CN112202203A - Cascade utilization optimization method for energy coupling of electric heating microgrid - Google Patents

Cascade utilization optimization method for energy coupling of electric heating microgrid Download PDF

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CN112202203A
CN112202203A CN202011057064.7A CN202011057064A CN112202203A CN 112202203 A CN112202203 A CN 112202203A CN 202011057064 A CN202011057064 A CN 202011057064A CN 112202203 A CN112202203 A CN 112202203A
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energy
model
electric
coupling
cost
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吴奎华
李文升
綦陆杰
王延朔
赵韧
李�昊
刘蕊
杨波
梁荣
冯亮
郑志杰
李昭
王耀雷
崔灿
杨慎全
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State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
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State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Shandong 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
    • H02J3/381—Dispersed generators
    • 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
    • H02J15/00—Systems for storing electric energy
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y02A30/60—Planning or developing urban green infrastructure
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/10—Photovoltaic [PV]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

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Abstract

The invention provides an electric heating microgrid energy coupling cascade utilization optimization method, which utilizes an energy cascade utilization principle and an electric heating coupling cascade utilization principle to establish an energy coupling model and an energy grade conversion model according to different energy grades, establishes energy supply and demand models of different buses through the energy coupling model and the energy grade conversion model, and further establishes an optimization model for energy cascade utilization. The invention has the advantages that the data is easy to obtain, the data is closer to the actual situation, the energy efficiency utilization rate of the comprehensive energy system can be enhanced, the electric heating microgrid is taken as the typical form of the comprehensive energy system, the characteristics of different energy forms can be utilized, the complementation and the cooperative optimization of the multi-energy advantages are realized, the comprehensive benefits of the energy system in the links of production, transmission and distribution, utilization, circulation and the like are improved, the clean development of energy sources can be effectively promoted, the high-efficiency utilization of the energy sources and the construction of energy conservation and emission reduction.

Description

Cascade utilization optimization method for energy coupling of electric heating microgrid
Technical Field
The invention relates to the technical field of power grid planning, in particular to a cascade utilization optimization method for energy coupling of an electric heating micro-grid.
Background
In the field of comprehensive energy, in order to promote the utilization of renewable energy, an energy utilization optimization strategy is provided by taking an electric heating microgrid as a typical form of a comprehensive energy system. The electric heating micro-grid is an integrated energy supply system for realizing multiple energy demands of electricity, heat and the like for a terminal user, and the integrated energy supply system is suitable for local conditions, developed in a coordinated mode and complementary in utilization of traditional energy and new energy. However, the current energy application strategy is single, and unified optimization of a multi-energy system is lacked.
Disclosure of Invention
The invention aims to provide an electric heating microgrid energy coupling cascade utilization optimization method, which aims to solve the problems that an electric heating microgrid in the prior art is single in energy utilization strategy and lacks of optimization, realize multi-energy advantage complementation and collaborative optimization, and improve the comprehensive benefits of an energy system in the links of production, transmission and distribution, utilization, circulation and the like.
In order to achieve the technical purpose, the invention provides an electric heating microgrid energy coupling cascade utilization optimization method, which comprises the following operations:
s1, establishing an energy coupling model according to different energy source grades by using an electric heating microgrid system;
s2, realizing conversion of energy grade from high to low between electric energy and heat energy and between different grades of heat energy through an electric heat pump, an absorption heat pump and spike heater equipment, and establishing an energy grade conversion model;
s3, analyzing the energy supply and demand conditions on different energy buses from the energy cascade utilization angle by utilizing the energy flow coupling model and the energy grade conversion model, and establishing energy supply and demand models of the different energy buses;
and S4, establishing an optimization model based on energy cascade utilization through an energy cascade utilization principle, and solving the model to obtain each energy optimization scheduling strategy.
Preferably, the energy coupling model comprises: the system comprises a combined heat and power generation model of a gas turbine, a gas boiler heating model, an electric heat pump and electric refrigeration model, an absorption heat pump and absorption refrigeration model, a peak heater model, a solar photo-thermal equipment model, an electric energy storage model and a cold and hot water energy storage model.
Preferably, the establishing of the energy grade conversion model specifically includes:
the multi-energy coupling relation among different heat energy product levels is as follows:
Figure BDA0002711117990000021
wherein x is an energy device type;
Figure BDA0002711117990000022
to output heat energy; cxIs an energy efficiency ratio constant;
Figure BDA0002711117990000023
inputting energy to the driving side;
Figure BDA0002711117990000024
inputting heat energy to the heated side;
Figure BDA0002711117990000025
and
Figure BDA0002711117990000026
respectively the flow rates of working media at a driving side and a heated side; h isin、hheated、houtThe heat energy ratio of the driving side input, the heated side input and the heated side output is respectively baked; h isbaseBaking at normal temperature in water ratio as a reference;
Figure BDA0002711117990000027
in the formula, RxIs the ratio of the heat input at the heated side to the drive side.
