CN109784573B - Multi-objective optimization method and device for energy Internet - Google Patents
Multi-objective optimization method and device for energy Internet Download PDFInfo
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Abstract
The invention discloses an energy internet multi-objective optimization method, a device, equipment and a computer readable storage medium, wherein the method comprises the following steps: establishing a comprehensive evaluation index system for representing the optimal path selection capacity of the energy network and representing the intercommunication capacity of the energy network; acquiring an actual evaluation parameter matrix according to each evaluation index in the comprehensive evaluation index system, normalizing the actual evaluation parameter matrix to obtain a utility function matrix, and optimizing a decision layer by using the utility function matrix; and coordinating all targets through a utility function matrix, constructing a system layer objective function according to the utility function matrix and all evaluation indexes, and optimizing a system layer by utilizing the system layer objective function and an optimization algorithm, wherein the targets comprise energy network optimal path selection and energy network intercommunication capability. According to the technical scheme, the energy Internet can be better evaluated, and the operation strategy optimization can be performed on the actually operated energy Internet.
Description
Technical Field
The present invention relates to the technical field of energy internet, and more particularly, to a method, an apparatus, a device and a computer readable storage medium for optimizing energy internet with multiple objectives.
Background
The energy internet can be understood as a novel power network, a petroleum network, a natural gas network and other energy nodes which are formed by a large amount of distributed energy acquisition devices, distributed energy storage devices and various loads are interconnected by comprehensively applying advanced power electronic technology, information technology and intelligent management technology so as to realize energy peer-to-peer exchange and sharing network of energy in bidirectional flow.
At present, an energy internet evaluation system mainly focuses on traditional fossil energy sources such as a coal-fired generator set, a gas generator set and a gas triple supply unit, and is mainly evaluated based on a first thermodynamic law and a second thermodynamic law, wherein evaluation indexes are mostly efficiency indexes, an evaluation index system is not established for the energy internet containing renewable energy sources, the energy internet is difficult to comprehensively evaluate from multiple angles, and operation optimization is not performed on the actually operated energy internet, so that planning construction and operation of the energy internet are inconvenient.
By integrating the above, how to better evaluate the energy internet and optimize the operation strategy of the actually operated energy internet is a technical problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the present invention aims to provide a method, a device and a computer readable storage medium for optimizing energy internet multi-objective, so as to better evaluate energy internet and optimize operation strategy of actually operated energy internet.
In order to achieve the above object, the present invention provides the following technical solutions:
an energy internet multi-objective optimization method comprises the following steps:
establishing a comprehensive evaluation index system for representing the optimal path selection capacity of the energy network and representing the intercommunication capacity of the energy network;
acquiring an actual evaluation parameter matrix according to each evaluation index in the comprehensive evaluation index system, normalizing the actual evaluation parameter matrix to obtain a utility function matrix, and optimizing a decision layer by using the utility function matrix;
and coordinating each target through the utility function matrix, constructing a system layer objective function according to the utility function matrix and each evaluation index, and optimizing the system layer by utilizing the system layer objective function and an optimization algorithm, wherein the targets comprise energy network optimal path selection and energy network intercommunication capability.
Preferably, the evaluation indexes in the comprehensive evaluation index system include an evaluation index for representing the optimal path selection capability of the energy network and an evaluation index for representing the intercommunication capability of the energy network, wherein:
The evaluation indexes for representing the optimal path selection capability of the energy network comprise comprehensive utilization rate of energy, non-renewable energy duty ratio, renewable energy consumption rate, and the like,
Efficiency, carbon emission rate, economic benefit, investment cost, energy network loss rate, energy network maximum load utilization hours;
the evaluation indexes for representing the intercommunication capability of the energy network comprise the fault tolerance of the energy network, the error rate of the communication network, the heat generation power ratio, the electric heating quantity ratio and the heat generation quantity ratio.
Preferably, the method includes the steps of obtaining an actual evaluation parameter matrix according to each evaluation index in the comprehensive evaluation index system, and performing normalization processing on the actual evaluation parameter matrix to obtain a utility function matrix, wherein the method includes the following steps:
determining h first-level fingers by AHP-entropy weight methodTarget weight coefficient X h The first-level index is the optimal path selection capability and the energy network intercommunication capability of the energy network;
calculating subjective weights x of j second-level indexes relative to first-level indexes by using AHP hj '：
Constructing the actual evaluation parameter matrix, and performing the evaluation on x hj ' normalization processing: />
Wherein, the comprehensive utilization rate of energy, the duty ratio of non-renewable energy, the consumption rate of renewable energy, economic benefit, the maximum load utilization hour of energy network, the fault tolerance of energy network, the duty ratio of heat generation power, the duty ratio of electric heating quantity and the duty ratio of heat generation quantity are positive indexes, and the energy consumption rate is increased by >
Efficiency, carbon emission rate, investment cost, energy network loss rate and communication network error rate are negative indexes;
weight coefficient X based on first-level index h And normalizing the obtained x hj Establishing the utility function matrix: g hj (x)＝X h ×x hj 。
Preferably, after constructing the actual evaluation parameter matrix, the method further includes:
and carrying out consistency test on the actual evaluation parameter matrix, and reconstructing the actual evaluation parameter matrix if the consistency is not met.
