CN111222699B - Multi-energy system capacity optimization method based on hydro-thermal hybrid energy storage device - Google Patents
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 9
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- 238000005868 electrolysis reaction Methods 0.000 claims description 3
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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 invention establishes a capacity optimizing configuration model taking the investment cost of the hydrogen-heat hybrid energy storage device as an objective function based on the hydrogen storage device model, the heat storage device model and the net load power, optimizes the optimal energy storage capacity configuration by adopting the artificial ant colony algorithm, and can reduce the investment cost on the premise of ensuring reliable energy supply of the system.
Description
Technical Field
The invention relates to the technical field of multi-source energy storage of a multi-energy system, in particular to a multi-energy system capacity optimization method based on a hydro-thermal hybrid energy storage device.
Background
Because in the multi-energy system, the fluctuation of the output of renewable energy sources such as wind, light and the like is large, the unbalanced condition of the output and the load demand of the renewable energy sources often occurs, and the energy supply reliability of the multi-energy system is seriously affected; for example, wind power output is large at night, load demand is small, and in order to consume waste wind and ensure stable operation of the system, an energy storage device is introduced to stabilize output power of the system, so how to configure capacity of the energy storage device is important to reliability and economy of the system. Because the traditional storage battery is expensive in energy storage and lacks of research on coordination configuration of the hydrogen storage device and the heat storage device, the traditional storage battery has defects in the coordination process of energy supply and load absorption, and the investment cost and the energy loss are high; the invention aims at solving the problems and considers the investment and single-machine capacity problems of the hydrogen storage device and the heat storage device according to the fluctuation of the output of the energy source and the different load demands, and proposes a coordinated configuration strategy based on the hydrogen storage device and the heat storage device, thereby ensuring the energy supply reliability and the economy of the multi-energy system.
Disclosure of Invention
In order to overcome the defects in the prior art, a multi-energy system capacity optimization method based on a 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 a single heat storage system. The hydrogen storage device and the heat storage device are combined to form the hydrogen-heat hybrid energy storage system, when the source output of the multi-energy system cannot meet the load demand, the oxyhydrogen fuel cell and the thermoelectric generation are adopted to supplement the electric energy, and the mechanism of the coordinated configuration system of the hydrogen storage and heat storage device is shown in figure 2.
In order to solve the technical problems, the invention adopts the following technical scheme: a multi-energy system capacity optimization method based on a hydro-thermal hybrid energy storage device is shown in a figure 1, and comprises the following steps:
step 1: collecting data predicted on the next day of the power grid, and predicting total power P of the collected renewable energy power generation pro Conventional load prediction total power P com Establishing payload power P net Mathematical model:
|P net (t)|＝P pro (t)-P com (t)
wherein ,Pnet (t) is the predicted t-hour payload power, P pro (t) the predicted total power of renewable energy power generation at t hours, P com (t) is the conventional load power predicted at hour t;
step 2: according to the power and efficiency of hydrogen storage and release of the hydrogen storage device, a mathematical model of capacity configuration of the hydrogen storage device is established as follows:
wherein ,electric energy proportion allocated to electrolytic water hydrogen plant for payload power, +.>The power H is the power used by the hydrogen production device for electrolyzing water at the moment t HV Is the high heating value (H) HV ＝3.509(kW·h)/(N·m 3 ))，/>Hydrogen production efficiency of hydrogen production device by electrolysis of water, +.>For the hydrogen production rate at time t, +.>The hydrogen consumption rate at the time t is the gas storage amount of an N (t) gas storage device, and N (t-1) is the gas storage amount at the time t-1 +.>Is the total hydrogen production at time t, +.>The total hydrogen consumption at time t is defined as Δt as the time variation.
Step 3: and establishing a mathematical model of capacity configuration of the heat storage device according to the heat absorption and heat release power and efficiency of the heat storage device, wherein the mathematical model is as follows:
wherein ,the proportion of electrical energy allocated to the heating device for the net load power, P eb (t) is the power consumption of the electric boiler at the moment t, H eb (t) is the heating power of the electric boiler at the moment t, lambda eb Is the electric heat conversion efficiency of the electric boiler, H HS (t) is the heat storage amount at time t, α is the heat dissipation loss rate of the heat storage device, Q HS_ch (t)、η hch The endothermic power and the efficiency at the time t are respectively Q HS_dis (t)、η hdis The heat release power and efficiency at time t are respectively.
