CN118095065A - Parameter configuration method and system for combined electric hydrogen-heat-power system based on multi-objective optimization - Google Patents
Parameter configuration method and system for combined electric hydrogen-heat-power system based on multi-objective optimization Download PDFInfo
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Abstract
The invention provides a parameter configuration method and a system of an electric hydrogen-heat combined power system based on multi-objective optimization, wherein the method comprises the steps of obtaining inherent parameters of the electric hydrogen-heat combined power system to be optimized; based on inherent parameters, adopting a multi-objective genetic algorithm to optimally solve a preset multi-objective function to obtain an optimal solution set of parameters to be configured of the initial system; performing pareto front analysis and statistical analysis on the initial system to-be-configured parameter optimal solution set to obtain a system to-be-configured parameter optimal solution; the preset multi-objective function comprises a cost objective function and an energy efficiency objective function; the cost objective function is added and set according to the fixed cost of the system energy supply side, the fixed cost of the system user side and the system operation cost; by considering the corresponding equipment parameter configuration at the user side and carrying out system parameter configuration analysis on a plurality of targets of cost and energy efficiency, comprehensive consideration of multiple aspects of system parameter configuration optimization is realized, and the balance of economy and energy efficiency is realized by the power-assisted system equipment configuration design.
Description
Technical Field
The invention belongs to the technical field of parameter configuration of an electric hydrogen-heat combined power system, and particularly relates to a parameter configuration method and system of the electric hydrogen-heat combined power system based on multi-objective optimization.
Background
The distributed energy system is different from the traditional centralized energy supply system with large capacity for centralized energy supply and large-scale energy supply by special facilities, is directly oriented to users, produces and supplies energy on site according to the demands of the users, has multiple functions and can meet the requirements of medium-and small-sized energy conversion and utilization systems of multiple targets.
The cogeneration technology is a cogeneration system which is based on the concept of cascade utilization of energy and generates heat energy and electric energy. The energy conversion device is utilized to convert other forms of energy into electric energy, and waste heat generated in the process is utilized to supply heat. The cogeneration technology can greatly improve the energy utilization efficiency, and can improve the energy utilization efficiency from about 40% to 80-90% of the conventional power generation system.
Hydrogen fuel cells of hydrogen fuel cell technology in distributed energy systems are power generation devices that directly convert chemical energy of hydrogen and oxygen into electrical energy. The basic principle is that the reverse reaction of electrolyzed water supplies hydrogen and oxygen to the anode and the cathode respectively, and after hydrogen diffuses outwards through the anode and reacts with electrolyte, electrons are released and reach the cathode through an external load. The hydrogen fuel cell product has only water, almost no pollution to the environment in the operation process, quiet operation, power generation efficiency up to more than 50% and operation temperature of 60-80 ℃.
The existing parameter configuration method of the electricity-hydrogen-heat-electricity combined supply system is mainly aimed at building or community micro-grid systems related to various distributed energy sources, and is not designed for corresponding equipment setting schemes in energy utilization scenes of residential users. In the prior art, the parameter configuration optimization of the combined electric hydrogen and heat power system is mostly optimized for a single target or strategy, and the system parameter configuration optimization considering a plurality of targets is lacking.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a parameter configuration method of an electric hydrogen-heat-power cogeneration system based on multi-objective optimization, which comprises the following steps:
acquiring inherent parameters of an electric hydrogen-heat-power cogeneration system to be optimized;
Based on the inherent parameters, adopting a multi-objective genetic algorithm to optimally solve a preset multi-objective function to obtain an optimal solution set of initial system parameters to be configured corresponding to the inherent parameters;
performing pareto front analysis and statistical analysis on the initial system to-be-configured parameter optimal solution set to obtain a system to-be-configured parameter optimal solution corresponding to the inherent parameters;
Wherein the multi-objective function includes a cost objective function and an energy efficiency objective function; the cost objective function is set according to the sum of the system energy supply side fixed cost, the system user side fixed cost and the system operation cost.
Preferably, the intrinsic parameters include a plant fixed parameter and a plant operating parameter;
the system to be configured parameters include at least one or more of the following:
device power rating and device capacity rating.
Preferably, the system operation cost comprises the following calculation process:
adding and converting the generated energy, the stored energy and the power consumption of the power equipment, and superposing the added and converted result of the heat consumption of the thermal equipment to obtain the daily maintenance cost of the equipment;
Adding the daily maintenance cost of the equipment and the power grid electricity purchasing cost in the running process of the system for annual, so as to obtain the running cost of the system;
the power equipment comprises system energy supply side power equipment and system user side power equipment;
the generated energy and the power consumption are calculated according to the rated power, the fixed parameters and the operation parameters of the corresponding equipment;
The electricity storage capacity and the heat consumption are calculated according to the rated capacity of the corresponding equipment, the equipment fixing parameters and the equipment operation parameters.
Preferably, the system energy supply side fixed cost includes the following calculation process:
the acquisition cost of each device at the energy supply side of the system is converted into annual average cost according to the set service life period of the device and added to obtain the fixed cost at the energy supply side of the system;
the fixed cost of the system user side comprises the following calculation processes:
The acquisition cost of each device at the system user side is converted by the annual average cost according to the set service life period of the device and added to obtain the fixed cost at the system user side;
the service life period of the equipment belongs to the fixed parameter of the equipment, and the acquisition cost is calculated according to the inherent parameter and the parameter to be configured of the system.
Preferably, the energy efficiency objective function includes the following calculation process:
determining total energy output by the system, net energy stored by the system and total energy input by the system;
calculating the total energy output by the system, the net energy stored by the system and the total energy input by the system to obtain the energy efficiency objective function;
the system output total energy, the system net energy and the system input total energy are calculated according to the system to-be-configured parameter, the equipment fixing parameter and the equipment operation parameter.
Preferably, the energy efficiency objective function is expressed as:
Where M 2 is the energy efficiency objective function, η total is the system level energy efficiency, E use is the total energy output by the system, E st ore is the net energy stored by the system, and E supply,in is the total energy input by the system.
