CN110120682B - Power supply optimization scheduling method for tower barrel elevator with minimum lost air volume - Google Patents
Power supply optimization scheduling method for tower barrel elevator with minimum lost air volume Download PDFInfo
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Abstract
The invention discloses a power supply optimal scheduling method for a tower barrel elevator with minimum lost air volume, which comprises the following steps: the method comprises the steps of constructing a power supply model of the tower elevator, constructing a daily power consumption model of the tower elevator, constructing a power supply optimized scheduling model of the tower elevator based on the minimum abandoned wind volume, introducing a particle algorithm, improving the particle algorithm, and combining the improved particle algorithm with the reality to complete optimization of the power supply scheduling method of the tower elevator.
Description
Technical Field
The invention relates to the field of energy storage of tower barrel elevators, in particular to a power supply optimization scheduling method for a tower barrel elevator with minimum lost air volume.
Background
With the gradual exhaustion of fossil energy on the earth, renewable energy is generally used at home and abroad, and the loading capacity of wind power as a renewable energy is continuously enlarged. The wind power generation field in Xinjiang is rich in wind energy resources, the installed capacity of the wind power generation field in Xinjiang is gradually increased according to the regional advantages of the wind resources, the proportion of the wind power generation field in the power grid frame is continuously increased, five million kilowatt-level wind power bases are formed in 2020, and the total installed scale reaches ten million kilowatts. Therefore, the scientific operation and the reduction of energy consumption cost of the large wind power plant are more and more concerned.
At present, two problems mainly exist in the operation and maintenance of a large wind power plant: one is serious wind abandonment, and the other is huge workload of maintenance of wind turbine generators in a wind power plant. Consequently, to above-mentioned two outstanding problems, if install the elevator on all wind turbine generator system tower barrels in wind-powered electricity generation field, will abandon wind-powered electricity generation volume and directly give wind turbine generator system tower barrel elevator power supply, install energy memory additional simultaneously, abandon the big time storage electric quantity of wind volume, abandon the little or do not abandon the wind stage of amount of wind, give tower barrel elevator power supply by energy memory, both solved daily maintenance and maintained the use elevator problem, make full use of abandons the amount of wind again. And the optimal power supply dispatching of the tower elevator with the minimum lost air volume is a difficult problem at present. How to improve the economy of tower section of thick bamboo elevator dispatch under the prerequisite that does not change current hardware, reduce the cost that tower section of thick bamboo elevator overhauld and maintains always is the main direction of tower section of thick bamboo elevator power supply optimal scheduling.
Disclosure of Invention
According to the invention, under the power supply mode of 'wind abandon amount + energy storage compensation electric quantity = elevator power consumption', an optimal economical efficiency is taken as a target, a power supply optimization scheduling model of the tower elevator based on the minimum wind abandon amount is constructed, the feasibility and effectiveness of the power supply optimization scheduling model for the tower elevator are verified through a calculation example, the wind abandon capacity can be effectively improved, the economical efficiency of the tower elevator scheduling is improved, and the maintenance cost of the tower elevator is effectively reduced.
In order to achieve the aim, the invention provides a power supply optimal scheduling method for a tower elevator with minimum lost air volume, the feasibility and the effectiveness of the power supply optimal scheduling method for the tower elevator are verified through an example, and the problems in the prior art are solved.
The invention adopts the technical scheme that a power supply optimization scheduling method for a tower elevator with minimum lost air volume comprises the following steps:
constructing a power supply mode model of a tower elevator;
secondly, constructing a daily power consumption model of the tower elevator;
thirdly, constructing a power supply optimized dispatching model of the tower elevator based on the minimum lost air volume;
(IV) introducing a particle algorithm and improving the particle algorithm; in the particle algorithm, since the particles have a certain inertial velocity and always follow the best particle position; when the parameters are selected improperly, the algorithm rate is low, and the particles can only search a certain area, the whole feasible solution space cannot be searched, and the global optimum cannot be achieved; thus, to solve the above problem, the particle algorithm is modified to redefine the velocity as the interchange of elements in the particle; the rule for multiplying the constant by the velocity is thus redefined: 1. element 0 in velocity is unchanged; 2. a plurality of elements 1 and-1 in the random selection speed are unchanged, and the rest elements 1 and-1 are all changed into 0, so that the feasible solution can be searched for by the particles in the improved particle algorithm in a large range, and the convergence speed of the algorithm is effectively improved;
and (V) the improved particle algorithm is combined with the actual particle algorithm to complete the optimization of the power supply dispatching method of the tower elevator.
