CN115375019A - Optimal configuration method for park integrated energy system and related equipment - Google Patents
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Abstract
The application provides a park comprehensive energy system optimal configuration method, which comprises the following steps: determining a park optimization objective function and a park optimization constraint condition according to pre-acquired park information; determining a park optimization comprehensive objective function according to the park optimization objective function and the park optimization constraint condition; inputting the park optimization comprehensive objective function into a pre-constructed simulated annealing model, and determining the optimal solution of the park optimization objective function; and determining the optimal configuration scheme of the park comprehensive energy system according to the optimal solution of the park optimization objective function.
Description
Technical Field
The application relates to the technical field of communication, in particular to a park comprehensive energy system optimal configuration method and related equipment.
Background
As is well known, energy is a key factor supporting the development of human society, and in recent years, as the variety and utilization rate of energy increase, human dependence on energy is higher and higher. Each device and area in the industrial park relate to energy consumption, the energy system of the existing industrial park usually seeks the optimal scheme of optimal configuration of the energy system of the park by simulating the process of natural biological evolution, but the method can only be based on local configuration or global configuration consideration of the park, cannot comprehensively consider all energy consumption devices and energy consumption areas in the park, and the obtained optimal configuration scheme of the energy system of the park cannot be applied to the optimization of the actual energy system.
Disclosure of Invention
In view of this, an object of the present application is to provide a method for optimally configuring a campus integrated energy system and related devices.
Based on the above purpose, the present application provides a method for optimally configuring a park integrated energy system, comprising:
determining a park optimization objective function and a park optimization constraint condition according to pre-acquired park information;
determining a campus optimization comprehensive objective function according to the campus optimization objective function and the campus optimization constraint condition;
inputting the park optimization comprehensive objective function into a pre-constructed simulated annealing model, and determining the optimal solution of the park optimization objective function;
and determining the optimal configuration scheme of the park comprehensive energy system according to the optimal solution of the park optimization objective function.
Optionally, the campus information includes: park total cost information and park comprehensive energy efficiency information;
the method for determining the park optimization objective function according to the pre-acquired park information comprises the following steps:
determining a first objective function which minimizes the total cost of the park according to the total cost information of the park; wherein the first objective function is:
F 1 ＝min[f in (x)+f op (p)+f mc (p)+f ce (p)]
wherein, F 1 Is a first objective function, f in (x) Is the initial investment cost of the park integrated energy system, and x is the installed capacity of each equipment of the park integrated energy system；f op (p) annual operating costs of the park energy system over the lifetime, f mc (p) annual maintenance costs for the campus complex energy system; f. of ce (p) annual carbon emission costs for the campus complex energy system; p is the effective power of each device of the park integrated energy system;
determining a second objective function which enables the comprehensive energy efficiency of the park to be maximum according to the comprehensive energy efficiency information of the park; wherein the second objective function is:
wherein, P e For the electric energy input of a district complex energy system, P g Inputting gas energy of a park comprehensive energy system; l is e Total electrical load for garden users, L h Total heat load for campus users, L c Total cooling load for campus users, S e Actual remaining stored energy, S, for the batteries of the park' S integrated energy system h Actual residual stored energy, S, of the thermal storage tank of the park complex energy system c Storing the actual residual energy of the ice storage tank of the park comprehensive energy system; d e For the actual release of energy, D, of the batteries of the park complex energy system h For the actual release of energy from the heat storage tanks of the park complex energy system, D c For the actual release of energy, lambda, from the ice storage tanks of the district's complex energy system E 、λ G 、λ H And λ C Energy coefficients of electricity, natural gas, heat and cold energy are respectively; lambda [ alpha ] e 、λ h And λ c Energy coefficients of electric, thermal and cold loads, respectively;
determining the campus optimization objective function according to the first objective function and the second objective function; wherein the campus optimization objective function is:
optionally, the campus information further includes any one of: the system comprises park building area data, park power grid energy supply data, park energy supply equipment power data, park natural gas network capacity data, park energy supply and demand data, park energy supply reliability data, park energy network transmission power data, park energy storage battery data, park heat storage tank capacity data and park ice storage tank capacity data;
the determining the campus optimization constraint conditions according to the pre-acquired campus information comprises the following steps:
determining a park building area constraint condition according to the park building area data; wherein, the park building area constraint conditions are as follows:
wherein m is i Indicating the area of the park occupied by the installation equipment i, AZ max Represents the usable campus building area of the installation device i;
or determining the park power grid energy supply constraint condition according to the park power grid energy supply data; wherein, the park electric wire netting energy supply constraint condition is:
wherein D is max Representing the maximum power supply capacity, P, of the park network line or park substation equipment max i Indicating the consumed power of device i, U max i Represents the generated power of the plant i, L q max Representing the power load designed in the park power grid, and S representing the safety power utilization coefficient of the park power grid;
or determining the power constraint condition of the park energy supply equipment according to the power data of the park energy supply equipment; wherein, the power constraint conditions of the park energy supply equipment are as follows:
wherein Q is i min Represents the minimum power, Q, of the cooling of the device i max i Representing the maximum power of heat supply of the equipment i; q i down Representing the maximum ramp rate, Δ Q, of the device i up i Represents the minimum ramp rate of device i;
