CN116402210A - Multi-objective optimization method, system, equipment and medium for comprehensive energy system - Google Patents
Multi-objective optimization method, system, equipment and medium for comprehensive energy system Download PDFInfo
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Abstract
The invention provides a multi-objective optimization method, a system, equipment and a medium for an integrated energy system, which comprise the following steps: acquiring installed capacity data of renewable energy sources in an electric-gas interconnection comprehensive energy system and related parameters of system operation; based on the installed capacity data of the renewable energy sources and related parameters of system operation, solving a multi-objective optimal configuration model which is built in advance and comprises electric conversion equipment and multi-class energy storage equipment by adopting a genetic algorithm to obtain an optimal configuration solution set; and selecting an optimal configuration result of the electric-gas interconnection comprehensive energy system from the optimal configuration solution set based on the optimization demand. According to the invention, the multi-objective optimal configuration model comprising the electric conversion equipment and the multi-type energy storage equipment is established, so that the collaborative planning of the electric conversion equipment and the multi-type energy storage equipment is performed, and a more comprehensive optimal configuration scheme which is more in line with the actual situation of the comprehensive energy system can be provided.
Description
Technical Field
The invention belongs to the field of comprehensive energy system optimization, and particularly relates to an electric-gas interconnection comprehensive energy system multi-objective optimization method based on electric conversion gas.
Background
With the gradual aggravation of the problems of fossil energy crisis and environmental pollution, the completion of energy transformation and the construction of modern energy systems are urgent. Because wind power generation has obvious intermittence, uncertainty and anti-peak shaving characteristics, the wind abandoning phenomenon is serious. In recent years, with the proposal of the concept of an integrated energy system, the mutual coupling of the integrated energy system and renewable energy sources greatly improves the energy utilization efficiency and reduces the pollution emission. Among them, the electric power network and the natural gas network are two most important large-scale transmission carriers in the current energy field, so the coupling relationship between the two is always receiving a great deal of attention. The natural gas can be converted into the electric energy by means of equipment such as a gas turbine, the electric energy can be converted into the natural gas by means of an electric conversion technology, and further, the bidirectional flow of energy between an electric power network and the natural gas network is achieved, meanwhile, the coupling of an electric-gas comprehensive energy system is further deepened, the bidirectional coupling of the two systems is achieved together with the gas turbine, and a new scheme is provided for the consumption of renewable energy sources such as wind power and the like. Meanwhile, the energy storage device is generally added, so that the utilization rate of renewable energy sources can be improved, the electric energy quality is improved, and the energy storage device has an important supporting effect on the operation of a comprehensive energy system.
The existing problems about the consumption of renewable energy sources by the integrated energy source system have been studied in a large amount, the modeling of the integrated energy source system currently comprises various devices, and the research emphasis of the integrated energy source system model based on different devices is different, for example: the comprehensive energy system optimization scheduling comprising the electric gas conversion equipment mainly analyzes economic benefits brought by wind power consumption to the system, and the research of the comprehensive energy system comprising various energy storage equipment mainly prefers to consume wind power by utilizing electric and thermal energy storage equipment, and considers the economical efficiency and feasibility of configuring different energy storage equipment by the system. At present, most of the prior art unilaterally considers the optimal configuration of the electric conversion gas or multi-type energy storage equipment, but in actual production work, the electric conversion gas and multi-type energy storage equipment often only singly consider the configuration scheme of the electric conversion gas or multi-type energy storage equipment in an electric-gas interconnection integrated energy system. In addition, when the comprehensive energy system is optimally configured, a multi-objective optimization solution generally exists, and in the process, the problem that the overall optimal solution cannot be quickly converged, so that the optimization efficiency is to be improved often exists.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a multi-objective optimization method of a comprehensive energy system, which comprises the following steps:
Acquiring installed capacity data of renewable energy sources in an electric-gas interconnection comprehensive energy system and related parameters of system operation;
based on the installed capacity data of the renewable energy sources and related parameters of system operation, solving a multi-objective optimal configuration model which is built in advance and comprises electric conversion equipment and multi-class energy storage equipment by adopting a genetic algorithm to obtain an optimal configuration solution set;
selecting an optimal configuration result of the electric-gas interconnection comprehensive energy system from the optimal configuration solution set based on the optimization demand;
wherein the multi-objective optimization model is based on minimum system economic cost and CO on the basis of meeting the maximum renewable energy consumption of an electric-gas interconnection comprehensive energy system 2 The least amount of emissions is established.
Preferably, the construction of the multi-objective optimization configuration model including the electric conversion equipment and the multi-class energy storage equipment comprises the following steps:
constructing an electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multiple types of energy storage equipment based on multiple constraint conditions, wherein the electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multiple types of energy storage equipment comprises one or more of the following electric power system models, a natural gas system model, a coupling equipment model and multiple types of energy storage equipment models;
The electric-gas interconnection comprehensive energy system comprising the electric conversion equipment and various energy storage equipmentEconomic cost of the system model is minimum, renewable energy consumption is maximum and CO 2 Constructing a multi-objective optimal configuration function by taking the minimum emission as the objective;
and constructing a multi-objective optimal configuration model comprising the electric conversion equipment and the multi-class energy storage equipment based on the electric-air interconnection comprehensive energy system model comprising the electric conversion equipment and the multi-class energy storage equipment and the multi-objective optimal configuration function.
Preferably, the constructing an electric-gas interconnection integrated energy system model including an electric gas conversion device and multiple types of energy storage devices based on multiple constraint conditions includes:
constructing the power system model based on power balance constraint, unit output constraint, node voltage constraint and branch power flow constraint;
constructing the natural gas system model based on gas source gas outlet quantity constraint, natural gas pipeline operation constraint, pipe village operation constraint, gas storage tank operation constraint, compressor operation constraint and node flow balance constraint;
constructing the coupling equipment model based on the gas turbine output constraint and the electric gas conversion equipment output constraint;
constructing the multi-class energy storage device model based on the electric storage device operation constraint, the heat storage device operation constraint and the cold storage device operation constraint;
And constructing an electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multi-type energy storage equipment by using the electric power system model, the natural gas system model, the coupling equipment model and the multi-type energy storage equipment model.
Preferably, the calculation formula corresponding to the multi-objective optimal configuration function is as follows:
minF 1 ＝F inv +F ope
wherein: f (F) 1 Economic cost for the system; f (F) inv Is investment cost; f (F) ope Is the running cost; f (F) 2 Is the renewable energy consumption rate; t is a scheduling period; n (N) W The total number of the wind turbines in the system is; n (N) V The total number of the photoelectric units in the system;
wind power is received for the plan of the t-period system for the wind turbine j; />
The photoelectric power is scheduled to be admitted for the photoelectric unit j for the t-period system; />
The ideal power of the wind turbine j; />
The ideal power of the photoelectric unit j; f (F) 3 Is CO 2 Discharge amount; />
The method comprises the steps of purchasing electric power from a power grid at a time t for a comprehensive energy system; />
The method comprises the steps of obtaining the air power from the air network at the moment t of the comprehensive energy system; alpha e To purchase electricity CO 2 An emission coefficient; alpha gas To purchase gas CO 2 Emission coefficient.
Preferably, the investment cost F inv The formula of (2) is as follows:
the running cost F ope The formula of (2) is as follows:
wherein: gamma ray i Installation cost for unit capacity of the device i; c (C) i The installation capacity for device i; i is the total number of devices in the integrated energy system; alpha is annual rate; y is Y i The operating life of device i; t is a scheduling period;
the electricity price of electricity purchased from the power grid at the moment t; j (J) G Is the price of natural gas; p (P) out,i Output power for device i in period t; beta i Maintenance costs for the unit operation of the device i.
Preferably, the solving, by using a genetic algorithm, a pre-built multi-objective optimal configuration model including an electric conversion device and a plurality of types of energy storage devices based on the installed capacity data of the renewable energy sources and related parameters of system operation to obtain an optimal configuration solution set includes:
determining that parent population individuals in a genetic algorithm based on a self-adaptive elite retention strategy are electric conversion equipment capacity and multi-type energy storage equipment capacity based on the installed capacity data of the renewable energy sources and related system operation parameters, wherein the fitness value of each individual in the population is the multi-objective optimal configuration function value;
and solving the multi-objective optimal configuration function by adopting the genetic algorithm based on the self-adaptive elite retention strategy to obtain an optimal configuration solution set.
