CN110752611A - Method for optimizing operation and energy storage capacity of town energy Internet - Google Patents

Method for optimizing operation and energy storage capacity of town energy Internet Download PDF

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CN110752611A
CN110752611A CN201911044562.5A CN201911044562A CN110752611A CN 110752611 A CN110752611 A CN 110752611A CN 201911044562 A CN201911044562 A CN 201911044562A CN 110752611 A CN110752611 A CN 110752611A
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CN110752611B (en
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张聂鹏
高丙团
王满商
李远梅
杨利荣
白少锋
胡航
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State Grid Jiangsu Electric Power Co Ltd Zhenjiang Power Supply Branch
Southeast University
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State Grid Jiangsu Electric Power Co Ltd Zhenjiang Power Supply Branch
Southeast University
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

The invention discloses a method for optimizing town energy Internet operation and energy storage capacity, which comprises the following steps: s1: aiming at the problem of optimizing the operation and energy storage capacity of the energy Internet, constructing an operation and energy storage configuration optimization model; s2: aiming at the problems of operation and optimal configuration, an improved coevolution algorithm is provided; s3: and solving the operation and energy storage configuration optimization model by adopting an improved coevolution algorithm to obtain an optimal operation optimization result and an optimal energy storage capacity configuration. The particle swarm optimization is adopted as a basic algorithm for improving the coevolution algorithm, and strategy cooperation and double-population cooperation are coupled; the strategy cooperation is composed of an initialization strategy based on average entropy, a Tent chaotic initialization strategy and an adaptive weight strategy, and the double-population strategy is composed of different populations by adopting different strategies. The method solves the model for optimizing the operation and the energy storage capacity of the town energy Internet, realizes system optimization, ensures high-efficiency energy supply of the town energy Internet system, and improves the energy utilization rate.

Description

Method for optimizing operation and energy storage capacity of town energy Internet
Technical Field
The invention relates to a method for optimizing town energy Internet operation and energy storage capacity, and belongs to the technical field of electric power.
Background
With the continuous expansion of the power generation grid-connected scale of renewable energy sources, the forms of power supplies are diversified, the system characteristics and the dynamic balance mechanism are obviously changed due to the coupling and uncertainty of various heterogeneous energy resources, the changes cause troubles for changing the energy structure and realizing the energy transformation of China, and meanwhile, the challenges are provided for the operation and energy storage configuration optimization of the town energy Internet. And a combined cooling heating and power system is adopted as the supply side of energy in the town energy Internet system, so that the utilization efficiency of the energy can be improved, the pollutant emission can be reduced, and the energy cost can be reduced. Meanwhile, with the access of renewable energy power generation, a certain influence is caused on the system, and in order to improve the consumption of renewable energy and realize peak clipping and valley filling, an energy storage system with a certain capacity needs to be configured for the system, so that the economic efficiency of the system and the environment-friendly effect need to be realized, and reasonable energy storage capacity and operation strategy need to be determined.
Currently, there are two basic operating strategies, with respect to operating strategies, primarily, to heat up and heat up with electricity. However, neither of these strategies can guarantee high energy utilization. In order to achieve maximum energy efficiency, reasonable energy storage system capacity should be configured and operational optimization must be performed to determine the optimal solution. The coevolution algorithm attracts attention of broad scholars due to the excellent synergistic performance of the coevolution algorithm. Therefore, the invention provides an improved coevolution algorithm for solving the problems of operation and energy storage capacity optimization.
Disclosure of Invention
The invention aims to provide a method for optimizing the operation and energy storage capacity of the town energy Internet based on an improved coevolution algorithm, which can solve the defects in the prior art.
The purpose of the invention is realized by the following technical scheme:
a method for optimizing operation and energy storage capacity of town energy Internet comprises the following steps:
s1: aiming at the operation and energy storage optimization problem of the energy Internet, constructing an operation and energy storage optimization configuration model;
s2: aiming at an operation and energy storage capacity optimization model, an improved coevolution algorithm is provided;
s3: by adopting an improved coevolution algorithm to solve the operation and energy storage optimization configuration model, the town energy Internet system obtains an optimal operation optimization result and optimal energy storage capacity configuration.
