CN111222699A - Multi-energy system capacity optimization method based on hydrogen-heat hybrid energy storage device - Google Patents

Multi-energy system capacity optimization method based on hydrogen-heat hybrid energy storage device Download PDF

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CN111222699A
CN111222699A CN202010013990.8A CN202010013990A CN111222699A CN 111222699 A CN111222699 A CN 111222699A CN 202010013990 A CN202010013990 A CN 202010013990A CN 111222699 A CN111222699 A CN 111222699A
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滕云
朱祥祥
左浩
郑晨
袁浦
徐震
魏来
鲍瑞
马俊雄
顾翔
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Abstract

The invention discloses a multi-energy system capacity optimization method based on a hydrogen-heat hybrid energy storage device, which belongs to the technical field of multi-energy system multi-source energy storage. The capacity optimization configuration model taking the investment cost of the hydrogen-heat hybrid energy storage device as the objective function is established based on the hydrogen storage device model, the heat storage device model and the net load power, the optimal energy storage capacity configuration is optimized by adopting the artificial ant colony algorithm, and the investment cost can be reduced on the premise of ensuring reliable energy supply of the system.

Description

Multi-energy system capacity optimization method based on hydrogen-heat hybrid energy storage device
Technical Field
The invention relates to the technical field of multi-source energy storage of multi-energy systems, in particular to a multi-energy system capacity optimization method based on a hydrogen-heat hybrid energy storage device.
Background
In the multi-energy system, the output fluctuation of the renewable energy sources such as wind, light and the like is large, so that the output and load requirements of the renewable energy sources are often unbalanced, and the energy supply reliability of the multi-energy system is seriously influenced; for example, the wind power output at night is large, the load requirement is small, and in order to absorb the abandoned wind and ensure the stable operation of the system, the energy storage device is introduced to stabilize the output power of the system, so that how to configure the capacity of the energy storage device is of great importance to the reliability and the economy of the system. Because the traditional storage battery is expensive in energy storage and lacks of researches on the coordination configuration of the hydrogen storage device and the heat storage device, the coordination process of energy supply and load consumption is insufficient, and the investment cost and the energy loss are high; the invention aims at the problems, and provides a coordination configuration strategy based on the hydrogen storage device and the heat storage device according to the problems of the investment and the single machine capacity of the hydrogen storage device and the heat storage device according to the fluctuation of the energy output and different load requirements, thereby ensuring the energy supply reliability and the economical efficiency of a multi-energy system.
Disclosure of Invention
Aiming at the defects of the prior art, the capacity optimization method of the multi-energy system based on the hydrogen-heat hybrid energy storage device is provided, and the method changes the traditional expensive storage battery energy storage and a single hydrogen storage system or heat storage system. The source-charge characteristic and the advantages and the disadvantages of the hydrogen storage device and the heat storage device are fully combined, the hydrogen storage device and the heat storage device are combined to form a hydrogen-heat mixed energy storage system, when the source output of the multi-energy system cannot meet the load requirement, the hydrogen-oxygen fuel cell and the temperature difference power generation are adopted to supplement the shortage electric energy, and the mechanism of the hydrogen storage and heat storage device coordinated configuration system is shown in figure 2.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a capacity optimization method of a multi-energy system based on a hydrogen-heat hybrid energy storage device is disclosed, and the flow of the method is shown in figure 1, and the method comprises the following steps:
step 1: collecting the predicted data of the next day of the power grid, and predicting the total power P of the collected renewable energy power generationproConventional load prediction total power PcomEstablishing a payload power PnetThe mathematical model is as follows:
|Pnet(t)|＝Ppro(t)-Pcom(t)
wherein ,Pnet(t) predicted t hour payload power, Ppro(t) Total renewable Power Generation predicted at the t hour, Pcom(t) the predicted normal load power at the tth hour;
step 2: according to the power and efficiency of hydrogen storage and hydrogen discharge of the hydrogen storage device, a mathematical model of hydrogen storage device capacity configuration is established as follows:
Figure BDA0002358180300000021
wherein ,
Figure BDA0002358180300000022
the proportion of the electric energy distributed to the water electrolysis hydrogen production device for net load power,
Figure BDA0002358180300000023
the power consumption H of the water electrolysis hydrogen production device at the time tHVHigh heating value for hydrogen(HHV＝3.509(kW·h)/(N·m3))，
Figure BDA0002358180300000024
In order to improve the hydrogen production efficiency of the water electrolysis hydrogen production device,
Figure BDA0002358180300000025
the hydrogen production rate at the time t is shown,
Figure BDA0002358180300000026
the hydrogen consumption rate at the time t, N (t) the gas storage amount of the gas storage device, N (t-1) the gas storage amount at the time t-1,
Figure BDA0002358180300000027
is the total hydrogen production at time t,
Figure BDA0002358180300000028
is the total hydrogen consumption at time t, and Δ t is the time variation.