Preferably, the method for solving the multi-objective optimization configuration function by adopting a genetic algorithm based on an adaptive elite retention policy to obtain an optimization configuration solution set includes:
Step S1: initializing a population, and setting population scale, iteration times, basic crossover probability and basic variation probability;
step S2: randomly generating a parent population P, wherein each individual in the parent population P represents the capacity of an electric conversion device and the capacity of a multi-type energy storage device, and calculating the fitness value of each individual in the parent population P, wherein the fitness value represents the multi-objective optimal configuration function value;
step S3: selecting the parent population P through a genetic algorithm, and carrying out self-adaptive cross mutation on the parent population P based on the individual fitness value, the basic cross probability and the basic mutation probability to generate a child population Q;
step S4: mixing the parent population P and the child population Q to obtain a new population R, and then carrying out rapid non-dominant sorting on the new population R to obtain a non-dominant population sequence;
step S5: selecting the population R by adopting a selection strategy of a reference point based on the fitness value to obtain a population Y as a parent population of the next iteration;
step S6: screening dominant individuals in the population R by adopting the self-adaptive elite retention strategy based on the non-dominant population sequence, and adding the dominant individuals into the population Y to serve as a parent population of the next iteration;
Step S7: and judging whether the iteration times are reached, if so, obtaining the capacity of the electric conversion equipment, the capacity of the multi-type energy storage equipment and the multi-objective optimal configuration function value corresponding to each body as an optimal configuration solution set, ending, and otherwise, returning to the step S3.
Preferably, said adaptively cross-mutating said parent population P based on said individual fitness value, said base cross probability and said base variation probability comprises:
determining an adaptive crossover probability and an adaptive variation probability based on the individual fitness value, the base crossover probability, and the base variation probability;
and carrying out adaptive cross mutation on the parent population P based on the adaptive cross probability and the adaptive mutation probability.
Preferably, the calculation formula of the adaptive crossover probability is as follows:
the calculation formula of the adaptive mutation probability is as follows:
wherein: p is p c Is an adaptive crossover probability; k (k) 1 Is a first base crossover probability; k (k) 2 Is a second base crossover probability; p is p m Is the adaptive mutation probability; k (k) 3 Is a first base variation probability; k (k) 4 Is a second base variation probability; f (f) m A fitness value for the individual currently to be mutated; f (f) m Is the maximum fitness value acceptable by the population; f (f) c A larger fitness value for the two individuals to be crossed; f (f) min The fitness minimum value of all individuals in the population; the first base crossover probability is less than the second base crossover probability; the first base variation probability is less than the second base variation probability.
Preferably, the calculation formula of the adaptive elite retention strategy is as follows:
wherein: n (N) e Reserving the number of individuals for elite; f (f) i The fitness value of the ith population individual is the fitness value; n is the number of individuals in the population; f (f) b Is the fitness value of the optimal individual in the population.
Preferably, the system operation related parameters include one or more of the following: the system comprises a photoelectric power predicted value, a load predicted value, a wind power predicted value, a power price list of different time periods, electric conversion equipment parameters, multi-type energy storage equipment parameters, cogeneration equipment operation parameters, gas boiler operation parameters, absorption refrigerator operation parameters, gas storage tank operation parameters and compressor operation parameters.
Based on the same inventive concept, the invention also provides an electric-gas interconnection comprehensive energy system multi-objective optimization system based on electric conversion gas, which comprises:
and a data acquisition module: the system is used for acquiring installed capacity data of renewable energy sources in the electric-gas interconnection comprehensive energy system and related parameters of system operation;
And a solving module: the system is used for solving a multi-objective optimal configuration model which is built in advance and comprises electric conversion equipment and multi-class energy storage equipment by adopting a genetic algorithm based on the installed capacity data of the renewable energy sources and related parameters of system operation to obtain an optimal configuration solution set;
the optimal configuration result acquisition module: selecting an optimal configuration result of the electric-gas interconnection comprehensive energy system from the optimal configuration solution set based on the optimization demand;
wherein the multi-objective optimization model is based on minimum system economic cost and CO on the basis of meeting the maximum renewable energy consumption of an electric-gas interconnection comprehensive energy system 2 The least amount of emissions is established.
Preferably, the construction of the multi-objective optimization configuration model including the electric conversion equipment and the multi-class energy storage equipment in the solving module includes:
constructing an electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multiple types of energy storage equipment based on multiple constraint conditions, wherein the electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multiple types of energy storage equipment comprises one or more of the following electric power system models, a natural gas system model, a coupling equipment model and multiple types of energy storage equipment models;
the economic cost of the model of the electric-gas interconnection integrated energy system comprising the electric gas conversion equipment and the multi-type energy storage equipment is minimum, the renewable energy consumption is maximum and the CO is generated 2 Constructing a multi-objective optimal configuration function by taking the minimum emission as the objective;
and constructing a multi-objective optimal configuration model comprising the electric conversion equipment and the multi-class energy storage equipment based on the electric-air interconnection comprehensive energy system model comprising the electric conversion equipment and the multi-class energy storage equipment and the multi-objective optimal configuration function.
Preferably, the solving module builds an electric-gas interconnection integrated energy system model including electric conversion equipment and multiple types of energy storage equipment based on a plurality of constraint conditions, and the solving module comprises:
constructing the power system model based on power balance constraint, unit output constraint, node voltage constraint and branch power flow constraint;
constructing the natural gas system model based on gas source gas outlet quantity constraint, natural gas pipeline operation constraint, pipe village operation constraint, gas storage tank operation constraint, compressor operation constraint and node flow balance constraint;
constructing the coupling equipment model based on the gas turbine output constraint and the electric gas conversion equipment output constraint;
constructing the multi-class energy storage device model based on the electric storage device operation constraint, the heat storage device operation constraint and the cold storage device operation constraint;
and constructing an electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multi-type energy storage equipment by using the electric power system model, the natural gas system model, the coupling equipment model and the multi-type energy storage equipment model.
Preferably, the calculation formula corresponding to the multi-objective optimization configuration function in the solving module is as follows:
minF 1 ＝F inv +F ope
wherein: f (F) 1 Economic cost for the system; f (F) inv Is investment cost; f (F) ope Is the running cost; f (F) 2 Is the renewable energy consumption rate; t is a scheduling period; n (N) W The total number of the wind turbines in the system is; n (N) V The total number of the photoelectric units in the system;
wind power is received for the plan of the t-period system for the wind turbine j; />
The photoelectric power is scheduled to be admitted for the photoelectric unit j for the t-period system; />
The ideal power of the wind turbine j; />
The ideal power of the photoelectric unit j; f (F) 3 Is CO 2 Discharge amount; />
The method comprises the steps of purchasing electric power from a power grid at a time t for a comprehensive energy system; />
The method comprises the steps of obtaining the air power from the air network at the moment t of the comprehensive energy system; alpha e To purchase electricity CO 2 An emission coefficient; alpha gas To purchase gas CO 2 Emission coefficient.
Preferably, the investment cost F in the solving module inv The formula of (2) is as follows:
the running cost F ope The formula of (2) is as follows:
wherein: gamma ray i Installation cost for unit capacity of the device i; c (C) i The installation capacity for device i; i is the total number of devices in the integrated energy system; alpha is annual rate; y is Y i The operating life of device i; t is a scheduling period;
electricity purchased from a grid for time t Price; j (J) G Is the price of natural gas; p (P) out,i Output power for device i in period t; beta i Maintenance costs for the unit operation of the device i.
Preferably, the solving module is specifically configured to:
determining that parent population individuals in a genetic algorithm based on a self-adaptive elite retention strategy are electric conversion equipment capacity and multi-type energy storage equipment capacity based on the installed capacity data of the renewable energy sources and related system operation parameters, wherein the fitness value of each individual in the population is the multi-objective optimal configuration function value;
and solving the multi-objective optimal configuration function by adopting the genetic algorithm based on the self-adaptive elite retention strategy to obtain an optimal configuration solution set.
Preferably, the solving module adopts a genetic algorithm based on a self-adaptive elite retention policy to solve the multi-objective optimization configuration function to obtain an optimization configuration solution set, and the method comprises the following steps:
step S1: initializing a population, and setting population scale, iteration times, basic crossover probability and basic variation probability;
step S2: randomly generating a parent population P, wherein each individual in the parent population P represents the capacity of an electric conversion device and the capacity of a multi-type energy storage device, and calculating the fitness value of each individual in the parent population P, wherein the fitness value represents the multi-objective optimal configuration function value;
Step S3: selecting the parent population P through a genetic algorithm, and carrying out self-adaptive cross mutation on the parent population P based on the individual fitness value, the basic cross probability and the basic mutation probability to generate a child population Q;
step S4: mixing the parent population P and the child population Q to obtain a new population R, and then carrying out rapid non-dominant sorting on the new population R to obtain a non-dominant population sequence;
step S5: selecting the population R by adopting a selection strategy of a reference point based on the fitness value to obtain a population Y as a parent population of the next iteration;
step S6: screening dominant individuals in the population R by adopting the self-adaptive elite retention strategy based on the non-dominant population sequence, and adding the dominant individuals into the population Y to serve as a parent population of the next iteration;
step S7: and judging whether the iteration times are reached, if so, obtaining the capacity of the electric conversion equipment, the capacity of the multi-type energy storage equipment and the multi-objective optimal configuration function value corresponding to each body as an optimal configuration solution set, ending, and otherwise, returning to the step S3.