The object of the invention can be further achieved by the following technical measures:
in the method for optimizing operation and energy storage capacity of the town energy Internet, the construction of the operation and energy storage optimization configuration model in the step S1 includes the following steps:
s1.1: establishing an objective function of the model, namely calculating the annual average energy comprehensive utilization rate of the system:
in the formula (1), η represents the average annual energy utilization rate PeFor annual energy production, PcFor annual cooling capacity, PhFor annual heat supply, PresFor producing energy from renewable sources, PgridFor purchasing electric power from the grid, F is the amount of natural gas purchased, lambdagasIs the heat value of natural gas;
s1.2: establishing constraint conditions of the model, comprising the following steps:
at runtime, the system should satisfy the following constraints:
s1.2.1: establishing an energy conservation constraint:
Figure BDA0002253778420000022
Figure BDA0002253778420000024
in the formulae (2) to (4),andrespectively representing the electric power and the thermal power generated by the gas turbine,
Figure BDA0002253778420000027
and
Figure BDA0002253778420000028
respectively represent the charge and discharge power of the electricity storage system,
Figure BDA0002253778420000029
and
Figure BDA00022537784200000210
respectively showing the output cold power and the input heat power of the absorption refrigerator,
Figure BDA00022537784200000211
and
Figure BDA00022537784200000212
respectively representing the output cold power and the input electric power of the electric refrigerating equipment,
Figure BDA00022537784200000213
represents the output thermal power of the gas boiler,
Figure BDA00022537784200000214
and
Figure BDA00022537784200000215
respectively representing the stored thermal power, p, of the heat storage deviceWTThe power is predicted for the wind power,
Figure BDA00022537784200000216
and
Figure BDA00022537784200000217
respectively representing the demand amounts of the electric load, the heat load and the cold load of the user;
s1.2.2: device force constraints
0 pjtpjt,max(5)
In the formula (5), pjtDenotes the output power of the j-th device, pjt,maxRepresenting the maximum output power of the j-th device.
S1.2.3: energy storage system restraint
Figure BDA00022537784200000218
In the formula (6), Sees(t)、STes(t) energy storage of the electrical and thermal energy storage device during a time period t, Sees(t-1)、STes(t-1) is the energy storage of the electric energy storage and thermal energy storage device during the time period t-1,
Figure BDA00022537784200000219
and
Figure BDA00022537784200000220
respectively representing the charge and discharge efficiency of the electrical energy storage device,
Figure BDA0002253778420000031
and
Figure BDA0002253778420000032
respectively represents the minimum value and the maximum value of the capacity of the electric energy storage device,
Figure BDA0002253778420000033
is the maximum value of the capacity of the heat storage device,
Figure BDA0002253778420000034
and
Figure BDA0002253778420000035
respectively representing the charge/discharge efficiency of the thermal energy storage device; mu is the energy self-loss coefficient of the thermal energy storage device.
In the method for optimizing the operation and the energy storage capacity of the town energy Internet, the improved coevolution algorithm in the step S2 comprises the following steps:
s2.1: initializing algorithm parameters including learning factors, fixed weight coefficients, maximum and minimum values of weights, maximum iteration times and variable value ranges;
s2.2: dual population strategy
Based on the principle of co-evolution of multiple species in ecology, a double-population strategy is designed, and co-evolution is realized through adaptation among different species; in order to improve the diversity of the population, two species groups are established to optimize variables, and therefore a double-population strategy is adopted to construct the two populations;
s2.3: respectively initializing the two groups by adopting different initialization strategies to generate initial groups, wherein the specific initialization steps are as follows:
s2.3.1: initialization based on mean entropy
Firstly, initializing m individuals as an initial population, then randomly generating a new individual, calculating the mean entropy of the population according to the formulas (7) to (9), and adding the new individual into the initial population when the mean entropy of the population is larger than a preset threshold value until N individuals are obtained. According to the entropy theory of information, the entropy of the population is equal to the sum of the individual coding entropies, namely:
Figure BDA0002253778420000036
Figure BDA0002253778420000037
Figure BDA0002253778420000038
in the formulae (7) to (9), H represents the entropy of the population, HjFor each coding entropy, N is the population number, D is the dimension of each particle, m is the existing initial individual number, k is the new initial individual, PikThe degree of similarity between the j-th dimension code of the ith individual and the j-th dimension code of the kth individual is determined,and
Figure BDA00022537784200000310
table j-dimension values, A, for the ith and kth individualsjAnd BjThe upper and lower bounds of the j-th dimension variable.
