CN106026077B - Wind-powered electricity generation receives the wind-powered electricity generation unit of capability assessment method to receive the indicator of costs to determine method to power grid based on multiple-objection optimization a few days ago - Google Patents
Wind-powered electricity generation receives the wind-powered electricity generation unit of capability assessment method to receive the indicator of costs to determine method to power grid based on multiple-objection optimization a few days ago Download PDFInfo
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Abstract
The invention discloses a kind of power grid based on multiple-objection optimization, wind-powered electricity generation receives the wind-powered electricity generation unit of capability assessment method to receive the indicator of costs to determine method a few days ago, and based on power grid, wind-powered electricity generation receives the wind-powered electricity generation unit of capability assessment model Pareto optimal solution sets to receive indicator of costs C a few days agoApu,i, it is calculated as follows：According to the definition that Pareto is optimal, respectively solve corresponding cost of electricity-generating and meet following relation：CG,1<CG,2<…CG,nWind-powered electricity generation receives the Pareto optimal solution sets of capability assessment model to be made of n different solutions, and electricity A is received by wind-powered electricity generationwSize Pareto optimal solution sets are ranked up, after sequence, there are following relation：AW,1<AW,2<…AW,n.The present invention proposes wind-powered electricity generation unit on the basis of wind-powered electricity generation a few days ago receives capability assessment model Pareto optimal solution sets and receives the indicator of costs, can weigh power grid to receive the cost price that wind-powered electricity generation is paid.
Description
The application is application No. 201510031432.3, application date: 2015-01-21, a divisional application entitled "wind power acceptance assessment method based on multiobjective optimization of power grid day ahead".
Technical Field
The invention relates to a renewable energy power grid-connected technology, in particular to a multi-objective optimization-based power grid day-ahead wind power acceptance capacity assessment method and a wind power unit acceptance cost index.
Background
In recent decades, with the gradual depletion of fossil fuels and the increasing increase of environmental pollution, the development of renewable energy sources represented by wind power is paid enough attention all over the world. By 2012, through years of high-speed development, the wind power permeability of partial regional power grids in regions rich in wind resources in China reaches a higher level, for example, the proportion of wind power installations of Monte-West power grids in the total installed power generation system is up to 22.2%. The large-scale wind power integration increases the difficulty of scheduling decision, deteriorates the power quality of a local power grid, and even worse, when the power grid scheduling resources cannot balance the random variation of wind power, serious 'wind abandon' may occur. In 2013, the 'wind abandon' power of the power grid in China is as high as 162 hundred million kilowatts, and accounts for about 10 percent of the total wind power generation amount in the year.
With the phenomenon of wind abandonment becoming more serious day by day, the academic community deeply analyzes the wind abandonment reason of the power grid and evaluates the wind power receiving capacity of the power grid from a plurality of time angles, thereby providing reference for scheduling decisions. According to analysis of Liaoning power grid wind power admission capacity analysis based on power balance (power system automation, 2010, volume 34, 3 rd, page 86 to 90), the main reason for 'wind abandon' at the present stage is that the transmission capacity and peak shaving capacity are restricted, so that the problems of system tide, voltage stability, power quality and the like caused by wind power grid connection can be solved in a local power grid, and the wind power consumption of the whole power grid is not restricted. The document two, related problems and measures for accessing large-scale wind power to a power grid (the report of the Chinese Motor engineering, 2010, volume 30, phase 25, pages 1 to 9), compares the power structures of the China and the German two countries, and considers that the unreasonable power structure is one of the main reasons for causing large-scale wind abandonment. In the third document, "assessment of real-time wind power absorption capacity considering network security constraints" (report of electrical engineering of china, 2013, volume 33, stage 16, pages 23 to 29), on the basis of considering network security constraints, the wind power receiving capacity of the power grid is assessed from the perspective of real-time operation, and the wind power receiving capacity of each node is analyzed emphatically. In the fourth document, "wind power absorption capacity assessment method based on day-ahead wind power prediction" (power grid technology, 2012, volume 36, stage 8, pages 69 to 75), the wind power absorption capacity of the power grid is analyzed from the day-ahead time perspective, a concept of wind power absorption "envelope band" is proposed, and a beneficial reference is provided for dispatchers.
