CN109885009B - Multi-energy complementary park energy optimization configuration method considering electricity-to-gas planning - Google Patents

Multi-energy complementary park energy optimization configuration method considering electricity-to-gas planning Download PDF

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CN109885009B
CN109885009B CN201910209869.XA CN201910209869A CN109885009B CN 109885009 B CN109885009 B CN 109885009B CN 201910209869 A CN201910209869 A CN 201910209869A CN 109885009 B CN109885009 B CN 109885009B
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CN109885009A (en
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陈铭
李峰
高亚静
刘刚刚
高长征
马顺
王秀娜
韩淳
高晓彬
赵名锐
韩超
胡晋岚
陈冠多
侯凯
姜玉梁
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Grid Planning Research Center of Guangdong Power Grid Co Ltd
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Power Construction Technology Economic Consulting Center Of China Electricity Council
Grid Planning Research Center of Guangdong Power Grid Co Ltd
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Abstract

The invention relates to a multi-energy complementary park energy optimization configuration method considering electricity-to-gas planning, and belongs to the field of distributed energy planning. The invention relates to a P2G device, and a double-layer multi-target optimization configuration model including various energy supply devices, which takes the influences of the economy, the environmental protection, the reliability and the like of the comprehensive energy system of the multi-energy complementary park into consideration. And the correctness and feasibility of the optimized configuration model are verified by combining with related examples. Compared with the traditional energy supply mode, the electricity/heat/cold multi-energy flow coordination complementary configuration is obtained by comparing the optimal configuration results under different energy supply strategies, so that the energy supply cost of the system is reduced, and the performance of various energy supply indexes is improved; and the addition of the P2G equipment improves the index performance of the energy supply system, and realizes the coordinated complementary utilization of the electricity/heat/cold/gas multiple energy flows of the energy supply system.

Description

Multi-energy complementary park energy optimization configuration method considering electricity-to-gas planning
Technical Field
The invention belongs to the field of distributed energy planning, and particularly relates to a multi-energy complementary park energy optimization configuration method considering electricity-to-gas planning.
Background
The increasing demand for energy and the constant access of distributed energy (DG) have a certain impact on the energy allocation of conventional multi-energy complementary parks. The reasonable configuration of the comprehensive energy supply center can not only reduce the loss in the energy transmission process and consume more DGs, but also realize the electric/heat/cold multi-energy flow coordination planning. However, for the current optimization configuration research of the multi-energy complementary system, most of the devices with a certain device type as the core or a few determined devices form a system structure, the diversity of the device types of the system is not fully considered, and the objective function is too simple, and generally only relevant discussion is made in terms of economy, and the energy supply efficiency and the DG output utilization rate of the system are not considered.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for optimizing and configuring energy of a multi-energy complementary park considering electricity-to-gas planning, and aims to integrate the electricity/heat/cold energy flow difference of the multi-energy complementary park and the energy conversion and space-time translation characteristics of power-to-gas (P2G) equipment to complete the optimal configuration of the energy.
In order to solve the technical problems, the technical scheme of the invention is as follows: the optimal configuration method of the multi-energy complementary park energy considering the electricity-to-gas planning is characterized by comprising the following optimal configuration steps of:
(1) constructing a multi-energy complementary park comprising energy production, conversion, transmission, storage and utilization units, said energy conversion units comprising electric to gas equipment;
(2) for a multi-energy complementary park, a double-layer multi-objective optimization configuration model is established by taking the minimum annual cost as an upper-layer objective function and taking the minimum annual operation cost, the maximum annual energy supply efficiency and the optimal wind-solar output utilization rate as a lower-layer objective function according to the constraint conditions of energy production, conversion, transmission, storage and utilization units;
(3) for the double-layer multi-objective optimization configuration model, an improved immune genetic algorithm based on fuzzy membership and variance weighting is adopted to perform nested solution on the model; and determining an optimal configuration scheme of the double-layer multi-objective optimal configuration model according to the solution result.
A further technical solution is that the energy production units typically include wind power, photovoltaic, micro gas turbines and gas boilers;
the energy conversion unit comprises a heat pump, an air conditioner, a heat exchanger, an electric refrigerator, an absorption refrigerator and an electric gas conversion device;
the energy storage unit comprises an electric energy storage device, a thermal energy storage device, a cold accumulation device and an air storage device;
the energy utilization units are electrical loads, thermal loads and cold loads in the park.
The further technical proposal is that the objective function of the upper layer is
Cyear＝Cinves+Cfuel+Cgrid+Copera+Cenvir (1)
Figure GDA0002035352630000021
Figure GDA0002035352630000022
Figure GDA0002035352630000023
Figure GDA0002035352630000031
Figure GDA0002035352630000032
Figure GDA0002035352630000033
In the formula: cyear，Cinves，Cfuel，Cgrid，Copera，CenvirRespectively providing annual cost, equipment investment cost, fuel cost, cost for interacting with a power grid, operation and maintenance cost and environmental cost of the multi-energy complementary park; n is a radical ofIThe type of investment equipment;
Figure GDA0002035352630000034
respectively, investment cost per unit volume, installation capacity and installation factor of the equipment i, wherein, if the equipment is installed,
Figure GDA0002035352630000035
otherwise, the value is 0;
Figure GDA0002035352630000036
is the present value coefficient of device i, ri equ，
Figure GDA0002035352630000037
Respectively showing the current sticking rate and the whole life cycle of the equipment i; n is a radical ofJThe total days in the planning period; n is a radical ofTTotal number of time segments for a typical day; pt Gas，Pt P2G_out，Pt grid，
Figure GDA0002035352630000038
Pt MT，Qt GB，ηt MT，ηGB，
Figure GDA0002035352630000039
Respectively, the power interacting with the natural gas network, the natural gas power value output by the electric gas conversion equipment, the electric power interacting with the power network, the output power of the equipment i, the electric/thermal power values and efficiencies of the micro gas turbine and the gas boiler, and the power factor interacting with the power network at the typical time t in the day, wherein if P ist grid>0, then
Figure GDA00020353526300000310
Otherwise, the value is 0; Δ t is the time interval, taken here as 1 h; etaGasIs the power transmission efficiency of the natural gas grid; cgas，vLHVRespectively the purchase price of the natural gas and the low heat value of the combustion of the natural gas; xiP2G，CP2GRespectively CO required by unit natural gas production in the process of converting electricity into gas2Coefficient and CO2A price; ct,sale，Ct,buyRespectively selling electricity and purchasing electricity at time t;
Figure GDA00020353526300000316
operating and maintaining costs for unit capacity of equipment i; n is a radical ofUIs the number of pollutant species; ruIs the environmental value of contaminant u;
Figure GDA00020353526300000312
Figure GDA00020353526300000317
the emission intensity of pollutants u under the power of a micro gas turbine, a gas boiler and the interaction with a power grid respectively; etagridThe transmission efficiency of the power grid is obtained;
Figure GDA00020353526300000314
as pollutant CO2The environmental value of (c);
Figure GDA00020353526300000315
for converting CO into CO in the process of electricity2The strength is consumed.