Preferably, a system layer objective function constructed according to the utility function matrix and each evaluation index is:
Preferably, the constraint condition of the system layer objective function includes an electric balance constraint, a thermal balance constraint, a cold balance constraint, a power constraint and a capacity constraint.
Preferably, optimizing the system layer by using the system layer objective function and the optimization algorithm includes:
11-dimensional Shan Lizi P= [ P ] composed of 11 scheduling variables including photovoltaic power generation, wind power generation, gas triple power generation unit power, gas triple power generation unit cooling power, gas triple power generation unit heating power, biomass power generation unit power, gas boiler heating power, outsourcing power, cold storage, heat storage and power storage 1 …P 11 ]，P i For the schedule variable, i=1, 2, … 11;
during optimizing, updating one dimension of the single particle P every time, and substituting the single particle P into the system layer objective function F (x);
comparing the mass of the single particle P before and after the k-th optimizing, and if the mass of the single particle P is improved, making the k+1-th optimizing correct speed
Correction speed equal to k-th optimization +.>
If the mass of the single particle P is not improved, correcting the speed by a correction factor>
Correction is performed, wherein the optimization increment delta P of the ith scheduling variable i k+1 By correction speed->
Random speed->
The composition, k=1, 2, … M-1, M is the prescribed number of optima;
after the optimizing times reach the preset optimizing times, the single particle P converges on the global optimal solution of the system layer objective function to realize the optimization of the system layer.
An energy internet multi-objective optimization device, comprising:
the establishing module is used for: establishing a comprehensive evaluation index system for representing the optimal path selection capacity of the energy network and representing the intercommunication capacity of the energy network;
a first optimization module for: acquiring an actual evaluation parameter matrix according to each index in the comprehensive evaluation index system, normalizing the actual evaluation parameter matrix to obtain a utility function matrix, and optimizing a decision layer by using the utility function matrix;
A second optimization module, configured to: and coordinating each target through the utility function matrix, constructing a system layer objective function according to the utility function matrix and each evaluation index, and optimizing the system layer by utilizing the system layer objective function and an optimization algorithm, wherein the targets comprise energy network optimal path selection and energy network intercommunication capability.
An energy internet multi-objective optimization device comprising:
a memory for storing a computer program;
a processor for implementing the steps of the energy internet multi-objective optimization method according to any one of the above when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the energy internet multi-objective optimization method according to any of the preceding claims.
The invention provides an energy internet multi-objective optimization method, device, equipment and a computer readable storage medium, wherein the method comprises the following steps: establishing a comprehensive evaluation index system for representing the optimal path selection capacity of the energy network and representing the intercommunication capacity of the energy network; acquiring an actual evaluation parameter matrix according to each evaluation index in the comprehensive evaluation index system, normalizing the actual evaluation parameter matrix to obtain a utility function matrix, and optimizing a decision layer by using the utility function matrix; and coordinating all targets through a utility function matrix, constructing a system layer objective function according to the utility function matrix and all evaluation indexes, and optimizing a system layer by utilizing the system layer objective function and an optimization algorithm, wherein the targets comprise energy network optimal path selection and energy network intercommunication capability.
According to the technical scheme, a comprehensive evaluation index system is established from the optimal path selection capability and the energy network intercommunication capability of the energy network, so that the energy Internet containing renewable energy sources is better evaluated by utilizing the comprehensive evaluation index system, an actual evaluation parameter matrix is obtained by utilizing the comprehensive evaluation index system, normalization processing is carried out on the actual evaluation parameter matrix to obtain a utility function matrix, a decision layer is optimized by utilizing the obtained utility function matrix, a system layer objective function is constructed by utilizing the utility function matrix, and then the system layer is optimized by utilizing the system layer objective function and an optimization algorithm, so that double-layer multi-objective optimization is realized, and therefore, operation strategy optimization is realized on the actually operated energy Internet, and a reference basis is provided for construction operation of the energy Internet.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an energy Internet multi-objective optimization method provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of energy conversion of an energy Internet system;
fig. 3 is a schematic structural diagram of an energy internet multi-objective optimization device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an energy internet multi-objective optimization device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of an energy internet multi-objective optimization method provided by an embodiment of the present invention may include:
s11: and establishing a comprehensive evaluation index system for representing the optimal path selection capability of the energy network and representing the intercommunication capability of the energy network.
Specifically, the energy internet has i (i is a positive integer) small-sized energy internet systems, including various non-renewable energy sources such as natural gas, solar energy, wind power, biomass energy and the like, and renewable energy sources. The park mainly comprises photovoltaic power generation, wind power generation, biomass power generation, a gas triple supply unit, a gas boiler, a heat storage/cooling and electricity storage device; the power purchase of the power grid outside the park mainly comprises a gas generator set and a coal-fired generator set. The energy internet park is mainly used for exchanging fuel and electric energy with the outside. Specifically, fig. 2 may be combined, which shows an energy internet system energy efficiency conversion schematic diagram, including a heat supply network, a cold supply network and a power grid, which may satisfy the heat demand, the cold demand and the electric demand of the user, and may internally implement heat refrigeration, electric refrigeration and electric heating.