or determining the constraint condition of the natural gas network capacity of the park according to the natural gas network capacity data of the park; wherein, the constraint conditions of the natural gas network capacity of the garden are as follows:
wherein PQ min,J Indicates the upper limit of the flow rate that the pipe l can withstand, PQ max,l Represents the lower limit of the sustainable flow of the pipeline l; cl (iii) l.y The pipeline safety fluctuation coefficient is obtained;
or determining the park energy supply and demand constraint condition according to the park energy supply and demand data; wherein, the energy supply and demand constraint conditions of the park are as follows:
E s (t)＝E load (t)
wherein, E s (t) is the supply of various energy sources in the park, and the unit is kW and E load (t) is the demand of the user for various energy sources;
or determining park energy supply reliability constraint conditions according to the park energy supply reliability data; wherein, park energy supply reliability constraint condition is:
ΔL b s ≤ΔL max
wherein, Δ L max The upper limit of electric energy consumption of the park;
or determining a park energy network transmission force constraint condition according to the park energy network transmission force data; the park energy network transmission force constraint conditions are as follows:
or determining the constraint conditions of the park energy storage battery according to the data of the park energy storage battery; wherein the park energy storage battery conditions are:
wherein, SOC (t) 0 ) Respectively representing the residual storage capacity of the battery at a certain moment; δ represents a self-discharge loss rate of the battery system; SOC min ,，SOC max A minimum security constraint and a maximum security constraint for remaining storage capacity; p ch_e,max ，P dis_e,max Rated maximum charge and discharge power;
or determining the capacity constraint condition of the park heat storage tank according to the capacity data of the park heat storage tank; wherein, the constraint conditions of the capacity of the heat storage tank in the park are as follows:
wherein Q is TS (t) represents the amount of heat remaining stored in the heat storage tank at time t; mu.s hloss The self-heat dissipation loss rate of the heat storage tank is represented; q TS (t 0 ) Denotes the initial t 0 The heat stored in the heat storage tank at any moment;
indicates from time t to time t 0 The stored heat of the heat storage tank;
indicates from time t to time t 0 The heat released from the heat storage tank;
the ratios of the maximum allowable stored heat amount, the minimum allowable stored heat amount and the stored heat capacity are respectively; c TS Is the heat storage capacity;
or determining the capacity constraint condition of the park ice storage tank according to the capacity data of the park ice storage tank; wherein, the capacity constraint conditions of the ice storage tank in the park are as follows:
CES min ≤CES(t)≤CES max
wherein, CES max 、CES min Is a maximum and minimum constraint on ice storage tank capacity, Q cesin,min 、Q cesin,max Is the minimum and maximum value of the ice storage power, Q cesout,min 、Q cesout,max Are the minimum and maximum values of ice melting power.
Optionally, the inputting the campus optimization comprehensive objective function into a pre-constructed simulated annealing model, and determining an optimal solution of the campus optimization objective function includes:
randomly generating an initial objective function solution set according to the park information; wherein the initial objective function solution set comprises a number of initial objective function solutions;
determining an initial fitness ranking of the plurality of initial objective function solutions according to the campus optimization objective function;
and determining the optimal solution of the campus optimization objective function in the iterative objective function solutions according to the initial fitness ranking.
Optionally, the determining an optimal solution of the campus optimization objective function in the iterative objective function solutions according to the initial fitness ranking includes:
performing simulated annealing operation according to the initial fitness ranking, and determining iterative fitness ranking of the plurality of initial objective function solutions;
sequencing and executing a crossover program and a variation program according to the iterative fitness, and determining an iterative objective function solution set; wherein the set of iterative objective function solutions comprises a number of iterative objective function solutions;
and executing simulated annealing operation according to the iterative fitness sequence, and determining the optimal solution of the park optimization objective function in the iterative objective function solutions.
Optionally, the determining the fitness ranking of the plurality of initial objective function solutions according to the campus optimization objective function includes:
matching the initial objective function solutions with the campus optimization comprehensive objective function to obtain a plurality of function solution adaptability values corresponding to the initial objective function solutions;
and sequencing the function solution fitness values according to a preset rule to obtain the initial fitness sequencing.
Optionally, the performing simulated annealing operation according to the iterative fitness ranking, and determining an optimal solution of the campus optimization objective function in the iterative objective function solutions includes:
according to the ranking according to the iterative fitness, matching the iterative objective function solutions with the campus optimization comprehensive objective function, and determining the iterative objective function solution with the maximum fitness in the iterative objective function solutions;
and determining the optimal solution of the campus optimization objective function according to the iteration objective function solution with the maximum fitness.
Based on same purpose, this application still provides a garden integrated energy system optimal configuration device, includes:
the system comprises a target determining module, a target setting module and a monitoring module, wherein the target determining module is configured to determine a campus optimization target function and a campus optimization constraint condition according to pre-acquired campus information;
a constraint determining module configured to determine a campus optimization comprehensive objective function according to the campus optimization objective function and the campus optimization constraint condition;
the simulated annealing module is configured to input the park optimization comprehensive objective function into a pre-constructed simulated annealing model and determine the optimal solution of the park optimization objective function;
and the output module is configured to determine a campus comprehensive energy system optimization configuration scheme according to the optimal solution of the campus optimization objective function.
In view of the above objects, the present application further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the optimal configuration method for the campus integrated energy system according to any one of the above aspects.
In view of the above, the present application further provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to execute any one of the above-mentioned methods for optimally configuring a campus integrated energy system.