A further technical solution consists in constructing a multi-energy complementary park comprising energy production, conversion, transmission, storage and utilization units, the underlying objective functions of which are:
Cyear_opera＝Cfuel+Cgrid+Copera+Cenvir (8)
Figure GDA0002035352630000041
Figure GDA0002035352630000042
Figure GDA0002035352630000043
in the formula: cyear_opera，fREL，fDG_utiRespectively providing annual operation cost, annual energy supply efficiency and wind and light output utilization rate in the multi-energy complementary park, wherein if the energy supply value at a certain moment is greater than the load value, the energy supply efficiency at the moment is 1; pt WT，Pt PV，Pt EC_in，Pt HP_H，Pt HP_C，Pt AIR_H，Pt AIR_C，Pt P2G_inRespectively representing the output power of wind power and photovoltaic power, the input electric power value of refrigeration of an electric refrigerator, the input electric power value of a heat pump and an air conditioner for heating and refrigeration and the electric power value consumed by electric gas conversion equipment at the typical moment t in a day;
Figure GDA0002035352630000044
Figure GDA0002035352630000045
the output thermal power of the micro gas turbine at the typical time t in the day, the input thermal power of the absorption refrigerator and the output thermal power value of the heat pump and the air conditioner are respectively;
Figure GDA0002035352630000046
Figure GDA0002035352630000047
the output cold power values of the electric refrigerator, the absorption refrigerator, the heat pump and the air conditioner at the typical moment t in the day are respectively; pt ES，
Figure GDA0002035352630000048
Respectively the net discharge values of the electric refrigerator, the heat energy storage device and the cold storage device,
Figure GDA0002035352630000049
and
Figure GDA00020353526300000410
the charging and discharging rate values of the electric energy storage device, the thermal energy storage device and the cold accumulation device at the typical moment t in the day are respectively;
Figure GDA00020353526300000411
and
Figure GDA00020353526300000412
respectively, the charging and discharging energy zone bits of the electric energy storage device, the thermal energy storage device and the cold accumulation device at the typical time t in a day, if the energy is discharged,
Figure GDA00020353526300000413
is 1, otherwise is 0, if the energy is charged,
Figure GDA00020353526300000414
Figure GDA00020353526300000415
is 1, otherwise is 0; etapch，ηhch，ηcch，ηpdis，ηhdis，ηcdisThe charging and discharging efficiency of the electric energy storage device, the thermal energy storage device and the cold accumulation device respectively; pt WT_pre，Pt PV_preWind power and photovoltaic prediction output values at the typical time t in a day are respectively.
A further technical solution consists in constructing a multi-energy complementary park comprising energy production, conversion, transmission, storage and utilization units, subject to the constraint of
Figure GDA00020353526300000416
Figure GDA0002035352630000051
Figure GDA0002035352630000052
Figure GDA0002035352630000053
Figure GDA0002035352630000054
Figure GDA0002035352630000055
Figure GDA0002035352630000056
Figure GDA0002035352630000057
Figure GDA0002035352630000058
Figure GDA0002035352630000059
In the formula: equation (12) is the electrical/thermal/cold/pneumatic power balance constraint at each moment; equation (13) is the device capacity configuration constraint; equation (14) is the output limit constraint for each energy unit; equation (15) is the MT ramp rate constraint; equation (16) is the grid interaction power constraint; equations (17) and (18) are respectively the output of the P2G equipment at each moment and the upper and lower limits of the supply flow of the air source point; equations (19) and (20) are respectively the maximum storage and release rate and the storage capacity constraint of the energy storage device; the formula (21) is (0,1) variable constraint, and the energy storage device cannot store and release energy at the same moment; wherein the content of the first and second substances,
Figure GDA00020353526300000510
the electric/heat/cold load value in the complementary garden of the multipotency at the time t in the typical day;
Figure GDA00020353526300000511
and
Figure GDA00020353526300000512
respectively is the air charging and discharging speed value and the air charging and discharging mark position of the air storage device at the typical time t in the day, if the air is discharged,
Figure GDA00020353526300000513
on the contrary, if the gas is inflated,
Figure GDA00020353526300000514
otherwise, the value is 0;
Figure GDA00020353526300000515
the natural gas consumption of the micro gas turbine and the gas boiler at the typical time t in a day respectively;
Figure GDA00020353526300000516
Figure GDA00020353526300000517
respectively the upper and lower limit values of the installation capacity of the equipment i;
Figure GDA00020353526300000518
Figure GDA00020353526300000519
respectively representing the output value and the scheduling factor of the energy unit i at the time t in the typical day, wherein if the energy unit is scheduled, the value is 1, otherwise, the value is 0;
Figure GDA00020353526300000520
the output values of the micro gas turbine at typical time t +1, t, t-1 in the day are respectively;
Figure GDA00020353526300000521
respectively generating natural gas power for each energy unit output, the climbing rate of the micro gas turbine, the transmission power of the power grid and the upper and lower limit values of the interaction power of the natural gas grid at each moment of the electric gas conversion equipment; en is an energy storage and gas storage device type and comprises an electric energy storage device, a thermal energy storage device, a cold storage device and a gas storage device; ent，
Figure GDA00020353526300000522
Figure GDA00020353526300000523
CEn，
Figure GDA00020353526300000524
The energy storage device change rate, the energy storage device capacity, the maximum energy charging and discharging rate, the upper and lower limit values of the energy charging state, the energy storage capacity value and the energy storage and discharge zone bit at the typical time t in a day are respectively; etagch，ηgdisRespectively the air charging and discharging efficiency of the air storage device.
