CN112364556A - Intelligent energy optimization configuration method based on multi-energy complementation and terminal equipment - Google Patents
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Abstract
The invention is suitable for the technical field of energy, and provides a smart energy optimal configuration method based on multi-energy complementation and terminal equipment, wherein the method comprises the following steps: acquiring equipment parameters of each equipment in an energy supply system, and establishing an equipment physical model to determine an electrical parameter interval value of each equipment operation; constructing a target function which takes the lowest total cost of an energy supply system as a target and meets a preset constraint condition according to the electrical parameter interval value of each device operation; according to the objective function, an optimization model of the intelligent energy system planning is constructed, the energy load requirement can be met, the energy complementation characteristic is considered, and a multi-energy complementation-based intelligent energy optimization model is established by taking the system economy and the environmental protection performance as the objective function, so that the problems that the multi-energy complementation facility is incomplete and the utilization rate of energy is low in the prior art can be solved.
Description
Technical Field
The invention belongs to the technical field of energy, and particularly relates to an intelligent energy optimization configuration method based on multi-energy complementation and terminal equipment.
Background
The energy industry, as a key factor for supporting activities such as daily life consumption and industrial production, has the characteristics of large energy demand, multiple energy types, large energy structure difference, rapid energy flow change and the like. Most of the existing energy production structures are independent of each other and operate independently, so that the energy conversion loss is serious easily, the systems are sparse in coupling relation with each other, and the structure is single. At the energy consumption end, the huge energy demand and the rapid change of energy consumption modes continuously impact the structure of the existing energy system, and great challenges are brought to the stability and the safety of the energy system.
The multi-energy complementary technology is an expansion on the traditional energy, and because the traditional power generation mode has certain limitation and the thermal power generation has huge environmental pollution, the multi-energy complementary system enables the application of distributed energy to be expanded from points to surfaces, and the multi-energy complementary technology adopts clean energy, so that the environmental pollution is almost zero. The core of the multi-energy complementary system is that cooperation and cooperation of each energy system are realized by optimizing aspects of energy production, transmission, storage, management, use and the like on the premise of considering the stable operation of the system, the utilization efficiency of energy is improved by an integrated method, and the production cost is reduced. However, at present, there are differences in the construction of some power supply infrastructures, conditions for developing multi-energy complementation are not particularly sufficient, especially multi-energy complementation facilities are not perfect, so that the utilization rate of energy sources is not high, which is contrary to the original purpose of developing multi-energy complementation, and the method has no economic benefit and is not environment-friendly.
Disclosure of Invention
In view of this, embodiments of the present invention provide an intelligent energy optimal configuration method and terminal device based on multi-energy complementation, and aim to solve the problems in the prior art that facilities of multi-energy complementation are incomplete and the utilization rate of energy is not high.
In order to achieve the above object, a first aspect of the embodiments of the present invention provides a smart energy optimization configuration method based on multi-energy complementation, including:
acquiring equipment parameters of each equipment in an energy supply system, and establishing an equipment physical model to determine an electrical parameter interval value of each equipment operation;
constructing a target function which takes the lowest total cost of an energy supply system as a target and meets a preset constraint condition according to the electrical parameter interval value of each device operation;
and constructing an optimization model of the intelligent energy system planning according to the objective function.
As another embodiment of the present application, after the constructing the optimization model of the smart energy system plan, the method further includes:
and establishing a multi-energy complementary evaluation index model based on the energy supply side parameters and the demand side parameters, and evaluating the optimization model.
As another embodiment of the present application, the device physical model includes: a cogeneration unit model, an electric boiler model, a gas boiler model and an energy storage model;
the combined heat and power generation unit model comprises
Wherein the content of the first and second substances,
indicates the natural gas power interval value, [ Q ] consumed by the micro-combustion engine at time tMGT(t)]±Indicating the natural gas flow rate interval, L, consumed by the micro-combustion engine at time tGCVIndicating the natural gas lower heating value at time t, deltat indicating the scheduled time,
represents the electric power interval value [ eta ] of the micro-combustion engine output at the time tMGT]±The value of the interval of the power generation efficiency is expressed,
represents the interval value of the residual heat power of the high-temperature flue gas at the moment t, [ eta ]q]±Which represents the efficiency of the waste heat transfer loss,
represents the thermal power interval value output by the waste heat boiler at the time t,
the heating coefficient of the waste heat boiler is shown,
indicates the recovery rate of flue gas, [ H ]HRB(t)]±Representing the heat interval value output by the waste heat boiler at the moment t;
the electric boiler model is
Wherein the content of the first and second substances,
represents the thermal power interval value output by the electric boiler at the time t,
represents the electric power interval value, [ eta ] consumed by the electric boiler at the time tEB]±Represents the interval value of electric heat conversion efficiency of the electric boiler, [ H ]EB(t)]±Representing the final output heat interval value of the electric boiler at the time t;
the gas boiler model is
Wherein the content of the first and second substances,
representing the value of the natural gas power interval consumed by the gas boiler at time t,
a value representing the natural gas flow interval consumed by the gas boiler at time t,
represents the thermal power interval value output by the gas boiler at the time t, [ eta ]GB]±Represents a gas-heat conversion efficiency interval value, [ H ]GB(t)]±The heat interval value which represents the final output of the gas boiler at the time t;
the energy storage model is
Wherein E isele(t) represents the amount of electricity stored by the electrical energy storage device at time t, Eele(t-1) represents the amount of electricity stored by the electrical energy storage device at time (t-1),
it is shown that the efficiency of the charging is,
representing the charging power of the electrical energy storage at time t,
which represents the discharge power at the time t,
indicating the discharge efficiency, Hheat(t) represents the amount of heat stored in the thermal energy store at time t, Hheat(t-1) represents the amount of heat stored by the thermal energy storage at the time (t-1),
indicating the efficiency of heat absorption
Represents the heat absorption power of the thermal energy storage at time t,
representing the heat-release power at time t,
showing the efficiency of heat release, Ggas(t) represents the amount of gas stored in the gas storage at time t, Ggas(t-1) represents the amount of gas stored in the gas storage tank at time (t-1),
the efficiency of the inflation is shown as,
representing the charge power of the intake charge energy storage at time t,
indicating the bleed power at the time t,
indicating the efficiency of the bleed.