Step 4: taking the investment cost of the hydrogen-heat hybrid energy storage device as an objective function, establishing a capacity optimization configuration model, and calculating the proportion value to be optimized of net power distributed to the hydrogen storage device and the heat storage device;
step 4.1: the capacity optimization configuration model is established as follows:
wherein ,f represents the investment cost of the hydrogen-heat hybrid energy storage device as an objective function, < >>Indicating the number of hydrogen storage devices, N H Representing the number of arrangements of the heat storage means, +.>Indicating the unit price of the hydrogen storage device, C H Indicating the unit price of the heat storage device, |P HTHS The I is hydrogen-heat mixed energy storage, positive represents energy release, and negative represents energy storage; p (P) lease The energy loss is represented by m, the rated capacity of a single hydrogen storage device is represented by m, and the rated capacity of the single heat storage device is represented by n;
step 4.2: the load and renewable energy power generation data are read, and the proportion value of net power distributed to the hydrogen storage device and the heat storage device to be optimized is calculated:
wherein ,to be optimized for the net power allocation to the hydrogen storage device, < >>The net power is distributed to the proportion to be optimized of the heat storage device.
Step 5: the optimal capacity configuration scheme of the hydrogen-heat hybrid energy storage device is calculated through a manual bee colony algorithm, and the flow is shown in fig. 3, and comprises the following steps:
step 5.1: initializing a configuration schemei=1, 2, …, NP is the number of configuration schemes, and the fitness evaluation value F of the solution corresponding to the quality of the configuration scheme i Setting leading bees and following bees with the number N=NP/2, the dimension D=2 and the maximum iteration number K max =100, maximum mining number limit=50, according to steps 1 to 4, generating an initial plan +_in the search space>
wherein ,to be optimized for the net power allocation to the hydrogen storage device in the ith variant, +.>The net power in the ith scheme is distributed to the proportion to be optimized of the heat storage device;
step 5.2: within the upper and lower limit ranges of the search space, N optimal configuration schemes are selected as leading bees according to the followingζ…ξ∈i＝2,3,4…,NP；
wherein L d and Ud Represents a lower limit 0 and an upper limit 100% of the search space, d=1, 2, respectively;
step 5.3: calculating N investment costs F of hydrogen-heat hybrid energy storage system corresponding to leading bees N And at N investment costs F N Screening out the optimal investment cost F α The fitness is made;
step 5.4: generating a neighborhood range by using the N optimal configuration schemes in the step 5.2 as the center through an Or-opt neighborhood search algorithm, and selecting the N optimal configuration schemes from the neighborhood range as the leading bees of the N optimal configuration schemes obtained in the step 5.2 according to the following formulaCalculating N investment costs F of hydrogen-heat hybrid energy storage systems corresponding to corresponding neighborhood leading bees N And at updated N investment costs F N Screening the updated optimal investment cost F β The fitness is made;
wherein, ψ is a random number uniformly distributed in [ -1,1], and the disturbance amplitude is determined, j=1, 2, …, NP, j+.i, which means that a configuration scheme which is not equal to i is randomly selected from NP configuration schemes;
step 5.5: f obtained by leading the bee in the step 5.2 α F obtained by leading bees in neighborhood corresponding to the step 5.4 β Comparing the two, selecting an optimal configuration scheme with low adaptability evaluation value by adopting a greedy selection method as
And determining a better configuration scheme according to a greedy selection method, wherein the calculation formula of the fitness evaluation value is as follows:
wherein ,Fi Represents the fitness evaluation value, F N An objective function representing the problem being optimized;
step 5.6: calculating the probability that the configuration scheme to be optimized found by the leading bee is followed according to the fitness evaluation value;
wherein ,Fi An fitness evaluation value of the ith configuration scheme transmitted by the leading bee;
step 5.7: the following bees select the lead bees by roulette, i.e. at 0,1]Generating a uniformly distributed random number r, if p i Above r, the following bee is in the configuration scheme according to the formula of step 5.4A new configuration scheme is generated around the (a), the following peak is searched in the same way as the leading bees, and a better configuration scheme is determined and reserved according to a greedy selection method;
step 5.8: judging configuration schemeWhether the abandoned condition is satisfied or not, i.e., whether the maximum mining frequency limit=50 is reached, is not updated; if yes, the configuration scheme is abandoned, the corresponding leading bee role becomes a reconnaissance bee, otherwise, the step 5.9 is directly transferred;
step 5.9: the scout bees randomly generate a new configuration scheme to replace the following
Step 5.10: and K=K+1, judging whether the algorithm meets a termination condition, if so, terminating, and outputting the optimal capacity configuration, otherwise, turning to step 5.2.
