CN111799778A - Wind power-containing power system energy storage capacity optimization method considering peak shaving requirements - Google Patents

Wind power-containing power system energy storage capacity optimization method considering peak shaving requirements Download PDF

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CN111799778A
CN111799778A CN202010531912.7A CN202010531912A CN111799778A CN 111799778 A CN111799778 A CN 111799778A CN 202010531912 A CN202010531912 A CN 202010531912A CN 111799778 A CN111799778 A CN 111799778A
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power
energy storage
unit
cost
capacity
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吴健
杨波
冯亮
郑志杰
崔灿
綦陆杰
刘蕊
王耀雷
王延朔
张雯
赵韧
张博颐
李�昊
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State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
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State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
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Abstract

The invention discloses a wind power-containing power system energy storage capacity optimization method considering peak regulation requirements, which comprises the following steps: considering an optimization target of the energy storage capacity of the wind power-containing power system of the dispatching plan, establishing an energy storage capacity optimization cost and income model, wherein the optimization cost and income model comprises an objective function and a constraint condition for realizing the maximum net profit of the system with the lowest energy storage investment cost; and adopting an improved adaptive genetic algorithm to solve the energy storage capacity optimization cost-benefit model to obtain the optimal capacity of the water pumping energy storage power station, and optimizing the energy storage capacity of the wind power-containing power system according to the optimal capacity of the water pumping energy storage power station. According to the method, the peak regulation requirement constraint is introduced into the pumped storage pumping/energy storage process, and the peak regulation requirement of the system is prevented from being increased due to wind power grid-connected power fluctuation in a system scheduling plan.

Description

Wind power-containing power system energy storage capacity optimization method considering peak shaving requirements
Technical Field
The invention relates to a method for optimizing the energy storage capacity of a power system, in particular to a method for optimizing the energy storage capacity of a wind power-containing power system considering peak shaving requirements.
Background
At present, a power supply in a power system mainly comprises a thermal power generating unit, and the power supply is slow in starting and stopping and high in cost, so that the power supply is kept in an operating state all the time and is most economical. However, the load on the grid is not constant, and is usually large during the day and small during the night. In a power system containing wind power, because of the influence of natural wind energy, the fluctuation of the wind power output is more severe than the load, and the fluctuation is often similar to the load and has a characteristic of inverse peak shaving, so that the condition is unfavorable for the operation of a power grid, and the thermal power generating unit is frequently started and stopped in order to adapt to the change of the wind power and the load. The pumped storage power station is used for balancing the condition that the load of a power grid and the output of wind power are not matched, and is also called peak clipping and valley filling. Compared with a thermal power generating unit, the hydroelectric generating unit has the advantages of being capable of being started and stopped quickly, capable of responding to changes of power grid load and wind power output, and low in water consumption during starting and stopping; the thermal power generating unit is slow to start and stop, needs additional coal and is high in economic cost. Therefore, in order to ensure the stable output of the thermal power generating unit, when the load of a power grid is suddenly increased or the wind power output is suddenly reduced, the pumped storage power station discharges water to generate power; when the load of the power grid is reduced or the wind power output is suddenly increased, pumping water through the pumped storage power station to consume redundant electric energy. Therefore, from the perspective of the whole power grid, although the construction cost is high, the pumped storage power station can maintain the safe and stable operation of the power grid, cut peaks and fill valleys to promote wind power consumption, save coal consumption, utilize water resources and increase the benefit of the whole power grid.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a wind power-containing power system energy storage capacity optimization method considering peak shaving requirements. On the basis of analyzing the influence of wind power grid connection on the system peak regulation demand, the peak regulation demand constraint is introduced into the pumped storage pumping/energy storage process, and the system peak regulation demand is prevented from being increased due to wind power grid connection power fluctuation in a system scheduling plan.
The purpose of the invention is realized by the following technical scheme.
The invention relates to a wind power-containing power system energy storage capacity optimization method considering peak shaving requirements, which comprises the following steps:
the method comprises the following steps: considering an optimization target of the energy storage capacity of the wind power-containing power system of the dispatching plan, establishing an energy storage capacity optimization cost and income model, wherein the optimization cost and income model comprises an objective function and a constraint condition for realizing the maximum net profit of the system with the lowest energy storage investment cost;
step two: and adopting an improved adaptive genetic algorithm to solve the energy storage capacity optimization cost-benefit model to obtain the optimal capacity of the water pumping energy storage power station, and optimizing the energy storage capacity of the wind power-containing power system according to the optimal capacity of the water pumping energy storage power station.
Establishing an objective function with the maximum net profit of a pumped storage power station in a power system containing wind power within the whole life cycle of the power system as a target:
maxf＝R(S)+W(S)+O(S)+CS-Ce
(1) cost of energy storage Ce
The energy storage cost comprises investment cost and operation and maintenance cost, and the energy storage cost is as follows:
Figure BDA0002535618390000021
in the formula: cz(r, n) is the capital recovery factor; cmThe unit capacity cost of the pumped storage power station;
Figure BDA0002535618390000022
the capacity of the pumped storage power station; coAnnual operating cost of unit capacity of the pumped storage power station is saved;
capital recovery factor Cz(r, n) is represented by
Figure BDA0002535618390000023
In the formula: r is depreciation rate, and n is the effective life of the pumped storage power station;
(2) operating revenue of pumped storage power station
1) Peak clipping and valley filling income Cs
Figure BDA0002535618390000024
In the formula:
Figure BDA0002535618390000025
respectively the electric energy capacity of the pumped storage power station during water discharge/water pumping; cdThe unit electricity price is the unit electricity price when the pumped storage power station discharges water; ccIs unit electricity price when pumping water;
2) benefits of system operation
Saving running cost of conventional unit O (S)
Figure BDA0002535618390000031
In the formula: o(s) increased operational benefits for operating the system over the life cycle of the energy storage system; o iskAnd O'kThe total operation cost of the system under the condition that the system contains and does not contain energy storage in the scheduling period T; k is the number of days of operation; pg,hThe output of the unit g in the h time period; n is a radical ofgThe number of units is;
Figure BDA0002535618390000032
for consumption characteristics of thermal power generating units, ag,bg,cgIs a coefficient; u. ofg,hThe starting and stopping state of the unit g in the h period; sgThe unit g unit start-stop cost is set;
wind abandon punishment cost saving W (S)
Figure BDA0002535618390000033
In the formula: eWlossAnd E'WlossThe wind power station abandoned wind power quantity under the conditions of containing and not containing an energy storage system respectively; c. CwindThe price of the wind power is the price of the wind power; w (S) saves cost for punishing cost of abandoned wind, and wind power income is obtained by reducing abandoned wind volume;
saving cost for power-lack loss R (S)
Figure BDA0002535618390000034
In the formula: rlossAnd R'lossThe power shortage electric quantity is respectively contained and not contained in the energy storage system; c. ClossThe unit economic loss cost is the power shortage loss; r (S) saves the cost for the power shortage loss, namely the benefit obtained by reducing the power shortage cost before and after the energy storage of the system configuration.