As another embodiment of the present application, the objective function is
Wherein C represents the total cost, CinvRepresenting annual investment costs, CFuelRepresents the fuel cost, CrunRepresenting annual operating costs, CgridIndicating the cost of electricity purchase and sale, CEnvRepresents an environmental cost; zetaqRepresenting the equivalent annual coefficient of the plant q, Hs,qRepresenting the installed capacity of the device q in the energy station s,
represents the construction cost per unit capacity of the plant q, ζrRepresenting the equivalent annual coefficient, L, of the r-th functional lines,kRepresenting the length of the pipe between the energy station s and the energy station k, alpharThe investment and construction cost of unit length and unit capacity of the energy supply pipeline is shown, subscript r belongs to { c, h, e }, and c, h, e respectively represent cold energy, hot energy and electric energy, phir,s,kRepresents the installation capacity, beta, of the r-th energy supply line between the energy station s and the energy station krCost coefficient, gamma, of the r-th functional line for building a unit length liner,s,kRepresenting installation factors corresponding to installation capacities of the r-th energy supply line between the energy station s and the energy station k; p is a radical ofi,s,qRepresenting the work output of a device q in a power station s at time tThe ratio of the total weight of the particles,
represents the operating costs of the plant q in the energy station s at time t; pigasRepresenting the price of natural gas, uLHVThe heat value of the combustion of the natural gas is shown,
represents the gas consumption of the plant s in the energy station j at time t;
representing the power purchase of the energy station s with the upper level grid at time t,
indicating the purchase price of electricity at time t,
representing the selling power of the energy station s and the upper level power grid at time t,
represents the electricity selling price at the time t; piCtaxDenotes a carbon tax, EburnIndicating CO emitted by burning natural gas2Carbon emission intensity of (a); egridIndicating CO emitted by outsourcing electric energy2Carbon emission intensity of (a); etagridRepresenting the grid transmission efficiency.
As another embodiment of the present application, the preset constraint condition includes: the system comprises equipment, equipment model constraint conditions, energy supply line constraint conditions, electricity purchasing and selling constraint conditions and system energy balance constraint conditions, wherein the equipment and the line planning constraint conditions among the equipment are adopted;
the constraint conditions of each device and the line planning among the devices are
Wherein x iss,qRepresenting arrangements in energy stations sAnd the mounting factor of the device q is prepared,
upper and lower limit values, I, respectively representing the installation capacity of the equipment q in the energy station ss,qA value, phi, representing the installation capacity of the equipment q in the energy station sr,s,kRepresenting the installed capacity, x, of the r-th supply line between the energy station s and the energy station kr,s,kRepresenting the installation factor of the r-th supply line between the energy station s and the energy station k,
respectively representing an upper limit value and a lower limit value of installation capacity of an r-th energy supply line between the energy station s and the energy station k;
the constraint condition of the equipment model is
Wherein, yt,s,qRepresenting the operational factor of the equipment in the energy station s,
respectively representing the upper and lower limits of the output power of a device q in the energy station s, Ot,s,qRepresenting the output power, p, of a device q in an energy station ss,q,min、ρs,q,maxRespectively representing the minimum load rate and the maximum load rate of the device q;
the constraint condition of the energy supply line is
Wherein, yt,r,skIndicating whether the r-th supply line delivers power from station s to station k at time t, yt,r,ksIndicating whether the r-th energy supply line delivers power from energy station k to energy station s at time t,
respectively representing the upper limit value and the lower limit value of the transmission power of the r-th energy supply line between the energy source station s and the energy source station k, qt,r,skRepresenting the power delivered by the r-th energy supply line from energy station s to energy station k at time t, qt,r,ksRepresenting the power, p, delivered by the r-th supply line from station k to station s at time tr,s,k,min、ρr,s,k,maxRespectively representing the minimum load rate, the maximum load rate, omega, of the r-th energy supply line between the energy station s and the energy station kqThe line loss rate is shown in the graph,
respectively representing the electric power and the thermal power transmitted from the energy station s to the energy station k at the head end at the time t, and the electric power and the thermal power received by the energy station k at the tail end;
the restriction conditions for purchasing and selling electricity are
Wherein the content of the first and second substances,
representing the purchased electric power of the energy station s at time t,
representing the power sold at the energy station s at time t,
respectively represent
The upper limit value of the power of (c),
represents the electricity purchasing factor of the utility grid,
representing a power selling factor;
the system energy balance constraint condition is
Wherein the content of the first and second substances,
As another embodiment of the present application, the constructing an optimization model of a smart energy system plan according to the objective function includes:
solving the objective function through a particle swarm algorithm to obtain the optimal equipment configuration of the energy supply system and configuration parameters of all equipment in the optimal equipment configuration;
and constructing an optimization model of the intelligent energy system planning according to the optimal equipment configuration and the configuration parameters of each piece of equipment.
As another embodiment of the present application, the multi-energy complementation evaluation index model includes an energy supply complementation evaluation index model and an energy consumption complementation evaluation index model;
the energy supply complementation evaluation index model comprises: the system comprises a first reliability index model, a first economic index model, a flexibility index model and an environmental protection index model;
the energy-use complementation evaluation index model comprises: a second reliability index model, a second economic index model, a sustainability index model, and a comfort index model.
As another embodiment of the present application, the first reliability index model is
Wherein σtIndicating the loss of load duration ratio, tLLIndicating the duration of the loss of complementary supply, tLLiRepresents the i-th single-energy-supply load loss duration, i is 1 and 2 … N, and N represents a positive integer;
the first economic indicator model is
Wherein σcRepresenting the energy cost ratio, cspRepresents the total cost of the complementary power supply,
represents the cost of the ith single energy supply;
the flexibility index model is
Wherein alpha ismmRepresents the absolute quantity of the output quantity change rate of the m-th form energy source in the (n) th to (n + 1) th sampling time periods, wherein m and n respectively represent positive integers, n is 1, 2 … v-1, v represents a positive integer larger than 1, and Om,n+1、Om,nRespectively represents the output quantity of the m-th form energy source at the n +1 th sampling value and the n-th sampling value, T represents the sampling time interval, A represents betajSet of (2), betajRepresenting the complementary measure, σ, of the various energy sources within a sampling periodfRepresenting the degree of functional complementarity between the energy sources;
the environmental protection index model is
Wherein σeRepresents the pollutant discharge ratio, epRepresents the total pollutant emissions of the complementary energy supply,
indicating the amount of pollutant emissions of the ith single energy supply.
As another embodiment of the present application, the second reliability index model is
Wherein, thetap-vRepresents the ratio of the peak-to-valley difference, muiRepresenting the peak-to-valley difference rate, upsilon, of the energy source of the i form under the condition of complementary energy usekRepresenting the load peak-to-valley difference rate of the kth form of energy source under the condition of non-complementary energy use, and I, K representing the types and the quantity of the energy sources under the conditions of complementary energy use and non-complementary energy use respectively;
the second economic indicator model is
Wherein, thetacThe energy-to-cost ratio is expressed,
representing the total cost of the complementary energy, cdThe cost of the ith energy source is shown, and N is the type and the quantity of the energy source;
the sustainability index model is
Wherein the content of the first and second substances,
indicates the utilization rate of non-renewable energy, Cele,TRepresenting the power consumed by the load during a period T, Cheat,TRepresenting the heat energy consumed by the load during the period T, Cgas,TNatural gas representing the consumption of the load during the period T, Ecoal,TLower calorific value, E, representing the consumption of coal by the system during the period Tgas,TRepresenting the lower heating value of the natural gas consumed by the system during the period T,
representing the utilization rate of the non-renewable energy of the complementary energy,
representing the non-renewable energy utilization, theta, of non-complementary energy usagesA sustainability value representing a complementary performance;
the comfort index model is
Wherein, thetacomfortIndicating the load interruption or transfer ratio,/traniRepresenting the rate of load interruption or transfer of the energy source of the i-th form in the case of complementary use of energy, dtrankIndicating the k-th shape under non-complementary energy useLoad interruption or transfer rate of the energy source.