And step 3: according to the heat absorption and heat release power and efficiency of the heat storage device, a capacity configuration mathematical model of the heat storage device is established, and the capacity configuration mathematical model comprises the following steps:
Figure BDA0002358180300000029
wherein ,
Figure BDA00023581803000000210
proportion of electric energy, P, distributed to the heating devices for the net load powereb(t) electric power consumption of the electric boiler at time t, Heb(t) heating power of the electric boiler at time t, λebTo the electric heat conversion efficiency of the electric boiler, HHS(t) the amount of heat stored at time t, α the heat dissipation loss of the heat storage device, QHS_ch(t)、ηhchRespectively the endothermic power and efficiency at time t, QHS_dis(t)、ηhdisRespectively the heat release power and efficiency at time t.
And 4, step 4: establishing a capacity optimization configuration model by taking the investment cost of the hydrogen-heat mixed energy storage device as an objective function, and calculating a proportion value to be optimized of net power distributed to the hydrogen storage device and the heat storage device;
step 4.1: establishing a capacity optimization configuration model as follows:
Figure BDA00023581803000000211
wherein ,
Figure BDA00023581803000000212
f represents the investment cost of the hydrogen-heat hybrid energy storage device as an objective function,
Figure BDA0002358180300000031
indicates the number of hydrogen storage devices arranged, NHThe number of the heat storage devices arranged is shown,
Figure BDA0002358180300000032
representing the unit price of the hydrogen storage unit, CHRepresenting the unit price, | P, of the heat storage deviceHTHSI is hydrogen-heat mixed energy storage, positive represents energy release, and negative represents energy storage; pleaseRepresenting energy loss, m representing the rated capacity of a single hydrogen storage device, and n representing the rated capacity of a single heat storage device;
step 4.2: reading the load and the power generation data of the renewable energy sources, and calculating the proportion value of the net power distributed to the hydrogen storage device and the heat storage device to be optimized:
Figure BDA0002358180300000033
Figure BDA0002358180300000034
wherein ,
Figure BDA0002358180300000035
for the proportion to be optimized of the net power distribution to the hydrogen storage means,
Figure BDA0002358180300000036
the proportion to be optimized for the net power distribution to the heat storage device.
And 5: the optimal capacity allocation scheme of the hydrogen-heat hybrid energy storage device is calculated through an artificial bee colony algorithm, the flow of the scheme is shown in figure 3, and the method comprises the following steps:
step 5.1: initialization configuration scheme
Figure BDA0002358180300000037
i is 1,2, …, NP is the number of allocation schemes, and the good or bad of the allocation schemes corresponds to the fitness evaluation value F of the solutioniSetting the number N of leading bees and following bees as NP/2, the dimension D as 2 and the maximum iteration number Kmax100, 50, and generating an initial scheme in the search space according to the steps 1 to 4
Figure BDA0002358180300000038
wherein ,
Figure BDA0002358180300000039
for the proportion to be optimized of the i-th scheme where net power is allocated to the hydrogen storage device,
Figure BDA00023581803000000310
the proportion to be optimized for distributing the net power to the heat storage device in the ith scheme;
step 5.2: within the range of the upper limit and the lower limit of the search space, N optimized configuration schemes are selected as leading bees according to the following formula
Figure BDA00023581803000000311
ζ…ξ∈i＝2,3,4…,NP；
Figure BDA00023581803000000312
wherein
Figure BDA00023581803000000313
Ld and UdRespectively representing the lower limit 0 and the upper limit of the search spaceLimit 100%, d ═ 1, 2;
step 5.3: calculating N investment costs F of leading bees corresponding to the hydrogen-heat mixed energy storage systemNAnd at N investment costs FNMiddle screening out optimal investment cost FαMaking fitness;
step 5.4: generating a neighborhood range by an Or-opt neighborhood search algorithm with the N optimized configuration schemes in the step 5.2 as the center, and selecting the N optimized configuration schemes from the neighborhood range according to the following formula to be used as the leading bees of the N optimized configuration schemes obtained in the step 5.2
Figure BDA0002358180300000041
Calculating N investment costs F of corresponding neighborhood leading bees to the hydrogen-heat hybrid energy storage systemNAnd N investment costs F after updatingNMiddle screening out updated optimum investment cost FβMaking fitness;