Constraint conditions in the step one:
(1) constraint taking into account peak shaving requirements
Pnet,t＝Pd,t-Pw,t
In the formula: pnet,tIs the net load at time t; pd,tIs the system load at time t; pw,tThe output power of the wind power plant at the moment t;
Figure BDA0002535618390000041
in the formula: delta Pd,tThe fluctuation quantity of the system load at the moment t; delta Pnet,tThe amount of fluctuation of the net load at time t. At this time, Δ Pnet,tThe peak load regulation requirement of the conventional unit of the system at the time t after the system is accessed with the wind power is obtained;
after the energy storage grid connection, the load can be regarded as a negative load to be added into the system, and the peak regulation requirement of the conventional unit of the system at the h hour is
Lpeakreq≈ΔPnet,h+ΔPs,h
In the formula: l ispeakreqThe peak regulation requirement of the conventional unit of the system in the h hour is met; delta Pnet,hThe net load requirement of the system after wind power integration is met; wherein Δ Ps,h＝Ps,h-Ps,h-1，Ps,hThe energy storage charging and discharging power;
therefore, considering the peak load pressure of the wind storage combined grid-connected system without increasing the system, the system climbing constraint considering the peak load demand is expressed as
Figure BDA0002535618390000042
In the formula: delta Pload,hIs a system load fluctuation;
Figure BDA0002535618390000043
the up/down climbing capacity of the unit of the No. g of the system per hour;
(2) system operating constraints
1) System power balance constraints
Figure BDA0002535618390000044
In the formula: n is a radical ofgThe number of units is; pg,hThe output of the unit g in the h time period; pw,hWind power output power is output for h time period; ps,hStoring energy charging and discharging power in h time period; plosd,hLoad for h period;
2) rotational reserve capacity constraint
Figure BDA0002535618390000045
In the formula: n is a radical ofgThe number of units is; pg,hThe output of the unit g in the h time period; pw,hWind power output power is output for h time period; ps,hStoring energy charging and discharging power in h time period; plosd,hLoad for h period; r is the system rotation standby requirement after the wind power plant is accessed; u. ofg,hThe starting and stopping state of the unit g in the h period;
(3) constraint conditions of conventional units
1) Conventional unit output constraints
Figure BDA0002535618390000051
In the formula:
Figure BDA0002535618390000052
the maximum and minimum output of the conventional unit; pg,hThe output of the unit g in the h time period;
2) constraint of starting and stopping of unit
Figure BDA0002535618390000053
In the formula: u. ofg,hThe starting and stopping state of the unit g in h hours is shown; u. ofg,h-1The unit g is in a starting and stopping state within h-1 hour;
Figure BDA0002535618390000054
respectively the minimum on-off time of the unit g;
Figure BDA0002535618390000055
respectively accumulating the starting time and the shutdown time of the unit g in 0-h-1 hour;
3) unit climbing restraint
Figure BDA0002535618390000056
In the formula:
Figure BDA0002535618390000057
the up/down climbing capacity of the unit of the No. g of the system per hour; pg,hThe output of the unit g in the h time period; pg.hThe output of the unit g in the h-1 time period;
(4) constraint condition of pumped storage power station unit
1) Power generation/pumping power constraints
Figure BDA0002535618390000058
Figure BDA0002535618390000059
In the formula:
Figure BDA00025356183900000510
for the pumping power of the pumped storage power station,
Figure BDA00025356183900000511
generating power for the pumped storage power station;
Figure BDA00025356183900000512
and
Figure BDA00025356183900000513
the maximum minimum pumping power of the pumped storage power station is obtained;
Figure BDA00025356183900000514
and
Figure BDA00025356183900000515
the maximum minimum generating power of the pumped storage power station is obtained; etadThe power generation efficiency of the pumped storage power station is improved; es,hStoring energy for the upper reservoir in the h time period; Δ h is the time period;
2) capacity constraint
Figure BDA0002535618390000061
In the formula: etacPumping efficiency, η, for pumped storage power stationsdGenerating power for the pumped storage power station; es,hStoring energy for the upper reservoir in the h time period; es.h+1Storing energy in the upper reservoir for h +1 time period;
Figure BDA0002535618390000062
pumping power for a pumped storage power station;
Figure BDA0002535618390000063
is the generated power.
In the second step, the improved adaptive genetic algorithm is adopted to solve the energy storage capacity optimization cost-benefit model in the following steps:
1) determining an initial value: wind power PvConventional unit and load data; setting minimum and maximum values of pumped storage generating capacity, i.e.
Figure BDA0002535618390000064
And
Figure BDA0002535618390000065
2) determining genetic operator parameters: selecting an operator, a crossover operator and a mutation operator;
3) determining initial power P of pumped storage power station in each time intervals,hCarrying out binary coding to generate an initial population;
4) eliminating chromosomes which do not meet the constraint conditions of power and storage capacity of the pumped storage power station;
5) calculating a fitness value, and calculating the energy storage benefit of the configuration of the wind power system; selecting, crossing and mutating genetic algorithms to generate a new generation of population;
6) judging whether evolution termination algebra is reached; if the iteration times are reached, terminating the evolution and outputting a population optimal result; otherwise, repeating (4) and (5).
