CN111668878A - Optimal configuration method and system for renewable micro-energy network - Google Patents

Optimal configuration method and system for renewable micro-energy network Download PDF

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CN111668878A
CN111668878A CN202010365203.6A CN202010365203A CN111668878A CN 111668878 A CN111668878 A CN 111668878A CN 202010365203 A CN202010365203 A CN 202010365203A CN 111668878 A CN111668878 A CN 111668878A
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energy
power
equipment
configuration
time
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金璐
闫华光
何伟
何桂雄
李德智
钟鸣
覃剑
郭炳庆
李克成
孙旻
成岭
刘铠诚
熊俊杰
唐艳梅
吴建章
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State Grid Jiangxi Electric Power Co ltd
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
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State Grid Jiangxi Electric Power Co ltd
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Abstract

The invention provides an optimal configuration method of a renewable micro energy network, which comprises the following steps: acquiring configuration parameters and cost parameters of the to-be-selected energy equipment, an electric heating and cooling load curve of a typical day in each season and a typical wind and light output scene in each season; inputting configuration parameters and cost parameters of the to-be-selected energy equipment, electric heating and cooling load curves of typical days in each season and typical wind and light output scenes in each season into an optimized configuration model to obtain a pareto solution set of the capacity of the to-be-selected energy equipment; screening a pareto solution set of the capacity of the energy equipment to be selected based on a fuzzy theory to obtain an optimal configuration scheme of the energy equipment to be selected; the optimization configuration model comprises the following steps: the invention sets the optimized configuration model by considering the operation effect, and the obtained configuration scheme can simultaneously realize the optimal operation effect and the optimal equipment configuration, thereby being more reasonable than the traditional optimized configuration result.

Description

Optimal configuration method and system for renewable micro-energy network
Technical Field
The invention belongs to the technical field of optimal configuration of power systems, and particularly relates to an optimal configuration method and system of a renewable micro-energy network.
Background
The diversification characteristic of suburban energy demand is increasingly remarkable, and higher requirements are also put forward on the quality of energy service. However, the existing suburban energy supply is only limited to electric energy, and does not effectively utilize renewable resources abundant in local areas, and meanwhile, the ever-sharp contradiction between human and nature also makes the planning and design of the suburban energy system unable to focus on economic factors only. The multi-energy complementary system integrating electricity collection, cold supply and heat supply is used for carrying out cascade utilization on different energy sources, and breaks through the mutual independent fence of energy supply in the traditional mode, so that the system is an effective way for promoting low carbon emission reduction and improving energy utilization efficiency in suburbs. Therefore, a plurality of renewable energy sources are reasonably configured, a suburb green micro-energy network system is constructed, and the method has great significance for accelerating suburb modernization construction.
At present, many planning designs for hybrid renewable energy systems exist, but the following problems still exist: firstly, local available resources cannot be fully utilized; secondly, the influence of the system operation mode on the planning result is not considered; thirdly, the effect cannot be evaluated after the configuration is optimized, so that how to solve the problems in the prior art is a problem to be solved by the technical personnel in the field.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an optimal configuration method of a renewable micro energy network, which comprises the following steps:
acquiring configuration parameters and cost parameters of the to-be-selected energy equipment, an electric heating and cooling load curve of a typical day in each season and a typical wind and light output scene in each season;
inputting configuration parameters and cost parameters of the to-be-selected energy equipment, electric heating and cooling load curves of typical days in each season and typical wind and light output scenes in each season into a preset optimization configuration model to obtain a pareto solution set of the capacity of the to-be-selected energy equipment;
screening the pareto solution set of the capacity of the to-be-selected energy equipment based on a fuzzy theory to obtain an optimal configuration scheme of the to-be-selected energy equipment;
the optimized configuration model comprises: the system comprises an upper-layer optimization configuration model and a lower-layer operation optimization scheduling model, wherein the upper-layer optimization configuration model is optimally constructed based on the quantity configuration of the energy equipment to be selected, and the lower-layer operation optimization scheduling model is optimally constructed based on the equipment output of the energy equipment to be selected.
Preferably, the alternative energy device comprises: the system comprises photovoltaic equipment, wind power equipment, biogas cogeneration equipment, a biogas boiler, a ground source heat pump, an electric refrigerator, an absorption refrigerator, an electric power storage device, a cold accumulation device and a heat accumulation device;
the configuration parameters of the candidate energy equipment comprise: rated power, output power range, maximum climbing coefficient, dissipation coefficient and waste heat recovery coefficient of the biogas cogeneration equipment; rated power, output power range, maximum climbing coefficient and thermal efficiency of the biogas boiler; rated power, output power range, maximum climbing coefficient, electric-heat conversion efficiency and heat exchange efficiency of the heat exchange pump of the ground source heat pump; the output power range and energy efficiency ratio of the electric refrigerator and the absorption refrigerator; the charging and discharging efficiency, the maximum charging and discharging coefficient, the dissipation coefficient and the energy storage coefficient of the electric power storage device, the cold accumulation device and the heat accumulation device;
the cost parameters of the candidate energy equipment comprise: procurement costs, installation costs, and maintenance costs.
Preferably, the setting of the optimal configuration model includes:
constructing an upper-layer optimal configuration objective function by taking the equipment capacity of the to-be-selected energy equipment as a decision variable and the minimum annual operation cost, pollution emission and energy utilization efficiency as targets based on a preset renewable micro energy network multi-dimensional evaluation index; setting an upper-layer optimal configuration model by taking the installation capacity of the energy equipment to be selected as an upper-layer optimal configuration constraint condition;
constructing a lower-layer operation optimization scheduling objective function by taking the equipment output of the to-be-selected energy equipment as a decision variable and the minimum purchasing cost, maintenance cost and light and wind abandoning cost of the to-be-selected energy equipment as targets based on the renewable micro energy network multi-dimensional evaluation index; and setting a lower-layer operation optimization scheduling model by taking the transmission power constraint of the bus, the interactive power constraint with the external connecting line, the energy coupling equipment constraint and the energy storage equipment constraint as constraint conditions.
Preferably, the upper layer optimal configuration objective function is as follows:
min f1＝(C，F，1/E)
wherein C is the annual operation cost, F is the pollution emission, and E is the energy utilization efficiency.
Preferably, the annual operating cost is calculated as follows:
Figure BDA0002476323780000021
wherein, CEIFor installation costs of candidate energy devices, CEPFor purchase costs of candidate energy plants, CEMFor maintenance costs of candidate energy devices, NiThe installation capacity of the i-th type of energy equipment to be selected is obtained; k is the reference discount rate, y is the use period of the energy equipment to be selected, ciThe unit price of the device for the i-th candidate energy source,
Figure BDA0002476323780000022
the price of purchasing electricity per unit power at the time t on the day d,
Figure BDA0002476323780000031
for the grid tie line interactive power at the d-th day t,
Figure BDA0002476323780000032
the price of electricity sold per unit power at the time t on the day d, cbioThe unit cubic meter of the production cost of the methane is,
Figure BDA0002476323780000033
is the methane consumption of the micro gas turbine at the time t on the day d,
Figure BDA0002476323780000034
is the biogas consumption of the biogas boiler at the time t on the day d,
Figure BDA0002476323780000035
the maintenance cost per unit power of the equipment of the i-th candidate energy source,
Figure BDA0002476323780000036
and (3) the output power of the equipment of the ith type of energy to be selected at the time T on the D day, D is the number of days of the whole year, and T is the number of hours per day.
Preferably, the calculation formula of the emission amount of pollutants is as follows:
Figure BDA0002476323780000037
wherein the content of the first and second substances,
Figure BDA0002476323780000038
for grid tie line interaction power at day d, time t, ηgenFor the power generation efficiency of the power plant, ηlossIs the loss rate of the power transmission line of the power plant,
Figure BDA0002476323780000039
the discharge coefficient of the j-type pollutants generated by coal-fired power generation,
Figure BDA00024763237800000310
is the methane consumption of the micro gas turbine at the time t on the day d,
Figure BDA00024763237800000311
is the biogas consumption of the biogas boiler at the time t on the day d, q is the standard biogas low heat value,
Figure BDA00024763237800000312
the discharge coefficient of j-type pollutants generated by biogas combustion is shown, D is the number of days in the whole year, and T is the number of hours per day.