Figure BDA0002358180300000042
where ψ is a random number of [ -1,1] uniformly distributed, determines the disturbance amplitude, j is 1,2, …, NP, j ≠ i, indicating that one configuration scheme not equal to i is randomly selected among NP configuration schemes;
step 5.5: f obtained by leading bees in the step 5.2αF obtained by leading bees in neighborhood corresponding to step 5.4βComparing the two, selecting the optimal configuration scheme with lower fitness evaluation value by a greedy selection method, and recording the scheme
Figure BDA0002358180300000043
And determining a better configuration scheme according to a greedy selection method, wherein a calculation formula of the fitness evaluation value is as follows:
Figure BDA0002358180300000044
wherein ,FiDenotes a fitness evaluation value, FNObject function representing problem to be optimizedCounting;
step 5.6: calculating the probability of the configuration scheme to be optimized found by the leading bee to be followed according to the fitness evaluation value;
Figure BDA0002358180300000045
wherein ,FiThe fitness evaluation value of the ith configuration scheme transferred for the leading bee;
step 5.7: selecting leading bees by roulette method following bees, i.e. [0, 1]]Generating a uniformly distributed random number r if piIf r, the follower bee is configured according to the formula of step 5.4
Figure BDA0002358180300000046
Generating a new configuration scheme around the peak, searching the following peak in the same way as the leading bee, and determining a configuration scheme with better reservation according to a greedy selection method;
step 5.8: determining a configuration scheme
Figure BDA0002358180300000047
Whether the condition of being abandoned is met or not is not updated after the maximum mining number limit is 50; if yes, the configuration scheme is abandoned, the corresponding leading bee role is changed into the scout bee, otherwise, the step 5.9 is directly carried out;
step 5.9: the scout bees randomly generate a new configuration scheme according to the following formula
Figure BDA0002358180300000048
Figure BDA0002358180300000049
Step 5.10: and (5) judging whether the algorithm meets a termination condition or not, if so, terminating, outputting the optimal capacity configuration, and otherwise, turning to the step 5.2.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
1. according to the invention, through the coordinated configuration of the hydrogen storage device and the heat storage device, the capacity optimization of the hydrogen-heat hybrid energy storage system is further realized, so that the purposes of output power and peak characteristics of a smooth multi-energy system are achieved, the energy waste is reduced, the balance of supply and demand of the system is met, and the energy supply reliability of the system is ensured;
2. the invention provides a coordinated configuration strategy of the hydrogen storage device and the heat storage device, optimizes the optimal configuration scheme of the hydrogen storage device and the heat storage device by adopting an artificial bee colony algorithm with accuracy and high speed, and calculates the configuration number of the devices, thereby ensuring the lowest investment cost of the system.
Drawings
FIG. 1 is a flow chart of a capacity optimization method of a multi-energy system based on a hydrogen-heat hybrid energy storage device according to the present invention;
FIG. 2 is a schematic diagram of a configuration of the hydrogen storage and heat storage device according to the present invention;
fig. 3 is a flow chart of the artificial bee colony algorithm of the invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
In this embodiment, taking a multi-energy system in a certain area as an example, when the initial time t is 1, the total amount of renewable energy power generation P ispro(t) 500MW, normal load power Pcom(t) is 301MW, &lTtT transfer = &α "&gTt α &lTt/T &gTt ═ 0.1 is the heat dissipation loss rate of the heat storage device, and the heat absorption power Q isHS_ch(1) 70MW endothermic efficiency ηhch0.9; heat release power QHS_dis(1) 16MW, exothermic efficiency ηhdis0.8; total hydrogen production of
Figure BDA0002358180300000051
Total hydrogen consumption of
Figure BDA0002358180300000052
Hydrogen storage quantity N (0) of hydrogen storage device is 33MW, heat storage quantity H of heat storage deviceHS(0) 50 MW; single machine rated capacity m of hydrogen storage device is 5MW, heat storage deviceThe rated capacity n of the single machine is 8 MW; the unit price of the hydrogen storage device is 5 ten thousand yuan, and the unit price of the heat storage device is 3 ten thousand yuan.