From the above, according to the optimal configuration method of the campus comprehensive energy system and the related equipment, the campus optimization objective function and the campus optimization constraint condition are determined according to the actual information of the campus, the campus optimization objective function and the campus optimization constraint condition are determined according to the campus optimization objective function and the campus optimization constraint condition, the comprehensive optimization purpose of combining the optimization objective of the campus and the optimization condition needing to be considered in the optimization process of the campus is achieved, further, the campus optimization objective function is input into the pre-constructed simulated annealing model, the optimal solution of the campus optimization objective function is determined, the optimal solution of the campus optimization objective function is sought in an algorithm form by using the pre-constructed simulated annealing model, the optimal solution of the campus optimization objective function is determined according to the optimal solution of the campus optimization objective function, the optimal configuration scheme of the campus comprehensive energy system is determined, the accuracy of the optimal configuration scheme of the campus comprehensive energy system is guaranteed, and the efficiency of the optimization process of the campus is improved.
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In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only the present application, and that other drawings can be obtained by those skilled in the art without inventive efforts.
Fig. 1 is a schematic flow chart of a method for optimally configuring a campus integrated energy system according to the present application.
Fig. 2 is a schematic diagram illustrating an execution flow of a simulated annealing model according to an embodiment of the present disclosure.
Fig. 3 is a schematic view of an optimal configuration device of a campus integrated energy system according to an embodiment of the present application.
Fig. 4 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
It is to be noted that, unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this disclosure belongs. As used in this application, the terms "first," "second," and the like do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
As described in the background section, the traditional genetic algorithm is a simple, universal and robust global search method. In each generation, many individuals cross-mutate according to a certain rule to generate offspring. Genetic algorithms can approach globally optimal solutions from different routes but their local search capabilities are not strong and slow to work. The generated result is greatly influenced by the design of an actual algorithm.
In the process of implementing the present disclosure, the applicant finds that the traditional genetic algorithm can perform effective global search, but cannot perform local search, and the obtained result is easy to converge to the local optimal solution. The simulated annealing algorithm can quickly find a local optimal solution, but cannot quickly find a global optimal solution. Therefore, the application provides a campus comprehensive energy system optimal configuration method and related equipment, aiming at improving the search speed of an optimal solution by selecting certain specific objective function solutions to breed, and avoiding randomness and blindness to a certain extent.
Hereinafter, the technical means of the present disclosure will be described in further detail with reference to specific examples.
Referring to fig. 1, a schematic flow chart of a method for optimally configuring a campus integrated energy system is provided.
And step S101, determining a campus optimization objective function and a campus optimization constraint condition according to pre-acquired campus information.
In specific implementation, the park information comprises park total cost information, the park total cost directly influences the energy input of the park energy system, the park total cost is higher, the energy input of the representing park energy system is also higher, and therefore the effect of optimizing the energy input of the park energy system can be achieved by reasonably controlling the park total cost.
As an alternative embodiment, the first objective function of the total annual garden cost may be established, but not limited to, in units of years, and the total annual garden cost mainly includes the initial investment cost of the integrated energy system in the early stage, the annual operation cost, wherein the annual operation cost includes the consumption of energy and the input of labor cost, the annual maintenance cost, the annual carbon emission cost, and the like.
The initial investment cost of the park integrated energy system mainly consists of the acquisition cost, the installation cost, the land arrangement cost and other expenses of park equipment, and can be represented by the following formula:
wherein y is the design life of the park integrated energy system, and r is the discount rate of the park integrated energy system; c. C i Purchasing cost for each equipment unit in the park comprehensive energy system; x is the number of i Planning optimal installed capacity for each device in the comprehensive energy system of the park; j is a function of i The use cost of land occupation for each device in the park comprehensive energy system; t is t i The installation cost of each equipment unit capacity in the park comprehensive energy system; el is the remaining cost spent in the campus construction phase.
The system operation cost to be considered by the park integrated energy system mainly includes annual fuel consumption cost and annual electric energy purchase cost in the whole life cycle, and can be expressed by the following formula:
wherein, P i The operating power condition of each device i in the park comprehensive energy system is obtained; eta i The power consumption proportion coefficient of each device i in the park comprehensive energy system is obtained; g i For natural gas consuming plant i efficiency case, kappa i Is the gas consumption proportionality coefficient of the equipment i.
The system maintenance cost of the park integrated energy system is also the subject of the first objective function, and can be expressed by the following formula:
wherein f is mc (p) the annual maintenance costs of all the equipment of the campus integrated energy system for the full life cycle; w is a i The unit maintenance cost of each device in the park comprehensive energy system.
The annual carbon emission cost of the park integrated energy system is also the subject of consideration by the first objective function and can be expressed by the following formula:
wherein f is ce (p) annual carbon emission cost for the full life cycle of the park integrated energy system; delta e A carbon emission coefficient for consuming electrical energy for a campus complex energy system; delta g Consuming the carbon emission coefficient of natural gas for the park integrated energy system; d ctax Is the carbon emission tax of the park integrated energy system.
In summary, a first objective function that minimizes the total cost of the campus may be determined according to the total cost information of the campus; wherein the first objective function may be expressed as:
wherein, F 1 Is a first objective function, f in (x) Is the initial investment cost of the park integrated energy system, and x is the installed capacity of each device of the park integrated energy system; f. of op (p) annual operating costs of the park energy system over the lifetime, f mc (p) is the annual maintenance cost of the campus complex energy system; f. of ce (p) annual carbon emission costs for the campus complex energy system; p is the effective power of each device of the park integrated energy system;
as an alternative embodiment, the second objective function of the campus comprehensive energy efficiency information may be established, but not limited to, in units of years, where the campus comprehensive energy efficiency information mainly includes inputs of external energy sources such as electric energy and gas of the campus, electric, heat and cold loads of users in the campus, actual remaining energy storage amounts of the cells, the heat storage tanks and the ice storage tanks of the campus after energy loss, energy actually released by the cells, the heat storage tanks and the ice storage tanks of the campus after energy loss occurs, and the like.
