CN106410780A - New energy source acceptance space discrete probability sequence calculation method - Google Patents

Abstract

The present invention relates to a new energy source acceptance space discrete probability sequence solution method. The method comprises the steps of firstly determining a power-on mode of a thermal power generating unit; bringing the output of a new energy source into the electric power balance of the thermal power generating unit to obtain the daily thermal power generating unit power-on capacity and the minimum technical output data; then considering the correlation of the daily maximum load and the thermal power generating unit minimum technical output to solve the joint probability distribution of the daily maximum load and the thermal power generating unit minimum technical output; in a similar way, solving the joint probability distribution of the daily maximum load and the load; finally, solving the volume difference of a load sequence corresponding to the same daily maximum load discrete values and a thermal power minimum output sequence, solving an acceptance space sequence corresponding to the daily maximum load discrete values, orderly solving the acceptance space sequences corresponding to each daily maximum load discrete value, and then collating, thereby obtaining the acceptance space discrete probability sequence of a system.

Description

New energy acceptance space discrete probability sequence calculation method

Technical Field

The invention relates to a discrete probability sequence calculation method in the field of new energy grid-connected simulation, in particular to a new energy acceptance space discrete probability sequence calculation method.

Background

In recent years, new energy has been rapidly developed worldwide, and some countries have taken the development and utilization of new energy as a major measure for improving energy structure, promoting environmental protection, and maintaining economic and social sustainable development, and actively promote the efficient utilization of new energy. At present, the new energy utilization in China is mainly wind power and photovoltaic power generation.

As an important member of renewable energy, wind energy has become the fastest growing, most promising green energy. Because wind power has the characteristics of randomness, intermittence and the like, the wind power brings huge challenges to the aspects of stability, power quality, scheduling mechanism and the like of a power grid after large-scale grid connection, so that the power grid has to pay corresponding cost while receiving the wind power.

The photovoltaic power generation grid connection relieves the energy crisis and the environmental pressure to a certain extent, and simultaneously brings new challenges to the safe operation of the power system. Different from conventional power generation modes such as thermal power, hydroelectric power and the like, the output of the photovoltaic power station has randomness due to the fluctuation and intermittency of solar radiation, so that the photovoltaic power station is difficult to dispatch and control. In addition, photovoltaic power generation has natural day and night alternation characteristic, and the photovoltaic power station output at night is zero, which has obvious difference with other renewable energy sources such as wind power and the like.

In order to ensure clean and efficient utilization of energy, new energy needs to be preferentially consumed. However, the new energy has inherent volatility and randomness on multiple time scales, the difficulty of the power grid for absorbing the new energy is increased, the new energy cannot be absorbed in all parts of regions, and the receiving space of the new energy must be researched to realize the quantitative calculation of the absorption electric quantity and the limited electric quantity of the new energy.

The receiving space of the new energy is the new energy electric power which can be received by the system at most, in order to ensure that the new energy is received as much as possible and the preferential consumption of the new energy is realized, the firing electric generating set is reduced as much as possible on the premise of ensuring the load requirement and the positive standby of the system, and the thermal power generating set is operated with the minimum technical output as much as possible, so that the receiving space of the new energy is the part of the load exceeding the minimum output of the thermal power generating set. Because the load and the minimum output of the thermal power generating unit are changed along with time, the receiving space of the new energy is also changed along with time.

Disclosure of Invention

In order to solve the defects in the prior art, the invention aims to provide a new energy admission space discrete probability sequence calculation method.

The purpose of the invention is realized by adopting the following technical scheme:

the invention provides a new energy admission space discrete probability sequence calculation method, which is improved in that the method is based on joint probability distribution and comprises the following steps:

step 1: calculating the replaceable capacity of the new energy output participating in the thermal power starting;

step 2: establishing a daily starting mode of the thermal power generating unit, and determining the minimum technical output P of the daily thermal power_min；

And step 3: determining daily maximum load_maxMinimum technical output P of thermal power every day_minA joint probability distribution of (a);

and 4, step 4: determining daily maximum load_maxJoint probability distribution with load;

and 5: and determining a new energy admission space probability sequence by using the volume difference.

Further, the step 1 comprises the following steps:

step 1.1: acquiring and arranging data: acquiring load, new energy (wind power and photovoltaic power generation) output and installation time sequence data of a certain time period, wherein the total data amount is T, the total days of a research period is D, and the data amount of the load, the new energy output and the installation every day is N, so that T is N.D; if the sampling interval of time series data such as load is 1h, N is 24; if the sampling interval is 15min, N is 96;

step 1.2: solving the replaceable capacity of the new energy participating in thermal power starting: prediction error of new energyThe difference is 20%, and 20% of the new energy source predicted value minus the installed energy source participates in the thermal power starting; if the predicted value of the new energy output is more than 20% of the installed capacity of the new energy output, the excess part is brought into the thermal power starting power balance, and the thermal power starting capacity is reduced; otherwise, the new energy does not participate in the starting of the thermal power generating unit, and the replaceable capacity of the new energy participating in the starting of the thermal power generating unit in the period T is researched; c_{renewableenergy}(t) satisfies the following formula:

C_{renewableenergy}(t)＝max(P_{renewableenergy}(t)-Capacity_{renewableenergy}(t)×20％,0)

P_{renewableenergy}(t)＝P_wind(t)+P_PV(t)

Capacity_{renewableenergy}(t)＝Capacity_wind(t)+Capacity_PV(t)

wherein T represents the tth time period in the study period, T is more than or equal to 0 and less than or equal to T, P_{renewableenergy}(t) is a predicted value of new energy output in the t period, Capacity_{renewableenergy}(t) New energy installed Capacity at time t, P_wind(t) is the predicted value of wind power generation at time t, Capacity_wind(t) wind installed capacity at time t, P_PV(t) is the predicted photovoltaic power generation value, Capacity, at time t_PV(t) photovoltaic installed capacity for time period t.

Further, the step 2 comprises the following steps,

step 2.1: calculating the equivalent load eqload and the daily maximum equivalent load eqload_max：C_{renewableenergy}(t) the equivalent load participating in the power balance of the starting of the thermal power generating unit meets the following formula:

eqload(t)＝load(t)-C_{renewableenergy}(t)

wherein eqload (t) is the equivalent load of time period t, load (t) is the load of time period t, C_{renewableenergy}(t) new energy alternative capacity for time period t;

the total amount of data for the eqload is also T,the daily data volume is N, the maximum value of the eqload every day is obtained, namely the daily maximum equivalent load eqload_max，eqload_maxThe total amount of data of (a) is D;

step 2.2: establishing a daily starting mode of the thermal power generating unit, and solving the minimum technical output of daily thermal power:

according to the maximum equivalent load and the positive standby of the system every day, the starting mode of every day can be sequentially solved by combining the parameters of the thermal power generating unit in the system, and the method comprises the following steps:

starting a power set which needs to be started in an electric power system, wherein the power set needs to be started in a heat supply period in order to guarantee heat supply, and the power set does not need to be started in a non-heat supply period;

if the maximum value of the equivalent load in the day plus the maximum output of the power system which is in reserve and is larger than the maximum output of the unit which must be started, the other units are increased; in order to enable the new energy to accept electric quantity as large as possible, under the condition of the same maximum output, the unit with the minimum output is started preferentially, and the starting is stopped until the maximum output of the thermal power unit in the power system meets the daily maximum equivalent load and the system is in reserve;

the thermal power generating unit is characterized in that an objective function expressed by a mixed integer programming model in a daily starting mode is as follows:

min P_min(d)

wherein, P_min(d) Representing the minimum output power of the thermal power generating unit on the day d;

the constraint conditions of the thermal power unit starting mode mixed integer programming model are as follows:

(1) the minimum output of the thermal power unit on the day d is equal to the sum of the minimum outputs of all the thermal power units started on the day d;

wherein, X_j(d) Showing the d-th operating state of the j machine set as a binary systemA variable, wherein 0 represents that the unit is stopped, and 1 represents that the unit is running; TP_j,min(d) The minimum output power of the jth thermal power generating unit on the day d;

(2) the maximum output of the thermal power generating unit when being started every day must ensure the load demand and system standby on the same day, namely:

wherein, TP_j,max(d) Is the maximum output power, eqload, of the jth thermal power unit on the day d_max(d) Is the daily maximum equivalent load on day d, P_reIs a positive backup for the system;

(3) the number of the starting machines of each thermal power generating unit is between the maximum number and the minimum number, namely:

S_j,min(d)≤S_j(d)≤S_j,max(d)

wherein S is_j(d) The number of starting units of the j th thermal power generating unit on the d th day, S_j,min(d) Is the minimum starting number S of the j thermal power generating units on the day d_j,max(d) The maximum starting number of the j thermal power generating units on the day d is obtained;

sequentially calculating the daily starting mode of the thermal power generating unit according to the model, and further calculating the daily minimum technical output P of the thermal power generating unit in the research period_min，P_minThe total amount of data of (a) is D.

Further, the step 3 comprises the following steps:

step 3.1: will load daily maximum load_maxMinimum technical output P of thermal power every day_minRespectively within their range of variation: load_maxAnd P_minThe total amount of data of (a) is D; choose daily maximum load_maxHas a discretization interval length of C₁Daily minimum technical contribution of thermal power P_minHas a discretization interval length of C₂，load_maxAnd P_minThe number of intervals of (A) is L₁And L₂；

Step 3.2: setting a cycle initial value: d is 1, n_ik0, wherein D represents day D, 0. ltoreq. d.ltoreq.D, n_ikIs load_max(d) Falls into its i-th discretization interval, and P_min(d) An approximation to its k-th discretized interval, i 1,2₁，k＝1,2,...,L₂；

Step 3.3: read in daily maximum load on day d_max(d) And the minimum technical output P of thermal power every day_min(d) And the data are classified into corresponding discretization intervals;

step 3.4: d +1, n_ik＝n_ik+1；

Step 3.5: if D is less than or equal to D, turning to step 3.3, otherwise, turning to step 3.6;

step 3.6: determining daily maximum load_maxAverage averageload of all data in each discretized interval_max(i)，i＝1,2...L₁(ii) a Determining daily thermal power minimum technical output P_minAverage averageP of all data in each interval_min(k)，k＝1,2...L₂；

Step 3.7: determining load_maxFalls into its i-th discretization interval, and P_minProbability P of falling into its k-th discretization interval_ik，load_maxAnd P_minThe included discretized interval combinations have L in common₁·L₂And sequentially calculating the probability of each discretization interval combination, and further obtaining the combined probability distribution of the daily maximum load and the daily thermal power minimum output.