As shown in fig. 1, the method of the present embodiment is as follows.
Step 1: collecting the predicted data of the next day of the power grid, namely predicting the total power P generated by the renewable energy sourcespro(t) and predicted total power P of conventional loadcom(t) establishing a payload power Pnet(t) a mathematical model;
|Pnet(t)|＝Ppro(t)-Pcom(t)
wherein ,Pnet(t) is the t hour net load power, Ppro(t) Total renewable Power Generation predicted at the t hour, Pcom(t) the predicted normal load power at the tth hour;
in this embodiment, taking the 1 st time as an example, the net load power at the 1 st time calculated by the step 1 is Pnet(1)＝199MW；
Step 2: according to the power and efficiency of hydrogen storage and hydrogen discharge of the hydrogen storage device, a mathematical model of hydrogen storage device capacity configuration is established as follows:
Figure BDA0002358180300000061
wherein ,
Figure BDA0002358180300000062
the proportion of electric energy, P, distributed to the water electrolysis hydrogen production plant for net load powerH2The power consumption H of the water electrolysis hydrogen production device at the time tHVIs a high heating value (H) of hydrogenHV＝3.509(kW·h)/(N·m3))，
Figure BDA0002358180300000063
In order to improve the hydrogen production efficiency of the water electrolysis hydrogen production device,
Figure BDA0002358180300000064
the hydrogen production rate at the time t is shown,
Figure BDA0002358180300000065
the hydrogen consumption rate at the time t, N (t) the gas storage amount of the gas storage device, N (t-1) the gas storage amount at the time t-1,
Figure BDA0002358180300000066
is the total hydrogen production at time t,
Figure BDA0002358180300000067
is the total hydrogen consumption at time t, and Δ t is the time variation.
And step 3: according to the heat absorption and heat release power and efficiency of the heat storage device, a capacity configuration mathematical model of the heat storage device is established, and the capacity configuration mathematical model comprises the following steps:
Figure BDA0002358180300000068
wherein ,
Figure BDA0002358180300000069
proportion of electric energy, P, distributed to the heating devices for the net load powereb(t) electric power consumption of the electric boiler at time t, Heb(t) heating power of the electric boiler at time t, λebTo the electric heat conversion efficiency of the electric boiler, HHS(t) the amount of heat stored at time t, α the heat dissipation loss of the heat storage device, QHS_ch(t)、ηhchRespectively the endothermic power and efficiency at time t, QHS_dis(t)、ηhdisRespectively the heat release power and efficiency at time t.
And 4, step 4: establishing a capacity optimization configuration model by taking the investment cost of the hydrogen-heat mixed energy storage device as an objective function, and calculating a proportional value of net power distributed to the hydrogen storage device and the heat storage device;
step 4.1: establishing a capacity optimization configuration model as follows:
Figure BDA0002358180300000071
wherein ,
Figure BDA0002358180300000072
f represents the investment cost of the hydrogen-heat hybrid energy storage device as an objective function,
Figure BDA0002358180300000073
indicates the number of hydrogen storage devices arranged, NHThe number of the heat storage devices arranged is shown,
Figure BDA0002358180300000074
representing the unit price of the hydrogen storage unit, CHRepresenting the unit price, | P, of the heat storage deviceHTHSI is hydrogen-heat mixed energy storage, positive represents energy release, and negative represents energy storage; pleaseRepresenting energy loss, m representing the rated capacity of a single hydrogen storage device, and n representing the rated capacity of a single heat storage device;
step 4.2: reading the load and the power generation data of the renewable energy sources, and calculating the proportion value of the net power distributed to the hydrogen storage device and the heat storage device to be optimized:
Figure BDA0002358180300000075
Figure BDA0002358180300000076
wherein ,
Figure BDA0002358180300000077
for the proportion to be optimized of the net power distribution to the hydrogen storage means,
Figure BDA0002358180300000078
the proportion to be optimized for the net power distribution to the heat storage device.
In this embodiment, N (1) ═ 111MW, H can be calculated through steps 2 to 4HS(1) 88MW, hydrogen storage apparatus to be optimized
Figure BDA0002358180300000079
Ratio to be optimized for heat storage devices
Figure BDA00023581803000000710
And 5: and calculating the optimal capacity configuration of the hydrogen-heat mixed energy storage device by an artificial bee colony algorithm.