S2.3.2: tent-based chaotic mapping initialization
Tent mapping is a typical chaotic system, and a chaotic sequence can be mapped to a solution space through an equation (10);
Figure BDA0002253778420000041
xija value representing the j dimension of an individual i;
s2.4: strategy co-evolution
Solving the optimization problem by adopting an optimization algorithm, wherein the solving method comprises the following steps: performing coevolution on the two populations by adopting a particle swarm algorithm as a basic algorithm and combining a fixed-step-length strategy and a variable-step-length strategy as an operator updating method, and performing elite interaction after the two populations are respectively evolved; the specific method comprises the following steps:
the particle swarm algorithm is an intelligent algorithm, and an optimal position is found by simulating the flying behavior of a bird swarm in a multi-dimensional space and continuously adjusting the motion and the distance of the bird swarm; the particle swarm optimization is realized by continuously moving to the global optimum in a solution space, the moving direction and distance are determined by the speed of the particles, the speed of the particles is continuously updated along with the change of the movement, the optimal value is found in the solution space through the process, and the iterative formula of the particles is as follows:
Figure BDA0002253778420000042
Figure BDA0002253778420000043
in formulae (11) to (12), Vi tAnd Vi t+1Representing the velocity of the ith particle in the t-th generation and the t +1 th generation,
Figure BDA0002253778420000044
and
Figure BDA0002253778420000045
indicating the position of the ith particle in the t-th generation and the t + 1-th generation,
Figure BDA0002253778420000046
representing the global optimal solution at the t-th generation,
Figure BDA0002253778420000047
represents the individual optimal solution at the t generation, omega represents the weight coefficient, c1And c2Is a learning factor, rand denotes [0,1 ]]A random number in between;
respectively adopting fixed inertia weight and nonlinear inertia weight factors for the two populations, wherein the fixed inertia weight is to set the weight coefficient as a fixed value, the nonlinear inertia weight is adaptively adjusted according to the fitness value of the particles, and the nonlinear inertia weight factors are described as follows:
Figure BDA0002253778420000048
in formula (13), ωmaxAnd ωminRespectively representing the maximum and minimum values of the weights, tmaxThe maximum number of iterations, t is the number of iterations.
S2.5 output results
If the termination condition is met, i.e. the maximum iteration number is reached, the optimal result is output, otherwise, the step S2.4 is carried out.
Compared with the prior art, the invention has the beneficial effects that: the invention relates to a method for optimizing the running of an improved coevolution algorithm-based town energy Internet in energy storage capacity, which adopts a particle swarm algorithm as a basic algorithm of the improved coevolution algorithm and couples strategy cooperation and double-population cooperation; the strategy cooperation is composed of an average entropy initialization strategy, a Tent chaotic initialization strategy and an adaptive weight strategy, and the double-population strategy is composed of different populations and different strategies, so that the diversity of the populations in the algorithm is increased, and the convergence speed of the algorithm is improved. And applying the improved coevolution algorithm to the solution of the optimization model, thereby obtaining the operation and energy storage capacity optimization result of the system and improving the energy utilization efficiency of the system.
Drawings
FIG. 1 is a flow chart of an improved coevolution algorithm of the present invention;
FIG. 2 is a diagram of a typical day 1 user energy usage scenario in an embodiment of the present invention;
FIG. 3 is a diagram illustrating an exemplary day 2 user energy usage scenario in accordance with an embodiment of the present invention;
FIG. 4 is a diagram illustrating an exemplary day 3 user energy usage scenario in accordance with an embodiment of the present invention;
FIG. 5 is a diagram illustrating an exemplary day 4 user energy usage scenario in accordance with an embodiment of the present invention;
FIG. 6 is a diagram illustrating the result of optimizing the operation of the power flow in the system according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating the optimization results of thermal energy flow operation in the system according to an embodiment of the present invention;
fig. 8 is a diagram illustrating a result of optimizing cold energy flow operation in the system according to the embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and specific examples.