The wind power acceptance capacity evaluation methods proposed in the third and fourth documents only give a single evaluation result, and focus is on displaying the maximum theoretical wind power acceptance capacity of the power grid. In addition, the existing wind power acceptance capacity evaluation model completely ignores the wind power acceptance cost in the evaluation, so that a corresponding wind power acceptance cost index is not provided.
Disclosure of Invention
The invention aims to provide a method for evaluating day-ahead wind power receiving capacity of a power grid based on multi-objective optimization, which is more comprehensive and simple in evaluation.
The technical solution of the invention is as follows:
a power grid day-ahead wind power receiving capacity assessment method based on multi-objective optimization is characterized by comprising the following steps: the day-ahead wind power admitting ability evaluation model of the power grid has two optimization targets of maximum wind power admitting ability and minimum conventional system power generation cost, which are as follows:
optimization objective 1:
optimization objective 2:
in the above formula, A w The wind power in the dispatching day is admitted with electric quantity, and the expectation of wind power abandoning is subtracted from the predicted electric quantity of the wind power in the dispatching day; p i,t The output power of the unit i in the time period t is obtained; u. of i Representing the running state of the unit i in a dispatching day, wherein '0' represents shutdown, and '1' represents startup; t is the number of scheduling time segments; f w,t The maximum theoretical output of the wind power in the time period t is the predicted value of the wind power; c w,t The expected electric quantity of the wind curtailment for the time period t, and P i,t And u i (ii) related; c G The power generation cost of the conventional system in the dispatching day; n is the number of conventional units; f. of i (P i,t ) Fitting a fuel cost function of the unit i in a time period t by a quadratic function;
the constraint conditions of the wind power receiving capacity evaluation model before the power grid day are as follows:
and (3) system active power balance constraint:
in the above formula, P d,t For the predicted value of the load at time t, P w,t The wind power grid-connected electric quantity at the moment t;
and (3) output constraint of a conventional unit:
P min,i ≤P i,t ≤P max,i
in the above formula, P max,i 、P min,i Respectively the maximum and minimum technical output of the unit i;
and (3) climbing restraint:
P i,t -P i,t-1 ≤ΔTR up,i
P i,t-1 -P i,t ≤ΔTR down,i
in the above formula, R up,i 、R down,i Respectively the maximum increasing and decreasing force rates of the unit i;
system security constraints:
V LOLP,t ≤R LOLP
in the above formula, V LOLP,t Probability of loss of load for scheduling period t, R LOLP A desired level of operational reliability;
wind power constraint:
P w,t ≤F w,t 。
the method comprises the following steps of solving a wind power receiving capacity evaluation model in the day ahead of a power grid by adopting a genetic algorithm based on non-dominated classification, and solving a Pareto optimal solution set of the evaluation model, wherein the specific steps are as follows:
step 1, randomly generating an initial chromosome population of a genetic algorithm, wherein the population scale is 10N; representing a chromosome in the chromosome population by using a binary code with the length of N; each chromosome gives the running states of N conventional units in a scheduling day, wherein '0' represents shutdown, and '1' represents startup;
step 2, carrying out reliability evaluation on the chromosome, carrying out economic dispatching calculation on the chromosome meeting the safety constraint, and calculating the power generation cost C of the conventional system on the basis G And calculating wind curtailment electric quantity expectation C of chromosomes meeting the safety constraint in a scheduling day w,t ；
Step 3, the chromosomes in the population are layered according to the non-inferior solution levels, the smaller the level index is, the higher the non-inferior solution level is, and the lowest the non-inferior solution level of the chromosome which does not meet the security constraint is during layering; assuming that the population can be divided into m layers, for an individual i, if the non-inferior solution level is j, the fitness V of the individual is fit,i Comprises the following steps:
V fit,i ＝10N-j
i＝0,1,…,10N j＝0,1,…,m
and 4, calculating the local crowding distance of each individual in the same non-inferior solution layer. When calculating the local crowding distance, the individuals are divided into two categories: individuals at the sorting edge and individuals in the middle of the sorting; for individuals at the sequencing edge, the local crowding distance is directly endowed with a larger numerical value, so that the individuals obtain the selection advantage; for the individuals in the middle of the sequence, the local crowding distance is the sum of the lengths of two sides of a rectangle formed by taking two adjacent individuals as vertexes;
step 5, directly copying the non-inferior solution set in the parent population to the child population as a part of the child population; selecting other individuals in the offspring population according to the individual fitness and the local crowding distance, namely randomly selecting two individuals from the parent, selecting the individual with high fitness if the fitness values are different, and selecting the individual with large local crowding distance if the fitness values are the same; the selection operation is repeated until a progeny population is formed. Carrying out cross and mutation operations on the offspring population according to a certain probability;
and 6, repeatedly executing the steps 2 to 5 until the algorithm meets the preset convergence condition.