For the optimization process of the park comprehensive energy system, the higher the park comprehensive energy efficiency is, the better the optimization effect is, so that a second objective function which enables the park comprehensive energy efficiency to be maximum can be determined according to the park comprehensive energy efficiency information; wherein the second objective function is:
wherein, P e For the electric energy input of a district complex energy system, P g Inputting gas energy of a park comprehensive energy system; l is e Total electrical load for garden users, L h Total heat load for campus users, L c Total cooling load for campus users, S e Actual remaining stored energy, S, for the batteries of the park Integrated energy System h Actual residual stored energy, S, of the thermal storage tank of the park complex energy system c Storing the actual residual energy of the ice storage tank of the park comprehensive energy system; d e For the actual release of energy, D, of the batteries of the park complex energy system h For the actual release of energy from the heat storage tanks of the park complex energy system, D c For the actual release of energy, lambda, from the ice storage tanks of the district's complex energy system E 、λ G 、λ H And λ C Energy coefficients of electricity, natural gas, heat and cold energy are respectively; lambda [ alpha ] e 、λ h And λ c The energy coefficients of the electrical, thermal and cold loads, respectively.
As an alternative embodiment, the solution provided by the present application aims to reduce the total cost of the campus and improve the comprehensive energy efficiency of the campus, so that according to the first objective function and the second objective function, a campus optimization objective function can be determined, and the campus optimization objective function can be expressed as:
furthermore, the optimal configuration method for the comprehensive energy system of the park, provided by the application, also comprehensively considers the influences of factors such as the amount of park resources, the size of an installable site, the maximum capacity which can be manufactured by the current technology and the like, and needs to be carried out under certain constraint conditions.
As an optional embodiment, the campus information further includes any one of: the park energy supply system comprises park building area data, park power grid energy supply data, park energy supply equipment power data, park natural gas network capacity data, park energy supply and demand data, park energy supply reliability data, park energy network transmission capacity data, park energy storage battery data, park heat storage tank capacity data and park ice storage tank capacity data, and can determine park optimization constraint conditions according to park information.
Specifically, the site limitation should be considered in the installation process of the energy equipment in the park, for example, the building area limitation of the relevant place should be considered in the installation of the photovoltaic panel, so the park building area constraint condition can be determined according to the park building area data; wherein, the park building area constraint conditions are as follows:
wherein m is i Indicating the area of the park occupied by the installation equipment i, AZ max Represents the usable campus building area of the installation device i;
furthermore, the energy system in the park also needs to consider power grid energy supply data of the park in the operation process, and needs to optimize and configure the comprehensive energy system in the bearable range of the park power grid energy supply, so that the park power grid energy supply constraint condition can be determined according to the power grid energy supply data of the park; wherein, the park electric wire netting energy supply constraint condition is:
wherein D is max Representing the maximum power supply capacity, P, of the park network line or park substation equipment max i Indicating the consumed power of device i, U max i Represents the generated power of the plant i, L q max Representing the power load designed in the park power grid, and S representing the safety power utilization coefficient of the park power grid;
furthermore, the working power of each device in the park is limited in the operation process, so that the power constraint condition of the park energy supply device can be determined according to the power data of the park energy supply device; wherein, the power constraint conditions of the park energy supply equipment are as follows:
wherein Q is i min Indicating the minimum power, Q, of cooling of the device i max i Representing the maximum power of heat supply of the equipment i; q i down Representing the maximum ramp rate, Δ Q, of the device i up i Represents the minimum ramp rate of device i;
furthermore, for the supply of the natural gas in the garden, the physical law between the air pressure and the power flow corresponding to the natural gas transmission network also needs to be considered, so that the constraint condition of the natural gas network capacity of the garden can be determined according to the natural gas network capacity data of the garden; the constraint conditions of the natural gas network capacity of the park are as follows:
wherein PQ min,J Indicates the upper limit of the flow rate that the pipe l can withstand, PQ max,l Represents the lower limit of the sustainable flow of the pipeline l; cl (iii) l.y The safe fluctuation coefficient of the pipeline is set;
furthermore, the energy supply and the win demand of the park are in a balanced state, so that the energy is ensured not to be wasted to the maximum extent, the investment cost is saved, and therefore, the energy supply and demand constraint condition of the park can be determined according to the energy supply and demand data of the park; wherein, the park energy supply and demand constraint conditions are as follows:
E s (t)＝E load (t)
wherein E is s (t) is the supply of various energy sources in the park, and the unit is kW and E load (t) is the demand of the user for various energy sources;
furthermore, for new equipment added at any time in a park, the energy supply safety of a power system cannot be influenced by the new equipment, so that the reliability constraint of the system operation safety needs to be considered, and the park energy supply reliability constraint condition can be determined according to the park energy supply reliability data; wherein, park energy supply reliability constraint condition is:
ΔL b s ≤ΔL max
wherein, Δ L max The upper limit of electric energy consumption of the park;
furthermore, new equipment is added to improve energy supply, meanwhile, the transmission capability of different branches in a transmission network is considered to ensure that energy can be transmitted to a user side for use, and a park energy network transmission force constraint condition is determined according to the park energy network transmission force data; the constraint conditions of the transmission power of the park energy network are as follows:
wherein, V i,t For the voltage of grid node i at time t, P i E Is the active power of the grid node i,
the maximum value of the active power of the grid node i,
and the minimum value of the active power of the grid node i.