The further technical scheme is that the solving process is as follows:
1) inputting original data, coding and initializing an upper layer population, randomly generating N antibodies, extracting m individuals from a memory base to form an initial population, setting the iteration number I to be 1, and setting the maximum iteration number I to bemaxAntibody N ═ 1, population size Nmax；
2) Inputting the capacity configuration result of the upper layer antibody n as original data into the lower layer population, coding and initializing the lower layer population, and setting the iteration number i to be 1 and the maximum iteration number to be imaxAntibody k is 1 and population size k ismax；
3) Calculating an antibody fitness value of the antibody k according to a lower-layer objective function;
4) judgment of k<kmaxIf yes, if k is k +1, the process proceeds to step 3), and if not, the process proceeds to step 5);
5) calculating the membership degree of each target function of each antibody of the lower layer according to the formula (22);
Figure GDA0002035352630000061
in the formula: gkjIs the jth target value for antibody k;
Figure GDA0002035352630000062
maximum and minimum values for target j among all antibodies;
6) weighting each objective function according to equation (23);
Figure GDA0002035352630000063
in the formula, wjThe weight of the target j, M is the number of the target functions, and N is the total number of the antibodies;
7) calculating the preferential selectivity of each antibody according to the formula (24), wherein the antibody with the highest selectivity is the compromise optimal solution;
Figure GDA0002035352630000064
8) updating the lower-layer parent population and the memory library;
9) carrying out self-adaptive selection, crossing and variation on the lower-layer parent population to form a new population;
10) judgment of i<imaxIf yes, the process proceeds to step 3) if i +1 and k 1 are satisfied, and proceeds to step 11) if not;
11) returning the antibody corresponding to the optimal solution and the lower-layer objective function to the upper layer;
12) calculating an upper antibody fitness value;
13) judging whether N < N is true, if so, turning to step 2), and turning to step 14) if not;
14) calculating the expected reproduction probability of each antibody in the upper layer by using the formula (25);
Figure GDA0002035352630000071
in the formula: f is the upper antibody fitness value;
15) sequentially arranging the initial population from large to small according to the expected reproduction rate P, selecting individual optimal solutions, extracting the first N individuals to form a parent population, and storing the first m individuals into a memory bank;
16) carrying out self-adaptive selection, crossing and mutation operations on the upper-layer parent population to form a new population;
17) judgment of I<ImaxAnd if the optimal configuration result is not satisfied, the iterative process is ended, and the optimal configuration result is output.
By adopting the technical scheme, the invention has the beneficial effects that: the invention combines the electricity/heat/cold multi-energy flow difference and the energy conversion and space-time translation characteristics of the P2G equipment, and combines the related examples to verify the correctness and feasibility of the provided optimization configuration model. Compared with the traditional energy supply mode, the electricity/heat/cold multi-energy flow coordination complementary configuration is obtained by comparing the optimal configuration results under different energy supply strategies, so that the energy supply cost of the system is reduced, and the performance of various energy supply indexes is improved; and the addition of the P2G equipment improves the index performance of the energy supply system, and realizes the coordinated complementary utilization of the electricity/heat/cold/gas multiple energy flows of the energy supply system.
Drawings
FIG. 1 is a flow diagram of the energy of a multi-energy complementary park; in the figure, the solid line, the thin dashed line, the dotted line, the thick dashed line and the arrows represent the electrical/thermal/cold/air load and the energy flow direction, respectively, in the multi-energy complementary park.
FIG. 2 is a flow chart of a nested solution of an improved immune genetic algorithm based on fuzzy membership and variance weighting.
FIG. 3 typical daily wind/photovoltaic and electric/heat/cold load output conditions; a) a wind power/photovoltaic output curve; b) the electrical/thermal/cold load output curves are shown.
FIG. 4 is a graph comparing DG output utilization at various times under different energy supply strategies.
FIG. 5 is a typical daily electrical/thermal/cold/pneumatic power balance diagram; FIG. 5a) is electrical power; FIG. 5b) shows thermal power; fig. 5c) is cold power; fig. 5d) is gas power.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
1. Double-layer multi-target optimization configuration model construction for comprehensive energy supply center of multi-energy complementary park
1.1 analysis of the flow characteristics of the energy of a multipotent complementary park
The multi-energy complementary park is used as a complex energy system, relates to the production, transfer, storage and utilization of various electric/heat/cold energy sources, and has the characteristics of complex load characteristics, large load demand, high energy supply efficiency requirement and the like. For a typical multi-energy complementary park, the system typically contains energy production, conversion, transmission, storage and utilization units within the system. Among them, the energy production unit generally includes Wind Turbine (WT), Photovoltaic (PV), micro-turbines (MT), Gas Boiler (GB), and so on; the energy conversion unit includes a Heat Pump (HP), an AIR conditioner (AIR conditioner, AIR), a heat-exchanger (HE), an electric refrigerator (EC), an absorption refrigerator (AC), and an electric gas converter (P2G); the energy storage unit includes an electric energy storage device (ES), a thermal energy storage device (HS), a cold storage device (CS), a gas storage device (GS), and the like; the energy utilization units are electric load (Eload), heat load (Hload), cold load (Cload), and the like in the campus. The electricity/heat/cold/gas energy flow relationship in the garden is shown in figure 1;
the method is characterized in that the double-layer multi-objective optimization configuration model of the comprehensive energy source system of the multi-energy complementary park is established by integrating the electricity/heat/cold energy flow difference of the multi-energy complementary park and the energy conversion and space-time translation characteristics of P2G equipment and considering the economic, environmental protection and reliability influences of the energy supply system. The upper layer takes the minimum annual cost as a target function, the target function comprises equipment installation cost, fuel cost, operation maintenance cost, power grid interaction cost and environment cost, and decision variables are various types of DG configuration capacity and configuration types; the lower layer takes the minimum annual operation cost, the maximum annual energy supply efficiency and the optimal DG output utilization rate as objective functions, and the decision variables are the scheduling values, the scheduling factors, the power values interacting with the distribution network and the like of various types of DGs at all times. And finally, nesting and solving the model by adopting an improved immune genetic algorithm.
1.2 objective function
1) Upper layer objective function
Cyear＝Cinves+Cfuel+Cgrid+Copera+Cenvir (1)
Figure GDA0002035352630000091
Figure GDA0002035352630000092
Figure GDA0002035352630000093
Figure GDA0002035352630000094
Figure GDA0002035352630000095
Figure GDA0002035352630000096
In the formula: cyear，Cinves，Cfuel，Cgrid，Copera，CenvirRespectively providing annual cost, equipment investment cost, fuel cost, cost for interacting with a power grid, operation and maintenance cost and environmental cost of the multi-energy complementary park; n is a radical ofIThe type of investment equipment;
Figure GDA0002035352630000097
respectively, investment cost per unit volume, installation capacity and installation factor of the equipment i, wherein, if the equipment is installed,
Figure GDA0002035352630000098
otherwise, the value is 0;
Figure GDA00020353526300000914
is the current value coefficient of the device i,
Figure GDA00020353526300000910
respectively representing the current sticking rate (generally 7%) and the life cycle of the device i; n is a radical ofJThe total days in the planning period; n is a radical ofTTotal number of time segments for a typical day; pt Gas，Pt P2G_out，Pt grid，
Figure GDA00020353526300000911
Pt MT，
Figure GDA00020353526300000912
ηGB，
Figure GDA00020353526300000913
Respectively, the power interacting with the natural gas network, the natural gas power value output by the electric gas conversion equipment, the electric power interacting with the power network, the output power of the equipment i, the electric/thermal power values and efficiencies of the micro gas turbine and the gas boiler, and the power factor interacting with the power network at the typical time t in the day, wherein if P ist grid>0, then
Figure GDA0002035352630000101
Otherwise, the value is 0; Δ t is the time interval, taken here as 1 h; etaGasIs the power transmission efficiency of the natural gas grid; cgas，vLHVRespectively the purchase price of the natural gas and the low heat value of the combustion of the natural gas; xiP2G，CP2GRespectively CO required by unit natural gas production in the process of converting electricity into gas2Coefficient and CO2A price; ct,sale，Ct,buyRespectively selling electricity and purchasing electricity at time t;
Figure GDA0002035352630000102
operating and maintaining costs for unit capacity of equipment i; n is a radical ofUIs the number of pollutant species, including CO2,SO2,NOxEtc.; ruIs the environmental value of contaminant u;
Figure GDA0002035352630000103
the emission intensity of pollutants u under the power of a micro gas turbine, a gas boiler and the interaction with a power grid respectively; etagridThe transmission efficiency of the power grid is obtained;
Figure GDA0002035352630000104
as pollutant CO2The environmental value of (c);
Figure GDA0002035352630000109
for converting CO into CO in the process of electricity2The strength is consumed.