For the energy Internet containing renewable energy sources, a comprehensive evaluation index system for representing the optimal path selection capability of the energy network and representing the intercommunication capability of the energy network is established, namely, the comprehensive evaluation index system is established from the two aspects of the optimal path selection of the energy network and the intercommunication capability of the energy network, and the established comprehensive evaluation index system not only comprises evaluation indexes for representing the optimal path selection capability of the energy network, but also comprises evaluation indexes for representing the intercommunication capability of the energy network.
The comprehensive evaluation index system established by the method can evaluate the energy Internet better and more comprehensively from multiple aspects.
S12: according to each evaluation index in the comprehensive evaluation index system, an actual evaluation parameter matrix is obtained, normalization processing is carried out on the actual evaluation parameter matrix, a utility function matrix is obtained, and a decision layer is optimized by utilizing the utility function matrix.
After the comprehensive evaluation index system is established, an actual evaluation parameter matrix can be obtained according to the importance of each evaluation index contained in the comprehensive evaluation index system. In order to facilitate subsequent optimization calculation, the obtained actual evaluation parameter matrix can be normalized to obtain a utility function matrix in consideration of incomplete unification of units and the like of each evaluation index in the comprehensive evaluation index system.
And optimizing the decision layer by using the obtained utility function matrix to decide the energy use ratio (heat, cold and electricity distribution ratio).
S13: and coordinating all targets through a utility function matrix, constructing a system layer objective function according to the utility function matrix and all evaluation indexes, and optimizing a system layer by utilizing the system layer objective function and an optimization algorithm, wherein the targets comprise energy network optimal path selection and energy network intercommunication capability.
After the utility function matrix is obtained, the two targets, namely the optimal path selection of the energy network and the intercommunication capability of the energy network, are coordinated through the utility function matrix, namely the consistency of the utility function matrix is checked for each target, so that the consistency of the utility function matrix is ensured.
And then, constructing a system layer objective function according to the utility function matrix and each evaluation index contained in the comprehensive evaluation index system, and optimizing the system layer by utilizing the constructed system layer objective function and an optimization algorithm to determine what channel and what transmission equipment are specifically utilized to transmit and utilize energy.
According to the technical scheme, a comprehensive evaluation index system is established from the optimal path selection capability and the energy network intercommunication capability of the energy network, so that the energy Internet containing renewable energy sources is better evaluated by utilizing the comprehensive evaluation index system, an actual evaluation parameter matrix is obtained by utilizing the comprehensive evaluation index system, normalization processing is carried out on the actual evaluation parameter matrix to obtain a utility function matrix, a decision layer is optimized by utilizing the obtained utility function matrix, a system layer objective function is constructed by utilizing the utility function matrix, and then the system layer is optimized by utilizing the system layer objective function and an optimization algorithm, so that double-layer multi-objective optimization is realized, and therefore, operation strategy optimization is realized on the actually operated energy Internet, and a reference basis is provided for construction operation of the energy Internet.
According to the energy Internet multi-objective optimization method provided by the embodiment of the invention, the evaluation indexes in the comprehensive evaluation index system comprise the evaluation indexes for representing the optimal path selection capacity of the energy network and the evaluation indexes for representing the intercommunication capacity of the energy network, wherein:
the evaluation index for representing the optimal path selection capability of the energy network can comprise the comprehensive utilization rate of energy, the duty ratio of non-renewable energy, the consumption rate of renewable energy, the energy consumption rate of the renewable energy,
Efficiency, carbon emission rate, economic benefit, investment cost, energy network loss rate, energy network maximum load utilization hours;
the evaluation indexes for representing the energy network intercommunication capability can comprise an energy network fault tolerance rate, a communication network error rate, a thermal power generation power ratio, an electric heating quantity ratio and a thermal power generation quantity ratio.
In the comprehensive evaluation index system, the evaluation indexes comprise an evaluation index for representing the optimal path selection capability of the energy network and an evaluation index for representing the intercommunication capability of the energy network: the evaluation indexes for representing the optimal path selection capability of the energy network comprise comprehensive utilization of energyRate eta, non-renewable energy source duty ratio xi and renewable energy source digestion rate R re 、
Efficiency eta ex1 Carbon emission rate v, economic benefit C total Investment cost C cost Energy network loss rate->
Energy network maximum load utilization hours beta d The method comprises the steps of carrying out a first treatment on the surface of the The evaluation index for representing the intercommunication capability of the energy network comprises an energy network fault tolerance delta 1 Error rate delta of communication network 2 Thermal power duty cycle ζ p,qe Electric heating power duty ratio xi p,eq Electric heating quantity duty ratio xi e,q Duty ratio ζ of thermal power generation e,E 。
The comprehensive energy utilization rate eta is the ratio of the total cold, heat and electricity demands to the fossil energy input quantity, namely the comprehensive energy utilization rate of the energy Internet formed by i parks. The evaluation index is used for purchasing energy E of an external power grid grid,T The method is characterized in that renewable energy electric energy, gas power generation and coal power generation are subdivided according to the permeability according to the sources, all the parts are converted to the power generation side according to the power generation efficiency of the corresponding unit, and the influence of the multi-energy flow characteristic of the comprehensive energy system and the renewable energy access is considered.