Under a power supply mode of 'wind abandon amount + energy storage compensation amount = elevator consumption amount', with the aim of optimal economy, researching the optimal power supply scheduling of the tower barrel elevator based on the minimum lost wind abandon amount, wherein the relation among the wind abandon amount, the energy storage compensation amount and the elevator consumption amount must be clarified, as shown in fig. 1, for the convenience of calculation and verification, the energy consumption in a lifting stage is uniformly used as the one-time operation energy consumption of the tower barrel elevator, and the total operation energy consumption of the whole tower barrel elevator in the wind power plant is calculated according to the energy consumption;
preferably, the step (one) of constructing the tower elevator power supply mode model is as follows:
air volume abandoning + energy storage compensation electric quantity = elevator consumption electric quantity
Because uncertain weighting factors exist and are related to basic requirements of maintenance workers, random uncertainty characteristics, the number of times of sudden failure of the wind turbine generator and the severity degree, the value of the weighting factors can be determined according to an expert investigation method, and the expert investigation method is adopted to evaluate the maintenance time of the wind turbine generator and the number of times of getting on and off the top of the wind turbine generator and has the following characteristics: in the maintenance process, the maintenance task is not completed when the staff have dinner, and the maintenance is continued after dinner by unifying the task; the polling time of the wind turbine generator is generally within 3 hours, and generally does not exceed the dining time; the overhaul duration of the major fault is determined according to the current situation, and the physiological reasons of the overhaul personnel are considered, so that the overhaul duration of 3-6h is uniformly increased by 2 times within 3h on the rise times, 3-6h is increased by 4 times, and 6-9h is increased by more than 6 times and 9h and 8 times; comprehensively considering various reasons and expert experience description, and setting the relationship between the overhaul time length and the rising times, thereby obtaining the overhaul quality;
preferably, the step (two) of constructing the daily power consumption model of the tower elevator is as follows:
a tower elevator daily power consumption model:
W sum ＝W plan +(α 1 +α 2 +α 3 )W
in the formula, W sum The total real-time daily electricity consumption of the tower elevator; w plan -tower elevator consumption according to a maintenance schedule; alpha is alpha 1 ,α 2 ,α 3 The weighting factors are integers and are determined by the basic requirements of maintenance personnel, the faults of the wind turbine generator and the fault degree; w is the power consumption of the elevator in one operation; wherein
In the formula, W represents the power consumption of the elevator in one-time operation; k f To be electricThe internal energy consumption coefficient of the ladder auxiliary device; q is elevator load; SWP-elevator deadweight; g-gravitational acceleration, H-tower height; eta d -motor efficiency; eta-elevator efficiency;
the real-time power consumption of the tower elevator has a direct relation with the operation times and duration of the tower elevator of the wind turbine generator. When the wind power station wind turbine generator is overhauled and maintained, according to the basic requirements of overhaul and maintenance workers of the wind power station wind turbine generator, the height of a tower barrel elevator and an overhaul and maintenance plan of the wind power station wind turbine generator, real-time random uncertainty characteristics are considered, and a long time threshold value of each operation interval of the elevator, the operation times of the elevator and the single operation power consumption of the elevator are determined. The elevator operation is a daily real-time power consumption calculation model, as shown in fig. 2.
The abandoned wind has great uncertainty in supplying power to the tower barrel elevator, a certain energy storage device needs to be configured to supply power to the tower barrel elevator when the abandoned wind does not exist, so that the requirement on the operation reliability of the tower barrel elevator is met, reasonable planning needs to be carried out according to actual conditions when the energy storage system is optimally configured, and the optimal economic capacity of the energy storage system is searched;
preferably, the step (three) of constructing the power supply optimized dispatching model of the tower elevator based on the minimum lost air volume comprises the following steps:
minF＝F ty +F dw -F hc
in the formula, F ty -energy storage costs; f dw -the electricity purchase cost inside the wind farm; f hc Energy storage compensates for thermal power pollution discharge and environmental governance costs;
because the energy storage cost is high, the research of the invention is based on day-ahead scheduling, and the electricity quantity required by the tower elevator in one day is only needed to be configured, so that the energy storage cost is low;
preferably, step (three) is carried out to build energy storage cost F in the power supply optimization scheduling model of the tower elevator based on the minimum lost air volume ty Including investment costs and operating costs, wherein:
the investment cost is as follows:
F tz ＝C m ×C ap
in the formula, F tz -energy storageSystem investment cost; c m -cost per system of energy storage capacity;
C ap -total capacity of stored energy;
the operation cost is as follows:
in the formula, F yy -energy storage operation costs; k 1 -energy storage operation cost compromise factor; p eni ——
The active output power of the stored energy at the moment; t is t 1 ,t 2 -energy storage system runtime domain.
Preferably, step (three) is carried out to construct wind power plant internal electricity purchase cost F in the tower elevator power supply optimization scheduling model based on minimum lost air volume dw Comprises the following steps:
F dw ＝c dw W dw
in the formula, c dw -electricity purchasing unit price inside the wind farm; w dw -purchasing power.
Preferably, the energy storage compensation thermal power pollution discharge and environment improvement cost F in the tower elevator power supply optimization scheduling model based on the minimum abandoned air volume is constructed in the step (III) hc Including thermal power unit blowdown cost and thermal power pollution control cost, wherein:
F hc ＝F ep +F pl
in the formula, F ep Thermal power unit pollution discharge cost, F pl -thermal power pollution control cost;
the thermal power unit pollution discharge cost is as follows:
in the formula, c ep Converting the thermal power pollution discharge cost into unit price of ten thousand/MW; n is the total number of the thermal power generating units; m represents the number of thermal power generating units participating in power generation; p ij The output power, MW, of the ith thermal power generating unit at the moment j；Wi(P j ) The unit output sewage discharge amount of the ith thermal power generating unit at the moment j is t/MW;
the thermal power pollution control cost is as follows:
in the formula, c pl The thermal power unit pollution control cost is ten thousand (m)/t; n is the total number of thermal power generating units; m represents the number of thermal power generating units participating in power generation; p is ij The output power, MW, of the ith thermal power generating unit at the moment j; w i (P j ) The unit output discharge capacity of the ith thermal power generating unit at the moment j is t/MW.