Preferably, the calculation formula of the energy use efficiency is as follows:
Figure BDA00024763237800000313
wherein Q isoutFor system output energy, QinFor system input, λeThe conversion coefficient of the electric energy is the conversion coefficient,
Figure BDA00024763237800000314
for the grid tie line interactive power at the d-th day t,
Figure BDA00024763237800000315
the rated output power of the photovoltaic power generation unit at the time t on the day d,
Figure BDA00024763237800000316
rated output power, lambda, of the wind power generation unit at the time of day d and tgThe conversion coefficient of the methane energy is calculated,
Figure BDA00024763237800000317
is the methane consumption of the micro gas turbine at the time t on the day d,
Figure BDA00024763237800000318
is the biogas consumption of the biogas boiler at the time t on the d-th day, q is the standard biogas low heat value, lambdapIn order to convert the cold energy into the system,
Figure BDA0002476323780000041
inputting ground source heat energy for the ground source heat exchange heat pump at the time t on the day d,
Figure BDA0002476323780000042
is the electric load power, lambda, of the user at time t on day dhIn order to convert the coefficient of heat energy into a coefficient,
Figure BDA0002476323780000043
is the thermal load power, lambda, of the user at time t on day dcIn order to convert the coefficient of the cold energy,
Figure BDA0002476323780000044
the cooling load power of the user at the time t on the day d.
Preferably, the installation capacity constraint of the candidate energy device is as follows:
Figure BDA0002476323780000045
wherein the content of the first and second substances,
Figure BDA0002476323780000046
for the lower limit of the installation capacity of the ith type of equipment,
Figure BDA0002476323780000047
upper limit of installation capacity for class i devices, NiCapacity is installed for class i devices.
Preferably, the lower-layer operation optimization scheduling objective function is as follows:
Figure BDA0002476323780000048
wherein, CEPFor purchase costs of candidate energy plants, CEMFor maintenance costs of the candidate energy plant, CAEIn order to avoid the cost of light and wind,
Figure BDA0002476323780000049
in order to discard the optical power,
Figure BDA00024763237800000410
to abandon the wind power, cPVCost of light rejection for unit power, cWTT is the wind curtailment cost per unit power, and T is the number of hours per day.
Preferably, the bus transmission power constraint is as follows:
Figure BDA00024763237800000411
wherein the content of the first and second substances,
Figure BDA00024763237800000412
for the actual output power of the photovoltaic at time t,
Figure BDA00024763237800000413
for wind power generation at time tThe power of the output is actually outputted,
Figure BDA00024763237800000414
for the actual output power of the cogeneration system at time t,
Figure BDA00024763237800000415
for grid tie line interaction power at time t,
Figure BDA00024763237800000416
for the electrical load power of the user at time t,
Figure BDA00024763237800000417
the power consumed by the ground source heat pump at the moment t,
Figure BDA00024763237800000418
for the time t the electric refrigerator consumes power,
Figure BDA00024763237800000419
for the power consumption of the electric storage device at time t,
Figure BDA00024763237800000420
for the heating power of the cogeneration system at time t,
Figure BDA00024763237800000421
for the heating power of the biogas boiler at the moment t,
Figure BDA00024763237800000422
the heating power of the ground source heat pump at the moment t,
Figure BDA00024763237800000423
for the thermal load power of the user at time t,
Figure BDA00024763237800000424
for the absorption heat power of the absorption refrigerator at time t,
Figure BDA00024763237800000425
is tThe output of the thermal energy storage at any moment,
Figure BDA00024763237800000426
the refrigerating power of the electric refrigerator at the time t,
Figure BDA00024763237800000427
the t-time absorption refrigerating machine refrigerating power,
Figure BDA00024763237800000428
the cold energy storage output of the ground source heat pump at the moment t,
Figure BDA00024763237800000429
the power of the cold load of the user,
Figure BDA00024763237800000430
the cold stored energy output at the moment t.
Preferably, the interaction power constraint with the external tie is as follows:
Figure BDA0002476323780000051
wherein the content of the first and second substances,
Figure BDA0002476323780000052
for grid tie line interaction power at time t,
Figure BDA0002476323780000053
the upper power limit is interacted with an external power grid tie line.
Preferably, the energy source coupling device is constrained by the following equation:
Figure BDA0002476323780000054
Figure BDA0002476323780000055
wherein the content of the first and second substances,
Figure BDA0002476323780000056
the output of the i-th class device at time t,
Figure BDA0002476323780000057
the lower limit of the output of the ith type device at the time t,
Figure BDA0002476323780000058
the upper limit of the output of the ith type device at the moment t,
Figure BDA0002476323780000059
the start-stop state of the ith type equipment at the time t,
Figure BDA00024763237800000510
the output, Δ S, of the i-th class device at time t-1iAnd the output climbing limit of the ith equipment is obtained.
Preferably, the step of inputting the configuration parameters and the cost parameters of the candidate energy device, the electric heating and cooling load curve of a typical day in each season, and the typical wind and light output scenes in each season into a preset optimal configuration model to obtain a pareto solution set of the capacity of the candidate energy device includes:
inputting the configuration parameters and cost parameters of the to-be-selected energy equipment into an upper-layer optimized configuration model, and solving the upper-layer optimized configuration model by adopting a non-dominated sorting genetic algorithm to obtain the configuration of the to-be-selected energy equipment corresponding to each generation of parent chromosomes;
inputting the configuration of the energy equipment to be selected corresponding to each generation parent chromosome, the electric heating and cooling load curve of a typical day in each season and the typical wind and light output scene in each season into a lower-layer operation optimization scheduling model to obtain the equipment output under the configuration of the energy equipment to be selected corresponding to each generation parent chromosome;
and screening the population of the non-dominated sorting genetic algorithm to obtain a new generation of population based on the device output under the configuration of the to-be-selected energy device corresponding to each generation of parent chromosome until the iteration times exceed the preset iteration upper limit of the non-dominated sorting genetic algorithm, and obtaining a pareto solution set of the capacity of the to-be-selected energy device corresponding to the last generation of population.
Preferably, the screening of the population of the non-dominated ranking genetic algorithm to obtain a new generation of population is performed based on the device output under the configuration of the candidate energy device corresponding to each generation of parent chromosome, and includes:
calculating a target function value of an upper-layer optimization configuration model corresponding to each generation of candidate energy equipment configuration based on equipment output under the candidate energy equipment configuration corresponding to each generation of parent chromosomes;
and screening the population of the non-dominated sorting genetic algorithm to obtain a new generation of population based on the objective function value of the upper-layer optimization configuration model corresponding to each generation of energy equipment to be selected.
Preferably, the method for obtaining the optimal configuration scheme of the candidate energy devices includes the steps of screening pareto solution sets of the capacity of the candidate energy devices based on a fuzzy theory:
respectively calculating corresponding membership functions of annual operation cost, pollution emission and energy utilization efficiency of an upper-layer optimized configuration target on the basis of the pareto solution set of the capacity of the to-be-selected energy equipment;
weighting and calculating the membership function corresponding to the annual operation cost, the pollution emission and the energy utilization efficiency to obtain a comprehensive membership function;
and calculating the comprehensive membership degree corresponding to each solution based on the pareto solution set of the capacity of the to-be-selected energy equipment, and determining the solution corresponding to the maximum value of the comprehensive membership degree, which is the optimal configuration scheme of the to-be-selected energy equipment.
Preferably, after obtaining the optimal configuration scheme of the candidate energy device, the method further includes: evaluating an optimized configuration result by adopting the renewable micro energy network multidimensional evaluation index;
the multidimensional evaluation index of the renewable micro energy network comprises the following indexes: a cost saving rate index, a pollution emission reduction rate index and an energy efficiency improvement rate index.
Preferably, the cost saving calculation formula is as follows:
Figure BDA0002476323780000061
wherein R isCSFor cost savings, C is the annual operating cost of the renewable micro-energy grid system, CSPThe annual operating cost of the energy production system is reduced.
Preferably, the calculation formula of the pollution emission reduction rate is as follows:
Figure BDA0002476323780000062
wherein R isPRF is the pollution emission amount of the renewable micro-energy network system for the pollution emission reduction rate, FSPThe pollution discharge amount of the energy production system is reduced.
Preferably, the energy efficiency improvement rate calculation formula is as follows:
Figure BDA0002476323780000063
wherein R isEIFor efficiency improvement, E is the energy utilization efficiency of the renewable micro-energy network system, ESPThe energy utilization efficiency of the energy separate production system is improved.
