CN108764519B - Optimal configuration method for capacity of park energy Internet energy equipment - Google Patents

Optimal configuration method for capacity of park energy Internet energy equipment Download PDF

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CN108764519B
CN108764519B CN201810321714.0A CN201810321714A CN108764519B CN 108764519 B CN108764519 B CN 108764519B CN 201810321714 A CN201810321714 A CN 201810321714A CN 108764519 B CN108764519 B CN 108764519B
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林晓明
张勇军
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South China University of Technology SCUT
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Abstract

The invention provides a method for optimally configuring the capacity of energy equipment of a park energy Internet. The method comprises the following specific steps: constructing a multi-scene model of photovoltaic output and electricity/heat/natural gas load; establishing an energy equipment capacity optimization upper layer model, and solving the optimal configuration capacity of the energy equipment; establishing a park energy Internet operation optimization lower layer model, solving a determined scene and determining an optimal operation mode of the park energy Internet under the capacity of energy equipment; and solving the upper layer model by using a genetic algorithm, and solving the lower layer model by using a cplex solver to obtain the optimal configuration capacity of the energy equipment. The method can realize the combined optimization of the configuration and the operation of the park energy Internet, and considers the multi-scene of photovoltaic output and load, so that the planning result is more practical.

Description

Optimal configuration method for capacity of park energy Internet energy equipment
Technical Field
The invention relates to a planning technology of a park energy Internet, in particular to a park energy Internet energy equipment capacity optimal configuration method.
Background
Currently, fossil energy is gradually exhausted and environmental pollution is becoming severe, and energy structures face the challenge of transformation and upgrading. The park energy internet is close to the user side, integrates renewable energy, can utilize the complementary advantages of various energy sources and improve the energy utilization efficiency, and is an effective way for realizing the sustainable development of energy sources.
The energy Internet of the garden comprises energy equipment for energy production, conversion, storage and the like, the capacity of the energy equipment in the garden is reasonably configured, and the energy Internet of the garden has very important significance. The optimal configuration of the capacity of the energy internet energy equipment in the park is to adopt different targets and reasonably configure the installation capacity of the internal energy equipment on the premise of meeting the safety constraint. In the current research, the planning of the park energy internet has the defects that the planning of a power supply system is only considered independently, the planning is performed by using a single scene, the types of energy equipment are few, and the like. Aiming at the problems, the invention provides a method for optimizing and configuring the capacity of energy equipment of the park energy Internet, which can realize the combined optimization of the configuration and the operation of the park energy Internet, and considers the multi-scenes of photovoltaic output and load, so that the planning result is more in line with the reality.
Disclosure of Invention
The invention aims to solve the problem of park energy Internet capacity configuration and realize the combined optimization of park energy Internet configuration and operation. In order to achieve the purpose, the invention provides a method for optimally configuring the capacity of energy equipment of park energy Internet, which comprises the following steps:
(1) constructing a multi-scene model of photovoltaic output and electricity/heat/natural gas load;
(2) establishing an energy equipment capacity optimization upper layer model, and solving the optimal configuration capacity of the energy equipment;
(3) establishing a park energy Internet operation optimization lower layer model, solving a determined scene and determining an optimal operation mode of the park energy Internet under the capacity of energy equipment;
(4) and solving the upper layer model by using a genetic algorithm, and solving the lower layer model by using a cplex solver to obtain the optimal configuration capacity of the energy equipment.
The energy equipment comprises photovoltaic equipment, cogeneration equipment, a gas boiler, an electric boiler and heat energy storage equipment.
The step (1) comprises the following steps:
(1-1) according to the annual illumination hour data, carrying out K-means clustering according to the conditions of spring and autumn, summer and winter to obtain NsIndividual lighting scene, NsIs a set value;
(1-2) obtaining N according to the illumination scene and the photovoltaic output modelsEach photovoltaic output scene;
Figure GDA0001782477530000021
wherein, beta is the illumination intensity, betaratedRated for the intensity of light, EPVIn order to provide photovoltaic output,
Figure GDA0001782477530000022
photovoltaic rated capacity;
(1-3) determining typical daily data of electricity/heat/natural gas load according to spring and autumn, summer and winter conditions, and combining the typical daily data with a photovoltaic output scene to form NsPhotovoltaic output and electricity/heat/natural gas load scenarios.
