CN110363362A - A kind of multiple target economic load dispatching model and method a few days ago of meter and flexible load - Google Patents
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Abstract
The invention discloses a kind of meter and the multiple target economic load dispatching model and methods a few days ago of flexible load.Initially set up wind power output uncertainty models and negative rules model, in the case where considering systematic economy cost and carbon emission amount at the same time, fired power generating unit in system is modeled respectively with flexible load, it is constrained using the spinning reserve of chance constrained programming processing system, pass through improved CMOPSO algorithm solving model, it is focused to find out optimal compromise from Pareto solution using fuzzy membership function to solve, i.e., the solution of balanced two objective functions.The present invention can made a few days ago unit output plan in second day and met the fluctuation of wind-powered electricity generation and load with certain confidence level, guarantee system reliability of operation；Demand-side flexible load participates in system call, can carry out cooperation with Generation Side, reach peak load shifting, alleviate the effect of peak period Voltage force；Modified hydrothermal process can increase the search range to object space, improve convergence and diversity.
Description
Technical Field
The invention relates to the field of economic dispatching of a single-area day-ahead power system, in particular to a multi-target day-ahead economic dispatching model and method considering flexible loads under the condition of considering source-load bilateral uncertainty.
Background
Along with the development of economy and the progress of society, a large amount of fossil fuels such as coal and petroleum are used, the consumption of global non-renewable energy resources is increased year by year, meanwhile, the carbon emission is greatly increased due to the large amount of combustion of the fossil fuels such as coal and petroleum, and the environmental problems of human beings are increasingly severe. Therefore, the concept of low carbon emission reduction is proposed and is approved and valued by people. In recent years, on the one hand, power generation technology of renewable energy sources is gradually mature and is already incorporated into power system scheduling. Renewable energy sources mainly include wind energy, solar energy, water energy and the like. Among them, the power generation technology of wind energy is the most mature and has been widely used. On the other hand, carbon emissions are also considered as a major factor in power system scheduling. At the same time, part of the loads on the demand side, which are referred to as flexible loads, may also participate in the economic dispatch of the power system. Due to the participation of the flexible load, the dispatching of the power system is more flexible, economic and reasonable.
Because wind power generation has strong volatility, intermittence and reverse peak-shaving characteristics, the influence of wind power fluctuation on a plan is considered when the plan of the next day is made in the day ahead. Most of the existing processing modes related to the wind power participation system directly give the rotation reserve rate which needs to be reserved for wind power fluctuation when planning in the day ahead, but the mode is too rigid and cannot be flexibly scheduled according to the actual condition of a power grid.
The load on the demand side also has fluctuations. Meanwhile, the demand side load can be divided into an inflexible load and a flexible load, and the scheduling of the demand side flexible load is one of important means for relieving the contradiction between power supply and demand, and is gradually brought into the problem of economic scheduling of a power system in recent years. But most of the existing documents are too simple for classifying and modeling the flexible load.
With the popularization of the energy saving and emission reduction concept, two targets of economic cost and carbon emission are required to be considered simultaneously in the power system scheduling. In the prior art, a multi-target problem processing mode adopts a weighting method to process two targets into one target according to weighting coefficients, and then a single-target algorithm is adopted to solve the problem. However, the setting of the weight coefficient is generally given by people according to subjective will and is not objective enough. In recent years, some scholars have proposed a plurality of intelligent optimization algorithms for processing multi-objective problems, but the algorithms have shortcomings in processing complex multi-objective optimization problems.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a flexible load-based multi-target day-ahead economic dispatching model and a flexible load-based multi-target day-ahead economic dispatching method, aiming at solving a reasonable day-ahead plan formulated through source-to-load interaction under the condition of considering source-to-load bilateral uncertainty by adopting an improved multi-target particle swarm algorithm based on a competition mechanism.
The invention adopts the following technical scheme for solving the technical problems:
a multi-target day-ahead economic dispatching model building method considering flexible loads takes a single region as a research object, a power generation side in the model mainly comprises a conventional thermal power generating unit and a wind power generating unit, a load side comprises a rigid load and a flexible load, and the flexible load is mainly divided into three types: the load can be reduced, the load can be translated and the load can be translated; the method is characterized in that the establishment and solving method of the multi-target day-ahead economic dispatching model is carried out according to the following steps:
step 1: establishing source-load bilateral uncertainty model
Step 1.1, establishing an uncertainty model of source side wind power
The wind power prediction error follows normal distribution with the mean value of 0:
the function relation between the standard deviation of the wind power prediction error and the wind power prediction value is as follows:
σW，t＝kW1PWZ，t+kW2 (2)
therefore, the actual power of the wind power is equal to the sum of the predicted wind power value and the predicted wind power error:
P′WZ，t＝PWZ，t+ΔPWZ，t (3)
in the formula: delta PWZ，tPredicting an error for the wind power at the time t; sigmaW，tThe standard deviation of the wind power prediction error in the t time period is obtained; pWZ，tPredicting total power for wind power at t time interval; k is a radical ofW1、kW2The prediction error coefficient of the wind power is obtained; p'WZ，tThe actual total power of wind power is t time period;
step 1.2, establishing uncertainty model of load side load
Similar to the wind power prediction error, the load prediction error can also be regarded as a random variable with an average value of 0 and obeying normal distribution:
the function relation between the standard deviation of the load prediction error and the load prediction value is as follows:
σL，t＝kLPL，t (5)
thus, the actual load value is equal to the sum of the predicted load value and the predicted load error:
P′L，t＝PL，t+ΔPL，t(6) in the formula: delta PL，tLoad prediction error for time period t; sigmaL，tStandard deviation of load prediction error for time t; k is a radical ofLPredicting an error coefficient for the load; p'L，tIs the actual load value in the t period; pL，tLoad prediction value is t time interval;
step 2: establishing cost model of conventional thermal power generating unit
Step 2.1, generating cost of conventional thermal power generating unit
In the formula: f. ofcThe power generation cost of the conventional thermal power generating unit is reduced; pi，tThe active power of the unit i at the moment t is obtained; ci(Pi，t) The coal burning cost of the conventional thermal power generating unit i at the time t is calculated; u. ofi，tStarting and stopping state variables of the unit i at the moment t, and ui，t1 indicates that the unit is in a power-on state, ui，t0 represents that the unit is in a shutdown state; t represents the number of time periods; i represents the number of the machine sets;
wherein,