Selecting an operator in the step 2):
in the random tournament selection mechanism, a number of individuals with a tournament scale are selected for the next generation each time, and the specific process of selection is as follows:
(ii) parent population PtB individuals in (1) are subjected to crossing and mutation operations to form a new generation of sub-population P'tThen combining the parent population and the offspring population to generate a new population, randomly extracting two individuals in the new population to compare fitness values, and transmitting the individuals with large fitness values to the next generation;
② repeating the above-mentioned operation process A times to obtain required A next-generation population individuals, recording them as new-generation population Pt+1。
The operator is crossed in the step 2):
the adaptive cross probability adjustment formula is
Figure BDA0002535618390000071
In the formula: f. ofmaxThe maximum value of the fitness of a certain individual in a certain generation of population; f. ofavgThe fitness average value of the population is obtained; f' is the greater fitness value in the individual of the two crossover operations; paIs the cross probability;
the position of the cross point can be determined according to the fitness values of two individuals in the previous generation populationPlacing; assuming that I is the length of the chromosome, fa、fbThe length L of the partial chromosome string generated by the intersection segmentation is divided into two individual fitness valuesaAnd LbAre respectively as
La＝faL/(fa+fb)
Lb＝L-La
Variation operator in step 2):
the adaptive variation probability is adjusted by the formula
Figure BDA0002535618390000072
In the formula: f is the fitness value of the variant individual; pbIs the variation probability; pbtThe mutation probability was taken to be 0.15.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
the method can optimize the energy storage capacity of the wind power system, and meet the power dispatching peak shaving and demand side power consumption requirements; the power utilization efficiency of the power system is improved, and the economic operation of the power system containing wind power is promoted.
Drawings
FIG. 1 is a flow chart for solving based on an improved adaptive genetic algorithm.
Fig. 2 is a typical daily load/wind power curve.
FIG. 3 is a plot of the pumped cost system revenue and net profit for different pumped capacity configurations.
Fig. 4 shows the result of energy storage capacity allocation under different investment cost of pumping unit.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The invention relates to a wind power-containing power system energy storage capacity optimization method considering peak shaving requirements, which comprises the following steps:
the method comprises the following steps: and (3) considering the optimization target of the wind power-containing power system energy storage capacity of the dispatching plan, and establishing an energy storage capacity optimization cost and income model, wherein the energy storage capacity optimization cost and income model comprises an objective function and a constraint condition for realizing the maximum net profit of the system with the lowest energy storage investment cost.
The wind power output is translated in time and space by using the energy storage system, a net load curve is smoothed, so that peak clipping and valley filling benefits and system operation benefits are obtained, the overall economical efficiency of the system operation after the energy storage system is put into operation is guaranteed to be optimal, and the maximum net profit is the target objective function within the whole life cycle of a pumped storage power station in the power system containing the wind power:
maxf＝R(S)+W(S)+O(S)+CS-Ce
(1) cost of energy storage Ce
The energy storage cost comprises investment cost and operation and maintenance cost, and the energy storage cost is as follows:
Figure BDA0002535618390000081
in the formula: cz(r, n) is the capital recovery factor; cmThe unit capacity cost of the pumped storage power station;
Figure BDA0002535618390000082
the capacity of the pumped storage power station; coThe annual operation cost of unit capacity of the pumped storage power station is saved.
Capital recovery factor Cz(r, n) can be represented as
Figure BDA0002535618390000083
In the formula: r is depreciation rate, and n is the effective life of the pumped storage power station.
(2) Operating revenue of pumped storage power station
1) Peak clipping and valley filling income Cs
Considering the influence of time-of-use electricity price, because the pumped storage power station pumps and stores water when the load is low in the valley electricity price and discharges water to generate electricity when the load is high in the peak electricity price, the income can be obtained as
Figure BDA0002535618390000084
In the formula:
Figure BDA0002535618390000085
respectively the electric energy capacity of the pumped storage power station during water discharge/water pumping; cdThe unit electricity price is the unit electricity price when the pumped storage power station discharges water; ccIs the unit electricity price when pumping water.
2) Benefits of system operation
The system operation benefits include the following cost savings: the conventional unit operation cost is saved, the wind abandoning punishment is saved, and the electricity shortage loss is saved.
Saving running cost of conventional unit O (S)
Wind power as a power supply can replace part of conventional units to output power, but the running cost of the conventional units is increased compared with that of the conventional units without wind power due to the anti-peak regulation characteristic of the conventional units. When the wind power is connected to the grid, the pumped storage power station is used for carrying out space-time translation on the wind power, the system peak load pressure increased by the wind power can be effectively reduced, the running cost of the system is reduced, the starting and stopping times of a unit can be effectively reduced, and the starting and stopping cost of a power system containing the wind power is reduced. The reduction of the operation cost of the conventional unit can be regarded as the pumping energy storage brought the operation benefit of the system.
Figure BDA0002535618390000091
In the formula: o(s) increased operational benefits for operating the system over the life cycle of the energy storage system; o iskAnd O'kThe total operation cost of the system under the condition that the system contains and does not contain energy storage in the scheduling period T; k is the number of days of operation; pg,hThe output of the unit g in the h time period; n is a radical ofgThe number of units is;
Figure BDA0002535618390000092
for consumption characteristics of thermal power generating units, ag,bg,cgIs a coefficient; u. ofg,hThe starting and stopping state of the unit g in the h period; sgThe unit g unit start-stop cost is obtained.
Wind abandon punishment cost saving W (S)
Within the operating life cycle of the energy storage system, the wind abandon penalty cost saving brought by wind abandon reduction before and after the energy storage of the system configuration can be regarded as part of the operating benefit of the system, namely
Figure BDA0002535618390000093
In the formula: eWlossAnd E'WlossThe wind power station abandoned wind power quantity under the conditions of containing and not containing an energy storage system respectively; c. CwindThe price of the wind power is the price of the wind power; w (S) saves cost for wind abandon punishment cost, and wind power income obtained by wind abandoning amount reduction.