Preferably, the adaptive cross mutation of the parent population P based on the individual fitness value, the basic cross probability and the basic mutation probability in the solving module includes:
Determining an adaptive crossover probability and an adaptive variation probability based on the individual fitness value, the base crossover probability, and the base variation probability;
and carrying out adaptive cross mutation on the parent population P based on the adaptive cross probability and the adaptive mutation probability.
Preferably, the calculation formula of the adaptive crossover probability in the solving module is as follows:
the calculation formula of the adaptive mutation probability is as follows:
wherein: p is p c Is an adaptive crossover probability; k (k) 1 Is a first base crossover probability; k (k) 2 Is a second base crossover probability; p is p m Is the adaptive mutation probability; k (k) 3 Is a first base variation probability; k (k) 4 Is a second base variation probability; f (f) m A fitness value for the individual currently to be mutated; f (f) m Is the maximum fitness value acceptable by the population; f (f) c To be crossed withA larger fitness value in two individuals; f (f) min The fitness minimum value of all individuals in the population; the first base crossover probability is less than the second base crossover probability; the first base variation probability is less than the second base variation probability.
Preferably, the calculation formula of the adaptive elite retention strategy in the solving module is as follows:
wherein: n (N) e Reserving the number of individuals for elite; f (f) i The fitness value of the ith population individual is the fitness value; n is the number of individuals in the population; f (f) b Is the fitness value of the optimal individual in the population.
Preferably, the system operation related parameters in the data acquisition module include one or more of the following: the system comprises a photoelectric power predicted value, a load predicted value, a wind power predicted value, a power price list of different time periods, electric conversion equipment parameters, multi-type energy storage equipment parameters, cogeneration equipment operation parameters, gas boiler operation parameters, absorption refrigerator operation parameters, gas storage tank operation parameters and compressor operation parameters.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention provides a multi-objective optimization method, a system, equipment and a medium for an integrated energy system, which comprise the following steps: acquiring installed capacity data of renewable energy sources in an electric-gas interconnection comprehensive energy system and related parameters of system operation; based on the installed capacity data of the renewable energy sources and related parameters of system operation, solving a multi-objective optimal configuration model which is built in advance and comprises electric conversion equipment and multi-class energy storage equipment by adopting a genetic algorithm to obtain an optimal configuration solution set; and selecting an optimal configuration result of the electric-gas interconnection comprehensive energy system from the optimal configuration solution set based on the optimization demand. According to the invention, the multi-objective optimal configuration model comprising the electric conversion equipment and the multi-type energy storage equipment is established, so that the collaborative planning of the electric conversion equipment and the multi-type energy storage equipment is performed, and a more comprehensive optimal configuration scheme which is more in line with the actual situation of the comprehensive energy system can be provided.
Drawings
FIG. 1 is a schematic flow chart of a multi-objective optimization method of an integrated energy system according to an embodiment of the present invention;
FIG. 2 is a graph of typical solar radiation intensity and wind speed for one embodiment provided by the present invention;
FIG. 3 is a load diagram of an integrated energy system according to one embodiment of the present invention;
FIG. 4 is a block diagram of an integrated energy system according to one embodiment of the present invention;
FIG. 5 is a flowchart of a genetic algorithm in accordance with one embodiment of the present invention;
FIG. 6 is a diagram of an optimal solution set for providing one embodiment of the present invention;
FIG. 7 is a graph of annual CO according to an embodiment of the present invention 2 A discharge amount comparison chart;
FIG. 8 is a graph showing renewable energy consumption rate versus time for an embodiment of the present invention;
FIG. 9 is a schematic diagram of a multi-objective optimization system for an integrated energy system according to the present invention.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
Example 1:
the invention provides a multi-objective optimization method of a comprehensive energy system, in particular to a flow diagram of the multi-objective optimization method of the comprehensive energy system provided by the embodiment of the application, which comprises the following steps:
step 1: acquiring installed capacity data of renewable energy sources in an electric-gas interconnection comprehensive energy system and related parameters of system operation;
step 2: based on the installed capacity data of the renewable energy sources and related parameters of system operation, solving a multi-objective optimal configuration model which is built in advance and comprises electric conversion equipment and multi-class energy storage equipment by adopting a genetic algorithm to obtain an optimal configuration solution set;
step 3: selecting an optimal configuration result of the electric-gas interconnection comprehensive energy system from the optimal configuration solution set based on the optimization demand;
wherein the multi-objective optimization model is based on minimum system economic cost and CO on the basis of meeting the maximum renewable energy consumption of an electric-gas interconnection comprehensive energy system 2 The least amount of emissions is established.
In the step 1, the installed capacity data of a renewable energy unit in the electric-gas interconnection comprehensive energy system and the related parameters of system operation are obtained. In the invention, the optimal configuration is calculated and planned mainly based on the installed capacity of the renewable energy generator set, and the renewable energy generator set can be a wind generator set, a photovoltaic generator set, a hydroelectric generator set or a marine energy generator set. In an embodiment of the present disclosure, the renewable energy unit is: the photovoltaic unit and the wind turbine generator. In the present disclosure, acquiring the installed capacity data of the renewable energy unit in the electric-gas interconnected integrated energy system includes: 9 sets of data for 3 scenarios are shown in table 1. The above 3 scenarios are respectively: consider only the scenario of an electrical switching apparatus, consider only the scenario of an energy storage apparatus, consider the scenario of an electrical switching apparatus and an energy storage apparatus.
TABLE 1
The acquired system operation related parameters comprise operation parameters of all devices in the system, electricity charge price of the system operation and other data. Specifically, embodiments of the present disclosure include one or more of the following: the system comprises a photoelectric power predicted value, a load predicted value, a wind power predicted value, a power price list of different time periods, electric conversion equipment parameters, multi-type energy storage equipment parameters, cogeneration equipment operation parameters, gas boiler operation parameters, absorption refrigerator operation parameters, gas storage tank operation parameters and compressor operation parameters.
In the embodiment of the disclosure, the predicted value of the photovoltaic power is selected from the obtained historical data according to the required data characteristics by taking into consideration the time difference of the solar radiation intensity and the load, and the solar radiation intensity and the load of the typical day represent the corresponding predicted value of the photovoltaic power. The load predicted value and the wind power predicted value are obtained from typical days in the history data. Specifically, the predicted value of the photovoltaic power and the predicted value of the wind power obtained in the present embodiment are shown in fig. 2, and the predicted value of the load is shown in fig. 3. Acquiring an electricity price meter of different time periods of the day, existing equipment parameters of the comprehensive energy system and electricity conversion gas and multi-type energy storage equipment parameters, wherein the acquired data are shown in table 2, table 3 and table 4, the table 2 is the electricity price meter of different time periods of the day, the table 3 is the existing equipment parameters of the comprehensive energy system, and the table 4 is the electricity conversion gas and multi-type energy storage equipment parameters.
TABLE 2
TABLE 3 Table 3
TABLE 4 Table 4
Step 2:
specifically, based on the installed capacity data of the renewable energy sources and related parameters of system operation, a genetic algorithm is adopted to solve a multi-objective optimization configuration model which is built in advance and comprises electric conversion equipment and multi-class energy storage equipment, and an optimization configuration solution set is obtained.
In an embodiment of the present disclosure, the building of the pre-built multi-objective optimization configuration model including the electric gas conversion device and the multi-class energy storage device includes: and constructing an electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multiple types of energy storage equipment based on the multiple constraint conditions. As shown in fig. 4, a schematic diagram of an electric-gas interconnection integrated energy system model established in an embodiment of the present disclosure is shown. The method specifically comprises one or more of the following models: a power system model, a natural gas system model, a coupling device model, and a multi-class energy storage device model.