The beneficial effects of adopting above-mentioned technical scheme to produce 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 realized, so that the purposes of smoothing the output power and peak characteristics of the multi-energy system are achieved, the energy waste is reduced, the supply and demand balance of the system is met, and the energy supply reliability of the system is ensured;
2. according to the invention, the optimal configuration scheme of the hydrogen storage device and the heat storage device is optimized accurately and quickly by proposing a coordinated configuration strategy of the hydrogen storage device and the heat storage device and calculating the configuration quantity of the devices, so that the minimum investment cost of the system is ensured.
Drawings
FIG. 1 is a flow chart of a method for optimizing the capacity of a multi-energy system based on a hydro-thermal hybrid energy storage device of the present invention;
FIG. 2 is a diagram showing a coordinated configuration of a hydrogen storage and heat storage device according to the present invention;
fig. 3 is a flowchart of the artificial bee colony algorithm of the invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
In this embodiment, taking a regional multi-energy system as an example, the total amount of renewable energy power generation P is at the initial time t=1 pro (t) 500MW, conventional load Power P com (t) is 301MW, α=0.1 is the heat dissipation loss rate of the heat storage device; heat absorption power Q HS_ch (1) =70 MW, heat absorption efficiency η hch =0.9; heat release power Q HS_dis (1) =16 MW, heat release efficiency η hdis =0.8; the total hydrogen yield isThe total hydrogen consumption is->Hydrogen storage amount N (0) =33mw of the hydrogen storage device, and heat storage amount H of the heat storage device HS (0) =50mw; the single-machine rated capacity m=5MW of the hydrogen storage device, and the single-machine rated capacity n=8MW of the heat storage device; 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 this embodiment is as follows.
Step 1: the data predicted on the next day of the power grid is collected, namely the total power P of renewable energy power generation is predicted pro (t) and predicting the total power of conventional load P com (t) establishing payload power P net (t) a mathematical model;
|P net (t)|＝P pro (t)-P com (t)
wherein ,Pnet (t) is the t hour payload power, P pro (t) the predicted total power of renewable energy power generation at t hours, P com (t) is the conventional load power predicted at hour t;
in this embodiment, taking time 1 as an example, the payload power at time 1 is calculated by step 1 to be P net (1)＝199MW；
Step 2: according to the power and efficiency of hydrogen storage and release of the hydrogen storage device, a mathematical model of capacity configuration of the hydrogen storage device is established as follows:
wherein ,electric energy proportion for distributing net load power to electrolytic water hydrogen production device, P H2 The power H is the power used by the hydrogen production device for electrolyzing water at the moment t HV Is the high heating value (H) HV ＝3.509(kW·h)/(N·m 3 ))，/>Hydrogen production efficiency of hydrogen production device by electrolysis of water, +.>For the hydrogen production rate at time t, +.>The hydrogen consumption rate at the time t is the gas storage amount of an N (t) gas storage device, and N (t-1) is the gas storage amount at the time t-1 +.>Is the total hydrogen production at time t, +.>The total hydrogen consumption at time t is DeltatTime variation.
Step 3: and establishing a mathematical model of capacity configuration of the heat storage device according to the heat absorption and heat release power and efficiency of the heat storage device, wherein the mathematical model is as follows:
wherein ,the proportion of electrical energy allocated to the heating device for the net load power, P eb (t) is the power consumption of the electric boiler at the moment t, H eb (t) is the heating power of the electric boiler at the moment t, lambda eb Is the electric heat conversion efficiency of the electric boiler, H HS (t) is the heat storage amount at time t, α is the heat dissipation loss rate of the heat storage device, Q HS_ch (t)、η hch The endothermic power and the efficiency at the time t are respectively Q HS_dis (t)、η hdis The heat release power and efficiency at time t are respectively.