A second aspect of an embodiment of the present invention provides a terminal device, including: the intelligent energy optimization configuration method based on the multi-energy complementation comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the intelligent energy optimization configuration method based on the multi-energy complementation according to any embodiment.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: compared with the prior art, the method has the advantages that the equipment physical model is established by acquiring the equipment parameters of each equipment in the energy supply system, so as to determine the electric parameter interval value of each equipment operation; constructing a target function which takes the lowest total cost of an energy supply system as a target and meets a preset constraint condition according to the electrical parameter interval value of each device operation; according to the objective function, an optimization model of the intelligent energy system planning is constructed, the energy load requirement can be met, the energy complementation characteristic is considered, and a multi-energy complementation-based intelligent energy optimization model is established by taking the system economy and the environmental protection performance as the objective function, so that the problems that the multi-energy complementation facility is incomplete and the utilization rate of energy is low in the prior art can be solved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart illustrating an implementation of a smart energy optimization configuration method based on multi-energy complementation according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating an implementation of a smart energy optimization configuration method based on multi-energy complementation according to another embodiment of the present invention;
FIG. 3 is a schematic illustration of an annual electrical load demand curve provided by an embodiment of the present invention;
FIG. 4 is an exemplary graph of an annual heat load demand curve provided by an embodiment of the present invention;
FIG. 5 is an exemplary graph of an annual cooling load demand curve provided by an embodiment of the present invention;
fig. 6 is a schematic energy flow diagram of an integrated energy system according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 1 is a schematic flow chart of an implementation of a smart energy optimization configuration method based on multi-energy complementation according to an embodiment of the present invention, which is described in detail below.
Optionally, the device physical model in this step includes: the system comprises a cogeneration unit model, an electric boiler model, a gas boiler model and an energy storage model. Constructing an equipment physical model, and calculating a natural gas power interval and an electric power interval of the cogeneration unit; a thermal power interval and a thermal energy interval of the electric boiler; the natural gas power interval, the thermal power interval and the heat interval value of the gas-fired boiler; the electric quantity, the heat quantity and the gas quantity stored by the energy storage model. Among them, factors such as electric power, thermal power and natural gas power are involved. The finally constructed objective function considers economy and environmental protection, wherein annual operation cost, fuel cost and electricity purchasing cost all relate to factors such as electricity, heat, gas power and the like of equipment; the constraint conditions also include equipment model constraint, electricity purchasing and selling constraint, system energy balance constraint and the like, and all the constraint conditions are calculated by taking the interval of the output power of the equipment as default premise. Therefore, the establishment of the physical model of the equipment and the calculation of the reasonable electric, thermal and pneumatic power interval values of the equipment are the basis of the target function and the constraint condition of the subsequent establishment model.
Optionally, the model of the cogeneration unit is
Wherein the content of the first and second substances,
indicates the natural gas power interval value, [ Q ] consumed by the micro-combustion engine at time tMGT(t)]±Indicating the natural gas flow rate interval, L, consumed by the micro-combustion engine at time tGCVThe low heat value of the natural gas at the time t is shown, and the value is generally 9.7 kW.h/m3And Δ t represents a scheduled time,
represents the electric power interval value [ eta ] of the micro-combustion engine output at the time tMGT]±The value of the interval of the power generation efficiency is expressed,
represents the interval value of the residual heat power of the high-temperature flue gas at the moment t, [ eta ]q]±Which represents the efficiency of the waste heat transfer loss,
represents the thermal power interval value output by the waste heat boiler at the time t,
the heating coefficient of the waste heat boiler is shown,
indicates the recovery rate of flue gas, [ H ]HRB(t)]±Indicating the waste heat boiler at time tThe heat output by the furnace.
Optionally, the electric boiler model is
Wherein the content of the first and second substances,
represents the thermal power interval value output by the electric boiler at the time t,
represents the electric power interval value, [ eta ] consumed by the electric boiler at the time tEB]±Represents the interval value of electric heat conversion efficiency of the electric boiler, [ H ]EB(t)]±Which represents the value of the interval of heat finally output by the electric boiler at time t.
Optionally, the gas boiler model is
Wherein the content of the first and second substances,
denotes the interval value of natural gas power consumed by the gas boiler at time t, [ Q ]GB(t)]±A value representing the natural gas flow interval consumed by the gas boiler at time t,
represents the thermal power interval value output by the gas boiler at the time t, [ eta ]GB]±Represents a gas-heat conversion efficiency interval value, [ H ]GB(t)]±Which represents the value of the interval of heat finally output by the gas boiler at time t.
Optionally, the energy storage model is
Wherein E isele(t) represents the amount of electricity stored by the electrical energy storage device at time t, Eele(t-1) represents the amount of electricity stored by the electrical energy storage device at time (t-1),
it is shown that the efficiency of the charging is,
representing the charging power of the electrical energy storage at time t,
which represents the discharge power at the time t,
indicating the discharge efficiency, Hheat(t) represents the amount of heat stored in the thermal energy store at time t, Hheat(t-1) represents the amount of heat stored by the thermal energy storage at the time (t-1),
indicating the efficiency of heat absorption
Represents the heat absorption power of the thermal energy storage at time t,
representing the heat-release power at time t,
showing the efficiency of heat release, Ggas(t) represents the amount of gas stored in the gas storage at time t, Ggas(t-1) represents the amount of gas stored in the gas storage tank at time (t-1),
the efficiency of the inflation is shown as,
representing the charge power of the intake charge energy storage at time t,
indicating the bleed power at the time t,
indicating the efficiency of the bleed.
The method comprises the steps of constructing an equipment physical model for setting a reasonable equipment operation interval value, and because the equipment operation is influenced by various factors, the equipment operation is difficult to keep a stable and unchangeable operation state, constructing the equipment model with a certain interval value, selecting an optimal solution in a small dynamic range, and being the basis of the equipment operation state when an optimization model is subsequently constructed and solved. The reasonable intervals of all parameters of the equipment can be formed by constructing the physical model of the equipment, and the condition that the final calculation result is not accordant is avoided.
And 102, constructing a target function which aims at the lowest total cost of the energy supply system and meets preset constraint conditions according to the electrical parameter interval value of each device operation.
In this embodiment, the optimization model of the intelligent energy system plan is constructed in consideration of the economy and environmental protection of the integrated energy system, so the objective function in this step is set to target the minimum sum of the annual investment cost, fuel cost, operation cost, electricity purchasing cost and environmental cost of energy production, conversion equipment and energy supply network.