Further, the step 4 comprises:

step 4.1: will load daily maximum load_maxIs separately from the load onThe variation range is divided into: load_maxSelecting load with total data D and total data of load T_maxHas a discretization interval length of C₁Selecting the discretization interval length of the load as C₃，load_maxAnd the number of load sections is L respectively₁And L₃；

Step 4.2: setting a cycle initial value, d is 1, n_is0, wherein d represents day d, n_isIs load_max(d) The load (t) of the t-period is classified into the ith discretization interval, i is 1,2₁，s＝1,2,...,L₃；

Step 4.3: read in daily maximum load on day d_max(d) Entering the corresponding interval;

step 4.4: an initial value is set, t ═ N +1 (d-1), at which time t denotes the 1 st period on day d.

Step 4.5: reading load (t) in a period t and classifying the load (t) into a corresponding discretization interval;

step 4.6: t +1, n_is＝n_is+1；

Step 4.7: if t is less than or equal to d.N, turning to the step 4.5, otherwise, turning to the step 4.8;

step 4.8: d is d + 1;

step 4.9: if D is less than or equal to D, turning to the step 4.3, otherwise, turning to the step 4.10;

step 4.10: determining daily maximum load_maxAverage averageload of all data in each discretized interval_max(i)，i＝1,2...L₁(ii) a Determining the average value averageload(s) of all data in each interval of the load, wherein s is 1 and 2₃；

Step 4.11: determining daily maximum load_maxClassified into the ith discretization interval and the minimum daily thermal power technical output P_minFall underProbability P of the s-th discretization interval_is，load_maxThe combination of the discretized interval in which load is included has L₁·L₃And sequentially calculating the probability of each section combination, and further obtaining the combined probability distribution of the daily maximum load and the load.

Further, the step 5 comprises: daily maximum load_maxAverage averageload of all data in each discretized interval_max(i) Discrete probability sequence gP corresponding to daily thermal power minimum technology output_mini，gP_miniIs a discrete power value and the second row is the corresponding probability, i.e.:

gP_mini(1,k)＝averageP_min(k)

wherein,is a load_maxFall under discrete value averageload_max(i) The probability of (d); k represents P_minThe kth discretization interval of (1);

daily maximum load_maxAverage averageload of all data in each discretized interval_max(i) Corresponding load discrete probability sequence gload_iSatisfies the following formula:

gload_i(1,s)＝averageload(s)

for gP_miniAnd gload_iDaily minimum technical output discrete power of thermal power generating unitThe number of all combinations of the value and the load discrete power value is L₂·L₃If in a certain combination, the load discrete power value is gload_i(1, s), the minimum technical output discrete power value of the thermal power generating unit every day is gP_mini(1, k), then the combined new energy acceptance space power gACCOM_i(1, j) satisfies the following formula:

gACCOM_i(1,j)＝gload_i(1,s)-gP_mini(1,k)

the probability of this combination is:

gACCOM_i(2,j)＝gload_i(2,s)·gP_mini(2,k)

determining the new energy admission space power values of all combinations and the probability thereof to obtain averageload_max(i) Corresponding new energy acceptance space discrete probability sequence gACCOM_i；

The above is to make gload_iAnd gP_miniA volume difference operation is performed, expressed as,

due to gACCOM_iIs averageload_max(i) Corresponding admission space probability sequence, in the whole probability space, when load_maxFall under discrete value averageload_max(i) Time admission space discrete probability sequence gACCOM'_iSatisfies the following formula,

gACCOM'_i(1,j)＝gACCOM_i(1,j)

determining gACCOM 'in turn'₁,gACCOM'₂,...,And combining and sequencing to obtain a new energy admission space probability sequence gACCOM.

The technical scheme provided by the invention has the following excellent effects:

(1) the output of the new energy is brought into the starting power balance of the thermal power generating unit, so that the starting capacity of the thermal power generating unit can be reduced, and the new energy can be better consumed.

(2) Different from the method for solving the admission space by directly subtracting the load prediction time sequence data and the corresponding thermal power minimum output time sequence data, the method for solving the admission space by adopting the probability sequence operation reduces the influence of the contingency of the load and the new energy time sequence prediction data on the calculation result of the admission space, and can better reflect the regularity of the admission space.