Specifically, in the embodiment of the present disclosure, the power system model, the natural gas system model, the coupling device model, and the multi-class energy storage device model are the following formulas:
the power network model includes: node power active and reactive balance equations, unit output constraint, node voltage constraint and branch power flow constraint;
(1) Equation of power balance
In which P is i cle Active power of the generator of the node i;
reactive power of the generator at node i; p (P) i wind Active power injected into a wind farm; v (V) i The voltage amplitude of the node i; v (V) j The voltage amplitude at node j; θ ij The voltage phase angle difference between the node i and the node j; g ij Is the real part of the admittance matrix of the node ij; b (B) ij The imaginary part of the admittance matrix of the node ij; j epsilon i Representing all nodes j directly connected to node i.
(2) Unit output constraint
Wherein:
the active output of the unit i at the moment t; />
The upper limit of the active output of the unit i; />
The lower limit of the active output of the unit i; />
Reactive output of the unit i at the moment t; />
The reactive output lower limit of the unit i; />
The reactive power output upper limit of the unit i; />
The active output of the wind turbine generator system i at the moment t; />
The upper limit of the active output of the wind turbine generator system i is set; />
For wind turbine iLower limit of active force.
(3) Node voltage constraint
U i,min ≤U i,t ≤U i,max
In U, U i,t The voltage of the node i at the moment t; u (U) i,min A lower voltage limit for node i; u (U) i,max Is the upper voltage limit of node i.
(4) Branch tide constraint
|P kl,t |≤P kl,max
In which P is kl,t The current value of the branch kl at the moment t; p (P) kl,max Is the upper limit of the power flow of the branch kl.
The natural gas network model includes: the natural gas source, the natural gas pipeline, the pipe storage, the gas storage tank, the compressor and the node flow balance;
(1) Air source
S i,min ≤S i,t ≤S i,max
Wherein: s is S i,t The natural gas supply quantity of the node i at the time t in the natural gas network is obtained; s is S i,max An upper limit for gas well gas production; s is S i,min Is the lower limit of gas well gas production.
(2) Natural gas pipeline
Wherein: f (F) ij The pipe flow for pipe ij; c (C) ij Is a constant related to the length, radius, temperature, gas density, compression factor, etc. of the tubing ij; pi i The pressure of the natural gas pipeline node i; pi j The pressure of the natural gas pipeline node j; sgn (pi) i ,π j ) The natural gas flow direction is represented by a sign function, when the pressure of the node i is greater than that of the node j, the value of the natural gas flow direction is 1, and conversely, the natural gas flow direction is-1;π i a lower pressure limit for the natural gas pipeline node i;
is the upper pressure limit of the natural gas pipeline node i.
(3) Tube stock
Wherein: l (L) ij,t The pipe is stored for the pipeline ij at the moment t; l (L) ij,t-1 The pipe is stored for a pipeline ij at the time t-1; c (C) ij Is a constant related to the length, radius, temperature, gas density, compression factor, etc. of the tubing ij;
represents the average pressure of the conduit ij; />
The air inflow of the pipeline ij at the moment t; />
The air output of the pipeline ij at the time t.
(4) Air storage tank
Wherein: s is S S,j,t The storage capacity of the air storage tank j at the moment t;
natural gas injection flow for t-moment gas storage tank jAn amount of;
the natural gas output flow of the gas storage tank j at the moment t; s is S S,j,max An upper limit of the storage capacity of the air storage tank j; s is S S,j,min A lower limit of the storage capacity of the air storage tank j; />
The upper limit of the natural gas injection flow of the gas storage tank j; />
Is the upper limit of the natural gas output flow of the gas storage tank j.
(5) Compressor with a compressor body having a rotor with a rotor shaft
P com ＝H com (0.7479×10 -5 )
Wherein: h com Power required for the compressor; f (F) ij Is the flow through the compressor; b is a constant; pi i The pressure of the natural gas pipeline node i; pi j The pressure of the natural gas pipeline node j; p (P) com Is an electrical load that electrically drives the compressor.
(6) Node traffic balancing
Wherein: q (Q) N,j,t The natural gas flow is the natural gas flow of a node j of the natural gas network at the moment t; i e j represents all nodes connected to node j;
the natural gas injection flow of the gas storage tank j at the moment t; />
Natural gas for t-moment gas storage tank jOutputting flow;
the air inflow of the pipeline ij at the moment t; />
The air outlet quantity of the pipeline ij at the moment t; q (Q) P2G,j,t The natural gas flow obtained by conversion of the electric gas conversion equipment j at the moment t; q (Q) GT,j,t The natural gas flow consumed by the gas turbine j at the time t; q (Q) com,j,t The natural gas flow consumed by the compressor j at the time t; q (Q) L,j,t Is the natural gas load of node j at time t.
The multi-type energy storage device model includes:
(1) Power storage device
Wherein: w (W) ES,t The energy stored at the moment t of the storage battery is used; mu (mu) ES Is the loss rate of the storage battery; p (P) ch,t The charging power of the storage battery at the moment t; p (P) dis,t The discharge power of the storage battery at the moment t; w (W) ES,t-1 The energy stored at the moment of the storage battery t-1 is used for storing energy; lambda (lambda) ES,ch The charging efficiency of the storage battery is improved; lambda (lambda) ES,dis Is the discharge efficiency of the storage battery; Δt is the scheduling time interval.
(2) Heat storage device
Wherein: w (W) HS,t The energy stored at the moment t for the heat storage device; mu (mu) HS The loss rate of the heat storage equipment; h ch,t The heat storage power at the moment t is the heat storage power; h dis,t The heat release power at the moment t of the heat storage equipment; lambda (lambda) HS,ch The heat charging efficiency of the heat storage equipment is improved; lambda (lambda) HS,dis The heat release efficiency of the heat storage device; w (W) HS,t-1 The energy stored for the heat storage equipment t-1 at the moment; Δt is the scheduling time interval.
(3) Cold storage device
Wherein:
the energy stored at the moment t for the cold accumulation device; />
The energy stored for the cold accumulation device t-1 at the moment; the method comprises the steps of carrying out a first treatment on the surface of the Mu (mu) CS The loss rate of the cold accumulation equipment; />
The cold-charging power at the moment t of the cold-storage equipment; />
The cooling power is the cooling power of the cold accumulation equipment at the moment t; lambda (lambda) CS,ch The cold-charging efficiency of the cold-storage device; lambda (lambda) CS,dis The cold storage efficiency of the cold storage equipment is; Δt is the scheduling time interval.
The coupling device model includes:
(1) Gas turbine model:
wherein:
natural gas consumed for the g time t of the gas turbine unit; beta g G is the gas coefficient of the gas turbine unit g; / >
Generating electric power for the time t of the gas turbine unit g; GHV is natural gas with high calorific value; />
The minimum value of electric power is output for the gas turbine unit g; />
The maximum value of electric power is output for the gas turbine unit g; omega shape T For a scheduling time interval; n (N) GAS The total number of the turbine units of the combustion engine.
(2) Electric gas conversion equipment model:
wherein:
gas production is carried out for the time t of the electric gas conversion equipment o; />
Natural gas power is consumed for the electricity-to-gas equipment o time t; GHV is natural gas with high calorific value; />
The conversion efficiency coefficient is the o conversion efficiency coefficient of the electric conversion equipment; />
Natural gas power is minimally consumed for the electric gas conversion equipment o; />
The natural gas power is consumed for the electricity-to-gas equipment o maximum; omega shape T For a scheduling time interval; n (N) P2G The total number of the electric conversion equipment is counted.
Then, based on the established electric-gas interconnection integrated energy system model comprising the electric conversion equipment and the multi-type energy storage equipment, the economic cost of the system is minimum, the renewable energy consumption is maximum and the CO is generated 2 The minimum emission is the goal to build a multi-objective optimal configuration function. The multi-objective optimal configuration function comprises at least one or more of the following objective functions: optimal objective function of economic cost of system, maximum objective function of renewable energy consumption rate and CO 2 Minimum emission objective function.
Specifically, in the embodiment of the present disclosure, the calculation formula of the optimal objective function of the economic cost of the system is as follows:
minF 1 ＝F inv +F ope
F ope ＝F be +F OM
F OM ＝β i P out,i
wherein: f (F) 1 Economic cost for the system; f (F) inv Is investment cost; f (F) ope Is the running cost; f (F) be Is the cost of purchasing energy; f (F) OM Maintenance costs for the system; c (C) i The installation capacity for device i; gamma ray i Installation cost for unit capacity of the device i; alpha is annual rate, 6% is taken here; y is Y i The operating life of device i; t is a scheduling period;
the method comprises the steps of purchasing electric power from a power grid at a time t for a comprehensive energy system; />
In an integrated energy systemthe pneumatic power purchased from the pneumatic network at the moment t; />
The electricity price of electricity purchased from the power grid at the moment t; j (J) G Is the price of natural gas; p (P) out,i Output power for device i in period t; beta i Maintenance costs for the unit operation of the device i.