Preferably, the building of the tower elevator power supply optimized dispatching model based on the minimum lost air volume in the step (three) further comprises main constraint conditions, wherein the main constraint conditions comprise: maintenance continuity constraint, maintenance start time constraint, energy storage discharge power constraint and energy storage residual capacity constraint, wherein:
and (4) maintenance continuity constraint:
in the formula: t is t i -constraint time, within a period of time, a certain overhaul or task needs to be completed; s i -a start time; d i -maintenance duration;
and (4) constraint of maintenance starting time:
in the formula:
-earliest time the ith wind turbine generator can be overhauled; />
-the latest time accessible to the ith team; />
-the ith cell overhaul time period;
energy storage and discharge power constraint:
0≤P b,t ≤P b,max
in the formula: p b,t -storing the discharge power of the energy at time t; p is b,max -storage discharge power;
and energy storage residual capacity constraint:
in the formula: c s (t) -remaining capacity at time t of energy storage; c bN -energy storage rated capacity; beta is a 1 、β 2 -protection factor for over-discharge and over-charge of stored energy.
Preferably, the particle algorithm is introduced and improved in the step (four); wherein:
particle algorithm:
wherein i is the number of particles; d is the number of times;
-the individual best and the global best of the ith particle in the d-th iteration; />
-the velocity and position of the ith particle in the d-th iteration; omega, c 1 、c 2 At speed is betterThe weight coefficients in the new process; f. of rand A random number of- (0, 1); δ -a constraint factor, typically set to 1, wherein the population of particles is based on particle algorithm >>
Updating; since the particles have a certain inertial velocity and always follow the best particle position; when the parameters are selected improperly, the algorithm rate is low, the particles can only search a certain area, the whole feasible solution space cannot be searched, and the global optimum cannot be achieved; thus, to solve the above problem, the velocity is redefined as the interchange of the 0 element and the 1 element in the particle x; when x is p ＝0,x q For exchanging elements 0 and 1 in these 2 particles x, the velocity v needs to be defined as: v. of p ＝1,v q = -1; by the formula x + v = x', the interchange of 0 and 1 in the particle x can be realized, and the number of 0 and 1 elements in the particle x is not changed; assuming that the speed v contains Q1 s and Q-1 s, the rest elements are all 0, and the constant c is more than 0; the rule for multiplying the constant c by the velocity v is thus redefined: 1. element 0 in velocity v is unchanged; 2. randomly selecting min { Q, int (c multiplied by Q) } elements 1 in the speed v to be unchanged with-1, and changing the rest elements 1 and-1 to be 0; if the maximum iteration number is M, the update formula of the speed and the position of the ith particle in the jth iteration is as follows:
x i ＝x i +v i
in the formula, w min ,w max -a minimum coefficient of inertia and a maximum coefficient of inertia, wherein 0 < w min ＜w max Less than 1; r- (0, 1); p is a radical of g -a global optimal solution; in order to ensure that the particles can search feasible solutions in a large range and effectively improve the convergence rate of the algorithm, when x exists i ＝p g When, v i It becomes to satisfy x i Arbitrary values of the rules are exchanged.
Preferably, step (five) is represented by the formula minF = F ty +F dw -F hc The optimal economy is a target function, whether an iteration termination condition is met or not is determined according to the fitness value of the particle swarm algorithm, 200 times of maximum iteration times are selected, an initial value is randomly set, and the particle swarm algorithm flow is improved; and combining the improved particle algorithm with the reality, wherein each particle represents a solution, a solution set is formed at last, the final result is compared with the original maintenance plan and the predicted air abandonment amount, whether the air abandonment amount at the moment is matched with the electric quantity required by the planned maintenance is judged, the maintenance plan is not required to be modified if the air abandonment amount is matched with the electric quantity required by the planned maintenance, and the maintenance plan is adjusted according to the requirement if the air abandonment amount is not matched with the electric quantity required by the planned maintenance, so that the optimization of the power supply scheduling method of the tower elevator is completed.
The invention has the beneficial effects that:
the optimization method aims at optimizing the power supply economy of the tower elevator when the wind power plant is operated and maintained, an improved algorithm optimization model based on the particle swarm algorithm is established by means of the basic principle of the particle swarm algorithm and by combining with the specific actual situation in the wind power plant, the calculation efficiency of the particle swarm algorithm is effectively improved by defining a new speed and an updating method of the new speed in the process of solving the model, and the optimization of the power supply dispatching method of the tower elevator is completed.