(3) And (3) fully considering the correlation between the daily maximum load and the minimum technical output of the thermal power generating unit and the correlation between the daily maximum load and the load, and calculating the receiving space sequence corresponding to the daily maximum load discrete value by subtracting the load probability sequence corresponding to the same daily maximum load discrete value from the thermal power minimum output probability sequence. The error caused by direct difference of discrete probability sequences converted by using the load and thermal power minimum output time sequence data is avoided.

Drawings

Fig. 1 is a flowchart of a new energy admission space discrete probability sequence calculation method provided by the present invention.

Detailed Description

The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.

The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. The scope of embodiments of the invention encompasses the full ambit of the claims, as well as all available equivalents of the claims. Embodiments of the invention may be referred to herein, individually or collectively, by the term "invention" merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed.

The invention provides a new energy admission space discrete probability sequence solving method based on joint probability distribution, which comprises 5 steps, and the flow chart is shown in figure 1:

step 1: and solving the replaceable capacity of the new energy which participates in the thermal power starting.

Step 1.1: sorting and acquiring data: the method comprises the steps of obtaining load, new energy (wind power and photovoltaic power generation) output and installation time sequence data of a certain time period (generally one year or several months), wherein the total data amount is T, the total days of a research period is D, the data amount of the load, the new energy output and the installation every day is N, and T is N.D. If the sampling interval of time series data such as load is 1h, N is 24; if the sampling interval is 15min, N is 96.

Step 1.2: solving the replaceable capacity of the new energy participating in thermal power starting: in order to accept new energy as much as possible, the generated power of the new energy at each time interval is brought into the calculation range of the starting capacity of the thermal power generating unit, and the new energy can replace part of thermal power and reduce the starting capacity of the thermal power. At present, the prediction error of new energy is about 20%, so that 20% of the installed energy is subtracted from the predicted value of the new energy to participate in thermal power starting. If the predicted value of the new energy output is more than 20% of the installed capacity of the new energy output, the excess part is brought into the thermal power starting power balance, and the thermal power starting capacity is reduced; otherwise, the new energy does not participate in the starting of the thermal power generating unit. Study period TReplaceable capacity C for enabling internal new energy to participate in thermal power starting_{renewableenergy}(t) satisfies the following formula:

C_{renewableenergy}(t)＝max(P_{renewableenergy}(t)-Capacity_{renewableenergy}(t)×20％,0)

P_{renewableenergy}(t)＝P_wind(t)+P_PV(t)

Capacity_{renewableenergy}(t)＝Capacity_wind(t)+Capacity_PV(t)

wherein T represents the tth time period in the study period, T is more than or equal to 0 and less than or equal to T, P_{renewableenergy}(t) is a predicted value of new energy output in the t period, Capacity_{renewableenergy}(t) New energy installed Capacity at time t, P_wind(t) is the predicted value of wind power generation at time t, Capacity_wind(t) wind installed capacity at time t, P_PV(t) is the predicted photovoltaic power generation value, Capacity, at time t_PV(t) photovoltaic installed capacity for time period t.

Step 2: establishing a daily starting mode of the thermal power generating unit, and determining the minimum technical output P of the daily thermal power_min。

Step 2.1: calculating the equivalent load eqload and the daily maximum equivalent load eqload_max：C_{renewableenergy}(t) the equivalent load participating in the power balance of the starting of the thermal power generating unit meets the following formula:

eqload(t)＝load(t)-C_{renewableenergy}(t)

wherein eqload (t) is the equivalent load of time period t, load (t) is the load of time period t, C_{renewableenergy}(t) is the new energy alternative capacity for the time period t found by step 1.

The total data quantity of the eqload is also T, the data quantity of each day is also N, and the maximum value of the eqload per day is obtained and is the maximum equivalent load per day_max，eqload_maxThe total amount of data of (a) is D.

Step 2.2: and (4) establishing a daily starting mode of the thermal power generating unit, and solving the daily thermal power minimum technical output.

According to the maximum equivalent load and the positive standby of the system every day, the starting mode of every day can be sequentially calculated by combining the parameters of the thermal power generating units in the system. The specific method comprises the following steps:

starting a power set which needs to be started in an electric power system, wherein the power set needs to be started in a heat supply period in order to guarantee heat supply, and the power set does not need to be started in a non-heat supply period;

if the maximum value of the equivalent load in the day plus the maximum output of the power system which is in reserve and is larger than the maximum output of the unit which must be started, the other units are increased; in order to enable the new energy to accept electric quantity as large as possible, the unit with small minimum output is started preferentially under the condition of the same maximum output, and the starting is stopped until the maximum output of the thermal power unit in the power system meets the daily maximum equivalent load and the system is in reserve.

The objective function expressed by the mixed integer programming model in the startup mode is as follows:

min P_min(d)

wherein, P_min(d) And representing the minimum output power of the thermal power generating unit on the day d. The above formula indicates that when a daily starting plan of the thermal power generating unit is formulated, the minimum output of the thermal power generating unit which is started every day should be made as small as possible.