Step 5.1: initialization configuration scheme
Figure BDA00023581803000000711
i is 1,2, …, NP is the number of allocation schemes, and the good or bad of the allocation schemes corresponds to the fitness evaluation value F of the solutioniSetting the number N of leading bees/following bees as NP/2, the dimension D as 2 and the maximum iteration number KmaxAnd generating an initial configuration scheme to be optimized in the search space according to the steps 1 to 4 by using the maximum mining times limit
Figure BDA00023581803000000712
wherein ,
Figure BDA00023581803000000713
for the proportion to be optimized of the i-th scheme where net power is allocated to the hydrogen storage device,
Figure BDA00023581803000000714
the proportion to be optimized for distributing the net power to the heat storage device in the ith scheme;
in this embodiment, taking the 1 st time as an example, the initial configuration scheme to be optimized is [ 55.78%, 44.22%]The number of configuration schemes NP is 100, and the maximum number of iterations Kmax100, 50, and the solution fitness evaluation value F corresponding to the quality of the arrangement plani＝0.008。
Step 5.2: within the range of the upper limit and the lower limit of the search space, N optimized configuration schemes are selected as leading bees according to the following formula
Figure BDA0002358180300000081
In this embodiment, N is 2, and N may be any integer from 1 to 50, and may be selected as needed.
Figure BDA0002358180300000082
wherein
Figure BDA0002358180300000083
Ld and UdRepresents the lower limit 0 and the upper limit 100% of the search space, respectively, and d is 1, 2;
step 5.3: calculating N investment costs F of leading bees corresponding to the hydrogen-heat mixed energy storage systemNAnd at N investment costs FNMiddle screening out optimal investment cost FαMaking fitness;
step 5.4: generating a neighborhood range by an Or-opt neighborhood search algorithm with the N optimized configuration schemes in the step 5.2 as the center, and selecting the N optimized configuration schemes from the neighborhood range according to the following formula to be used as the leading bees of the N optimized configuration schemes obtained in the step 5.2
Figure BDA0002358180300000084
Calculating N investment costs F of corresponding neighborhood leading bees to the hydrogen-heat hybrid energy storage systemNAnd N investment costs F after updatingNMiddle screening out updated optimum investment cost FβMaking fitness;
Figure BDA0002358180300000085
where ψ is a random number of [ -1,1] uniformly distributed, determines the disturbance amplitude, j is 1,2, …, NP, j ≠ i, indicating that one configuration scheme not equal to i is randomly selected among NP configuration schemes;
step 5.5: f obtained by leading bees in the step 5.2αF obtained by leading bees in neighborhood corresponding to step 5.4βComparing the two, selecting the optimal configuration scheme with lower fitness evaluation value by a greedy selection method, and recording the scheme
Figure BDA0002358180300000086
And determining a better configuration scheme to be kept according to a greedy selection method, wherein the calculation formula of the fitness evaluation value is as follows:
Figure BDA0002358180300000087
wherein ,FiDenotes a fitness evaluation value, FNAn objective function representing an optimization problem;
step 5.6: calculating the probability of the configuration scheme to be optimized found by the leading bee to be followed according to the fitness evaluation value;
Figure BDA0002358180300000088
wherein ,FiThe fitness evaluation value of the ith scheme to be optimized transmitted for the leading bee;
step 5.7: selecting leading bees by roulette method following bees, i.e. [0, 1]]Generating a uniformly distributed random number r if piIf r, the follower bee is configured according to the formula of step 5.4
Figure BDA0002358180300000091
Generating a new configuration scheme around the peak, searching the following peak in the same way as the leading bee, and determining a configuration scheme with better reservation according to a greedy selection method;
step 5.8: determining a configuration scheme
Figure BDA0002358180300000092
Whether the condition of being abandoned is met or not is not updated after the maximum mining number limit is 50; if yes, the configuration scheme is abandoned, the corresponding leading bee role is changed into the scout bee, otherwise, the step 5.9 is directly carried out;
step 5.9: the scout bees randomly generate a new configuration scheme according to the following formula
Figure BDA0002358180300000093
Figure BDA0002358180300000094
Step 5.10: and (5) judging whether the algorithm meets a termination condition or not, if so, terminating, outputting the optimal capacity configuration, and otherwise, turning to the step 5.2.
