctuation sub-interval;

The parameter setting module is used to set the segmented robust optimization parameters of each sub-interval by using the historical predicted output data of the random energy station and the historical actual output data of the random energy station;

The model building module is used to establish the linearization constraints by using the segmented robust optimization parameters and dual variables of each sub-interval, and establish the optimal scheduling model of the unit with the minimum combustion cost and start-stop cost of the thermal power unit as the objective function.

Preferably, the model establishment module includes an objective function establishment unit, a constraint condition processor, a linearization processor and a converter; the objective function establishment unit is connected with the linearization processor; the converter is connected with the constraint condition processor;

The objective function establishment module is used to establish the objective function that minimizes the sum of the fuel cost and the start-stop cost of the thermal power unit;

The constraint condition processor is used to use each sub-interval segmented robust optimization parameters to establish deterministic constraints;

The linearization processor is used to linearize the objective function using a piecewise linearization method;

The converter is used to linearize the positive spinning reserve constraint and the negative spinning reserve constraint by introducing dual variables to establish the optimal scheduling model of the unit.

Preferably, the sub-interval segmented robust optimization parameters include an upper limit and a lower limit of the number of deviation multiples of each sub-interval. Preferably, the constraints include power balance constraints, upper and lower limits of unit output, unit ramp constraints, unit start-stop logic constraints, positive spinning reserve constraints and negative spinning reserve constraints.

Preferably, the output fluctuation interval of the random energy source is expressed as follows:

为第k个随机能源场站在第t时段的出力偏差，且满足条件：

为第k个随机能源场站在第t时段的实际出力；

为第k个随机能源场站在第t时段的预测出力；

in,

is the output deviation of the kth random energy station in the tth period, and it satisfies the conditions:

is the actual output of the kth random energy station in the tth period;

Output for the prediction of the kth random energy station in the tth period;

Preferably, the linearized objective function is:

δ _l,g,t ≤H _l,g -H _{l-1,g ,} l∈NLg ,

δ _{l,g,t ≥0} ,l∈NLg ,

为机组g的出力下限；

为机组g的出力上限；f_G为机组燃料成本；N_G系统中火电机组集合；a_g、b_g、c_g为火电机组二次、一次、常数成本系数；

为第g台火电机组在第t时刻的出力；u_g,t为第g台火电机组在第t时刻的运行状态。Among them, δ _{l, g, t} is an additional variable, representing the output power of unit g in the first section of time period t; NL _g is the segment number of the piecewise linearization of the fuel cost characteristic curve of unit g; A _g is the unit g in the The minimum fuel cost in the power-on state; F _l,g is the slope of the fuel cost quadratic curve of the unit g in the first section; H _l,g is the segment point of the first section of the unit g;

is the lower output limit of unit g;

is the output upper limit of unit g; f _G is the fuel cost of the unit; the set of thermal power units in the _NG system; a _g , b _g , and c _g are the secondary, primary and constant cost coefficients of thermal power units;

is the output of the g-th thermal power unit at the t-th moment; u _g,t is the operating state of the g-th thermal power unit at the t-th moment.

Preferably, the positive spinning reserve constraint translated into a deterministic constraint is:

为对偶变量；

为t时段系统的正旋转备用容量需求；

为时段t全系统的负荷需求；u_g,t为第g台火电机组在第t时刻的运行状态；

为第k个随机能源场站在第t时段的出力偏差；

为第k个随机能源场站在第t时段的预测出力；

为机组g的出力上限；

和

分别为偏差倍数在各子区间分段的个数上限和下限。in,

is a dual variable;

is the positive spinning reserve capacity requirement of the system in period t;

is the load demand of the whole system in the period t; u _g,t is the operating state of the gth thermal power unit at the tth time;

is the output deviation of the kth random energy station in the tth period;

Output for the prediction of the kth random energy station in the tth period;

is the output upper limit of unit g;

and

are the upper and lower limits of the number of segments of the deviation multiple in each sub-interval, respectively.

The method for establishing an optimal scheduling model of a unit disclosed in the present invention can be stored in a computer-readable storage medium, and when a computer program is executed by a processor, the method for establishing an optimal scheduling model for a unit provided by the present invention can be implemented.