Preferably, the optimizing and solving a preset multi-objective function by adopting a multi-objective genetic algorithm based on the intrinsic parameters to obtain an optimal solution set of the initial system parameters to be configured corresponding to the intrinsic parameters, including:
based on the inherent parameters, randomly generating initial values of populations corresponding to the inherent parameters under the constraint of preset constraint conditions; each individual in the population corresponds to a system to-be-configured parameter solution of the multi-objective function;
Calculating non-dominant grades of the to-be-configured parameter solutions of all the systems by adopting a rapid non-dominant sorting algorithm, selecting a plurality of the most excellent to-be-configured parameter solutions from the population to perform crossing and mutation operations according to a principle of lowest non-dominant grades, obtaining a new to-be-configured parameter solution of the system, and repeatedly and iteratively updating the to-be-configured parameter solution of the system until the iteration number reaches a set maximum value to obtain a new population;
And outputting a plurality of system to-be-configured parameter solutions from the new population as an initial system to-be-configured parameter optimal solution set by adopting a principle of lowest non-dominant level according to the non-dominant level of each system to-be-configured parameter solution in the new population.
Preferably, the constraint conditions include a power balance constraint condition, a power generation amount constraint condition, and a power consumption constraint condition.
Preferably, the performing pareto front analysis and statistical analysis on the initial system to-be-configured parameter optimal solution set to obtain a system to-be-configured parameter optimal solution includes:
Performing pareto front analysis on the optimal solution set of the parameters to be configured of the initial system to obtain the optimal solution set of the pareto;
and carrying out statistical analysis on the pareto optimal solution set to obtain an optimal solution of the system to be configured parameters.
Preferably, the performing pareto front analysis on the parameter optimal solution set to be configured of the initial system to obtain a pareto optimal solution set includes:
Judging all solutions in the optimal solution set of the parameters to be configured of the initial system respectively, and if one solution does not exist and can be better than the solution on all objective functions of the multiple objective functions, marking the solution as a pareto optimal solution; and the set of all pareto optimal solutions is the pareto optimal solution set.
Preferably, the performing statistical analysis on the pareto optimal solution set to obtain an optimal solution of the system to be configured parameters includes:
carrying out statistical analysis on the pareto optimal solution set by adopting a good-bad solution distance method, and determining optimal ideal solutions and worst ideal solutions of all objective functions of the system;
and calculating and scoring according to the distance between each solution in the pareto optimal solution set and the optimal ideal solution and the worst ideal solution, wherein the pareto optimal solution corresponding to the minimum scoring value is the optimal solution of the parameters to be configured of the system.
Based on the same inventive concept, the invention also provides an electro-hydro-thermal cogeneration system parameter configuration system based on multi-objective optimization, which comprises:
The system parameter acquisition module is used for acquiring inherent parameters of the combined electric hydrogen and heat power system to be optimized;
the initial solving module is used for optimizing and solving a preset multi-objective function by adopting a multi-objective genetic algorithm based on the inherent parameters to obtain an optimal solution set of the initial system parameters to be configured corresponding to the inherent parameters;
the optimal solution analysis determining module is used for performing pareto front edge analysis and statistical analysis on the initial system to-be-configured parameter optimal solution set to obtain a system to-be-configured parameter optimal solution;
Wherein the multi-objective function includes a cost objective function and an energy efficiency objective function; the cost objective function is set according to the sum of the system energy supply side fixed cost, the system user side fixed cost and the system operation cost.
Preferably, the intrinsic parameters in the system parameter acquisition module include equipment fixing parameters and equipment operation parameters;
the system to be configured parameters include at least one or more of the following:
device power rating and device capacity rating.
Preferably, the system operation cost in the initial solving module includes the following calculation process:
adding and converting the generated energy, the stored energy and the power consumption of the power equipment, and superposing the added and converted result of the heat consumption of the thermal equipment to obtain the daily maintenance cost of the equipment;
Adding the daily maintenance cost of the equipment and the power grid electricity purchasing cost in the running process of the system for annual, so as to obtain the running cost of the system;
the power equipment comprises system energy supply side power equipment and system user side power equipment;
the generated energy and the power consumption are calculated according to the rated power, the fixed parameters and the operation parameters of the corresponding equipment;
The electricity storage capacity and the heat consumption are calculated according to the rated capacity of the corresponding equipment, the equipment fixing parameters and the equipment operation parameters.
Preferably, the system energy supply side fixed cost in the initial solving module includes the following calculation process:
the acquisition cost of each device at the energy supply side of the system is converted into annual average cost according to the set service life period of the device and added to obtain the fixed cost at the energy supply side of the system;
The system user side fixed cost in the initial solving module comprises the following calculation process:
The acquisition cost of each device at the system user side is converted by the annual average cost according to the set service life period of the device and added to obtain the fixed cost at the system user side;
the service life period of the equipment belongs to the fixed parameter of the equipment, and the acquisition cost is calculated according to the inherent parameter and the parameter to be configured of the system.
Preferably, the energy efficiency objective function in the initial solving module includes the following calculation process:
determining total energy output by the system, net energy stored by the system and total energy input by the system;
calculating the total energy output by the system, the net energy stored by the system and the total energy input by the system to obtain the energy efficiency objective function;
the system output total energy, the system net energy and the system input total energy are calculated according to the system to-be-configured parameter, the equipment fixing parameter and the equipment operation parameter.
Preferably, the energy efficiency objective function in the initial solving module is expressed as:
Where M 2 is the energy efficiency objective function, η total is the system level energy efficiency, E use is the total energy output by the system, E store is the net energy stored by the system, and E supply,in is the total energy input by the system.
Preferably, the initial solving module is specifically configured to:
based on the inherent parameters, randomly generating initial values of populations corresponding to the inherent parameters under the constraint of preset constraint conditions; each individual in the population corresponds to a system to-be-configured parameter solution of the multi-objective function;
Calculating non-dominant grades of the to-be-configured parameter solutions of all the systems by adopting a rapid non-dominant sorting algorithm, selecting a plurality of the most excellent to-be-configured parameter solutions from the population to perform crossing and mutation operations according to a principle of lowest non-dominant grades, obtaining a new to-be-configured parameter solution of the system, and repeatedly and iteratively updating the to-be-configured parameter solution of the system until the iteration number reaches a set maximum value to obtain a new population;
And outputting a plurality of system to-be-configured parameter solutions from the new population as an initial system to-be-configured parameter optimal solution set by adopting a principle of lowest non-dominant level according to the non-dominant level of each system to-be-configured parameter solution in the new population.
Preferably, the constraint conditions in the initial solving module include a power balance constraint condition, a power generation capacity constraint condition and a power consumption constraint condition.