The optimal configuration scheme of the energy storage capacity at the 1 st moment obtained finally through the artificial bee colony algorithm is X [ 57%, 43% ], at the moment, 23 hydrogen storage devices are configured, 11 heat storage devices are configured, and the investment cost is 124 ten thousand.

Claims (5)

1. A multi-energy system capacity optimization method based on a hydrogen-heat hybrid energy storage device is characterized by comprising the following steps:
step 1: collecting the predicted data of the next day of the power grid, and predicting the total power P of the collected renewable energy power generationproConventional load prediction total power PcomEstablishing a payload power PnetThe mathematical model is as follows:
|Pnet(t)|＝Ppro(t)-Pcom(t)
wherein ,Pnet(t) predicted t hour payload power, Ppro(t) Total renewable Power Generation predicted at the t hour, Pcom(t) the predicted normal load power at the tth hour;
step 2: establishing a capacity configuration mathematical model of the hydrogen storage device according to the hydrogen storage and hydrogen discharge rate and efficiency of the hydrogen storage device;
and step 3: establishing a capacity configuration mathematical model of the heat storage device according to the heat absorption and heat release power and efficiency of the heat storage device;
and 4, step 4: taking the investment cost of the hydrogen-heat mixed energy storage device as an objective function, establishing a capacity optimization configuration model by combining the models in the steps 1 to 3, and calculating a proportion value to be optimized of net power distributed to the hydrogen storage device and the heat storage device;
and 5: and calculating the optimal capacity configuration of the hydrogen-heat mixed energy storage device by an artificial bee colony algorithm.
2. The method according to claim 1, wherein the mathematical model for configuring the capacity of the gas storage device is as follows:
Figure FDA0002358180290000011
wherein ,
Figure FDA0002358180290000012
the proportion of the electric energy distributed to the water electrolysis hydrogen production device for net load power,
Figure FDA0002358180290000013
the power consumption H of the water electrolysis hydrogen production device at the time tHVIs a high heating value of the hydrogen gas,
Figure FDA0002358180290000014
in order to improve the hydrogen production efficiency of the water electrolysis hydrogen production device,
Figure FDA0002358180290000015
the hydrogen production rate at the time t is shown,
Figure FDA0002358180290000016
the hydrogen consumption rate at the time t, N (t) the gas storage amount of the gas storage device, N (t-1) the gas storage amount at the time t-1,
Figure FDA0002358180290000017
is the total hydrogen production at time t,
Figure FDA0002358180290000018
is the total hydrogen consumption at time t, and Δ t is the time variation.
3. The capacity optimization method for the multi-energy system based on the hydrogen-heat hybrid energy storage device as claimed in claim 1, wherein the mathematical model for the capacity configuration of the heat storage device is as follows:
Figure FDA0002358180290000019
wherein ,
Figure FDA0002358180290000021
proportion of electric energy, P, distributed to the heating devices for the net load powereb(t) electric power consumption of the electric boiler at time t, Heb(t) heating power of the electric boiler at time t, λebTo the electric heat conversion efficiency of the electric boiler, HHS(t) the amount of heat stored at time t, α the heat dissipation loss of the heat storage device, QHS_ch(t)、ηhchRespectively the endothermic power and efficiency at time t, QHS_dis(t)、ηhdisRespectively the heat release power and efficiency at time t.
4. The capacity optimization method for the multi-energy system based on the hydrogen-heat hybrid energy storage device according to claim 1, wherein the process of step 4 is as follows:
step 4.1: establishing a capacity optimization configuration model as follows:
Figure FDA0002358180290000022
wherein ,
Figure FDA0002358180290000023
f represents the investment cost of the hydrogen-heat hybrid energy storage device as an objective function,
Figure FDA0002358180290000024
indicates the number of hydrogen storage devices arranged, NHThe number of the heat storage devices arranged is shown,
Figure FDA0002358180290000025
representing the unit price of the hydrogen storage unit, CHRepresenting the unit price, | P, of the heat storage deviceHTHSI is hydrogen-heat mixed energy storage, positive represents energy release, and negative represents energy storage; pleaseRepresents energy loss, m representsThe rated capacity of a single hydrogen storage device, and n represents the rated capacity of the single heat storage device;
step 4.2: reading the load and the power generation data of the renewable energy sources, and calculating the proportion value of the net power distributed to the hydrogen storage device and the heat storage device to be optimized:
Figure FDA0002358180290000026
Figure FDA0002358180290000027
wherein ,
Figure FDA0002358180290000028
for the proportion to be optimized of the net power distribution to the hydrogen storage means,
Figure FDA0002358180290000029
the proportion to be optimized for the net power distribution to the heat storage device.