The invention also provides an optimal configuration system of the renewable micro energy network based on the same concept, which comprises the following steps:
the data acquisition module is used for acquiring configuration parameters and cost parameters of the to-be-selected energy equipment, an electric heating and cooling load curve of a typical day in each season and a typical wind and light output scene in each season;
the calculation module is used for inputting the configuration parameters and the cost parameters of the to-be-selected energy equipment, the electric heating and cooling load curves of typical days in each season and the typical wind and light output scenes in each season into a preset optimization configuration model to obtain a pareto solution set of the capacity of the to-be-selected energy equipment;
the optimization module is used for screening the pareto solution set of the capacity of the to-be-selected energy equipment based on a fuzzy theory to obtain an optimal configuration scheme of the to-be-selected energy equipment;
the optimized configuration model comprises: the system comprises an upper-layer optimization configuration model and a lower-layer operation optimization scheduling model, wherein the upper-layer optimization configuration model is optimally constructed based on the quantity configuration of the energy equipment to be selected, and the lower-layer operation optimization scheduling model is optimally constructed based on the equipment output of the energy equipment to be selected.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention provides an optimal configuration method of a renewable micro energy network, which comprises the following steps: acquiring configuration parameters and cost parameters of the to-be-selected energy equipment, an electric heating and cooling load curve of a typical day in each season and a typical wind and light output scene in each season; inputting configuration parameters and cost parameters of the to-be-selected energy equipment, electric heating and cooling load curves of typical days in each season and typical wind and light output scenes in each season into a preset optimization configuration model to obtain a pareto solution set of the capacity of the to-be-selected energy equipment; screening the pareto solution set of the capacity of the to-be-selected energy equipment based on a fuzzy theory to obtain an optimal configuration scheme of the to-be-selected energy equipment; the optimized configuration model comprises: the method comprises the steps that an upper-layer optimization configuration model and a lower-layer operation optimization scheduling model are established, wherein the upper-layer optimization configuration model is established based on the number of the to-be-selected energy devices, and the lower-layer operation optimization scheduling model is established based on the device output of the to-be-selected energy devices.
The optimal configuration model of the invention integrates various renewable energy sources such as wind, light, marsh and geothermal, and is beneficial to realizing the maximum utilization of the renewable clean energy sources in suburbs.
Meanwhile, the evaluation index of the renewable micro energy network system is set, the optimized configuration result is evaluated, and the optimized configuration effect can be clearly evaluated.
Drawings
Fig. 1 is a schematic diagram of an optimized configuration method of a renewable micro energy network according to the present invention;
fig. 2 is a schematic diagram of an optimized configuration system of a renewable micro energy network according to the present invention;
fig. 3 is a schematic diagram of a renewable micro energy network architecture provided in an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating a calculation procedure of a double-layer optimized configuration of a renewable micro energy network according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating a typical load curve and a wind-solar power scenario provided in an embodiment of the present disclosure;
fig. 6 is a schematic diagram of a Pareto non-inferior solution set provided in an embodiment of the present invention;
fig. 7 is a comparative schematic diagram of a multivariate evaluation index provided in an embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
Example 1:
the embodiment of the invention discloses an optimal configuration method of a renewable micro energy network, which is shown in figure 1 and comprises the following steps:
s1, configuration parameters and cost parameters of the selected energy equipment, electric heating and cooling load curves of typical days in each season, and typical wind and light output scenes in each season;
s2, inputting configuration parameters and cost parameters of the to-be-selected energy equipment, electric heating and cooling load curves of typical days in each season and typical wind and light output scenes in each season into a preset optimization configuration model to obtain a pareto solution set of the capacity of the to-be-selected energy equipment;
s3, screening the pareto solution set of the capacity of the candidate energy equipment in a fuzzy theory to obtain an optimal configuration scheme of the candidate energy equipment.
S2, inputting configuration parameters and cost parameters of the candidate energy device, an electric heating and cooling load curve of a typical day in each season, and a typical wind and light output scene in each season into a preset optimal configuration model, to obtain a pareto solution set of the capacity of the candidate energy device, which specifically includes:
s2-1 construction of mathematical model of candidate energy equipment
S2-1-1 biogas cogeneration system mathematical model
At present, biomass energy produced in agriculture, forestry and animal husbandry in suburban areas is abundant in reserve, but due to the fact that the resource distribution is dispersed, most of the biomass energy is only used for living heating, and the idle waste situation is serious. Therefore, the invention adopts a moderate centralized treatment mode, takes biomass methane as a gas source, selects a Combined Heating and Power (CHP) system which is jointly composed of a Micro Turbine (MT) and a flue gas waste heat recovery device as main energy supply equipment of a suburban micro energy network, and the output model can be described as follows:
Figure BDA0002476323780000081
in the formula:
Figure BDA0002476323780000082
the power generation efficiency, the output thermal power, the heat dissipation coefficient and the waste heat recovery rate of the CHP at the moment t are respectively;
Figure BDA0002476323780000083
respectively the output electric power, the methane consumption, the available residual heat quantity and the rated power of the MT at the time t; q is the standard biogas low heat value, and the invention takes 5.98 kW.h/m 3.
S2-1-2 biogas boiler mathematical model
A Biogas Boiler (BB) is a gas-thermal coupling device using biomass biogas as fuel, can convert biomass energy into heat energy, is an auxiliary heat source device in a micro energy grid, has the advantages of low cost, light pollution, high thermal efficiency, and the like, and can be expressed by a mathematical model:
Figure BDA0002476323780000091
in the formula:
Figure BDA0002476323780000092
respectively inputting biogas quantity and outputting heat power for the biogas boiler at the time t, ηBBThe heating efficiency of the methane boiler is improved.
S2-1-3 ground source heat pump mathematical model
As a new energy conversion means, the heat pump technology has the energy efficiency ratio (COP) which is usually larger than 4 and far higher than that of most existing energy supply equipment, can work in a refrigeration or refrigeration state according to the requirements of users, and has the advantages of energy conservation, environmental protection, safety, reliability, easiness in maintenance and the like, so that the heat pump technology is well paid attention to by people in the world, and has a very wide development prospect. In addition, the heat pump is taken as a typical multi-energy coupling device, and the introduction of the heat pump also promotes the reasonable distribution of the energy on the supply side of the multi-energy complementary system, so that the comprehensive energy utilization level is improved. The invention selects a Ground Source Heat Pump (GSHP) as energy supply equipment of the micro energy network, the device is composed of a ground source heat exchange pump and a heat pump host, and the energy consumption and output model can be expressed as follows:
Figure BDA0002476323780000093
in the formula:
Figure BDA0002476323780000094
the ground source heat energy output by the ground source heat exchange pump at the time t, the GSHP output heat (cold) power and the operation energy efficiency of the GSHP output heat (cold) power are respectively;
Figure BDA0002476323780000095
the power of a ground source heat pump, a ground source heat exchange pump and a heat pump host machine at the moment t respectively;
Figure BDA0002476323780000096
ηCP、ηHPthe GSHP rated heating (cold) power, the ground source heat exchange pump heat exchange efficiency and the heat pump host machine electric heat conversion efficiency are respectively.
S2-1-4 source heat pump energy storage equipment mathematical model
The energy storage device is one of important components of a multi-energy complementary system under the background of an energy internet, plays an important role in relieving energy supply and demand contradiction, can store residual energy and fill up vacancy demand according to the output state and the load size of energy equipment at different moments, further changes the space-time distribution state of multi-energy flow, and is beneficial to the cooperative optimization operation of the multi-energy complementary system. Considering that the proportion of renewable energy sources in a micro energy network is high, the output of a unit has obvious fluctuation, and the daily load demand change of suburban areas is large, the invention arranges multiple energy storage devices including storage batteries (ES), heat storage tanks (HS) and cold storage tanks (CS) in a model so as to improve the economic and environmental benefits of an energy supply system. Because energy storage devices of different energy types have similar operation characteristics, the invention only takes a storage battery as an example, and gives mathematical models of output and energy storage as shown in formulas (5) and (6) respectively:
Figure BDA0002476323780000101
Figure BDA0002476323780000102
in the formula:
Figure BDA0002476323780000103
the electric energy storage capacity, the input power and the output power of the storage battery at the moment t are respectively;
Figure BDA0002476323780000104
is the self-discharge rate;
Figure BDA0002476323780000105
respectively charge and discharge efficiency;
Figure BDA0002476323780000106
is a variable 0-1 indicating the state of charge and discharge, and
Figure BDA0002476323780000107
s2-2 construction of renewable micro energy network multidimensional evaluation index
S2-2-1 cost saving rate index
Whether SP systems orThe annual operating cost C of the micro-energy network system is the equipment installation cost CEIEnergy procurement cost CEPAnd equipment maintenance cost CEMThe common composition, namely:
Figure BDA0002476323780000108
in the formula: n is a radical ofiInstalling capacity for the i-th equipment; k and y represent the benchmark discount rate and the equipment service cycle, and are respectively set to be 7 percent and 20 years; pgridPower is interacted for a power grid tie line, electric energy is purchased for positive representation, and electric energy is sold for negative representation; c. Ci、
Figure BDA0002476323780000109
Respectively maintaining the unit price and unit power of the ith equipment; c. Cbuy、csell、cbioRespectively purchasing electricity and selling electricity price in unit power and producing the methane in unit cubic meter; siOutputting power for the ith equipment; D. t is the number of days and hours per day throughout the year, 365 days and 24 hours, respectively, are taken herein. The invention refers to a Cost Saving Rate (CSR) index of a micro energy network formulated by an SP system to measure the economic benefit brought by a multi-energy complementary system, wherein the CSR is specifically defined as the ratio of the annual operation cost saved by the micro energy network relative to the SP system to the annual operation cost of the SP system, namely:
Figure BDA00024763237800001010
s2-2-2 pollution emission reduction rate index
The pollutants generated in the energy conversion process mainly comprise greenhouse gas CO2And acid gas SO2、NOxMainly contaminated by power generation from external power plants FgridAnd biomass gas combustion pollution FbioAnd (4) forming. For SP systems and micro energy networks, the pollutant emissions FSPAnd FRMENCan be expressed as:
Figure BDA0002476323780000111
in the formula ηgen、ηlossRespectively representing the power generation efficiency and the loss rate of the transmission line of the power plant;
Figure BDA0002476323780000112
the discharge coefficients of the j-th pollutants generated by coal-fired power generation and methane combustion are respectively. On the basis, the Pollution Reduction Rate (PRR) of the multi-energy complementary system is defined as shown in the formula (10), and the environmental protection performance of the combined supply system can be effectively evaluated according to the index.