Step 4: taking the investment cost of the hydrogen-heat hybrid energy storage device as an objective function, establishing a capacity optimization configuration model, and calculating the proportion value of net power distributed to the hydrogen storage device and the heat storage device;
step 4.1: the capacity optimization configuration model is established as follows:
wherein ,f represents the investment cost of the hydrogen-heat hybrid energy storage device as an objective function, < >>Indicating the number of hydrogen storage devices, N H Representing the number of arrangements of the heat storage means, +.>Indicating the unit price of the hydrogen storage device, C H Indicating the unit price of the heat storage device, |P HTHS The I is hydrogen-heat mixed energy storage, positive represents energy release, and negative represents energy storage; p (P) lease The energy loss is represented by m, the rated capacity of a single hydrogen storage device is represented by m, and the rated capacity of the single heat storage device is represented by n;
step 4.2: the load and renewable energy power generation data are read, and the proportion value of net power distributed to the hydrogen storage device and the heat storage device to be optimized is calculated:
wherein ,to be optimized for the net power allocation to the hydrogen storage device, < >>The net power is distributed to the proportion to be optimized of the heat storage device.
In this embodiment, N (1) =111 mw and h are calculated in steps 2 to 4 HS (1) =88 MW, the hydrogen storage device to be optimizedProportion to be optimized of the heat storage device>
Step 5: and calculating the optimal capacity configuration of the hydrogen-heat hybrid energy storage device through a manual bee colony algorithm.
Step 5.1: initializing a configuration schemei=1, 2, …, NP is the number of configuration schemes, and the fitness evaluation value F of the solution corresponding to the quality of the configuration scheme i Setting leading bee/following bee number N=NP/2, dimension D=2, and maximum iteration number K max Maximum exploitation times limit, according to step 1 to step 4, generating an initial configuration scheme to be optimized in the search space +.>
wherein ,to be optimized for the net power allocation to the hydrogen storage device in the ith variant, +.>The net power in the ith scheme is distributed to the proportion to be optimized of the heat storage device;
in this embodiment, the 1 st time is taken as an example, and the configuration scheme to be optimized is initialized to be [55.78%,44.22%]The number of configuration schemes np=100, the maximum number of iterations K max Maximum mining number limit=50, and fitness evaluation value F of quality corresponding solution of configuration scheme i ＝0.008。
Step 5.2: within the upper and lower limit ranges of the search space, N optimal configuration schemes are selected as leading bees according to the followingIn this embodiment, N is 2, N may be any integer from 1 to 50, and may be selected according to requirements.
wherein L d and Ud Represents a lower limit 0 and an upper limit 100% of the search space, d=1, 2, respectively;
step 5.3: calculating N investment costs F of hydrogen-heat hybrid energy storage system corresponding to leading bees N And at N investment costs F N Screening out the optimal investment cost F α The fitness is made;
step 5.4: generating a neighborhood range by using the N optimal configuration schemes in the step 5.2 as the center through an Or-opt neighborhood search algorithm, and selecting the N optimal configuration schemes from the neighborhood range as the leading bees of the N optimal configuration schemes obtained in the step 5.2 according to the following formulaCalculating N investment costs F of hydrogen-heat hybrid energy storage systems corresponding to corresponding neighborhood leading bees N And at updated N investment costs F N Screening the updated optimal investment cost F β The fitness is made;
wherein, ψ is a random number uniformly distributed in [ -1,1], and the disturbance amplitude is determined, j=1, 2, …, NP, j+.i, which means that a configuration scheme which is not equal to i is randomly selected from NP configuration schemes;
step 5.5: f obtained by leading the bee in the step 5.2 α F obtained by leading bees in neighborhood corresponding to the step 5.4 β Comparing the two, selecting an optimal configuration scheme with low adaptability evaluation value by adopting a greedy selection method as
And determining a reserved better configuration scheme according to a greedy selection method, wherein the calculation formula of the fitness evaluation value is as follows:
wherein ,Fi Represents the fitness evaluation value, F N An objective function representing the problem being optimized;
step 5.6: calculating the probability that the configuration scheme to be optimized found by the leading bee is followed according to the fitness evaluation value;
wherein ,Fi The method comprises the steps of transmitting an i-th evaluation value of the adaptability of the configuration scheme to be optimized for leading bees;