Saving cost for power-lack loss R (S)
In the operating life cycle of the energy storage system, the reduction of the power shortage loss cost before and after the energy storage of the system configuration can be regarded as a part of the operating benefit of the system:
Figure BDA0002535618390000101
in the formula: rlossAnd R'lossThe power shortage electric quantity is respectively contained and not contained in the energy storage system; c. ClossThe unit economic loss cost is the power shortage loss; r (S) saves the cost for the power shortage loss, namely the benefit obtained by reducing the power shortage cost before and after the energy storage of the system configuration.
Constraint conditions are as follows:
(1) constraint taking into account peak shaving requirements
In order to more specifically display the relationship between the wind power and the system load, the wind power can be used as a negative load to be superposed with the load of the power grid to obtain the system net load.
Pnet,t＝Pd,t-Pw,t
In the formula: pnet,tIs the net load at time t; pd,tIs the system load at time t; pw,tAnd the output power of the wind power plant at the moment t.
Figure BDA0002535618390000102
In the formula: delta Pd,tThe fluctuation quantity of the system load at the moment t; delta Pnet,tThe amount of fluctuation of the net load at time t. At this time, Δ Pnet,tNamely the peak shaving requirement of the conventional set of the system at the time t after the system is accessed into the wind power.
The operation cost of the conventional set is increased due to the anti-peak regulation characteristic of the wind power, so that the positivity of the conventional set on wind power peak regulation is low, and a large amount of abandoned wind occurs. If the system energy storage device relaxes the peak regulation requirement of the system by performing space-time translation on the wind power when the wind power shows the inverse peak regulation characteristic, the fluctuation rule of the wind power is made to be as close as possible to the fluctuation rule of the system load, and the wind power consumption can be promoted on the basis of ensuring the economical efficiency of the operation of the conventional units in the system. After the energy storage grid connection, the load can be regarded as a negative load to be added into the system, and the peak regulation requirement of the conventional unit of the system at the h hour is
Lpeakreq≈ΔPnet,h+ΔPs,h
In the formula: l ispeakreqThe peak regulation requirement of the conventional unit of the system in the h hour is met; delta Pnet,hThe net load requirement of the system after wind power integration is met; wherein Δ Ps,h＝Ps,h-Ps,h-1，Ps,hThe energy storage charging and discharging power.
Therefore, considering the peak load pressure of the wind storage combined grid-connected system without increasing the system, the system climbing constraint considering the peak load demand is expressed as
Figure BDA0002535618390000111
In the formula: delta Pload,hIs a system load fluctuation;
Figure BDA0002535618390000112
the up/down climbing capacity of the unit of the g-th unit of the system per hour. The peak load shifting requirement constraint can limit the peak load shifting degree, so that the peak load shifting requirement of the wind power integration aggravation system is limited.
(2) System operating constraints
1) System power balance constraints
Figure BDA0002535618390000113
In the formula: n is a radical ofgThe number of units is; pg,hThe output of the unit g in the h time period; pw,hWind power output power is output for h time period; ps,hStoring energy charging and discharging power in h time period; plosd,hLoad for a period of h.
2) Rotational reserve capacity constraint
Figure BDA0002535618390000114
In the formula: n is a radical ofgThe number of units is; pg,hThe output of the unit g in the h time period; pw,hWind power output power is output for h time period; ps,hStoring energy charging and discharging power in h time period; plosd,hLoad for h period; r is the system rotation standby requirement after the wind power plant is accessed; u. ofg,hThe starting and stopping state of the unit g in the h period.
(3) Constraint conditions of conventional units
1) Conventional unit output constraints
Figure BDA0002535618390000115
In the formula:
Figure BDA0002535618390000116
the maximum and minimum output of the conventional unit; pg,hThe output of the unit g in the h time period.
2) Constraint of starting and stopping of unit
As an energy source, pumped storage can stabilize wind power and load fluctuation in dispatching, can successfully avoid excessive starting and stopping operations of a thermal power generator in dispatching operation, and reduces the total generating cost.
Figure BDA0002535618390000121
In the formula: u. ofg,hThe starting and stopping state of the unit g in h hours is shown; u. ofg,h-1The unit g is in a starting and stopping state within h-1 hour;
Figure BDA0002535618390000122
respectively the minimum on-off time of the unit g;
Figure BDA0002535618390000123
the cumulative on-off time of the unit g in 0 to h-1 hour is respectively.
3) Unit climbing restraint
Figure BDA0002535618390000124
In the formula:
Figure BDA0002535618390000125
the up/down climbing capacity of the unit of the No. g of the system per hour; pg,hThe output of the unit g in the h time period; pg.hIs the output of the unit g in the h-1 time period.
(4) Constraint condition of pumped storage power station unit
1) Power generation/pumping power constraints
Figure BDA0002535618390000126
Figure BDA0002535618390000127
In the formula:
Figure BDA0002535618390000128
for the pumping power of the pumped storage power station,
Figure BDA0002535618390000129
generating power for the pumped storage power station;
Figure BDA00025356183900001210
and
Figure BDA00025356183900001211
the maximum minimum pumping power of the pumped storage power station is obtained;
Figure BDA00025356183900001212
and
Figure BDA00025356183900001213
the maximum minimum generating power of the pumped storage power station is obtained; etadThe power generation efficiency of the pumped storage power station is improved; es,hStoring energy for the upper reservoir in the h time period; Δ h is the period length.
2) Capacity constraint
Figure BDA00025356183900001214
In the formula: etacPumping efficiency, η, for pumped storage power stationsdGenerating power for the pumped storage power station; es,hStoring energy for the upper reservoir in the h time period; es.h+1Storing energy in the upper reservoir for h +1 time period;
Figure BDA00025356183900001215
pumping power for a pumped storage power station;
Figure BDA00025356183900001216
is the generated power.