Ci(Pi，t)＝aiPi，t 2+biPi，t+ci(8) in the formula: a isi、bi、ciThe coal cost coefficient of the unit i is obtained;
step 2.2, starting and stopping cost of conventional thermal power generating unit
In the formula: f. ofsThe starting and stopping cost of the conventional thermal power generating unit is reduced; si *The starting cost coefficient of the unit i is obtained;
step 2.3, carbon emission of conventional thermal power generating unit
On the premise of not considering the loss of the power grid, the carbon emission caused by the consumption of the primary energy on the power generation side should theoretically be equal to the carbon emission caused by the consumption of the load electric energy on the demand side; therefore, the calculation of CO from the power generation side angle is selected2Emissions, usually expressed as a quadratic function:
in the formula: f. of2The carbon emission of the conventional thermal power generating unit; x is the number ofi、yi、ziThe emission coefficient of the polluted gas of the unit i is obtained;
and step 3: establishing flexible load compensation cost model
The flexible load at the demand side is generally divided into three types according to the response characteristic of the flexible load to participate in the system economic dispatching, namely, the load can be reduced, the load can be translated and the load can be translated;
step 3.1, load scheduling cost can be reduced
The load reduction capability generally refers to a load which is partially or entirely reduced according to the supply and demand conditions of the electric power, and the compensation cost after the load reduction is
In the formula: f. ofcutThe compensation cost for load scheduling can be reduced; ccutfix，tA fixed compensation cost for load reduction at time t; ccut，pCompensation cost for active reduction of load units; u. ofcut，tIs in a load reduction state at time t, and ucut，t1 indicates that the load is reduced, ucut，t0 means that the load is not reduced; p is a radical ofcut，tThe reduction amount of the load at the time t can be reduced;
step 3.2, load scheduling cost can be translated
The translatable load is restricted by the production flow, and the power utilization time period has strict continuity, so that the translatable load can only be translated integrally within a certain time period in a fixed continuous time period; the compensation cost after the translational load scheduling is
In the formula: f. ofshA penalty fee for translatable load scheduling; j is the set of original starting time of the translatable load; j. n is the original starting time and the original ending time of the translatable load respectively; t is tshFor load to be translatable after translationStarting time;a translation period interval that is load acceptable; csh，p，tThe compensation cost of the load unit active power translation at the moment t; p is a radical ofsh，tIs an original interval [ j, n]The active power of the load can be shifted at the internal time t, andwhen is, Psh，t＝0；Is a translational state of the load, andrepresenting loads from [ j, n]Time interval is translated to tsh，tsh+n-j]The period of time is,indicating that the load is not translating;
step 3.3, transferable load scheduling cost
Transferable load generally refers to a load in which the total power consumption remains unchanged during the whole scheduling period T, but can be flexibly adjusted in a part of time period; there are no continuous and chronological limitations compared to translatable loads; the compensation cost after the load dispatching can be transferred to
In the formula: f. oftrA penalty fee for transferable load scheduling; m is a set of transferable loads at the original running time; a transition period interval in which the load is acceptable; ctr，p，tFor time t load sheetThe compensation cost of the position active power transfer; p is a radical oftr，tFor time t to shift to ttrThe active power at a moment;is a transition state of the load, and indicates the load shift to ttrAt the moment of time, the time of day,indicates that the load is not transferred to ttrTime of day;
and 4, step 4: establishing multi-target day-ahead economic dispatching model
The objective function of the multi-objective day-ahead economic dispatching model comprises the economic operation cost of the system and the carbon emission of the conventional thermal power generating unit; the economic operation cost of the system mainly comprises the operation cost, the start-stop cost and the dispatching cost of three types of flexible loads on demand sides of a conventional thermal power generating unit; the model scheduling cycle is 24 periods a day, and the two objective functions are:
f1＝fc+fs+fcut+fsh+ftr (14)
a multi-target day-ahead economic dispatch model solving method considering flexible loads is characterized in that an improved multi-target particle swarm algorithm based on a competition mechanism is adopted to solve the model, and the method comprises the following steps:
step 1: inputting data and related parameters required by model solution; the model data mainly comprises: the method comprises the following steps of (1) relevant parameters of 10 units, parameters of 3 types of flexible loads, data of day-ahead wind power, data of day-ahead loads and the like; the parameters mainly comprise: the method comprises the following steps of (1) population scale, maximum iteration times, polynomial intersection, variation probability and start-stop variation probability of a thermal power generating unit;
step 2: and (3) encoding: encoding the particles in the particle swarm in a real number encoding mode;
and step 3: initializing a population to obtain the position and the speed of particles;
step 3.1, adjusting the particles in the generated initial population, and performing adaptability evaluation on the adjusted individuals; checking whether the adjusted particles meet the rotation standby constraint of the system or not, and processing the particles which do not meet the rotation standby constraint in a penalty function mode; the objective function value of the particle meeting the rotating standby constraint is the fitness value of the particle;
step 3.2, if the constraint violation degree of the partial particles after multiple adjustments is still higher, discarding the partial particles and regenerating the partial particles; then, adjusting the newly generated particles again, and performing adaptability evaluation on the adjusted particles; checking whether the adjusted particles meet the rotation standby constraint of the system or not, and processing the particles which do not meet the rotation standby constraint in a penalty function mode; the objective function value of the particle meeting the rotating standby constraint is the fitness value of the particle;
and 4, step 4: archive set update operation: obtaining a first layer of non-dominated individuals from the current population and adding the first layer of non-dominated individuals into an archive set;
and 5: executing a learning strategy based on a competition mechanism to update the positions of the particles to be updated in the population;
step 6: performing a polynomial mutation operation on the updated particle;
and 7: performing unit start-stop variation operation on the varied particles;
and 8: adjusting the particles obtained after the variation operation of the start and the stop of the unit, performing adaptability evaluation on the adjusted particles, checking whether the adjusted particles meet the rotation standby constraint of the system, and processing the particles which do not meet the rotation standby constraint in a penalty function mode; the objective function value of the particle meeting the rotating standby constraint is the fitness value of the particle;
and step 9: combining the updated population with the initial population, and deleting a part of particles to obtain a new population;
step 10: merging the new population and the archive set, and performing the update operation on the archive set again to obtain a new archive set;
step 11: performing an evolutionary search strategy on the new archive set;
step 11.1, creating a set S, merging the set S with a new archive set, and then executing the update operation of the archive set again to obtain an updated archive set;
step 12: judging whether a termination condition is met, and if so, outputting particles in a file set; otherwise, return to step 5.