Wherein the content of the first and second substances,
the highest temperature that can be borne at the node i when the heat supply network normally transmits heat energy,
the maximum and minimum temperatures that can be sustained at node i when the heat network is normally transmitting heat energy,
the maximum flow rate of the heat medium which can be borne at the node i when the heat supply network normally transmits heat energy,
the maximum value of the heat transfer power which can be borne by the pipe sections i-j when the heat supply network normally transmits heat energy,
the minimum value of the heat transfer power which can be borne by the pipe sections i-j when the heat supply network normally transfers heat energy is obtained.
Furthermore, the storage battery capacity of the park energy system also influences the working efficiency of the system, so that the park energy storage battery constraint condition can be determined according to the park energy storage battery data; wherein the park energy storage battery conditions are:
wherein, SOC (t) 0 ) Respectively representing the residual storage capacity of the battery at a certain moment; δ represents a self-discharge loss rate of the battery system; SOC min ,，SOC max A minimum security constraint and a maximum security constraint for remaining storage capacity; p ch_e,max ，P dis_e,max Rated maximum charge and discharge power;
furthermore, because a large amount of energy-consuming equipment exists in the park and a large amount of heat energy is generated in the working process of the energy-consuming equipment, the capacity constraint condition of the park heat storage tank can be determined according to the capacity data of the park heat storage tank; wherein, the constraint conditions of the capacity of the heat storage tank in the park are as follows:
wherein Q is TS (t) represents the amount of heat remaining stored in the heat storage tank at time t; mu.s hloss The self-heat dissipation loss rate of the heat storage tank is represented; q TS (t 0 ) Denotes the initial t 0 The heat stored in the heat storage tank at any moment;
indicates from time t to time t 0 The stored heat of the heat storage tank;
indicating from time t to time t 0 The heat released by the heat storage tank;
the ratios of the maximum allowable stored heat amount, the minimum allowable stored heat amount and the stored heat capacity are respectively; c TS Is the heat storage capacity;
furthermore, the capacity constraint condition of the park ice storage tank can be determined according to the capacity data of the park ice storage tank; wherein, the capacity constraint conditions of the ice storage tank in the park are as follows:
CES min ≤CES(t)≤CES max
wherein, CES max 、CES min Is a maximum and minimum constraint on ice storage tank capacity, Q cesin,min 、Q cesin,max Is the minimum and maximum value of the ice storage power, Q cesout,min 、Q cesout,max Is the minimum of ice melting powerA value and a maximum value.
It should be noted that the campus optimization constraint condition provided by the present application may be any one of the above-mentioned campus building area constraint condition, power supply constraint condition of a power grid of a campus, power supply constraint condition of energy supply equipment of a campus, natural gas network capacity constraint condition of a campus, energy supply and demand constraint condition of a campus, reliability constraint condition of energy supply of a campus, transmission power constraint condition of an energy network of a campus, capacity constraint condition of a heat storage tank of a campus, and capacity constraint condition of an ice storage tank of a campus, or may be a set of all constraint conditions.
And S102, determining a campus optimization comprehensive objective function according to the campus optimization objective function and the campus optimization constraint conditions.
In this embodiment of the present application, on the premise of the campus optimization objective function and all the constraint conditions in step S101, a campus optimization comprehensive objective function is determined according to the campus optimization objective function and the campus optimization constraint conditions, where the campus optimization comprehensive objective function may be expressed as:
in the specific implementation, in the optimal configuration process of the campus comprehensive energy system, the optimal solution of the campus optimization objective function can be obtained only by simultaneously meeting the campus optimization objective function and the campus optimization constraint condition.
Step S103, inputting the park optimization comprehensive objective function into a pre-constructed simulated annealing model, and determining the optimal solution of the park optimization objective function.
Simulated annealing comes from the metallurgical proper term annealing. Annealing is the heating of the material followed by cooling at a specific rate in order to increase the volume of the grains and to reduce defects in the crystal lattice. Atoms in the material will stay in a position where the internal energy has a local minimum, heating will increase the energy, and the atoms will leave the original position and move randomly in other positions. Annealing cools at a slower rate so that the atoms are more likely to find a lower internal energy than they were originally.
Simulated annealing (SAA) is a general probabilistic algorithm for finding the optimal solution of a topic in a large search space.
In general, the global search capability of simulated annealing algorithms is achieved by mutations, which are random changes in population members. However, in the improved simulated annealing algorithm applied by the simulated annealing model proposed in the present application, diversity is achieved by generating new, random individuals in each generation of population, and by performing local search in simulated annealing.
In specific implementation, an initial objective function solution set is randomly generated according to the park information; the initial objective function solution set comprises a plurality of initial objective function solutions, and the initial objective function solution set is used as an initial population of the model input.
Further, determining an initial fitness ranking of a plurality of initial objective function solutions according to the campus optimization objective function, specifically, matching the plurality of initial objective function solutions with the campus optimization comprehensive objective function to obtain a plurality of function solution fitness values corresponding to the plurality of initial objective function solutions, where the fitness values refer to a matching degree of an initial objective function solution in an initial population with the campus optimization objective function and a matching relative ability of the initial objective function solution with the campus optimization objective function in a next execution round. The larger the fitness value is, the higher the matching rate of the fitness value with the campus optimization comprehensive objective function is.
Specifically, the initial population K is generated through a certain random mechanism, and the characteristics of the initial population K mainly comprise a random position and an initial moving speed; assuming that there are N individuals in the population, each having a decision variable of i dimensions (i.e., the installed capacity of the capacity expansion device), the genetic characteristics of each population of individuals can be expressed as:
wherein, X N Is the location of the individuals of the initial population, V N The moving speed of the individuals in the initial population.