Wind curtailment electric quantity expectation C for each scheduling period by adopting analytic probability algorithm w,t And the probability of losing load V LOLP,t The calculation is carried out by the following steps:
step 1, representing the probability characteristic of random fluctuation of wind power near a predicted value by adopting a general probability distribution function, wherein the probability density function and the accumulative probability distribution function are respectively shown as the following formulas:
F(x)＝{1+exp[-α(x-γ)]} -β
step 2, adopting normal distribution N (P) d,t ,σ d,t ) Representing the probability characteristic of the load fluctuating randomly around the predicted value, and adopting 7 discrete probability points to normal distribution N (P) d,t ,σ d,t ) Performing approximate approximation, namely:
step 3, expressing the random fault characteristics of the unit by adopting an improved double-state model without considering element repair, and expressing the fault rate of the unit i in a time period tf i,t Comprises the following steps:
f i,t ＝1-exp[-λ i (T LD +t)]≈λ i (T LD +t)
in the formula, T LD Evaluating the advance time for the wind power receiving capacity; lambda [ alpha ] i The average failure rate of the unit i.
Step 4, assuming that m sets are in a starting state in a time period t, and under the condition of neglecting the simultaneous failure of more than two sets, a discrete probability expression of the available generating capacity of the conventional set in the time period is as follows:
in the above formula, G 0 Is the available generating capacity p when the units are all in normal state 0 For the corresponding probability, it can be calculated by:
G j (j =1,2, \ 8230; m) is the available power generation capacity in case of a single unit failure, p j Is the corresponding probability. Assume that the index of the failed set is k, G j 、p j Respectively as follows:
G j ＝G 0 -P max,k
G j (j = m +1, m +2, \ 8230and m (m + 1)/2) is the available power generation capacity when two units fail simultaneously, and p is the available power generation capacity j Is the probability of an event occurring. Assume the index of the failed group is k 1 、k 2 ，G j 、p j Respectively as follows:
step 5, assuming that m sets are in a starting state in a time period t, and under the condition of neglecting the simultaneous failure of more than two sets, a discrete probability expression of the total minimum technical output of the conventional set in the time period is as follows:
in the above formula, G 0 The total minimum technical output, p, of the conventional unit when the units are all in a normal state 0 For the corresponding probability, it can be calculated by:
G j (j =1,2, \ 8230; m) is the total minimum technical output, p, of a conventional unit in the event of a single unit failure j Is the corresponding probability. Assume that the index of the failed group is k, G j 、p j Respectively as follows:
G j ＝G 0 -P min,k
G j (j = m +1, m +2, \ 8230m (m + 1)/2) is two machinesTotal minimum technical output, p, of a conventional unit in the event of a simultaneous group failure j Is the probability of an event occurring. Assume the index of the failed group is k 1 、k 2 ，G j 、p j Respectively as follows:
step 6, calculating the expected abandoned wind electric quantity C of each scheduling period w,t And the probability of losing load V LOLP,t As shown in the following formula:
if P d,l -G j <0 P d,l -G j ＝0
if P d,l -G j >G wind P d,l -G j ＝G wind
if x 0 <0 x 0 ＝0
if x 0 >1 x 0 ＝1
a wind power unit acceptance cost index of a power grid day-ahead wind power acceptance capacity evaluation method based on multi-objective optimization is characterized in that: wind power unit acceptance cost index C based on power grid day-ahead wind power acceptance capability evaluation model Pareto optimal solution set Apu,i Push and pressThe following formula is calculated:
according to the optimal definition of Pareto, the power generation cost corresponding to each solution satisfies the following relation:
C G,1 <C G,2 <…C G,n