Optionally, the objective function is
Wherein C represents the total cost, CinvRepresenting annual investment costs, CFuelRepresents the fuel cost, CrunRepresenting annual operating costs, CgridIndicating the cost of electricity purchase and sale, CEnvRepresents an environmental cost; zetaqRepresenting the equivalent annual coefficient of the plant q, Hs,qRepresenting the installed capacity of the device q in the energy station s,
represents the construction cost per unit capacity of the plant q, ζrRepresenting the equivalent annual coefficient, L, of the r-th functional lines,kRepresenting the length of the pipeline alpha between the energy station s and the energy station krThe investment and construction cost of unit length and unit capacity of the energy supply pipeline is shown, subscript r belongs to { c, h, e }, and c, h, e respectively represent cold energy, hot energy and electric energy, phir,s,kRepresents the installation capacity of the r-th energy supply line between the energy station s and the energy station k, here the installation capacity of the grid and the heat supply network, respectively, betarCost coefficient, gamma, of the r-th functional line for building a unit length liner,s,kRepresenting installation factors corresponding to installation capacities of the r-th energy supply line between the energy station s and the energy station k; p is a radical ofi,s,qRepresenting the output power of the device q in the energy station s at time t,
represents the operating costs of the plant q in the energy station s at time t; pigasRepresenting the price of natural gas, uLHVThe heat value of the combustion of the natural gas is shown,
represents the gas consumption of the plant s in the energy station j at time t;
representing the power purchase of the energy station s with the upper level grid at time t,
indicating the purchase price of electricity at time t,
representing the selling power of the energy station s and the upper level power grid at time t,
represents the electricity selling price at the time t; piCtaxDenotes a carbon tax, EburnIndicating CO emitted by burning natural gas2Carbon emission intensity of (a); egridIndicating CO emitted by outsourcing electric energy2Carbon emission intensity of (a); etagridRepresenting the grid transmission efficiency.
Optionally, the objective function needs to be satisfied under a certain constraint condition, so the preset constraint condition includes: and the equipment and the line between the equipment are planned and constrained, and the equipment is modeled and constrained, and the equipment is powered and sold.
Optionally, the constraint condition of route planning among the devices and the devices is
Wherein x iss,qA variable representing the installation factor of a device q in the energy station s, whose value is 0 or 1, indicates whether the device is installed or not. Note that 0 may denote non-installation apparatus q and 1 may denote installation apparatus q, or 1 may denote non-installation apparatus q and 0 may denote installation apparatus q.
Upper and lower limit values, I, respectively representing the installation capacity of the equipment q in the energy station ss,qA value, phi, representing the installation capacity of the equipment q in the energy station sr,s,kRepresenting the installed capacity, x, of the r-th supply line between the energy station s and the energy station kr,s,kRepresenting the installation factor of the r-th supply line between the energy station s and the energy station k,
the upper limit value and the lower limit value of the installation capacity of the r-th energy supply line between the energy station s and the energy station k are respectively shown.
Optionally, the constraint condition of the equipment model is
Wherein, yt,s,qA variable representing an operation factor of the equipment in the energy station s, whose value may be 0 or 1, represents whether the equipment in the energy station s is operating or not.
Respectively representing the upper and lower limits of the output power of a device q in the energy station s, Ot,s,qRepresenting the output power, p, of a device q in an energy station ss,q,min、ρs,q,maxRespectively representing the minimum load rate and the maximum load rate of the device q.
Optionally, the constraint condition of the energy supply line is
Wherein, yt,r,skRepresenting an operating factor, a variable having a value of 0 or 1, yt,r,skIt can be provided that at time t the r-th energy supply line delivers power from energy station s to energy station k, yt,r,skIs 1, otherwise is 0, yt,r,ksIt can be provided that at time t the r-th energy supply line delivers power from energy station k to energy station s, yt,r,ksIs 1, otherwise is 0.
Respectively representing the upper limit value and the lower limit value of the transmission power of the r-th energy supply line between the energy source station s and the energy source station k, qt,r,skRepresenting the power delivered by the r-th energy supply line from energy station s to energy station k at time t, qt,r,ksRepresenting the power, p, delivered by the r-th supply line from station k to station s at time tr,s,k,min、ρr,s,k,maxRespectively representing the minimum load rate, the maximum load rate, omega, of the r-th energy supply line between the energy station s and the energy station kqThe line loss rate is shown in the graph,
respectively represents the electric power and the thermal power transmitted from the energy station s to the energy station k at the head end and the electric power and the thermal power received by the energy station k at the tail end at the time t.
Optionally, the restriction condition of electricity purchase and sale is
Wherein the content of the first and second substances,
representing the purchased electric power of the energy station s at time t,
representing the power sold at the energy station s at time t,
respectively represent
The upper limit value of the power of (c),
represents the electricity purchasing factor of the utility grid,
indicating a power selling factor.
And ensuring that electricity purchasing and selling can not be carried out at the same time in each time period t.
Optionally, the constraint condition of energy balance of the system is
Wherein, the first of the three formulas in the system energy balance constraint condition represents an electric power balance constraint condition, the second represents a thermal power balance constraint condition, and the third represents a cold power balance constraint condition.
The objective function and the constraint condition for constructing the optimization model are to construct a model which meets a certain constraint condition by taking the lowest total cost of the intelligent energy system as a target. And (3) constructing a mathematical model containing influencing factors according to the variables influencing the total cost of the system, the efficiency of the equipment and the like, such as the power of the equipment, the capacity of the equipment and the like, and determining the optimal parameters of each component of the system by calculating a specific objective function. The optimization model is a model algorithm tool for obtaining the optimal configuration of each device of a certain intelligent energy system, and the direct effect of constructing the optimization model is to obtain the optimal device configuration of the intelligent energy system and specific parameters of each device.
And 103, constructing an optimization model of the intelligent energy system planning according to the objective function.
Optionally, this step may include: solving the objective function through a particle swarm algorithm to obtain the optimal equipment configuration of the energy supply system and configuration parameters of all equipment in the optimal equipment configuration; and constructing an optimization model of the intelligent energy system planning according to the optimal equipment configuration and the configuration parameters of each piece of equipment.
Optionally, the optimization model of the integrated energy system in this embodiment is solved by a particle swarm algorithm, the algorithm is derived from analysis of biological predation behaviors, and the population can be rapidly optimized by collective division of labor cooperation and information sharing among animals. The problem solution to be solved in the model can be represented as particles, and for the particles, searching is carried out in an N-dimensional space, the characteristics of the speed and the position of each particle are given, and the final adjusting direction is obtained through self information and comparison with other particles.
When a particle swarm algorithm is used, the particle swarm algorithm includes the velocity, position variables, and fitness function of the particle. In the operation scene, the fitness value function of the intelligent energy system is set to be the reciprocal of the annual total cost
I.e., when the total cost is minimized, the fitness value is maximized, the position variable of the particle corresponds to the constraint variable in the system (including the installation capacity of the equipment and the line, the output power of each equipment, the transmission power of the functional line, the power for purchase and sale, etc.), the speed variable of the particle corresponds to the variation value of the variable, and the position of the particle can be expressed as (x)1,x2,…,xm) The particle velocity is expressed as (v)1,v2,…,vm) And m represents that the intelligent energy system has m-dimensional variables. In the iteration process, the control variable information corresponding to the current particles is updated in each generation, and the superiority of the current optimization parameters is reflected through the objective function values and the fitness values.