The upper layer model in the step (2) comprises an objective function and a constraint condition;
the objective function of the upper layer model is annual comprehensive cost C1And (3) minimizing:
Figure GDA0001782477530000023
wherein,
Figure GDA0001782477530000024
for operating costs in the scene s, psProbability of occurrence of scene s; n is a radical ofdecThe total number of the energy devices is,
Figure GDA0001782477530000025
for the installation capacity of the ith energy source device,
Figure GDA0001782477530000026
the installation cost of the unit capacity of the ith energy equipment, the value range of i is 1-Ndec；
Figure GDA0001782477530000027
For the annual fund recovery rate of the ith energy equipment, the following calculation is carried out:
Figure GDA0001782477530000028
wherein r is the discount rate; y isiThe service life of the ith energy equipment is the life cycle of the ith energy equipment;
operating cost under scene s
Figure GDA0001782477530000029
By operating maintenance costs
Figure GDA00017824775300000210
Cost of fuel
Figure GDA00017824775300000211
Electric energy transaction fee
Figure GDA00017824775300000212
And carbon emission tax
Figure GDA00017824775300000213
The composition is calculated as follows:
Figure GDA00017824775300000214
Figure GDA0001782477530000031
wherein, Δ T is an optimization time interval, and T is an optimization total time interval;
Figure GDA0001782477530000032
the input power of ith energy equipment in a T time period under a scene s is set, and the value range of T is 1-T;
Figure GDA0001782477530000033
operating and maintaining cost coefficients for the ith energy equipment;
Figure GDA0001782477530000034
Figure GDA0001782477530000035
and
Figure GDA0001782477530000036
the power purchasing/selling power and the power purchasing/selling price of the park energy Internet and the power grid at the time t under the scene s are respectively;
Figure GDA0001782477530000037
the gas purchasing power v of the park energy Internet at the time t under the scene sLHVLow heating value for natural gas combustion; a iseAnd agCarbon emission coefficient of electric energy and natural gas, respectively, ccIs the carbon tax price;
the constraint conditions of the upper layer model are as follows:
0≤Pi rated≤Pi max
wherein,
Figure GDA0001782477530000038
the maximum installation capacity of the ith energy device.
The lower layer model in the step (3) comprises an objective function and a constraint condition;
the objective function of the lower layer model is the running cost under the scene s
Figure GDA0001782477530000039
And (3) minimizing:
Figure GDA00017824775300000310
the constraint conditions of the lower layer model comprise energy power balance constraint, energy equipment operation constraint and energy interaction power constraint;
the energy power balance constraint is as follows:
Figure GDA00017824775300000311
wherein,
Figure GDA00017824775300000312
and
Figure GDA00017824775300000313
electric power of photovoltaic, cogeneration equipment, an electric boiler and an electric load at a time t under a scene s;
Figure GDA00017824775300000314
and
Figure GDA00017824775300000315
electric boiler, gas boiler and cogeneration at t time period under scene sThermal power of equipment, thermal load;
Figure GDA00017824775300000316
and
Figure GDA00017824775300000317
respectively the heat charging and discharging power of the heat energy storage equipment;
Figure GDA0001782477530000041
and
Figure GDA0001782477530000042
respectively the gas power of a gas boiler, cogeneration equipment and a natural gas load at the time t under the scene s;
the energy equipment operation constraint is as follows:
Figure GDA0001782477530000043
wherein,
Figure GDA0001782477530000044
and
Figure GDA0001782477530000045
respectively the maximum charge-discharge energy power of the thermal energy storage equipment;
Figure GDA0001782477530000046
and
Figure GDA0001782477530000047
respectively optimizing the energy storage proportion of the initial time interval and the ending time interval for the thermal energy storage equipment;
Figure GDA0001782477530000048
and
Figure GDA0001782477530000049
respectively an electric boiler, a cogeneration device and a gas boilerAn amount;
Figure GDA00017824775300000410
and
Figure GDA00017824775300000411
the upward climbing rate limit values of the cogeneration equipment and the gas boiler are respectively set;
Figure GDA00017824775300000412
and
Figure GDA00017824775300000413
the downward climbing rate limit values of the cogeneration equipment and the gas boiler are respectively;
the energy source interaction power constraint is as follows:
Figure GDA00017824775300000414
wherein,
Figure GDA00017824775300000415
and
Figure GDA00017824775300000416
respectively the maximum values of the gas purchasing power, the electricity purchasing power and the electricity selling power of the energy Internet of the park;
Figure GDA00017824775300000417
in order to represent the variable of the electricity purchasing state of the park energy Internet, when the park energy Internet purchases electricity
Figure GDA00017824775300000418
Equal to 1, in the rest state
Figure GDA00017824775300000419
Equal to 0.