The optimization algorithm process related to the present embodiment is shown in fig. 1, and the process is as follows: the first step is as follows: initializing parameters such as the town energy Internet system, renewable energy, an optimization algorithm and the like. The second step is that: constructing two populations; the third step: and generating an initialization population of double populations by adopting an initialization strategy corresponding to each population. The fourth step: and updating the speed and the position of the particle i according to strategy cooperation, and calculating the fitness value of each particle, wherein the calculation of the fitness value is obtained according to the established optimization model. The historical optimal position and the overall optimal position of each particle are updated. The fifth step: and returning to output when the maximum iteration times are reached, or returning to the step four.
The specific embodiment discloses a method for optimizing town energy Internet operation and energy storage capacity by improving a coevolution algorithm, which comprises the following steps:
s1: aiming at the operation and energy storage optimization problem of the energy Internet, constructing an operation and energy storage optimization configuration model;
s2: aiming at an operation and energy storage capacity optimization model, an improved coevolution algorithm is provided;
s3: by adopting an improved coevolution algorithm to solve the operation and energy storage optimization configuration model, the town energy Internet system obtains an optimal operation optimization result and optimal energy storage capacity configuration.
The construction of the operation and energy storage optimization configuration model in the step S1 includes the following steps:
s1.1: establishing an objective function of the model, namely calculating the annual average energy comprehensive utilization rate of the system:
Figure BDA0002253778420000051
in the formula (1), η represents the average annual energy utilization rate PeFor annual energy production, PcFor annual cooling capacity, PhFor annual heat supply, PresFor producing energy from renewable sources, PgridFor purchasing electric power from the grid, F is the amount of natural gas purchased, lambdagasIs the heat value of natural gas;
s1.2: establishing constraint conditions of the model, comprising the following steps:
at runtime, the system should satisfy the following constraints:
s1.2.1: establishing an energy conservation constraint:
Figure BDA0002253778420000061
Figure BDA0002253778420000063
in the formulae (2) to (4),andrespectively representing the electric power and the thermal power generated by the gas turbine,and
Figure BDA0002253778420000067
respectively represent the charge and discharge power of the electricity storage system,
Figure BDA0002253778420000068
and
Figure BDA0002253778420000069
respectively showing the output cold power and the input heat power of the absorption refrigerator,
Figure BDA00022537784200000610
and
Figure BDA00022537784200000611
respectively representing the output cold power and the input electric power of the electric refrigerating equipment,
Figure BDA00022537784200000612
represents the output thermal power of the gas boiler,
Figure BDA00022537784200000613
and
Figure BDA00022537784200000614
respectively representing the stored thermal power, p, of the heat storage deviceWTThe power is predicted for the wind power,
Figure BDA00022537784200000615
andrespectively representing the demand of the electrical load, the thermal load and the cold load of the user.
S1.2.2: device force constraints
0 pjtpjt,max(5)
In the formula (5), pjtDenotes the output power of the j-th device, pjt,maxRepresenting the maximum output power of the j-th device.
S1.2.3: energy storage system restraint
Figure BDA00022537784200000617
In the formula (6), Sees(t)、STes(t) energy storage of the electrical and thermal energy storage device during a time period t, Sees(t-1)、STes(t-1) is the energy storage of the electric energy storage and thermal energy storage device during the time period t-1,
Figure BDA00022537784200000618
and
Figure BDA00022537784200000619
respectively representing the charge and discharge efficiency of the electrical energy storage device,
Figure BDA00022537784200000620
andrespectively represents the minimum value and the maximum value of the capacity of the electric energy storage device,
Figure BDA00022537784200000622
is the maximum value of the capacity of the heat storage device,andrespectively representing the heat storage/release efficiency of the heat storage device; mu is the energy self-loss coefficient of the thermal energy storage device.