According to the method, the wind curtailment absorption capacity is effectively improved, the economical efficiency of the dispatching of the tower elevator is improved, and the cost of the maintenance of the tower elevator is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a power supply mode structure diagram of a tower elevator in a power supply optimized dispatching method for the tower elevator with the minimum lost air volume;
FIG. 2 is a diagram of a model for real-time power consumption calculation in elevator operation;
FIG. 3 is a flow chart of an improved particle swarm algorithm;
FIG. 4 is an element interchange diagram;
FIG. 5 is a graph of power consumption and energy storage required by an elevator;
FIG. 6 is a diagram of prediction of wind curtailment power at a certain day in an electric field;
FIG. 7 is a comparison of particle swarm optimization and improved particle swarm optimization;
fig. 8 is an optimization plot.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1 to 8, a power supply optimized scheduling method for a tower elevator with a minimum loss air volume includes the following steps:
constructing a power supply mode model of a tower elevator;
secondly, constructing a daily power consumption model of the tower elevator;
thirdly, constructing a power supply optimized dispatching model of the tower elevator based on the minimum lost air volume;
(IV) introducing a particle algorithm and improving the particle algorithm; in the particle algorithm, since the particles have a certain inertial velocity and always follow the best particle position; when the parameters are selected improperly, the algorithm rate is low, the particles can only search a certain area, the whole feasible solution space cannot be searched, and the global optimum cannot be achieved; thus, to solve the above problem, the particle algorithm is modified to redefine the velocity as the interchange of elements in the particle; the rule for multiplying the constant by the velocity is thus redefined: 1. element 0 in velocity is unchanged; 2. a plurality of elements 1 and-1 in the random selection speed are unchanged, and the rest elements 1 and-1 are all changed into 0, so that the feasible solution of the particles can be searched in a large range in the improved particle algorithm, and the convergence speed of the algorithm is effectively improved;
and (V) the improved particle algorithm is combined with the actual particle algorithm to complete the optimization of the power supply dispatching method of the tower elevator.
Under a power supply mode of 'wind abandon amount + energy storage compensation amount = elevator consumption amount', with the aim of optimal economy, researching power supply optimization scheduling of a tower barrel elevator based on minimum lost wind abandon amount, wherein the relation among the wind abandon amount, the energy storage compensation amount and the elevator consumption amount must be determined;
further, the power supply mode model of the tower elevator is constructed in the step (I) as follows:
abandon amount of wind + energy storage compensation electric quantity = elevator power consumption
Because uncertain weighting factors exist, and the weighting factors are related to the basic requirements of maintenance workers, random uncertainty characteristics, the number of times of sudden failure of the wind turbine generator and the severity, the value of the weighting factors can be determined according to an expert survey method, and the expert survey method is adopted to evaluate the maintenance time of the wind turbine generator and the number of times of loading and unloading the top of the wind turbine generator, so that the method has the following characteristics: in the maintenance process, the maintenance task is not completed when the staff have dinner, and the maintenance is continued after dinner by unifying the task; the inspection time of the wind turbine generator is generally within 3h, and the dining time is not generally passed; the overhaul duration of the major fault is determined according to the current situation, and the physiological reasons of the overhaul personnel are considered, so that the overhaul duration of 3-6h is uniformly increased by 2 times within 3h on the rise times, 3-6h is increased by 4 times, and 6-9h is increased by more than 6 times and 9h and 8 times; and comprehensively considering various reasons and expert experience description, and setting the relationship between the overhaul time length and the rise times.
Further, the step (II) of constructing a daily power consumption model of the tower elevator is as follows:
a tower elevator daily power consumption model:
W sum ＝W plan +(α 1 +α 2 +α 3 )W
in the formula, W sum The total real-time daily electricity consumption of the tower elevator; w plan Push checkRepairing planned tower elevator consumption; alpha is alpha 1 ,α 2 ,α 3 The weighting factors are integers and are determined by the basic requirements of maintenance personnel, the faults of the wind turbine generator and the fault degree; w is the power consumption of the elevator in one operation; wherein
In the formula, W represents the power consumption of the elevator in one-time operation; k f -is the internal energy consumption coefficient of the elevator auxiliary device; q is the elevator load; SWP-Elevator dead weight; g-gravitational acceleration, H-tower height; eta d -motor efficiency; eta-elevator efficiency;
abandoning wind and having great uncertainty for tower section of thick bamboo elevator power supply, need dispose certain energy memory and be used for supplying power for tower section of thick bamboo elevator when not abandoning wind to satisfy tower section of thick bamboo elevator operational reliability's requirement, when carrying out energy storage system's optimal configuration, need carry out reasonable planning according to actual conditions, look for the energy storage system economic nature optimal capacity.
Further, the step (three) of constructing a power supply optimized dispatching model of the tower elevator based on the minimum lost air volume comprises the following steps:
minF＝F ty +F dw -F hc
in the formula, F ty -energy storage costs; f dw -the electricity purchase cost inside the wind farm; f hc Energy storage compensation thermal power pollution discharge and environmental management cost;
because the energy storage cost is high, the research of the invention is based on day-ahead scheduling, and therefore, the electric quantity required by the tower elevator in one day only needs to be configured.
Further, the energy storage cost F in the tower elevator power supply optimization scheduling model based on the minimum lost air volume is constructed in the step (III) ty Including investment costs and operating costs, wherein:
the investment cost is as follows:
F tz ＝C m ×C ap
in the formula, F tz -energy storage system investment cost; c m -cost per system of energy storage capacity;
C ap -total capacity of stored energy;
the operation cost is as follows:
in the formula, F yy -energy storage operation costs; k 1 -energy storage operation cost compromise factor; p eni ——
The active output power of the stored energy at the moment; t is t 1 ,t 2 The energy storage system runtime domain.
Further, the internal electricity purchasing cost F of the wind power field in the tower elevator power supply optimization scheduling model based on the minimum lost air volume is constructed in the step (III) dw Comprises the following steps:
F dw ＝c dw W dw
in the formula, c dw -electricity purchasing unit price inside the wind farm; w dw -purchasing power.