Specifically, the comprehensive utilization rate of energy
Wherein, the external power grid purchases electric energy
Energy of intra-park energy internet system
The power supply quantity, the cold supply quantity and the heat supply quantity of the park are respectively as follows: />
Wherein E is other,T Energy of energy Internet system in park, P L,T 、C L,T 、Q L,T Respectively supplying power, cooling and heating in the period T in the park, wherein the unit is MJ; p (t), C (t) and Q (t) are park electric, cold and heat load power at the moment t, and the unit is kW; pv, wp, b, coal, gas the energy sources are solar energy, wind energy, biomass energy, coal and natural gas; ζ is the nonrenewable coefficient of energy, ζ of renewable energy is 0, and ζ of nonrenewable is 1; v (t) is the permeability of different primary energy sources in outsourcing electricity at the moment t; lambda is the power generation efficiency of the corresponding unit; f (t) is the lower heating value of the consumed unit fuel, and the unit is MJ; b (t) is the consumption of the corresponding fuel, and the unit is t; p (P) buy (t) purchasing electric energy and power of a power grid at the moment t, wherein the unit is kW; .
The non-renewable energy duty ratio xi is the ratio of the output energy and the non-renewable energy of the system, and the higher the non-renewable energy duty ratio is, the better the energy conservation of the system is. In particular, non-renewable energy duty cycle
Respectively the proportion of non-renewable energy sources such as coal, petroleum, natural gas and the like in the processes of electricity production, heating and refrigeration,
wherein P is gas The unit of power generated by the gas triple power supply unit is kW; p (P) h,gas1 、P h,gas2 、P h,e The heating power of the gas triple supply unit, the gas boiler and the electric heating unit is kW; p (P) c,gas1 、P c,gas2 、P c,e Refrigeration power of gas triple supply unit, gas boiler and electric heatingRate in kW.
Renewable energy source digestion rate R re The evaluation index mainly solves the problem that the traditional energy efficiency evaluation index mainly aims at non-renewable energy sources such as coal, natural gas and the like, and reflects the capability of the energy Internet for absorbing renewable energy sources such as solar energy, biomass energy and the like. In particular, renewable energy consumption rate
The carbon emission rate v, the evaluation index belongs to an environment-friendly index, and mainly characterizes various energy sources such as: the unit energy carbon emission generated by natural gas, biomass and outsourcing electric energy is t. Specifically, carbon emission rate
Wherein alpha is g Is natural gas CO 2 Emission coefficient, alpha b Is biomass CO 2 Emission coefficient, alpha coal Is coal CO 2 Emission coefficient.
Economic benefit C total And characterizing the difference value between the sum of the cold, heat and electricity benefits and the running cost of the energy Internet. Specifically, carbon emission rate
Wherein P is e,sal 、P cool,sal 、P heat,sal The selling price of electricity, cold and heat is respectively shown in the unit of yuan/kWh; p (P) buy (t) purchasing electric energy and power of a power grid at the moment t, wherein the unit is kW; p (P) gas 、P b,buy The price of natural gas and the price of biomass fuel are respectively shown in the unit of yuan/kg; p (P) e,buy For electricity purchase price, the unit is Yuan/kWh.
Investment cost C cost And the total investment of the comprehensive energy system of electric energy, heat energy and cold energy under the operation of the energy internet is represented. In particular, investment costs 
Wherein P is c,pv 、P c,wp 、P c,b 、P c,gas1 、P c,gas2 、P c,se 、P c,sc 、P c,sh 、P c,td 、P c,hp 、P c,ec 、P c,eh The unit price of photovoltaic, wind power, biomass energy, gas triple supply, gas boiler, electricity storage, cold storage, heat storage, power grid, heating power grid, electric refrigeration and electric heating equipment is respectively calculated, and the unit price is calculated according to the unit price/kW; w (W) pv 、W wp 、W b 、W gas1 、W gas2 、W se 、W sc 、W sh 、W td 、W hp 、W ec 、W eh The unit is kW, which is the capacity of photovoltaic, wind power, biomass energy, gas triple supply, gas boiler, electricity storage, cold storage, heat storage, power grid, heating power grid, electric refrigeration and electric heating equipment.
Loss rate of energy network
And the energy loss rate of an energy transmission network of the comprehensive energy system for representing electric energy, heat energy and cold energy under the operation of the energy Internet. Specifically, the energy network loss rate +.>
Wherein mu e 、μ c 、μ h The energy loss coefficient, the cold energy loss coefficient and the heat energy loss coefficient of the energy network are respectively.
Energy network maximum load utilization hours beta d And the utilization rate of energy transmission network equipment in the energy internet system is characterized. Specifically, the energy network maximum load utilization hours
Wherein P is e,max 、P c,max 、P h,max The maximum transmission power of electric energy, cold energy and heat energy of the energy network is kW; alpha 1 、α 2 、α 3 The duty ratios of electric energy, cold energy and heat energy in the energy network are respectively.
Energy network fault tolerance delta 1 The evaluation index comprehensively considers a plurality of energy storage, heat storage and cold storage systems and power capacity limits of heating, refrigerating and electric equipment, and represents fault tolerance and reliability of a dispatching optimization scheme of the multi-energy system. Specifically, the energy network fault tolerance 
Wherein P is c,S (t)、P h,S (t)、P e,S And (t) respectively storing power of the cold storage, heat storage and electricity storage devices in the i energy internet systems, wherein the unit is kW.