The constraint conditions of the thermal power unit starting mode mixed integer programming model are as follows:

(1) and the minimum output of the thermal power unit on the day d is equal to the sum of the minimum outputs of all the thermal power units started on the day d.

Wherein, X_j(d) And (3) showing the operation state of the jth unit on the d day, wherein the operation state is a binary variable, 0 represents that the unit is stopped, and 1 represents that the unit is operating. TP_j,min(d) Is the firstj minimum output power of the thermal power generating unit on day d.

(3) The maximum output of the thermal power generating unit when being started every day must ensure the load requirement and system standby on the same day,

wherein, TP_j,max(d) Is the maximum output power, eqload, of the jth thermal power unit on the day d_max(d) Is the daily maximum equivalent load on day d, P_reIs a positive backup for the system.

(4) The number of the starting machines of each thermal power generating unit in each day is between the maximum number and the minimum number, namely

S_j,min(d)≤S_j(d)≤S_j,max(d)

Wherein S is_j(d) The number of starting units of the j th thermal power generating unit on the d th day, S_j,min(d) Is the minimum starting number S of the j thermal power generating units on the day d_j,max(d) And the maximum starting number of the j thermal power generating units on the day d.

According to the method in the step 2, the daily minimum technical output P of the thermal power generating unit in the research period can be obtained by sequentially obtaining the starting modes of each day_min，P_minThe total amount of data of (a) is D.

And step 3: finding daily maximum load_maxAnd P_minThe joint probability distribution of (c).

Minimum technical output P due to thermal power_minThe daily maximum equivalent load is determined by the sum of the daily maximum equivalent load and the system positive standby, the system positive standby is generally a fixed value, the daily maximum equivalent load has strong correlation with the daily maximum load, therefore, the daily maximum load is required to be obtained by a method of joint probability distribution_maxAnd P_minThe joint probability distribution of (c).

Step 3.1: will be the most dailyHeavy load_maxMinimum technical output P of thermal power every day_minRespectively within their range of variation: load_maxAnd P_minThe total amount of data of (a) is D. Choose daily maximum load_maxHas a discretization interval length of C₁Daily minimum technical contribution of thermal power P_minHas a discretization interval length of C₂，load_maxAnd P_minThe number of intervals of (A) is L₁、L₂；

Step 3.2: setting a cycle initial value: d is 1, n_ik0, wherein D represents day D, 0. ltoreq. d.ltoreq.D, n_ikIs load_max(d) Falls into its i-th discretization interval, and P_min(d) An approximation to its k-th discretized interval, i 1,2₁，k＝1,2,...,L₂；

Step 3.3: read in daily maximum load on day d_max(d) And the minimum technical output P of thermal power every day_min(d) And the data are classified into corresponding discretization intervals;

step 3.4: d +1, n_ik＝n_ik+1；

Step 3.5: if D is less than or equal to D, turning to step 3.3, otherwise, turning to step 3.6;

step 3.6: determining daily maximum load_maxAverage averageload of all data in each discretized interval_max(i)，i＝1,2...L₁(ii) a Determining daily thermal power minimum technical output P_minAverage averageP of all data in each interval_min(k)，k＝1,2...L₂；

Step 3.7: determining load_maxFalls into its i-th discretization interval, and P_minProbability P of falling into its k-th discretization interval_ik，load_maxAnd P_minThe included discretized interval combinations have L in common₁·L₂The number of the main components is one,and sequentially calculating the probability of each interval combination to obtain the combined probability distribution of the daily maximum load and the daily minimum output of the thermal power.

Found load_maxAnd P_minIs given a joint probability distribution of_2×1As shown in table 1.

TABLE 1 load_maxAnd P_minIs given by the joint probability distribution table

And 4, step 4: load calculation_maxJoint probability distribution with load:

load_maxthe method has strong correlation with the load, and also obtains the daily maximum load by a method of combining probability distribution_maxJoint probability distribution with load.

Step 4.1: will load daily maximum load_maxAnd the load is divided in the variation range thereof respectively: load_maxThe total data amount of (1) is D, the total data amount of load is T, and the daily maximum load is_maxSelecting load as the partitioning method in step 3.1_maxHas a discretization interval length of C₁Selecting the discretization interval length of the load as C₃，load_maxAnd the number of load sections is L respectively₁And L₃；

Step 4.2: setting a cycle initial value, d is 1, n_is0, wherein d represents day d, n_isIs load_max(d) The load (t) of the t-period is classified into the ith discretization interval, i is 1,2₁，s＝1,2,...,L₃；

Step 4.3: read in daily maximum load on day d_max(d) Entering the corresponding interval;

step 4.4: an initial value is set, t ═ N +1 (d-1), at which time t denotes the 1 st period on day d.