Further, step (three), energy storage compensation thermal power pollution discharge and environment improvement cost F in tower elevator power supply optimization scheduling model based on minimum lost wind volume is established hc Including thermal power unit blowdown cost and thermal power pollution control cost, wherein:
F hc ＝F ep +F pl
wherein, = F ep Thermal power unit pollution discharge cost, F pl -thermal power pollution control cost;
the thermal power unit pollution discharge cost is as follows:
in the formula, c ep Converting the thermal power pollution discharge cost into unit price of ten thousand/MW; n is the total number of the thermal power generating units; m represents the number of thermal power generating units participating in power generation; p ij -ith thermal power generating unitOutput at time j, MW; w i (P j ) The unit output of the ith thermal power generating unit at the moment j is the discharge capacity t/MW;
the thermal power pollution control cost is as follows:
in the formula, c pl The thermal power unit pollution control cost is ten thousand (m)/t; n is the total number of thermal power generating units; m represents the number of thermal power generating units participating in power generation; p ij The output power, MW, of the ith thermal power generating unit at the moment j; w i (P j ) The unit output of the ith thermal power generating unit at the moment j is the discharge capacity t/MW.
Further, the power supply optimization scheduling model of the tower elevator based on the minimum lost air volume constructed in the step (three) further comprises main constraint conditions, wherein the main constraint conditions comprise: maintenance continuity constraint, maintenance start time constraint, energy storage discharge power constraint and energy storage residual capacity constraint, wherein:
and (4) maintenance continuity constraint:
in the formula: t is t i -constraint time, within a period of time, a certain overhaul or task needs to be completed; s i -a start time; d i -maintenance duration;
and (4) constraint of maintenance starting time:
in the formula:
-earliest time the ith wind turbine generator can be overhauled; />
-the latest time accessible to the ith team; />
-an ith unit overhaul time period;
energy storage and discharge power constraint:
0≤P b,t ≤P b,max
in the formula: p b,t -storing the discharge power of the energy at time t; p is b,max -storage discharge power;
and (4) energy storage residual capacity constraint:
in the formula: c s (t) -remaining capacity at time t of energy storage; c bN -energy storage rated capacity; beta is a 1 、β 2 -protection factor for over-discharge and over-charge of stored energy.
Further, a particle algorithm is introduced and improved in the step (four), wherein:
particle algorithm:
wherein i is the number of particles; d is the number of times;
-the individual best and the global best of the ith particle in the d iteration; />
-the velocity and position of the ith particle in the d-th iteration; omega, c 1 、c 2 -weight coefficients in the velocity update process; f. of rand A random number of- (0, 1); δ -a constraint factor, typically set to 1, wherein the population of particles is based on particle algorithm >>
Updating; since the particles have a certain inertial velocity and always follow the best particle position; when the parameters are selected improperly, the algorithm rate is low, and the particles can only search a certain area, the whole feasible solution space cannot be searched, and the global optimum cannot be achieved; thus, to solve the above problem, the velocity is redefined as the interchange of the 0 element and the 1 element in the particle x; when x is p ＝0,x q For exchanging the elements 0 and 1 in the two particles x, the velocity v is defined as: v. of p ＝1,v q = -1; by the formula x + v = x', the interchange of 0 and 1 in the particle x can be realized, and the number of 0 and 1 elements in the particle x is not changed; assuming that the speed v contains Q1 s and Q-1 s, the rest elements are all 0, and the constant c is more than 0; the rule for multiplying the constant c by the velocity v is thus redefined: 1. element 0 in velocity v is unchanged; 2. randomly selecting min { Q, int (c multiplied by Q) } elements 1 in the speed v to be unchanged with-1, and changing the rest elements 1 and-1 to be 0; if the maximum iteration number is M, the update formula of the speed and the position of the ith particle in the jth iteration is as follows:
x i ＝x i +v i
in the formula, w min ,w max -a minimum coefficient of inertia and a maximum coefficient of inertia, wherein
0＜w min ＜w max Less than 1; r- (0, 1) is a random number; p is a radical of g -a global optimal solution;
in order to ensure that the particles can search feasible solutions in a large range and effectively improve the convergence rate of the algorithm, when x exists i ＝p g When, v i It becomes to satisfy x i Exchange rulesAny value of (c).
Further, step (five) is represented by the formula minF = F ty +F dw -F hc The optimal economy is a target function, whether an iteration termination condition is met or not is determined according to the fitness value of the particle swarm algorithm, 200 times of maximum iteration times are selected, an initial value is randomly set, and the particle swarm algorithm flow is improved; and combining the improved particle algorithm with the actual algorithm, wherein each particle represents a solution, a solution set is formed at last, the final result is compared with the original maintenance plan and the predicted air abandonment amount, whether the air abandonment amount at the moment is matched with the electric quantity required by the planned maintenance is judged, if so, the maintenance plan is not required to be modified, and if not, the maintenance plan is adjusted according to the requirement, so that the optimization of the power supply scheduling method of the tower elevator is completed.
Example (b):
taking a certain wind power plant in Daban City, xinjiang as an example, the wind power plant has 25 wind power units of 2MW, and a unit maintenance plan of 24 periods of a day is considered. Under a power supply mode of 'wind abandon amount + energy storage compensation electric quantity = elevator power consumption', with the aim of optimal economy, researching power supply optimization scheduling of a tower barrel elevator based on minimum wind abandon amount, wherein the relation among the wind abandon amount, the energy storage compensation electric quantity and the elevator power consumption is required to be determined; in order to facilitate calculation and verification, energy consumption in a lifting stage is uniformly used as one-time operation energy consumption of the tower elevator, and the total operation energy consumption of the tower elevator in the whole wind power plant is calculated according to the energy consumption; taking the wind power plant as an example, H =80m, and the primary power consumption W =0.175kWh of the tower elevator of the wind power plant is calculated.