Error rate delta of communication network 2 The evaluation index comprehensively characterizes the transmission reliability of the communication network in the comprehensive energy system. In particular, the error rate of a communication network
M is the total code number transmitted by a communication network in an energy internet system in a time period T, and the unit is kb; m is m 1 The unit is kb for the number of bit errors in the t time period of the communication network.
Thermal power duty cycle ζ pqe The evaluation index characterizes the thermal power generation efficiency in the comprehensive energy system. Specifically, the thermal power duty ratio
Wherein P is pv,max 、P wp,max 、P b,max 、P gas,max The power generation unit is kW and is rated power for photovoltaic, wind power, biomass power generation and fuel gas triple power generation in a comprehensive energy system.
Electric heating power ratio xi p,eq The evaluation index characterizes the efficiency of converting electric energy into heat energy in the comprehensive energy system. Specifically, the electric heating power duty ratio
Wherein P is hgas1,max 、P hgas2,max 、P he,max The unit is kW for rated power of gas triple supply, gas boiler and electric heating in the comprehensive energy system.
Electric heating quantity duty ratio xi e,q The evaluation index comprehensively considers factors such as a plurality of gas triple heating/cooling systems, a gas boiler heating system, a heat storage system, a cold storage system and the like, and represents the ratio of electric heating to total heat. Specifically, the electric heating amount duty ratio 
Wherein P is h,e And the electric heating power is t time.
Thermal power generation duty cycle ζ e,E The evaluation index comprehensively considers the power capacity limit of a plurality of heating, refrigerating and electric equipment, and represents the ratio of the total power generation of the thermal power generation in the multi-energy system. Specifically, the thermal power generation rate
The comprehensive evaluation index system established by utilizing the 9 evaluation indexes for representing the optimal path selection capability of the energy network and the 6 evaluation indexes for representing the intercommunication capability of the energy network can better reflect the efficiency and the multi-energy complementary capability of the energy Internet system, and can comprehensively evaluate the energy Internet from multiple angles.
Of course, other evaluation indexes capable of reflecting the system efficiency, the energy network conversion and the intercommunication energy flow in the energy internet system can be added into the established comprehensive evaluation index system.
The method for optimizing the energy Internet by multiple targets, provided by the embodiment of the invention, acquires an actual evaluation parameter matrix according to each evaluation index in a comprehensive evaluation index system, and performs standardization processing on the actual evaluation parameter matrix to obtain a utility function matrix, and can comprise the following steps:
determining weight coefficients X of h first-level indexes by using AHP-entropy weight method h The first-level index is the optimal path selection capability and the energy network intercommunication capability of the energy network;
subjective weight x of j second-level indexes relative to first-level indexes is calculated by using AHP hj '：
Constructing an actual evaluation parameter matrix, and for x hj ' normalization processing: />
Wherein, the comprehensive utilization rate of energy, the duty ratio of non-renewable energy, the consumption rate of renewable energy, economic benefit, the maximum load utilization hour of energy network, the fault tolerance of energy network, the duty ratio of heat generation power, the duty ratio of electric heating quantity and the duty ratio of heat generation quantity are positive indexes, and the energy consumption rate is increased by>
Efficiency, carbon emission rate, investment cost, energy network loss rate and communication network error rate are negative indexes;
weight coefficient X based on first-level index h And normalizing the obtained x hj Establishing a utility function matrix: g hj (x)＝X h ×x hj 。
The specific process of obtaining the utility function matrix according to the comprehensive evaluation index system can be as follows:
step 1: determining the weight coefficient X of each index in the h first-level indexes by using AHP (Analytic Hierarchy Process), analytic hierarchy process and entropy weight process h The first-level index specifically comprises an energy network optimal path selection capability and an energy network intercommunication capability.
Step 2: calculating the weight coefficient X of each evaluation index in j second-level indexes relative to the first-level index by using analytic hierarchy process h Subjective weight x of (2) hj '，
And constructing a comparison matrix of the second-level index relative to the first-level index, namely constructing an actual evaluation parameter matrix.
The second level index is specifically the above 15 evaluation indexes, i.e., j=15. In the 15 evaluation indexes, the comprehensive utilization rate eta of energy, the non-renewable energy duty ratio xi and the renewable energy consumption rate R re Economic benefit C total Maximum load utilization hour beta of energy network d Fault tolerance delta of energy network 1 Thermal power duty cycle ζ p,qe Electric heating power duty ratio xi p,eq Electric heating quantity duty ratio xi e,q Duty ratio ζ of thermal power generation e,E Is used as a positive index to indicate that the current position is positive,
efficiency eta ex1 Carbon emission rate v, investment cost C cost Energy network loss rate->
Error rate delta of communication network 2 Is a negative index. When r is hj For positive index, the drug is added>
When r is hj For negative indicators->
The subjective weight x of the second-level index relative to the first-level index is obtained through calculation hj ' after, for x hj ' normalization processing:
step 3: after each index weight of each level is obtained, the weight coefficient X of the index of the first level can be based on h X after normalization process hj Establishing a utility function matrix: g hj (x)＝X h ×x hj 。
The energy internet multi-objective optimization method provided by the embodiment of the invention can further comprise the following steps after the actual evaluation parameter matrix is constructed:
and carrying out consistency test on the actual evaluation parameter matrix, and reconstructing the actual evaluation parameter matrix if the consistency is not met.
