CN115564139A - Energy value evaluation-based energy system running state optimization method and device - Google Patents

Energy value evaluation-based energy system running state optimization method and device Download PDF

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CN115564139A
CN115564139A CN202211354491.0A CN202211354491A CN115564139A CN 115564139 A CN115564139 A CN 115564139A CN 202211354491 A CN202211354491 A CN 202211354491A CN 115564139 A CN115564139 A CN 115564139A
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energy value
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CN115564139B (en
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苏姣月
汤健康
周孟雄
郭仁威
章浩文
纪捷
陈帅
赵环宇
杜董生
黄佳惠
孙娜
张楚
彭甜
黄慧
王晞
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Yulin Wenhao Energy Conservation Service Co.,Ltd.
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Abstract

The invention discloses an energy value evaluation-based energy system running state optimization method and device, which comprise the following steps: collecting energy input parameters of each energy source, wherein the energy input parameters are energy data of each energy source, and specifically comprise an energy flow B, a renewable energy source R, a non-renewable energy source N and a purchased energy source F; converting different kinds of energy input parameters into energy value parameters of an Ethernet energy value-energy value baseline, wherein the energy values comprise a renewable energy value, a non-renewable energy value, a purchase energy value and an output energy value; constructing an energy value evaluation model for the multi-energy system, and calculating the energy value parameters by using the energy value evaluation model to obtain core parameters; and optimizing the core parameters by using a multi-objective slime mold algorithm to obtain the optimized core parameters. Compared with the prior art, the optimization method has the advantages that the multi-target slime mold algorithm is used for optimizing the energy evaluation system, and the functions of displaying components needing to be optimized and the optimized system evaluation result can be realized.

Description

Energy value evaluation-based energy system running state optimization method and device
Technical Field
The invention relates to the technical field of renewable energy collection automatic control devices, in particular to an energy evaluation-based energy system operation state optimization method and device.
Background
The comprehensive evaluation of the technology, economy and environment of the comprehensive energy system at the present stage mainly comprises the modes of an analytic hierarchy process, a fuzzy analytic hierarchy process, an early warning method and the like. None of these approaches highlights this factor to the environment, is difficult to evaluate the sustainability of the power generation system, and is computationally complex relative to the energy value evaluation. The energy system is different from the evaluation mode, the boundary is wider, the evaluation is more comprehensive, and the calculation amount is far less than that of the method.
The core parameters of the energy evaluation model represent the current running state of the multi-energy system, the main functions of the parameters are to indicate parts needing to be optimized, in the prior art, various evaluation models are mainly used for evaluating the multi-energy system, then, various costs of the system are optimized, the method is single, and the scheme is difficult to guarantee to be the optimal solution of all indexes.
The slime mold algorithm is known for its efficient global search capability, and compared with other competitive algorithms, the multi-target slime mold algorithm can find more pareto solutions in a target space, and the pareto solutions are not random, so that the multi-parameter can be accurately and efficiently optimized by the multi-target slime mold algorithm. The adjustable and controllable part of the system is optimized through a multi-target slime mold algorithm, so that the output index is in a proper position, and the running state of the system is improved.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the background technology, the invention provides an energy system running state optimization method and device based on energy value evaluation, which can monitor the running state of a multi-energy system in real time, realize the optimization of the running state through a multi-target slime mold algorithm and reduce unnecessary energy consumption.
The technical scheme is as follows: the invention provides an energy system running state optimization method based on energy value evaluation, which comprises the following steps:
step 1: collecting energy input parameters of each energy source, wherein the energy input parameters are energy data of each energy source, specifically energy flow B, renewable energy source R, non-renewable energy source N and purchased energy source F;
step 2: converting different kinds of energy input parameters into energy value parameters of an Ethernet energy value-energy value baseline, wherein the energy values comprise a renewable energy value, a non-renewable energy value, a purchase energy value and an output energy value;
and step 3: calculating the energy value parameters by using an energy value evaluation model to obtain core parameters, wherein the core parameters comprise an energy value output rate (EYR), an energy value investment rate (EIR), an Environmental Load Rate (ELR), a sustainability index (ESI), a renewable energy ratio (phi R) and an energy value self-sufficiency rate (ESR);
and 4, step 4: and (4) optimizing the core parameters in the step (3) by using a multi-target slime mold algorithm to obtain the optimized core parameters.