Step 4.5: reading load (t) in a period t and classifying the load (t) into a corresponding discretization interval;

step 4.6: t +1, n_is＝n_is+1；

Step 4.7: if t is less than or equal to d.N, turning to the step 4.5, otherwise, turning to the step 4.8;

step 4.8: d is d + 1;

step 4.9: if D is less than or equal to D, turning to the step 4.3, otherwise, turning to the step 4.10;

step 4.10: determining daily maximum load_maxAverage averageload of all data in each discretized interval_max(i)，i＝1,2...L₁(ii) a Determining the average value averageload(s) of all data in each interval of the load, wherein s is 1 and 2₃；

Step 4.11: determining daily maximum load_maxClassified into the ith discretization interval and the minimum daily thermal power technical output P_minProbability P of being classified into its s-th discretization interval_is，load_maxThe combination of the discretized interval in which load is included has L₁·L₃And sequentially calculating the probability of each section combination, and further obtaining the combined probability distribution of the daily maximum load and the load. Finding load_maxThe joint probability distribution with load is shown in table 2.

TABLE 2 load_maxJoint probability distribution table with load

And 5: and solving an admission space probability sequence by the volume difference.

As can be seen from Table 2, each averageload_max(i) All corresponding to a discrete probability sequence gP of daily minimum output of thermal power generating unit_mini，gP_miniIs a discrete power value, the second row is a corresponding probability, i.e.,

gP_mini(1,k)＝averageP_min(k)

wherein,is a load_maxFall under discrete value averageload_max(i) The probability of (d); k represents P_minThe kth discretization interval of (1);

similarly, each averageload_max(i) Corresponding load discrete probability sequence gload_iSatisfies the following formula,

gload_i(1,s)＝averageload(s)

for gP_miniAnd gload_iAll possible combination numbers of daily minimum technology output discrete power value and load discrete power value of the thermal power generating unit are L₂·L₃If in a certain combination, the load discrete power value is gload_i(1, s) the minimum power output discrete power value of thermal power day is gP_mini(1, k), then the combined new energy acceptance space power gACCOM_i(1, j) satisfies the following formula:

gACCOM_i(1,j)＝gload_i(1,s)-gP_mini(1,k)

the probability of this combination is:

gACCOM_i(2,j)＝gload_i(2,s)·gP_mini(2,k)

the new energy admission space power value of all the combinations and the probability thereof are calculated, and averageload can be obtained_max(i) Corresponding new energy acceptance space discrete probability sequence gACCOM_i。

The fact is that the gload is_iAnd gP_miniA volume difference operation, which may be expressed as,

and due to the fact that the gACCOM_iIs averageload_max(i) Corresponding admission space probability sequence, so in the whole probability space, when load_maxFall under discrete value averageload_max(i) Time admission space discrete probability sequence gACCOM'_iSatisfies the following formula,

gACCOM'_i(1,j)＝gACCOM_i(1,j)

sequentially obtaining gACCOM'₁,gACCOM'₂,...,And merging and sequencing to obtain a new energy admission space probability sequence gACCOM.

Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can make modifications and equivalents to the embodiments of the present invention without departing from the spirit and scope of the present invention, which is set forth in the claims of the present application.

Claims (6)

1. A new energy admission space discrete probability sequence calculation method is based on joint probability distribution and comprises the following steps:

step 1: calculating the replaceable capacity of the new energy output participating in the thermal power starting;

step 2: establishing a daily starting mode of the thermal power generating unit, and determining the minimum technical output P of the daily thermal power_min；

And step 3: determining daily maximum load_maxMinimum technical output P of thermal power every day_minA joint probability distribution of (a);

and 4, step 4: determining daily maximum load_maxJoint probability distribution with load;

and 5: and determining a new energy admission space probability sequence by using the volume difference.

2. The method for calculating the discrete probability sequence of the new energy admission space according to claim 1, wherein the step 1 comprises the following steps:

step 1.1: acquiring and arranging data: acquiring load, new energy output and installation time sequence data of a certain time period, wherein the total data amount is T, the total days of a research period is D, and the data amount of the load, the new energy output and the installation every day is N, so that T is N.D; if the sampling interval of time series data such as load is 1h, N = 24; if the sampling interval is 15min, then N = 96;

step 1.2: solving the replaceable capacity of the new energy participating in thermal power starting: the prediction error of the new energy is 20%, and 20% of the installed energy is subtracted from the predicted value of the new energy to participate in thermal power starting; if the predicted value of the new energy output is more than 20% of the installed capacity of the new energy output, the excess part is brought into the thermal power starting power balance, and the thermal power starting capacity is reduced; otherwise, the new energy does not participate in the starting of the thermal power generating unit, and the replaceable capacity C of the new energy participating in the starting of the thermal power generating unit in the period T is researched_{renewableenergy}(t) satisfies the following formula:

C_{renewableenergy}(t)＝max(P_{renewableenergy}(t)-Capacity_{renewableenergy}(t)×20%,0)

P_{renewableenergy}(t)＝P_wind(t)+P_PV(t)

Capacity_{renewableenergy}(t)＝Capacity_wind(t)+Capacity_PV(t)

wherein T represents the tth time period in the study period, T is more than or equal to 0 and less than or equal to T, P_{renewableenergy}(t) is a predicted value of new energy output in the t period, Capacity_{renewableenergy}(t) New energy installed Capacity at time t, P_wind(t) is the predicted value of wind power generation at time t, Capacity_wind(t) wind installed capacity at time t, P_PV(t) is for a period of tPredicted value of photovoltaic power generation, Capacity_PV(t) photovoltaic installed capacity for time period t.