In the step (4), the specific method comprises the following steps:
(4-1) data initialization, inputting of photovoltaic output and N of electric/thermal/natural gas loadsIndividual scene data;
(4-2) coding decision variables of the upper layer, randomly generating an initial population, and enabling each individual to represent the capacity configuration of energy Internet energy equipment of a park;
(4-3) for each individual in the population, performing lower-layer operation optimization of each scene by using a cplex solver, and returning the optimal operation cost to the upper layer;
(4-4) calculating the fitness function value of each individual by the upper layer according to the optimization result of the lower layer;
(4-5) judging whether a termination condition is met, if so, outputting an optimal capacity configuration scheme of the energy equipment, and otherwise, performing the step (4-6);
(4-6) carrying out selection, crossing and mutation operations on the population to form a next generation population, and returning to the step (4-3).
Compared with the prior art, the invention has the beneficial effects that:
(1) capacity optimization configuration of energy production, conversion and storage equipment is integrated, and an optimization configuration scheme is more comprehensive and has wider applicability;
(2) the combined optimization of the park energy Internet capacity configuration and the operation strategy is realized, and the safety and the economy of operation are taken into consideration in the optimized configuration scheme;
(3) the photovoltaic output and the electricity/heat/natural gas load are considered, and the optimal configuration scheme is more practical.
Drawings
FIG. 1 is a schematic diagram of the steps of a method for optimally configuring the capacity of energy equipment of a park energy Internet;
FIG. 2 is a flow chart of the solution of the two-layer model;
FIG. 3 is a block diagram of a typical campus energy Internet;
FIGS. 4 a-4 c are graphs of multi-scene curves of photovoltaic output;
fig. 5 a-5 c are multi-scenario graphs of electricity/heat/natural gas load.
Detailed Description
The following description of the embodiments of the present invention is provided in connection with the accompanying drawings and examples. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments obtained by persons skilled in the art without inventive labor based on the embodiments of the present invention and all other embodiments obtained by persons skilled in the art without inventive labor are within the scope of the present invention.
(1) Constructing a multi-scene model of photovoltaic output and electricity/heat/natural gas load;
(2) establishing an energy equipment capacity optimization upper layer model, and solving the optimal configuration capacity of the energy equipment;
(3) establishing a park energy Internet operation optimization lower layer model, solving a determined scene and determining an optimal operation mode of the park energy Internet under the capacity of energy equipment;
(4) and solving the upper layer model by using a genetic algorithm, and solving the lower layer model by using a cplex solver to obtain the optimal configuration capacity of the energy equipment.
The energy equipment comprises photovoltaic equipment, cogeneration equipment, a gas boiler, an electric boiler and heat energy storage equipment.
The step (1) comprises the following steps:
(1-1) according to annual illumination hour data, performing K-means clustering according to spring and autumn, summer and winter conditions respectively, wherein the number of categories is 3, and obtaining 9 illumination scenes, wherein the principle of the K-means clustering is as follows:
for set X ═ X1，x2，…，xnC, the number of categories is c, and the objective function of the clustering is the sum of squared errors JcMinimum:
Figure GDA0001782477530000061
wherein, wiThe cluster center of the ith category; dijIs a coefficient of 0-1, and satisfies the following conditions:
Figure GDA0001782477530000062
(1-2) obtaining N according to the illumination scene and the photovoltaic output modelsEach photovoltaic output scene;
Figure GDA0001782477530000063
wherein, beta is the illumination intensity, betaratedRated for the intensity of light, EPVIn order to provide photovoltaic output,
Figure GDA0001782477530000064
photovoltaic rated capacity;
(1-3) determining typical daily data of the electricity/heat/natural gas load according to spring and autumn, summer and winter conditions, and combining the typical daily data with a photovoltaic output scene to form 9 photovoltaic output and electricity/heat/natural gas load scenes.