The maximum objective function of renewable energy consumption rate is calculated as follows:
wherein: f (F) 2 Is the renewable energy consumption rate;
wind power is received for the plan of the t-period system for the wind turbine j; />
The photoelectric power is scheduled to be admitted for the photoelectric unit j for the t-period system; />
The ideal power of the wind turbine j;
the ideal power of the photoelectric unit j; t is a scheduling period; n (N) W The total number of the wind turbine generators is; n (N) V Is the total number of the photoelectric units.
CO 2 The emission minimum objective function is calculated as follows:
wherein: f (F) 3 Is CO 2 Discharge amount;
the method comprises the steps of purchasing electric power from a power grid at a time t for a comprehensive energy system; />
The method comprises the steps of obtaining the air power from the air network at the moment t of the comprehensive energy system; alpha e To purchase electricity CO 2 An emission coefficient; alpha gas To purchase gas CO 2 Emission coefficient.
And finally, constructing a multi-objective optimal configuration model comprising the electric conversion equipment and the multi-class energy storage equipment based on the electric-air interconnection comprehensive energy system model comprising the electric conversion equipment and the multi-class energy storage equipment and the multi-objective optimal configuration function.
And solving the multi-objective optimal configuration function by adopting a genetic algorithm based on a self-adaptive elite retention strategy for the established multi-objective optimal configuration model to obtain an optimal configuration solution set. According to the invention, the genetic algorithm is improved based on the self-adaptive elite retention strategy and the self-adaptive cross variation, and the genetic algorithm is applied to the solution of the optimization model, so that the convergence speed can be increased, and the optimization efficiency can be further improved.
Specifically, in the embodiment of the disclosure, based on the installed capacity data of the renewable energy source and the related parameters of system operation, the capacity of the electric conversion equipment and the capacity of the multi-type energy storage equipment of the parent population individuals in the genetic algorithm based on the adaptive elite retention strategy are determined, and the fitness value of each individual in the population is the multi-objective optimal configuration function value. In the embodiment of the present disclosure, as shown in fig. 5, which is a flowchart of a genetic algorithm, the genetic algorithm based on the adaptive elite retention policy solves the multi-objective optimization configuration function to obtain an optimization configuration solution set, and specifically includes:
Step S1, initializing a population: setting the population scale as 100, the maximum iteration number as 100, k 1 0.5, k 2 Is 0.9, k 3 Is of the order of 01, k 4 0.1.
Step S2: randomly generating a parent population P, wherein each individual in the parent population P represents the capacity of an electric conversion device and the capacity of a multi-type energy storage device, and calculating the fitness value of each individual in the parent population P, wherein the fitness value represents an objective function;
step S3: and generating a offspring population Q through selection of a genetic algorithm and adaptive crossover mutation operation based on the individual fitness value, the base crossover probability and the base mutation probability. The calculation formula of the self-adaptive cross mutation probability and the self-adaptive mutation probability in the self-adaptive cross mutation operation is as follows:
adaptive crossover, variation:
wherein: p is p c Is an adaptive crossover probability; k (k) 1 And k 2 Is based on cross probability, and k 2 ＞k 1 ；f m Is the maximum fitness value acceptable by the population; f (f) c A larger fitness value for the two individuals to be crossed; f (f) min The fitness minimum value of all individuals in the population; p is p m Is the adaptive mutation probability; k (k) 3 And k 4 Based on the probability of variation, and k 4 ＞k 3 ；f m And (3) adapting the value to the individual to be mutated currently. Setting k in adaptive crossover probability 2 ＞k 1 And k 4 ＞k 3 The crossover probability and the mutation probability can be made to be k based on the fitness value 2 And k 4 The self-adaptive change is generated near the value, so that the evolution direction of individuals in the population is changed towards the optimal direction, and the optimization efficiency of the algorithm is improved.
Step S4: mixing the parent population P and the child population Q to obtain a new population R, and then carrying out rapid non-dominant sorting on the new population R to obtain a non-dominant population sequence;
step S5: selecting the population R by adopting a selection strategy of a reference point based on the fitness value to obtain a population Y as a parent population of the next iteration;
step S6: and screening dominant individuals in the population R by adopting an adaptive elite retention strategy based on the non-dominant population sequence, and adding the dominant individuals into the population Y to serve as a parent population of the next iteration.
The expression of the adaptive elite retention strategy is as follows:
in the formula, N e Reserving the number of individuals for elite; n is the number of individuals in the population; f (f) b Is the fitness value of the optimal individual in the population. Compared with the common elite retention strategy, the self-adaptive elite retention strategy has the advantages that the self-adaptive elite retention strategy can dynamically change along with the fitness value, the most excellent individuals in the population are saved, the accuracy of the algorithm is improved, and meanwhile, the optimization efficiency is improved.
Step S7: judging whether the preset iteration times are reached, if so, outputting the capacity of the electric conversion equipment, the capacity of the multi-type energy storage equipment and the objective function value corresponding to each individual as an optimal configuration solution set, otherwise, returning to the step S3.
And solving the multi-objective optimal configuration function through a genetic algorithm based on the adaptive elite retention strategy based on the installed capacity data of the renewable energy sources and the system operation related parameters acquired in different scenes to obtain an optimal configuration solution set as shown in fig. 6. In the invention, the output after solving is an optimal solution set, and the final optimal configuration is selected based on the requirement. Specifically, in the embodiment of the present disclosure, an optimal optimization configuration is selected based on different objective functions, and fig. 7 is a CO of 9 sets of data corresponding to an optimal solution 2 Fig. 8 is a graph of the emission data, and fig. 8 is a graph of the renewable energy consumption rate of the optimal solution corresponding to the 9 sets of data. For simulation results from year CO 2 Comparing and analyzing two aspects of emission and renewable energy consumption rate can find that: the adoption of the electric gas conversion equipment and the multi-energy storage equipment can greatly improve the renewable energy consumption rate and reduce CO 2 Annual emissions. The energy system has the advantages that multiple energies are mutually converted and stored in the integrated energy system, and the wind and light discarding rate is greatly reduced, so that the energy utilization rate is improved, and the economic cost is reduced.
TABLE 5
The invention provides a multi-objective optimization method of an integrated energy system based on electric conversion, which aims at an electric-gas interconnection integrated energy system, establishes an electric power system, a natural gas system model, an energy storage equipment model and a coupling equipment model which meet a plurality of constraints, then establishes a multi-objective optimization model with minimum economic cost, maximum renewable energy consumption and minimum CO2 emission, and solves by using an improved genetic algorithm. Compared with the prior art, the collaborative planning of the electric conversion equipment and the multi-type energy storage equipment is performed, a plurality of targets of the comprehensive energy system are optimized, the actual situation of the comprehensive energy system is more met, and the economical efficiency and the environmental performance of the system are improved. As shown in table 5, it can be seen that the genetic algorithm obtains an optimal solution better, and the genetic algorithm based on the adaptive elite retention strategy adopted in the present technical solution has better ability to obtain an optimal solution than the unmodified genetic algorithm (NSGA-iii).
Example 2:
based on the same inventive concept, the invention also provides an electric-gas interconnection comprehensive energy system multi-objective optimization system based on electric conversion gas. The system structure is shown in fig. 9, and includes:
and a data acquisition module: the system is used for acquiring installed capacity data of renewable energy sources in the electric-gas interconnection comprehensive energy system and related parameters of system operation;
And a solving module: the system is used for solving a multi-objective optimal configuration model which is built in advance and comprises electric conversion equipment and multi-class energy storage equipment by adopting a genetic algorithm based on the installed capacity data of the renewable energy sources and related parameters of system operation to obtain an optimal configuration solution set;
the optimal configuration result acquisition module: selecting an optimal configuration result of the electric-gas interconnection comprehensive energy system from the optimal configuration solution set based on the optimization demand;
wherein the multi-objective optimization model is based on minimum system economic cost and CO on the basis of meeting the maximum renewable energy consumption of an electric-gas interconnection comprehensive energy system 2 The least amount of emissions is established.