2) Lower layer objective function
Cyear_opera＝Cfuel+Cgrid+Copera+Cenvir (8)
The energy supply efficiency of the multi-energy complementary park is the ratio of the scheduling value of the energy supply system to the functional load value, the ratio is less than 1, but the larger the ratio is, the higher the energy supply efficiency is; the energy supply efficiency is:
Figure GDA0002035352630000106
the wind-solar output utilization rate is the ratio of the actual wind power and photovoltaic modulation values of the energy supply system to the predicted value, the ratio is less than or equal to 1, and the larger the ratio is, the better the wind-solar output utilization rate is; the wind-solar output utilization rate is as follows:
Figure GDA0002035352630000107
Figure GDA0002035352630000108
in the formula: cyear_opera，fREL，fDG_utiRespectively providing annual operation cost, annual energy supply efficiency and wind and light output utilization rate in the multi-energy complementary park, wherein if the energy supply value at a certain moment is greater than the load value, the energy supply efficiency at the moment is 1; pt WT，Pt PV，Pt EC_in，Pt HP_H，Pt HP_C，Pt AIR_H，Pt AIR_C，Pt P2G_inRespectively representing the output power of wind power and photovoltaic power, the input electric power value of refrigeration of an electric refrigerator, the input electric power value of a heat pump and an air conditioner for heating and refrigeration and the electric power value consumed by electric gas conversion equipment at the typical moment t in a day;
Figure GDA0002035352630000111
Figure GDA0002035352630000112
the output thermal power of the micro gas turbine at the typical time t in the day, the input thermal power of the absorption refrigerator and the output thermal power value of the heat pump and the air conditioner are respectively;
Figure GDA0002035352630000113
Figure GDA0002035352630000114
the output cold power values of the electric refrigerator, the absorption refrigerator, the heat pump and the air conditioner at the typical moment t in the day are respectively;
Figure GDA0002035352630000115
respectively the net discharge values of the electric refrigerator, the heat energy storage device and the cold storage device,
Figure GDA0002035352630000116
and
Figure GDA0002035352630000117
the charging and discharging rate values of the electric energy storage device, the thermal energy storage device and the cold accumulation device at the typical moment t in the day are respectively;
Figure GDA0002035352630000118
and
Figure GDA0002035352630000119
respectively, the charging and discharging energy zone bits of the electric energy storage device, the thermal energy storage device and the cold accumulation device at the typical time t in a day, if the energy is discharged,
Figure GDA00020353526300001110
is 1, otherwise is 0, if the energy is charged,
Figure GDA00020353526300001111
Figure GDA00020353526300001112
is 1, otherwise is 0; etapch，ηhch，ηcch，ηpdis，ηhdis，ηcdisThe charging and discharging efficiency of the electric energy storage device, the thermal energy storage device and the cold accumulation device respectively; pt WT_pre，Pt PV_preWind power and photovoltaic prediction output values at the typical time t in a day are respectively.
1.3 constraint Condition
Figure GDA00020353526300001113
Figure GDA00020353526300001114
Figure GDA00020353526300001115
Figure GDA00020353526300001116
Figure GDA00020353526300001117
Figure GDA00020353526300001118
Figure GDA00020353526300001119
Figure GDA00020353526300001120
Figure GDA00020353526300001121
Figure GDA00020353526300001122
In the formula: equation (12) is the electrical/thermal/cold/pneumatic power balance constraint at each moment; equation (13) is the device capacity configuration constraint; equation (14) is the output limit constraint for each energy unit; equation (15) is the MT ramp rate constraint; equation (16) is the grid interaction power constraint; equations (17) and (18) are respectively the output of the P2G equipment at each moment and the upper and lower limits of the supply flow of the air source point; equations (19) and (20) are respectively the maximum storage and release rate and the storage capacity constraint of the energy storage device; the formula (21) is (0,1) variable constraint, and it is ensured that the energy storage device can not store energy (gas) and release energy (gas) at the same time. Wherein the content of the first and second substances,
Figure GDA0002035352630000121
the electric/heat/cold load value in the complementary garden of the multipotency at the time t in the typical day;
Figure GDA0002035352630000122
and
Figure GDA00020353526300001213
respectively is the air charging and discharging speed value and the air charging and discharging mark position of the air storage device at the typical time t in the day, if the air is discharged,
Figure GDA0002035352630000124
on the contrary, if the gas is inflated,
Figure GDA0002035352630000125
otherwise, the value is 0;
Figure GDA0002035352630000126
the natural gas consumption of the micro gas turbine and the gas boiler at the typical time t in a day respectively;
Figure GDA0002035352630000127
respectively the upper and lower limit values of the installation capacity of the equipment i;
Figure GDA0002035352630000128
respectively representing the output value and the scheduling factor of the energy unit i at the time t in the typical day, wherein if the energy unit is scheduled, the value is 1, otherwise, the value is 0;
Figure GDA0002035352630000129
the output values of the micro gas turbine at typical time t +1, t, t-1 in the day are respectively;
Figure GDA00020353526300001210
respectively generating natural gas power for each energy unit output, the climbing rate of the micro gas turbine, the transmission power of the power grid and the upper and lower limit values of the interaction power of the natural gas grid at each moment of the electric gas conversion equipment; en is an energy storage and gas storage device type and comprises an electric energy storage device, a thermal energy storage device, a cold storage device and a gas storage device; ent，
Figure GDA00020353526300001211
CEn，
Figure GDA00020353526300001212
The change rate of the energy storage device, the capacity of the energy storage device, the maximum energy (gas) charging and discharging rate, the upper and lower limit values of the energy charging state, the energy (gas) storage capacity value and the energy (gas) storage and discharging zone bit at the typical moment t in the day are respectively; etagch，ηgdisRespectively the air charging and discharging efficiency of the air storage device.