After the actual evaluation parameter matrix is constructed, consistency test can be performed on the actual evaluation parameters, and if the constructed actual evaluation parameter matrix does not meet consistency, the actual evaluation parameters are reconstructed according to the step 1 and the step 2 until the actual evaluation parameters meeting consistency are obtained.
The energy Internet multi-objective optimization method provided by the embodiment of the invention is characterized in that a system layer objective function constructed according to a utility function matrix and each evaluation index is as follows:
In obtaining the utility function matrix g hj (x) Then according to the utility function matrix g hj (x) And the system layer objective function constructed by each evaluation index is specifically as follows: 
Wherein f j (x) The calculation formulas corresponding to the evaluation indexes representing the optimal path selection capability of the energy network and the calculation formulas corresponding to the evaluation indexes representing the intercommunication capability of the energy network are respectively adopted.
Specifically, in the system layer objective function F (x), when j takes a value from 1 to 9, F j (x) Corresponding to a calculation formula of 9 evaluation indexes for representing the optimal path selection capability of the energy network; when j takes a value from 10 to 15, f j (x) Corresponding to the calculation formula of 6 evaluation indexes for representing the intercommunication capability of the energy network.
The constraint conditions of the system layer objective function comprise electric balance constraint, thermal balance constraint, cold balance constraint, power constraint and capacity constraint.
Constraints of the constructed system layer objective function include electric balance constraint, thermal balance constraint, cold balance constraint, power constraint and capacity constraint.
Wherein, the expressions of the electric balance constraint, the thermal balance constraint and the cold balance constraint are respectively:
P L,T (t)+P buy (t)＝P e,Storage (t)+P load (t)
C L,T (t)＝P cool,Storage (t)+P cool,load (t)
Q L,T (t)＝P heat,Storage (t)+P heat,load (t)
in the above three expressions, P Storage To store energy power, P load Is the load power.
The power constraint and the capacity constraint are that all refrigeration, heating and electric power, and cold storage, heat storage and electric power are smaller than the sum of the power of each corresponding device, and the storage capacity of each electric storage, heat storage and heat storage is smaller than the storage capacity of the corresponding device.
The multi-objective optimization method for the energy Internet provided by the embodiment of the invention optimizes a system layer by utilizing a system layer objective function and an optimization algorithm, and can comprise the following steps:
11-dimensional Shan Lizi P= [ P ] composed of 11 scheduling variables including photovoltaic power generation, wind power generation, gas triple power generation unit power, gas triple power generation unit cooling power, gas triple power generation unit heating power, biomass power generation unit power, gas boiler heating power, outsourcing power, cold storage, heat storage and power storage 1 …P 11 ]，P i For the schedule variable, i=1, 2, … 11;
during optimizing, updating one dimension of the single particle P every time, and substituting the single particle P into a system layer objective function F (x);
comparing the mass of the single particle P before and after the k-th optimizing, and if the mass of the single particle P is improved, enabling the k+1-th optimizing to correct the speed
Correction speed equal to k-th optimization +.>
If the mass of the single particle P is not improved, the correction speed is modified by the correction factor>
Correction is performed, wherein the optimization increment delta P of the ith scheduling variable i k+1 By correction speed->
Random speed->
The composition of the composite material comprises the components,
k=1, 2, … M-1, M being the specified number of optima;
after the optimizing times reach the preset optimizing times, the single particle P converges on the global optimal solution of the objective function of the system layer, and the optimization of the system layer is realized.
In the energy internet, an energy management center is used as a management and control mechanism in the running process of an energy internet system, the management and control mechanism has the processes of state monitoring, data communication, task calculation, instruction execution and the like, cold, heat and electric loads of the management and control mechanism are predicted through historical load curves (cold, heat and electric historical load curves) of energy internet energy networks, and photovoltaic power generation capacity, wind power generation capacity, gas triple power generation unit power generation capacity, cold/heat supply, biomass power generation unit power generation capacity, gas boiler power supply capacity, outsourcing power quantity, cold storage capacity, heat storage capacity and electricity storage capacity are optimized through multi-objective optimization indexes based on real-time natural gas, weather forecast and electric energy buying and selling prices. The feasible solution of the energy internet real-time scheduling comprises 11 scheduling variables, namely photovoltaic power generation, wind power generation, gas triple power supply unit cooling power, gas triple power supply unit heating power, biomass power generation unit power generation, gas boiler heating power, outsourcing power, cold storage, heat storage and electricity storage power.
When the system layer is optimized by utilizing the constructed system layer objective function F (x) and the optimization algorithm, the global optimal solution searching can be specifically performed based on the intelligent particle algorithm so as to optimize the system layer.
The specific process of carrying out global optimal solution searching based on the intelligent particle algorithm is as follows:
using the 11 schedule variables to form 11-dimensional Shan Lizi p= [ P ] 1 …P 11 ]Wherein, the initial value of each scheduling variable is randomly generated in the respective value interval.
In the process of multi-objective optimization, only one dimension of the single particle P is updated in each optimizing, namely only one of the 11 scheduling variables is updated, and the single particle P is substituted into the constructed system layer objective function F (x) to evaluate the merits of F (P). The larger the value of F (P), the better the quality of single particle P.