Figure BDA0002476323780000113
S2-2-3 energy efficiency improvement rate index
Because of the quality difference among different kinds of energy sources, the invention simultaneously considers the comprehensive energy utilization level and the renewable energy consumption potential from the perspective of the second theorem of thermodynamics and defines the energy utilization efficiency E as the system output energy QoutAnd input energy QinThe ratio of. For SP systems and micro energy networks, the input and output energy is shown as formula (11):
Figure BDA0002476323780000114
in the formula: pload、Qload,h、Qload,cRespectively the electricity, heat and cold load power of suburban users;
Figure BDA0002476323780000115
rated output power of the photovoltaic power generation unit and rated output power of the wind power generation unit are respectively set; lambda [ alpha ]e、λh、λcRespectively, the conversion coefficients of electric, heat and cold energy, lambdag、λpRespectively the conversion coefficients of methane and geothermal energy.
Respectively calculating the energy utilization efficiency E of SP and micro energy network according to the above formulaSPAnd ERMENAnd thereby define a renewable micro energy gridThe energy Efficiency Improvement Rate (EIR) is:
Figure BDA0002476323780000116
s2-3 construction of renewable micro energy network double-layer optimization configuration model
S2-3-1 construction of an objective function
According to the invention, by combining the evaluation indexes, 3 different targets of annual operation cost, pollution emission and energy utilization efficiency are selected as an upper-layer optimization configuration target function f1
min f1＝(CRMEN，FRMEN，1/ERMEN) (13)
The lower layer day optimization scheduling mainly considers the energy purchasing cost CEPAnd the maintenance cost of the apparatus CEMAnd cost of light and wind abandoning CAEAnd defining daily integrated running cost f2As an objective function. Wherein, CEPAnd CEMCan be seen in formula (7), CAEThe calculation method is shown in formula (14):
Figure BDA0002476323780000121
in the formula:
Figure BDA0002476323780000122
respectively, the light abandoning power and the wind abandoning power; c. CPV、cWTThe cost of light abandoning and wind abandoning of unit power is respectively 0.15 and 0.12 ￥/kW.h.
S2-3-2 construction constraint condition
S2-3-2-1, constructing the constraint conditions of the upper layer model: equipment installation capacity constraints
The invention limits the installation capacity of the equipment so as to ensure that the equipment is not redundant under the maximum load value, and the constraint conditions are as follows:
Figure BDA0002476323780000123
in the formula:
Figure BDA0002476323780000124
respectively, an upper limit and a lower limit of the installation capacity of the ith type of equipment. CHP, BB and GSHP are configured according to the number of the devices, and the rest devices are set by taking kW as a unit.
S2-3-2-2, constructing constraints of an upper layer model:
1) transmission power constraint
The renewable micro-energy grid system needs to meet the electricity, heat and cold energy requirements of users in an area at any time, so that the following bus power transmission constraints exist:
Figure BDA0002476323780000125
in the formula:
Figure BDA0002476323780000126
the actual output power of a Photovoltaic (PV) cell, a wind power generator (WT) and the power consumption power of an electric refrigerator (EC) at the time t are respectively;
Figure BDA0002476323780000127
the heating power of GSHP, the absorption heat power of an absorption refrigerator (AC) and the heat storage output at the time t are respectively;
Figure BDA0002476323780000128
Figure BDA0002476323780000129
the refrigeration power and the cold stored energy output of EC, AC and GSHP at the time t are respectively. Furthermore, the tie-line to the external grid should also satisfy the following constraints:
Figure BDA0002476323780000131
in the formula:
Figure BDA0002476323780000132
is the upper power limit for interaction with the external power grid.
2) Energy coupling device restraint
The energy coupling devices CHP, BB, GSHP, PV, WT, EC and AC herein all need to satisfy the device output constraints:
Figure BDA0002476323780000133
in the formula:
Figure BDA0002476323780000134
respectively representing the upper output limit, the lower output limit and the start-stop state of the ith equipment at the moment t. In addition, CHP, BB and GSHP also have unit ramp constraints:
Figure BDA0002476323780000135
in the formula: delta SiAnd the output climbing limit of the ith equipment is obtained.
3) Energy storage device restraint
For multiple types of energy storage devices, taking the storage battery as an example, the operation constraint conditions are as follows:
Figure BDA0002476323780000136
in the formula:
Figure BDA0002476323780000137
respectively the maximum charge and discharge power of the storage battery;
Figure BDA0002476323780000138
respectively an upper limit and a lower limit of the energy storage capacity.
S3, screening the pareto solution set of the capacity of the candidate energy equipment in a fuzzy theory to obtain an optimal configuration scheme of the candidate energy equipment, specifically:
within the universe of discourse U, muAIs to map any U ∈ U to [0,1 [ ]]A function of, i.e. muA:U→[0,1]，u→μA(u), then, μAIs a function of degree of membership in U, muAAnd (u) is the degree of membership of u to the fuzzy set A. Let A be { mu ═ muA(U) | U ∈ U }, then A is the fuzzy set on U, the fuzzy set is completely determined by its membership function, according to the fuzzy theory, the membership function μ (x) can be used to fuzzify each objective function, and μ (x) ∈ [0, 1U }, then]. Herein the membership function is taken as:
Figure BDA0002476323780000139
in the formula: mu.si(x) As a function of degree of membership, fi(x) Is an objective function;
Figure BDA00024763237800001310
and
Figure BDA00024763237800001311
the maximum and minimum values of each objective function are obtained under single-objective optimization; after the membership functions of all objective functions are solved, the membership functions are weighted and added to obtain a single objective optimization function:
Figure BDA0002476323780000141
in the formula: w is aiIs a weight coefficient of each objective function, and wi≥0,∑wi＝1。
The invention adopts a non-dominated sorting genetic algorithm (NSGA-II) and IBM business software Cplex to solve the model, the capacity of equipment of upper-layer decision variables is used, an objective function is annual operation cost, pollution emission and energy utilization efficiency, the output of the equipment is used as lower-layer decision variables, the objective function is annual comprehensive operation cost, a comprehensive optimal configuration scheme and evaluation indexes of the renewable micro energy network are output, and the establishment and solving processes of the model can be summarized as follows:
step 1, building a renewable micro energy network model, and inputting NSGA-II algorithm parameters, basic configuration parameters of a unit to be selected, cost parameters, typical daily electric heating and cooling load curves of seasons and typical wind and light output scenes;
and 2, solving the multi-objective double-layer optimization configuration model, wherein the upper layer model adopts NSGA-II to solve, and the lower layer model calls IBM commercial software Cplex to solve. The specific solving process is as follows: firstly, randomly generating a decision variable by an upper model, bringing the decision variable into a lower model, calling a Cplex solver to carry out optimized scheduling on the output of equipment, calculating an upper objective function based on a lower optimization result, transmitting the upper objective function into the upper model, continuously solving by an NSGA-II, repeating the steps until the upper iteration times exceed an iteration upper limit, and finally outputting a pareto solution set of multi-objective optimized configuration;
and 3, deciding a pareto frontier solution set obtained by the multi-objective optimization configuration model based on a fuzzy theory, and outputting a renewable micro energy network compromise optimal configuration scheme and multi-dimensional evaluation indexes thereof.
The specific steps of calculating the double-layer optimized configuration of the renewable micro energy network are shown in fig. 4.
Example 2:
the invention is further illustrated by the following example.