step 5.7: the following bees select the lead bees by roulette, i.e. at 0,1]Generating a uniformly distributed random number r, if p i Above r, the following bee is in the configuration scheme according to the formula of step 5.4A new configuration scheme is generated around the (a), the following peak is searched in the same way as the leading bees, and a better configuration scheme is determined and reserved according to a greedy selection method;
step 5.8: judging configuration schemeWhether the abandoned condition is satisfied or not, i.e., whether the maximum mining frequency limit=50 is reached, is not updated; if yes, the configuration scheme is abandoned, the corresponding leading bee role becomes a reconnaissance bee, otherwise, the step 5.9 is directly transferred;
step 5.9: the scout bees randomly generate a new configuration scheme to replace the following
Step 5.10: and K=K+1, judging whether the algorithm meets a termination condition, if so, terminating, and outputting the optimal capacity configuration, otherwise, turning to step 5.2.
The optimal configuration scheme of the energy storage capacity at the 1 st moment is X57 percent and 43 percent finally obtained through the artificial bee colony algorithm, 23 hydrogen storage devices are configured at the moment, 11 heat storage devices are configured, and the investment cost is 124 ten thousand.
Claims (2)
1. The capacity optimization method of the multi-energy system based on the hydro-thermal hybrid energy storage device is characterized by comprising the following steps of:
step 1: collecting data predicted on the next day of the power grid, and predicting total power P of the collected renewable energy power generation pro Conventional load prediction total power P com Establishing payload power P net Mathematical model:
|P net (t)|＝P pro (t)-P com (t)
wherein ,Pnet (t) is the predicted t-hour payload power, P pro (t) the predicted total power of renewable energy power generation at t hours, P com (t) is the conventional load power predicted at hour t;
step 2: according to the rate and efficiency of hydrogen storage and hydrogen release of the hydrogen storage device, a mathematical model of capacity configuration of the hydrogen storage device is established as follows:
wherein ,electric energy proportion allocated to electrolytic water hydrogen plant for payload power, +.>The power H is the power used by the hydrogen production device for electrolyzing water at the moment t HV Is the high heat value of hydrogen>Hydrogen production efficiency of hydrogen production device by electrolysis of water, +.>For the hydrogen production rate at time t, +.>The hydrogen consumption rate at the time t is the gas storage rate of the N (t) gas storage device, N (t-1) is the gas storage rate at the time t-1,is the total hydrogen production at time t, +.>The total hydrogen consumption at the time t is delta t, and the time variation is delta t;
step 3: and establishing a mathematical model of capacity configuration of the heat storage device according to the heat absorption and heat release power and efficiency of the heat storage device, wherein the mathematical model is as follows:
wherein ,the proportion of electrical energy allocated to the heating device for the net load power, P eb (t) is the power consumption of the electric boiler at the moment t, H eb (t) is the heating power of the electric boiler at the moment t, lambda eb Is the electric heat conversion efficiency of the electric boiler, H HS (t) is the heat storage amount at time t, α is the heat dissipation loss rate of the heat storage device, Q HS_ch (t)、η hch The endothermic power and the efficiency at the time t are respectively Q HS_dis (t)、η hdis The heat release power and the efficiency at the time t are respectively;
step 4: taking the investment cost of the hydrogen-heat hybrid energy storage device as an objective function, combining the models of the steps 1 to 3 to establish a capacity optimization configuration model, and calculating the to-be-optimized ratio value of net power distributed to the hydrogen storage device and the heat storage device;
step 4.1: the capacity optimization configuration model is established as follows:
wherein ,as an objective function, F represents the investment cost of the hydrogen-heat hybrid energy storage device,indicating the number of hydrogen storage devices, N H Representing the number of arrangements of the heat storage means, +.>Indicating the unit price of the hydrogen storage device, C H Indicating the unit price of the heat storage device, |P HTHS The I is hydrogen-heat mixed energy storage, positive represents energy release, and negative represents energy storage; p (P) lease The energy loss is represented by m, the rated capacity of a single hydrogen storage device is represented by m, and the rated capacity of the single heat storage device is represented by n;
step 4.2: the load and renewable energy power generation data are read, and the proportion value of net power distributed to the hydrogen storage device and the heat storage device to be optimized is calculated:
wherein ,to be optimized for the net power allocation to the hydrogen storage device, < >>The net power is distributed to the proportion to be optimized of the heat storage device;
step 5: and calculating the optimal capacity configuration of the hydrogen-heat hybrid energy storage device through a manual bee colony algorithm.