After the kth optimizing, comparing the quality of the single particle P before and after optimizing, if the quality of the single particle P is improved, the optimizing is successful, and the k+1th optimizing correction speed can be made
A correction rate equal to the k-th optimization
If the quality of the single particles P is not improved, a correction factor is used + ->
Correction speed for k+1th time optimization
And (3) correcting to change the optimization direction. Where i denotes a scheduling variable, i=1, 2, … 11, k=1, 2, … M-1, M is a prescribed number of optimizations (i.e., iterations).
In the optimizing process, in order to ensure that the optimizing speed has certain randomness, the optimizing range can cover the feasible solution area as far as possible, and then the random speed can be generated according to the specified optimizing times and optimizing radius 
Accordingly, the optimization increment ΔP of the ith schedule variable i k+1 Then the correction speed can be made +.>
Random speed->
These two parts are made up of.
After the optimizing times reach the preset optimizing times, the single particle P can finally converge on the global optimal solution of the system layer objective function F (x), namely the optimal output of each scheduling variable, so that a reference basis can be provided for the construction and operation of the energy Internet.
Of course, besides the global optimal solution searching by using the intelligent particle algorithm, the optimization calculation can be performed by using a gray correlation TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method, an AHP method, an entropy weight method, a neural network algorithm, a support vector machine algorithm and other intelligent optimization algorithms.
The embodiment of the invention also provides an energy internet multi-objective optimization device, please refer to fig. 3, which shows a schematic structural diagram of the energy internet multi-objective optimization device provided by the embodiment of the invention, which may include:
a building module 11 for: establishing a comprehensive evaluation index system for representing the optimal path selection capacity of the energy network and representing the intercommunication capacity of the energy network;
a first optimization module 12 for: acquiring an actual evaluation parameter matrix according to each index in the comprehensive evaluation index system, carrying out standardization processing on the actual evaluation parameter matrix to obtain a utility function matrix, and optimizing a decision layer by using the utility function matrix;
A second optimization module 13 for: and coordinating all targets through a utility function matrix, constructing a system layer objective function according to the utility function matrix and all evaluation indexes, and optimizing a system layer by utilizing the system layer objective function and an optimization algorithm, wherein the targets comprise energy network optimal path selection and energy network intercommunication capability.
The embodiment of the invention also provides an energy internet multi-objective optimizing device, and particularly referring to fig. 4, which shows a schematic structural diagram of the energy internet multi-objective optimizing device provided by the embodiment of the invention, the energy internet multi-objective optimizing device may include:
a memory 21 for storing a computer program;
a processor 22 for implementing the steps of any of the energy internet multi-objective optimization methods described above when executing a computer program.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium is stored with a computer program, and the computer program realizes the steps of any energy Internet multi-objective optimization method when being executed by a processor.
The description of the related parts in the energy internet multi-objective optimization device, the device and the computer readable storage medium provided by the embodiment of the invention refers to the detailed description of the corresponding parts in the energy internet multi-objective optimization method provided by the embodiment of the invention, and the detailed description is omitted here.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements is inherent to. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. In addition, the parts of the above technical solutions provided in the embodiments of the present invention, which are consistent with the implementation principles of the corresponding technical solutions in the prior art, are not described in detail, so that redundant descriptions are avoided.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. The multi-objective optimization method for the energy Internet is characterized by comprising the following steps of:
establishing a comprehensive evaluation index system for representing the optimal path selection capacity of the energy network and representing the intercommunication capacity of the energy network;
acquiring an actual evaluation parameter matrix according to each evaluation index in the comprehensive evaluation index system, normalizing the actual evaluation parameter matrix to obtain a utility function matrix, and optimizing a decision layer by using the utility function matrix;
coordinating each target through the utility function matrix, constructing a system layer objective function according to the utility function matrix and each evaluation index, and optimizing a system layer by utilizing the system layer objective function and an optimization algorithm, wherein the targets comprise energy network optimal path selection and energy network intercommunication capability;
the evaluation indexes in the comprehensive evaluation index system comprise evaluation indexes representing the optimal path selection capability of the energy network and evaluation indexes representing the intercommunication capability of the energy network, wherein:
the evaluation indexes for representing the optimal path selection capability of the energy network comprise comprehensive utilization rate of energy, non-renewable energy duty ratio, renewable energy consumption rate, and the like, 
Efficiency, carbon emission rate, economic benefits, investment costs, energy network loss rate, and energy network maximum load utilization hours;
the evaluation indexes for representing the intercommunication capability of the energy network comprise the fault tolerance of the energy network, the error rate of the communication network, the heat generation power ratio, the electric heating quantity ratio and the heat generation quantity ratio;
acquiring an actual evaluation parameter matrix according to each evaluation index in the comprehensive evaluation index system, and performing normalization processing on the actual evaluation parameter matrix to obtain a utility function matrix, wherein the method comprises the following steps:
determining weight coefficients X of h first-level indexes by using AHP-entropy weight method h The first-level index is the optimal path selection capability and the energy network intercommunication capability of the energy network;
calculating subjective weights x of j second-level indexes relative to first-level indexes by using AHP hj ′：
Constructing the actual evaluation parameter matrix, and performing the evaluation on x hj ' normalization processing: />
Wherein, the comprehensive utilization rate of energy, the non-renewable energy duty ratio, the renewable energy consumption rate, the economic benefit, the maximum load utilization hour of the energy network, the fault tolerance of the energy network, the heat generation power duty ratio, the electric heating quantity duty ratio and the heat generation quantity duty ratio are positive indexes, and the energy consumption rate is increased by >
Efficiency, carbon emission rate, investment cost, energy network loss rate and communication network error rate are negative indexes;
weight coefficient X based on first-level index h And normalizing the obtained x hj Establishing the utility function matrix: g hj (x)＝X h ×x hj ；
After constructing the actual evaluation parameter matrix, the method further comprises:
and carrying out consistency test on the actual evaluation parameter matrix, and reconstructing the actual evaluation parameter matrix if the consistency is not met.