Step two: and adopting an improved adaptive genetic algorithm to solve the energy storage capacity optimization cost-benefit model to obtain the optimal capacity of the water pumping energy storage power station, and optimizing the energy storage capacity of the wind power-containing power system according to the optimal capacity of the water pumping energy storage power station.
Improved adaptive genetic algorithm: and considering the peak regulation requirement, and setting the pumped storage power station to increase the capacity by a certain step length. The constraint conditions are mainly processed, and the cross and variation probability is optimized by adopting an adaptive genetic algorithm.
For a certain pumped storage power generation capacity, the pumped storage capacity is generally according to
Figure BDA0002535618390000131
The arrangement (gamma is a proportionality coefficient, generally 1-1.2).
(1) Constraint processing
The first six constraints in the model are added to the fitness function in the form of a penalty function. The specific method comprises the following steps: a maximum positive coefficient is used as a penalty factor, and the penalty function does not play a role in the fitness function when each constraint condition is met; when each constraint condition is not met, the punishment factor can reduce the fitness value of the individual, so that the individual cannot be propagated to the next generation.
For the power and reservoir capacity constraints of the latter two pumped storage power stations, individuals which do not meet the two constraints can be directly eliminated.
(2) Genetic operator parameters
The method is improved on the basis of the traditional genetic algorithm by adopting adaptive factors, wherein the adaptive factors comprise an adaptive crossover operator and an adaptive mutation operator. The adoption of the self-adaptive crossover operator can automatically adjust the crossover position and the crossover probability according to the size of the fitness value during population evolution. The adaptive mutation operator can automatically adjust the mutation probability during population evolution according to the size of the fitness value.
The improved adaptive genetic algorithm can make the calculation process more convergent. The genetic operators used in detail are as follows:
1) selection operator
The key to whether the optimal solution of the problem to be optimized can be obtained and the key to the success of the genetic algorithm are selection operation. The random tournament selection operation is selected, so that the problem that the global optimal solution cannot be obtained due to dead evolution and blind search caused by too small change of the fitness value in the later stage of the evolution process can be solved. Because the tournament selection mechanism does not need large difference between fitness values, only the fitness values of the selected individuals are compared, and the individuals with the maximum fitness values are passed to the offspring. In the random tournament selection mechanism, a number of individuals with a tournament scale are selected for the next generation each time, and the specific process of selection is as follows:
(ii) parent population PtB individuals in (1) are subjected to crossing and mutation operations to form a new generation of sub-population P't,. The parent population and the offspring population are then combined to produce a new population. Randomly extracting two individuals in the new population to compare fitness values, and inheriting the individuals with large fitness values to the next generation.
② repeating the above-mentioned operation process A times to obtain required A next-generation population individuals, recording them as new-generation population Pt+1.
2) Crossover operator
An adaptive crossover operator is adopted in the crossover process, and the crossover position and the probability can be automatically adjusted in the population evolution process according to the magnitude of the fitness value. Comparing the population average fitness value with the fitness value of each individual, reducing the crossing probability of the individual with the fitness value higher than the average fitness value, and promoting the individual to be inherited to the next generation. It is necessary to increase the cross probability of an individual having a fitness value lower than the average fitness value, thereby eliminating the individual.
The adaptive cross probability adjustment formula is
Figure BDA0002535618390000141
In the formula: f. ofmaxThe maximum value of the fitness of a certain individual in a certain generation of population; f. ofavgThe fitness average value of the population is obtained; f' is the greater fitness value in the individual of the two crossover operations; paIs the cross probability.
The position of the intersection point can be determined according to the fitness values of the two individuals in the previous generation population. Assuming that I is the length of the chromosome, fa、fbThe length L of the partial chromosome string generated by the intersection segmentation is divided into two individual fitness valuesaAnd LbAre respectively as
La＝faL/(fa+fb)
Lb＝L-La
3) Mutation operator
And an adaptive mutation operator is adopted, and the mutation probability can be automatically adjusted in the evolution process according to the size of the fitness value. Comparing the population average fitness value with the fitness value of each individual, reducing the variation probability of the individual with the fitness value higher than the average fitness value, and promoting the individual to be inherited to the next generation. It is necessary to increase the variation probability of an individual having a fitness value lower than the average fitness value, thereby eliminating the individual. The specific adaptive mutation probability adjustment formula is
Figure BDA0002535618390000142
In the formula: f is the fitness value of the variant individual; pbIs the variation probability; pbtThe mutation probability was taken to be 0.15.
The process of solving the energy storage capacity optimization cost-benefit model is as follows:
1) determining an initial value: wind power PvConventional unit and load data; setting minimum and maximum values of pumped storage generating capacity, i.e.
Figure BDA0002535618390000151
And
Figure BDA0002535618390000152
2) determining genetic operator parameters: selecting an operator, a crossover operator and a mutation operator;
3) determining initial power P of pumped storage power station in each time intervals,hCarrying out binary coding to generate an initial population;
4) eliminating chromosomes which do not meet the constraint conditions of power and storage capacity of the pumped storage power station;
5) calculating a fitness value, and calculating the energy storage benefit of the configuration of the wind power system; selecting, crossing and mutating genetic algorithms to generate a new generation of population;
6) judging whether evolution termination algebra is reached; if the iteration times are reached, terminating the evolution and outputting a population optimal result; otherwise, repeating (4) and (5).
The specific embodiment is as follows:
1. basic data
Take the power structure and load characteristics of a certain practical power grid system in China as an example. Wind power, load and net load values per hour for a typical day are shown in fig. 2. The parameters of the related units of the system are shown in the table, the maximum load is 8950MW, and the installed capacity of wind power is 2000 MW. The wind power permeability is 22%. According to relevant data of China, the unit investment cost of the pumped storage power station is calculated according to 450 ten thousand yuan/MW, the annual operation and maintenance cost is considered according to 4%, the service life is 50 years, and the depreciation rate is 12%.
TABLE 1 Unit parameter Table
Figure BDA0002535618390000153
And (I) considering the energy storage capacity optimization result of the small-scale peak regulation requirement.