the Pareto optimal solution set of the wind power acceptance capacity evaluation model consists of n different solutions and is based on wind power acceptance electric quantity A w The Pareto optimal solution set is sorted according to the size of the solution, and after sorting, the following relations exist:
A W,1 <A W,2 <…A W,n 。
has the advantages that: compared with the prior art, the invention has the outstanding advantages that: firstly, the operation cost of a power system is considered in the evaluation of the day-ahead wind power receiving capacity of the power grid, a day-ahead wind power receiving capacity evaluation model based on multi-objective optimization is constructed, the model is closer to the actual dispatching of the power grid, and the evaluation is more comprehensive; secondly, the existing evaluation model can only give a single evaluation result, namely the maximum theoretical wind power acceptance capacity of the system, while the evaluation method disclosed by the invention can give a Pareto optimal solution set which consists of a series of evaluation results and corresponding costs; finally, an acceptance cost index of a wind power unit is provided on the basis of a day-ahead wind power acceptance capability evaluation model Pareto optimal solution set, and the cost paid by a power grid for accepting wind power can be measured.
Detailed Description
The invention is further described below with reference to the accompanying drawings and examples.
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a graph of the relationship between the amount of the abandoned wind and the power generation cost.
Example 1
In order to evaluate the wind power acceptance of a power grid in the time of day ahead and analyze the cost problem of wind power acceptance, the invention discloses a multi-objective optimization-based method for evaluating the wind power acceptance of the power grid in the day ahead, and wind power unit acceptance cost is calculated on the basis of a Pareto optimal solution set given by the evaluation method, wherein the general flow is shown in the attached figure 1.
Two optimization targets of the power grid day-ahead wind power receiving capacity evaluation model based on multi-target optimization are respectively as follows:
optimization objective 1:
optimization objective 2:
the optimization objectives given by the two formulas are respectively the maximum wind power receiving electric quantity and the minimum conventional system power generation cost. In the formula, A w Receiving electric quantity for wind power in a dispatching day; p i,t The output power of the unit i in the time period t is obtained; u. u i Representing the running state of the unit i in the dispatching day, wherein '0' represents shutdown and '1' represents startup; t is the number of scheduling time segments; f w,t The maximum theoretical output of the wind power in the time period t is given by a wind power prediction system; c w,t Expected electric quantity for 'wind curtailment' in time period t, and P i,t And u i (ii) related; c G The power generation cost of the conventional system in the dispatching day; n is the number of conventional units; f. of i (P i,t ) For the fuel cost function of unit i over time period t, it can be fitted by a quadratic function as shown in the following equation:
in the above formula, a i 、b i And c i Respectively the fuel cost coefficient of the unit i.
The constraints of the evaluation model are as follows:
(1) And (3) system active power balance constraint:
in the above formula, P d,t For the predicted value of the load at time t, P w,t The wind power grid-connected electric quantity at the moment t.
(2) And (3) output constraint of a conventional unit:
P min,i ≤P i,t ≤P max,i
in the above formula, P max,i 、P min,i The maximum and minimum technical output of the unit i are respectively.
(3) And (3) climbing restraint:
P i,t -P i,t-1 ≤ΔTR up,i
P i,t-1 -P i,t ≤ΔTR down,i
in the above formula, R up,i 、R down,i The maximum increasing and decreasing power rates of the unit i are respectively.
(4) System security constraints:
V LOLP,t ≤R LOLP
in the above formula, V LOLP,t The operation risk can be quantified for the load loss probability of the scheduling time t; r LOLP To the desired level of operational reliability.