The method for establishing the multi-target day-ahead economic dispatching model considering the flexible load is characterized in that the constraint conditions of the multi-target day-ahead economic dispatching model are as follows:
PLreal，t＝PLb，t-ucut，tpcut，t+pLsh，t+pLtr，t (17)
-rdiΔT≤Pi，t-Pi，t-1≤ruiΔT (19)
equations (16) to (17) are power balance constraints, where PLb，tLoad at time t when the load can be transferred and the load can be transferred is not counted; pLreal，tThe total load participating in scheduling for the flexible load at the time t; p is a radical ofLsh，tTo translate to a translatable loadtranslation amount at time t; p is a radical ofLtr，tThe transferable load is transferred to the transfer amount at the time t; wherein, the translation amount has two conditions: the translation amount is pLsh，tWhen the load is shifted, the load is not in the original interval or the acceptable shifting interval; ② the translation amount is The translation load is in the original interval or the acceptable translation interval after translation; there are also two cases of the amount of transfer: (ii) the amount of load transfer pLtr，t0, this means that the transferable load is not transferred at the original operating time tcNor within an acceptable transition interval; ② amount of transfer In the formula ptr，cFrom the original initial moment t for transferable loadcTransfer to ttrThe transferable amount of time indicates the transferable load at that time at the original operation time tcOr within an acceptable transition interval; the formula (18) is the constraint of the upper and lower output limits of the thermal power generating unit, wherein,the method comprises the following steps of respectively obtaining the minimum value and the maximum value of the i output of the thermal power generating unit at the t-time period;
formula (19) is a climbing constraint, wherein rdi、ruiThe downward climbing speed and the upward climbing speed of the unit i are respectively; Δ T is the time interval of the scheduling period;
equation (20) is the unit start-stop time constraint, where MDTi、MUTiRespectively the minimum outage time and the minimum operation time of the unit i;
the upper and lower limit constraints of the load can be reduced by the equation (21),an upper limit and a lower limit for reducing the load reduction amount at time t, respectively;
equations (22) to (25) are the load shedding minimum duration constraints; wherein, the expressions (22) to (23) represent that the load reduction due to the limitation of the minimum duration time is continuously executed at the end of the last scheduling period, the expression (24) is used for restricting the load reduction minimum duration time in the scheduling period, and the expression (25) is used for restricting the load reduction minimum duration time at the end of the scheduling period; wherein,minimum duration for load shedding;the number of the reduced duration periods of the initial period is obtained from the data of the last scheduling period;
equations (26) to (28) are the load shedding maximum duration constraints; wherein, expressions (26) to (27) represent maximum duration constraints considering the reduction state at the end of the previous scheduling period, and expression (28) is used for constraining the maximum duration of load reduction in the scheduling period; wherein,maximum duration for load shedding;the number of the time intervals of which the initial time interval is continuously reduced can be obtained from the data of the last scheduling cycle;
equation (29) is the limit of the number of load reductions, where CcutfixFixed compensation cost for each reduction;the number of allowed maximum loads in a scheduling period is reduced;
equations (30) - (31) are system rotation reserve capacity constraints, wherein α and β respectively represent confidence degrees that positive and negative rotation reserves meet requirements;
equation (32) is the start time constraint after the translatable load is translated;
formula (33) is transferable load transfer to ttrThe constraint of the time of day.
The method for solving the multi-target day-ahead economic dispatch model considering the flexible load is characterized in that a real number coding mode is adopted for particles in the step 2, and the method specifically comprises the following variables: the I conventional thermal power generating units at all the moments in the whole dispatching cycle are used for planning power values, the load reduction amount can be reduced at all the moments in the whole dispatching cycle, the translation amount of all the moments after the load can be translated in the whole dispatching cycle and the translation amount of all the moments after the load can be translated in the whole dispatching cycle.
The method for solving the multi-target day-ahead economic dispatching model considering the flexible load is characterized in that in the particle adjusting links of the steps 3.1, 3.2 and 8, the specific adjusting steps are as follows:
firstly, obtaining an expected output value of each conventional thermal power generating unit at 24 moments according to a power balance equality relation, then randomly generating an output planned value of each conventional thermal power generating unit at 24 moments, and then obtaining a difference value between the output planned value and the output planned value, namely a power unbalance amount;
judging whether the randomly generated transferable loads meet the constraint that the total amount is unchanged in the whole scheduling period, and if not, adjusting to ensure that the total amount is unchanged at 24 moments in one day; the specific adjustment steps are as follows: calculating the proportion of the transferable loads at all times generated randomly before adjustment to the total transferable loads at all times, and then redistributing the transferable loads according to the proportion;
judging whether the randomly generated reducible load meets upper and lower limit constraints for reducing the load amount and constraints for reducing time and reducing times; if not, adjusting the upper and lower limit constraints capable of reducing the load, then adjusting the reduction time capable of reducing the load according to the initial continuous reduction time period number, the minimum and large time constraints capable of reducing the load, and finally performing the reduction number constraint limitation on the reduction time, so as to adjust the reducible load to be within a reasonable range;
recalculating the power unbalance after the adjustment of the steps;
and then adjusting the conventional thermal power generating unit: firstly, judging whether the output upper limit and the output lower limit of a conventional thermal power generating unit are met, if not, adjusting, and recalculating the power unbalance; judging the climbing constraint of the conventional thermal power generating unit, if the climbing constraint is not met, adjusting, and recalculating the power unbalance; finally, judging the minimum starting and stopping time constraint of the conventional thermal power generating unit, if the minimum starting and stopping time constraint is not met, adjusting, and recalculating the power unbalance;
and respectively calculating the maximum value of the positive and negative rotation reserve of each conventional thermal power generating unit at each moment, thereby obtaining the maximum value of the total positive and negative rotation reserve provided by 10 thermal power generating units at each moment.
The method for solving the multi-target day-ahead economic dispatch model considering the flexible load is characterized in that the archive set updating operation in the steps 4, 10 and 11.1 comprises the following specific processes:
firstly, sorting particles in a population according to a rapid non-dominated sorting method;
then the crowding degree of the population particles is obtained through the crowding degree distance calculation;
selecting the particles according to the number of layers and the crowding degree of the particles;
the method for solving the multi-target day-ahead economic dispatch model considering the flexible load is characterized in that in the learning strategy based on the competition mechanism in the step 5, two particles are arbitrarily selected from an archive set to execute competition operation, and winning particles are used for guiding the update of the particles to be updated.
The method for solving the multi-target day-ahead economic dispatch model considering the flexible load is characterized in that the specific operation of deleting partial particles of the combined population in the step 9 is as follows:
and executing environment selection operation on the combined population, deleting the inferior particles and keeping the superior particles. Wherein, the main steps of environment selection are as follows:
firstly, sorting particles in a population according to a rapid non-dominated sorting method;
particles exceeding a predetermined size are deleted by a chopping operation.
Compared with the prior art, the invention has the beneficial effects that:
the established single-region day-ahead economic dispatching model is solved through the improved multi-target particle swarm optimization based on the competition mechanism, the planned value of the output of the day-ahead conventional thermal power generating unit and the dispatching planned value of the flexible load can be obtained, the searching range of the multi-target particle swarm optimization to the target space can be enlarged aiming at the condition that the convergence and diversity are poor when the traditional multi-target intelligent optimization algorithm is used for solving the problem, and the convergence and diversity of the algorithm can be improved to a certain extent.