Taking a demonstration project of a multi-energy complementary system to be reconstructed in Jiangxi province as an example, the system is optimally designed by adopting the method, and the schematic diagram of the renewable micro energy network architecture is shown in FIG. 3. Dividing the whole year into summer (6 months to 8 months), winter (12 months to 2 months) and transition seasons according to local climate conditions, wherein typical daily electric heating and cooling load curves and typical wind and light output scenes of all seasons are shown in figure 5, and energy peak values of three seasons are 1382.7kW & h, 1159.3kW & h and 976.4kW & h respectively; the electricity purchase price adopts the local peak valley time-of-use electricity price, specifically, as shown in table 1, the electricity price of the new energy internet post is 0.4593 mm/kw.h, and the unit methane production cost is 0.58 mm/m 3; the device parameters available for configuration are shown in table 2; in the NSGA-II algorithm, the population size is 150, the iteration number is 100, and the cross rate and the variation rate are respectively 0.9 and 0.1.
TABLE 1 time-of-use electricity price table
Figure BDA0002476323780000142
TABLE 2 parameter table of candidate energy source equipment
Figure BDA0002476323780000143
Figure BDA0002476323780000151
The Pareto non-inferior solution set obtained in a grid-connected mode according to the NSGA-II optimization algorithm is shown in FIG. 6, and it can be seen that the method can effectively capture the Pareto frontier of a multi-objective planning design problem for investors to carry out balance selection, and meanwhile, obvious conflict exists between the economic benefit index and the environmental performance of the micro-energy network.
And selecting three groups of typical schemes 1, 2 and 3 from the solution set according to the optimal principle of each sub-target, wherein the optimal configuration result is shown in table 3. It can be seen that, the solutions 1 and 2 have the lowest annual operation cost and the lowest total pollutant emission amount respectively, but the environmental protection performance of the solution 1 and the economic performance of the solution 2 are also the worst of all the solutions, because the solution 1 selects less expensive new energy power generation equipment and energy storage devices, so that the equipment purchase cost is greatly reduced, but this also results in the reduction of the electric energy ratio generated by pollution-free wind energy and light energy in the system, and in turn depends on biomass energy for electric energy supply, thereby causing the increase of pollutant emission amount. The scheme 2 reduces the energy yield of the biogas heat and power cogeneration unit, a large number of clean energy devices including WT, PV and GSHP are arranged, the number of energy storage devices is increased, and the environment-friendly performance is improved at the cost of economic cost. Compared with the former two, scheme 3 uses more CHP units capable of realizing energy gradient utilization, saves the operating cost expenditure through selling electric energy, obtains good energy utilization efficiency, but a large amount of methane consumption also generates certain damage to the local ecological environment.
TABLE 3 exemplary scheme optimization configuration results
Figure BDA0002476323780000152
Figure BDA0002476323780000161
In order to comprehensively consider the investment cost, the environmental protection performance and the operation energy efficiency of the renewable micro energy network, based on a fuzzy theory, a compromise optimization configuration scheme with the highest comprehensive satisfaction degree, namely a scheme 4 and a scheme 5, is selected under the grid-connected operation condition and the off-grid operation condition respectively, and is compared with an SP system to measure the comprehensive energy supply potential, wherein the optimization configuration result and the operation parameters of the system are shown in a table 4. It can be known that the SP system has a simple energy supply structure and a low unit price of equipment, and is superior in terms of energy equipment purchasing and operation and maintenance costs, but it does not fully utilize local renewable resources, and does not realize the cascade utilization of energy, so that the environmental protection performance and the energy utilization efficiency are quite behind.
In contrast, the operation indexes of the scheme 4 and the scheme 5 have obvious advantages. The grid-connected scheme mainly depends on various local renewable resources in the aspect of energy consumption, electric energy is rarely purchased, and meanwhile, a certain number of energy storage devices are arranged to ensure flexible operation of the system. In the aspect of energy output, in the scheme 4, relatively cheap WT and CHP units are more selected as power generation equipment, so that the load demand of suburban users is met, clean electric energy is also transmitted to an external power grid, and the economic benefit is good. Compared with a grid-connected mode, the coupling characteristic between the multi-energy flows of the off-grid system is more remarkable, so that the investment of energy storage equipment is increased in the configuration scheme of the off-grid system, the reasonable distribution of system energy in different time periods is realized, and the operation cost of the off-grid system is greatly improved. In addition, the scheme 5 reduces the wind and light installed capacity to reduce the total amount of wind and light abandoning, and improves the new energy consumption level by means of GSHP in two seasons of summer and winter, thereby achieving the aim of improving the energy utilization efficiency of the system. In order to maintain the power balance of the system, especially the electric and thermal power balance in the cooling season, the scheme 5 also selects an absorption refrigerator rather than an electric refrigerator as a cold source. Because the off-grid system only needs to meet self energy consumption, the biomass gas consumption is low, and the environmental benefit is better than that of a grid-connected system.
TABLE 4 comparison of optimized configuration results
Figure BDA0002476323780000162
Figure BDA0002476323780000171
The system operation indexes of schemes 1-5 can be respectively obtained by referring to the SP system, and the result is shown in FIG. 7. The operation indexes of the scheme obtained by the method are greatly improved compared with those of the traditional energy supply mode, and the method is particularly outstanding in the aspects of low carbon and environmental protection, so that the great advantage of the multi-energy coupling system in the aspect of green suburban energy supply is reflected. Meanwhile, the fuzzy optimal solution also realizes effective balance of multiple indexes, can fully explore the energy supply potential of the micro energy network, and avoids the blindness of the conventional single target planning.
Example 3
An optimized configuration system of renewable micro energy network, as shown in fig. 2, includes:
the data acquisition module is used for acquiring configuration parameters and cost parameters of the to-be-selected energy equipment, an electric heating and cooling load curve of a typical day in each season and a typical wind and light output scene in each season;
the calculation module is used for inputting the configuration parameters and the cost parameters of the to-be-selected energy equipment, the electric heating and cooling load curves of typical days in each season and the typical wind and light output scenes in each season into a preset optimization configuration model to obtain a pareto solution set of the capacity of the to-be-selected energy equipment;
the optimization module is used for screening the pareto solution set of the capacity of the to-be-selected energy equipment based on a fuzzy theory to obtain an optimal configuration scheme of the to-be-selected energy equipment;
the optimized configuration model comprises: the system comprises an upper-layer optimization configuration model and a lower-layer operation optimization scheduling model, wherein the upper-layer optimization configuration model is optimally constructed based on the quantity configuration of the energy equipment to be selected, and the lower-layer operation optimization scheduling model is constructed based on the equipment output of the energy equipment to be selected.
Candidate energy equipment includes: the system comprises photovoltaic equipment, wind power equipment, biogas cogeneration equipment, a biogas boiler, a ground source heat pump, an electric refrigerator, an absorption refrigerator, an electric power storage device, a cold accumulation device and a heat accumulation device;
the configuration parameters of the candidate energy equipment comprise: rated power, output power range, maximum climbing coefficient, dissipation coefficient and waste heat recovery coefficient of the biogas cogeneration equipment; rated power, output power range, maximum climbing coefficient and thermal efficiency of the biogas boiler; rated power, output power range, maximum climbing coefficient, electric-heat conversion efficiency and heat exchange efficiency of the heat exchange pump of the ground source heat pump; the output power range and energy efficiency ratio of the electric refrigerator and the absorption refrigerator; the charging and discharging efficiency, the maximum charging and discharging coefficient, the dissipation coefficient and the energy storage coefficient of the electric power storage device, the cold accumulation device and the heat accumulation device;
the cost parameters of the candidate energy equipment comprise: procurement costs, installation costs, and maintenance costs.
The system also comprises an optimized configuration model setting module, wherein the optimized configuration model setting module comprises:
the upper-layer optimization configuration model setting module is used for constructing an upper-layer optimization configuration objective function by taking the equipment capacity of the to-be-selected energy equipment as a decision variable and the minimum annual operation cost, pollution emission and energy utilization efficiency as targets based on preset renewable micro energy network multi-dimensional evaluation indexes; setting an upper-layer optimal configuration model by taking the installation capacity of the energy equipment to be selected as an upper-layer optimal configuration constraint condition;
the lower-layer operation optimization scheduling model setting module is used for constructing a lower-layer operation optimization scheduling objective function by taking the equipment output of the to-be-selected energy equipment as a decision variable and the minimum purchasing cost, maintenance cost and light and wind abandoning cost of the to-be-selected energy equipment as targets based on the renewable micro energy network multi-dimensional evaluation index; and setting a lower-layer operation optimization scheduling model by taking the transmission power constraint of the bus, the interactive power constraint with the external connecting line, the energy coupling equipment constraint and the energy storage equipment constraint as constraint conditions.
The upper layer optimal configuration objective function is as follows:
min f1＝(C，F，1/E)
wherein C is the annual operation cost, F is the pollution emission, and E is the energy utilization efficiency.