Further, sorting the function solution fitness values according to a preset rule to obtain the initial fitness sorting. Specifically, the initial objective function solutions may be sorted in the order from high to low of the fitness value, or may be sorted according to other rules according to actual needs.
Further, determining an optimal solution of the campus optimization objective function in the plurality of iterative objective function solutions according to the initial fitness ranking.
As an optional embodiment, the simulated annealing operation is executed according to the initial fitness ranking, and the iterative fitness ranking of a plurality of initial objective function solutions is determined, where the simulated annealing operation can be understood as fully utilizing the properties and characteristics of the solution problem, and the specific steps are as follows:
referring to fig. 2, a schematic diagram of an execution flow of a simulated annealing model according to an embodiment of the present application is shown.
Step S201, the randomly selected pattern of genetic factors of the starting population is closed.
In particular implementation, one of the differences between the present application and the prior art is that the search speed of the optimal solution is increased by selecting some specific objective function solutions for propagation, while randomness and blindness are avoided to some extent, so that a mode of random selection of genetic factors of the initial objective function solution needs to be turned off.
Step S202, calculating the fitness of the generated individuals based on the objective function, sequencing the fitness, and carrying out evolution operation on excellent individuals in the initial population based on an improved simulated annealing algorithm.
In a specific implementation, the improved simulated annealing algorithm can be expressed as:
wherein P represents probability density, exp (-) represents an exponential function with a natural number E as a base, k is Boltzmann constant, T is temperature, E is i Representing the energy in the i-th state, E j Representing the energy in the j-th state.
And step S203, calculating the fitness value of the generated new generation individuals based on the generated new generation individuals, and combining the fitness value with the previous generation population to generate a new generation population.
In particular implementations, to avoid randomness and blindness, the present application determines a new generation of individuals based on fitness values of each individual in the initial population.
Step S204, responding to the situation that the population fitness value obtained by the simulated annealing operation is larger than the population fitness value before the simulated annealing operation is executed, receiving the result of the simulated annealing operation,
if the fitness function value of this resulting solution is lower than the previous best solution, it may be selected, or it may be randomly selected based on a probabilistic acceptance function to prevent local optimality. Here, the probability of accepting the differential solution decreases with decreasing temperature, i.e., the temperature T increases after each annealing round, and the entire solution becomes stable as the number of cycles increases.
Further, a cross program and a variation program are executed according to the iterative fitness sequence, and an iterative objective function solution set is determined; wherein the set of iterative objective function solutions comprises a number of iterative objective function solutions.
In particular, crossover programs as well as mutation programs can combine attributes of two different parent solutions to generate a new population. Generally, in the genetic algorithm mechanism, the wheel disk selection is used for determining which two parents are combined to form a new generation individual, wherein the execution formula of the cross program is as follows:
wherein f is i r Is an individual V i max The probability of being selected is determined by the probability of being selected,
is the probability of inheritance and crossover.
As an optional embodiment, the simulated annealing operation is performed according to the iterative fitness ranking, and an optimal solution of the campus optimization objective function in the plurality of iterative objective function solutions is determined.
Specifically, according to the iterative fitness ranking, a plurality of iterative objective function solutions are matched with the campus optimization comprehensive objective function, the iterative objective function solution with the maximum fitness in the iterative objective function solutions is determined, and the optimal solution of the campus optimization objective function is determined according to the iterative objective function solution with the maximum fitness.
It should be noted that the optimal solution is a final result calculated through continuous iteration, and in a specific implementation, the optimal solution can be obtained through iteration calculation according to the actual situation of the park equipment.
From the above, according to the optimal configuration method of the park integrated energy system, the park optimization objective function and the park optimization constraint condition are determined according to the actual information of the park, the park optimization integrated objective function is determined according to the park optimization objective function and the park optimization constraint condition, so that the comprehensive optimization purpose of combining the optimization objective of the park and the optimization condition to be considered in the park optimization process is achieved, further, the park optimization integrated objective function is input into the pre-constructed simulated annealing model, the optimal solution of the park optimization objective function is determined, the optimal solution of the park optimization objective function is sought in an algorithm form by utilizing the pre-constructed simulated annealing model, the optimal solution of the park integrated energy system is determined according to the optimal solution of the park optimization objective function, the accuracy of the optimal configuration scheme of the park integrated energy system is guaranteed, and the efficiency of the park optimization process is improved.
It should be noted that the method of the embodiment of the present application may be executed by a single device, such as a computer or a server. The method of the embodiment of the application can also be applied to a distributed scene and is completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment, and the multiple devices interact with each other to complete the method.
It should be noted that the above-mentioned description describes specific embodiments of the present application. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Based on the same conception, the application also provides a device for optimizing and configuring the park comprehensive energy system.
Referring to fig. 3, a schematic diagram of an optimal configuration apparatus for a campus integrated energy system according to an embodiment of the present application is shown.
The device includes: a goal determination module 301, a constraint determination module 302, a simulated annealing module 303, and an output module 304.
The target determining module 301 is configured to determine a campus optimization target function and a campus optimization constraint condition according to pre-acquired campus information;
a constraint determining module 302 configured to determine a campus optimization comprehensive objective function according to the campus optimization objective function and the campus optimization constraint condition;
a simulated annealing module 303, configured to input the campus optimization comprehensive objective function into a simulated annealing model constructed in advance, and determine an optimal solution of the campus optimization objective function;
and the output module 304 is configured to determine a campus comprehensive energy system optimization configuration scheme according to the optimal solution of the campus optimization objective function.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functions of the modules may be implemented in the same or multiple software and/or hardware when implementing the embodiments of the present application.