Further, the energy value parameter for converting the different kinds of energy input parameters into the ethernet energy value-based value in step 2 is specifically:
the energy flow is marked as B, the solar conversion coefficient is T, and the solar energy value is defined as
M＝B*T。
Further, the energy value evaluation model in step 3 specifically includes:
energy production ratio (EYR) is the ratio of total energy input to energy purchased;
Figure BDA0003918753490000021
the Environmental Load Rate (ELR) represents the environmental impact of a system:
Figure BDA0003918753490000022
sustainability index is the ratio of EYR to ELR, systems with sustainability index higher than 1 are sustainable, but higher than 10 is an indication of underutilization of resources:
Figure BDA0003918753490000023
the renewable ratio (Φ R) represents the percentage of new energy used during the operation of the system:
Figure BDA0003918753490000024
the Energy Investment Ratio (EIR) represents the share of the purchased energy in the whole energy, i.e. the sum of the energy values of the new energy and the non-renewable energy, and the purchased energy includes the purchased values of natural gas, electricity and maintenance service:
Figure BDA0003918753490000025
the energy self-sufficiency ratio (ESR) is an index for evaluating the self-organization capability of a system, and is used for evaluating the supporting capability of a natural environment:
Figure BDA0003918753490000031
further, the process of performing optimization and update on the core parameters in the step 3 by using the multi-target slime mold algorithm in the step 4 is as follows:
step 4.1: initializing algorithm parameters: overall size N, problem dimension (dim), lower bound lb, upper bound ub, and maximum number of iterations maxim;
the mucus model is as follows:
Figure BDA0003918753490000032
wherein ,vb Is a parameter between-a and a, v c Is a variable which is linearly decreased within the range of 1 to 0 and represents the feedback relationship between the food concentration and the slime quality, X (t) represents the position of the slime at t, X b Is the position of the individual having the highest concentration of odor so far, X A and XB Is two solutions randomly selected from bee colony, W is the weight of slime, and the variable p is calculated as follows:
p＝tanh|S(i)-DF|
and 4.2: random initialization solving methodA scheme: x = rand (N, dim) * (ub-lb)+lb；
Step 4.3: main optimization circulation, stopping the standard not to meet;
step 4.4: applying upper and lower boundaries: allFitness = fobj (lb < X > ub)
Step 4.5: and storing an archive of the non-dominant solutions, and deleting the dominant solution with the minimum congestion distance from the archive:
step 4.6: calculating a crowding distance CD for an archive solution
Figure BDA0003918753490000033
wherein ,
Figure BDA0003918753490000034
is the minimum value of the jth objective function,
Figure BDA0003918753490000035
is the maximum value of the jth objective function. The CD estimate for one solution m is the average distance of its two rounded/adjacent solutions (m-1, m + 1).
Step 4.7: ordering the non-dominance enforcement based on the congestion distance;
[SmellOrder，SmellIndex]＝sort(AllFitness)，worstFitness＝SmellOrder(N)，
bestFitness＝SmellOrder(1)，S＝bestFitness-worstFitness+eps
smellorder refers to a fitness value sequence sorted according to the odor sequence, smellIndex refers to a fitness value sequence sorted according to the ascending sequence, worstfit refers to the worst solution, bestfittness refers to the best solution, and S refers to the fitness.
Step 4.8: selecting a first ranked target as an optimal solution;
step 4.9: updating the optimal fitness value and the optimal position;
step 4.10: updating the location of the search agent;
the mucus mold updates its position as follows:
Figure BDA0003918753490000041
wherein lb and ub are the lower and upper bounds, respectively, i.e. the limiting bounds of the core parameter, r, respectively i ∈R，r i ∈[0，1]For i =1,2, variable v b and vc Respectively at [ -a, a [ -a ]]And [ -1,1]With the number of iterations increasing, v b and vc Gradually approaching zero.