Preferably, the annual operating cost is calculated as follows:
Figure BDA0002476323780000181
wherein, CEIFor installation costs of candidate energy devices, CEPFor purchase costs of candidate energy plants, CEMFor maintenance costs of candidate energy devices, NiThe installation capacity of the i-th type of energy equipment to be selected is obtained; k is the reference discount rate, y is the use period of the energy equipment to be selected, ciThe unit price of the device for the i-th candidate energy source,
Figure BDA0002476323780000182
the price of purchasing electricity per unit power at the time t on the day d,
Figure BDA0002476323780000183
for the grid tie line interactive power at the d-th day t,
Figure BDA0002476323780000184
the price of electricity sold per unit power at the time t on the day d, cbioThe unit cubic meter of the production cost of the methane is,
Figure BDA0002476323780000185
is the methane consumption of the micro gas turbine at the time t on the day d,
Figure BDA0002476323780000186
is the biogas consumption of the biogas boiler at the time t on the day d,
Figure BDA0002476323780000187
the maintenance cost per unit power of the equipment of the i-th candidate energy source,
Figure BDA0002476323780000188
and (3) the output power of the equipment of the ith type of energy to be selected at the time T on the D day, D is the number of days of the whole year, and T is the number of hours per day.
The calculation formula of the pollution emission is as follows:
Figure BDA0002476323780000189
wherein the content of the first and second substances,
Figure BDA00024763237800001810
for grid tie line interaction power at day d, time t, ηgenFor the power generation efficiency of the power plant, ηlossIs the loss rate of the power transmission line of the power plant,
Figure BDA0002476323780000191
the discharge coefficient of the j-type pollutants generated by coal-fired power generation,
Figure BDA0002476323780000192
is the methane consumption of the micro gas turbine at the time t on the day d,
Figure BDA0002476323780000193
is the biogas consumption of the biogas boiler at the time t on the day d, q is the standard biogas low heat value,
Figure BDA0002476323780000194
the discharge coefficient of j-type pollutants generated by biogas combustion is shown, D is the number of days in the whole year, and T is the number of hours per day.
The calculation formula of the energy utilization efficiency is as follows:
Figure BDA0002476323780000195
wherein Q isoutFor system output energy, QinFor system input, λeThe conversion coefficient of the electric energy is the conversion coefficient,
Figure BDA0002476323780000196
for the grid tie line interactive power at the d-th day t,
Figure BDA0002476323780000197
the rated output power of the photovoltaic power generation unit at the time t on the day d,
Figure BDA0002476323780000198
rated output power, lambda, of the wind power generation unit at the time of day d and tgThe conversion coefficient of the methane energy is calculated,
Figure BDA0002476323780000199
is the methane consumption of the micro gas turbine at the time t on the day d,
Figure BDA00024763237800001910
is the biogas consumption of the biogas boiler at the time t on the d-th day, q is the standard biogas low heat value, lambdapIn order to convert the cold energy into the system,
Figure BDA00024763237800001911
inputting ground source heat energy for the ground source heat exchange heat pump at the time t on the day d,
Figure BDA00024763237800001912
is the electric load power, lambda, of the user at time t on day dhIn order to convert the coefficient of heat energy into a coefficient,
Figure BDA00024763237800001913
is the thermal load power, lambda, of the user at time t on day dcIn order to convert the coefficient of the cold energy,
Figure BDA00024763237800001914
the cold load power of suburban users at the time t of day d.
The installation capacity constraints of the candidate energy devices are as follows:
Figure BDA00024763237800001915
wherein the content of the first and second substances,
Figure BDA00024763237800001916
for the lower limit of the installation capacity of the ith type of equipment,
Figure BDA00024763237800001917
upper limit of installation capacity for class i devices, NiCapacity is installed for class i devices.
The lower run optimization scheduling objective function is as follows:
Figure BDA0002476323780000201
wherein, CEPFor purchase costs of candidate energy plants, CEMFor maintenance costs of the candidate energy plant, CAEIn order to avoid the cost of light and wind,
Figure BDA0002476323780000202
in order to discard the optical power,
Figure BDA0002476323780000203
to abandon the wind power, cPVCost of light rejection for unit power, cWTT is the wind curtailment cost per unit power, and T is the number of hours per day.
The bus transmission power constraints are as follows:
Figure BDA0002476323780000204
wherein the content of the first and second substances,
Figure BDA0002476323780000205
for the actual output power of the photovoltaic at time t,
Figure BDA0002476323780000206
for the actual output power of the wind power at the moment t,
Figure BDA0002476323780000207
for combined heat and power systems at time tThe actual output power of the power converter is,
Figure BDA0002476323780000208
for grid tie line interaction power at time t,
Figure BDA0002476323780000209
for the electrical load power of the user at time t,
Figure BDA00024763237800002010
the power consumed by the ground source heat pump at the moment t,
Figure BDA00024763237800002011
for the time t the electric refrigerator consumes power,
Figure BDA00024763237800002012
for the power consumption of the electric storage device at time t,
Figure BDA00024763237800002013
for the heating power of the cogeneration system at time t,
Figure BDA00024763237800002014
for the heating power of the biogas boiler at the moment t,
Figure BDA00024763237800002015
the heating power of the ground source heat pump at the moment t,
Figure BDA00024763237800002016
for the thermal load power of the user at time t,
Figure BDA00024763237800002017
for the absorption heat power of the absorption refrigerator at time t,
Figure BDA00024763237800002018
the thermal energy storage output at the time t,
Figure BDA00024763237800002019
for refrigeration of the electric refrigerator at time tThe power of the electric motor is controlled by the power controller,
Figure BDA00024763237800002020
the t-time absorption refrigerating machine refrigerating power,
Figure BDA00024763237800002021
the cold energy storage output of the ground source heat pump at the moment t,
Figure BDA00024763237800002022
the power of the cold load of the user,
Figure BDA00024763237800002023
the cold stored energy output at the moment t.
The interaction power constraint with the external tie is as follows:
Figure BDA00024763237800002024
wherein the content of the first and second substances,
Figure BDA00024763237800002025
for grid tie line interaction power at time t,
Figure BDA00024763237800002026
the upper power limit is interacted with an external power grid tie line.
The energy coupling device constraints are as follows:
Figure BDA00024763237800002027
Figure BDA00024763237800002028
wherein the content of the first and second substances,
Figure BDA00024763237800002029
the output of the i-th class device at time t,
Figure BDA00024763237800002030
the lower limit of the output of the ith type device at the time t,
Figure BDA00024763237800002031
the upper limit of the output of the ith type device at the moment t,
Figure BDA0002476323780000211
the start-stop state of the ith type equipment at the time t,
Figure BDA0002476323780000212
the output, Δ S, of the i-th class device at time t-1iAnd the output climbing limit of the ith equipment is obtained.
The calculation module comprises:
the computing module 1 is used for inputting the configuration parameters and the cost parameters of the to-be-selected energy equipment into an upper-layer optimized configuration model, and solving the upper-layer optimized configuration model by adopting a non-dominated sorting genetic algorithm to obtain the configuration of the to-be-selected energy equipment corresponding to each generation of parent chromosomes;
the calculation module 2 is used for inputting the configuration of the energy equipment to be selected corresponding to each generation parent chromosome, the electric heating and cooling load curve of a typical day in each season and the typical wind and light output scene in each season into a lower-layer operation optimization scheduling model to obtain the output of the equipment under the configuration of the energy equipment to be selected corresponding to each generation parent chromosome;
and the calculating module 3 is used for screening the population of the non-dominated sorting genetic algorithm to obtain a new generation of population based on the device output under the configuration of the to-be-selected energy device corresponding to each generation of parent chromosome until the iteration times exceed an iteration upper limit preset by the non-dominated sorting genetic algorithm, and obtaining a pareto solution set of the capacity of the to-be-selected energy device corresponding to the last generation of population.
A calculation module 3, comprising:
the target function calculation module is used for calculating a target function value of an upper-layer optimized configuration model corresponding to each generation of the to-be-selected energy equipment configuration based on the equipment output under the to-be-selected energy equipment configuration corresponding to each generation of the parent chromosome;
and the new population generation module is used for screening the population of the non-dominated sorting genetic algorithm to obtain a new generation of population based on the objective function value of the upper-layer optimization configuration model corresponding to each generation of the energy equipment to be selected.
An optimization module comprising:
the membership function calculation module is used for respectively calculating membership functions corresponding to annual operation cost, pollution emission and energy utilization efficiency of an upper-layer optimized configuration target based on the pareto solution set of the capacity of the to-be-selected energy equipment;
the comprehensive membership function calculation module is used for carrying out weighted operation on the membership functions corresponding to the annual operation cost, the pollution emission and the energy utilization efficiency to obtain comprehensive membership functions;
and the configuration generation module is used for calculating the comprehensive membership corresponding to each solution and determining the solution corresponding to the maximum value of the comprehensive membership based on the pareto solution set of the capacity of the energy equipment to be selected, and the solution is the optimal configuration scheme of the energy equipment to be selected.