The upper layer model in the step (2) comprises an objective function and a constraint condition;
the objective function of the upper layer model is annual comprehensive cost C1And (3) minimizing:
Figure GDA0001782477530000065
wherein,
Figure GDA0001782477530000066
for operating costs in the scene s, psProbability of occurrence of scene s; n is a radical ofdecThe total number of the energy devices is,
Figure GDA0001782477530000067
for the installation capacity of the ith energy source device,
Figure GDA0001782477530000068
the installation cost of the unit capacity of the ith energy equipment, the value range of i is 1-Ndec；
Figure GDA0001782477530000069
For the annual fund recovery rate of the ith energy equipment, the following calculation is carried out:
Figure GDA00017824775300000610
wherein r is the discount rate; y isiThe service life of the ith energy equipment is the life cycle of the ith energy equipment;
operating cost under scene s
Figure GDA00017824775300000611
By operating maintenance costs
Figure GDA00017824775300000612
Cost of fuel
Figure GDA00017824775300000613
Electric energy transaction fee
Figure GDA00017824775300000614
And carbon emission tax
Figure GDA0001782477530000071
The composition is calculated as follows:
Figure GDA0001782477530000072
Figure GDA0001782477530000073
wherein, Δ T is an optimization time interval, and T is an optimization total time interval;
Figure GDA0001782477530000074
the input power of ith energy equipment in a T time period under a scene s is set, and the value range of T is 1-T;
Figure GDA0001782477530000075
operating and maintaining cost coefficients for the ith energy equipment;
Figure GDA0001782477530000076
Figure GDA0001782477530000077
and
Figure GDA0001782477530000078
the power purchasing/selling power and the power purchasing/selling price of the park energy Internet and the power grid at the time t under the scene s are respectively;
Figure GDA0001782477530000079
the gas purchasing power v of the park energy Internet at the time t under the scene sLHVLow heating value for natural gas combustion; a iseAnd agCarbon emission coefficient of electric energy and natural gas, respectively, ccIs the carbon tax price;
the constraint conditions of the upper layer model are as follows:
0≤Pi rated≤Pi max
wherein,
Figure GDA00017824775300000710
the maximum installation capacity of the ith energy device.
The lower layer model in the step (3) comprises an objective function and a constraint condition;
the objective function of the lower layer model is the running cost under the scene s
Figure GDA00017824775300000711
And (3) minimizing:
Figure GDA00017824775300000712
the constraint conditions of the lower layer model comprise energy power balance constraint, energy equipment operation constraint and energy interaction power constraint;
the energy power balance constraint is as follows:
Figure GDA00017824775300000713
wherein,
Figure GDA0001782477530000081
and
Figure GDA0001782477530000082
electric power of photovoltaic, cogeneration equipment, an electric boiler and an electric load at a time t under a scene s;
Figure GDA0001782477530000083
and
Figure GDA0001782477530000084
respectively thermal powers of an electric boiler, a gas boiler, cogeneration equipment and a thermal load at a time t under a scene s;
Figure GDA0001782477530000085
and
Figure GDA0001782477530000086
respectively the heat charging and discharging power of the heat energy storage equipment;
Figure GDA0001782477530000087
and
Figure GDA0001782477530000088
respectively the gas power of a gas boiler, cogeneration equipment and a natural gas load at the time t under the scene s;
the energy equipment operation constraint is as follows:
Figure GDA0001782477530000089
wherein,
Figure GDA00017824775300000810
and
Figure GDA00017824775300000811
respectively the maximum charge-discharge energy power of the thermal energy storage equipment;
Figure GDA00017824775300000812
and
Figure GDA00017824775300000813
respectively optimizing the energy storage proportion of the initial time interval and the ending time interval for the thermal energy storage equipment;
Figure GDA00017824775300000814
and
Figure GDA00017824775300000815
respectively the installation capacities of an electric boiler, cogeneration equipment and a gas boiler;
Figure GDA00017824775300000816
and
Figure GDA00017824775300000817
the upward climbing rate limit values of the cogeneration equipment and the gas boiler are respectively set;
Figure GDA00017824775300000818
and
Figure GDA00017824775300000819
the downward climbing rate limit values of the cogeneration equipment and the gas boiler are respectively;
the energy source interaction power constraint is as follows:
Figure GDA00017824775300000820
wherein,
Figure GDA00017824775300000821
and
Figure GDA00017824775300000822
respectively the maximum values of the gas purchasing power, the electricity purchasing power and the electricity selling power of the energy Internet of the park;
Figure GDA00017824775300000823
in order to represent the variable of the electricity purchasing state of the park energy Internet, when the park energy Internet purchases electricity
Figure GDA00017824775300000824
Equal to 1, in the rest state
Figure GDA00017824775300000825
Equal to 0.