Step 4.11: updating the weight vector explained in the equation;
Figure BDA0003918753490000042
wherein R is in the range of R, R is in the range of 0,1]，b F and wF Respectively obtaining an optimal solution and a worst solution in the process of iterating t times, wherein S (i) is the fitness of X, and SmellIndex refers to a sequence of fitness values sorted in an ascending order;
step 4.12: updating the current solution position according to the specification of the equation;
SmellIndex＝sort(S)
s is the fitness of X, and the SmellIndex refers to a fitness value sequence sorted in ascending order;
step 4.13: returning to the best solution.
The invention also discloses an energy system running state optimization device based on energy value evaluation, which comprises the following steps:
the system comprises acquisition equipment, a data acquisition unit and a data processing unit, wherein the acquisition equipment is used for acquiring energy input parameters of various energy sources, and the energy input parameters are energy data of the various energy sources, specifically energy flow B, renewable energy source R, non-renewable energy source N and purchased energy source F;
the short-time storage equipment is used for storing the energy input parameters acquired by the acquisition equipment within one hour;
the parameter analysis computing equipment is used for converting the energy input parameters of different types into energy value parameters with the Ethernet energy value as an energy value baseline, wherein the energy value parameters comprise a renewable energy value, a non-renewable energy value, a purchase energy value and an output energy value;
the design unit is used for calculating the energy value parameters by using an energy value evaluation model to obtain core parameters, wherein the core parameters comprise an energy value output rate (EYR), an energy value investment rate (EIR), an Environmental Load Rate (ELR), a sustainability index (ESI), a renewable energy ratio (phi R) and an energy value self-sufficiency rate (ESR);
the control module is used for optimizing the core parameters by utilizing a multi-target slime mold algorithm to obtain the optimized core parameters;
and the display is used for displaying the optimized core parameters of the control module.
Preferably, the harvesting device is disposed in the path of energy input and in the path of energy system output.
Has the advantages that:
the invention obtains corresponding input energy values through the acquisition equipment for inputting renewable resources such as wind, light and water energy and non-renewable resources such as biomass and natural gas; the data processing center of the equipment obtains the energy value of the output power of the system through the converter, and the evaluation of parameters such as investment ratio, energy self-sufficiency rate, sustainability and environmental load ratio of the system is realized; the equipment optimizes the energy evaluation system through the multi-objective slime mold algorithm, and can realize the function of displaying components to be optimized and the optimized system evaluation result. The multi-target slime mold algorithm can find more pareto solutions in a target space, the pareto solutions are not random, and the multi-target slime mold algorithm can be used for accurately and efficiently optimizing multiple parameters.
Drawings
FIG. 1 is a system block diagram of the apparatus of the present invention;
FIG. 2 is a flow chart of the operation of the apparatus of the present invention;
FIG. 3 is a flow chart of the optimization algorithm of the present invention;
FIG. 4 is an evaluation result obtained by calculating an acquired energy value through a model, taking a wind-solar-thermal-electric-coupled system as an example, according to the present invention;
fig. 5 shows the results of various optimized core parameters of the wind, light, heat and electricity combined system of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
The invention discloses an energy system running state optimization method based on energy value evaluation, which comprises the following steps:
step 1: energy input parameters of various energy sources are collected, wherein the energy input parameters are energy data of the various energy sources, specifically energy flow B, renewable energy sources R, non-renewable energy sources N and purchased energy sources F.
And 2, step: converting the energy input parameters of different kinds into energy value parameters with the Ethernet energy value as the energy value baseline, wherein the energy value parameters comprise renewable energy values, non-renewable energy values, purchase energy values and output energy values.
Different types of energy vary in quality and value. High quality energy, such as electrical energy, may be easily converted to low quality energy, which is difficult and costly to convert to high quality, so that different types of energy cannot be directly compared.
Energy value refers to the amount of another energy contained or stored in the energy stream, which is essentially the energy contained in the substance. The conversion of solar energy values is very important in the calculation of energy values, and all energy forms on the earth come from solar energy directly or indirectly. Thus, any energy type is typically measured in terms of solar energy, and the present invention uses solar energy as a baseline for energy values.