(5) Wind power constraint:
P w,t ≤F w,t
the model is a multi-objective optimization model, and two optimization objectives conflict with each other, the method adopts a genetic algorithm based on non-dominated classification to solve the model, so that a Pareto optimal solution set of the evaluation model can be solved, and the method specifically comprises the following steps:
step 1, randomly generating an initial chromosome population of a genetic algorithm, wherein the population size is 10N. A binary code of length N is used to represent a chromosome in the chromosome population. Each chromosome gives the running states of N conventional units in a scheduling day, wherein '0' represents shutdown, and '1' represents startup;
step 2, carrying out reliability evaluation on the chromosome, carrying out economic dispatching calculation on the chromosome meeting the safety constraint, and calculating the power generation cost C of the conventional system on the basis G And calculating wind curtailment power expectation C of the chromosomes meeting the safety constraint in the scheduling day w,t ；
And 3, layering the chromosomes in the population according to the non-inferior solution levels, wherein the smaller the hierarchical index is, the higher the non-inferior solution level is, and the lowest the non-inferior solution level of the chromosome which does not meet the security constraint is during layering. Assuming that the population can be divided into m layers, for an individual i, if the non-inferior solution level is j, the fitness V of the individual is fit,i Comprises the following steps:
V fit,i ＝10N-j
i＝0,1,…,10N j＝0,1,…,m
and 4, calculating the local crowding distance of each individual in the same non-inferior solution layer. When calculating the local crowding distance, the individuals are divided into two categories: individuals at the edges of the ranking and individuals in the middle of the ranking (individuals a, B, and C in the following figures). For individuals at the sequencing edge, a larger numerical value is directly assigned to the local crowding distance of the individuals, so that the individuals obtain the selection advantage; for the individuals in the middle of the ranking (individual B in the lower graph), the local crowding distance is the sum of the lengths of two sides of a rectangle with two adjacent individuals (such as individuals a and C in fig. 2) as vertices.
And 5, directly copying the non-inferior solution set in the parent population to the child population as a part of the child population. And performing selection operation according to the individual fitness and the local crowding distance to generate other individuals in the offspring population, namely randomly selecting two individuals from the parent, selecting the individual with high fitness if the fitness values are different, and selecting the individual with large local crowding distance if the fitness values are the same. The selection operation is repeated until a progeny population is formed. And carrying out cross and mutation operations on the filial generation population according to a certain probability.
And 6, repeatedly executing the steps 2 to 5 until the algorithm meets the preset convergence condition.
In the solving process of the wind power acceptance capacity evaluation model before the day of the day based on the non-dominated classification genetic algorithm, the invention adopts an analytic probability algorithm to calculate the wind curtailment electric quantity expectation C w,t And the probability of losing load V LOLP,t The method comprises the following specific steps:
step 1, representing the probability characteristic of random fluctuation of wind power near a predicted value by adopting a general probability distribution function, wherein the probability density function and the accumulative probability distribution function are respectively shown as the following formulas:
F(x)＝{1+exp[-α(x-γ)]} -β
step 2, adopting normal distribution N (P) d,t ,σ d,t ) Probability distribution characteristic (σ) representing random fluctuation of load in the vicinity of predicted value d,t Standard deviation of random fluctuations in load, typically within 5% of the predicted value of load). To avoid more complicated convolution operation, 7 discrete probability point pairs are adopted to distribute N (P) in a normal distribution d,t ,σ d,t ) Performing an approximation, namely:
step 3, expressing the random fault characteristics of the unit by adopting an improved double-state model without considering element repair, and expressing the fault rate f of the unit i in a time period t i,t Comprises the following steps:
f i,t ＝1-exp[-λ i (T LD +t)]≈λ i (T LD +t)
in the formula, T LD Evaluating the advance time for the wind power receiving capacity; lambda [ alpha ] i The average failure rate of the unit i.