Preferably, the construction of the multi-objective optimization configuration model including the electric conversion equipment and the multi-class energy storage equipment in the solving module includes:
constructing an electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multiple types of energy storage equipment based on multiple constraint conditions, wherein the electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multiple types of energy storage equipment comprises one or more of the following electric power system models, a natural gas system model, a coupling equipment model and multiple types of energy storage equipment models;
the economic cost of the model of the electric-gas interconnection integrated energy system comprising the electric gas conversion equipment and the multi-type energy storage equipment is minimum, the renewable energy consumption is maximum and the CO is generated 2 Constructing a multi-objective optimal configuration function by taking the minimum emission as the objective;
and constructing a multi-objective optimal configuration model comprising the electric conversion equipment and the multi-class energy storage equipment based on the electric-air interconnection comprehensive energy system model comprising the electric conversion equipment and the multi-class energy storage equipment and the multi-objective optimal configuration function.
Preferably, the construction of the electric-gas interconnection comprehensive energy system model including the electric conversion equipment and the multi-type energy storage equipment based on a plurality of constraint conditions in the solving module comprises the following steps:
constructing the power system model based on power balance constraint, unit output constraint, node voltage constraint and branch power flow constraint;
constructing the natural gas system model based on gas source gas outlet quantity constraint, natural gas pipeline operation constraint, pipe village operation constraint, gas storage tank operation constraint, compressor operation constraint and node flow balance constraint;
constructing the coupling equipment model based on the gas turbine output constraint and the electric gas conversion equipment output constraint;
constructing the multi-class energy storage device model based on the electric storage device operation constraint, the heat storage device operation constraint and the cold storage device operation constraint;
and constructing an electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multi-type energy storage equipment by using the electric power system model, the natural gas system model, the coupling equipment model and the multi-type energy storage equipment model.
Preferably, the calculation formula corresponding to the multi-objective optimization configuration function in the solving module is as follows:
minF 1 ＝F inv +F ope
wherein: f (F) 1 Economic cost for the system; f (F) inv Is investment cost; f (F) ope Is the running cost; f (F) 2 Is the renewable energy consumption rate; t is a scheduling period; n (N) W The total number of the wind turbines in the system is; n (N) V The total number of the photoelectric units in the system;
wind power is received for the plan of the t-period system for the wind turbine j; />
The photoelectric power is scheduled to be admitted for the photoelectric unit j for the t-period system; />
The ideal power of the wind turbine j; />
The ideal power of the photoelectric unit j;F 3 is CO 2 Discharge amount; />
The method comprises the steps of purchasing electric power from a power grid at a time t for a comprehensive energy system; />
The method comprises the steps of obtaining the air power from the air network at the moment t of the comprehensive energy system; alpha e To purchase electricity CO 2 An emission coefficient; alpha gas To purchase gas CO 2 Emission coefficient.
Preferably, the investment cost F in the solving module inv The formula of (2) is as follows:
the running cost F ope The formula of (2) is as follows:
wherein: gamma ray i Installation cost for unit capacity of the device i; c (C) i The installation capacity for device i; i is the total number of devices in the integrated energy system; alpha is annual rate; y is Y i The operating life of device i; t is a scheduling period;
the electricity price of electricity purchased from the power grid at the moment t; j (J) G Is the price of natural gas; p (P) out,i Output power for device i in period t; beta i Maintenance costs for the unit operation of the device i.
Preferably, the solving module is specifically configured to:
determining that parent population individuals in a genetic algorithm based on a self-adaptive elite retention strategy are electric conversion equipment capacity and multi-type energy storage equipment capacity based on the installed capacity data of the renewable energy sources and related system operation parameters, wherein the fitness value of each individual in the population is the multi-objective optimal configuration function value;
and solving the multi-objective optimal configuration function by adopting the genetic algorithm based on the self-adaptive elite retention strategy to obtain an optimal configuration solution set.
Preferably, the solving module adopts a genetic algorithm based on a self-adaptive elite retention policy to solve the multi-objective optimization configuration function to obtain an optimization configuration solution set, and the method comprises the following steps:
step S1: initializing a population, and setting population scale, iteration times, basic crossover probability and basic variation probability;
step S2: randomly generating a parent population P, wherein each individual in the parent population P represents the capacity of an electric conversion device and the capacity of a multi-type energy storage device, and calculating the fitness value of each individual in the parent population P, wherein the fitness value represents the multi-objective optimal configuration function value;
Step S3: selecting the parent population P through a genetic algorithm, and carrying out self-adaptive cross mutation on the parent population P based on the individual fitness value, the basic cross probability and the basic mutation probability to generate a child population Q;
step S4: mixing the parent population P and the child population Q to obtain a new population R, and then carrying out rapid non-dominant sorting on the new population R to obtain a non-dominant population sequence;
step S5: selecting the population R by adopting a selection strategy of a reference point based on the fitness value to obtain a population Y as a parent population of the next iteration;
step S6: screening dominant individuals in the population R by adopting the self-adaptive elite retention strategy based on the non-dominant population sequence, and adding the dominant individuals into the population Y to serve as a parent population of the next iteration;
step S7: and judging whether the iteration times are reached, if so, obtaining the capacity of the electric conversion equipment, the capacity of the multi-type energy storage equipment and the multi-objective optimal configuration function value corresponding to each body as an optimal configuration solution set, ending, and otherwise, returning to the step S3.
Preferably, the adaptive cross mutation of the parent population P based on the individual fitness value, the basic cross probability and the basic mutation probability in the solving module includes:
Determining an adaptive crossover probability and an adaptive variation probability based on the individual fitness value, the base crossover probability, and the base variation probability;
and carrying out adaptive cross mutation on the parent population P based on the adaptive cross probability and the adaptive mutation probability.
Preferably, the calculation formula of the adaptive crossover probability in the solving module is as follows:
the calculation formula of the adaptive mutation probability is as follows:
wherein: p is p c Is an adaptive crossover probability; k (k) 1 Is a first base crossover probability; k (k) 2 Is a second base crossover probability; p is p m Is the adaptive mutation probability; k (k) 3 Is a first base variation probability; k (k) 4 Is a second base variation probability; f (f) m A fitness value for the individual currently to be mutated; f (f) m Is the maximum fitness value acceptable by the population; f (f) c A larger fitness value for the two individuals to be crossed; f (f) min The fitness minimum value of all individuals in the population; the first base crossover probability is less than the second base crossover probability; the first base variation probability is less than the second base variation probability.
Preferably, the calculation formula of the adaptive elite retention strategy in the solving module is as follows:
wherein: n (N) e Reserving the number of individuals for elite; f (f) i The fitness value of the ith population individual is the fitness value; n is the number of individuals in the population; f (f) b Is the fitness value of the optimal individual in the population.
Preferably, the system operation related parameters in the data acquisition module include one or more of the following: the system comprises a photoelectric power predicted value, a load predicted value, a wind power predicted value, a power price list of different time periods, electric conversion equipment parameters, multi-type energy storage equipment parameters, cogeneration equipment operation parameters, gas boiler operation parameters, absorption refrigerator operation parameters, gas storage tank operation parameters and compressor operation parameters.
According to the method and the device, the optimal configuration scheme of the comprehensive energy system comprising the electric energy conversion equipment and the multi-type energy storage equipment is solved through the data acquisition module, the solving module and the optimal configuration result acquisition module, and the collaborative planning of the electric energy conversion equipment and the multi-type energy storage equipment is performed by establishing the multi-objective optimal configuration model comprising the electric energy conversion equipment and the multi-type energy storage equipment, so that the more comprehensive optimal configuration scheme which is more in line with the actual situation of the comprehensive energy system can be provided. In addition, the solving module adopts a genetic algorithm based on a self-adaptive elite retention strategy and self-adaptive cross variation, and the optimization efficiency is further improved in the solving process.
Example 3:
based on the same inventive concept, the invention also provides a computer device comprising a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application SpecificIntegrated Circuit, ASIC), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular to load and execute one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding functions, to implement the steps of a multi-objective optimization method for an integrated energy system in the above embodiments.
Example 4:
based on the same inventive concept, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a computer device, for storing programs and data. It is understood that the computer readable storage medium herein may include both built-in storage media in a computer device and extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the steps of a multi-objective optimization method for an integrated energy system in the above embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the scope of protection thereof, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that various changes, modifications or equivalents may be made to the specific embodiments of the application after reading the present invention, and these changes, modifications or equivalents are within the scope of protection of the claims appended hereto.
Claims (24)
1. The multi-objective optimization method for the comprehensive energy system is characterized by comprising the following steps of:
acquiring installed capacity data of renewable energy sources in an electric-gas interconnection comprehensive energy system and related parameters of system operation;
based on the installed capacity data of the renewable energy sources and related parameters of system operation, solving a multi-objective optimal configuration model which is built in advance and comprises electric conversion equipment and multi-class energy storage equipment by adopting a genetic algorithm to obtain an optimal configuration solution set;
selecting an optimal configuration result of the electric-gas interconnection comprehensive energy system from the optimal configuration solution set based on the optimization demand;
wherein the multi-objective optimization model is based on minimum system economic cost and CO on the basis of meeting the maximum renewable energy consumption of an electric-gas interconnection comprehensive energy system 2 The least amount of emissions is established.