Preferably, the optimal solution analysis determining module is specifically configured to:
Performing pareto front analysis on the optimal solution set of the parameters to be configured of the initial system to obtain the optimal solution set of the pareto;
and carrying out statistical analysis on the pareto optimal solution set to obtain an optimal solution of the system to be configured parameters.
Preferably, the optimal solution analysis determining module performs pareto front analysis on the optimal solution set of the parameters to be configured of the initial system to obtain a pareto optimal solution set, including:
Judging all solutions in the optimal solution set of the parameters to be configured of the initial system respectively, and if one solution does not exist and can be better than the solution on all objective functions of the multiple objective functions, marking the solution as a pareto optimal solution; and the set of all pareto optimal solutions is the pareto optimal solution set.
Preferably, the optimal solution analysis determining module performs statistical analysis on the pareto optimal solution set to obtain an optimal solution of a parameter to be configured of the system, including:
carrying out statistical analysis on the pareto optimal solution set by adopting a good-bad solution distance method, and determining optimal ideal solutions and worst ideal solutions of all objective functions of the system;
and calculating and scoring according to the distance between each solution in the pareto optimal solution set and the optimal ideal solution and the worst ideal solution, wherein the pareto optimal solution corresponding to the minimum scoring value is the optimal solution of the parameters to be configured of the system.
Based on the same inventive concept, the present invention also provides a computer device, comprising: one or more processors;
a memory for storing one or more programs;
the parameter configuration method of the combined electric hydrogen and heat power system is realized when the one or more programs are executed by the one or more processors.
Based on the same inventive concept, the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed, implements the parameter configuration method of the combined electric hydrogen and heat power system as described above.
Compared with the closest prior art, the invention has the following beneficial effects:
The invention provides a parameter configuration method and a system of an electric hydrogen-heat combined power system based on multi-objective optimization, wherein the method comprises the steps of obtaining inherent parameters of the electric hydrogen-heat combined power system to be optimized; based on the inherent parameters, adopting a multi-objective genetic algorithm to optimally solve a preset multi-objective function to obtain an optimal solution set of initial system parameters to be configured corresponding to the inherent parameters; performing pareto front analysis and statistical analysis on the initial system to-be-configured parameter optimal solution set to obtain a system to-be-configured parameter optimal solution corresponding to the inherent parameters; wherein the multi-objective function includes a cost objective function and an energy efficiency objective function; the cost objective function is added and set according to the fixed cost of the system energy supply side, the fixed cost of the system user side and the system operation cost; by considering the corresponding equipment parameter configuration in the energy utilization scene of the resident user side and carrying out system parameter configuration analysis on a plurality of targets of cost and energy efficiency, comprehensive consideration of multiple aspects of system parameter configuration optimization is realized, and the power assisting system equipment configuration design realizes balance of economy and energy efficiency.
Drawings
FIG. 1 is a schematic flow chart of a parameter configuration method of an electric hydrogen-heat cogeneration system based on multi-objective optimization;
Fig. 2 is a schematic diagram of a parameter configuration system of an electric hydrogen-heat power cogeneration system based on multi-objective optimization.
Detailed Description
The following describes the embodiments of the present invention in further detail with reference to the drawings.
Example 1:
the flow chart of the parameter configuration method of the combined electrical hydrogen and heat power system based on multi-objective optimization is shown in figure 1, and the method comprises the following steps:
s1, acquiring inherent parameters of an electric hydrogen-heat-power cogeneration system to be optimized;
S2, based on the inherent parameters, adopting a multi-objective genetic algorithm to optimally solve a preset multi-objective function to obtain an optimal solution set of initial system parameters to be configured corresponding to the inherent parameters;
S3, performing pareto front analysis and statistical analysis on the initial system to-be-configured parameter optimal solution set to obtain a system to-be-configured parameter optimal solution corresponding to the inherent parameters;
wherein the multi-objective function includes a cost objective function and an energy efficiency objective function; the cost objective function is added and set according to the fixed cost of the system energy supply side, the fixed cost of the system user side and the system operation cost;
The intrinsic parameters in step S1 include a device fixed parameter and a device operation parameter;
the system to be configured parameters include at least one or more of the following:
device power rating and device capacity rating.
Wherein the intrinsic parameters reflect the composition characteristics of the system equipment and the intrinsic relation among the equipment.
In this embodiment, the equipment fixed parameters include a discount rate, an equipment life cycle, a photovoltaic power generation equipment unit power cost, a wind power generation equipment unit power cost, a fuel cell unit power cost, a storage battery unit capacity cost, a hydrogen storage tank unit volume cost, a heat storage water tank unit volume cost, a power equipment unit maintenance cost, a thermal equipment unit maintenance cost, and a municipal electricity purchasing unit cost;
The equipment operation parameters comprise the generated energy of the photovoltaic power generation equipment on a typical working day, typical illumination time, the generated energy of the wind power generation equipment on a typical working day, typical wind power generation time, the generated energy of a fuel cell on a typical working day, the typical working time of the fuel cell on a typical working day, the accumulated charge quantity of a storage battery on a typical working day, the accumulated discharge quantity of the storage battery on a typical working day, the hydrogen storage quantity of a hydrogen storage tank on a typical working day, the maximum hydrogen storage pressure, the hydrogen storage temperature, the working voltage of a single cell of the fuel cell, the constant specific heat capacity of water, the inlet and outlet water temperature difference of a heat storage water tank, the total electricity consumption of a user and the total heat consumption of the user.
The total amount of user power usage is determined by the average daily power usage of a typical user. The length of off-peak electricity is determined by government-enforced electricity price policies. The typical illumination time period and the typical wind power generation time period are determined by domestic average illumination and wind power generation time periods. The unit power cost of the fuel cell and the electrolytic cell, the unit capacity cost of the storage battery, the unit volume cost of the hydrogen storage tank and the heat storage water tank, the maintenance unit cost of the electric equipment and the thermal equipment and other parameters are all determined by the main stream price level of the market. The fuel cell and cell voltages are determined by market mainstream product parameters, reflecting the energy efficiency levels of the fuel cell and cell, respectively. The maximum pressure and the temperature of the hydrogen storage are determined by the type of the hydrogen storage bottle, and the maximum hydrogen storage amount of the hydrogen storage tank is influenced by the volume of the hydrogen storage tank. The gas constant and the specific heat capacity of water are necessary constants for calculating the hydrogen storage amount and the heat exchange amount. The temperature difference between the inlet and the outlet of the heat storage water tank is usually 50 ℃ under the condition of meeting the requirement of hot water for residential users. The discount rate is the interest rate for converting the future amount into the present value, and can be generally calculated as 3%. The device life cycle is determined by the specific type of device.