The system further includes an evaluation module, wherein the evaluation module includes: the system comprises a cost saving rate evaluation module, a pollution emission reduction rate evaluation module and an energy efficiency improvement rate evaluation module;
the cost saving rate evaluation module is used for evaluating the cost saving of the renewable micro energy network with the optimal configuration scheme;
the pollution emission reduction rate evaluation module is used for evaluating the pollution emission reduction rate of the renewable micro energy network with the optimal configuration scheme;
and the energy efficiency improvement rate evaluation module is used for evaluating the energy efficiency improvement rate of the renewable micro energy network with the optimal configuration scheme.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present application and not for limiting the scope of protection thereof, and although the present application is described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that after reading the present application, they can make various changes, modifications or equivalents to the specific embodiments of the application, but these changes, modifications or equivalents are all within the scope of protection of the claims to be filed.

Claims (20)

1. An optimal configuration method of a renewable micro energy network is characterized by comprising the following steps:
acquiring configuration parameters and cost parameters of the to-be-selected energy equipment, an electric heating and cooling load curve of a typical day in each season and a typical wind and light output scene in each season;
inputting configuration parameters and cost parameters of the to-be-selected energy equipment, electric heating and cooling load curves of typical days in each season and typical wind and light output scenes in each season into a preset optimization configuration model to obtain a pareto solution set of the capacity of the to-be-selected energy equipment;
screening the pareto solution set of the capacity of the to-be-selected energy equipment based on a fuzzy theory to obtain an optimal configuration scheme of the to-be-selected energy equipment;
the optimized configuration model comprises: the system comprises an upper-layer optimization configuration model and a lower-layer operation optimization scheduling model, wherein the upper-layer optimization configuration model is optimally constructed based on the quantity configuration of the energy equipment to be selected, and the lower-layer operation optimization scheduling model is optimally constructed based on the equipment output of the energy equipment to be selected.
2. The method of claim 1, wherein the candidate energy devices comprise: the system comprises photovoltaic equipment, wind power equipment, biogas cogeneration equipment, a biogas boiler, a ground source heat pump, an electric refrigerator, an absorption refrigerator, an electric power storage device, a cold accumulation device and a heat accumulation device;
the configuration parameters of the candidate energy equipment comprise: rated power, output power range, maximum climbing coefficient, dissipation coefficient and waste heat recovery coefficient of the biogas cogeneration equipment; rated power, output power range, maximum climbing coefficient and thermal efficiency of the biogas boiler; rated power, output power range, maximum climbing coefficient, electric-heat conversion efficiency and heat exchange efficiency of the heat exchange pump of the ground source heat pump; the output power range and energy efficiency ratio of the electric refrigerator and the absorption refrigerator; the charging and discharging efficiency, the maximum charging and discharging coefficient, the dissipation coefficient and the energy storage coefficient of the electric power storage device, the cold accumulation device and the heat accumulation device;
the cost parameters of the candidate energy equipment comprise: procurement costs, installation costs, and maintenance costs.
3. The method of claim 1, wherein the setting of the optimal configuration model comprises:
constructing an upper-layer optimal configuration objective function by taking the equipment capacity of the to-be-selected energy equipment as a decision variable and the minimum annual operation cost, pollution emission and energy utilization efficiency as targets based on a preset renewable micro energy network multi-dimensional evaluation index; setting an upper-layer optimal configuration model by taking the installation capacity of the energy equipment to be selected as an upper-layer optimal configuration constraint condition;
constructing a lower-layer operation optimization scheduling objective function by taking the equipment output of the to-be-selected energy equipment as a decision variable and the minimum purchasing cost, maintenance cost and light and wind abandoning cost of the to-be-selected energy equipment as targets based on the renewable micro energy network multi-dimensional evaluation index; and setting a lower-layer operation optimization scheduling model by taking the transmission power constraint of the bus, the interactive power constraint with the external connecting line, the energy coupling equipment constraint and the energy storage equipment constraint as constraint conditions.
4. The method of claim 3, wherein the upper layer optimization configuration objective function is as follows:
min f1＝(C，F，1/E)
wherein C is the annual operation cost, F is the pollution emission, and E is the energy utilization efficiency.
5. The method of claim 4, wherein the annual operating cost is calculated as follows:
Figure FDA0002476323770000021
wherein, CEIFor installation costs of candidate energy devices, CEPFor purchase costs of candidate energy plants, CEMFor maintenance costs of candidate energy devices, NiThe installation capacity of the i-th type of energy equipment to be selected is obtained; k is the reference discount rate, y is the use period of the energy equipment to be selected, ciThe unit price of the device for the i-th candidate energy source,
Figure FDA0002476323770000022
the price of purchasing electricity per unit power at the time t on the day d,
Figure FDA0002476323770000023
for the grid tie line interactive power at the d-th day t,
Figure FDA0002476323770000024
the price of electricity sold per unit power at the time t on the day d, cbioThe unit cubic meter of the production cost of the methane is,
Figure FDA0002476323770000025
is the methane consumption of the micro gas turbine at the time t on the day d,
Figure FDA0002476323770000026
is the biogas consumption of the biogas boiler at the time t on the day d,
Figure FDA0002476323770000027
the maintenance cost per unit power of the equipment of the i-th candidate energy source,
Figure FDA0002476323770000028
and (3) the output power of the equipment of the ith type of energy to be selected at the time T on the D day, D is the number of days of the whole year, and T is the number of hours per day.
6. The method of claim 4, wherein the pollutant emissions are calculated as follows:
Figure FDA0002476323770000029
wherein the content of the first and second substances,
Figure FDA00024763237700000210
for grid tie line interaction power at day d, time t, ηgenFor the power generation efficiency of the power plant, ηlossIs the loss rate of the power transmission line of the power plant,
Figure FDA00024763237700000211
the discharge coefficient of the j-type pollutants generated by coal-fired power generation,
Figure FDA00024763237700000212
is the methane consumption of the micro gas turbine at the time t on the day d,
Figure FDA00024763237700000213
is the biogas consumption of the biogas boiler at the time t on the day d, q is the standard biogas low heat value,
Figure FDA00024763237700000214
the discharge coefficient of j-type pollutants generated by biogas combustion is shown, D is the number of days in the whole year, and T is the number of hours per day.
7. The method of claim 4, wherein the energy use efficiency is calculated as follows:
Figure FDA0002476323770000031
wherein Q isoutFor system output energy, QinFor system input, λeThe conversion coefficient of the electric energy is the conversion coefficient,
Figure FDA0002476323770000032
for the grid tie line interactive power at the d-th day t,
Figure FDA0002476323770000033
the rated output power of the photovoltaic power generation unit at the time t on the day d,
Figure FDA0002476323770000034
rated output power, lambda, of the wind power generation unit at the time of day d and tgThe conversion coefficient of the methane energy is calculated,
Figure FDA0002476323770000035
is the methane consumption of the micro gas turbine at the time t on the day d,
Figure FDA0002476323770000036
is the biogas consumption of the biogas boiler at the time t on the d-th day, q is the standard biogas low heat value, lambdapIn order to convert the cold energy into the system,
Figure FDA0002476323770000037
inputting ground source heat energy for the ground source heat exchange heat pump at the time t on the day d,
Figure FDA0002476323770000038
is the electric load power, lambda, of the user at time t on day dhIn order to convert the coefficient of heat energy into a coefficient,
Figure FDA0002476323770000039
is the thermal load power, lambda, of the user at time t on day dcIn order to convert the coefficient of the cold energy,
Figure FDA00024763237700000310
the cooling load power of the user at the time t on the day d.
8. The method according to claim 3, wherein the installation capacity constraints of the candidate energy devices are as follows:
Figure FDA00024763237700000311
wherein the content of the first and second substances,
Figure FDA00024763237700000312
for the lower limit of the installation capacity of the ith type of equipment,
Figure FDA00024763237700000313
upper limit of installation capacity for class i devices, NiCapacity is installed for class i devices.
9. The method of claim 3, wherein the lower run optimization scheduling objective function is as follows:
Figure FDA00024763237700000314
wherein, CEPFor purchase costs of candidate energy plants, CEMFor maintenance costs of the candidate energy plant, CAEIn order to avoid the cost of light and wind,
Figure FDA00024763237700000315
in order to discard the optical power,
Figure FDA00024763237700000316
to abandon the wind power, cPVCost of light rejection for unit power, cWTT is the wind curtailment cost per unit power, and T is the number of hours per day.