Step 4, assuming that m sets are in a starting state in a time period t, and under the condition of neglecting the simultaneous failure of more than two sets, the discrete probability expression of the available generating capacity of the conventional set in the time period is as follows:
in the above formula, G 0 Is the available generating capacity p when the units are all in normal state 0 For the corresponding probability, it can be calculated by:
G j (j =1,2, \ 8230; m) is the available power generation capacity in case of a single unit failure, p j Is the corresponding probability. Assume that the index of the failed set is k, G j 、p j Respectively as follows:
G j ＝G 0 -P max,k
G j (j = m +1, m +2, \ 8230and m (m + 1)/2) is the available power generation capacity when two units fail simultaneously, and p is the available power generation capacity j Is the probability of an event occurring. Assume the index of the failed group is k 1 、k 2 ，G j 、p j Respectively as follows:
step 5, assuming that m sets are in a starting state in a time period t, and under the condition of neglecting the simultaneous failure of more than two sets, the discrete probability expression of the total minimum technical output of the conventional set in the time period is as follows:
in the above formula, G 0 The total minimum technical output, p, of the conventional unit when the units are all in the normal state 0 For the corresponding probability, it can be calculated by:
G j (j =1,2, \8230m) is the total minimum technical output of the conventional unit when a single unit fails, p j Is the corresponding probability. Assume that the index of the failed group is k, G j 、p j Respectively as follows:
G j ＝G 0 -P min,k
G j (j = m +1, m +2, \ 8230m (m + 1)/2) is the total minimum technical output of the conventional unit when two units simultaneously fail, p j Is the probability of an event occurring. Assume the index of the failed group is k 1 、k 2 ，G j 、p j Respectively as follows:
and 6, once the sum of the minimum technical output and the wind power of the conventional unit is greater than the load, the wind abandon can be caused due to the restriction of the peak shaving capacity of the conventional unit. Based on the electric quantity expectation C of 'wind curtailment' in time period t w,t Can be calculated from the following formula:
if x 0 <0 x 0 ＝0
if x 0 >1 x 0 ＝1
once the actual load is larger than the sum of the available power generation capacity and the wind power, partial load power failure can be caused due to insufficient available power generation capacity, and based on the partial load power failure probability V of the time period t LOLP,t Can be calculated from the following formula:
if P d,l -G j <0 P d,l -G j ＝0
if P d,l -G j >G wind P d,l -G j ＝G wind
the existing wind power acceptance evaluation method only gives a single evaluation result, namely the maximum theoretical wind power acceptance capability of a power grid is mainly emphasized and displayed, and the cost problem of wind power acceptance is ignored. Therefore, the invention discloses an acceptance cost index of a wind power unit, which is used for measuring the cost price paid by a power grid for accepting wind power. The index can be calculated on the basis of a Pareto optimal solution set given by a wind power acceptance capability evaluation model, and the method is as follows.
Assuming that a Pareto optimal solution set of the wind power acceptance capacity evaluation model consists of n different solutions, for convenience of description, the wind power acceptance capacity A is used w The Pareto optimal solution set is sorted according to the size of the solution, and after sorting, the following relations exist:
A W,1 <A W,2 <…A W,n
according to the optimal definition of Pareto, the power generation cost corresponding to each solution satisfies the following relation:
C G,1 <C G,2 <…C G,n
generating cost C corresponding to the 1 st solution in the sorted Pareto optimal solution set G,1 Lowest, but corresponding, wind power acceptance level A W,1 And minimum. That is, the dispatcher only considers the lowest power generation cost at the moment, and completely ignores the acceptance of the wind power quantity. At the moment, the wind power receiving electric quantity A W,1 The admission cost is zero for the wind power that the system can naturally admit. For other solutions in the Pareto optimal solution set, the power generation cost of the system is improved to different degrees in order to accommodate more wind power. Obviously, the additionally increased conventional system power generation cost can be regarded as the wind power acceptance cost, and the unit wind power acceptance cost C disclosed by the invention Apu,i The cost price paid by the system for accepting more wind power can be calculated according to the following formula:
Claims (1)