Preferably, the establishing of the energy supply and demand models of the different energy buses specifically includes:
energy sources on each bus:
Figure BDA0002711117990000031
in the formula:
Figure BDA0002711117990000032
the energy sources of the buses are respectively electric power, steam, high-temperature hot water, Chinese hot water, low-temperature hot water and cold water.
Considering the energy interaction of electricity, cold and hot energy storage and corresponding buses, the energy consumption model of each bus is as follows:
Figure BDA0002711117990000033
in the formula:
Figure BDA0002711117990000034
respectively is the energy consumption sum of electric power, steam, high-temperature hot water, medium-temperature water, low-temperature hot water and cold water buses;
Figure BDA0002711117990000035
Figure BDA0002711117990000036
respectively, electric load, steam load, warm hot water load, medium warm hot water load and cold water load.
Preferably, the optimization model aims at the lowest daily operation cost, and the objective function consists of the natural gas and electric energy purchase cost and the equipment operation cost:
Call＝Cng+Cgnd+Cdevice
the purchase cost of natural gas is as follows:
Figure BDA0002711117990000037
in the formula, i represents the number in the same type of equipment;
the micro-grid and the main power grid are in a grid-connected operation state, electricity is purchased from the main power grid according to the time-of-use electricity price, and the electricity purchasing and selling cost of the power grid is as follows:
Figure BDA0002711117990000041
in the formula (I), the compound is shown in the specification,
Figure BDA0002711117990000042
the price of the time-of-use electricity is the price of the time-of-use electricity;
the equipment running cost can be divided into energy equipment operation and maintenance cost and battery energy storage depreciation cost, and the equipment maintenance cost is defined by equipment unit power cost, and battery energy storage depreciation cost is relevant with the charge-discharge electric quantity, assumes that the electric energy storage is along with the linear depreciation of charge-discharge increase, then equipment running cost is:
Figure BDA0002711117990000043
in the formula, pxFor unit output power costs of different energy devices, cbatReplacement of cost for electrical energy storage, Qess,maxThe total charge and discharge amount of the storage battery in the whole life cycle is obtained.
Preferably, the operating constraints of the optimization model are:
the constraints of each bus are:
Figure BDA0002711117990000044
in addition, the energy equipment needs to meet the maximum minimum power and the constraint of climbing slope during operation:
Figure BDA0002711117990000045
in the formula, Px,min、Px,maxRespectively minimum maximum operating power, D, of different energy devicesx、BxRespectively the downward and upward climbing rates of different energy devices.
The effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
compared with the prior art, the method utilizes an energy cascade utilization principle and an electrothermal coupling cascade utilization principle, establishes an energy coupling model and an energy grade conversion model according to different energy grades, establishes energy supply and demand models of different buses through the energy coupling model and the energy grade conversion model, and further establishes an optimization model for energy cascade utilization. The invention has the advantages that the data is easy to obtain, the data is closer to the actual situation, the energy efficiency utilization rate of the comprehensive energy system can be enhanced, the electric heating microgrid is taken as the typical form of the comprehensive energy system, the characteristics of different energy forms can be utilized, the complementation and the cooperative optimization of the multi-energy advantages are realized, the comprehensive benefits of the energy system in the links of production, transmission and distribution, utilization, circulation and the like are improved, the clean development of energy sources can be effectively promoted, the high-efficiency utilization of the energy sources and the construction of energy conservation and emission reduction.
Drawings
Fig. 1 is a flowchart of a cascade utilization optimization method for energy coupling of an electric heating microgrid provided in an embodiment of the present invention;
fig. 2 is a schematic diagram of an electric heating microgrid structure and energy flow provided in an embodiment of the present invention;
FIG. 3 is a schematic illustration of a method of energy staging for a gas turbine provided in an embodiment of the present invention;
FIG. 4 is a schematic diagram of the source-charge-storage relationship in energy step utilization provided in an embodiment of the present invention;
FIG. 5 is a schematic diagram of an energy flow structure for electro-thermally coupled cascade utilization provided in an embodiment of the present invention;
fig. 6 is a schematic diagram of multi-taste energy conversion provided in an embodiment of the present invention.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
The following describes in detail a cascade utilization optimization method for energy coupling of an electric heating microgrid provided by an embodiment of the present invention with reference to the accompanying drawings.