For the planning scheme, the capacity of the pumped-storage power station is set in steps of 100 MW. The maximum benefit brought to the system by pumped storage in the next day, the pumped storage power station cost and the net profit curve of the configuration scheme with different capacities are obtained according to the optimization model and are shown in fig. 3.
The following points can be obtained:
(1) the pumped-hydro energy storage power station cost is a proportional function of the pumped-hydro energy storage power station capacity, and increases as the pumped-hydro energy storage power station capacity increases.
(2) The pumped storage power station profit increases with the increase of the capacity of the pumped storage power station, because when the pumping capacity is smaller, the profit obtained by improving the peak shaving demand of the system gradually increases with the increase of the pumping capacity, and the profit comprises the reduction of the peak shaving and valley filling profit, the reduction of the conventional operation cost of the system and the reduction of the wind abandoning punishment and the cost of the power shortage loss. However, after the pumping capacity is increased to a certain value, the system benefit tends to be stable, because the net load of the system is stable at the moment, the increase of the improvement system operation benefit is reduced.
(3) Since the net system profit is the difference between the system benefit and the pumped storage cost, the net system profit tends to increase and then decrease as the pumped storage capacity increases. When the cost curve of the pumped-storage power station is tangent to the pumping-storage income curve, the net profit obtained by the system is the maximum, and the capacity of the pumped-storage power station is between 400MW and 500MW at the moment. It can be obtained from fig. 3 that the optimal pumping capacity in the wind power system obtained by the known model is configured to be 500MW, and the net profit of the system is the maximum at this time, which is 26.3 ten thousand yuan. When the capacity of the pumped storage power station reaches 800MW, the net profit of the system is reduced to negative, and the energy storage investment is not suitable to be increased continuously.
(II) influence of investment cost of pumped storage power station on optimal capacity
The investment cost of the pumped storage power station has obvious influence on the optimal capacity of the pumped storage power station, and the influence of the unit investment cost of the pumped storage power station on the capacity configuration of the pumped storage power station is researched in the section. And increasing the unit investment cost of the pumped storage power station to 650 ten thousand yuan MW from 350 ten thousand yuan MW, namely changing the slope of the investment cost in the graph 3, and sequentially calculating the optimal capacity of the pumped storage power station according to the step length of 50 ten thousand yuan MW. The energy storage capacity configuration results for different pumping unit investment costs are shown in fig. 4.
With the increase of the pumping cost, the capacity of the optimal pumped storage power station is gradually reduced, and the net profit of the system is reduced. When the minimum pumped storage cost is 350 ten thousand yuan MW, the capacity of the optimal pumped storage power station is 800MW, and the maximum system net profit can be obtained at the moment and is 396 ten thousand yuan. However, when the pumping cost rises to 650 ten thousand yuan MW, the optimal capacity of the pumped storage power station is 200MW, and the net profit of the system which can benefit is reduced to 6 ten thousand yuan.
Although the present invention has been described above, the present invention is not limited to the above-described embodiments, and those skilled in the art can make many modifications without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims (7)

1. A wind power-containing power system energy storage capacity optimization method considering peak shaving requirements is characterized by comprising the following steps:
the method comprises the following steps: considering an optimization target of the energy storage capacity of the wind power-containing power system of the dispatching plan, establishing an energy storage capacity optimization cost and income model, wherein the optimization cost and income model comprises an objective function and a constraint condition for realizing the maximum net profit of the system with the lowest energy storage investment cost;
step two: and adopting an improved adaptive genetic algorithm to solve the energy storage capacity optimization cost-benefit model to obtain the optimal capacity of the water pumping energy storage power station, and optimizing the energy storage capacity of the wind power-containing power system according to the optimal capacity of the water pumping energy storage power station.
2. The method of claim 1, wherein the step one is to establish an objective function that the net profit of the pumped storage power station in the wind power system is the maximum target during the whole life cycle:
maxf＝R(S)+W(S)+O(S)+CS-Ce
(1) cost of energy storage Ce
The energy storage cost comprises investment cost and operation and maintenance cost, and the energy storage cost is as follows:
Figure FDA0002535618380000011
in the formula: cz(r, n) is the capital recovery factor; cmThe unit capacity cost of the pumped storage power station;
Figure FDA0002535618380000012
the capacity of the pumped storage power station; coAnnual operating cost of unit capacity of the pumped storage power station is saved;
capital recovery factor Cz(r, n) is represented by
Figure FDA0002535618380000013
In the formula: r is depreciation rate, and n is the effective life of the pumped storage power station;
(2) operating revenue of pumped storage power station
1) Peak clipping and valley filling income Cs
Figure FDA0002535618380000014
In the formula:
Figure FDA0002535618380000015
respectively the electric energy capacity of the pumped storage power station during water discharge/water pumping; cdThe unit electricity price is the unit electricity price when the pumped storage power station discharges water; ccIs unit electricity price when pumping water;
2) benefits of system operation
Saving running cost of conventional unit O (S)
Figure FDA0002535618380000021
In the formula: o(s) increased operational benefits for operating the system over the life cycle of the energy storage system; o iskAnd O'kThe total operation cost of the system under the condition that the system contains and does not contain energy storage in the scheduling period T; k is the number of days of operation; pg,hThe output of the unit g in the h time period; n is a radical ofgThe number of units is;
Figure FDA0002535618380000022
for consumption characteristics of thermal power generating units, ag,bg,cgIs a coefficient; u. ofg,hThe starting and stopping state of the unit g in the h period; sgThe unit g unit start-stop cost is set;
wind abandon punishment cost saving W (S)
Figure FDA0002535618380000023
In the formula: eWlossAnd E'WlossThe wind power station abandoned wind power quantity under the conditions of containing and not containing an energy storage system respectively; c. CwindThe price of the wind power is the price of the wind power; w (S) saves cost for punishing cost of abandoned wind, and wind power income is obtained by reducing abandoned wind volume;
saving cost for power-lack loss R (S)
Figure FDA0002535618380000024
In the formula: rlossAnd R'lossThe power shortage electric quantity is respectively contained and not contained in the energy storage system; c. ClossThe unit economic loss cost is the power shortage loss; r (S) saves the cost for the power shortage loss, namely the benefit obtained by reducing the power shortage cost before and after the energy storage of the system configuration.