2. Model solution
For the multi-objective double-layer optimization configuration model of the comprehensive energy supply center of the multi-energy complementary park established by the invention, an improved immune genetic algorithm based on fuzzy membership and variance weighting is adopted for nested solution. The immune genetic algorithm is characterized in that a genetic algorithm and a concentration regulation mechanism are introduced on the basis of improving a self-adaptive evolution immune algorithm, and a fuzzy membership degree and variance weighting method is introduced to process a multi-target problem in a lower model, wherein the model nesting solving process is as follows:
1) the original data is input and the data is input,coding and initializing upper layer population, randomly generating N antibodies, and extracting m individuals from a memory bank to form an initial population, wherein the iteration number I is 1, and the maximum iteration number is ImaxAntibody N ═ 1, population size Nmax；
2) Inputting the capacity configuration result of the upper layer antibody n as original data into the lower layer population, coding and initializing the lower layer population, and setting the iteration number i to be 1 and the maximum iteration number to be imaxAntibody k is 1 and population size k ismax；
3) Calculating an antibody fitness value of the antibody k according to a lower-layer objective function;
4) judgment of k<kmaxIf yes, if k is k +1, the process proceeds to step 3), and if not, the process proceeds to step 5);
5) calculating the membership degree of each target function of each antibody of the lower layer according to the formula (22);
Figure GDA0002035352630000131
in the formula: gkjIs the jth target value for antibody k;
Figure GDA0002035352630000132
maximum and minimum values for target j among all antibodies;
6) weighting each objective function according to equation (23);
Figure GDA0002035352630000133
in the formula, wjThe weight of the target j, M is the number of the target functions, and N is the total number of the antibodies;
7) calculating the preferential selectivity of each antibody according to the formula (24), wherein the antibody with the highest selectivity is the compromise optimal solution;
Figure GDA0002035352630000134
8) updating the lower-layer parent population and the memory library;
9) carrying out self-adaptive selection, crossing and variation on the lower-layer parent population to form a new population;
10) judgment of i<imaxIf yes, the process proceeds to step 3) if i +1 and k 1 are satisfied, and proceeds to step 11) if not;
11) returning the antibody corresponding to the optimal solution and the lower-layer objective function to the upper layer;
12) calculating an upper antibody fitness value;
13) judging whether N < N is true, if so, turning to step 2), and turning to step 14) if not;
14) calculating the expected reproduction probability of each antibody in the upper layer by using the formula (25);
Figure GDA0002035352630000141
in the formula: f is the upper antibody fitness value;
15) sequentially arranging the initial population from large to small according to the expected reproduction rate P, selecting individual optimal solutions, extracting the first N individuals to form a parent population, and storing the first m individuals into a memory bank;
16) carrying out self-adaptive selection, crossing and mutation operations on the upper-layer parent population to form a new population;
1)17) judgment of I<ImaxAnd if the optimal configuration result is not satisfied, the iterative process is ended, and the optimal configuration result is output.
In conclusion, a flow chart 2 of a nested solution of an improved immune genetic algorithm based on fuzzy membership and variance weighting is shown;
3. example analysis
3.1 summary of the examples
Considering the optimal configuration of a multi-energy complementary park comprehensive energy supply center on a certain subarea of the park comprehensive energy supply center, the plan area of the park comprehensive energy supply center is about 23 ten thousand meters2It is composed of industrial area, commercial area, city and residential area, and each area has electric/heat/cold load demand. Wherein the park has a demand for electric/thermal loadThe demand is large and the cold load demand is relatively small. And, because the inside utility tunnel that is equipped with of garden, its electricity/heat/cold load adopts concentrated energy supply mode more. The maximum installation capacity of wind power/photovoltaic in a park is 780MW and 600MW respectively, the maximum load of electricity/heat/cold year is 1260MW, 1140MW and 680MW respectively, the maximum power of interaction with a power grid and a natural gas grid is 400MW and 120MW respectively, the configuration parameters of each energy supply device are shown in an attached table 3-1, the time-sharing price of energy is shown in an attached table 3-2, and the related parameters of operation pollutants of each device are shown in an attached table 3-3. The upper limit value and the lower limit value of the climbing speed of the micro gas turbine are both 30MW/h, the heat dissipation loss coefficient is 0.15, the minimum load rate is 0.3, the minimum load rates of other equipment are 0, and the maximum load rates are both 1; CO required for producing unit natural gas in electric gas conversion process2Coefficient and CO2The prices are 0.2 and 0.36$/MW.h respectively. The configuration information of each energy supply device of the multi-energy complementary park is shown in tables 3-1, 3-2 and 3-3:
TABLE 3-1 multiple energy complementary park energy supply device configuration parameters
Figure GDA0002035352630000151
TABLE 3-2 time-of-use price of energy
Figure GDA0002035352630000152
Figure GDA0002035352630000161
Tables 3-3 operating contamination related parameters for each of the plants
Figure GDA0002035352630000162
Here, a typical solar wind/light and electricity/heat/cold load curve in the selected park is shown in fig. 3;
3.2 optimal configuration results and analysis of comprehensive energy supply center of multi-energy complementary park
1) Comparison of optimized configuration results under different energy supply strategies
Based on the wind power/photovoltaic and electricity/heat/cold load output conditions in a certain typical day, the provided model and algorithm are adopted to optimally configure the following four multi-energy complementary park energy supply strategies:
energy supply strategy 1: the traditional energy supply modes are power supply of a power distribution network, GB heat supply and EC cold supply;
energy supply strategy 2: the combined electric heating and cooling mode is characterized in that MT supplies power and heat simultaneously, EC and AC supply cooling together, and a power distribution network, GB and energy storage equipment supply power, heat and cold in an auxiliary mode;
energy supply strategy 3: the electric heating and cooling combined supply mode considers the addition of DG;
energy supply strategy 4: the combined supply of electricity, heat and cold, consider the addition of DG and P2G equipment;
the optimized configuration results and various target function pairs under 4 different energy supply strategies are shown in tables 4-1 and 4-2:
TABLE 4-1 optimized configuration results under different energy supply strategies
Figure GDA0002035352630000163
Figure GDA0002035352630000171
TABLE 4-2 comparison of various objective functions under different energy supply strategies
Figure GDA0002035352630000172
As can be seen from tables 4-1 and 4-2:
comparing energy supply strategies 1 and 2: in the energy supply strategy 2, due to the addition of various energy supply devices, the equipment investment cost is obviously increased compared with that of the energy supply strategy 1, and 106.89e is added4The operation and maintenance cost is slightly increased to about 10.56e4$ 3; but fuel costs, interaction costs with the grid and environmental costsAll are obviously reduced, and are about 96.65e4$、454.31e4$ and 103.38e4$ therefore, overall cost of energy is reduced 536.89e compared to energy strategy 14And $ 44.25%. Compared with the traditional energy supply mode, the MT in the combined cooling heating and power supply mode generates electricity and heat through fuel consumption so as to meet the demand of partial electricity/heat load in the garden, so that the heat generation demand of GB is reduced, the coordinated complementary utilization of electricity/heat/cold multiple energy flows is realized, the fuel cost is reduced, the electricity purchasing demand of a power grid is reduced, and the environmental cost generated by electricity purchasing is reduced.