Optionally, as shown in fig. 2, the intelligent energy optimization configuration method based on multi-energy complementation further includes:
and 104, establishing a multi-energy complementary evaluation index model based on the energy supply side parameters and the demand side parameters, and evaluating the optimization model.
Optionally, the construction of the multi-energy complementary rating index is used as an evaluation criterion for evaluating the effect of the optimization model. The complementary evaluation index is used for evaluating whether the constructed model can reach the optimization target on the basis of successfully constructing the model objective function and the constraint condition. The evaluation factors of the economical efficiency and flexibility indexes in the energy supply complementary evaluation indexes respectively comprise cost and energy output factors in the optimization model; economic and sustainable indexes which can complement evaluation indexes respectively comprise cost and consumption factors of electricity, heat and gas in the optimization model. Therefore, the multi-energy complementary evaluation index model is a model for quantifying the optimization degree of the optimization model after the optimization model is constructed.
Optionally, the energy supply complementation evaluation index model includes: the system comprises a first reliability index model, a first economic index model, a flexibility index model and an environmental protection index model;
the energy-use complementation evaluation index model comprises: a second reliability index model, a second economic index model, a sustainability index model, and a comfort index model.
Optionally, in the present embodiment, the loss load duration ratio is selected to describe the effect of complementary energization on system reliability from a temporal perspective. The unloading duration ratio refers to the ratio of the total unloading duration of the complementary supplies to the sum of the unloading durations of the single supplies. The first reliability index model is
Wherein σtRepresenting the ratio of the duration of the lost load, σtThe smaller the value, the higher the reliability of the complementary energization, and the better the complementary performance. t is tLLIndicating complementary energy suppliesDuration of lost load, tLLiRepresents the i-th single-energy-supply load loss duration, i is 1 and 2 … N, and N represents a positive integer.
Alternatively, the energy cost ratio is selected in the present embodiment to describe the impact of complementary energy supply on system economy. The energy supply cost ratio refers to the ratio of the total cost of complementary energy supply to the sum of the cost of single energy supply, and the first economic indicator model is
Wherein σcThe smaller the value of the energy cost ratio, the higher the economical efficiency of complementary energy supply, and the better the complementary performance. c. CspRepresents the total cost of the complementary power supply,
representing the cost of the ith single energy supply.
Optionally, the flexibility index model is
Wherein alpha ismmRepresents the absolute quantity of the output quantity change rate of the m-th form energy source in the (n) th to (n + 1) th sampling time periods, wherein m and n respectively represent positive integers, n is 1, 2 … v-1, v represents a positive integer larger than 1, and Om,n+1、Om,nRespectively represents the output quantity of the m-th form energy source at the n +1 th sampling value and the n-th sampling value, T represents the sampling time interval, A represents betajSet of (2), betajRepresenting the complementary measures of the various energy sources over the sampling period.
When beta isjWhen the output quantity of each energy source is equal to 0, the change rates of the output quantities of the energy sources just offset each other, and the energy sources in various forms realize complete complementary energy supply; when beta isj>At 0, there is an un-offset portion in the rate of change of each energy output, where βjShowing interaction of multiple energy sources during the sampling periodAnd (5) complementing degree.
σfRepresenting the degree of functional complementarity, σ, between the energy sourcesfThe closer to 0, the more the variation of each energy output quantity is mutually offset, the stronger the complementarity, and the stronger the flexibility of complementary energy supply; otherwise, σfThe larger the magnitude of the change in the output of the respective energy sources, the less complementary the greater the flexibility of the complementary energy supply.
Optionally, in this embodiment, the pollutant discharge ratio is selected to describe the influence of complementary energy supply on the environmental protection of the system operation. The pollutant discharge ratio refers to the ratio of the total pollutant discharge amount of complementary energy supply to the sum of the pollutant discharge amount of single energy supply, and the environmental protection index model is
Wherein σeRepresents the pollutant emission ratio, the value range of the pollutant emission ratio is 0 to 1, the smaller the value, the stronger the environmental protection performance of complementary energy supply, the better the complementarity, epRepresents the total pollutant emissions of the complementary energy supply,
indicating the amount of pollutant emissions of the ith single energy supply.
Optionally, in this embodiment, the peak-to-valley ratio is selected to describe the effect of the complementary energy on the system reliability. The peak-to-valley rate ratio refers to the ratio of the peak-to-valley rate of the average load of the complementary energy to the peak-to-valley rate of the average load of the non-complementary energy. Wherein the peak-to-valley difference rate refers to a ratio of the peak-to-valley difference to the highest load.
The second reliability index model is
Wherein, thetap-vThe peak-to-valley ratio is represented by a value range of 0 to 1, and the smaller the value, the higher the reliability of the complementation energy and the complementarityThe better, muiRepresenting the peak-to-valley difference rate, upsilon, of the energy source of the i form under the condition of complementary energy usekRepresenting the peak-to-valley load difference rate of the kth form of energy source in the case of non-complementary use of energy, I, K representing the number of types of energy source in the case of complementary use of energy and non-complementary use of energy, respectively.
Optionally, in this embodiment, the energy-to-cost ratio is selected to describe the impact of complementary energy on system economy. The energy cost ratio refers to the ratio of the total cost of complementary energy to the sum of the cost of non-complementary energy, and the second economic indicator model is
Wherein, thetacThe energy cost ratio is expressed, and the value ranges from 0 to 1, and the smaller the value, the higher the economy of the energy for complementation is, the better the complementarity is.
Representing the total cost of the complementary energy, cdRepresents the cost of the ith energy source, and N represents the number of types of energy sources.
Optionally, a ratio of the non-renewable energy utilization rate of the complementary energy usage to the non-renewable energy utilization rate of the non-complementary energy usage, that is, the non-renewable energy utilization ratio is selected to describe an influence of the complementary energy usage on the system sustainability, and the sustainability index model is
Wherein the content of the first and second substances,
indicates the utilization rate of non-renewable energy, Cele,TRepresenting the power consumed by the load during a period T, Cheat,TRepresenting the heat energy consumed by the load during the period T, Cgas,TNatural gas representing the consumption of the load during the period T, Ecoal,TIndicating system consumption during the T periodLower calorific value of coal, Egas,TRepresenting the lower heating value of the natural gas consumed by the system during the period T,
representing the utilization rate of the non-renewable energy of the complementary energy,
representing the non-renewable energy utilization, theta, of non-complementary energy usagesA sustainability value representing the complementation energy ranges from 1 to infinity, with a larger value indicating a higher sustainability of the complementation energy, the better the complementation.
Optionally, the load break or transfer ratio is selected to describe the impact of complementary energy on user comfort. The load break or transfer ratio refers to the ratio of the average load break or transfer rate of the complementary energy to the average load break or transfer rate of the non-complementary energy. The comfort index model is
Wherein, thetacomfortRepresenting a load break or transfer ratio, the value of which ranges from 0 to 1, the smaller the value, the more comfortable the complementation energy is, the better the complementarity. ltraniRepresenting the rate of load interruption or transfer of the energy source of the i-th form in the case of complementary use of energy, dtrankRepresenting the rate of load interruption or transfer of the energy source of the kth form in the case of non-complementary use of energy.