2. The energy internet multi-objective optimization method according to claim 1, wherein a system layer objective function constructed according to the utility function matrix and each of the evaluation indexes is:
3. The energy internet multi-objective optimization method according to claim 2, wherein the constraint conditions of the system layer objective function include an electric balance constraint, a thermal balance constraint, a cold balance constraint, a power constraint and a capacity constraint.
4. The energy internet multi-objective optimization method according to claim 2, wherein optimizing the system layer by using the system layer objective function and the optimization algorithm comprises:
11-dimensional Shan Lizi P= [ P ] formed by 11 scheduling variables of photovoltaic power generation, wind power generation, gas triple power generation unit power, gas triple power generation unit cooling power, gas triple power generation unit heating power, biomass power generation unit power, gas boiler heating power, outsourcing power, cold storage, heat storage and power storage are constructed 1 …P 11 ]，P i For the schedule variable, i=1, 2, … 11;
during optimizing, updating one dimension of the single particle P every time, and substituting the single particle P into the system layer objective function F (x);
comparing the mass of the single particle P before and after the k-th optimizing, and if the mass of the single particle P is improved, making the k+1-th optimizing correct speed
Correction speed equal to k-th optimization +.>
If the mass of the single particle P is not improved, correcting the speed by a correction factor>
Correction is performed, wherein the optimization increment delta P of the ith scheduling variable i k +1 By correction speed->
Random speed->
The composition, k=1, 2, … M-1, M is the prescribed number of optima;
After the optimizing times reach the preset optimizing times, the single particle P converges on the global optimal solution of the system layer objective function to realize the optimization of the system layer.
5. An energy internet multi-objective optimization device, comprising:
the establishing module is used for: establishing a comprehensive evaluation index system for representing the optimal path selection capacity of the energy network and representing the intercommunication capacity of the energy network;
a first optimization module for: acquiring an actual evaluation parameter matrix according to each index in the comprehensive evaluation index system, normalizing the actual evaluation parameter matrix to obtain a utility function matrix, and optimizing a decision layer by using the utility function matrix;
a second optimization module, configured to: coordinating each target through the utility function matrix, constructing a system layer objective function according to the utility function matrix and each evaluation index, and optimizing a system layer by utilizing the system layer objective function and an optimization algorithm, wherein the targets comprise energy network optimal path selection and energy network intercommunication capability;
the evaluation indexes in the comprehensive evaluation index system comprise evaluation indexes representing the optimal path selection capability of the energy network and evaluation indexes representing the intercommunication capability of the energy network, wherein:
The evaluation indexes for representing the optimal path selection capability of the energy network comprise comprehensive utilization rate of energy, non-renewable energy duty ratio, renewable energy consumption rate, and the like,
Efficiency, carbon emission rate, warpEconomic benefit, investment cost, energy network loss rate and energy network maximum load utilization hours;
the evaluation indexes for representing the intercommunication capability of the energy network comprise the fault tolerance of the energy network, the error rate of the communication network, the heat generation power ratio, the electric heating quantity ratio and the heat generation quantity ratio;
the first optimizing module is specifically configured to: determining weight coefficients X of h first-level indexes by using AHP-entropy weight method h The first-level index is the optimal path selection capability and the energy network intercommunication capability of the energy network; calculating subjective weights x of j second-level indexes relative to first-level indexes by using AHP hj '：
Constructing the actual evaluation parameter matrix, and performing the evaluation on x hj ' normalization processing: />
Wherein, the comprehensive utilization rate of energy, the non-renewable energy duty ratio, the renewable energy consumption rate, the economic benefit, the maximum load utilization hour of the energy network, the fault tolerance of the energy network, the heat generation power duty ratio, the electric heating quantity duty ratio and the heat generation quantity duty ratio are positive indexes, and the energy consumption rate is increased by >
Efficiency, carbon emission rate, investment cost, energy network loss rate and communication network error rate are negative indexes; weight coefficient X based on first-level index h And normalizing the obtained x hj Establishing the utility function matrix: g hj (x)＝X h ×x hj ；
Further comprises:
and a module for carrying out consistency check on the actual evaluation parameter matrix after the actual evaluation parameter matrix is constructed, and reconstructing the actual evaluation parameter matrix if consistency is not met.
6. An energy internet multi-objective optimization device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the energy internet multi-objective optimization method according to any one of claims 1 to 4 when executing the computer program.
7. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the energy internet multi-objective optimization method according to any of claims 1 to 4.
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