Under a model with the minimum lost wind power and the optimal economy as targets, according to the comparative analysis of historical abandoned wind data and the consumed power of the tower elevator, the following results can be obtained: the power consumption of the tower elevator is low, and the power consumption of the tower elevator is far less than the abandoned wind power; therefore, the abandoned wind power generated by the wind power plant at a certain moment is inevitably enough for the power supply of the tower elevator, and the condition that the abandoned wind power is lower than the power consumption of the elevator can be generated only when the abandoned wind is not generated and the output power of the electric field is zero due to the reason that the wind speed is too small and the like.
The abandoned wind has great uncertainty in supplying power to the tower barrel elevator, a certain energy storage device needs to be configured to supply power to the tower barrel elevator when the abandoned wind does not exist, so that the requirement on the operation reliability of the tower barrel elevator is met, reasonable planning needs to be carried out according to actual conditions when the energy storage system is optimally configured, and the optimal economic capacity of the energy storage system is searched; because the energy storage cost is high, the research of the invention is based on day-ahead scheduling, and therefore, the energy storage method only needs to be configured to meet the electric quantity required by the tower elevator in one day, namely, the power consumed by the elevator minus the abandoned wind power, and the part of the energy storage method larger than 0 is the place where the abandoned wind power is insufficient and the energy storage is required to be configured. Fig. 5 is a graph comparing the power consumption of an elevator and the power consumption of an elevator requiring energy storage. In fig. 5, a curve a represents the power consumption curve of the elevator, and a curve b represents the positive part of the result obtained by subtracting the wind curtailment curve from the power consumption curve of the elevator with the curve a, and the power consumption of the elevator is allocated to the energy storage part. As can be seen from fig. 5, the curve b and the curve a partially overlap each other, which is caused by the fact that the wind dump is 0 and the place for storing energy needs to be configured. The maximum of the energy storage capacity and the power does not exceed the maximum value in the data record, and the minimum of the power is not lower than the mode value of the data record.
Because uncertain weighting factors exist and are related to basic requirements of maintenance workers, random uncertainty characteristics, the number of times of sudden failure of the wind turbine generator and the severity degree, the value of the weighting factors can be determined according to an expert investigation method, and the expert investigation method is adopted to evaluate the maintenance time of the wind turbine generator and the number of times of getting on and off the top of the wind turbine generator and has the following characteristics:
1) In the maintenance process, the maintenance task is not completed when the staff have dinner, and the maintenance is continued after dinner by unifying the task;
2) The inspection time of the wind turbine generator is generally within 3h, and the dining time is not generally passed;
3) The overhaul time of the large fault is determined according to the current situation, and the physiological reasons of the overhaul personnel are considered, so that the overhaul time of 3-6h is uniformly increased by 2 times within 3h, the overhaul time of 3-6h is increased by 4 times, and the overhaul time of 6-9h is increased by more than 6 times and by 8 times within 9 h;
TABLE 1
The relationship between the overhaul duration and the rise times is set as shown in table 1 by comprehensively considering various reasons and expert experience description, and the forced outage rate R for the wind turbine generator can be calculated by the following formula:
in the formula, lambda and mu represent failure rate and repair rate; t is t MTTF 、t MTTR -mean time to failure and mean time to repair;
TABLE 2
The forced outage rates of the wind turbines obtained in the formula are shown in table 2 (wherein 1# and 23# are in fault shutdown), and the forced outage rates of the wind turbines are small and can be considered from table 2; according to the evaluation of the power consumption of the tower elevator and the analysis of the energy storage configuration, T is taken h ＝24h，T h -overhaul length measurement; let the number of particles be 30, the maximum number of iterations be 200, w max ＝0.9,w min ＝0.4，c 1 ＝c 2 =2, root analysis according to above, day α 1 ＝2，α 2 ＝2，α 3 =4, and each parameter is as shown in table 3.
TABLE 3
Because the power supply mode of the tower elevator is related to the abandoned wind power and the stored energy compensation power, the abandoned wind power can be predicted by analyzing historical power data of a wind power plant and power data of a wind power generation unit, the difference between the two sets of data is used as a abandoned wind power time sequence, then the sequences are decomposed in an empirical mode for signal processing, and each component is predicted by combining a Markov chain; the wind power station is maintained at 4#, 8#, 10# and 21#, and maintained at 3#, 5# and 6# daily, the cut-in wind speed of the wind power generation set is 3m/s, the rated wind speed is 10.9m/s, and the cut-out wind speed is 25m/s; the wind curtailment power prediction of the wind farm is shown in fig. 6.
TABLE 4
The energy storage system is configured to meet the electric quantity required by the tower elevator in one day by scheduling in the day ahead on one day, the optimization result of the energy storage system is shown in a table 4, and a table 5 is an optimization comparison result of a daily maintenance plan for maintaining according to a plan and considering the minimum lost air quantity; it is obvious from table 5 that the maintenance cost of the method of the invention considering the minimum lost air volume is reduced to half of the original maintenance cost compared with the original plan, so that the scheduling method of the invention improves the economy of the tower elevator scheduling and reduces the maintenance cost of the tower elevator.