The invention aims at the situation of a given rotation reserve rate existing in the prior literature on the problem of processing uncertainty of wind power and load, and selects the reserve capacity problem required by an opportunity constraint planning processing system. And a positive and negative system rotation standby confidence interval is given, so that the output of the unit can be increased or reduced to meet the requirement at each moment in a scheduling period.
According to the invention, the load of the demand side and the power generation side are coordinated and matched to participate in the economic dispatching of the power system, so that the power utilization time of the power system can be adjusted by a user, and the satisfaction degree of the user is increased; the effects of peak clipping and valley filling and relieving the voltage during the peak period are achieved to a certain extent.
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FIG. 1 is an overall flow chart of the present invention.
Fig. 2 is a schematic diagram of a single-zone power system according to the present invention.
FIG. 3 is a flow chart of the present invention for solving a model using the modified CMOPSO algorithm.
Detailed Description
A multi-target day-ahead economic dispatching model and method considering flexible loads are applied to a single-area power system shown in figure 2 and mainly comprise the following steps: a wind generating set, a conventional thermal generating set and a demand side load; the demand side loads are mainly divided into two main categories: rigid and flexible loads; wherein the flexible load mainly comprises a load reducible load, a load translatable and a load translatable. In consideration of uncertainty of wind power generation and load requirements, an improved multi-target particle swarm algorithm based on a competition mechanism is adopted to solve a day-ahead plan of the system, and the obtained dispatching plan can meet wind power and load fluctuation at each moment in the day within a certain confidence interval, so that the economy and flexibility of the system are improved. The flow chart is shown in fig. 1 and 3.
The invention relates to a multi-target day-ahead economic dispatching model and a method considering flexible load, which are carried out according to the following steps:
step 1: establishing a source-load bilateral uncertainty model, which comprises a power generation side wind generating set output uncertainty model and a demand side load uncertainty;
step 2: establishing a multi-target day-ahead economic dispatching model considering flexible loads, which mainly comprises the cost of a conventional thermal power generating unit, the establishment of 3 types of flexible load cost models, power balance constraint, related constraint of the conventional thermal power generating unit, related constraint of 3 types of flexible loads and rotation standby constraint of a system; the method comprises the following steps that an opportunity constraint planning mode is adopted to process the rotation standby problem of the system, a series of wind power samples and load samples are randomly generated according to the characteristic that the wind power prediction error and the load prediction error are in normal distribution, and the variation condition of wind power output and load is simulated;
and step 3: the method comprises the following steps of adopting a real number coding mode for particles, and solving the model through an improved multi-target particle swarm algorithm based on a competition mechanism;
the specific steps of the uncertainty model building in step 1 are as follows:
step 1.1, establishing an uncertainty model of source side wind power
The wind power prediction error follows normal distribution with the mean value of 0:
the function relation between the standard deviation of the wind power prediction error and the wind power prediction value is as follows:
σW，t＝kW1PWZ，t+kW2 (2)
therefore, the actual power of the wind power is equal to the sum of the predicted wind power value and the predicted wind power error:
P′WZ，t＝PWZ，t+ΔPWZ，t (3)
in the formula: delta PWZ，tPredicting an error for the wind power at the time t; sigmaW，tThe standard deviation of the wind power prediction error in the t time period is obtained; pWZ，tPredicting total power for wind power at t time interval; k is a radical ofW1、kW2The prediction error coefficient of the wind power is obtained; p'WZ，tThe actual total power of wind power is t time period;
step 1.2, establishing uncertainty model of load side load
Similar to the wind power prediction error, the load prediction error can also be regarded as a random variable with an average value of 0 and obeying normal distribution:
the function relation between the standard deviation of the load prediction error and the load prediction value is as follows:
σL，t＝kLPL，t (5)
thus, the actual load value is equal to the sum of the predicted load value and the predicted load error:
P′L，t＝PL，t+ΔPL，t(6) in the formula: delta PL，tLoad prediction error for time period t; sigmaL，tStandard deviation of load prediction error for time t; k is a radical ofLPredicting an error coefficient for the load; p'L，tIs the actual load value in the t period; pL，tLoad prediction value is t time interval;
the specific steps of establishing the day-ahead economic dispatch model in the step 2 are as follows:
step 2.1, generating cost of conventional thermal power generating unit
In the formula: f. ofcThe power generation cost of a conventional generator set; pi，tThe active power of the unit i at the moment t is obtained; ci(Pi，t) The coal burning cost of the unit i at the time t is calculated; u. ofi，tStarting and stopping state variables of the unit i at the moment t, and ui，t1 indicates that the unit is in a power-on state, ui，t0 represents that the unit is in a shutdown state; t represents the number of time periods; i represents the number of the machine sets;
wherein,
Ci(Pi，t)＝aiPi，t 2+biPi，t+ci(8) in the formula: a isi、bi、ciThe coal cost coefficient of the unit i is obtained;
step 2.2, starting and stopping cost of conventional thermal power generating unit
In the formula: f. ofsThe starting and stopping cost of the conventional thermal power generating unit is reduced; si *The starting cost coefficient of the unit i is obtained;
step 2.3, carbon emission of conventional thermal power generating unit
On the premise of not considering the loss of the power grid, the carbon emission caused by the consumption of the primary energy on the power generation side should theoretically be equal to the carbon emission caused by the consumption of the load electric energy on the demand side; therefore, the calculation of CO from the power generation side angle is selected2Emissions, usually expressed as a quadratic function:
in the formula: f. of2The carbon emission of the conventional thermal power generating unit; x is the number ofi、yi、ziThe emission coefficient of the polluted gas of the unit i is obtained;
step 2.4, establishing a flexible load compensation cost model
The flexible load at the demand side is generally divided into three types according to the response characteristic of the flexible load to participate in the system economic dispatching, namely, the load can be reduced, the load can be translated and the load can be translated;
step 2.5, load scheduling cost can be reduced
The load reduction capability generally refers to a load which is partially or entirely reduced according to the supply and demand conditions of the electric power, and the compensation cost after the load reduction is
In the formula: f. ofcutThe compensation cost for load scheduling can be reduced; ccutfix，tA fixed compensation cost for load reduction at time t; ccut，pCompensation cost for active reduction of load units; u. ofcut，tIs in a load reduction state at time t, and ucut，t1 indicates that the load is reduced, ucut，t0 means that the load is not reduced; p is a radical ofcut，tThe reduction amount of the load at the time t can be reduced;
step 2.6, load scheduling cost can be translated
The translatable load is restricted by the production flow, and the power utilization time period has strict continuity, so that the translatable load can only be translated integrally within a certain time period in a fixed continuous time period; the compensation cost after the translational load scheduling is
In the formula: f. ofshA penalty fee for translatable load scheduling; j is the set of original starting time of the translatable load; j. n is the original starting time and the original ending time of the translatable load respectively; t is tshThe initial time of the load which can be translated after translation;a translation period interval that is load acceptable; csh，p，tThe compensation cost of the load unit active power translation at the moment t; p is a radical ofsh，tIs an original interval[j，n]The active power of the load can be shifted at the internal time t, andwhen is, psh，t＝0；Is a translational state of the load, andrepresenting loads from [ j, n]Time interval is translated to tsh，tsh+n-j]The period of time is,indicating that the load is not translating;