The equipment rated power comprises the rated power of the photovoltaic power generation equipment, the rated power of the wind power generation equipment, the rated output electric power of the fuel cell and the heat recovered by the heat storage water tank through waste heat utilization;
the rated capacity of the equipment comprises the capacity of a storage battery and the volume of a hydrogen storage tank.
The step S2 specifically comprises the following steps:
Based on the inherent parameters, randomly generating initial values of N populations corresponding to the inherent parameters under the constraint of preset constraint conditions; each individual in the population corresponds to a system to-be-configured parameter solution of the multi-objective function;
Aiming at a cost objective function M 1 and an energy efficiency objective function M 2, calculating non-dominant grades of to-be-configured parameter solutions of all systems by adopting a rapid non-dominant sorting algorithm, selecting M most excellent to-be-configured parameter solutions of the systems from the population according to a principle of lowest non-dominant grades to perform crossing and mutation operations to obtain new to-be-configured parameter solutions of the systems, and repeatedly and iteratively updating the to-be-configured parameter solutions of the systems until the iteration number reaches a set maximum value to obtain a new population;
and outputting M system parameter solutions to be configured from the new population as an initial system parameter optimal solution set to be configured according to the non-dominant level of each system parameter solution to be configured in the new population by adopting a principle of lowest non-dominant level, wherein M is less than N.
The step S3 specifically comprises the following steps:
Performing pareto front analysis on the optimal solution set of the parameters to be configured of the initial system to obtain the optimal solution set of the pareto;
and carrying out statistical analysis on the pareto optimal solution set to obtain an optimal solution of the system to be configured parameters.
Performing pareto front analysis on the parameter optimal solution set to be configured of the initial system to obtain a pareto optimal solution set, wherein the method comprises the following steps:
judging all solutions in the optimal solution set of the parameters to be configured of the initial system respectively, and if one solution is not better than the solution in the cost objective function and the energy efficiency objective function, but worse than the solution in at least one objective function, marking the solution as the pareto optimal solution; and the pareto optimal solution set is the pareto optimal solution set with the cost and the system energy efficiency as targets, and the pareto optimal solution is presented as the pareto front by taking the cost and the system energy efficiency as the abscissa and the ordinate respectively.
The statistical analysis is performed on the pareto optimal solution set to obtain an optimal solution of the system to be configured parameters, including:
carrying out statistical analysis on the pareto optimal solution set by adopting a good-bad solution distance method, and determining optimal ideal solutions and worst ideal solutions of all objective functions of the system;
and calculating and scoring according to the distance between each solution in the pareto optimal solution set and the optimal ideal solution and the worst ideal solution, wherein the pareto optimal solution corresponding to the minimum scoring value is the optimal solution of the parameters to be configured of the system.
In this embodiment, the method for determining the optimal ideal solution z min is as follows: and taking the minimum value of each objective function in the pareto optimal solution set as the corresponding objective function value of the optimal ideal solution. The determination method of the worst ideal solution z max is as follows: and taking the maximum value of each objective function in the pareto optimal solution set as the corresponding objective function value of the worst ideal solution.
The distance d min between the pareto optimal solution and the optimal ideal solution z min and the distance d max between the pareto optimal solution and the worst ideal solution z max are calculated:
Wherein z x is the xth objective function value of the pareto optimal solution; d min,y、dmax,y is the distance between the y-th pareto optimal solution and the optimal and worst ideal solutions.
The pareto optimal solutions can be scored S y according to the distance, and the calculation formula is as follows:
And sequencing S y, wherein the pareto optimal solution corresponding to the minimum value of S y is the selected system optimal solution. And configuring an optimization scheme by using all equipment parameters corresponding to the optimal solution of the system as finally output system equipment parameters.
The multi-objective function and constraint conditions need to be preset before step S2, specifically:
when the multi-objective function is preset, the system energy supply side fixed cost comprises the following calculation process:
the acquisition cost of each device at the energy supply side of the system is converted into annual average cost according to the set service life period of the device and added to obtain the fixed cost at the energy supply side of the system;
In this embodiment, each system energy supply side device i includes a photovoltaic power generation device and a wind power generation device, and the system energy supply side fixed cost C 1 has a calculation formula as follows:
Wherein C 1,i is the annual average fixed cost/yuan of the system energy supply side device i; r is the discount rate; n is the service life period of the equipment, and the service life of the storage battery is shorter than that of other equipment; c i is the acquisition cost per unit of the system energy supply side device i;
Wherein, C i includes photovoltaic power generation equipment acquisition cost C PV and wind power generation equipment acquisition cost C WD, and the computational formula is:
Wherein P PV is rated power/W of the photovoltaic power generation equipment; e PV is the power generation amount/J of the photovoltaic power generation equipment in a typical working day; k PV is the unit power cost per unit of the photovoltaic power generation equipment/W -1;t1 is typical illumination time length, namely the ratio of typical solar power generation capacity of the photovoltaic power generation equipment to rated power of the photovoltaic power generation equipment;
P WD is rated power/W of wind power generation equipment; e WD is the power generation amount/J of the wind power generation equipment in a typical working day; k WD is the unit power cost per unit of the wind power generation equipment/W -1;t2 is the typical wind power generation time length, namely the ratio of the typical daily power generation amount of the wind power generation equipment to the rated power of the wind power generation equipment.
The fixed cost of the system user side comprises the following calculation processes:
The acquisition cost of each device at the system user side is converted by the annual average cost according to the set service life period of the device and added to obtain the fixed cost at the system user side;
the service life period of the equipment belongs to the fixed parameter of the equipment, and the acquisition cost is calculated according to the inherent parameter and the parameter to be configured of the system.