TABLE 5
FIG. 7 is a comparison between the particle swarm optimization and the improved particle swarm optimization, and FIG. 8 is an optimization curve of the two schemes; obviously, considering that the economical efficiency of the maintenance of the minimum abandoned wind volume is better than that of the maintenance according to the plan, the feasibility of the power supply optimization scheduling model of the tower elevator based on the minimum abandoned wind volume is verified under the power supply mode of 'abandoned wind volume + energy storage compensation electric quantity = elevator power consumption electric quantity', and a new path is opened up for abandoned wind consumption; the overhaul time interval of the wind turbine generator obtained by the algorithm is almost arranged in the time interval with sufficient wind power abandon, so that the wind turbine generator overhaul method is quite consistent with the full utilization of wind power, the overhaul rule of the wind turbine generator is reasonably arranged, and the rationality of the algorithm is verified.
In summary, according to the power supply optimization scheduling method for the tower elevator with the minimum lost wind volume, disclosed by the invention, under the power supply mode of the tower elevator with the wind volume abandoned + energy storage compensation electric quantity = power consumption of the tower elevator, the optimal economical efficiency is taken as a target, and a power supply optimization scheduling model for the tower elevator based on the minimum lost wind volume is constructed. In order to efficiently solve the problem of power supply optimization scheduling of the tower barrel elevator, an improved particle swarm algorithm is provided, and the calculation efficiency of the particle swarm algorithm is effectively improved by defining a new speed and an updating method of the new speed. Meanwhile, the feasibility and the effectiveness of the power supply optimization scheduling of the tower elevator by the model are verified through an example, the problems in the prior art are solved, the economy of the power supply scheduling of the tower elevator is improved, and the maintenance cost of the tower elevator is reduced.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (6)
1. A power supply optimization scheduling method for a tower elevator with minimum lost air volume is characterized by comprising the following steps:
constructing a power supply mode model of a tower elevator;
secondly, constructing a daily power consumption model of the tower elevator;
thirdly, constructing a power supply optimized dispatching model of the tower elevator based on the minimum lost air volume;
(IV) introducing a particle algorithm and improving the particle algorithm; in the particle algorithm, since the particles have a certain inertial velocity and always follow the best particle position; when the parameters are selected improperly, the algorithm rate is low, the particles can only search a certain area, the whole feasible solution space cannot be searched, and the global optimum cannot be achieved; thus, to solve the above problem, the particle algorithm is modified to redefine the velocity as the interchange of elements in the particle; the rule for multiplying the constant by the velocity is thus redefined: 1. element 0 in velocity is unchanged; 2. a plurality of elements 1 and-1 in the random selection speed are unchanged, and the rest elements 1 and-1 are all changed into 0, so that the feasible solution of the particles can be searched in a large range in the improved particle algorithm, and the convergence speed of the algorithm is effectively improved;
fifthly, the improved particle algorithm is combined with the actual particle algorithm to complete the optimization of the power supply dispatching method of the tower elevator; in the step (V), the formula is minF = F ty +F dw -F hc The optimal economy is a target function, whether an iteration termination condition is met or not is determined according to the fitness value of the particle swarm algorithm, 200 times of maximum iteration times are selected, an initial value is randomly set, and the particle swarm algorithm flow is improved; combining the improved particle algorithm with the reality, wherein each particle represents a solution, a solution set is formed at last, the final result is compared with the original maintenance plan and the predicted air abandonment amount, whether the air abandonment amount is matched with the electric quantity required by the planned maintenance or not is judged, if yes, the maintenance plan is not required to be modified, and if not, the maintenance plan is adjusted according to the requirement, so that the optimization of the power supply scheduling method of the tower elevator is completed;
the step (I) of constructing a power supply model of the tower elevator comprises the following steps:
the air volume abandoning + energy storage compensation electric quantity = the electric quantity consumed by the elevator;
the step (II) of constructing the daily power consumption model of the tower elevator is as follows:
a tower elevator daily power consumption model:
W sum ＝W plan +(α 1 +α 2 +α 3 )W
in the formula, W sum The total real-time daily electricity consumption of the tower elevator; w is a group of plan -tower elevator consumption according to a maintenance schedule; alpha is alpha 1 ,α 2 ,α 3 The weighting factor is an integer and is determined by the basic requirement of maintenance personnel, the fault of the wind turbine generator and the fault degree; w is the power consumption of the elevator in one operation; wherein
In the formula, W represents the power consumption of the elevator in one-time operation; k f -the energy consumption coefficient inside the elevator auxiliary equipment; q is elevator load; SWP-Elevator dead weight; g-gravitational acceleration, H-tower height; eta d -motor efficiency; eta-elevator efficiency;
and (III) constructing a power supply optimized dispatching model of the tower elevator based on the minimum lost wind volume as follows:
minF＝F ty +F dw -F hc
in the formula, F ty -energy storage costs; f dw -the electricity purchase cost inside the wind farm; f hc The energy storage compensates the thermal power pollution discharge and environmental management cost.