1. a wind power unit acceptance cost index determining method of a power grid day-ahead wind power acceptance capacity evaluation method based on multi-objective optimization is characterized by comprising the following steps: wind power unit acceptance cost index C based on power grid day-ahead wind power acceptance capability evaluation model Pareto optimal solution set Apu,i Calculated as follows:
according to the optimal definition of Pareto, the power generation cost corresponding to each solution satisfies the following relation:
C G,1 <C G,2 <…C G,n
the Pareto optimal solution set of the wind power acceptance capacity evaluation model consists of n different solutions and is based on wind power acceptance electric quantity A w The Pareto optimal solution set is sorted according to the size of the Pareto optimal solution set, and after sorting, the following relations exist:
A W,1 <A W,2 <…A W,n ；
the method for evaluating the day-ahead wind power receiving capacity of the power grid based on multi-objective optimization comprises the following steps:
the day-ahead wind power admitting ability evaluation model of the power grid has two optimization targets of maximum wind power admitting ability and minimum conventional system power generation cost, which are as follows:
optimization objective 1:
optimization objective 2:
in the above formula, A w The wind power in the dispatching day is admitted with electric quantity, and the expectation of wind power abandoning is subtracted from the predicted electric quantity of the wind power in the dispatching day; p i,t The output power of the unit i in the time period t is obtained; u. of i Representing the running state of the unit i in a dispatching day, wherein '0' represents shutdown, and '1' represents startup; t is the number of scheduling time segments; f w,t The maximum theoretical output of wind power in the time period t is the predicted value of wind power; c w,t The expected electric quantity of the wind curtailment for the time period t, and P i,t And u i (ii) related; c G The power generation cost of the conventional system in the dispatching day; n is the number of conventional units; f. of i (P i,t ) Fitting a fuel cost function of the unit i in a time period t by a quadratic function;
the constraint conditions of the wind power receiving capacity evaluation model before the power grid day are as follows:
and (3) system active balance constraint:
in the above formula, P d,t For the predicted value of the load at time t, P w,t The wind power grid-connected electric quantity at the moment t;
and (3) output constraint of a conventional unit:
P min,i ≤P i,t ≤P max,i
in the above formula, P max,i 、P min,i Respectively the maximum and minimum technical output of the unit i;
and (3) climbing restraint:
P i,t -P i,t-1 ≤ΔTR up,i
P i,t-1 -P i,t ≤ΔTR down,i
in the above formula, R up,i 、R down,i Respectively the maximum increasing and decreasing force rates of the unit i;
system security constraints:
V LOLP,t ≤R LOLP
in the above formula, V LOLP,t Probability of loss of load for scheduling period t, R LOLP A desired level of operational reliability;
wind power constraint:
P w,t ≤F w,t ；
the method comprises the following steps of solving a day-ahead wind power receiving capacity evaluation model of a power grid by adopting a genetic algorithm based on non-dominated classification, and solving a Pareto optimal solution set of the evaluation model, wherein the method comprises the following specific steps:
step 1, randomly generating an initial chromosome population of a genetic algorithm, wherein the population size is 10N; representing a chromosome in the chromosome population by using a binary code with the length of N; each chromosome gives the running state of N conventional units in a scheduling day, wherein '0' represents shutdown and '1' represents startup;
step 2, evaluating the reliability of the chromosomeEstimating, carrying out economic dispatching calculation on chromosomes meeting safety constraint, and calculating the power generation cost C of the conventional system on the basis G And calculating wind curtailment power expectation C of the chromosomes meeting the safety constraint in the scheduling day w,t ；
Step 3, the chromosomes in the population are layered according to the non-inferior solution levels, the smaller the level index is, the higher the non-inferior solution level is, and the lowest the non-inferior solution level of the chromosome which does not meet the security constraint is during layering; assuming that the population can be divided into m layers, for an individual i, if the non-inferior solution level is j, the fitness V of the individual is fit,i Comprises the following steps:
V fit,i ＝10N-j
i＝0,1,…,10N j＝0,1,…,m