In this embodiment, each system user side device j includes a fuel cell, an electrolytic cell, a storage battery, a hydrogen storage tank, and a hot water storage tank, where a calculation formula of the system user side fixed cost C 3 is:
Wherein, C 3,j is the annual average fixed cost/yuan of the system user side equipment j; c j is the acquisition cost/unit of the system energy supply side device j;
Wherein, C j includes fuel cell purchase cost C FC, electrolytic cell purchase cost C EL, battery purchase cost C BT, hydrogen storage tank purchase cost C HS, heat storage water tank purchase cost C TANK, and the computational formula is:
Wherein P FC is rated output electric power/W of the fuel cell; e FC is the power generation per J of the fuel cell on a typical workday; n FC is the typical daily operating time of the fuel cell per hour; k FC is the fuel cell unit power cost per unit of W -1;
P EL is the power consumption/W of electrolysis Chi Eding; e EL is the cell power consumption per J at a typical workday, which is related to the fuel cell daily hydrogen consumption; n EL is the typical daily working time per hour of the electrolytic cell; k EL is the cell system unit power cost per unit of W -1;
E BT is the capacity/J of the storage battery, namely the electricity storage capacity; e BT,in is the accumulated charge of the battery per J at a typical workday; e BT,out is the accumulated discharge amount/J of the storage battery in a typical working day; k BT is the cost per unit capacity of the storage battery per unit of Wh -1;
V HS is the hydrogen storage tank volume/m 3;NHS is the hydrogen storage consumption per mol of the hydrogen storage tank on a typical workday, which is consistent with the daily hydrogen consumption of the fuel cell; p max is the maximum pressure of hydrogen storage/Pa; t is hydrogen storage temperature/K; u cell,FC is the fuel cell operating voltage/V; k HS is the cost per unit volume of the hydrogen storage tank per unit m -3; r is a gas constant; f is Faraday constant;
Q recycle is heat/J recovered and utilized by the heat storage water tank through waste heat utilization; k TANK is the unit volume cost of the heat storage water tank/yuan.L -1;CP is the constant pressure specific heat capacity of water/J.kg -1·K-1; delta T is the temperature difference of water at the inlet and outlet of the heat storage water tank/. Degree.C.
The system operation cost comprises the following calculation processes:
adding and converting the generated energy, the stored energy and the power consumption of the power equipment, and superposing the added and converted result of the heat consumption of the thermal equipment to obtain the daily maintenance cost of the equipment;
in this embodiment, the calculation formula of the daily maintenance cost C 2,1 of the device is:
Wherein, C e is the unit maintenance cost of the power equipment/Yuan-kWh -1;Cq is the unit maintenance cost of the thermal equipment/Yuan-MJ -1;EEL is the power consumption of the electrolytic cell/J under the typical working day, and E PV is the power generation amount of the photovoltaic power generation equipment/J under the typical working day; e WD is the power generation amount/J of the wind power generation equipment in a typical working day; e FC is the power generation per J of the fuel cell on a typical workday; e BT is battery capacity/J;
Adding the daily maintenance cost of the equipment and the power grid electricity purchasing cost in the running process of the system for annual, so as to obtain the running cost of the system;
In this embodiment, the power grid purchase cost C 2,2 in the system operation process is obtained by converting the power grid power supply amount calculated by coupling between the user side power load and the equipment parameter, and the calculation formula is as follows:
C2,2＝∫CgPg(t)dt＝CgEg＝Cg(EEL+Euser-EPV-EWD-EFC+EBT,out-EBT,in),
wherein, C g is municipal electricity purchasing unit cost/yuan; p g (t) is the power transmission power/W of the power grid; e g is the power supply quantity/J of the power grid, namely the total quantity of the power grid actually supplying power to the system; e user is the total daily electricity load of the user/J; e BT,in is the accumulated charge of the battery per J at a typical workday; e BT,out is the accumulated discharge amount/J of the storage battery in a typical working day;
Finally, the calculation formula of the system operation cost C 2 is:
C2＝365×(C2,1+C2,2)。
The power equipment comprises system energy supply side power equipment and system user side power equipment;
In this embodiment, the system energy supply side power device includes a photovoltaic power generation device and a wind power generation device; the system user side power equipment comprises a fuel cell, an electrolytic cell and a storage battery; the thermal device comprises a heat storage water tank at the user side.
The generated energy and the power consumption are calculated according to the rated power, the fixed parameters and the operation parameters of the corresponding equipment;
The electricity storage capacity and the heat consumption are calculated according to the rated capacity of the corresponding equipment, the equipment fixing parameters and the equipment operation parameters.
In this embodiment, the calculation formula of the cost objective function M 1 is:
M1＝C1+C2+C3；
Wherein, C 1 is the fixed cost of the system energy supply side, C 2 is the system operation cost, and C 3 is the fixed cost of the system user side.
The system operation process involves the mutual conversion of electric energy and hydrogen energy and the conversion of the electric energy and the hydrogen energy into heat energy, and the energy efficiency objective function comprises the following calculation processes:
determining total energy output by the system, net energy stored by the system and total energy input by the system;
calculating the total energy output by the system, the net energy stored by the system and the total energy input by the system to obtain the energy efficiency objective function;
the system output total energy, the system net energy and the system input total energy are calculated according to the system to-be-configured parameter, the equipment fixing parameter and the equipment operation parameter.
The energy efficiency objective function is expressed as:
Where M 2 is the energy efficiency objective function, η total is the system level energy efficiency, E use is the total energy output by the system, E store is the net energy stored by the system, and E supply,in is the total energy input by the system.
In the energy efficiency calculation in this embodiment, because renewable energy sources such as wind power and photovoltaic are adopted by the system, the renewable energy sources belong to energy sources which can be utilized in the system, are not input energy of an external system, are inexhaustible, and therefore, the renewable energy sources do not account for an energy inlet; but the renewable energy source can practically meet the energy demand of the park and reduce the input energy of an external system, so that the load quantity met by the renewable energy source is counted into an energy outlet. Therefore, the calculation formula of the total energy input by the system is as follows:
Esupply,in＝Eg；
e g is the power supply quantity/J of the power grid;
the system output energy consists of the system net stored energy and the system total output energy. The total energy output by the system consists of system output electric quantity and system output heat, the system output electric quantity depends on the actual electric quantity of a user, and for the convenience of statistics, a system heat boundary is defined to an inlet of the heat storage water tank, and the heat entering the heat storage water tank is considered to be the system output heat, so that the calculation formula is as follows:
Euse＝Euse,e+Euse,h＝Euser,e+Qrecycle；
Wherein E use,e is the output power of the system/J; e user,e is the total electricity consumption of the user/J; e use,h is system output heat/J; q recycle is heat/J recovered and utilized by the heat storage water tank through waste heat utilization;
because the system thermal boundary is defined, the system net energy storage only calculates the electric quantity stored by the storage battery, the storage battery has higher charge and discharge efficiency in the day, and the loss is basically negligible, so the system net energy storage calculation formula is as follows:
Estore＝Estore,e＝Estore,BT,net＝Eg-EEL+EFC+EPV+EWD-Euser,e；
wherein E store,e is the net stored power of the system; e store,BT,net is the net storage capacity/J of the storage battery;
therefore, the system level energy efficiency calculation is:
Therefore, the energy efficiency objective function is calculated as:
Wherein E EL is the power consumption per J of the electrolytic cell at a typical working day, and E PV is the power generation per J of the photovoltaic power generation equipment at a typical working day; e WD is the power generation amount/J of the wind power generation equipment in a typical working day; e FC is the power generation per J of the fuel cell on a typical workday;
the preset constraint conditions comprise a power balance constraint condition, a power generation capacity constraint condition and a power consumption constraint condition.