Implementation

In order to reduce the conservatism of limit robust optimization and take into account the economics of power system operation, the paper "Research on the optimization method of power system operation with large-scale wind farms" applies Seng-Cheol Kang robust optimization to power system unit scheduling. However, the Seng-Cheol Kang robust optimization model can only consider the limit case of each uncertain variable, and cannot consider the general situation inside the fluctuation interval of the uncertain variable.

However, the present invention considers in detail the limit situation and general situation when random energy output fluctuates, divides the traditional robust interval, and performs robust optimization based on random energy output fluctuation sub-intervals. The constructed sub-interval effectively reduces the conservatism of traditional robust optimization methods, and is more conducive to finding the balance between robustness and economy of robust unit scheduling results; on this basis, dual theory and quadratic function segmentation are adopted. The linearization method realizes the transformation from nonlinear model to linearized model. The interval segment robust model based on the interval segment robust optimization provided by the present invention is compared and analyzed with the scheduling results of the traditional limit robust optimization model and the Seng-Cheol Kang robust optimization model to further verify the interval segment robustness. Effectiveness of optimization methods.

Based on the method for establishing an interval segment robust model provided by the present invention, two simulation comparison schemes are proposed:

Scheme 1. Scheduling scheme for wind farm system with 10 machines, 39 nodes and 1 wind farm

In this scheme, the limit robust optimization model, the Seng-Cheol Kang robust optimization model and the interval segment robust model provided by the present invention are used to compare and analyze the system operation cost and system robustness after system unit scheduling. Robustness includes testing the average air curtailment, average curtailment times and average regulation amount of the three models of unit dispatching results; system operating costs represent the economy of dispatching results.

Scheme 2. Scheduling scheme for wind farm systems with 54 machines, 118 nodes and 3 wind farms

This scheme is based on the comparative analysis of the Seng-Cheol Kang robust optimization model and the unit scheduling results of the interval segment robust model provided by the present invention based on a large-scale multi-generator power system. It focuses on the analysis of the robustness adjustment ability of the Seng-Cheol Kang robust optimization model and the interval piecewise robust model.

The present invention will be further described in detail below with reference to the embodiments.

This embodiment performs simulation based on MATLAB combined with the CPLEX solver. In the simulation, the random energy is set as wind power, and the prediction error of wind power is 20%, that is, the actual wind power fluctuates in the interval of 0.8 times to 1.2 times of the predicted wind power. The specific simulation scheme is shown in Table 1. Table 2 is an example of interval segmentation of the interval segmentation robust optimization method, where the segmentation coefficient is set to M=2, there are 2M+1=5 sub-intervals, and the values of each sub-interval are shown in Table 2; Table 3 is the scheme Correspondence of subjective parameters between the Seng-Cheol Kang robust model (SCK-RO) and the interval piecewise robust model (MBU-RO). Both the Seng-Cheol Kang robust model (SCK-RO) and the interval segment robust model (MBU-RO) in scheme (1) were simulated for 20 times; the Seng-Cheol Kang robust model (SCK-RO) in scheme 2 ) simulation experiments were performed 3 times, and the interval segment robust model (MBU-RO) simulation experiments were performed 20 times.

Table 1

Table 2

table 3

Figure 2 is a comparison chart of unit output between a Seng-Cheol Kang robust model (SCK-RO) and an interval segmented robust model (MBU-RO). Among them, the Seng-Cheol Kang robust model (SCK-RO) The uncertainty parameter of Γ _t =1, and the segmental coefficient M of the interval piecewise robust model (MBU-RO) is 20. At this time, the two robust models are their respective most conservative cases. It can be seen from Figure 2 that the unit output of the Seng-Cheol Kang robust model (SCK-RO) and the interval piecewise robust model (MBU-RO) is very different in the most conservative case. The generator G5 is the most obvious.

Figure 3 is a comparison diagram of the rotation backup between the Seng-Cheol Kang robust model (SCK-RO) and the interval segment robust model (MBU-RO) in scheme one. It can be seen from Figure 3 that the rotation reserve of the Seng-Cheol Kang robust model (SCK-RO) and the interval piecewise robust model (MBU-RO) is very close, but there is a certain gap in individual time periods. From this, it can be judged that the Seng-Cheol Kang robust model (SCK-RO) and the interval piecewise robust model (MBU-RO) have different rotation reserves in their most conservative cases, namely the Seng-Cheol Kang robust model ( SCK-RO) and the interval piecewise robust model (MBU-RO) in the most conservative case have different capacity in coping with wind power fluctuations.