Comparing energy supply strategies 2 and 3: due to the addition of DG in the energy supply strategy 3, the equipment investment cost and the operation and maintenance cost are increased to about 76.50e compared with those of the energy supply strategy 24$ and 12.15e4$ 3; fuel cost, interaction cost with the power grid and environmental cost are reduced to different degrees, which are 81.44e respectively4$、110.69e4$ and 95.87e4$ therefore, overall cost of energy supply is reduced 199.35e compared to energy supply strategy 24And $ 29.47%. In addition, the annual energy supply efficiency is slightly reduced compared with the energy supply strategy 2. The DG is added, so that the partial electric load requirement is met, the MT electric output is slightly reduced, the natural gas fuel requirement and the power grid electricity purchasing requirement are reduced, and the environmental protection benefit is improved. In addition, considering that the DG output has a certain uncertainty, its annual energy supply efficiency is reduced, but its annual energy supply efficiency is slightly reduced due to the presence of the energy storage device.
Comparing energy supply strategies 3 and 4: the equipment investment cost and the operation and maintenance cost of the energy supply strategy 4 are increased compared with the energy supply strategy 3 due to the addition of P2G equipment, and are 86.57e4$ and 9.66e4$ 3; the fuel cost and the interaction cost with the power grid are obviously reduced and are respectively 74.04e4$ and 68.69e4$ 3; the environmental cost is slightly reduced to 14.32e4And $ 3. Because the benefits brought by the reduction of electricity purchase are far greater than the cost of the increase of the cost of fuel and the like, the total cost of energy supply is reduced 60.82e compared with that of energy supply strategy 37And $ 12.75%, and annual energy supply efficiency and DG output utilization rate are improved to different degrees compared with the energy supply strategy 3. This is due to the addition of the P2G device during the low-valley period of the electrical loadRedundant wind and light output can be absorbed, and the electrical load requirement of the system is increased; at the peak period of the electric load, the net electric load peak value at the peak period of the load is reduced by increasing MT and GB output, and the electricity purchasing requirements of natural gas fuel and a power grid are reduced by consuming generated natural gas, so that the environmental benefit is increased, the energy supply cost is reduced, and the annual energy supply efficiency and the DG output utilization rate are improved to a certain extent compared with the energy supply strategy 3. Wherein, comparing the DG output utilization rate at each time in a typical day before and after the P2G equipment is added, as shown in FIG. 4;
as can be seen from fig. 4: for the DG output utilization rate at each moment, under the condition that the addition of P2G equipment is not considered, the DG output utilization rate is stabilized between 0.7 and 0.85, and a certain degree of abandoned wind and abandoned light can be generated; under the condition that P2G equipment is added, the DG output utilization rate corresponding to each moment is improved to a certain degree and is stabilized between 0.9 and 1.0. This is because full DG uptake cannot be achieved despite the presence of energy storage devices; the P2G equipment further stabilizes the uncertainty of DG output, increases the DG output at each moment, basically realizes complete absorption, and alleviates the problems of wind and light abandonment.
Therefore, compared with the four energy supply strategies, the addition of the combined electric-heat-cold supply system can realize coordinated complementary utilization of electricity/heat/cold multiple energy flows in the multiple energy complementary park, reduce the energy supply cost of the system and improve the energy supply efficiency of the system; the addition of the DG meets the requirements of partial electric loads of the system, the energy supply cost is further reduced, but the energy supply efficiency is slightly reduced considering that the DG output has certain confidence coefficient; in addition, the coordinated utilization of the P2G device and the MT plays a role of 'peak clipping and valley filling' on the system net load, the uncertainty of DG output is stabilized to a certain extent, and the characteristics of each objective function are improved to different extents.
2) Optimizing scheduling result analysis under typical day
Based on the typical daily optimization configuration result of the multi-energy complementary park, the electric/thermal/cold/pneumatic power optimization scheduling situation at each moment is shown in fig. 5;
as can be seen from fig. 5: for typical time-of-day electrical power balancing, WT, PV, MT, ES and the power purchased from the grid are used to meet the demand for electrical loads, P2G equipment, AIR, HP and EC refrigeration loads on the campus. During the nighttime electrical load valley period 23: 00-5: 00, the wind power output is large, the photovoltaic output is 0, at the moment, in order to further reduce the wind power, the MT output is relatively reduced, the P2G equipment consumes redundant wind power and converts the redundant wind power into natural gas, and the ES stores the electricity; during daytime peak electrical load 10: 00-21: 00, wind and photovoltaic output cannot meet the electrical load demand, MT output is further increased, where at 18: 00-21: 00 the photovoltaic is no longer applied, MT is applied to the maximum value, and the insufficient electric load requirement is met by the power grid purchase and the ES discharge.
For typical day-to-day heat/cold load balancing, MT, GB, HS, HP, and AIR heat production collectively meet campus heat load and AC cold load demands, and EC, AC, CS, HP, and AIR co-refrigerate to meet campus cold load demands. In consideration of the difference of heat/cold loads of various functional areas of the park, the heat load is large in the daytime, the heat load is relatively small at night, and the overall cold load demand is relatively small. In the heat load valley period at night, as the MT power output is reduced, the heat output is relatively reduced in consideration of the 'fixed power with heat' operation mode, so that the GB power output is increased and the HS power is released in order to meet the heat load requirement at night; in the daytime at the peak of heat load, the MT electric output is increased, the heat output value obtained by the HE is increased, the GB output is relatively reduced, and the excess heat energy is stored in the HS.
For typical time-of-day gas power balance, the production of natural gas, GS by the P2G plant, and the purchase of natural gas from the natural gas grid collectively meet MT and GB natural gas demand. For night 1: 00-9: 00, because the wind power output is large, redundant wind power is converted into natural gas through P2G equipment, partial MT and GB requirements are met, and redundant natural gas is stored; for 10: 00-24: 00, wind power output is reduced firstly and then increased, photovoltaic output is increased firstly and then is 0, at the moment, because the electric load is increased, the output of P2G equipment is reduced to 0, MT (multi-terminal) power output is increased, the natural gas load demand is increased continuously, natural gas purchased from a natural gas network is increased continuously, and the insufficient part is met by GS (gas supply) deflation, so that the balance of gas power at each moment in a typical day is realized.