The method is characterized in that a multi-energy complementary evaluation index model is constructed, various optimization aspects of an optimized intelligent energy system planning optimization model are specifically quantized into a data form, starting from the aspects of cost, operation efficiency, pollutant emission and the like, and the advantages and disadvantages of different performance indexes are visually represented by the size of data. The multi-energy complementary evaluation index model is constructed to form a complete system for evaluating the optimization degree of the constructed optimization model of the intelligent energy system planning.
According to the intelligent energy optimization configuration method based on multi-energy complementation, the equipment physical model is established by obtaining the equipment parameters of each equipment in the energy supply system, so as to determine the electrical parameter interval value of each equipment operation; constructing a target function which takes the lowest total cost of an energy supply system as a target and meets a preset constraint condition according to the electrical parameter interval value of each device operation; and constructing an optimization model of the intelligent energy system planning according to the objective function. The invention can ensure that the energy load demand is met, considers the energy complementary characteristic, and establishes the intelligent energy optimization configuration method based on the multi-energy complementation by taking the system economy and the environmental protection as the objective function, thereby solving the problems of incomplete multi-energy complementary facilities and low utilization rate of energy in the prior art, realizing the multi-angle requirements of the comprehensive energy system on the economy, the environment and the reliability, reducing the unit area heating unit price, saving the energy cost of a user, and preferentially selecting the scene with lower carbon emission while ensuring the economy not to be damaged, so that the ecological environment protection, the energy conservation and the emission reduction are realized.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The following uses a certain park as an example to perform an exemplary description of the intelligent energy optimization configuration method based on multi-energy complementation. A certain park is positioned in the west of Jilin province, and the total area is 506.84km2. 27307 general population in the whole area, 888.21km total width of the audience2. Total floor area of 100000.00m of Chagan lake transfer center and auxiliary matched infrastructure engineering construction project2Wherein: building floor area 9702.00m2Road and hardened area 21253.7m2Greening area 10000.00m2Parking area 59044.22m2And 1370 parking spaces are arranged. Project new construction 1 transfer center with building area 38808.00m2And corresponding ancillary facilities.
And carrying out simulation on the basis of the annual electric load data, and planning and setting a scene as a simulated residential group project. The overall power demand of the residential group project is 500-1800 kW, the load peak may reach 2000-2400 kW, the load valley may be about 300kW, and the power load demand is shown in figure 3. The residential group project occupies about 31100 square meters of floor space and 46600 square meters of building space. The annual heat load fluctuation is increased, the maximum heat load is 1 month, 2 months, 11 months and 12 months, and the whole heat load is 2500kW-4800 kW; the heat load is less in summer, mainly is the hot water load, and is about 100kW, and the heat load demand is shown in the attached figure 4. The cooling load demand is derived from the actual cooling area data and the unit cooling load of the provided residential group project as shown in fig. 5.
The equipment with higher adaptation degree to residential projects of park centers and affiliated matching infrastructure residences is selected, and comprises a photovoltaic system, a fan, an energy storage battery, a gas boiler, a heat storage electric boiler, a heat storage tank, a double-working-condition ice cold storage unit and the like, and three ways of a power grid, a heat supply network and a cold supply network are selected for energy transportation.
And analyzing resources and terrains in the garden, and setting the upper limit of installed capacity of a fan to 1000kW and the upper limit of installed capacity of a photovoltaic to 1000 kW. Due to lack of alternative equipment data, the method can measure and calculate in kilowatt units, and set the charge-discharge power and capacity ratio of the energy storage and heat storage equipment as 1: 4, and setting the range of the available energy storage capacity to be 10-95%. The heat accumulating type electric boiler and the double-working-condition ice cold accumulation machine set in the planning park enjoy double-accumulation price of electricity, and the prices of other equipment and electricity purchase are conventional time-of-use prices of electricity. Wherein, the physical model of the key device may include:
firstly, a fan; the fan mainly comprises a wind-time part, a current collector, a shutter, a windowing mechanism, a motor, a belt pulley, an air inlet cover, an inner frame, a volute and the like, wherein the motor drives the fan blades to rotate when the fan is started, the windowing mechanism opens the shutter to exhaust air, and the shutter is automatically closed when the fan is stopped.
Secondly, a photovoltaic power generation device; the core component of photovoltaic power generation is a battery plate, which converts solar energy into electric energy and stores the generated electric energy in a storage battery through a controller.
Thirdly, an energy storage battery; the energy storage battery model can comprise a battery management unit, a power supply unit and a lithium battery energy storage module unit, wherein the battery management unit is respectively connected with the battery unit and the lithium battery energy storage module unit, and the battery power supply and the lithium battery energy storage module unit are managed.
Optionally, as shown in the energy flow diagram of the comprehensive energy system shown in fig. 6, according to the requirements of regional users, a multi-energy complementary smart energy system of "photovoltaic + fan + energy storage battery + power grid + heat storage type electric boiler + gas-fired boiler + dual-operating-condition ice storage" is set. In fig. 6, photovoltaic power generation, wind turbine power generation, and grid power generation are respectively used for battery charging and discharging, an electric refrigerator, an electric boiler, and other devices through an electric hub, thereby obtaining an electric load, a heat load, and a cold load that can be used by a user, and meanwhile, natural gas can be combusted through a gas boiler to obtain heat energy, which is further converted into a heat load that can be used by a user.
It should be noted that, in order to achieve the lowest total cost of the energy supply system, in the heat system, if the time period is a valley price period, the gas boiler is preferentially used for supplying heat, and the electric boiler is used for storing heat for the heat storage tank; if the peak price period is reached, the heat storage tank is used for supplying heat preferentially, and then the heat source is a gas boiler and an electric boiler respectively. In the cold system, if the time is in the valley price period, the electric refrigerating unit is preferentially used for supplying cold and storing ice for the ice storage tank; if the time is in the peak price period, the ice storage tank supplies cold preferentially, and the electric refrigerating unit supplements. In the electric system, the energy storage battery is charged in the valley period, and if the photovoltaic and the wind power cannot meet the electric load and the charging requirement, electricity is purchased from the power grid. And the peak period preferably uses the energy storage battery to meet the residual load, and if the residual load is insufficient, the power is purchased to the power grid.
The output result obtained according to the constraint condition and the objective function established by the model is as follows: the distributed equipment which can be installed in the residential group project under the scene mainly comprises a fan, a photovoltaic, an energy storage battery, a gas boiler, a heat accumulation type electric boiler and double-working-condition ice storage. Based on the existing data analysis, the measurement and calculation of installed capacity of each device are as follows: the installed capacity of the fan is 983kW, the installed capacity of the photovoltaic is 991kW, the installed capacity of the energy storage battery is 100kW, the installed capacity of the gas boiler is 4679kW, the installed capacity of the heat accumulation type electric boiler is 977kW, the installed capacity of the heat accumulation tank is 2242kW, the installed capacity of the electric refrigerating unit is 5470kW, and the installed capacity of the ice storage tank is 1357 kW.