In the step (4), the specific method comprises the following steps:
(4-1) data initialization, inputting of photovoltaic output and N of electric/thermal/natural gas loadsIndividual scene data;
(4-2) coding decision variables of the upper layer, randomly generating an initial population, and enabling each individual to represent the capacity configuration of energy Internet energy equipment of a park;
(4-3) for each individual in the population, performing lower-layer operation optimization of each scene by using a cplex solver, and returning the optimal operation cost to the upper layer;
(4-4) calculating the fitness function value of each individual by the upper layer according to the optimization result of the lower layer;
(4-5) judging whether a termination condition is met, if so, outputting an optimal capacity configuration scheme of the energy equipment, and otherwise, performing the step (4-6);
(4-6) carrying out selection, crossing and mutation operations on the population to form a next generation population, and returning to the step (4-3).
A typical campus energy internet is taken as an example for illustration, and the structure is shown in fig. 3.
The multi-scene curves of photovoltaic output are shown in fig. 4a to 4c (corresponding to spring and autumn, summer and winter respectively), and the multi-scene curves of electricity/heat/natural gas load are shown in fig. 5a to 5c (corresponding to spring and autumn, summer and winter respectively). The number of days each scene appeared is shown in table 1.
TABLE 1 number of days of photovoltaic and load scene occurrence
Figure GDA0001782477530000091
The parameters and capacity configuration results of each energy device in the campus energy internet are shown in table 2.
TABLE 2 parameter and Capacity Allocation for energy plants
Figure GDA0001782477530000092
In conclusion, the invention provides a park energy Internet capacity configuration optimization method, which can realize the combined optimization of park energy Internet configuration and operation, and considers the multi-scenes of photovoltaic output and load, so that the planning result is more practical.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents and are intended to be included in the scope of the present invention.

Claims (3)

1. A method for optimally configuring the capacity of park energy Internet energy equipment is characterized by comprising the following steps:
(1) constructing a multi-scene model of photovoltaic output and electricity/heat/natural gas load:
(1-1) according to the annual illumination hour data, carrying out K-means clustering according to the conditions of spring and autumn, summer and winter to obtain NsIndividual lighting scene, NsIs a set value;
(1-2) obtaining N according to the illumination scene and the photovoltaic output modelsEach photovoltaic output scene;
Figure FDA0003201525660000011
wherein, beta is the illumination intensity, betaratedRated for the intensity of light, EPVIn order to provide photovoltaic output,
Figure FDA0003201525660000012
photovoltaic rated capacity;
(1-3) determining typical daily data of electricity/heat/natural gas load according to spring and autumn, summer and winter conditions, and combining the typical daily data with a photovoltaic output scene to form NsPhotovoltaic output and electricity/heat/natural gas load scenarios;
(2) establishing an energy equipment capacity optimization upper layer model, and solving the optimal configuration capacity of the energy equipment;
the upper layer model comprises an objective function and a constraint condition;
the objective function of the upper layer model is annual comprehensive cost C1And (3) minimizing:
Figure FDA0003201525660000013
wherein,
Figure FDA0003201525660000014
for operating costs in the scene s, psProbability of occurrence of scene s; n is a radical ofdecThe total number of the energy devices is,
Figure FDA0003201525660000015
for the installation capacity of the ith energy source device,
Figure FDA0003201525660000016
the installation cost of the unit capacity of the ith energy equipment, the value range of i is 1-Ndec；
Figure FDA0003201525660000017
For the annual fund recovery rate of the ith energy equipment, the following calculation is carried out:
Figure FDA0003201525660000018
wherein r is the discount rate; y isiThe service life of the ith energy equipment is the life cycle of the ith energy equipment;
operating cost under scene s
Figure FDA0003201525660000019
By operating maintenance costs
Figure FDA00032015256600000110
Cost of fuel
Figure FDA00032015256600000111
Electric energy transaction fee
Figure FDA00032015256600000112
And carbon emission tax
Figure FDA00032015256600000113
The composition is calculated as follows:
Figure FDA00032015256600000114
Figure FDA0003201525660000021
wherein, Δ T is an optimization time interval, and T is an optimization total time interval;
Figure FDA0003201525660000022
for the input power of the ith energy source device during the time t under the scene s,the value range of T is 1-T;
Figure FDA0003201525660000023
operating and maintaining cost coefficients for the ith energy equipment;
Figure FDA0003201525660000024
and
Figure FDA0003201525660000025