The device in the foregoing embodiment is used to implement the corresponding optimal configuration method for the campus comprehensive energy system in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described again here.
Based on the same concept, corresponding to the method of any embodiment, the application further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the optimal configuration method of the campus integrated energy system according to any embodiment is implemented.
Fig. 4 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present specification.
The Memory 1020 may be implemented in the form of a ROM (Read, only, memory), a RAM (Random, access, random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only the components necessary to implement the embodiments of the present disclosure, and need not include all of the components shown in the figures.
The electronic device of the above embodiment is used to implement the corresponding optimal configuration method for the campus integrated energy system in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same concept, the present application also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the optimal configuration method for a campus integrated energy system according to any of the above embodiments, corresponding to any of the above embodiments.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
The storage medium of the above embodiments stores computer instructions for causing the computer to execute the optimal configuration method for the campus integrated energy system according to any of the above embodiments, and has the beneficial effects of corresponding method embodiments, which are not described herein again.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the context of the present application, features from the above embodiments or from different embodiments may also be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of brevity.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures for simplicity of illustration and discussion, and so as not to obscure the embodiments of the application. Furthermore, devices may be shown in block diagram form in order to avoid obscuring embodiments of the application, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the embodiments of the application are to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the application, it should be apparent to one skilled in the art that the embodiments of the application can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present application has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures, such as Dynamic RAM (DRAM), may use the discussed embodiments.
The present embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of the embodiments of the present application are intended to be included within the scope of the present application.
Claims (10)
1. A park integrated energy system optimal configuration method is characterized by comprising the following steps:
determining a park optimization objective function and a park optimization constraint condition according to pre-acquired park information;
determining a park optimization comprehensive objective function according to the park optimization objective function and the park optimization constraint condition;
inputting the park optimization comprehensive objective function into a pre-constructed simulated annealing model, and determining the optimal solution of the park optimization objective function;
and determining the optimal configuration scheme of the park comprehensive energy system according to the optimal solution of the park optimization objective function.
2. The method of claim 1, wherein the campus information comprises: park total cost information and park comprehensive energy efficiency information;
the method for determining the park optimization objective function according to the pre-acquired park information comprises the following steps:
determining a first objective function which minimizes the total cost of the park according to the total cost information of the park; wherein the first objective function is:
F 1 ＝min[f in (x)+f op (p)+f mc (p)+f ce (p)]
wherein, F 1 Is a first objective function, f in (x) Is the initial investment cost of the park integrated energy system, and x is the installed capacity of each device of the park integrated energy system; f. of op (p) annual operating costs of the park energy system over the lifetime, f mc (p) annual maintenance costs for the campus complex energy system; f. of ce (p) annual carbon emission costs for the campus complex energy system; p is the effective power of each device of the park integrated energy system;
determining a second objective function which enables the comprehensive energy efficiency of the park to be maximum according to the comprehensive energy efficiency information of the park; wherein the second objective function is:
wherein, P e For the electric energy input of a district complex energy system, P g Inputting gas energy for a park comprehensive energy system; l is e Total electrical load for garden users, L h Total heat load for campus users, L c Total cooling load for campus users, S e Actual remaining stored energy, S, for the batteries of the park' S integrated energy system h Actual surplus of heat storage tanks for a park integrated energy systemResidual stored energy, S c Storing the actual residual energy of the ice storage tank of the park comprehensive energy system; d e Actual release of energy, D, for the batteries of the park Integrated energy System h For the actual release of energy from the heat storage tanks of the park complex energy system, D c For the actual release of energy, lambda, from the ice storage tanks of the district's complex energy system E 、λ G 、λ H And λ C Energy coefficients of electricity, natural gas, heat and cold energy are respectively; lambda [ alpha ] e 、λ h And λ c Energy coefficients of electric, thermal and cold loads, respectively;
determining the campus optimization objective function according to the first objective function and the second objective function; wherein the campus optimization objective function is:
3. the method of claim 1, wherein the campus information further comprises any one of: the system comprises park building area data, park power grid energy supply data, park energy supply equipment power data, park natural gas network capacity data, park energy supply and demand data, park energy supply reliability data, park energy network transmission power data, park energy storage battery data, park heat storage tank capacity data and park ice storage tank capacity data;
the determining the campus optimization constraint conditions according to the pre-acquired campus information comprises the following steps:
determining a park building area constraint condition according to the park building area data; wherein, the park building area constraint conditions are as follows:
wherein m is i Indicating the area of the park occupied by the installation equipment i, AZ max Show installationThe usable park building area of the backup device i;
or determining the park power grid energy supply constraint condition according to the park power grid energy supply data; the park power grid energy supply constraint conditions are as follows:
wherein D is max Representing the maximum power supply capacity, P, of the park network line or park substation equipment max i Indicating the power consumed by the device i, U max i Represents the generated power of the plant i, L q max Representing the power load designed in the park power grid, and S representing the safety power utilization coefficient of the park power grid;
or determining the power constraint condition of the park energy supply equipment according to the power data of the park energy supply equipment; wherein, the power constraint conditions of the park energy supply equipment are as follows:
wherein Q is i min Indicating the minimum power, Q, of cooling of the device i max i Representing the maximum power of heat supply of the equipment i; q i down Representing the maximum ramp rate, Δ Q, of the device i up i Represents the minimum ramp rate of device i;
or determining the constraint condition of the natural gas network capacity of the park according to the natural gas network capacity data of the park; wherein, the constraint conditions of the natural gas network capacity of the garden are as follows:
wherein PQ min,J Indicates the upper limit of the flow rate that the pipe l can withstand, PQ max,l Represents the lower limit of the sustainable flow of the pipeline l; cl is l.y The pipeline safety fluctuation coefficient is obtained;
or determining the park energy supply and demand constraint condition according to the park energy supply and demand data; wherein, the park energy supply and demand constraint conditions are as follows:
E s (t)＝E load (t)
wherein E is s (t) is the supply of various energy sources in kW and E load (t) is the demand of the user for various energy sources;
or determining the park energy supply reliability constraint condition according to the park energy supply reliability data; wherein, park energy supply reliability constraint condition is:
ΔL b s ≤ΔL max
wherein, Δ L max The upper limit of the electric energy consumption of the park;
or determining a park energy network transmission force constraint condition according to the park energy network transmission force data; the park energy network transmission force constraint conditions are as follows:
or determining the constraint conditions of the park energy storage battery according to the data of the park energy storage battery; wherein the park energy storage battery conditions are as follows:
wherein, SOC (t) 0 ) Respectively representing the residual storage capacity of the battery at a certain moment; δ represents a self-discharge loss rate of the battery system; SOC min ，SOC max Minimum and maximum security constraints for remaining storage capacity；P ch_e,max ，P dis_e,max Rated maximum charge and discharge power;
or determining the capacity constraint condition of the park heat storage tank according to the capacity data of the park heat storage tank; wherein, the constraint conditions of the capacity of the heat storage tank in the park are as follows:
wherein Q is TS (t) represents the amount of heat remaining stored in the heat storage tank at time t; mu.s hloss The self-heat dissipation loss rate of the heat storage tank is represented; q TS (t 0 ) Represents the initial t 0 The heat stored in the heat storage tank at all times;
indicating from time t to time t 0 The stored heat of the heat storage tank;
indicates from time t to time t 0 The heat released by the heat storage tank;
the ratios of the maximum allowable stored heat amount, the minimum allowable stored heat amount and the stored heat capacity are respectively; c TS Is the heat storage capacity;
or determining the capacity constraint condition of the park ice storage tank according to the capacity data of the park ice storage tank; wherein, the capacity constraint conditions of the ice storage tank in the park are as follows:
CES min ≤CES(t)≤CES max
wherein, CES max 、CES min Is a maximum and minimum constraint on ice storage tank capacity, Q cesin,min 、Q cesin,max Is the minimum and maximum value of the ice storage power, Q cesout,min 、Q cesout,max Are the minimum and maximum values of ice melting power.
4. The method of claim 1, wherein inputting the campus optimization comprehensive objective function into a pre-constructed simulated annealing model, and determining an optimal solution for the campus optimization objective function, comprises:
randomly generating an initial objective function solution set according to the park information; wherein the initial objective function solution set comprises a number of initial objective function solutions;
determining an initial fitness ranking of the plurality of initial objective function solutions according to the campus optimization objective function;
and determining the optimal solution of the campus optimization objective function in the iterative objective function solutions according to the initial fitness ranking.
5. The method of claim 4, wherein determining the optimal solution for the campus optimization objective function of the plurality of iterative objective function solutions according to the initial fitness ranking comprises:
performing simulated annealing operation according to the initial fitness ranking, and determining iterative fitness ranking of the plurality of initial objective function solutions;
sequencing and executing a cross program and a variation program according to the iteration fitness to determine an iteration objective function solution set; wherein the set of iterative objective function solutions comprises a number of iterative objective function solutions;
and executing simulated annealing operation according to the iterative fitness sequence, and determining the optimal solution of the park optimization objective function in the iterative objective function solutions.
6. The method of claim 4, wherein determining a fitness ranking of the plurality of initial objective function solutions according to the campus optimization objective function comprises:
matching the initial objective function solutions with the campus optimization comprehensive objective function to obtain a plurality of function solution adaptability values corresponding to the initial objective function solutions;
and sequencing the function solution fitness values according to a preset rule to obtain the initial fitness sequencing.
7. The method of claim 5, wherein said performing a simulated annealing operation according to said iterative fitness metric ordering to determine an optimal solution for said campus optimization objective function of said plurality of iterative objective function solutions comprises:
according to the ranking according to the iterative fitness, matching the iterative objective function solutions with the campus optimization comprehensive objective function, and determining the iterative objective function solution with the maximum fitness in the iterative objective function solutions;
and determining the optimal solution of the park optimization objective function according to the iteration objective function solution with the maximum fitness.
8. An optimal configuration device for a park integrated energy system, comprising:
the system comprises a target determining module, a data processing module and a data processing module, wherein the target determining module is configured to determine a park optimization target function and a park optimization constraint condition according to pre-acquired park information;
a constraint determining module configured to determine a campus optimization comprehensive objective function according to the campus optimization objective function and the campus optimization constraint condition;
the simulated annealing module is configured to input the park optimization comprehensive objective function into a pre-constructed simulated annealing model and determine the optimal solution of the park optimization objective function;
and the output module is configured to determine the optimal configuration scheme of the park comprehensive energy system according to the optimal solution of the park optimization objective function.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the program.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 7.
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| CN117332991B (en) * | 2023-11-24 | 2024-03-19 | 国网天津市电力公司电力科学研究院 | System energy efficiency optimization method and device based on comprehensive energy demand |
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