Under the mode of grid connection and no network connection of electric energy, the annual total cost is 1148.74 ten thousand yuan, and the annual investment is 256.78 ten thousand yuan; the annual carbon emission is 7012253.35 kg; the net present value is 72.10 ten thousand yuan which is far greater than 0; when the heating price is lower than two yuan of standard price, namely 26 yuan per square meter, the internal yield is 8.67 percent and exceeds the standard yield by 8 percent; the investment recovery period is 9.78 years.
Therefore, the intelligent energy optimal configuration method based on multi-energy complementation can well realize the operation scheduling of the park energy. By introducing the intelligent energy system planning optimization model, the multi-angle requirements of the operation of the comprehensive energy system on economy, environment and reliability are met, the unit area heating unit price is reduced, and the energy consumption cost of a user is saved. Meanwhile, the scenes with low carbon emission are preferably selected while the economy is not damaged. The energy utilization scheme has the advantages of ecological environmental protection, energy conservation and emission reduction.
Fig. 7 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 7, the terminal device 700 of this embodiment includes: a processor 701, a memory 702, and a computer program 703, such as a smart energy optimization configuration program based on multi-energy complementation, stored in the memory 702 and executable on the processor 701. The processor 701 executes the computer program 703 to implement the steps of the aforementioned smart energy optimization configuration method based on multi-energy complementation, such as steps 101 to 103 shown in fig. 1 or steps 101 to 104 shown in fig. 2.
Illustratively, the computer program 703 may be partitioned into one or more program modules, which are stored in the memory 702 and executed by the processor 701 to implement the present invention.
The terminal device 700 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 701, a memory 702. Those skilled in the art will appreciate that fig. 7 is merely an example of a terminal device 700 and does not constitute a limitation of terminal device 700 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 701 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 702 may be an internal storage unit of the terminal device 700, such as a hard disk or a memory of the terminal device 700. The memory 702 may also be an external storage device of the terminal device 700, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the terminal device 700. Further, the memory 702 may also include both an internal storage unit and an external storage device of the terminal device 700. The memory 702 is used for storing the computer programs and other programs and data required by the terminal device 700. The memory 702 may also be used to temporarily store data that has been output or is to be output.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A smart energy optimization configuration method based on multi-energy complementation is characterized by comprising the following steps:
acquiring equipment parameters of each equipment in an energy supply system, and establishing an equipment physical model to determine an electrical parameter interval value of each equipment operation;
constructing a target function which takes the lowest total cost of an energy supply system as a target and meets a preset constraint condition according to the electrical parameter interval value of each device operation;
and constructing an optimization model of the intelligent energy system planning according to the objective function.
2. The smart energy optimization configuration method based on multi-energy complementation according to claim 1, wherein after the constructing the optimization model of the smart energy system plan, the method further comprises:
and establishing a multi-energy complementary evaluation index model based on the energy supply side parameters and the demand side parameters, and evaluating the optimization model.
3. The intelligent energy optimization configuration method based on multi-energy complementation according to claim 1, wherein the equipment physical model comprises: a cogeneration unit model, an electric boiler model, a gas boiler model and an energy storage model;
the combined heat and power generation unit model comprises
Wherein the content of the first and second substances,
indicates the natural gas power interval value, [ Q ] consumed by the micro-combustion engine at time tMGT(t)]±Indicating the natural gas flow rate interval, L, consumed by the micro-combustion engine at time tGCVIndicating the natural gas lower heating value at time t, deltat indicating the scheduled time,
represents the electric power interval value [ eta ] of the micro-combustion engine output at the time tMGT]±The value of the interval of the power generation efficiency is expressed,
represents the interval value of the residual heat power of the high-temperature flue gas at the moment t, [ eta ]q]±Which represents the efficiency of the waste heat transfer loss,
represents the thermal power interval value output by the waste heat boiler at the time t,
the heating coefficient of the waste heat boiler is shown,
indicates the recovery rate of flue gas, [ H ]HRB(t)]±Representing the heat interval value output by the waste heat boiler at the moment t;
the electric boiler model is
Wherein the content of the first and second substances,
represents the thermal power interval value output by the electric boiler at the time t,
represents the electric power interval value, [ eta ] consumed by the electric boiler at the time tEB]±Represents the interval value of electric heat conversion efficiency of the electric boiler, [ H ]EB(t)]±Representing the final output heat interval value of the electric boiler at the time t;
the gas boiler model is
Wherein the content of the first and second substances,
denotes the interval value of natural gas power consumed by the gas boiler at time t, [ Q ]GB(t)]±A value representing the natural gas flow interval consumed by the gas boiler at time t,
represents the thermal power interval value output by the gas boiler at the time t, [ eta ]GB]±Represents a gas-heat conversion efficiency interval value, [ H ]GB(t)]±The heat interval value which represents the final output of the gas boiler at the time t;
the energy storage model is
Wherein E isele(t) represents the amount of electricity stored by the electrical energy storage device at time t, Eele(t-1) represents the amount of electricity stored by the electrical energy storage device at time (t-1),
it is shown that the efficiency of the charging is,
representing the charging power of the electrical energy storage at time t,
which represents the discharge power at the time t,
indicating the discharge efficiency, Hheat(t) represents the stored thermal energy at time tHeat of (H)heat(t-1) represents the amount of heat stored by the thermal energy storage at the time (t-1),
indicating the efficiency of heat absorption
Represents the heat absorption power of the thermal energy storage at time t,
representing the heat-release power at time t,
showing the efficiency of heat release, Ggas(t) represents the amount of gas stored in the gas storage at time t, Ggas(t-1) represents the amount of gas stored in the gas storage tank at time (t-1),
the efficiency of the inflation is shown as,
representing the charge power of the intake charge energy storage at time t,
indicating the bleed power at the time t,
indicating the efficiency of the bleed.
4. The intelligent energy optimization configuration method based on multi-energy complementation according to any one of the claims 1-3, wherein the objective function is
Wherein C represents the total cost, CinvRepresenting annual investment costs, CFuelRepresents the fuel cost, CrunRepresenting annual operating costs, CgridIndicating the cost of electricity purchase and sale, CEnvRepresents an environmental cost; zetaqRepresenting the equivalent annual coefficient of the plant q, Hs,qRepresenting the installed capacity of the device q in the energy station s,
represents the construction cost per unit capacity of the plant q, ζrRepresenting the equivalent annual coefficient, L, of the r-th functional lines,kRepresenting the length of the pipe between the energy station s and the energy station k, alpharThe investment and construction cost of unit length and unit capacity of the energy supply pipeline is shown, subscript r belongs to { c, h, e }, and c, h, e respectively represent cold energy, hot energy and electric energy, phir,s,kRepresents the installation capacity, beta, of the r-th energy supply line between the energy station s and the energy station krCost coefficient, gamma, of the r-th functional line for building a unit length liner,s,kRepresenting installation factors corresponding to installation capacities of the r-th energy supply line between the energy station s and the energy station k; p is a radical ofi,s,qRepresenting the output power of the device q in the energy station s at time t,
represents the operating costs of the plant q in the energy station s at time t; pigasRepresenting the price of natural gas, uLHVThe heat value of the combustion of the natural gas is shown,
represents the gas consumption of the plant s in the energy station j at time t;
representing the power purchase of the energy station s with the upper level grid at time t,
indicating at time tThe price of the electricity purchased is as follows,
representing the selling power of the energy station s and the upper level power grid at time t,
represents the electricity selling price at the time t; piCtaxDenotes a carbon tax, EburnIndicating CO emitted by burning natural gas2Carbon emission intensity of (a); egridIndicating CO emitted by outsourcing electric energy2Carbon emission intensity of (a); etagridRepresenting the grid transmission efficiency.