5. The capacity optimization method for the multi-energy system based on the hydrogen-heat hybrid energy storage device according to claim 1, wherein the process of step 5 is as follows:
step 5.1: initialization configuration scheme
Figure FDA00023581802900000210
NP is the number of the configuration schemes, and the fitness evaluation value F of the solution corresponding to the good or the bad of the configuration schemesiSetting the number N of leading bees/following bees as NP/2, the dimension D as 2 and the maximum iteration number KmaxAnd generating an initial scheme in the search space according to the steps 1 to 4 by using the maximum mining times limit
Figure FDA0002358180290000031
wherein ,
Figure FDA0002358180290000032
for the proportion to be optimized of the i-th scheme where net power is allocated to the hydrogen storage device,
Figure FDA0002358180290000033
the proportion to be optimized for distributing the net power to the heat storage device in the ith scheme;
step 5.2: within the range of the upper limit and the lower limit of the search space, N optimized configuration schemes are selected as leading bees according to the following formula
Figure FDA0002358180290000034
Figure FDA0002358180290000035
wherein
Figure FDA0002358180290000036
Ld and UdRepresents the lower limit 0 and the upper limit 100% of the search space, respectively, and d is 1, 2;
step 5.3: calculating N investment costs F of leading bees corresponding to the hydrogen-heat mixed energy storage systemNAnd at N investment costs FNMiddle screening out optimal investment cost FαMaking fitness;
step 5.4: generating a neighborhood range by an Or-opt neighborhood search algorithm with the N optimized configuration schemes in the step 5.2 as the center, and selecting the N optimized configuration schemes from the neighborhood range according to the following formula to be used as the leading bees of the N optimized configuration schemes obtained in the step 5.2
Figure FDA0002358180290000037
Calculating N investment costs F of corresponding neighborhood leading bees to the hydrogen-heat hybrid energy storage systemNAnd N investment costs F after updatingNMiddle screening out updated optimum investment cost FβMaking fitness;
Figure FDA0002358180290000038
where ψ is a random number of [ -1,1] uniformly distributed, determines the disturbance amplitude, j is 1,2, …, NP, j ≠ i, indicating that one configuration scheme not equal to i is randomly selected among NP configuration schemes;
step 5.5: f obtained by leading bees in the step 5.2αF obtained by leading bees in neighborhood corresponding to step 5.4βComparing the two, selecting the optimal configuration scheme with lower fitness evaluation value by a greedy selection method, and recording the scheme
Figure FDA0002358180290000039
And determining a better configuration scheme according to a greedy selection method, wherein a calculation formula of the fitness evaluation value is as follows:
Figure FDA00023581802900000310
wherein ,FiDenotes a fitness evaluation value, FNAn objective function representing an optimization problem;
step 5.6: calculating the probability of the configuration scheme to be optimized found by the leading bee to be followed according to the fitness evaluation value;
Figure FDA0002358180290000041
wherein ,FiThe fitness evaluation value of the ith configuration scheme transferred for the leading bee;
step 5.7: selecting leading bees by roulette method following bees, i.e. [0, 1]]Generating a uniformly distributed random number r if piIf r, the follower bee is configured according to the formula of step 5.4
Figure FDA0002358180290000042
A new configuration scheme is generated around, the following peak is searched in the same way as the leading bee, and better reservation is determined according to a greedy selection methodThe configuration scheme of (1);
step 5.8: determining a configuration scheme
Figure FDA0002358180290000043
Whether the condition of being abandoned is met or not is not updated after the maximum mining number limit is 50; if yes, the configuration scheme is abandoned, the corresponding leading bee role is changed into the scout bee, otherwise, the step 5.9 is directly carried out;
step 5.9: the scout bees randomly generate a new configuration scheme according to the following formula
Figure FDA0002358180290000044
Figure FDA0002358180290000045
Step 5.10: and (5) judging whether the algorithm meets a termination condition or not, if so, terminating, outputting the optimal capacity configuration, and otherwise, turning to the step 5.2.
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