2. The method of claim 1, wherein the constructing of the multi-objective optimal configuration model comprising the electrical switching apparatus and the plurality of classes of energy storage devices comprises:
constructing an electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multiple types of energy storage equipment based on multiple constraint conditions, wherein the electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multiple types of energy storage equipment comprises one or more of the following electric power system models, a natural gas system model, a coupling equipment model and multiple types of energy storage equipment models;
The economic cost of the model of the electric-gas interconnection integrated energy system comprising the electric gas conversion equipment and the multi-type energy storage equipment is minimum, the renewable energy consumption is maximum and the CO is generated 2 Constructing a multi-objective optimal configuration function by taking the minimum emission as the objective;
and constructing a multi-objective optimal configuration model comprising the electric conversion equipment and the multi-class energy storage equipment based on the electric-air interconnection comprehensive energy system model comprising the electric conversion equipment and the multi-class energy storage equipment and the multi-objective optimal configuration function.
3. The method of claim 2, wherein constructing an electric-to-gas interconnected integrated energy system model comprising an electric gas conversion device and a plurality of types of energy storage devices based on the plurality of constraints comprises:
constructing the power system model based on power balance constraint, unit output constraint, node voltage constraint and branch power flow constraint;
constructing the natural gas system model based on gas source gas outlet quantity constraint, natural gas pipeline operation constraint, pipe village operation constraint, gas storage tank operation constraint, compressor operation constraint and node flow balance constraint;
constructing the coupling equipment model based on the gas turbine output constraint and the electric gas conversion equipment output constraint;
constructing the multi-class energy storage device model based on the electric storage device operation constraint, the heat storage device operation constraint and the cold storage device operation constraint;
And constructing an electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multi-type energy storage equipment by using the electric power system model, the natural gas system model, the coupling equipment model and the multi-type energy storage equipment model.
4. The method according to claim 2, wherein the calculation formula corresponding to the multi-objective optimal configuration function is as follows:
minF 1 ＝F inv +F ope
wherein: f (F) 1 Economic cost for the system; f (F) inv Is investment cost; f (F) ope Is the running cost; f (F) 2 Is the renewable energy consumption rate; t is a scheduling period; n (N) W For stroke motor of systemTotal number of groups; n (N) V The total number of the photoelectric units in the system;
wind power is received for the plan of the t-period system for the wind turbine j; />
The photoelectric power is scheduled to be admitted for the photoelectric unit j for the t-period system; />
The ideal power of the wind turbine j; />
The ideal power of the photoelectric unit j; f (F) 3 Is CO 2 Discharge amount; />
The method comprises the steps of purchasing electric power from a power grid at a time t for a comprehensive energy system; />
The method comprises the steps of obtaining the air power from the air network at the moment t of the comprehensive energy system; alpha e To purchase electricity CO 2 An emission coefficient; alpha gas To purchase gas CO 2 Emission coefficient.
5. The method according to claim 4, wherein the investment cost F inv The formula of (2) is as follows:
the running cost F ope The formula of (2) is as follows:
wherein: gamma ray i Installation cost for unit capacity of the device i; c (C) i The installation capacity for device i; i is the total number of devices in the integrated energy system; alpha is annual rate; y is Y i The operating life of device i; t is a scheduling period;
the electricity price of electricity purchased from the power grid at the moment t; j (J) G Is the price of natural gas; p (P) out,i Output power for device i in period t; beta i Maintenance costs for the unit operation of the device i.
6. The method according to claim 2, wherein the solving the pre-built multi-objective optimal configuration model including the electric conversion equipment and the multi-class energy storage equipment by using a genetic algorithm based on the installed capacity data of the renewable energy source and the system operation related parameters to obtain an optimal configuration solution set comprises:
determining that parent population individuals in a genetic algorithm based on a self-adaptive elite retention strategy are electric conversion equipment capacity and multi-type energy storage equipment capacity based on the installed capacity data of the renewable energy sources and related system operation parameters, wherein the fitness value of each individual in the population is the multi-objective optimal configuration function value;
and solving the multi-objective optimal configuration function by adopting the genetic algorithm based on the self-adaptive elite retention strategy to obtain an optimal configuration solution set.
7. The method of claim 6, wherein solving the multi-objective optimal configuration function using a genetic algorithm based on an adaptive elite retention strategy to obtain an optimal configuration solution set comprises:
step S1: initializing a population, and setting population scale, iteration times, basic crossover probability and basic variation probability;
step S2: randomly generating a parent population P, wherein each individual in the parent population P represents the capacity of an electric conversion device and the capacity of a multi-type energy storage device, and calculating the fitness value of each individual in the parent population P, wherein the fitness value represents the multi-objective optimal configuration function value;
step S3: selecting the parent population P through a genetic algorithm, and carrying out self-adaptive cross mutation on the parent population P based on the individual fitness value, the basic cross probability and the basic mutation probability to generate a child population Q;
step S4: mixing the parent population P and the child population Q to obtain a new population R, and then carrying out rapid non-dominant sorting on the new population R to obtain a non-dominant population sequence;
step S5: selecting the population R by adopting a selection strategy of a reference point based on the fitness value to obtain a population Y as a parent population of the next iteration;
Step S6: screening dominant individuals in the population R by adopting the self-adaptive elite retention strategy based on the non-dominant population sequence, and adding the dominant individuals into the population Y to serve as a parent population of the next iteration;
step S7: and judging whether the iteration times are reached, if so, obtaining the capacity of the electric conversion equipment, the capacity of the multi-type energy storage equipment and the multi-objective optimal configuration function value corresponding to each body as an optimal configuration solution set, ending, and otherwise, returning to the step S3.
8. The method of claim 7, wherein said adaptively cross-mutating said parent population P based on said individual fitness value, said base cross probability, and said base variation probability, comprises:
determining an adaptive crossover probability and an adaptive variation probability based on the individual fitness value, the base crossover probability, and the base variation probability;
and carrying out adaptive cross mutation on the parent population P based on the adaptive cross probability and the adaptive mutation probability.
9. The method of claim 8, wherein the adaptive crossover probability is calculated as:
the calculation formula of the adaptive mutation probability is as follows:
Wherein: p is p c Is an adaptive crossover probability; k (k) 1 Is a first base crossover probability; k (k) 2 Is a second base crossover probability; p is p m Is the adaptive mutation probability; k (k) 3 Is a first base variation probability; k (k) 4 Is a second base variation probability; f (f) m A fitness value for the individual currently to be mutated; f (f) m Is the maximum fitness value acceptable by the population; f (f) c A larger fitness value for the two individuals to be crossed; f (f) min The fitness minimum value of all individuals in the population; the first base crossover probability is less than the second base crossover probability; the first base variation probability is less than the second base variation probability.
10. The method of claim 7, wherein the adaptive elite retention strategy is calculated as follows:
wherein: n (N) e Reserving the number of individuals for elite; f (f) i The fitness value of the ith population individual is the fitness value; n is the number of individuals in the population; f (f) b Is the fitness value of the optimal individual in the population.
11. The method of claim 1, wherein the system operation-related parameters include one or more of: the system comprises a photoelectric power predicted value, a load predicted value, a wind power predicted value, a power price list of different time periods, electric conversion equipment parameters, multi-type energy storage equipment parameters, cogeneration equipment operation parameters, gas boiler operation parameters, absorption refrigerator operation parameters, gas storage tank operation parameters and compressor operation parameters.
12. An electric-gas interconnection comprehensive energy system multi-objective optimization system based on electric conversion gas is characterized by comprising:
and a data acquisition module: the system is used for acquiring installed capacity data of renewable energy sources in the electric-gas interconnection comprehensive energy system and related parameters of system operation;
and a solving module: the system is used for solving a multi-objective optimal configuration model which is built in advance and comprises electric conversion equipment and multi-class energy storage equipment by adopting a genetic algorithm based on the installed capacity data of the renewable energy sources and related parameters of system operation to obtain an optimal configuration solution set;
the optimal configuration result acquisition module: the method comprises the steps of selecting an optimal configuration result of an electric-gas interconnection comprehensive energy system from the optimal configuration solution set based on an optimization requirement;
wherein the multi-objective optimization model is based on minimum system economic cost and CO on the basis of meeting the maximum renewable energy consumption of an electric-gas interconnection comprehensive energy system 2 The least amount of emissions is established.