The energy flow is marked as B, the solar conversion coefficient is T, and the solar energy value is defined as
M＝B*T。
And 3, step 3: and constructing an energy value evaluation model aiming at the multi-energy system, and calculating the energy value parameters by using the energy value evaluation model to obtain core parameters, wherein the core parameters comprise energy output rate (EYR), energy Investment Rate (EIR), environmental Load Rate (ELR), sustainability index (ESI), renewable energy ratio (phi R) and energy self-sufficiency rate (ESR).
The energy production ratio (EYR) is the ratio of total energy input to energy purchased. The higher the EYR, the lower the system's dependence on the purchased energy, and the more competitive the system. R is renewable energy, N is non-renewable energy, and F is purchased energy.
Figure BDA0003918753490000061
The Environmental Load Rate (ELR) represents the environmental impact of a system. Therefore, if a system has a high ELR, it will have a severe impact on the environment.
Figure BDA0003918753490000062
The sustainability index is the ratio of EYR to ELR. In general, systems with a sustainability index higher than 1 will be sustainable, but higher than 10 will be an indication of underutilization of resources.
Figure BDA0003918753490000063
The renewable ratio (φ R) represents the percentage of new energy used during system operation. The higher the value of phir, the greater the degree of dependence of the system on new energy input.
Figure BDA0003918753490000071
The Energy Investment Ratio (EIR) represents the share of the purchased energy in the overall energy (i.e., the sum of the energy values of the new energy and the non-renewable energy). The EIR values represent the level of economic development. The larger the EIR value, the higher the economic development level and the lower the dependence on environmental resources. The procurement of energy includes the procurement values of natural gas, electricity and maintenance services, all of which require expensive purchases.
Figure BDA0003918753490000072
The energy self-sufficiency ratio (ESR) is an index for evaluating the self-organizing ability of a system and is used for evaluating the supporting ability of the natural environment. The higher the ESR value, the greater the self-organizing ability of the system.
Figure BDA0003918753490000073
Energy conversion represents the energy input required to obtain a product. High energy conversion refers to products having a high energy level in the system. However, for a certain product, lower energy conversion means that less energy is required to obtain the product and the efficiency of the system is higher.
And 4, step 4: and (4) optimizing the core parameters in the step (3) by utilizing a multi-objective slime mold algorithm to obtain the optimized core parameters.
Optimizing a multi-target slime bacteria algorithm:
the mucus model is as follows:
Figure BDA0003918753490000074
wherein ,vb Is a parameter between-a and a, v c Is a variable which is linearly decreased within the range of 1 to 0 and represents the feedback relationship between the food concentration and the slime quality, X (t) represents the position of the slime at t, X b Is the position of the individual having the highest concentration of odor so far, X A and XB Two solutions randomly selected from bee colonies are adopted, W is the weight of slime bacteria, and the variable p is calculated by the following method:
p＝tanh|S(i)-DF|
given that i ∈ 1, 2.... Said., n, S (i) is the fitness of X, DF | is the best fitness value obtained so far, and the variable v ∈ is set to zero b The calculation method of (2) is as follows:
v b ＝[-a，a]
Figure BDA0003918753490000081
the variable W is defined in the equation:
Figure BDA0003918753490000082
SmellIndex＝sort(S)
wherein R is in the range of R, R is in the range of 0,1]，b F and wF The best solution and the worst solution obtained in the process of iterating t times respectively, S (i) is the fitness of X, and the SmellIndex refers to a sequence of fitness values sorted in ascending order.
The mucus mold updates its position as follows:
Figure BDA0003918753490000083
wherein lb and ub are the lower and upper bounds, respectively, i.e. the limiting bounds of the core parameter, r, respectively i ∈R，r i ∈[0，1]For i =1,2, variable v b and vc Respectively in [ -a, a [ - ]]And [ -1,1]With the number of iterations increasing, v b and vc Gradually approaching zero.