The proposal of the improved coevolution algorithm in the step S2 comprises the following steps:
s2.1: initializing algorithm parameters including learning factors, fixed weight coefficients, maximum and minimum values of weights, maximum iteration times and variable value ranges;
s2.2: dual population strategy
Based on the principle of co-evolution of multiple species in ecology, a double-population strategy is designed, and co-evolution is realized through adaptation among different species. In order to improve the diversity of the population, two species groups are established to optimize variables, and therefore a double-population strategy is adopted to construct the two populations;
s2.3: respectively initializing the two groups by adopting different initialization strategies to generate initial groups, wherein the specific initialization steps are as follows:
s2.3.1: initialization based on mean entropy
Firstly, initializing m individuals as an initial population, then randomly generating a new individual, calculating the average entropy of the population according to formulas (7) to (9), and adding the new individual into the initial population when the average entropy of the population is larger than a preset threshold value until N individuals are obtained. According to the entropy theory of information, the entropy of the population is equal to the sum of the individual coding entropies, namely:
Figure BDA0002253778420000071
Figure BDA0002253778420000072
Figure BDA0002253778420000073
in the formulae (7) to (9), H represents the entropy of the population, HjFor each coding entropy, N is the population number, D is the dimension of each particle, m is the existing initial individual number, k is the new initial individual, PikThe degree of similarity between the j-th dimension code of the ith individual and the j-th dimension code of the kth individual is determined,and
Figure BDA0002253778420000075
table j-dimension values, A, for the ith and kth individualsjAnd BjThe upper and lower bounds of the j-th dimension variable.
S2.3.2: tent-based chaotic mapping initialization
Tent mapping is a typical chaotic system, and a chaotic sequence can be mapped to a solution space by equation (10).
Figure BDA0002253778420000076
xijA value representing the j dimension of an individual i.
S2.4: strategy co-evolution
Solving the optimization problem by adopting an optimization algorithm, wherein the solving method comprises the following steps: and carrying out coevolution on the two populations by adopting a particle swarm algorithm as a basic algorithm and combining a fixed step length strategy and a variable step length strategy as an operator updating method, and carrying out elite interaction after the two populations are respectively evolved. The specific method comprises the following steps:
the particle swarm algorithm is an intelligent algorithm, and the optimal position is found by simulating the flying behavior of a bird swarm in a multidimensional space and continuously adjusting the motion and the distance of the bird swarm. The particle swarm optimization is realized by continuously moving to the global optimum in a solution space, the moving direction and distance are determined by the speed of the particles, the speed of the particles is continuously updated along with the change of the movement, the optimal value is found in the solution space through the process, and the iterative formula of the particles is as follows:
Figure BDA0002253778420000081
Figure BDA0002253778420000082
in formulae (11) to (12), Vi tAnd Vi t+1Representing the velocity of the ith particle in the t-th generation and the t +1 th generation,
Figure BDA0002253778420000083
and
Figure BDA0002253778420000084
indicating the position of the ith particle in the t-th generation and the t + 1-th generation,
Figure BDA0002253778420000085
representing the global optimal solution at the t-th generation,
Figure BDA0002253778420000086
represents the individual optimal solution at the t generation, omega represents the weight coefficient, c1And c2Is a learning factor, rand denotes [0,1 ]]A random number in between.
And respectively adopting fixed inertia weight factors and nonlinear inertia weight factors for the two populations. The fixed inertia weight is to set the weight coefficient to a fixed value, and the nonlinear inertia weight is adaptively adjusted according to the fitness value of the particle, and the nonlinear inertia weight factor is described as follows:
Figure BDA0002253778420000087
in formula (13), ωmaxAnd ωminRespectively representing the maximum and minimum values of the weights, tmaxThe maximum number of iterations, t is the number of iterations.
S2.5, outputting a result: if the termination condition is met, namely the maximum iteration number is reached, outputting the optimal result, otherwise, turning to S2.4.
In this embodiment, the capacity device related to the town energy internet system includes: the system comprises a gas turbine, a waste heat boiler, a gas boiler, an absorption refrigerator, an electric refrigerator, a storage battery and a heat storage tank, wherein the renewable energy power generation system is composed of a fan. Assume that the whole year is divided into four typical days according to seasons, constituting four typical day scenes as shown in fig. 2 to 5, each typical day being composed of 24 time periods each of which is 1 hour. The electricity price adopts a time-of-use electricity price mechanism, as shown in table 1. The algorithm parameters and system parameters are shown in table 2.