3. The method for optimizing the energy storage capacity of the wind power system in consideration of the peak shaving demand according to claim 1, wherein the constraint conditions in the first step are as follows:
(1) constraint taking into account peak shaving requirements
Pnet,t＝Pd,t-Pw,t
In the formula: pnet,tIs the net load at time t; pd,tIs the system load at time t; pw,tThe output power of the wind power plant at the moment t;
Figure FDA0002535618380000031
in the formula: delta Pd,tThe fluctuation quantity of the system load at the moment t; delta Pnet,tThe amount of fluctuation of the net load at time t. At this time, Δ Pnet,tThe peak load regulation requirement of the conventional unit of the system at the time t after the system is accessed with the wind power is obtained;
after the energy storage grid connection, the load can be regarded as a negative load to be added into the system, and the peak regulation requirement of the conventional unit of the system at the h hour is
Lpeakreq≈ΔPnet,h+ΔPs,h
In the formula: l ispeakreqThe peak regulation requirement of the conventional unit of the system in the h hour is met; delta Pnet,hThe net load requirement of the system after wind power integration is met; wherein Δ Ps,h＝Ps,h-Ps,h-1，Ps,hThe energy storage charging and discharging power;
therefore, considering the peak load pressure of the wind storage combined grid-connected system without increasing the system, the system climbing constraint considering the peak load demand is expressed as
Figure FDA0002535618380000032
In the formula: delta Pload,hIs a system load fluctuation;
Figure FDA0002535618380000033
the up/down climbing capacity of the unit of the No. g of the system per hour;
(2) system operating constraints
1) System power balance constraints
Figure FDA0002535618380000034
In the formula: n is a radical ofgThe number of units is; pg,hThe output of the unit g in the h time period; pw,hWind power output power is output for h time period; ps,hStoring energy charging and discharging power in h time period; plosd,hLoad for h period;
2) rotational reserve capacity constraint
Figure FDA0002535618380000041
In the formula: n is a radical ofgThe number of units is; pg,hThe output of the unit g in the h time period; pw,hWind power output power is output for h time period; ps,hStoring energy charging and discharging power in h time period; plosd,hLoad for h period; r is the system rotation standby requirement after the wind power plant is accessed; u. ofg,hThe starting and stopping state of the unit g in the h period;
(3) constraint conditions of conventional units
1) Conventional unit output constraints
Figure FDA0002535618380000042
In the formula:
Figure FDA0002535618380000043
the maximum and minimum output of the conventional unit; pg,hThe output of the unit g in the h time period;
2) constraint of starting and stopping of unit
Figure FDA0002535618380000044
In the formula: u. ofg,hThe starting and stopping state of the unit g in h hours is shown; u. ofg,h-1The unit g is in a starting and stopping state within h-1 hour;
Figure FDA0002535618380000045
respectively the minimum on-off time of the unit g;
Figure FDA0002535618380000046
respectively accumulating the starting time and the shutdown time of the unit g in 0-h-1 hour;
3) unit climbing restraint
Figure FDA0002535618380000047
In the formula:
Figure FDA0002535618380000048
the up/down climbing capacity of the unit of the No. g of the system per hour; pg,hThe output of the unit g in the h time period; pg.hThe output of the unit g in the h-1 time period;
(4) constraint condition of water pumping and energy storage power station unit
1) Power generation/pumping power constraints
Figure FDA0002535618380000049
Figure FDA00025356183800000410
In the formula:
Figure FDA0002535618380000051
for the pumping power of the pumped storage power station,
Figure FDA0002535618380000052
generating power for the pumped storage power station;
Figure FDA0002535618380000053
and
Figure FDA0002535618380000054
the maximum minimum pumping power of the pumped storage power station is obtained;
Figure FDA0002535618380000055
and
Figure FDA0002535618380000056
the maximum minimum generating power of the pumped storage power station is obtained; etadThe power generation efficiency of the pumped storage power station is improved; es,hStoring energy for the upper reservoir in the h time period; Δ h is the time period;
2) capacity constraint
Figure FDA0002535618380000057
In the formula: etacPumping efficiency, η, for pumped storage power stationsdGenerating power for the pumped storage power station; es,hStoring energy for the upper reservoir in the h time period; es.h+1Storing energy in the upper reservoir for h +1 time period;
Figure FDA0002535618380000058
pumping power for a pumped storage power station;
Figure FDA0002535618380000059
is the generated power.
4. The method for optimizing the energy storage capacity of the wind power system with consideration of the peak shaving demand according to claim 1, wherein a process of solving the energy storage capacity optimization cost-benefit model by using the improved adaptive genetic algorithm in the second step is as follows:
1) determining an initial value: wind power PvConventional unit and load data; setting minimum and maximum values of pumped storage generating capacity, i.e.
Figure FDA00025356183800000510
And
Figure FDA00025356183800000511
2) determining genetic operator parameters: selecting an operator, a crossover operator and a mutation operator;
3) determining initial power P of pumped storage power station in each time intervals,hCarrying out binary coding to generate an initial population;
4) eliminating chromosomes which do not meet the constraint conditions of power and storage capacity of the pumped storage power station;
5) calculating a fitness value, and calculating the energy storage benefit of the configuration of the wind power system; selecting, crossing and mutating genetic algorithms to generate a new generation of population;
6) judging whether evolution termination algebra is reached; if the iteration times are reached, terminating the evolution and outputting a population optimal result; otherwise, repeating (4) and (5).