Figure 4 is a schematic diagram showing the comparison of system operating costs of the extreme robust model (CRO), the Seng-Cheol Kang robust model (SCK-RO) and the interval segment robust model (MBU-RO). Figure 5 is a schematic diagram of the robustness comparison of scheduling schemes between the limit robust model (CRO), the Seng-Cheol Kang robust model (SCK-RO) and the interval segment robust model (MBU-RO) (Figure 5(a) is a Comparison chart of average abandoned air volume;

Figure 5(b) is a comparison chart of the average number of abandoned winds; Figure 5(c) is a comparison chart of the average adjustment amount). From Figure 4 and Figure 5 (Figure 5(a), Figure 5(b) and Figure 5(c)), it can be seen that the unit scheduling result of the limit robust model (CRO) is the Seng-Cheol Kang in the most conservative case The unit scheduling results of the robust model (SCK-RO), by comparing the scheduling results of the Seng-Cheol Kang robust model (SCK-RO) and the interval segment robust model (MBU-RO), it can be seen that although Seng-Cheol The Kang robust model (SCK-RO) can adjust the economy and robustness of the optimization results, but it still cannot obtain the same balance of economy and robustness as the interval piecewise robust model (MBU-RO).

Similarly, Figure 6 is a schematic diagram of the system operating cost comparison between the Seng-Cheol Kang robust model (SCK-RO) and the interval segment robust model (MBU-RO) in the second solution, and Figure 7 is the Seng-Cheol Kang in the second solution. Schematic diagram of the robustness comparison of scheduling methods between the robust model (SCK-RO) and the interval segment robust model (MBU-RO) (Fig. Times comparison chart; Figure 7(c) is the average adjustment amount comparison chart). In the case of multiple wind farms, although the uncertainty parameter Γ _t of the Seng-Cheol Kang robust model (SCK-RO) can be adjusted, there is still a cost waste when the most conservative Γ _t =3. The interval segment robust model (MBU-RO) takes into account the economy and robustness of the unit scheduling results, and there is a balance between the two, which is between the Seng-Cheol Kang robust model (SCK-RO) Γ _t = Optimal operating point between 2 and Γ _t =3.

Through the analysis of the economy and robustness of the scheduling results in the simulation, it can be found that if the interval segmentation is not performed, the operation cost of the power system will be wasted.

Those skilled in the art can easily understand that the above are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention, etc., All should be included within the protection scope of the present invention.

Claims (3)

1. a method for establishing an optimal dispatch model for unit, is characterized in that, comprises the following steps: S1: Construct random energy output fluctuation sub-intervals based on the interval segmented robust optimization method; S2: Use the historical predicted output data and historical actual output data of the random energy station to set the sub-interval robust optimization parameters; S3: Use the segmented robust optimization parameters and dual variables of each sub-interval to establish linearization constraints, and take the minimum combustion cost and start-stop cost of thermal power units as the objective function to establish the unit optimization scheduling model; The step S3 specifically includes: Establish an objective function that minimizes the sum of fuel cost and start-stop cost of thermal power units, and use sub-interval segmented robust optimization parameters to establish deterministic constraints; Use piecewise linearization method to linearize the objective function; By introducing dual variables, the positive spinning reserve constraint and the negative spinning reserve constraint are linearized to complete the optimal scheduling model of the unit; The constraints include power balance constraints, unit output upper and lower limit constraints, unit ramping constraints, unit start-stop logic constraints, positive spinning reserve constraints and negative spinning reserve constraints; Among them, S1 is specifically based on the interval segmented robust optimization method, which represents the output fluctuation interval of random energy in segments, and constructs the output fluctuation sub-interval of random energy; The random energy output is expressed as:

Among them, N _w is the number of random energy stations; T is the total dispatch period;

is the actual output of the kth random energy station in the tth period;

Output for the prediction of the kth random energy station in the tth period;

for

relative to

the deviation multiple;

d- ^M is the negative deviation percentage; d ^M is the positive deviation percentage;