TABLE 1
Time period Price of electricity Time period Price of electricity
0：00-07：00 0.330 19：00-22：00 1.113
07：00-10：00 1.113 22：00：24：00 0.300
10：00-19：00 0.685
TABLE 2
Figure BDA0002253778420000091
And (3) solving the optimization model by adopting an improved coevolution algorithm, wherein the obtained result is shown in fig. 6-8, the optimal capacity of the storage battery is 1.2MW, the optimal capacity of the heat storage tank is 0.8MW, and the total cost at the moment is 72023 yuan.
In order to show the superiority of the method, the results are shown in table 3 compared with the method for solving the optimization model by adopting the basic particle swarm algorithm and the genetic algorithm.
TABLE 3
Algorithm Improved coevolution algorithm Basic particle swarm algorithm Genetic algorithm
Efficiency of comprehensive utilization 0.92 0.89 0.90
As can be seen from FIGS. 6-8, the method can be used for effectively solving the optimization model. As can be seen from table 3, compared with the basic particle swarm algorithm and the genetic algorithm, the improved coevolution algorithm can obtain a better solution and obtain the maximum comprehensive utilization rate.
In addition to the above embodiments, the present invention may have other embodiments, and any technical solutions formed by equivalent substitutions or equivalent transformations fall within the scope of the claims of the present invention.

Claims (3)

1. A method for optimizing operation and energy storage capacity of town energy Internet is characterized by comprising the following steps:
s1: aiming at the operation and energy storage optimization problem of the energy Internet, constructing an operation and energy storage optimization configuration model;
s2: aiming at the problems of operation and optimal configuration, an improved coevolution algorithm is provided;
s3: by adopting an improved coevolution algorithm to solve the operation and energy storage optimization configuration model, the town energy Internet system obtains an optimal operation optimization result and optimal energy storage capacity configuration.
2. The method for optimizing town energy Internet operation and energy storage capacity according to claim 1, wherein: the building of the operation and energy storage optimization configuration model in the step S1 includes the following steps:
s1.1: establishing an objective function of the model, namely calculating the annual average energy comprehensive utilization rate of the system:
in the formula (1), η represents the average annual energy utilization rate PeFor annual energy production, PcFor annual cooling capacity, PhFor annual heat supply, PresFor producing energy from renewable sources, PgridFor purchasing electric power from the grid, F is the natural gas purchasedQuantity, λgasIs the heat value of natural gas;
s1.2: establishing constraint conditions of the model, comprising the following steps:
at runtime, the system should satisfy the following constraints:
s1.2.1: establishing an energy conservation constraint:
Figure FDA0002253778410000012
Figure FDA0002253778410000014
in the formulae (2) to (4),
Figure FDA0002253778410000015
and
Figure FDA0002253778410000016
respectively representing the electric power and the thermal power generated by the gas turbine,
Figure FDA0002253778410000017
and
Figure FDA0002253778410000018
respectively represent the charge and discharge power of the electricity storage system,
Figure FDA0002253778410000019
and
Figure FDA00022537784100000110
respectively showing the output cold power and the input heat power of the absorption refrigerator,
Figure FDA00022537784100000111
and
Figure FDA00022537784100000112
respectively representing the output cold power and the input electric power of the electric refrigerating equipment,
Figure FDA00022537784100000113
represents the output thermal power of the gas boiler,
Figure FDA00022537784100000114
and
Figure FDA00022537784100000115
respectively representing the stored thermal power, p, of the heat storage deviceWTThe power is predicted for the wind power,
Figure FDA00022537784100000116
and
Figure FDA00022537784100000117
respectively representing the demand amounts of the electric load, the heat load and the cold load of the user;
s1.2.2: device force constraints
0 pjtpjt,max(5)
In the formula (5), pjtDenotes the output power of the j-th device, pjt,maxRepresents the maximum output power of the j device;
s1.2.3: energy storage system restraint
In the formula (6), Sees(t)、STes(t) energy storage of the electrical and thermal energy storage device during a time period t, Sees(t-1)、STes(t-1) is the energy storage of the electric energy storage and thermal energy storage device during the time period t-1,
Figure FDA0002253778410000022
and
Figure FDA0002253778410000023
respectively representing the charge and discharge efficiency of the electrical energy storage device,
Figure FDA0002253778410000024
and
Figure FDA0002253778410000025
respectively represents the minimum value and the maximum value of the capacity of the electric energy storage device,
Figure FDA0002253778410000026
is the maximum value of the capacity of the heat storage device,and
Figure FDA0002253778410000028
respectively representing the charge/discharge efficiency of the thermal energy storage device; mu is the energy self-loss coefficient of the thermal energy storage device.