step 2.7, transferable load scheduling cost
Transferable load generally refers to a load in which the total power consumption remains unchanged during the whole scheduling period T, but can be flexibly adjusted in a part of time period; there are no continuous and chronological limitations compared to translatable loads; the compensation cost after the load dispatching can be transferred to
In the formula: f. oftrA penalty fee for transferable load scheduling; m is a set of transferable loads at the original running time; a transition period interval in which the load is acceptable; ctr，p，tThe compensation cost of the active power transfer of the load unit at the moment t; p is a radical oftr，tFor time t to shift to ttrThe active power at a moment;is a transition state of the load, and indicates the load shift to ttrAt the moment of time, the time of day,indicates that the load is not transferred to ttrTime of day;
step 2.8, establishing a multi-target day-ahead economic dispatching model
The objective function of the multi-objective day-ahead economic dispatching model comprises the economic operation cost of the system and the carbon emission of the conventional thermal power generating unit. The economic operation cost of the system mainly comprises the established conventional thermal power unit model and the 3-type demand side flexible load model. The model scheduling cycle is 24 periods a day, and the two objective functions are:
f1＝fc+fs+fcut+fsh+ftr (14)
in the specific implementation, each relevant constraint condition in the system scheduling process in step 2 is as follows:
PLreal，t＝PLb，t-ucut，tpcut，t+pLsh，t+pLtr，t (17)
-rdiΔT≤Pi，t-Pi，t-1≤ruiΔT (19)
equations (16) to (17) are power balance constraints, where PLb，tLoad at time t when the load can be transferred and the load can be transferred is not counted; pLreal，tThe total load participating in scheduling for the flexible load at the time t; p is a radical ofLsh，tThe translation amount of the translatable load translated to the t moment; p is a radical ofLtr，tThe transferable load is transferred to the transfer amount at the time t; wherein, the translation amount has two conditions: the translation amount is pLsh，tWhen the load is shifted, the load is not in the original interval or the acceptable shifting interval; ② the translation amount is The translation load is in the original interval or the acceptable translation interval after translation; there are also two cases of the amount of transfer: (ii) the amount of load transfer pLtr，t0, this means that the transferable load is not transferred at the original operating time tcNor within an acceptable transition interval; ② amount of transfer In the formula ptr，cFrom the original initial moment t for transferable loadcTransfer to ttrThe transferable amount of time indicates the transferable load at that time at the original operation time tcOr within an acceptable transition interval; the formula (18) is the constraint of the upper and lower output limits of the thermal power generating unit, wherein,the method comprises the following steps of respectively obtaining the minimum value and the maximum value of the i output of the thermal power generating unit at the t-time period;
formula (19) is a climbing constraint, wherein rdi、ruiThe downward climbing speed and the upward climbing speed of the unit i are respectively; Δ T is the time interval of the scheduling period;
equation (20) is the unit start-stop time constraint, where MDTi、MUTiRespectively the minimum outage time and the minimum operation time of the unit i;
the upper and lower limit constraints of the load can be reduced by the equation (21),an upper limit and a lower limit for reducing the load reduction amount at time t, respectively;
equations (22) to (25) are the load shedding minimum duration constraints; wherein, the expressions (22) to (23) represent that the load reduction due to the limitation of the minimum duration time is continuously executed at the end of the last scheduling period, the expression (24) is used for restricting the load reduction minimum duration time in the scheduling period, and the expression (25) is used for restricting the load reduction minimum duration time at the end of the scheduling period; wherein,minimum duration for load shedding;the number of the reduced duration periods of the initial period is obtained from the data of the last scheduling period;
equations (26) to (28) are the load shedding maximum duration constraints; wherein, expressions (26) to (27) represent maximum duration constraints considering the reduction state at the end of the previous scheduling period, and expression (28) is used for constraining the maximum duration of load reduction in the scheduling period; wherein,maximum duration for load shedding;the number of the time intervals of which the initial time interval is continuously reduced can be obtained from the data of the last scheduling cycle;
equation (29) is the limit of the number of load reductions, where CcutfixFixed compensation cost for each reduction;the number of allowed maximum loads in a scheduling period is reduced;
equations (30) - (31) are system rotation reserve capacity constraints, wherein α and β respectively represent confidence degrees that positive and negative rotation reserves meet requirements;
equation (32) is the start time constraint after the translatable load is translated;
formula (33) is transferable load transfer to ttrThe constraint of the time of day.
In step 3, a real number encoding mode is adopted for the particles, and the modified model is solved through an improved multi-target particle swarm algorithm based on a competition mechanism, and the method comprises the following steps:
step 3.1: inputting data and related parameters required by model solution; the model data mainly comprises: the method comprises the following steps of (1) relevant parameters of 10 units, parameters of 3 types of flexible loads, data of day-ahead wind power, data of day-ahead loads and the like; the parameters mainly comprise: the method comprises the following steps of (1) population scale, maximum iteration times, polynomial intersection, variation probability and start-stop variation probability of a thermal power generating unit;
step 3.2: and (3) encoding: encoding the particles in the particle swarm in a real number encoding mode;
step 3.3: initializing a population to obtain the position and the speed of particles;
step 3.3.1, adjusting the particles in the generated initial population, and performing adaptability evaluation on the adjusted individuals; checking whether the adjusted particles meet the rotation standby constraint of the system or not, and processing the particles which do not meet the rotation standby constraint in a penalty function mode; the objective function value of the particle meeting the rotating standby constraint is the fitness value of the particle;
step 3.3.2, if the constraint violation degree of partial particles is still higher after multiple times of adjustment, discarding the partial particles and regenerating the partial particles; then, adjusting the newly generated particles again, and performing adaptability evaluation on the adjusted particles; checking whether the adjusted particles meet the rotation standby constraint of the system or not, and processing the particles which do not meet the rotation standby constraint in a penalty function mode; the objective function value of the particle meeting the rotating standby constraint is the fitness value of the particle;
step 3.4: archive set update operation: obtaining a first layer of non-dominated individuals from the current population and adding the first layer of non-dominated individuals into an archive set;
step 3.5: executing a learning strategy based on a competition mechanism to update the positions of the particles to be updated in the population;
step 3.6: performing a polynomial mutation operation on the updated particle;
step 3.7: performing unit start-stop variation operation on the varied particles;
step 3.8: adjusting the particles obtained after the variation operation of the start and the stop of the unit, performing adaptability evaluation on the adjusted particles, checking whether the adjusted particles meet the rotation standby constraint of the system, and processing the particles which do not meet the rotation standby constraint in a penalty function mode; the objective function value of the particle meeting the rotating standby constraint is the fitness value of the particle;
step 3.9: combining the updated population with the initial population, and deleting a part of particles to obtain a new population;
step 3.10: merging the new population and the archive set, and performing the update operation on the archive set again to obtain a new archive set;
step 3.11: performing an evolutionary search strategy on the new archive set;
step 3.11.1, creating a set S, merging the set S with the new archive set, and then executing the update operation of the archive set again to obtain an updated archive set;
step 3.12: judging whether a termination condition is met, and if so, outputting particles in a file set; otherwise, return to step 3.5.