4 conclusion
For the optimal configuration of the comprehensive energy supply center of the multi-energy complementary park, the comprehensive electricity/heat/cold multi-energy flow difference of the section and the energy conversion and space-time translation characteristics of the P2G equipment, a double-layer multi-objective optimal configuration model which takes the P2G equipment into account and gives consideration to the influences of the economy, the environmental protection, the reliability and the like of the comprehensive energy system of the multi-energy complementary park and comprises various energy supply equipment is provided. And the correctness and feasibility of the optimized configuration model are verified by combining with related examples. Compared with the traditional energy supply mode, the electricity/heat/cold multi-energy flow coordination complementary configuration is obtained by comparing the optimal configuration results under different energy supply strategies, so that the energy supply cost of the system is reduced, and the performance of various energy supply indexes is improved; and the addition of the P2G equipment improves the index performance of the energy supply system, and realizes the coordinated complementary utilization of the electricity/heat/cold/gas multiple energy flows of the energy supply system.

Claims (4)

1. The optimal configuration method of the multi-energy complementary park energy considering the electricity-to-gas planning is characterized by comprising the following optimal configuration steps of:
(1) constructing a multi-energy complementary park comprising energy production, conversion, transmission, storage and utilization units, said energy conversion units comprising electric to gas equipment;
(2) for a multi-energy complementary park, a double-layer multi-objective optimization configuration model is established by taking the minimum annual cost as an upper-layer objective function and taking the minimum annual operation cost, the maximum annual energy supply efficiency and the optimal wind-solar output utilization rate as a lower-layer objective function according to the constraint conditions of energy production, conversion, transmission, storage and utilization units;
(3) for the double-layer multi-objective optimization configuration model, an improved immune genetic algorithm based on fuzzy membership and variance weighting is adopted to perform nested solution on the model; determining an optimal configuration scheme of a double-layer multi-objective optimal configuration model according to the solving result;
the energy production units typically include wind power, photovoltaic, micro gas turbines and gas boilers;
the energy conversion unit comprises a heat pump, an air conditioner, a heat exchanger, an electric refrigerator, an absorption refrigerator and an electric gas conversion device;
the energy storage unit comprises an electric energy storage device, a thermal energy storage device, a cold accumulation device and an air storage device;
the energy utilization units are electric loads, heat loads and cold loads in the garden;
constructing a multi-energy complementary park comprising energy production, conversion, transmission, storage and utilization units, the upper objective function of which is
Cyear＝Cinves+Cfuel+Cgrid+Copera+Cenvir (1)
Figure FDA0003357235370000011
Figure FDA0003357235370000012
Figure FDA0003357235370000013
Figure FDA0003357235370000014
Figure FDA0003357235370000015
Figure FDA0003357235370000021
In the formula: cyear，Cinves，Cfuel，Cgrid，Copera，CenvirRespectively providing annual cost, equipment investment cost, fuel cost, cost for interacting with a power grid, operation and maintenance cost and environmental cost of the multi-energy complementary park; n is a radical ofITo be thrown intoThe type of equipment;
Figure FDA0003357235370000022
respectively, investment cost per unit volume, installation capacity and installation factor of the equipment i, wherein, if the equipment is installed,
Figure FDA0003357235370000023
otherwise, the value is 0;
Figure FDA0003357235370000024
is the present value coefficient of device i, ri equ，Lfi equRespectively showing the current sticking rate and the whole life cycle of the equipment i; n is a radical ofJThe total days in the planning period; n is a radical ofTTotal number of time segments for a typical day; pt Gas，Pt P2G_out，Pt grid，
Figure FDA0003357235370000025
Pt MT，Qt GB，
Figure FDA0003357235370000026
ηGB，
Figure FDA0003357235370000027
Respectively, the power interacting with the natural gas network, the natural gas power value output by the electric gas conversion equipment, the electric power interacting with the power network, the output power of the equipment i, the electric/thermal power values and efficiencies of the micro gas turbine and the gas boiler, and the power factor interacting with the power network at the typical time t in the day, wherein if P ist gridIf greater than 0, then
Figure FDA0003357235370000029
Otherwise, the value is 0; Δ t is the time interval, taken here as 1 h; etaGasIs the power transmission efficiency of the natural gas grid; cgas，vLHVFor purchase price of natural gas and combustion of natural gas, respectivelyLow heat value; xiP2G，CP2GRespectively CO required by unit natural gas production in the process of converting electricity into gas2Coefficient and CO2A price; ct,sale，Ct,buyRespectively selling electricity and purchasing electricity at time t;
Figure FDA00033572353700000210
operating and maintaining costs for unit capacity of equipment i; n is a radical ofUIs the number of pollutant species; ruIs the environmental value of contaminant u;
Figure FDA00033572353700000211
Figure FDA00033572353700000212
the emission intensity of pollutants u under the power of a micro gas turbine, a gas boiler and the interaction with a power grid respectively; etagridThe transmission efficiency of the power grid is obtained;
Figure FDA00033572353700000213
as pollutant CO2The environmental value of (c);
Figure FDA00033572353700000214
for converting CO into CO in the process of electricity2The strength is consumed.