5. The wind power system energy storage capacity optimization method taking into account peak shaving requirements according to claim 4, characterized in that in step 2) an operator is selected:
in the random tournament selection mechanism, a number of individuals with a tournament scale are selected for the next generation each time, and the specific process of selection is as follows:
(ii) parent population PtAfter the B individuals are subjected to cross and mutation operations, a new generation of sub-population P is formedt' then combining the parent population and the offspring population to generate a new population, and randomly extracting two individuals in the new population to carry out fitness valueComparing, and inheriting the individuals with large fitness values to the next generation;
② repeating the above-mentioned operation process A times to obtain required A next-generation population individuals, recording them as new-generation population Pt+1。
6. The method for optimizing the energy storage capacity of the wind power system in consideration of the peak shaving demand according to claim 4, wherein the crossover operator in the step 2):
the adaptive cross probability adjustment formula is
Figure FDA0002535618380000061
In the formula: f. ofmaxThe maximum value of the fitness of a certain individual in a certain generation of population; f. ofavgThe fitness average value of the population is obtained; f' is the greater fitness value in the individual of the two crossover operations; paIs the cross probability;
the position of the cross point can be determined according to the fitness values of the two individuals in the previous generation population; assuming that I is the length of the chromosome, fa、fbThe length L of the partial chromosome string generated by the intersection segmentation is divided into two individual fitness valuesaAnd LbAre respectively La＝faL/(fa+fb)
Lb＝L-La
7. The wind power system energy storage capacity optimization method taking peak shaving requirements into account of claim 4, wherein the variation operator in step 2):
the adaptive variation probability is adjusted by the formula
Figure FDA0002535618380000062
In the formula: f is the fitness value of the variant individual; pbIs the variation probability; pbtThe mutation probability was taken to be 0.15.
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CN113890071A (en) * 2021-10-26 2022-01-04 国网经济技术研究院有限公司 Electrochemical energy storage capacity collaborative optimization configuration method considering pumped storage power station
CN114142536A (en) * 2021-12-03 2022-03-04 国家电网有限公司西北分部 Multi-type unit coordination method considering capacity reserve
CN114498699A (en) * 2021-12-29 2022-05-13 国网吉林省电力有限公司经济技术研究院 Echelon utilization energy storage system capacity configuration method for reducing abandoned wind
CN114498637A (en) * 2022-04-13 2022-05-13 清华四川能源互联网研究院 Method for analyzing influence of new energy field configuration energy storage on utilization rate
CN114707403A (en) * 2022-03-10 2022-07-05 国网湖北省电力有限公司宜昌供电公司 Multi-energy coordination optimization scheduling method for regional power distribution network based on pumped storage adjustment
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CN112564187A (en) * 2020-12-15 2021-03-26 深圳供电局有限公司 Wind-storage combined planning method for power system
CN113162091B (en) * 2021-05-13 2022-09-02 北方工业大学 Energy storage system configuration method for improving wind power smoothness
CN113162091A (en) * 2021-05-13 2021-07-23 北方工业大学 Energy storage system configuration method for improving wind power smoothness
CN113361715A (en) * 2021-06-24 2021-09-07 南通大学 Wind-storage hybrid power station daily operation strategy optimization method based on genetic algorithm
CN113555908A (en) * 2021-06-24 2021-10-26 国网山东省电力公司济宁市任城区供电公司 Energy storage optimization configuration method for intelligent power distribution network
CN113361715B (en) * 2021-06-24 2023-07-21 南通大学 Genetic algorithm-based daily operation strategy optimization method for wind-storage hybrid power station
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CN113765141A (en) * 2021-09-08 2021-12-07 内蒙古呼和浩特抽水蓄能发电有限责任公司 Wind power grid-connected down-peak regulation capability assessment method and system
WO2023060815A1 (en) * 2021-10-12 2023-04-20 广西电网有限责任公司电力科学研究院 Energy storage capacity optimization configuration method for improving reliability of power distribution network
CN113890071B (en) * 2021-10-26 2024-02-27 国网经济技术研究院有限公司 Electrochemical energy storage capacity collaborative optimization configuration method considering pumping and storage power station
CN113890071A (en) * 2021-10-26 2022-01-04 国网经济技术研究院有限公司 Electrochemical energy storage capacity collaborative optimization configuration method considering pumped storage power station
CN114142536A (en) * 2021-12-03 2022-03-04 国家电网有限公司西北分部 Multi-type unit coordination method considering capacity reserve
CN114142536B (en) * 2021-12-03 2023-09-26 国家电网有限公司西北分部 Multi-type unit coordination method considering capacity reserve
CN114498699B (en) * 2021-12-29 2024-02-09 国网吉林省电力有限公司经济技术研究院 Cascade utilization energy storage system capacity configuration method for reducing abandoned wind
CN114498699A (en) * 2021-12-29 2022-05-13 国网吉林省电力有限公司经济技术研究院 Echelon utilization energy storage system capacity configuration method for reducing abandoned wind
CN114707403A (en) * 2022-03-10 2022-07-05 国网湖北省电力有限公司宜昌供电公司 Multi-energy coordination optimization scheduling method for regional power distribution network based on pumped storage adjustment
WO2023179118A1 (en) * 2022-03-23 2023-09-28 中国能源建设集团江苏省电力设计院有限公司 Method and system for optimizing direct-current power transmission curve of multi-energy complementary integrated external transmission base
CN114498637B (en) * 2022-04-13 2022-07-12 清华四川能源互联网研究院 Method for analyzing influence of new energy field configuration energy storage on utilization rate
CN114498637A (en) * 2022-04-13 2022-05-13 清华四川能源互联网研究院 Method for analyzing influence of new energy field configuration energy storage on utilization rate
WO2024113353A1 (en) * 2022-11-30 2024-06-06 中国科学院深圳先进技术研究院 Multi-objective real-time scheduling method based on prediction mechanism and energy storage mechanism
CN117709098A (en) * 2023-12-14 2024-03-15 上海勘测设计研究院有限公司 Wind-solar-water-storage integrated capacity optimization configuration method and system based on genetic algorithm
CN118071036A (en) * 2024-04-19 2024-05-24 广东采日能源科技有限公司 Method and device for determining installation capacity of energy storage equipment and electronic equipment

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