3. The method for calculating discrete probability sequence of new energy admission space according to claim 1, characterized in that the step 2 comprises the following steps,

step 2.1: calculating the equivalent load eqload and the daily maximum equivalent load eqload_max：C_{renewableenergy}(t) the equivalent load participating in the power balance of the starting of the thermal power generating unit meets the following formula:

eqload(t)＝load(t)-C_{renewableenergy}(t)

wherein eqload (t) is the equivalent load of time period t, load (t) is the load of time period t, C_{renewableenergy}(t) new energy alternative capacity for time period t;

the total data quantity of the eqload is also T, the data quantity of each day is N, the maximum value of the eqload per day is obtained, and the maximum value of the eqload per day is the maximum equivalent load per day_max，eqload_maxThe total amount of data of (a) is D;

step 2.2: establishing a daily starting mode of the thermal power generating unit, and solving the minimum technical output of daily thermal power:

according to the maximum equivalent load and the positive standby of the system every day, the starting mode of every day can be sequentially solved by combining the parameters of the thermal power generating unit in the system, and the method comprises the following steps:

starting a power set which needs to be started in an electric power system, wherein the power set needs to be started in a heat supply period in order to guarantee heat supply, and the power set does not need to be started in a non-heat supply period;

if the maximum value of the equivalent load in the day plus the maximum output of the power system which is in reserve and is larger than the maximum output of the unit which must be started, the other units are increased; in order to enable the new energy to accept electric quantity as large as possible, under the condition of the same maximum output, the unit with the minimum output is started preferentially, and the starting is stopped until the maximum output of the thermal power unit in the power system meets the daily maximum equivalent load and the system is in reserve;

the thermal power generating unit is characterized in that an objective function expressed by a mixed integer programming model in a daily starting mode is as follows:

minP_min(d)

wherein, P_min(d) Representing the minimum output power of the thermal power generating unit on the day d;

the constraint conditions of the thermal power unit starting mode mixed integer programming model are as follows:

(1) the minimum output of the thermal power unit on the day d is equal to the sum of the minimum outputs of all the thermal power units started on the day d;

$P_{\min }\left(d\right)=\underset{j}{\Σ}{\mathrm{TP}}_{j,\min }\left(d\right)\·X_j\left(d\right)$

wherein, X_j(d) Showing the operation state of the jth unit on the d day, wherein the operation state is a binary variable, 0 indicates that the unit is stopped, and 1 indicates that the unit is operating; TP_j,min(d) The minimum output power of the jth thermal power generating unit on the day d;

(2) the maximum output of the thermal power generating unit when being started every day must ensure the load demand and system standby on the same day, namely:

$\underset{j}{\Σ}{\mathrm{TP}}_{j,max}\left(d\right)\≥{\mathrm{eqload}}_{max}\left(d\right)+P_{re}$

wherein, TP_j,max(d) Is the maximum output power, eqload, of the jth thermal power unit on the day d_max(d) Is the daily maximum equivalent load on day d, P_reIs a positive backup for the system;

(3) the number of the starting machines of each thermal power generating unit is between the maximum number and the minimum number, namely:

S_j,min(d)≤S_j(d)≤S_j,max(d)

wherein S is_j(d) The number of starting units of the j th thermal power generating unit on the d th day, S_j,min(d) Is the minimum starting number S of the j thermal power generating units on the day d_j,max(d) The maximum starting number of the j thermal power generating units on the day d is obtained;

sequentially calculating the daily starting mode of the thermal power generating unit according to the model, and further calculating the daily minimum technical output P of the thermal power generating unit in the research period_min，P_minThe total amount of data of (a) is D.

4. The method for calculating the discrete probability sequence of the new energy admission space as claimed in claim 1, wherein the step 3 comprises the following steps:

step 3.1: will load daily maximum load_maxMinimum technical output P of thermal power every day_minRespectively within their range of variation: load_maxAnd P_minThe total amount of data of (a) is D; choose daily maximum load_maxHas a discretization interval length of C₁Daily minimum technical contribution of thermal power P_minHas a discretization interval length of C₂，load_maxAnd P_minThe number of intervals of (A) is L₁And L₂；

Step 3.2: setting a cycle initial value: d =1, n_ik0, wherein D represents day D, 0. ltoreq. d.ltoreq.D, n_ikIs load_max(d) Falls into its i-th discretization interval, and P_min(d) An approximation to its k-th discretized interval, i 1,2₁，k＝1,2,...,L₂；

Step 3.3: read in daily maximum load on day d_max(d) And the minimum technical output P of thermal power every day_min(d) And the data are classified into corresponding discretization intervals;

step 3.4: d +1, n_ik＝n_ik+1；

Step 3.5: if D is less than or equal to D, turning to step 3.3, otherwise, turning to step 3.6;

step 3.6: determining daily maximum load_maxAverage averageload of all data in each discretized interval_max(i)，i＝1,2...L₁(ii) a Determining daily thermal power minimum technical output P_minAverage averageP of all data in each interval_min(k)，k＝1,2...L₂；

Step 3.7: determining load_maxFalls into its i-th discretization interval, and P_minProbability P of falling into its k-th discretization interval_ik，load_maxAnd P_minThe included discretized interval combinations have L in common₁·L₂And sequentially calculating the probability of each discretization interval combination, and further obtaining the combined probability distribution of the daily maximum load and the daily thermal power minimum output.