The interval form of is as follows:

where [d - ^M , d ^M ] is called

relative to

The deviation percentage interval of ; based on the interval piecewise robust optimization method, [d - ^M , d ^M ] is expressed in the form of multiple subintervals: d ^-M <...<d ^m-1 <d ^m <...<d ^M Among them, M is the segment coefficient of the interval piecewise robust optimization method, m is the number of each subinterval, and there are m∈{Q|-M,...,-1,0,1,...,M }, Q is the numbered set of m; When m=-M, the subinterval is a single number d- ^M ; when m∈{-M+1,...,-1,0,1,...,M}, the subinterval is (d ^{m -1} , d ^m ];

The subinterval form of is expressed as:

in,

when

hour,

relative to

No deviation occurs; when

hour,

relative to

deviation occurs,

Expressed as:

in,

is the upper limit of the output deviation of the kth random energy station in the tth period, and meets the following conditions:

The above completes the output fluctuation interval segmentation of random energy, and constructs the sub-interval of random energy output; Step S2 sets the sub-interval segmented robust optimization parameters, and the specific steps are: The segmentation coefficient M divides [d ^-M , _d ^M ] into 2M+1 sub-intervals, and defines lm to represent

The lower limit of the number of deviation multiples belonging to the sub-interval (1+d ^m-1 , 1+d ^m ], which defines _um to represent

The upper limit of the number of deviation multiples belonging to the subinterval (1+d ^m-1 , 1+d ^m ], and 0<l _m <u _m <N _W T; based on

historical data of

Determine the lm and _um values for each subinterval (1+d ^m-1 , 1+ _d ^m ]; suppose

Yes

historical samples,

Yes

The historical samples of , _{d is the number of the sample day, D is the set of sample days, the specific steps to determine lm and um are} as follows: S2.1: Based on

and

The value of is calculated using the following

historical value of

S2.2: Calculate each sub-interval (1+d ^m-1 , 1+d ^m ] in each time period

The number N ^{d, m} , and filter out the maximum number of deviation multiples corresponding to each sub-interval

and minimum

and

represents each subinterval (1+d ^m-1 , 1+d ^m ] in the period

The upper limit and the lower limit of the number; S2.3: Calculate the maximum probability of occurrence of each sub-interval (1+d ^m-1 , 1+d ^m ] within the time period

with minimum probability of occurrence

S2.4: Calculate the lm and _um values of the deviation multiples of each sub-interval (1+d ^m-1 , 1+ _d ^m ];

Among them, the introduction of dual variables

Obtain the linearized form of the positive spinning reserve constraint:

Introduce dual variables

Obtain the linearized form of the negative spinning reserve constraint:

After linearizing the positive and negative rotation reserve constraints, an interval piecewise robust model is obtained; Among them, the set of thermal power units in the NG system; u _{g , t} is the operating state of the gth thermal power unit at the t th time;

is the load demand of the whole system in period t; g is the thermal power unit;

is the lower output limit of unit g;

is the output upper limit of unit g;

and

are the upper and lower limits of the number of segments of the deviation multiple in each sub-interval, respectively;

Output for the prediction of the kth random energy station in the tth period;

is the positive spinning reserve capacity requirement of the system in period t;

is the negative spinning reserve capacity requirement of the system during period t. 2. a kind of establishment device based on the establishment method of unit optimization scheduling model, it is characterized in that, comprise successively connected sub-interval building module, parameter setting module and model building module; The sub-interval building module is used to represent the output fluctuation interval of random energy in segments based on the interval segmented robust optimization method, so as to construct a random energy output fluctuation sub-interval; The parameter setting module is used to set the segmented robust optimization parameters of each sub-interval by using the historical predicted output data and historical actual output data of the random energy station; The model establishment module is used for using each sub-interval segmented robust optimization parameters and dual variables to establish linearization constraints, and takes the minimum sum of the combustion cost and the start-stop cost of the thermal power unit as the objective function to establish the unit optimization scheduling model; The model establishment module includes an objective function establishment unit, a constraint condition processor, a linearization processor and a converter; Described objective function establishment unit is connected with described linearization processor; Described converter is connected with described constraint condition processor; The objective function establishment module is used to establish an objective function with the minimum sum of the fuel cost and the start-stop cost of the thermal power unit; The constraint condition processor is configured to use each sub-interval segmented robust optimization parameter to establish a deterministic constraint condition; The linearization processor is used to linearize the objective function by using a piecewise linearization method; The converter is used to linearize the positive spinning reserve constraint and the negative spinning reserve constraint by introducing dual variables to establish an optimal scheduling model for the unit; the constraints include power balance constraints, upper and lower output limits of the unit, ramp constraints of the unit, start and stop of the unit Logical constraints, positive spinning reserve constraints, and negative spinning reserve constraints; Among them, based on the interval segmented robust optimization method, the output fluctuation interval of random energy is represented by segments, and the output fluctuation sub-interval of random energy is constructed; The random energy output is expressed as:

Among them, N _w is the number of random energy stations; T is the total dispatch period;

is the actual output of the kth random energy station in the tth period;

Output for the prediction of the kth random energy station in the tth period;

for

relative to

the deviation multiple;

d- ^M is the negative deviation percentage; d ^M is the positive deviation percentage;

The interval form of is as follows:

where [d - ^M , d ^M ] is called

relative to

The deviation percentage interval of ; based on the interval piecewise robust optimization method, [d - ^M , d ^M ] is expressed in the form of multiple subintervals: d ^-M <...<d ^m-1 <d ^m <...<d ^M Among them, M is the segment coefficient of the interval piecewise robust optimization method, m is the number of each subinterval, and there are m∈{Q|-M,...,-1,0,1,...,M }, Q is the numbered set of m; When m=-M, the sub-interval is a single number d- ^M ; when m∈{-M+1,...,-1,0,1,...,M}, the sub-interval is (d ^{m -1} , d ^m ];

The subinterval form of is expressed as:

in,

when

hour,

relative to

No deviation occurs; when

hour,

relative to

deviation occurs,

Expressed as:

in,

is the upper limit of the output deviation of the kth random energy station in the tth period, and meets the following conditions:

The above completes the output fluctuation interval segmentation of random energy, and constructs the sub-interval of random energy output; Among them, the sub-interval segment robust optimization parameters are set, and the specific steps are: The segmentation coefficient M divides [d ^-M , _d ^M ] into 2M+1 sub-intervals, and defines lm to represent

The lower limit of the number of deviation multiples belonging to the sub-interval (1+d ^m-1 , 1+d ^m ], which defines _um to represent

The upper limit of the number of deviation multiples belonging to the subinterval (1+d ^m-1 , 1+d ^m ], and 0<l _m <u _m <N _W T; based on

historical data of

Determine the lm and _um values for each subinterval (1+d ^m-1 , 1+ _d ^m ]; suppose

Yes

historical samples,

Yes

The historical samples of , _{d is the number of the sample day, D is the set of sample days, the specific steps to determine lm and um are} as follows: based on

and

The value of is calculated using the following

historical value of

Calculate each sub-interval (1+d ^m-1 , 1+d ^m ] in each time period

The number N ^{d, m} , and filter out the maximum number of deviation multiples corresponding to each sub-interval

and minimum

and

represents each subinterval (1+d ^m-1 , 1+d ^m ] in the period

The upper limit and the lower limit of the number; Calculate the maximum probability of occurrence of each subinterval (1+d ^m-1 , 1+d ^m ] within the time period

with minimum probability of occurrence

Calculate the lm and _um values of the deviation multiples of each subinterval (1+d ^m-1 , 1+ _d ^m ];

Among them, the introduction of dual variables

Obtain the linearized form of the positive spinning reserve constraint:

Introduce dual variables

Obtain the linearized form of the negative spinning reserve constraint:

After linearizing the positive and negative rotation reserve constraints, an interval piecewise robust model is obtained; Among them, the set of thermal power units in the NG system; u _{g , t} is the operating state of the gth thermal power unit at the t th time;

is the load demand of the whole system in period t; g is the thermal power unit;

is the lower output limit of unit g;

is the output upper limit of unit g;

and

are the upper and lower limits of the number of segments of the deviation multiple in each sub-interval, respectively;

Output for the prediction of the kth random energy station in the tth period;

is the positive spinning reserve capacity requirement of the system in period t;

is the negative spinning reserve capacity requirement of the system during period t. 3 . A computer-readable storage medium on which a computer program is stored, characterized in that, when the computer program is executed by a processor, the steps of the establishment method of claim 1 are implemented.

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