The updating process of the slime mold algorithm is as follows:
step 4.1: determining the number of core parameters needing to be optimized and determining the limit boundary of each core parameter; overall size N, problem dimension (dim), lower bound lb, upper bound ub, and maximum number of iterations maxims;
the mucus model is as follows:
Figure BDA0003918753490000084
wherein ,vb Is a parameter between-a and a, v c Is a variable which is linearly decreased within the range of 1 to 0 and represents the feedback relationship between the food concentration and the slime quality, X (t) represents the position of the slime at t, X b Is the position of the individual having the highest concentration of odor so far, X A and XB Are two solutions randomly selected from a bee colony,w is the weight of the slime, and the variable p is calculated as follows:
p＝tanh|S(i)-DF|
step 4.2: random initialization solution: x = rand (N, dim) * (ub-lb)+lb；
Step 4.3: main optimization circulation, stopping the standard not to meet;
step 4.4: applying upper and lower boundaries: allFitness = fobj (lb < X > ub)
Step 4.5: saving an archive of non-dominant solutions, and deleting the dominant solution with the minimum congestion distance from the archive:
step 4.6: calculating a crowding distance CD for an archive solution
Figure BDA0003918753490000091
wherein ,
Figure BDA0003918753490000092
is the minimum value of the jth objective function,
Figure BDA0003918753490000093
is the maximum value of the jth objective function. The CD estimate for one solution m is the average distance of its two rounded/adjacent solutions (m-1, m + 1).
Step 4.7: ordering the non-dominance enforcement based on the congestion distance;
[SmellOrder，SmellIndex]＝sort(AllFitness)，worstFitness＝SmellOrder(N)，
bestFitness＝SmellOrder(1)，S＝bestFitness-worstFitness+eps
smellorder refers to a fitness value sequence sorted according to the odor sequence, smellIndex refers to a fitness value sequence sorted according to the ascending sequence, worstfit refers to the worst solution, bestfittness refers to the best solution, and S refers to the fitness.
Step 4.8: selecting a first ranked target as an optimal solution;
step 4.9: updating the optimal fitness value and the optimal position;
step 4.10: updating the location of the search agent;
the mucus mold updates its position as follows:
Figure BDA0003918753490000094
wherein lb and ub are the lower and upper bounds, respectively, i.e. the limiting bounds of the core parameter, r, respectively i ∈R，r i ∈[0，1]For i =1,2, variable v b and vc Respectively in [ -a, a [ - ]]And [ -1,1]With the number of iterations increasing, v b and vc Gradually approaching zero.
Step 4.11: updating the weight vector explained in the equation;
Figure BDA0003918753490000101
wherein R belongs to R, R belongs to [0,1 ]]，b F and wF Respectively obtaining an optimal solution and a worst solution in the process of iterating t times, wherein S (i) is the fitness of X, and the SmellIndex refers to a fitness value sequence sorted in an ascending order;
step 4.12: updating the current solution position according to the specification of the equation;
SmellIndex＝sort(S)
s is the fitness of X, and SmellIndex refers to a sequence of fitness values sorted in ascending order;
step 4.13: returning to the best solution.
The invention discloses an energy system running state optimization device based on energy value evaluation aiming at the energy system running state optimization method based on energy value evaluation, which comprises the following steps:
the energy input parameters are energy data of each energy source, specifically energy flow B, renewable energy R, non-renewable energy N and purchased energy F.
And the short-time storage device is used for storing the energy input parameters acquired by the acquisition device within one hour and covering the information before the one hour later.
The parameter analysis computing equipment is used for converting the energy input parameters of different kinds into energy value parameters of the Ethernet energy value-to-energy value baseline, wherein the energy values comprise a renewable energy value, a non-renewable energy value, a purchase energy value and an output energy value.
And the design unit is used for calculating the energy value parameters by utilizing an energy value evaluation model to obtain core parameters, and the core parameters comprise energy value output rate (EYR), energy value investment rate (EIR), environmental Load Rate (ELR), sustainability index (ESI), renewable energy ratio (phi R) and energy value self-sufficiency rate (ESR).
And the control module is used for optimizing the core parameters by utilizing a multi-target slime mold algorithm to obtain the optimized core parameters. The control module is realized by an STM32F103ZET6 single chip microcomputer.
And the display is used for displaying the optimized core parameters of the control module.
The input end of the collecting device is connected in an energy input path and an energy system output path, the input path of the renewable energy is a receiving panel, the input path of the non-renewable resource is generally a pipeline, the input total amount of the energy can be obtained through the input path, and the output total amount of the energy system can be obtained through the output path. The output end of the acquisition equipment is connected with the input end of the parameter analysis and calculation equipment, the output end of the parameter analysis and calculation equipment is connected with the design unit, the design unit outputs the result after optimization calculation to the control module, and the output end of the control module is connected with the display.