5. The method for calculating the discrete probability sequence of the new energy admission space as claimed in claim 1, wherein the step 4 comprises:

step 4.1: will load daily maximum load_maxAnd the load is divided in the variation range thereof respectively: load_maxSelecting load with total data D and total data of load T_maxHas a discretization interval length of C₁Selecting the discretization interval length of the load as C₃，load_maxAnd the number of load sections is L respectively₁And L₃；

Step 4.2: setting a cycle initial value of d =1, n_is0, wherein d represents day d, n_isIs load_max(d) The load (t) of the t-period is classified into the ith discretization interval, i is 1,2₁，s＝1,2,...,L₃；

Step 4.3: read in daily maximum load on day d_max(d) Entering the corresponding interval;

step 4.4: setting an initial value, t ═ N +1 (d-1), when t represents the 1 st period on day d;

step 4.5: reading load (t) in a period t and classifying the load (t) into a corresponding discretization interval;

step 4.6: t +1, n_is＝n_is+1；

Step 4.7: if t is less than or equal to d.N, turning to the step 4.5, otherwise, turning to the step 4.8;

step 4.8: d is d + 1;

step 4.9: if D is less than or equal to D, turning to the step 4.3, otherwise, turning to the step 4.10;

step 4.10: determining daily maximum load_maxAverage averageload of all data in each discretized interval_max(i)，i＝1,2...L₁(ii) a Determining the average value averageload(s) of all data in each interval of the load, wherein s is 1 and 2₃；

Step 4.11: determining daily maximum load_maxClassified into the ith discretization interval and the minimum daily thermal power technical output P_minProbability P of being classified into its s-th discretization interval_is，load_maxThe combination of the discretized interval in which load is included has L₁·L₃And sequentially calculating the probability of each section combination, and further obtaining the combined probability distribution of the daily maximum load and the load.

6. The method according to claim 1, wherein the step 5 comprises: daily maximum load_maxOf all data within each discretized intervalMean averageload_max(i) Discrete probability sequence gP corresponding to daily thermal power minimum technology output_mini，gP_miniIs a discrete power value and the second row is the corresponding probability, i.e.:

gP_mini(1,k)＝averageP_min(k)

${\mathrm{gP}}_{\min i}(2,k)=\frac{P_{ik}}{\underset{k=1}{\overset{L_2}{\Σ}}{P}_{ik}}$

wherein,is a load_maxFall under discrete value averageload_max(i) The probability of (d); k represents P_minThe kth discretization interval of (1);

daily maximum load_maxAverage averageload of all data in each discretized interval_max(i) Corresponding load discrete probability sequence gload_iSatisfies the following formula:

gload_i(1,s)＝averageload(s)

${\mathrm{gload}}_i(2,s)=\frac{P_{is}}{\underset{s=1}{\overset{L_3}{\Σ}}{P}_{is}}$

for gP_miniAnd gload_iAnd the combined number of the minimum technical output discrete power value and the load discrete power value of the thermal power generating unit is L₂·L₃If in a certain combination, the load discrete power value is gload_i(1, s), the minimum technical output discrete power value of the thermal power generating unit every day is gP_mini(1, k), then the combined new energy acceptance space power gACCOM_i(1, j) satisfies the following formula:

gACCOM_i(1,j)＝gload_i(1,s)-gP_mini(1,k)

the probability of this combination is:

gACCOM_i(2,j)＝gload_i(2,s)·gP_mini(2,k)

determining the new energy admission space power values of all combinations and the probability thereof to obtain averageload_max(i) Corresponding new energy acceptance space discrete probability sequence gACCOM_i；

The above is to make gload_iAnd gP_miniA volume difference operation is performed, expressed as,

due to gACCOM_iIs averageload_max(i) Corresponding admission space probability sequence, in the whole probability space, when load_maxFall under discrete value averageload_max(i) Time admission space discrete probability sequence gACCOM'_iSatisfies the following formula,

gACCOM'_i(1,j)＝gACCOM_i(1,j)

${{\mathrm{gACCOM}}^{\′}}_i(2,j)={\mathrm{gACCOM}}_i(2,j)\·\underset{k=1}{\overset{L_2}{\Σ}}{P}_{ik}$

in turn determineAnd combining and sequencing to obtain a new energy admission space probability sequence gACCOM.