3. The method for optimizing town energy Internet operation and energy storage capacity according to claim 2, wherein: the improved coevolution algorithm in the step S2 includes the following steps:
s2.1: initializing algorithm parameters including learning factors, fixed weight coefficients, maximum and minimum values of weights, maximum iteration times and variable value ranges;
s2.2: dual population strategy
Based on the principle of co-evolution of multiple species in ecology, a double-population strategy is designed, co-evolution is carried out through adaptation among different species, in order to improve population diversity, two species groups are established to optimize variables, and therefore the double-population strategy is adopted to construct the two populations;
s2.3: respectively initializing the two groups by adopting different initialization strategies to generate initial groups, wherein the specific initialization steps are as follows:
s2.3.1: initialization based on mean entropy
Firstly, initializing m individuals as an initial population, then randomly generating a new individual, calculating the average entropy of the population according to formulas (7) to (9), and adding the new individual into the initial population when the average entropy of the population is larger than a preset threshold value until N individuals are obtained; according to the entropy theory of information, the entropy of the population is equal to the sum of the individual coding entropies, namely:
Figure FDA0002253778410000029
Figure FDA00022537784100000211
in the formulae (7) to (9), H represents the entropy of the population, HjFor each coding entropy, N is the population number, D is the dimension of each particle, m is the existing initial individual number, k is the new initial individual, PikThe degree of similarity between the j-th dimension code of the ith individual and the j-th dimension code of the kth individual is determined,
Figure FDA0002253778410000031
and
Figure FDA0002253778410000032
table j-dimension values, A, for the ith and kth individualsjAnd BjThe upper and lower bounds of the j dimension variable are set;
s2.3.2: tent-based chaotic mapping initialization
Tent mapping is a typical chaotic system, and a chaotic sequence can be mapped to a solution space by equation (10).
Figure FDA0002253778410000033
xijA value representing the j dimension of an individual i;
s2.4: strategy co-evolution
Solving the optimization problem by adopting an optimization algorithm, wherein the solving method comprises the following steps: performing coevolution on the two populations by adopting a particle swarm algorithm as a basic algorithm and combining a fixed-step-length strategy and a variable-step-length strategy as an operator updating method, and performing elite interaction after the two populations are respectively evolved, wherein the specific method comprises the following steps of:
the particle swarm algorithm is an intelligent algorithm, and the optimal position is found by simulating the flying behavior of a bird swarm in a multidimensional space and continuously adjusting the motion and the distance of the bird swarm. The particle swarm optimization is realized by continuously moving to the global optimum in a solution space, the moving direction and distance are determined by the speed of the particles, the speed of the particles is continuously updated along with the change of the movement, the optimal value is found in the solution space through the process, and the iterative formula of the particles is as follows:
Figure FDA0002253778410000034
in formulae (11) to (12), Vi tAnd Vi t+1Representing the velocity of the ith particle in the t-th generation and the t +1 th generation,
Figure FDA0002253778410000036
and
Figure FDA0002253778410000037
indicating the position of the ith particle in the t-th generation and the t + 1-th generation,
Figure FDA0002253778410000038
representing the global optimal solution at the t-th generation,
Figure FDA0002253778410000039
represents the individual optimal solution at the t generation, omega represents the weight coefficient, c1And c2Is a learning factor, rand denotes [0,1 ]]A random number in between;
respectively adopting fixed inertia weight and nonlinear inertia weight factors for the two populations, wherein the fixed inertia weight is to set the weight coefficient as a fixed value, the nonlinear inertia weight is adaptively adjusted according to the fitness value of the particles, and the nonlinear inertia weight factors are described as follows:
in formula (13), ωmaxAnd ωminRespectively representing the maximum and minimum values of the weights, tmaxIs the maximum iteration number, and t is the iteration number;
s2.5 output results
If the termination condition is met, i.e. the maximum iteration number is reached, the optimal result is output, otherwise, the step S2.4 is carried out.
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