the power purchasing/selling power and the power purchasing/selling price of the park energy Internet and the power grid at the time t under the scene s are respectively;
Figure FDA0003201525660000026
the gas purchasing power v of the park energy Internet at the time t under the scene sLHVLow heating value for natural gas combustion; a iseAnd agCarbon emission coefficient of electric energy and natural gas, respectively, ccIs the carbon tax price;
the constraint conditions of the upper layer model are as follows:
0≤Pi rated≤Pi max
wherein,
Figure FDA0003201525660000027
the maximum installation capacity of the ith energy equipment;
(3) establishing a park energy Internet operation optimization lower layer model, solving a determined scene and determining an optimal operation mode of the park energy Internet under the capacity of energy equipment;
the lower layer model comprises an objective function and a constraint condition;
the objective function of the lower layer model is the running cost under the scene s
Figure FDA0003201525660000028
And (3) minimizing:
Figure FDA0003201525660000029
the constraint conditions of the lower layer model comprise energy power balance constraint, energy equipment operation constraint and energy interaction power constraint;
the energy power balance constraint is as follows:
Figure FDA00032015256600000210
wherein,
Figure FDA00032015256600000211
and
Figure FDA00032015256600000212
electric power of photovoltaic, cogeneration equipment, an electric boiler and an electric load at a time t under a scene s;
Figure FDA00032015256600000213
and
Figure FDA00032015256600000214
respectively thermal powers of an electric boiler, a gas boiler, cogeneration equipment and a thermal load at a time t under a scene s;
Figure FDA00032015256600000215
and
Figure FDA00032015256600000216
respectively the heat charging and discharging power of the heat energy storage equipment;
Figure FDA00032015256600000217
and
Figure FDA00032015256600000218
respectively the gas power of a gas boiler, cogeneration equipment and a natural gas load at the time t under the scene s;
the energy equipment operation constraint is as follows:
Figure FDA0003201525660000031
wherein,
Figure FDA0003201525660000032
and
Figure FDA0003201525660000033
respectively the maximum charge-discharge energy power of the thermal energy storage equipment;
Figure FDA0003201525660000034
and
Figure FDA0003201525660000035
respectively optimizing the energy storage proportion of the initial time interval and the ending time interval for the thermal energy storage equipment;
Figure FDA0003201525660000036
and
Figure FDA0003201525660000037
respectively the installation capacities of an electric boiler, cogeneration equipment and a gas boiler;
Figure FDA0003201525660000038
and
Figure FDA0003201525660000039
the upward climbing rate limit values of the cogeneration equipment and the gas boiler are respectively set;
Figure FDA00032015256600000310
and
Figure FDA00032015256600000311
are respectively asA downward climbing rate limit value of the cogeneration equipment and the gas boiler;
the energy source interaction power constraint is as follows:
Figure FDA00032015256600000312
wherein,
Figure FDA00032015256600000313
and
Figure FDA00032015256600000314
respectively the maximum values of the gas purchasing power, the electricity purchasing power and the electricity selling power of the energy Internet of the park;
Figure FDA00032015256600000315
in order to represent the variable of the electricity purchasing state of the park energy Internet, when the park energy Internet purchases electricity
Figure FDA00032015256600000316
Equal to 1, in the rest state
Figure FDA00032015256600000317
Equal to 0;
(4) and solving the upper layer model by using a genetic algorithm, and solving the lower layer model by using a cplex solver to obtain the optimal configuration capacity of the energy equipment.
2. The method of claim 1, wherein the energy facilities include photovoltaic, cogeneration, gas fired boiler, electric boiler, and thermal energy storage facilities.
3. The optimal configuration method for the capacity of the park energy Internet energy equipment according to claim 2, wherein the specific method in the step (4) comprises the following steps:
(4-1) Data initialization, inputting of photovoltaic output and N of electric/thermal/natural gas loadsIndividual scene data;
(4-2) coding decision variables of the upper layer, randomly generating an initial population, and enabling each individual to represent the capacity configuration of energy Internet energy equipment of a park;
(4-3) for each individual in the population, performing lower-layer operation optimization of each scene by using a cplex solver, and returning the optimal operation cost to the upper layer;
(4-4) calculating the fitness function value of each individual by the upper layer according to the optimization result of the lower layer;
(4-5) judging whether a termination condition is met, if so, outputting an optimal capacity configuration scheme of the energy equipment, and otherwise, performing the step (4-6);
(4-6) carrying out selection, crossing and mutation operations on the population to form a next generation population, and returning to the step (4-3).
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