In this embodiment, the power balance constraint condition, that is, the total input amount of electric energy to the system, must be not less than the total consumption amount of electric energy. The total electric energy input quantity comprises the output electric quantity of the fuel cell, the power supply quantity of the power grid, the photovoltaic power generation quantity and the discharge quantity of the storage battery, and the total electric energy consumption quantity comprises the user power load quantity, the working power consumption of the electrolytic cell and the storage battery charge quantity. The power balance constraint expression is as follows:
Wherein P g is the power grid input power/W, and P FC is the fuel cell output power/W; p EL is the output power/W of the electrolytic cell; p PV photovoltaic power generation equipment power generation/W; p WD is the power generated by the wind power generation equipment per W; p user,e is the power/W for the user; p BT,out is the discharge power/W of the storage battery; p BT,in is the battery charging power/W; e BT,in is the accumulated charge of the battery per J at a typical workday; e BT,out is the accumulated discharge amount/J of the storage battery in a typical working day;
the power generation amount constraint conditions comprise fuel cell power generation amount constraint, power grid power supply amount constraint, photovoltaic power generation amount constraint, wind power generation amount constraint and discharge amount constraint of the storage battery, and each constraint expression is as follows:
EFC,min≤EFC≤EFC,max;Eg,min≤Eg≤Eg,max；
EPV,min≤EPV≤EPV,max;EWD,min≤EWD≤EWD,max;0≤EBT,out≤EBT,out,max；
Wherein E *,min is a minimum power generation threshold value of a power generation main body, and E *,max is a maximum power generation threshold value of a power generation main body, wherein the power generation main body includes a fuel cell FC, a power grid g, a photovoltaic power generation device PV and a wind power generation device WD; e BT,out,max is the maximum discharge threshold of the storage battery;
The power consumption includes the user power consumption, the cell operation power consumption, and the battery charge. The power consumption of the user cannot be constrained, and the power consumption constraint condition expression is as follows:
EEL,min≤EEL≤EEL,max;0≤EBT,in≤EBT,in,max；
Wherein E EL,min is the minimum power consumption threshold of the electrolytic cell; e EL,max is the maximum power consumption threshold of the electrolytic cell; a maximum charge threshold of the E BT,in,max battery;
in this embodiment, besides the electrical input amount and the electrical consumption amount, other parameter constraint conditions including energy storage constraint of the heat storage water tank and hydrogen flow constraint exist in the system, and expression of other parameter constraint conditions is as follows:
QFC,min≤QFC≤QFC,max;NEL-NFC≥0；
Wherein Q FC is the heat generation amount/J of the fuel cell; q FC,min is the minimum heat generation threshold of the fuel cell; q FC,max is the maximum heat generation threshold of the fuel cell, and N FC is the hydrogen consumption per mol of the fuel cell; n EL is hydrogen yield per mol of the electrolytic cell.
The resident user distributed type electricity-hydrogen-heat-power cogeneration system based on the fuel cell integrates various renewable energy sources such as wind energy, photovoltaic energy, hydrogen energy and the like, is mainly designed for resident domestic energy scenes, can realize the application expansion of hydrogen energy at the user side, promotes the development of the electricity-hydrogen industry, and is beneficial to helping to realize the energy revolution and the double-carbon target of China.
The invention brings the devices such as the fuel cell, the electrolytic cell, the hydrogen storage tank, the heat storage water tank, the storage battery and the like contained in the typical user side fuel cell combined electric heating and power system into the configuration optimization range, and can realize the overall configuration parameter optimization of the user side fuel cell combined electric heating and power system configuration.
The parameter configuration method provided by the invention considers various indexes such as economy, energy efficiency and the like, can realize multi-objective comprehensive optimization of the fuel cell electricity-hydrogen-heat cogeneration system at the user side, and realizes balance of economy and energy efficiency by the configuration design of the power assisting system equipment.
Example 2:
based on the same inventive concept, the invention also provides an electro-hydro-thermal cogeneration system parameter configuration system based on multi-objective optimization, as shown in fig. 2, comprising:
The system parameter acquisition module is used for acquiring inherent parameters of the combined electric hydrogen and heat power system to be optimized;
the initial solving module is used for optimizing and solving a preset multi-objective function by adopting a multi-objective genetic algorithm based on the inherent parameters to obtain an optimal solution set of the initial system parameters to be configured corresponding to the inherent parameters;
the optimal solution analysis determining module is used for performing pareto front edge analysis and statistical analysis on the initial system to-be-configured parameter optimal solution set to obtain a system to-be-configured parameter optimal solution;
Wherein the multi-objective function includes a cost objective function and an energy efficiency objective function; the cost objective function is set according to the sum of the system energy supply side fixed cost, the system user side fixed cost and the system operation cost.
The inherent parameters in the system parameter acquisition module comprise equipment fixed parameters and equipment operation parameters;
the system to be configured parameters include at least one or more of the following:
device power rating and device capacity rating.
The system operation cost in the initial solving module comprises the following calculation process:
adding and converting the generated energy, the stored energy and the power consumption of the power equipment, and superposing the added and converted result of the heat consumption of the thermal equipment to obtain the daily maintenance cost of the equipment;
Adding the daily maintenance cost of the equipment and the power grid electricity purchasing cost in the running process of the system for annual, so as to obtain the running cost of the system;
the power equipment comprises system energy supply side power equipment and system user side power equipment;
the generated energy and the power consumption are calculated according to the rated power, the fixed parameters and the operation parameters of the corresponding equipment;
The electricity storage capacity and the heat consumption are calculated according to the rated capacity of the corresponding equipment, the equipment fixing parameters and the equipment operation parameters.