step 4, calculating the local crowding distance of each individual in the same non-inferior solution layer; when calculating the local crowding distance, the individuals are divided into two categories: individuals at the sorting edge and individuals in the middle of the sorting; for individuals at the sequencing edge, a larger numerical value is directly assigned to the local crowding distance of the individuals, so that the individuals obtain the selection advantage; for the individuals in the middle of the ranking, the local crowding distance is the sum of the lengths of two sides of a rectangle formed by taking two adjacent individuals as vertexes;
step 5, directly copying the non-inferior solution set in the parent population to the child population as a part of the child population; selecting other individuals in the offspring population according to the individual fitness and the local crowding distance, namely randomly selecting two individuals from the parent, selecting the individual with high fitness if the fitness values are different, and selecting the individual with large local crowding distance if the fitness values are the same; repeating the selection operation until a filial generation population is formed; carrying out cross and mutation operations on the offspring population according to a certain probability;
step 6, repeatedly executing the steps 2 to 5 until the algorithm meets the preset convergence condition;
wind curtailment electric quantity expectation C for each scheduling period by adopting analytic probability algorithm w,t And the probability of losing load V LOLP,t The calculation is carried out by the following steps:
step 1, representing the probability characteristic of random fluctuation of wind power near a predicted value by adopting a general probability distribution function, wherein the probability density function and the accumulative probability distribution function are respectively shown as the following formulas:
F(x)＝{1+exp[-α(x-γ)]} -β
step 2, adopting normal distribution N (P) d,t ,σ d,t ) Representing the probability characteristic of the load fluctuating randomly around the predicted value, and adopting 7 discrete probability points to normal distribution N (P) d,t ,σ d,t ) Performing approximate approximation, namely:
step 3, expressing the random fault characteristics of the unit by adopting an improved two-state model without considering element repair, and determining the fault rate f of the unit i in a time period t i,t Comprises the following steps:
f i,t ＝1-exp[-λ i (T LD +t)]≈λ i (T LD +t)
in the formula, T LD Evaluating the advance time for the wind power receiving capacity; lambda [ alpha ] i The average failure rate of the unit i is obtained;
step 4, assuming that m sets are in a starting state in a time period t, and under the condition of neglecting the simultaneous failure of more than two sets, the discrete probability expression of the available generating capacity of the conventional set in the time period is as follows:
in the above formula, G 0 Is the available generating capacity p when the units are all in normal state 0 For the corresponding probability, it can be calculated by:
G j (j =1,2, \ 8230; m) is the available power generation capacity in case of a single unit failure, p j Is the corresponding probability; assume that the index of the failed group is k, G j 、p j Respectively as follows:
G j ＝G 0 -P max,k
G j (j = m +1, m +2, \ 8230and m (m + 1)/2) is the available power generation capacity when two units fail simultaneously, and p is the available power generation capacity j Is the probability of an event occurring; assume the index of the failed group is k 1 、k 2 ，G j 、p j Respectively as follows:
step 5, assuming that m sets are in a starting state in a time period t, and under the condition of neglecting the simultaneous failure of more than two sets, a discrete probability expression of the total minimum technical output of the conventional set in the time period is as follows:
in the above formula, G 0 The total minimum technical output, p, of the conventional unit when the units are all in a normal state 0 To correspond toCan be calculated by:
G j (j =1,2, \8230m) is the total minimum technical output of the conventional unit when a single unit fails, p j Is the corresponding probability; assume that the index of the failed set is k, G j 、p j Respectively as follows:
G j ＝G 0 -P min,k
G j (j = m +1, m +2, \ 8230m (m + 1)/2) is the total minimum technical output of the conventional unit when two units simultaneously fail, p j Is the probability of an event occurring; assume the index of the failed group is k 1 、k 2 ，G j 、p j Respectively as follows:
step 6, calculating the expected abandoned wind electric quantity C of each scheduling period w,t And the loss of load probability V LOLP,t As shown in the following formula:
if P d,l -G j <0 P d,l -G j ＝0
if P d,l -G j >G wind P d,l -G j ＝G wind
if x 0 <0 x 0 ＝0
if x 0 >1 x 0 ＝1。
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