In step 3.2, a real number encoding mode is adopted for the particles, and each particle specifically comprises the following variables: the I conventional thermal power generating units at all the moments in the whole dispatching cycle are used for planning power values, the load reduction amount can be reduced at all the moments in the whole dispatching cycle, the translation amount of all the moments after the load can be translated in the whole dispatching cycle and the translation amount of all the moments after the load can be translated in the whole dispatching cycle.
The particles are coded in a real number coding mode, so that the calculation of the fitness value is faster and more convenient, and the calculation complexity is reduced;
step 3.3.1, 3.3.2 and 3.8, the particle rationality adjustment procedure is as follows:
firstly, obtaining an expected output value of each conventional thermal power generating unit at 24 moments according to a power balance equality relation, then randomly generating an output planned value of each conventional thermal power generating unit at 24 moments, and then obtaining a difference value between the output planned value and the output planned value, namely a power unbalance amount;
judging whether the randomly generated transferable loads meet the constraint that the total amount is unchanged in the whole scheduling period, and if not, adjusting to ensure that the total amount is unchanged at 24 moments in one day; the specific adjustment steps are as follows: calculating the proportion of the transferable loads at all times generated randomly before adjustment to the total transferable loads at all times, and then redistributing the transferable loads according to the proportion;
judging whether the randomly generated reducible load meets upper and lower limit constraints for reducing the load amount and constraints for reducing time and reducing times; if not, adjusting the upper and lower limit constraints capable of reducing the load, then adjusting the reduction time capable of reducing the load according to the initial continuous reduction time period number, the minimum and large time constraints capable of reducing the load, and finally performing the reduction number constraint limitation on the reduction time, so as to adjust the reducible load to be within a reasonable range;
recalculating the power unbalance after the adjustment of the steps;
and then adjusting the conventional thermal power generating unit: firstly, judging whether the output upper limit and the output lower limit of a conventional thermal power generating unit are met, if not, adjusting, and recalculating the power unbalance; judging the climbing constraint of the conventional thermal power generating unit, if the climbing constraint is not met, adjusting, and recalculating the power unbalance; finally, judging the minimum starting and stopping time constraint of the conventional thermal power generating unit, if the minimum starting and stopping time constraint is not met, adjusting, and recalculating the power unbalance;
and respectively calculating the maximum value of the positive and negative rotation reserve of each conventional thermal power generating unit at each moment, thereby obtaining the maximum value of the total positive and negative rotation reserve provided by 10 thermal power generating units at each moment.
The archive set updating operation in steps 3.4, 3.10 and 3.11.1 includes the following specific processes:
firstly, sorting particles in a population according to a rapid non-dominated sorting method; then the crowding degree of the population particles is obtained through the crowding degree distance calculation; the number of layers to which the particles belong and the degree of crowding are selected according to the number of layers to which the particles belong.
The learning strategy based on the competition mechanism in step 3.5 specifically operates as follows: two particles are arbitrarily selected from the archive set to perform a competition operation, and the winning particle is used for guiding the update of the particle to be updated.
The specific operation of deleting part of the particles from the combined population in step 3.9 is as follows: and executing environment selection operation on the combined population, deleting the inferior particles and keeping the superior particles. Wherein, the main steps of environment selection are as follows:
firstly, sorting particles in a population according to a rapid non-dominated sorting method; particles exceeding a predetermined size are deleted by a chopping operation.
The method adopts an improved multi-target particle swarm algorithm based on a competition mechanism to solve the single-region day-ahead economic scheduling problem under the condition of considering source-load bilateral uncertainty, can enlarge the search range in a target space, and increases the convergence and diversity of the algorithm to a certain extent.
The invention is not to be limited to the details of the description and the embodiments, but may be modified and adapted, with due regard to their suitability, for use in the field of application of the invention without departing from the scope of the claims.
Claims (8)
1. A multi-target day-ahead economic dispatching model building method considering flexible loads takes a single region as a research object, a power generation side in the model mainly comprises a conventional thermal power generating unit and a wind power generating unit, a load side is divided into two parts, namely a rigid load and a flexible load, wherein the flexible load is mainly divided into three types: the load can be reduced, the load can be translated and the load can be translated; the method for establishing the multi-target day-ahead economic dispatching model is characterized by comprising the following steps of:
step 1: establishing source-load bilateral uncertainty model
Step 1.1, establishing an uncertainty model of source side wind power
The wind power prediction error follows normal distribution with the mean value of 0:
the function relation between the standard deviation of the wind power prediction error and the wind power prediction value is as follows:
σW,t＝kW1PWZ,t+kW2 (2)
therefore, the actual power of the wind power is equal to the sum of the predicted wind power value and the predicted wind power error:
P′WZ,t＝PWZ,t+ΔPWZ,t (3)
in the formula: delta PWZ,tPredicting an error for the wind power at the time t; sigmaW,tThe standard deviation of the wind power prediction error in the t time period is obtained; pWZ,tPredicting total power for wind power at t time interval; k is a radical ofW1、kW2The prediction error coefficient of the wind power is obtained; p'WZ,tThe actual total power of wind power is t time period;
step 1.2, establishing uncertainty model of load side load
Similar to the wind power prediction error, the load prediction error can also be regarded as a random variable with an average value of 0 and obeying normal distribution:
the function relation between the standard deviation of the load prediction error and the load prediction value is as follows:
σL,t＝kLPL,t (5)
thus, the actual load value is equal to the sum of the predicted load value and the predicted load error:
P′L,t＝PL,t+ΔPL,t (6)
in the formula: delta PL,tLoad prediction error for time period t; sigmaL,tStandard deviation of load prediction error for time t; k is a radical ofLPredicting an error coefficient for the load; p'L,tIs the actual load value in the t period; pL,tLoad prediction value is t time interval;
step 2: establishing cost model of conventional thermal power generating unit
Step 2.1, generating cost of conventional thermal power generating unit
In the formula: f. ofcThe power generation cost of the conventional thermal power generating unit is reduced; pi,tThe active power of the unit i at the moment t is obtained; ci(Pi,t) The coal burning cost of the unit i at the time t is calculated; u. ofi,tStarting and stopping state variables of the unit i at the moment t, and ui,t1 indicates that the unit is in a power-on state, ui,t0 represents that the unit is in a shutdown state; t represents the number of time periods; i represents the number of the machine sets;
wherein,
Ci(Pi,t)＝aiPi,t 2+biPi,t+ci (8)
in the formula: a isi、bi、ciThe coal cost coefficient of the unit i is obtained;
step 2.2, starting and stopping cost of conventional thermal power generating unit