The system energy supply side fixed cost in the initial solving module comprises the following calculation process:
the acquisition cost of each device at the energy supply side of the system is converted into annual average cost according to the set service life period of the device and added to obtain the fixed cost at the energy supply side of the system;
The system user side fixed cost in the initial solving module comprises the following calculation process:
The acquisition cost of each device at the system user side is converted by the annual average cost according to the set service life period of the device and added to obtain the fixed cost at the system user side;
the service life period of the equipment belongs to the fixed parameter of the equipment, and the acquisition cost is calculated according to the inherent parameter and the parameter to be configured of the system.
The energy efficiency objective function in the initial solving module comprises the following calculation process:
determining total energy output by the system, net energy stored by the system and total energy input by the system;
calculating the total energy output by the system, the net energy stored by the system and the total energy input by the system to obtain the energy efficiency objective function;
the system output total energy, the system net energy and the system input total energy are calculated according to the system to-be-configured parameter, the equipment fixing parameter and the equipment operation parameter.
The energy efficiency objective function in the initial solving module is expressed as:
Where M 2 is the energy efficiency objective function, η total is the system level energy efficiency, E use is the total energy output by the system, E store is the net energy stored by the system, and E supply,in is the total energy input by the system.
The initial solving module is specifically configured to:
based on the inherent parameters, randomly generating initial values of populations corresponding to the inherent parameters under the constraint of preset constraint conditions; each individual in the population corresponds to a system to-be-configured parameter solution of the multi-objective function;
Calculating non-dominant grades of the to-be-configured parameter solutions of all the systems by adopting a rapid non-dominant sorting algorithm, selecting a plurality of the most excellent to-be-configured parameter solutions from the population to perform crossing and mutation operations according to a principle of lowest non-dominant grades, obtaining a new to-be-configured parameter solution of the system, and repeatedly and iteratively updating the to-be-configured parameter solution of the system until the iteration number reaches a set maximum value to obtain a new population;
And outputting a plurality of system to-be-configured parameter solutions from the new population as an initial system to-be-configured parameter optimal solution set by adopting a principle of lowest non-dominant level according to the non-dominant level of each system to-be-configured parameter solution in the new population.
The constraint conditions in the initial solving module comprise a power balance constraint condition, a power generation capacity constraint condition and a power consumption constraint condition.
The optimal solution analysis determining module is specifically configured to:
Performing pareto front analysis on the optimal solution set of the parameters to be configured of the initial system to obtain the optimal solution set of the pareto;
and carrying out statistical analysis on the pareto optimal solution set to obtain an optimal solution of the system to be configured parameters.
The optimal solution analysis determining module performs pareto front analysis on the optimal solution set of parameters to be configured of the initial system to obtain the pareto optimal solution set, and the method comprises the following steps:
Judging all solutions in the optimal solution set of the parameters to be configured of the initial system respectively, and if one solution does not exist and can be better than the solution on all objective functions of the multiple objective functions, marking the solution as a pareto optimal solution; and the set of all pareto optimal solutions is the pareto optimal solution set.
The optimal solution analysis determining module performs statistical analysis on the pareto optimal solution set to obtain an optimal solution of a system to be configured parameter, and the method comprises the following steps:
carrying out statistical analysis on the pareto optimal solution set by adopting a good-bad solution distance method, and determining optimal ideal solutions and worst ideal solutions of all objective functions of the system;
and calculating and scoring according to the distance between each solution in the pareto optimal solution set and the optimal ideal solution and the worst ideal solution, wherein the pareto optimal solution corresponding to the minimum scoring value is the optimal solution of the parameters to be configured of the system.
Example 3:
The invention further provides computer equipment based on the same multi-objective optimization-based parameter configuration method and conception of the combined electrical hydrogen and heat power system, wherein the computer equipment comprises a processor and a memory, the memory is used for storing a computer program, the computer program comprises program instructions, and the processor is used for executing the program instructions stored in the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processor, digital signal processor (DIGITAL SIGNAL Processor, DSP), application specific integrated circuit (Application SpecificIntegrated Circuit, ASIC), off-the-shelf Programmable gate array (Field-Programmable GATEARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., which are a computing core and control core of the terminal adapted to implement one or more instructions, specifically adapted 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 multiple-objective optimization-based combined electrical and thermal power system parameter configuration method in the above embodiments.
Example 4:
Based on the same design of the parameter configuration method of the combined electrical hydrogen and heat power system based on multi-objective optimization, the invention also provides a storage medium, in particular a computer readable storage medium (Memory), wherein the computer readable storage medium is a Memory device in a computer device and is used 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 method for configuring parameters of an electrical hydrogen cogeneration system based on multi-objective optimization 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 the present invention, 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 present invention after reading the present invention, and those changes, modifications or equivalents are within the scope of the claims of the present invention.
Claims (15)
1. The parameter configuration method of the combined electrical hydrogen and heat power system based on multi-objective optimization is characterized by comprising the following steps of:
acquiring inherent parameters of an electric hydrogen-heat-power cogeneration system to be optimized;
Based on the inherent parameters, adopting a multi-objective genetic algorithm to optimally solve a preset multi-objective function to obtain an optimal solution set of initial system parameters to be configured corresponding to the inherent parameters;
performing pareto front analysis and statistical analysis on the initial system to-be-configured parameter optimal solution set to obtain a system to-be-configured parameter optimal solution corresponding to the inherent parameters;
Wherein the multi-objective function includes a cost objective function and an energy efficiency objective function; the cost objective function is set according to the sum of the system energy supply side fixed cost, the system user side fixed cost and the system operation cost.
2. The method of claim 1, wherein the intrinsic parameters include a device fixed parameter and a device operating parameter;
the system to be configured parameters include at least one or more of the following:
device power rating and device capacity rating.
3. The method of claim 2, wherein the system operating cost comprises the following calculation process:
adding and converting the generated energy, the stored energy and the power consumption of the power equipment, and superposing the added and converted result of the heat consumption of the thermal equipment to obtain the daily maintenance cost of the equipment;
Adding the daily maintenance cost of the equipment and the power grid electricity purchasing cost in the running process of the system for annual, so as to obtain the running cost of the system;
the power equipment comprises system energy supply side power equipment and system user side power equipment;
the generated energy and the power consumption are calculated according to the rated power, the fixed parameters and the operation parameters of the corresponding equipment;
The electricity storage capacity and the heat consumption are calculated according to the rated capacity of the corresponding equipment, the equipment fixing parameters and the equipment operation parameters.