2. The method for optimizing the capacity of a multi-energy system based on a hybrid hydro-thermal energy storage device according to claim 1, wherein the process of step 5 is as follows:
step 5.1: initializing a configuration schemeNP is the number of configuration schemes, and the fitness evaluation value F of the solution corresponding to the quality of the configuration schemes i Setting leading bee/following bee number N=NP/2, dimension D=2, and maximum iteration number K max Maximum mining frequency limit, according to steps 1 to 4, generating an initial plan in the search space
wherein ,to be optimized for the net power allocation to the hydrogen storage device in the ith variant, +.>The net power in the ith scheme is distributed to the proportion to be optimized of the heat storage device;
step 5.2: within the upper and lower limit ranges of the search space, N optimal configuration schemes are selected as leading bees according to the following
wherein L d and Ud Represents a lower limit 0 and an upper limit 100% of the search space, d=1, 2, respectively;
step 5.3: calculating N investment costs F of hydrogen-heat hybrid energy storage system corresponding to leading bees N And at N investment costs F N Screening out the optimal investment cost F α The fitness is made;
step 5.4: generating a neighborhood range by using the N optimal configuration schemes in the step 5.2 as the center through an Or-opt neighborhood search algorithm, and selecting the N optimal configuration schemes from the neighborhood range as the leading bees of the N optimal configuration schemes obtained in the step 5.2 according to the following formulaCalculating N investment costs F of hydrogen-heat hybrid energy storage systems corresponding to corresponding neighborhood leading bees N And at updated N investment costs F N Screening the updated optimal investment cost F β The fitness is made;
wherein ψ is a uniformly distributed random number of [ -1,1] determining the disturbance amplitude, j=1, 2,..np, j+.i, meaning that one configuration scheme not equal to i is randomly selected among NP configuration schemes;
step 5.5: f obtained by leading the bee in the step 5.2 α F obtained by leading bees in neighborhood corresponding to the step 5.4 β Comparing the two, selecting an optimal configuration scheme with low adaptability evaluation value by adopting a greedy selection method as
And determining a better configuration scheme according to a greedy selection method, wherein the calculation formula of the fitness evaluation value is as follows:
wherein ,Fi Represents the fitness evaluation value, F N An objective function representing the problem being optimized;
step 5.6: calculating the probability that the configuration scheme to be optimized found by the leading bee is followed according to the fitness evaluation value;
wherein ,Fi An fitness evaluation value of the ith configuration scheme transmitted by the leading bee;
step 5.7: the following bees select the lead bees by roulette, i.e. at 0,1]Generating a uniformly distributed random number r, if p i Above r, the following bee is in the configuration scheme according to the formula of step 5.4A new configuration scheme is generated around the (a), the following peak is searched in the same way as the leading bees, and a better configuration scheme is determined and reserved according to a greedy selection method;
step 5.8: judging configuration schemeWhether the abandoned condition is satisfied or not, i.e., whether the maximum mining frequency limit=50 is reached, is not updated; if yes, the configuration scheme is abandoned, the corresponding leading bee role becomes a reconnaissance bee, otherwise, the step 5.9 is directly transferred;
step 5.9: the scout bees randomly generate a new configuration scheme to replace the following
Step 5.10: and K=K+1, judging whether the algorithm meets a termination condition, if so, terminating, and outputting the optimal capacity configuration, otherwise, turning to step 5.2.
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