In the formula: f. ofsThe starting and stopping cost of the conventional thermal power generating unit is reduced; si *The starting cost coefficient of the unit i is obtained;
step 2.3, carbon emission of conventional thermal power generating unit
On the premise of not considering the loss of the power grid, the carbon emission caused by the consumption of the primary energy on the power generation side should theoretically be equal to the carbon emission caused by the consumption of the load electric energy on the demand side; therefore, the calculation of CO from the power generation side angle is selected2Emissions, usually expressed as a quadratic function:
in the formula: f. of2The carbon emission of the conventional thermal power generating unit; x is the number ofi、yi、ziThe emission coefficient of the polluted gas of the unit i is obtained;
and step 3: establishing flexible load compensation cost model
The flexible load at the demand side is generally divided into three types according to the response characteristic of the flexible load to participate in the system economic dispatching, namely, the load can be reduced, the load can be translated and the load can be translated;
step 3.1, load scheduling cost can be reduced
The load reduction capability generally refers to a load which is partially or entirely reduced according to the supply and demand conditions of the electric power, and the compensation cost after the load reduction is
In the formula: f. ofcutThe compensation cost for load scheduling can be reduced; ccutfix,tA fixed compensation cost for load reduction at time t; ccut,pCompensation cost for active reduction of load units; u. ofcut,tIs in a load reduction state at time t, and ucut,t1 indicates that the load is reduced, ucut,t0 means that the load is not reduced; p is a radical ofcut,tThe reduction amount of the load at the time t can be reduced;
step 3.2, load scheduling cost can be translated
The translatable load is restricted by the production flow, and the power utilization time period has strict continuity, so that the translatable load can only be translated integrally within a certain time period in a fixed continuous time period; the compensation cost after the translational load scheduling is
In the formula: f. ofshA penalty fee for translatable load scheduling; j is the set of original starting time of the translatable load; j. n is the original starting time and the original ending time of the translatable load respectively; t is tshThe initial time of the load which can be translated after translation;a translation period interval that is load acceptable; csh,p,tThe compensation cost of the load unit active power translation at the moment t; p is a radical ofsh,tIs an original interval [ j, n]The active power of the load can be shifted at the internal time t, andwhen is, psh,t＝0；Is a translational state of the load, andrepresenting loads from [ j, n]Time interval is translated to tsh,tsh+n-j]The period of time is,indicating that the load is not translating;
step 3.3, transferable load scheduling cost
Transferable load generally refers to a load in which the total power consumption remains unchanged during the whole scheduling period T, but can be flexibly adjusted in a part of time period; there are no continuous and chronological limitations compared to translatable loads; the compensation cost after the load dispatching can be transferred to
In the formula: f. oftrA penalty fee for transferable load scheduling; m is a set of transferable loads at the original running time; a transition period interval in which the load is acceptable; ctr,p,tThe compensation cost of the active power transfer of the load unit at the moment t; p is a radical oftr,tFor time t to shift to ttrThe active power at a moment;is a transition state of the load, and indicates the load shift to ttrAt the moment of time, the time of day,indicates that the load is not transferred to ttrTime of day;
and 4, step 4: establishing multi-target day-ahead economic dispatching model
The objective function of the multi-objective day-ahead economic dispatching model comprises the economic operation cost of the system and the carbon emission of the conventional thermal power generating unit; the economic operation cost of the system mainly comprises the operation cost, the start-stop cost and the dispatching cost of three types of flexible loads on demand sides of a conventional thermal power generating unit; the model scheduling cycle is 24 periods a day, and the two objective functions are:
f1＝fc+fs+fcut+fsh+ftr (14)
therefore, the establishment of the multi-target day-ahead economic dispatching model is completed.
2. A multi-target day-ahead economic dispatching model solving method considering flexible loads is characterized in that an improved multi-target particle swarm algorithm based on a competition mechanism is adopted to solve the multi-target day-ahead economic dispatching model, and the method comprises the following steps:
step 1: inputting data and related parameters required by model solution; the model data mainly comprises: the method comprises the following steps of (1) relevant parameters of 10 units, parameters of 3 types of flexible loads, data of day-ahead wind power, data of day-ahead loads and the like; the parameters mainly comprise: the method comprises the following steps of (1) population scale, maximum iteration times, polynomial intersection, variation probability, start-stop variation probability of a thermal power generating unit and the like;
step 2: and (3) encoding: encoding the particles in the particle swarm in a real number encoding mode;
and step 3: initializing a population to obtain the position and the speed of particles;
step 3.1, adjusting the particles in the generated initial population, and performing adaptability evaluation on the adjusted individuals; checking whether the adjusted particles meet the rotation standby constraint of the system or not, and processing the particles which do not meet the rotation standby constraint in a penalty function mode; the objective function value of the particle meeting the rotating standby constraint is the fitness value of the particle;
step 3.2, if the constraint violation degree of the partial particles after multiple adjustments is still higher, discarding the partial particles and regenerating the partial particles; then, adjusting the newly generated particles again, and performing adaptability evaluation on the adjusted particles; checking whether the adjusted particles meet the rotation standby constraint of the system or not, and processing the particles which do not meet the rotation standby constraint in a penalty function mode; the objective function value of the particle meeting the rotating standby constraint is the fitness value of the particle;
and 4, step 4: archive set update operation: obtaining a first layer of non-dominated individuals from the current population and adding the first layer of non-dominated individuals into an archive set;
and 5: executing a learning strategy based on a competition mechanism to update the positions of the particles to be updated in the population;
step 6: performing a polynomial mutation operation on the updated particle;
and 7: performing unit start-stop variation operation on the varied particles;
and 8: adjusting the particles obtained after the variation operation of the start and the stop of the unit, performing adaptability evaluation on the adjusted particles, checking whether the adjusted particles meet the rotation standby constraint of the system, and processing the particles which do not meet the rotation standby constraint in a penalty function mode; the objective function value of the particle meeting the rotating standby constraint is the fitness value of the particle;
and step 9: combining the updated population with the initial population, and deleting a part of particles to obtain a new population;
step 10: merging the new population and the archive set, and executing the update operation of the archive set again to obtain a new archive set;
step 11: performing an evolutionary search strategy on the new archive set;
step 11.1, creating a set S, merging the set S with a new archive set, and then executing the update operation of the archive set again to obtain an updated archive set;
step 12: judging whether a termination condition is met, and if so, outputting particles in a file set; otherwise, return to step 5.