2. The method of claim 1, wherein the energy-optimized configuration of the complementary multi-energy park comprises energy production, conversion, transmission, storage and utilization units, and the lower objective function is:
Cyear_opera＝Cfuel+Cgrid+Copera+Cenvir (8)
Figure FDA00033572353700000215
Figure FDA00033572353700000216
Figure FDA0003357235370000031
in the formula: cyear_opera，fREL，fDG_utiRespectively providing annual operation cost, annual energy supply efficiency and wind and light output utilization rate in the multi-energy complementary park, wherein if the energy supply value at a certain moment is greater than the load value, the energy supply efficiency at the moment is 1; pt WT，Pt PV，Pt EC _in，Pt HP_H，Pt HP_C，Pt AIR_H，Pt AIR_C，Pt P2G_inRespectively representing the output power of wind power and photovoltaic power, the input electric power value of refrigeration of an electric refrigerator, the input electric power value of a heat pump and an air conditioner for heating and refrigeration and the electric power value consumed by electric gas conversion equipment at the typical moment t in a day;
Figure FDA0003357235370000032
Figure FDA0003357235370000033
the output thermal power of the micro gas turbine at the typical time t in the day, the input thermal power of the absorption refrigerator and the output thermal power value of the heat pump and the air conditioner are respectively;
Figure FDA0003357235370000034
Figure FDA0003357235370000035
the output cold power values of the electric refrigerator, the absorption refrigerator, the heat pump and the air conditioner at the typical moment t in the day are respectively; pt ES，
Figure FDA0003357235370000036
Respectively the net discharge values of the electric refrigerator, the heat energy storage device and the cold storage device,
Figure FDA0003357235370000037
and
Figure FDA0003357235370000038
the charging and discharging rate values of the electric energy storage device, the thermal energy storage device and the cold accumulation device at the typical moment t in the day are respectively;
Figure FDA0003357235370000039
and
Figure FDA00033572353700000310
respectively, the charging and discharging energy zone bits of the electric energy storage device, the thermal energy storage device and the cold accumulation device at the typical time t in a day, if the energy is discharged,
Figure FDA00033572353700000311
is 1, otherwise is 0, if the energy is charged,
Figure FDA00033572353700000312
Figure FDA00033572353700000313
is 1, otherwise is 0; etapch，ηhch，ηcch，ηpdis，ηhdis，hcdisThe charging and discharging efficiency of the electric energy storage device, the thermal energy storage device and the cold accumulation device respectively; pt WT_pre，Pt PV_preWind power and photovoltaic prediction output values at the typical time t in a day are respectively.
3. The method of claim 1, wherein the energy-optimized configuration of the complementary multi-energy park comprises energy production, conversion, transmission, storage and utilization units, subject to the constraints of
Figure FDA00033572353700000314
Figure FDA00033572353700000315
Figure FDA00033572353700000316
Figure FDA0003357235370000041
Figure FDA0003357235370000042
Figure FDA0003357235370000043
Figure FDA0003357235370000044
Figure FDA0003357235370000045
Figure FDA0003357235370000046
Figure FDA0003357235370000047
In the formula: equation (12) is the electrical/thermal/cold/pneumatic power balance constraint at each moment; equation (13) is the device capacity configuration constraint; equation (14) is the output limit constraint for each energy unit; equation (15) is the MT ramp rate constraint; equation (16) is the grid interaction power constraint; equations (17) and (18) are respectively the output of the P2G equipment at each moment and the upper and lower limits of the supply flow of the air source point; equations (19) and (20) are respectively the maximum storage and release rate and the storage capacity constraint of the energy storage device; the formula (21) is (0,1) variable constraint, and the energy storage device cannot store and release energy at the same moment; wherein, Pt load，
Figure FDA0003357235370000048
The electric/heat/cold load value in the complementary garden of the multipotency at the time t in the typical day;
Figure FDA0003357235370000049
and
Figure FDA00033572353700000410
respectively is the air charging and discharging speed value and the air charging and discharging mark position of the air storage device at the typical time t in the day, if the air is discharged,
Figure FDA00033572353700000411
on the contrary, if the gas is inflated,
Figure FDA00033572353700000412
otherwise, the value is 0;
Figure FDA00033572353700000413
the natural gas consumption of the micro gas turbine and the gas boiler at the typical time t in a day respectively;
Figure FDA00033572353700000414
Figure FDA00033572353700000415
respectively the upper and lower limit values of the installation capacity of the equipment i;
Figure FDA00033572353700000416
respectively representing the output value and the scheduling factor of the energy unit i at the time t in the typical day, wherein if the energy unit is scheduled, the value is 1, otherwise, the value is 0;
Figure FDA00033572353700000417
the output values of the micro gas turbine at typical time t +1, t, t-1 in the day are respectively;
Figure FDA00033572353700000418
respectively generating natural gas power for each energy unit output, the climbing rate of the micro gas turbine, the transmission power of the power grid and the upper and lower limit values of the interaction power of the natural gas grid at each moment of the electric gas conversion equipment; en is an energy storage and gas storage device type and comprises an electric energy storage device, a thermal energy storage device, a cold storage device and a gas storage device; ent，
Figure FDA00033572353700000419
Figure FDA00033572353700000420
CEn，
Figure FDA00033572353700000421
The energy storage device change rate, the energy storage device capacity, the maximum energy charging and discharging rate, the upper and lower limit values of the energy charging state, the energy storage capacity value and the energy storage and discharge zone bit at the typical time t in a day are respectively; etagch，ηgdisRespectively the air charging and discharging efficiency of the air storage device.
4. The method of claim 1, wherein the solving process comprises:
1) inputting original data, coding and initializing upper layer population, and generating randomlyForming N antibodies, extracting m individuals from the memory bank to form an initial population, setting the iteration number I to be 1, and setting the maximum iteration number ImaxAntibody N ═ 1, population size Nmax；
2) Inputting the capacity configuration result of the upper layer antibody n as original data into the lower layer population, coding and initializing the lower layer population, and setting the iteration number i to be 1 and the maximum iteration number to be imaxAntibody k is 1 and population size k ismax；
3) Calculating an antibody fitness value of the antibody k according to a lower-layer objective function;
4) judgment of k<kmaxIf yes, if k is k +1, the process proceeds to step 3), and if not, the process proceeds to step 5);
5) calculating the membership degree of each target function of each antibody of the lower layer according to the formula (22);
Figure FDA0003357235370000051
in the formula: gkjIs the jth target value for antibody k;
Figure FDA0003357235370000052
maximum and minimum values for target j among all antibodies;
6) weighting each objective function according to equation (23);
Figure FDA0003357235370000053
in the formula, wjThe weight of the target j, M is the number of the target functions, and N is the total number of the antibodies;
7) calculating the preferential selectivity of each antibody according to the formula (24), wherein the antibody with the highest selectivity is the compromise optimal solution;
Figure FDA0003357235370000054
8) updating the lower-layer parent population and the memory library;
9) carrying out self-adaptive selection, crossing and variation on the lower-layer parent population to form a new population;
10) judgment of i<imaxIf yes, the process proceeds to step 3) if i +1 and k 1 are satisfied, and proceeds to step 11) if not;
11) returning the antibody corresponding to the optimal solution and the lower-layer objective function to the upper layer;
12) calculating an upper antibody fitness value;
13) judging whether N < N is true, if so, turning to step 2), and turning to step 14) if not;
14) calculating the expected reproduction probability of each antibody in the upper layer by using the formula (25);
Figure FDA0003357235370000061
in the formula: f is the upper antibody fitness value;
15) sequentially arranging the initial population from large to small according to the expected reproduction rate P, selecting individual optimal solutions, extracting the first N individuals to form a parent population, and storing the first m individuals into a memory bank;
16) carrying out self-adaptive selection, crossing and mutation operations on the upper-layer parent population to form a new population;
17) judgment of I<ImaxAnd if the optimal configuration result is not satisfied, the iterative process is ended, and the optimal configuration result is output.
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