As shown in fig. 1-6, the invention discloses a cascade utilization optimization method for energy coupling of an electric heating microgrid, which comprises the following operations:
s1, establishing an energy coupling model according to different energy source grades by using the electric heating microgrid system, and realizing heterogeneous energy flow coupling mutual aid under different operation conditions.
Different from the traditional micro-grid, the electric heating micro-grid has various energy flow forms, various coupling relations exist among the energy flows, the relations are fully utilized, and under different operation conditions, the realization of heterogeneous energy flow coupling mutual aid is an effective method for improving the comprehensive utilization efficiency of energy and saving the operation cost.
The cogeneration model of a gas turbine is as follows:
Figure BDA0002711117990000061
in the formula (I), the compound is shown in the specification,
Figure BDA0002711117990000062
is the power of the gas turbine; lambda is natural gas low-level combustion heat value;
Figure BDA0002711117990000063
is the air input of the gas turbine; etaGTGenerating efficiency for the gas turbine;
Figure BDA0002711117990000064
is the thermal power recovered from the exhaust gas; etaexhIs the waste heat recovery efficiency;
Figure BDA0002711117990000065
is the heat power of the extracted steam; etaextIs the air extraction coefficient; the upper corner t indicates time.
The gas boiler consumes natural gas as well, and high-temperature steam is generated by combustion to directly supply heat, and the heating model is as follows:
HGB(t)＝ηGB·λFGB(t)
in the formula, FGBIs the air input of the gas boiler; hGBHeat energy contained in steam generated by a gas boiler; etaGBTo gas boiler efficiency; t represents time.
The electric heat pump is similar to the electric refrigeration working principle, and the model can be represented by energy efficiency ratio coefficients:
Figure BDA0002711117990000066
Figure BDA0002711117990000071
in the formula (I), the compound is shown in the specification,
Figure BDA0002711117990000072
heating power for the electric heating pump;
Figure BDA0002711117990000073
the electric heating pump consumes power; cHPThe energy efficiency ratio of the electric heat pump is obtained;
Figure BDA0002711117990000074
refrigeration power for electric refrigeration;
Figure BDA0002711117990000075
consuming power for electric refrigeration; cREThe energy efficiency ratio of electric refrigeration is obtained; the upper corner t indicates time.
The absorption heat pump is similar to the absorption refrigeration working principle, and the mathematical model is as follows:
Figure BDA0002711117990000076
Figure BDA0002711117990000077
in the formula (I), the compound is shown in the specification,
Figure BDA0002711117990000078
heating power for the absorption heat pump; cAHPThe energy efficiency ratio of the absorption heat pump is;
Figure BDA0002711117990000079
consuming steam heat power for the absorption heat pump;
Figure BDA00027111179900000710
the refrigeration power is absorption refrigeration power; cACHThe energy efficiency ratio of absorption refrigeration;
Figure BDA00027111179900000711
low temperature heat energy generated for absorption refrigeration; k is a radical ofACHIs a proportionality constant based on absorption refrigeration equipment characteristics; the upper corner t indicates time.
The spike heater is capable of heating hot water to high temperatures with steam, similar in mathematical model to the heat exchanger model:
Figure BDA00027111179900000712
in the formula (I), the compound is shown in the specification,
Figure BDA00027111179900000713
thermal energy delivered for a spike heater; cPHThe heat exchange efficiency is improved;
Figure BDA00027111179900000714
steam heat energy consumed for spike heaters; the upper corner t indicates time.
For solar photo-thermal equipment, sunlight is focused to generate high-temperature steam, one part of the high-temperature steam is used for generating electricity through a steam turbine, and the rest part of the high-temperature steam is used for supplying heat, wherein a mathematical model of the solar photo-thermal equipment is as follows:
Figure BDA00027111179900000715
in the formula, PPTThe power is the photo-thermal power generation power; etaSTGenerating efficiency for the steam turbine; esolarProviding heat energy for the heat collecting system; x is the number ofpThe heat energy proportional coefficient of the steam turbine is entered; hPT_HThe power is generated by photo-thermal heat; etaexTo the heat exchanger efficiency; etaWHThe waste heat recovery efficiency of the steam turbine is improved.
For electrical energy storage, its mathematical model is represented by the state of charge:
Figure BDA0002711117990000081
in the formula (I), the compound is shown in the specification,
Figure BDA0002711117990000082
is an electrical energy storage state of charge;
Figure BDA0002711117990000083
charging and discharging power (charging is positive, discharging is negative); eess.maxIs capacity; etachrTo the charging efficiency; etadchTo discharge efficiency; sigmaessIs the self-discharge coefficient; Δ t is the time interval; the upper corner t indicates time.