3. The method as claimed in claim 1, wherein the constraint conditions of the multi-target day-ahead economic dispatch model are as follows:
PLreal,t＝PLb,t-ucut,tpcut,t+pLsh,t+pLtr,t (17)
-rdiΔT≤Pi,t-Pi,t-1≤ruiΔT (19)
equations (16) to (17) are power balance constraints, where PLb,tLoad at time t when the load can be transferred and the load can be transferred is not counted; pLreal,tThe total load participating in scheduling for the flexible load at the time t; p is a radical ofLsh,tThe translation amount of the translatable load translated to the t moment; p is a radical ofLtr,tThe transferable load is transferred to the transfer amount at the time t; wherein, the translation amount has two conditions: the translation amount is pLsh,tWhen the load is shifted, the load is not in the original interval or the acceptable shifting interval; ② the translation amount is The translation load is in the original interval or the acceptable translation interval after translation; there are also two cases of the amount of transfer: (ii) the amount of load transfer pLtr,t0, this means that the transferable load is not transferred at the original operating time tcNor within an acceptable transition interval; ② amount of transfer In the formula ptr,cFrom the original initial moment t for transferable loadcTransfer to ttrThe transferable amount of time indicates the transferable load at that time at the original operation time tcOr within an acceptable transition interval; the formula (18) is the constraint of the upper and lower output limits of the thermal power generating unit, wherein,the method comprises the following steps of respectively obtaining the minimum value and the maximum value of the i output of the thermal power generating unit at the t-time period;
formula (19) is a climbing constraint, wherein rdi、ruiThe downward climbing speed and the upward climbing speed of the unit i are respectively; Δ T is the time interval of the scheduling period;
equation (20) is a unit start-stop time constraint, where MUTi、MUTiRespectively the minimum outage time and the minimum operation time of the unit i;
the upper and lower limit constraints of the load can be reduced by the equation (21),an upper limit and a lower limit for reducing the load reduction amount at time t, respectively;
equations (22) to (25) are the load shedding minimum duration constraints; wherein, the expressions (22) to (23) represent that the load reduction due to the limitation of the minimum duration time is continuously executed at the end of the last scheduling period, the expression (24) is used for restricting the load reduction minimum duration time in the scheduling period, and the expression (25) is used for restricting the load reduction minimum duration time at the end of the scheduling period; wherein,minimum duration for load shedding;the number of the reduced duration periods of the initial period is obtained from the data of the last scheduling period;
equations (26) to (28) are the load shedding maximum duration constraints; wherein, expressions (26) to (27) represent maximum duration constraints considering the reduction state at the end of the previous scheduling period, and expression (28) is used for constraining the maximum duration of load reduction in the scheduling period; wherein,maximum duration for load shedding;the number of the time intervals of which the initial time interval is continuously reduced can be obtained from the data of the last scheduling cycle;
equation (29) is the limit of the number of load reductions, where CcutfixFixed compensation cost for each reduction;the number of allowed maximum loads in a scheduling period is reduced;
equations (30) - (31) are system rotation reserve capacity constraints, wherein α and β respectively represent confidence degrees that positive and negative rotation reserves meet requirements;
equation (32) is the start time constraint after the translatable load is translated;
formula (33) is transferable load transfer to ttrThe constraint of the time of day.
4. The method as claimed in claim 2, wherein the particles in the population are encoded in a real number encoding manner in step 2, and each particle mainly comprises the following variables: the I conventional thermal power generating units at all the moments in the whole dispatching cycle are used for planning power values, the load reduction amount can be reduced at all the moments in the whole dispatching cycle, the translation amount of all the moments after the load can be translated in the whole dispatching cycle and the translation amount of all the moments after the load can be translated in the whole dispatching cycle.
5. The method for solving the multi-target day-ahead economic dispatch model considering the flexible load as claimed in claim 2, wherein in the particle adjustment links of steps 3.1, 3.2 and 8, the specific adjustment steps are as follows:
firstly, obtaining an expected output value of each conventional thermal power generating unit at 24 moments according to a power balance equality relation, then randomly generating an output planned value of each conventional thermal power generating unit at 24 moments, and then obtaining a difference value between the output planned value and the output planned value, namely a power unbalance amount;
judging whether the randomly generated transferable loads meet the constraint that the total amount is unchanged in the whole scheduling period, and if not, adjusting to ensure that the total amount is unchanged at 24 moments in one day; the specific adjustment steps are as follows: calculating the proportion of the transferable loads at all times generated randomly before adjustment to the total transferable loads at all times, and then redistributing the transferable loads according to the proportion;
judging whether the randomly generated reducible load meets upper and lower limit constraints for reducing the load amount and constraints for reducing time and reducing times; if not, adjusting the upper and lower limit constraints capable of reducing the load, then adjusting the reduction time capable of reducing the load according to the initial continuous reduction time period number, the minimum and large time constraints capable of reducing the load, and finally performing the reduction number constraint limitation on the reduction time, so as to adjust the reducible load to be within a reasonable range;
recalculating the power unbalance after the adjustment of the steps;
and then adjusting the conventional thermal power generating unit: firstly, judging whether the output upper limit and the output lower limit of a conventional thermal power generating unit are met, if not, adjusting, and recalculating the power unbalance; judging the climbing constraint of the conventional thermal power generating unit, if the climbing constraint is not met, adjusting, and recalculating the power unbalance; finally, judging the minimum starting and stopping time constraint of the conventional thermal power generating unit, if the minimum starting and stopping time constraint is not met, adjusting, and recalculating the power unbalance;
and respectively calculating the maximum value of the positive and negative rotation reserve of each conventional thermal power generating unit at each moment, thereby obtaining the maximum value of the total positive and negative rotation reserve quantity provided by 10 thermal power generating units at each moment.
6. The method for solving the multi-target day-ahead economic dispatch model considering the flexible load as claimed in claim 2, wherein the archive set update operation in steps 4, 10 and 11.1 is as follows: firstly, sorting particles in a population according to a rapid non-dominated sorting method; then the crowding degree of the particles in the population is obtained through the crowding degree distance calculation; according to the number of layers to which the particles belong and the degree of congestion, the particles with a low number of layers and a small distance of the degree of congestion are selected as the particles in the archive set.
7. The method as claimed in claim 2, wherein in the competition mechanism-based learning strategy in step 5, two particles are arbitrarily selected from the archive set to perform competition operations, and the winning particle is used to guide the update of the particle to be updated.
8. The method for solving the multi-target day-ahead economic dispatch model considering the flexible load as claimed in claim 2, wherein the specific operation of deleting part of the particles from the combined population in step 9 is as follows: executing environment selection operation on the combined population, deleting inferior particles and keeping the superior particles; wherein, the main steps of environment selection are as follows: firstly, sorting particles in a population according to a rapid non-dominated sorting method; particles exceeding a predetermined size are deleted by a chopping operation.
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