4. The method of claim 2, wherein the system energy supply side fixed cost comprises the following calculation process:
the acquisition cost of each device at the energy supply side of the system is converted into annual average cost according to the set service life period of the device and added to obtain the fixed cost at the energy supply side of the system;
the fixed cost of the system user side comprises the following calculation processes:
The acquisition cost of each device at the system user side is converted by the annual average cost according to the set service life period of the device and added to obtain the fixed cost at the system user side;
the service life period of the equipment belongs to the fixed parameter of the equipment, and the acquisition cost is calculated according to the inherent parameter and the parameter to be configured of the system.
5. The method of claim 2, wherein the energy efficiency objective function comprises a calculation process of:
determining total energy output by the system, net energy stored by the system and total energy input by the system;
calculating the total energy output by the system, the net energy stored by the system and the total energy input by the system to obtain the energy efficiency objective function;
the system output total energy, the system net energy and the system input total energy are calculated according to the system to-be-configured parameter, the equipment fixing parameter and the equipment operation parameter.
6. The method of claim 5, wherein the energy efficiency objective function is expressed as:
Where M 2 is the energy efficiency objective function, η total is the system level energy efficiency, E use is the total energy output by the system, E store is the net energy stored by the system, and E supply,in is the total energy input by the system.
7. The method of claim 1, wherein the optimizing and solving a preset multi-objective function by using a multi-objective genetic algorithm based on the intrinsic parameters to obtain an optimal solution set of initial system parameters to be configured corresponding to the intrinsic parameters comprises:
based on the inherent parameters, randomly generating initial values of populations corresponding to the inherent parameters under the constraint of preset constraint conditions; each individual in the population corresponds to a system to-be-configured parameter solution of the multi-objective function;
Calculating non-dominant grades of the to-be-configured parameter solutions of all the systems by adopting a rapid non-dominant sorting algorithm, selecting a plurality of the most excellent to-be-configured parameter solutions from the population to perform crossing and mutation operations according to a principle of lowest non-dominant grades, obtaining a new to-be-configured parameter solution of the system, and repeatedly and iteratively updating the to-be-configured parameter solution of the system until the iteration number reaches a set maximum value to obtain a new population;
And outputting a plurality of system to-be-configured parameter solutions from the new population as an initial system to-be-configured parameter optimal solution set by adopting a principle of lowest non-dominant level according to the non-dominant level of each system to-be-configured parameter solution in the new population.
8. The method of claim 7, wherein the constraints include a power balance constraint, a power generation capacity constraint, and a power consumption constraint.
9. The method of claim 1, wherein performing pareto front analysis and statistical analysis on the initial set of optimal solutions for the parameters to be configured of the system to obtain an optimal solution for the parameters to be configured of the system comprises:
Performing pareto front analysis on the optimal solution set of the parameters to be configured of the initial system to obtain the optimal solution set of the pareto;
and carrying out statistical analysis on the pareto optimal solution set to obtain an optimal solution of the system to be configured parameters.
10. The method of claim 9, wherein the performing pareto front analysis on the optimal solution set of parameters to be configured of the initial system to obtain the optimal solution set of pareto includes:
Judging all solutions in the optimal solution set of the parameters to be configured of the initial system respectively, and if one solution does not exist and can be better than the solution on all objective functions of the multiple objective functions, marking the solution as a pareto optimal solution; and the set of all pareto optimal solutions is the pareto optimal solution set.
11. The method of claim 9, wherein the performing a statistical analysis on the pareto optimal solution set to obtain a system to be configured parameter optimal solution comprises:
carrying out statistical analysis on the pareto optimal solution set by adopting a good-bad solution distance method, and determining optimal ideal solutions and worst ideal solutions of all objective functions of the system;
and calculating and scoring according to the distance between each solution in the pareto optimal solution set and the optimal ideal solution and the worst ideal solution, wherein the pareto optimal solution corresponding to the minimum scoring value is the optimal solution of the parameters to be configured of the system.
12. A multi-objective optimization-based parameter configuration system for an cogeneration system, comprising:
The system parameter acquisition module is used for acquiring inherent parameters of the combined electric hydrogen and heat power system to be optimized;
the initial solving module is used for optimizing and solving a preset multi-objective function by adopting a multi-objective genetic algorithm based on the inherent parameters to obtain an optimal solution set of the initial system parameters to be configured corresponding to the inherent parameters;
the optimal solution analysis determining module is used for performing pareto front edge analysis and statistical analysis on the initial system to-be-configured parameter optimal solution set to obtain a system to-be-configured parameter optimal solution;
Wherein the multi-objective function includes a cost objective function and an energy efficiency objective function; the cost objective function is set according to the sum of the system energy supply side fixed cost, the system user side fixed cost and the system operation cost.
13. The system of claim 12, wherein the initial solution module is specifically configured to:
based on the inherent parameters, randomly generating initial values of populations corresponding to the inherent parameters under the constraint of preset constraint conditions; each individual in the population corresponds to a system to-be-configured parameter solution of the multi-objective function;
Calculating non-dominant grades of the to-be-configured parameter solutions of all the systems by adopting a rapid non-dominant sorting algorithm, selecting a plurality of the most excellent to-be-configured parameter solutions from the population to perform crossing and mutation operations according to a principle of lowest non-dominant grades, obtaining a new to-be-configured parameter solution of the system, and repeatedly and iteratively updating the to-be-configured parameter solution of the system until the iteration number reaches a set maximum value to obtain a new population;
And outputting a plurality of system to-be-configured parameter solutions from the new population as an initial system to-be-configured parameter optimal solution set by adopting a principle of lowest non-dominant level according to the non-dominant level of each system to-be-configured parameter solution in the new population.
14. A computer device, comprising: one or more processors;
a memory for storing one or more programs;
When the one or more programs are executed by the one or more processors, a multi-objective optimization-based co-generation system parameter configuration method of any one of claims 1 to 11 is implemented.
15. A computer-readable storage medium, on which a computer program is stored, which computer program, when executed, implements a multi-objective optimization-based parameter configuration method for an cogeneration system according to any one of claims 1 to 11.
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