5. The intelligent energy optimization configuration method based on multi-energy complementation according to claim 4, wherein the preset constraints comprise: the system comprises equipment, equipment model constraint conditions, energy supply line constraint conditions, electricity purchasing and selling constraint conditions and system energy balance constraint conditions, wherein the equipment and the line planning constraint conditions among the equipment are adopted;
the constraint conditions of each device and the line planning among the devices are
Wherein x iss,qRepresenting the installation factor of the device q in the energy station s,
upper and lower limit values, I, respectively representing the installation capacity of the equipment q in the energy station ss,qA value, phi, representing the installation capacity of the equipment q in the energy station sr,s,kRepresenting the installed capacity, x, of the r-th supply line between the energy station s and the energy station kr,s,kRepresenting the installation factor of the r-th supply line between the energy station s and the energy station k,
respectively represent energyThe upper limit value and the lower limit value of the installation capacity of the r-th energy supply line between the source station s and the energy source station k;
the constraint condition of the equipment model is
Wherein, yt,s,qRepresenting the operational factor of the equipment in the energy station s,
respectively representing the upper and lower limits of the output power of a device q in the energy station s, Ot,s,qRepresenting the output power, p, of a device q in an energy station ss,q,min、ρs,q,maxRespectively representing the minimum load rate and the maximum load rate of the device q;
the constraint condition of the energy supply line is
Wherein, yt,r,skIndicating whether the r-th supply line delivers power from station s to station k at time t, yt,r,ksIndicating whether the r-th energy supply line delivers power from energy station k to energy station s at time t,
respectively representing the upper limit value and the lower limit value of the transmission power of the r-th energy supply line between the energy source station s and the energy source station k, qt,r,skRepresenting the power delivered by the r-th energy supply line from energy station s to energy station k at time t, qt,r,ksRepresenting the power, p, delivered by the r-th supply line from station k to station s at time tr,s,k,min、ρr,s,k,maxRespectively representing the minimum load rate, the maximum load rate, omega, of the r-th energy supply line between the energy station s and the energy station kqThe line loss rate is shown in the graph,
respectively representing the electric power and the thermal power transmitted from the energy station s to the energy station k at the head end at the time t, and the electric power and the thermal power received by the energy station k at the tail end;
the restriction conditions for purchasing and selling electricity are
Wherein the content of the first and second substances,
representing the purchased electric power of the energy station s at time t,
representing the power sold at the energy station s at time t,
respectively represent
The upper limit value of the power of (c),
represents the electricity purchasing factor of the utility grid,
representing a power selling factor;
the system energy balance constraint condition is
Wherein the content of the first and second substances,
6. The smart energy optimization configuration method based on multi-energy complementation according to any one of the claims 1-3, wherein the constructing an optimization model of smart energy system planning according to the objective function comprises:
solving the objective function through a particle swarm algorithm to obtain the optimal equipment configuration of the energy supply system and configuration parameters of all equipment in the optimal equipment configuration;
and constructing an optimization model of the intelligent energy system planning according to the optimal equipment configuration and the configuration parameters of each piece of equipment.
7. The intelligent energy optimization configuration method based on multi-energy complementation, according to claim 2, wherein the multi-energy complementation evaluation index model comprises an energy complementation evaluation index model and an energy complementation evaluation index model;
the energy supply complementation evaluation index model comprises: the system comprises a first reliability index model, a first economic index model, a flexibility index model and an environmental protection index model;
the energy-use complementation evaluation index model comprises: a second reliability index model, a second economic index model, a sustainability index model, and a comfort index model.
8. The method according to claim 7, wherein the first reliability index model is
Wherein σtIndicating the loss of load duration ratio, tLLIndicating the duration of the loss of load of the complementary supply,
represents the i-th single-energy-supply load loss duration, i is 1 and 2 … N, and N represents a positive integer;
the first economic indicator model is
Wherein σcRepresenting the energy cost ratio, cspRepresents the total cost of the complementary power supply,
represents the cost of the ith single energy supply;
the flexibility index model is
Wherein alpha ismmRepresents the absolute quantity of the output quantity change rate of the m-th form energy source in the (n) th to (n + 1) th sampling time periods, wherein m and n respectively represent positive integers, n is 1, 2 … v-1, v represents a positive integer larger than 1, and Om,n+1、Om,nRespectively represents the output quantity of the m-th form energy source at the n +1 th sampling value and the n-th sampling value, T represents the sampling time interval, A represents betajSet of (2), betajRepresenting the complementary measure, σ, of the various energy sources within a sampling periodfRepresenting the degree of functional complementarity between the energy sources;
the environmental protection index model is
Wherein σeRepresents the pollutant discharge ratio, epRepresents the total pollutant emissions of the complementary energy supply,
indicating the amount of pollutant emissions of the ith single energy supply.
9. The method of claim 7, wherein the second reliability index model is
Wherein, thetap-vRepresents the ratio of the peak-to-valley difference, muiRepresenting the peak-to-valley difference rate, upsilon, of the energy source of the i form under the condition of complementary energy usekRepresenting the peak-to-valley load difference rate of the energy source of the kth form in the case of non-complementary use of energy, I, K representing the peak-to-valley load difference rate in the case of complementary use of energy and the case of non-complementary use of energy, respectivelyThe number of types of energy;
the second economic indicator model is
Wherein, thetacThe energy-to-cost ratio is expressed,
representing the total cost of the complementary energy, cdThe cost of the ith energy source is shown, and N is the type and the quantity of the energy source;
the sustainability index model is
Wherein the content of the first and second substances,
indicates the utilization rate of non-renewable energy, Cele,TRepresenting the power consumed by the load during a period T, Cheat,TRepresenting the heat energy consumed by the load during the period T, Cgas,TNatural gas representing the consumption of the load during the period T, Ecoal,TLower calorific value, E, representing the consumption of coal by the system during the period Tgas,TRepresenting the lower heating value of the natural gas consumed by the system during the period T,
representing the utilization rate of the non-renewable energy of the complementary energy,
representing the non-renewable energy utilization, theta, of non-complementary energy usagesA sustainability value representing a complementary performance;
the comfort index model is
Wherein, thetacomfortIndicating the load interruption or transfer ratio,/traniRepresenting the rate of load interruption or transfer of the energy source of the i-th form in the case of complementary use of energy, dtrankRepresenting the rate of load interruption or transfer of the energy source of the kth form in the case of non-complementary use of energy.
10. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 9 when executing the computer program.
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