Taking a wind, light, heat and electricity combined system as an example, the collected energy values can be calculated through a model to obtain an evaluation result shown in fig. 4, and energy value analysis shows that the wind, light, cold and heat electricity combined system has high environmental load rate and large influence on the environment, the economic development level of the region is low, the dependence on environmental resources is high, but self-sufficiency can be met, the supporting capability on the environment is high, the self-organizing capability is strong, the dependence on energy purchase is low, and sustainable development cannot be realized. The results of the core parameters optimized by the optimization method are shown in fig. 5, and it can be seen from fig. 5 that the ratio of renewable energy resources is significantly increased, the environmental load rate is significantly reduced, and the sustainability index is significantly increased. The method optimizes the energy evaluation system through the multi-target slime mold algorithm, makes certain efforts on sustainable development of the system, greatly reduces pollution to the environment and reduces environmental pressure. .
The above embodiments are only for illustrating the technical idea and features of the present invention, and the purpose of the embodiments is to enable those skilled in the art to understand the content of the present invention and implement the present invention, and not to limit the protection scope of the present invention by this means. All equivalent changes and modifications made according to the spirit of the present invention should be covered in the protection scope of the present invention.

Claims (6)

1. An energy system operation state optimization method based on energy value evaluation is characterized by comprising the following steps:
step 1: collecting energy input parameters of each energy source, wherein the energy input parameters are energy data of each energy source, and specifically comprise an energy flow B, a renewable energy source R, a non-renewable energy source N and a purchased energy source F;
and 2, step: converting the energy input parameters of different kinds into energy value parameters taking the Ethernet energy value as an energy value baseline, wherein the energy value parameters comprise a renewable energy value, a non-renewable energy value, a purchase energy value and an output energy value;
and step 3: constructing an energy value evaluation model aiming at the multi-energy system, and calculating the energy value parameters by using the energy value evaluation model to obtain core parameters, wherein the core parameters comprise energy output rate (EYR), energy Investment Rate (EIR), environmental Load Rate (ELR), sustainability index (ESI), renewable energy ratio (phi R) and energy self-sufficiency rate (ESR);
and 4, step 4: and (4) optimizing the core parameters in the step (3) by using a multi-target slime mold algorithm to obtain the optimized core parameters.
2. The energy system operating condition optimizing method based on energy value evaluation according to claim 1, wherein the energy value parameter for converting the different kinds of energy input parameters into the energy value baseline with the ethernet energy value in the step 2 is specifically:
the energy flow is marked as B, the solar conversion coefficient is T, and the solar energy value is defined as
M＝B*T。
3. The energy system operating state optimization method based on energy value evaluation according to claim 1, wherein the energy value evaluation model in step 3 is specifically:
energy production ratio (EYR) is the ratio of total energy input to energy purchased;
Figure FDA0003918753480000011
the Environmental Load Rate (ELR) represents the environmental impact of a system:
Figure FDA0003918753480000012
sustainability index is the ratio of EYR to ELR, systems with sustainability index higher than 1 are sustainable, but higher than 10 is an indication of underutilization of resources:
Figure FDA0003918753480000013
the renewable ratio (Φ R) represents the percentage of new energy used during the operation of the system:
Figure FDA0003918753480000021
the Energy Investment Ratio (EIR) represents the share of the purchased energy in the whole energy, i.e. the sum of the energy values of the new energy and the non-renewable energy, and the purchased energy includes the purchased values of natural gas, electricity and maintenance service:
Figure FDA0003918753480000022
the energy self-sufficiency ratio (ESR) is an index for evaluating the self-organizing ability of a system, and is used for evaluating the supporting ability of the natural environment:
Figure FDA0003918753480000023
4. the energy system operation state optimization method based on energy value evaluation as claimed in claim 1, wherein the process of optimizing and updating the core parameters in step 3 by using the multi-objective slime mold algorithm in step 4 is as follows:
step 4.1: determining the number of core parameters needing to be optimized and determining the limit boundary of each core parameter; overall size N, problem dimension (dim), lower bound lb, upper bound ub, and maximum number of iterations maxims;
the mucus model is as follows:
Figure FDA0003918753480000024
wherein ,vb Is a parameter between-a and a, v c Is a variable which is linearly decreased within the range of 1 to 0 and represents the feedback relationship between the food concentration and the slime quality, X (t) represents the position of the slime at t, X b Is the position of the individual having the highest concentration of odor so far, X A and XB Is two solutions randomly selected from bee colony, W is the weight of slime, and the variable p is calculated as follows:
p＝tanh|S(i)-DF|
step 4.2: random initialization solution: x = rand (N, dim) * (ub-lb)+lb；
Step 4.3: a main optimization cycle, stopping searching which does not meet the standard;
step 4.4: applying the upper and lower boundaries, evaluating a target space F of the target solution:
AllFitness＝fobj(lb<X>ub)
step 4.5: saving the archive of the non-dominant solutions, and deleting the dominant solution with the minimum congestion distance from the archive;
step 4.6: calculating the crowding distance CD of the archive solution:
Figure FDA0003918753480000031
wherein ,
Figure FDA0003918753480000032
is the minimum value of the jth objective function,
Figure FDA0003918753480000033
is the maximum value of the jth objective function. The CD estimate for one solution m is the average distance of its two rounded/adjacent solutions (m-1, m + 1);
step 4.7: ordering the non-dominance enforcement based on the congestion distance;
[SmellOrder，SmellIndex]＝sort(AllFitness)，worstFitness＝SmellOrder(N)，
bestFitness＝SmellOrder(1)，S＝bestFitness-worstFitness+eps
wherein Smellorder refers to a fitness value sequence sorted according to the odor sequence, smellIndex refers to a fitness value sequence sorted according to the ascending sequence, worstfit refers to the worst solution, bestfittess refers to the best solution, and S refers to the fitness;
step 4.8: selecting a first ranked target as an optimal solution;
step 4.9: updating the optimal fitness value and the optimal position;
step 4.10: updating the location of the search agent;
the mucus mold updates its position as follows:
Figure FDA0003918753480000034
wherein lb and ub are respectively a lower bound and an upper bound, i.e. the limiting bounds of the core parameter, r i ∈R，r i ∈[0，1]For i =1,2, variable v b and vc Respectively in [ -a, a [ - ]]And [ -1,1]With the number of iterations increasing, v b and vc Gradually approaches zero;
step 4.11: updating the weight vector explained in the equation;
Figure FDA0003918753480000035
wherein R is in the range of R, R is in the range of 0,1]，b F and wF Respectively obtaining an optimal solution and a worst solution in the process of iterating t times, wherein S (i) is the fitness of X, and SmellIndex refers to a sequence of fitness values sorted in an ascending order;
step 4.12: updating the current solution position according to the specification of the equation;
SmellIndex＝sort(S)
s is the fitness of X, and the SmellIndex refers to a fitness value sequence sorted in ascending order;
step 4.13: the best solution is returned.
5. An energy system operating condition optimizing device based on energy value evaluation, comprising:
the energy input parameters are energy data of each energy source, specifically energy flow B, renewable energy source R, non-renewable energy source N and purchased energy source F;
the short-time storage equipment is used for storing the energy input parameters collected by the collecting equipment within one hour;
the parameter analysis computing equipment is used for converting the energy input parameters of different types into energy value parameters of a Ethernet energy value-to-energy value baseline, wherein the energy values comprise a renewable energy value, a non-renewable energy value, a purchase energy value and an output energy value;
the design unit is used for calculating the energy value parameters by using an energy value evaluation model to obtain core parameters, wherein the core parameters comprise an energy value output rate (EYR), an energy value investment rate (EIR), an Environmental Load Rate (ELR), a sustainability index (ESI), a renewable energy ratio (phi R) and an energy value self-sufficiency rate (ESR);
the control module is used for optimizing the core parameters by utilizing a multi-target slime mold algorithm to obtain the optimized core parameters;
and the display is used for displaying the optimized core parameters of the control module.
6. The energy system operating condition optimizing device based on energy value evaluation according to claim 5, wherein the collection means is provided in an energy input path and an energy system output path.
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