10. The method of claim 3, wherein the bus transmission power constraints are as follows:
Figure FDA0002476323770000041
wherein, Pt PVFor the actual photovoltaic output power at time t, Pt WTIs the actual wind power output power at the moment t, Pt CHPIs the actual output power, P, of the cogeneration system at time tt gridAt time tGrid tie line interaction power, Pt loadElectric load power, P, for the user at time tt GSHPIs the power consumed by the ground source heat pump at the moment t, Pt ECFor the consumption of power by the electric refrigerator at time t, Pt ESFor the power consumption of the accumulator at time t, Qt CHPFor the heating power of the cogeneration system at time t,
Figure FDA0002476323770000042
for the heating power of the biogas boiler at the moment t,
Figure FDA0002476323770000043
the heating power of the ground source heat pump at the moment t,
Figure FDA0002476323770000044
for the thermal load power of the user at time t,
Figure FDA0002476323770000045
for the absorption heat power of the absorption refrigerator at time t,
Figure FDA0002476323770000046
the thermal energy storage output at the time t,
Figure FDA0002476323770000047
the refrigerating power of the electric refrigerator at the time t,
Figure FDA0002476323770000048
the t-time absorption refrigerating machine refrigerating power,
Figure FDA0002476323770000049
the cold energy storage output of the ground source heat pump at the moment t,
Figure FDA00024763237700000410
the power of the cold load of the user,
Figure FDA00024763237700000411
the cold stored energy output at the moment t.
11. The method of claim 3, wherein the interaction power constraint with the external tie is as follows:
Figure FDA00024763237700000412
wherein, Pt gridFor grid tie line interaction power at time t,
Figure FDA00024763237700000413
the upper power limit is interacted with an external power grid tie line.
12. The method of claim 3, wherein the energy coupling device constraints are as follows:
Figure FDA00024763237700000414
Figure FDA00024763237700000415
wherein the content of the first and second substances,
Figure FDA00024763237700000416
the output of the i-th class device at time t,
Figure FDA00024763237700000417
the lower limit of the output of the ith type device at the time t,
Figure FDA00024763237700000418
the upper limit of the output of the ith type device at the moment t,
Figure FDA00024763237700000419
the start-stop state of the ith type equipment at the time t,
Figure FDA00024763237700000420
the output, Δ S, of the i-th class device at time t-1iAnd the output climbing limit of the ith equipment is obtained.
13. The method of claim 1, wherein the inputting the configuration parameters and cost parameters of the candidate energy devices, the heat and cold load curves of the typical days of each season, and the wind and light output scenes of each season into a preset optimal configuration model to obtain the pareto solution set of the capacities of the candidate energy devices comprises:
inputting the configuration parameters and cost parameters of the to-be-selected energy equipment into an upper-layer optimized configuration model, and solving the upper-layer optimized configuration model by adopting a non-dominated sorting genetic algorithm to obtain the configuration of the to-be-selected energy equipment corresponding to each generation of parent chromosomes;
inputting the configuration of the energy equipment to be selected corresponding to each generation parent chromosome, the electric heating and cooling load curve of a typical day in each season and the typical wind and light output scene in each season into a lower-layer operation optimization scheduling model to obtain the equipment output under the configuration of the energy equipment to be selected corresponding to each generation parent chromosome;
and screening the population of the non-dominated sorting genetic algorithm to obtain a new generation of population based on the device output under the configuration of the to-be-selected energy device corresponding to each generation of parent chromosome until the iteration times exceed the preset iteration upper limit of the non-dominated sorting genetic algorithm, and obtaining a pareto solution set of the capacity of the to-be-selected energy device corresponding to the last generation of population.
14. The method of claim 13, wherein the screening the population of the non-dominated ranking genetic algorithm to obtain a new generation population based on device output under the candidate energy device configuration corresponding to each generation parent chromosome comprises:
calculating a target function value of an upper-layer optimization configuration model corresponding to each generation of candidate energy equipment configuration based on equipment output under the candidate energy equipment configuration corresponding to each generation of parent chromosomes;
and screening the population of the non-dominated sorting genetic algorithm to obtain a new generation of population based on the objective function value of the upper-layer optimization configuration model corresponding to each generation of energy equipment to be selected.
15. The method of claim 1, wherein the screening the pareto solution set of the capacity of the candidate energy devices based on the fuzzy theory to obtain the optimal configuration scheme of the candidate energy devices comprises:
respectively calculating corresponding membership functions of annual operation cost, pollution emission and energy utilization efficiency of an upper-layer optimized configuration target on the basis of the pareto solution set of the capacity of the to-be-selected energy equipment;
weighting and calculating the membership function corresponding to the annual operation cost, the pollution emission and the energy utilization efficiency to obtain a comprehensive membership function;
and calculating the comprehensive membership degree corresponding to each solution based on the pareto solution set of the capacity of the to-be-selected energy equipment, and determining the solution corresponding to the maximum value of the comprehensive membership degree, which is the optimal configuration scheme of the to-be-selected energy equipment.
16. The method according to claim 3, wherein after obtaining the optimal configuration scheme of the candidate energy device, the method further comprises: evaluating an optimized configuration result by adopting the renewable micro energy network multidimensional evaluation index;
the multidimensional evaluation index of the renewable micro energy network comprises the following indexes: a cost saving rate index, a pollution emission reduction rate index and an energy efficiency improvement rate index.
17. The method of claim 16, wherein the cost savings ratio is calculated as follows:
Figure FDA0002476323770000051
wherein R isCSFor cost savings, C is the annual operating cost of the renewable micro-energy grid system, CSPThe annual operating cost of the energy production system is reduced.
18. The method of claim 16, wherein the pollution reduction rate is calculated as follows:
Figure FDA0002476323770000061
wherein R isPRF is the pollution emission amount of the renewable micro-energy network system for the pollution emission reduction rate, FSPThe pollution discharge amount of the energy production system is reduced.
19. The method according to claim 16, wherein the energy efficiency improvement ratio is calculated as follows:
Figure FDA0002476323770000062
wherein R isEIFor efficiency improvement, E is the energy utilization efficiency of the renewable micro-energy network system, ESPThe energy utilization efficiency of the energy separate production system is improved.
20. An optimal configuration system for a renewable micro energy network, comprising:
the data acquisition module is used for acquiring configuration parameters and cost parameters of the to-be-selected energy equipment, an electric heating and cooling load curve of a typical day in each season and a typical wind and light output scene in each season;
the calculation module is used for inputting the configuration parameters and the cost parameters of the to-be-selected energy equipment, the electric heating and cooling load curves of typical days in each season and the typical wind and light output scenes in each season into a preset optimization configuration model to obtain a pareto solution set of the capacity of the to-be-selected energy equipment;
the optimization module is used for screening the pareto solution set of the capacity of the to-be-selected energy equipment based on a fuzzy theory to obtain an optimal configuration scheme of the to-be-selected energy equipment;
the optimized configuration model comprises: the system comprises an upper-layer optimization configuration model and a lower-layer operation optimization scheduling model, wherein the upper-layer optimization configuration model is optimally constructed based on the quantity configuration of the energy equipment to be selected, and the lower-layer operation optimization scheduling model is optimally constructed based on the equipment output of the energy equipment to be selected.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112434853A (en) * 2020-11-23 2021-03-02 上海电气分布式能源科技有限公司 Capacity configuration method and device of energy supply system, electronic equipment and storage medium
CN113313416A (en) * 2021-06-22 2021-08-27 上海交通大学 Distribution network power resource optimal distribution method considering biogas power generation and new energy consumption mechanism
CN113361779A (en) * 2021-06-08 2021-09-07 深圳供电局有限公司 Commercial power utilization optimization method and system and storage medium
CN114595584A (en) * 2022-03-14 2022-06-07 南方电网数字电网研究院有限公司 Multi-energy complementary regional terminal energy use configuration method and device
CN114744624A (en) * 2022-06-10 2022-07-12 国网湖北省电力有限公司经济技术研究院 Planning optimization method for biomass-solar distributed power supply
CN116187209A (en) * 2023-05-04 2023-05-30 山东大学 High-proportion new energy system capacity optimal configuration method, equipment, medium and device

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112434853A (en) * 2020-11-23 2021-03-02 上海电气分布式能源科技有限公司 Capacity configuration method and device of energy supply system, electronic equipment and storage medium
CN113361779A (en) * 2021-06-08 2021-09-07 深圳供电局有限公司 Commercial power utilization optimization method and system and storage medium
CN113313416A (en) * 2021-06-22 2021-08-27 上海交通大学 Distribution network power resource optimal distribution method considering biogas power generation and new energy consumption mechanism
CN114595584A (en) * 2022-03-14 2022-06-07 南方电网数字电网研究院有限公司 Multi-energy complementary regional terminal energy use configuration method and device
CN114744624A (en) * 2022-06-10 2022-07-12 国网湖北省电力有限公司经济技术研究院 Planning optimization method for biomass-solar distributed power supply
CN116187209A (en) * 2023-05-04 2023-05-30 山东大学 High-proportion new energy system capacity optimal configuration method, equipment, medium and device
CN116187209B (en) * 2023-05-04 2023-08-08 山东大学 High-proportion new energy system capacity optimal configuration method, equipment, medium and device

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