13. The system of claim 12, wherein the construction of the multi-objective optimal configuration model including the electrical switching apparatus and the plurality of types of energy storage apparatuses in the solution module comprises:
constructing an electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multiple types of energy storage equipment based on multiple constraint conditions, wherein the electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multiple types of energy storage equipment comprises one or more of the following electric power system models, a natural gas system model, a coupling equipment model and multiple types of energy storage equipment models;
The economic cost of the model of the electric-gas interconnection integrated energy system comprising the electric gas conversion equipment and the multi-type energy storage equipment is minimum, the renewable energy consumption is maximum and the CO is generated 2 Constructing a multi-objective optimal configuration function by taking the minimum emission as the objective;
and constructing a multi-objective optimal configuration model comprising the electric conversion equipment and the multi-class energy storage equipment based on the electric-air interconnection comprehensive energy system model comprising the electric conversion equipment and the multi-class energy storage equipment and the multi-objective optimal configuration function.
14. The system of claim 13, wherein the solution module builds an electric-to-gas interconnected integrated energy system model comprising an electric conversion device and a plurality of classes of energy storage devices based on a plurality of constraints, comprising:
constructing the power system model based on power balance constraint, unit output constraint, node voltage constraint and branch power flow constraint;
constructing the natural gas system model based on gas source gas outlet quantity constraint, natural gas pipeline operation constraint, pipe village operation constraint, gas storage tank operation constraint, compressor operation constraint and node flow balance constraint;
constructing the coupling equipment model based on the gas turbine output constraint and the electric gas conversion equipment output constraint;
Constructing the multi-class energy storage device model based on the electric storage device operation constraint, the heat storage device operation constraint and the cold storage device operation constraint;
and constructing an electric-gas interconnection comprehensive energy system model comprising electric conversion equipment and multi-type energy storage equipment by using the electric power system model, the natural gas system model, the coupling equipment model and the multi-type energy storage equipment model.
15. The system of claim 13, wherein the calculation formula corresponding to the multi-objective optimization configuration function in the solution module is as follows:
minF 1 ＝F inv +F ope
wherein: f (F) 1 Economic cost for the system; f (F) inv Is investment cost; f (F) ope Is the running cost; f (F) 2 Is the renewable energy consumption rate; t is a scheduling period; n (N) W The total number of the wind turbines in the system is; n (N) V The total number of the photoelectric units in the system;
wind power is received for the plan of the t-period system for the wind turbine j; />
The photoelectric power is scheduled to be admitted for the photoelectric unit j for the t-period system; />
The ideal power of the wind turbine j; />
The ideal power of the photoelectric unit j; f (F) 3 Is CO 2 Discharge amount; />
The method comprises the steps of purchasing electric power from a power grid at a time t for a comprehensive energy system; />
The method comprises the steps of obtaining the air power from the air network at the moment t of the comprehensive energy system; alpha e To purchase electricity CO 2 An emission coefficient; alpha gas To purchase gas CO 2 Emission coefficient.
16. The system of claim 15, wherein the cost of investment F in the solution module inv The formula of (2) is as follows:
the running cost F ope The formula of (2) is as follows:
wherein: gamma ray i Installation cost for unit capacity of the device i; c (C) i The installation capacity for device i; i is the total number of devices in the integrated energy system; alpha is annual rate; y is Y i The operating life of device i; t is a scheduling period;
the electricity price of electricity purchased from the power grid at the moment t; j (J) G Is the price of natural gas; p (P) out,i Output power for device i in period t; beta i Maintenance costs for the unit operation of the device i.
17. The system of claim 13, wherein the solution module is specifically configured to:
determining that parent population individuals in a genetic algorithm based on a self-adaptive elite retention strategy are electric conversion equipment capacity and multi-type energy storage equipment capacity based on the installed capacity data of the renewable energy sources and related system operation parameters, wherein the fitness value of each individual in the population is the multi-objective optimal configuration function value;
and solving the multi-objective optimal configuration function by adopting the genetic algorithm based on the self-adaptive elite retention strategy to obtain an optimal configuration solution set.
18. The system of claim 17, wherein the solving module solves the multi-objective optimal configuration function using a genetic algorithm based on an adaptive elite retention policy to obtain an optimal configuration solution set, comprising:
step S1: initializing a population, and setting population scale, iteration times, basic crossover probability and basic variation probability;
step S2: randomly generating a parent population P, wherein each individual in the parent population P represents the capacity of an electric conversion device and the capacity of a multi-type energy storage device, and calculating the fitness value of each individual in the parent population P, wherein the fitness value represents the multi-objective optimal configuration function value;
step S3: selecting the parent population P through a genetic algorithm, and carrying out self-adaptive cross mutation on the parent population P based on the individual fitness value, the basic cross probability and the basic mutation probability to generate a child population Q;
step S4: mixing the parent population P and the child population Q to obtain a new population R, and then carrying out rapid non-dominant sorting on the new population R to obtain a non-dominant population sequence;
step S5: selecting the population R by adopting a selection strategy of a reference point based on the fitness value to obtain a population Y as a parent population of the next iteration;
Step S6: screening dominant individuals in the population R by adopting the self-adaptive elite retention strategy based on the non-dominant population sequence, and adding the dominant individuals into the population Y to serve as a parent population of the next iteration;
step S7: and judging whether the iteration times are reached, if so, obtaining the capacity of the electric conversion equipment, the capacity of the multi-type energy storage equipment and the multi-objective optimal configuration function value corresponding to each body as an optimal configuration solution set, ending, and otherwise, returning to the step S3.
19. The system of claim 18, wherein the solution module adaptively cross-mutates the parent population P based on the individual fitness value, the base cross probability, and the base variation probability, comprising:
determining an adaptive crossover probability and an adaptive variation probability based on the individual fitness value, the base crossover probability, and the base variation probability;
and carrying out adaptive cross mutation on the parent population P based on the adaptive cross probability and the adaptive mutation probability.
20. The system of claim 13, wherein the adaptive crossover probability in the solution module is calculated as:
The calculation formula of the adaptive mutation probability is as follows:
wherein: p is p c Is an adaptive crossover probability; k (k) 1 Is a first base crossover probability; k (k) 2 Is a second base crossover probability; p is p m Is the adaptive mutation probability; k (k) 3 Is a first base variation probability; k (k) 4 Is a second base variation probability; f (f) m A fitness value for the individual currently to be mutated; f (f) m Is the maximum fitness value acceptable by the population; f (f) c A larger fitness value for the two individuals to be crossed; f (f) min The fitness minimum value of all individuals in the population; the first base crossover probability is less than the second base crossover probability; the first base variation probability is less than the second base variation probability.
21. The system of claim 13, wherein the adaptive elite retention strategy in the solution module is calculated as follows:
wherein: n (N) e Reserving the number of individuals for elite; f (f) i The fitness value of the ith population individual is the fitness value; n is the number of individuals in the population; f (f) b Is the fitness value of the optimal individual in the population.
22. The system of claim 12, wherein the system operation related parameters in the data acquisition module include one or more of: the system comprises a photoelectric power predicted value, a load predicted value, a wind power predicted value, a power price list of different time periods, electric conversion equipment parameters, multi-type energy storage equipment parameters, cogeneration equipment operation parameters, gas boiler operation parameters, absorption refrigerator operation parameters, gas storage tank operation parameters and compressor operation parameters.
23. A computer device, comprising: one or more processors;
a memory for storing one or more programs;
a comprehensive energy system multi-objective optimization method according to any one of claims 1 to 11, when said one or more programs are executed by said one or more processors.
24. A computer readable storage medium, characterized in that a computer program is stored thereon, which computer program, when executed, implements a multi-objective optimization method of an integrated energy system according to any of claims 1 to 11.
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| CN117217500A (en) * | 2023-11-08 | 2023-12-12 | 湘潭大学 | Electric-gas comprehensive energy system source network collaborative planning method considering flexibility requirement |
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| CN116663936A (en) * | 2023-07-24 | 2023-08-29 | 长江三峡集团实业发展(北京)有限公司 | Capacity expansion planning method, device, equipment and medium for electric comprehensive energy system |
| CN116663936B (en) * | 2023-07-24 | 2024-01-09 | 长江三峡集团实业发展(北京)有限公司 | Capacity expansion planning method, device, equipment and medium for electric comprehensive energy system |
| CN117217500A (en) * | 2023-11-08 | 2023-12-12 | 湘潭大学 | Electric-gas comprehensive energy system source network collaborative planning method considering flexibility requirement |
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