2. The power supply optimized dispatching method for the tower elevator with the minimum air loss rate as claimed in claim 1, wherein the step (three) is implemented to construct the energy storage cost F in the power supply optimized dispatching model for the tower elevator based on the minimum air loss rate ty Including investment costs and operating costs, wherein:
the investment cost is as follows:
F tz ＝C m ×C ap
in the formula, F tz -energy storage system investment cost; c m -cost per system of energy storage capacity; c ap -total capacity of stored energy;
the operation cost is as follows:
in the formula, F yy -energy storage operation costs; k 1 -energy storage operation cost compromise factor;
P eni -the active output power of the energy storage at the moment; t is t 1 ,t 2 -energy storage system runtime domain.
3. The power supply optimized dispatching method for the tower elevator with the minimum air loss rate as claimed in claim 1, wherein the step (three) is implemented to construct the power purchase cost F inside the wind power plant in the power supply optimized dispatching model of the tower elevator based on the minimum air loss rate dw Comprises the following steps:
F dw ＝c dw W dw
in the formula, c dw -electricity purchasing unit price inside the wind farm; w dw -purchasing power.
4. The power supply optimized dispatching method for the tower elevator with the minimum air loss and the minimum air loss as claimed in claim 1, wherein the energy storage compensation thermal power pollution discharge and environmental improvement cost F in the power supply optimized dispatching model for the tower elevator based on the minimum air loss and the minimum air loss is constructed in the third step hc Including thermal power unit blowdown cost and thermal power pollution control cost, wherein:
F hc ＝F ep +F pl
in the formula, F ep Thermal power unit pollution discharge cost, F pl -thermal power pollution control cost;
the thermal power unit pollution discharge cost is as follows:
in the formula, c ep The cost of thermal power pollution discharge is converted into unit price,
n is the total number of the thermal power generating units; m represents the number of thermal power generating units participating in power generation; p ij The output power, MW, of the ith thermal power generating unit at the moment j; w is a group of i (P j ) The unit output sewage discharge amount of the ith thermal power generating unit at the moment j is t/MW;
the thermal power pollution control cost is as follows:
in the formula, c pl -the pollution control cost of the thermal power unit is
n is the total number of thermal power generating units; m represents the number of thermal power generating units participating in power generation; p ij The output power, MW, of the ith thermal power generating unit at the moment j; w i (P j ) The unit output discharge capacity of the ith thermal power generating unit at the moment j is t/MW.
5. The power supply optimized dispatching method for the tower elevator with the minimum air loss rate according to claim 1, wherein the step (three) of constructing the power supply optimized dispatching model for the tower elevator based on the minimum air loss rate further comprises main constraint conditions, and the main constraint conditions comprise: maintenance continuity constraint, maintenance start time constraint, energy storage discharge power constraint and energy storage residual capacity constraint, wherein:
and (4) maintenance continuity constraint:
in the formula: t, restraining time, and completing a certain maintenance task within a period of time; s i -a start time; d i -maintenance duration;
and (4) constraint of maintenance starting time:
in the formula:
-earliest time the ith wind turbine generator can be overhauled; />
-the latest time accessible to the ith team;
-an ith unit overhaul time period;
energy storage and discharge power constraint:
0≤P b,t ≤P b,max
in the formula: p b,t -the discharge power of the stored energy at time t; p is b,max -storage discharge power;
and energy storage residual capacity constraint:
in the formula: c s (t) -remaining capacity at time t of energy storage; c bN -energy storage rated capacity;
β 1 、β 2 -protection factor for over-discharge and over-charge of stored energy.
6. The tower elevator power supply optimization scheduling method with minimum air loss amount according to claim 1, wherein a particle algorithm is introduced and improved in the step (IV), wherein:
particle algorithm:
wherein i is the number of particles; d is the number of times;
-the individual best and the global best of the ith particle in the d-th iteration; />
-the velocity and position of the ith particle in the d-th iteration; omega, c 1 、c 2 -weight coefficients in the velocity update process; f. of rand A random number of- (0, 1); δ -a constraint factor, typically set to 1, wherein the population of particles is based on particle algorithm >>
Updating; since the particles have a certain inertial velocity and always follow the best particle position; when the parameters are selected improperly, the algorithm rate is low, the particles can only search a certain area, the whole feasible solution space cannot be searched, and the global optimum cannot be achieved; thus, to solve the above problem, the velocity is redefined as the interchange of the 0 element and the 1 element in the particle x; when x is p ＝0,x q For exchanging the elements 0 and 1 in the two particles x, the velocity v is defined as: v. of p ＝1,v q = -1; by the formula x + v = x', the interchange of 0 and 1 in the particle x can be realized, and the number of 0 and 1 elements in the particle x is not changed; assuming that the speed v contains Q1 s and Q-1 s, the rest elements are all 0, and the constant c is more than 0; the rule for multiplying the constant c by the velocity v is thus redefined: 1. element 0 in velocity v is unchanged; 2. randomly selecting min { Q, int (c multiplied by Q) } elements 1 in the speed v to be unchanged with-1, and changing the rest elements 1 and-1 to be 0; if the maximum iteration number is M, the update formula of the speed and the position of the ith particle in the jth iteration is as follows: />
x i ＝x i +v i (2)
In the formula, w min ,w max -a minimum coefficient of inertia and a maximum coefficient of inertia, wherein 0 < w min ＜w max Less than 1; r- (0, 1); p is a radical of formula g -a global optimal solution;
in order to ensure that the particles can search feasible solutions in a large range and effectively improve the convergence rate of the algorithm, when x exists i ＝p g When, v i It becomes to satisfy x i Arbitrary values of the rules are exchanged.
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