For cold and hot water energy storage, the cold and hot water energy storage is constant temperature storage, the change of water storage reflects the energy storage state, and the mathematical model is as follows:
Figure BDA0002711117990000084
in the formula (I), the compound is shown in the specification,
Figure BDA0002711117990000085
heat energy for storage of the water tank; sigmaMIs the thermal self-loss coefficient;
Figure BDA0002711117990000086
the heat power of the water tank is adopted, the positive is input, and the negative is output;
Figure BDA0002711117990000087
cold energy for storage of the water tank; sigmaCIs the cold self-loss coefficient;
Figure BDA0002711117990000088
the cold power of the water tank is input positively, and output negatively; the upper corner t indicates time.
S2, conversion of energy grade from high to low is achieved through electric heating pumps, absorption heat pumps, spike heaters and other equipment between electric energy and heat energy and between different grades of heat energy, an energy grade conversion model is built, and energy mutual aid between the electric energy and the heat energy and between the different grades of heat energy is achieved.
Analyzing the multi-energy coupling relation among different thermal energy product positions based on the definition of the energy conservation theorem and the thermal energy ratio:
Figure BDA0002711117990000089
wherein x is an energy device type;
Figure BDA00027111179900000810
to output heat energy; cxIs an energy efficiency ratio constant;
Figure BDA00027111179900000811
inputting energy to the driving side;
Figure BDA0002711117990000091
inputting heat energy to the heated side;
Figure BDA0002711117990000092
and
Figure BDA0002711117990000093
respectively the flow rates of working media at a driving side and a heated side; h isin、hheated、houtThe heat energy ratio of the driving side input, the heated side input and the heated side output is respectively baked; h isbaseThe normal temperature water ratio is used as a reference.
Figure BDA0002711117990000094
In the formula, RxThe proportionality factor, which is the ratio of the heated side to the drive side input heat energy, depends on the energy efficiency ratio of the device and the design input-output flame ratio, which can be considered as a constant for a particular device.
Figure BDA0002711117990000095
In the formula, RxIs a scaling factor.
And analyzing the coupling conversion relation among different heat energy levels, namely rewriting input and output mathematical models of the electric heat pump, the absorption heat pump and the spike heater based on the original mathematical model, and limiting the space and not repeating the description.
S3, analyzing the energy supply and demand conditions on different energy buses from the energy cascade utilization angle by utilizing the energy flow coupling model and the energy grade conversion model, and establishing the energy supply and demand models of the different energy buses.
Energy sources on each bus:
Figure BDA0002711117990000096
in the formula:
Figure BDA0002711117990000097
are respectively electricityThe energy sources of the power, the steam, the high-temperature hot water, the Chinese hot water, the low-temperature hot water and the cold water bus are the total.
Considering the energy interaction of electricity, cold and hot energy storage and corresponding buses, the energy consumption model of each bus is as follows:
Figure BDA0002711117990000101
in the formula:
Figure BDA0002711117990000102
respectively is the energy consumption sum of electric power, steam, high-temperature hot water, medium-temperature water, low-temperature hot water and cold water buses;
Figure BDA0002711117990000103
Figure BDA0002711117990000104
respectively, electric load, steam load, warm hot water load, medium warm hot water load and cold water load.
And S4, establishing an optimization model based on energy cascade utilization through an energy cascade utilization principle, and solving the model to obtain each energy optimization scheduling strategy.
The optimization strategy aims at the lowest daily operation cost, and an objective function consists of natural gas and electric energy purchase cost and equipment operation cost:
Call＝Cng+Cgnd+Cdevice
the natural gas is composed of gas turbine and gas boiler consumption, and then the natural gas purchase cost is:
Figure BDA0002711117990000105
in the formula, i represents the number in the same type of equipment.
The micro-grid and the main power grid are in a grid-connected operation state, electricity is purchased from the main power grid according to the time-of-use electricity price, and the electricity purchasing and selling cost of the power grid is as follows:
Figure BDA0002711117990000106
in the formula (I), the compound is shown in the specification,
Figure BDA0002711117990000107
the price of the time-of-use electricity is the price of the time-of-use electricity.
The equipment running cost can be divided into energy equipment operation and maintenance cost and battery energy storage depreciation cost, and the equipment maintenance cost is defined by equipment unit power cost, and battery energy storage depreciation cost is relevant with the charge-discharge electric quantity, assumes that the electric energy storage is along with the linear depreciation of charge-discharge increase, then equipment running cost is:
Figure BDA0002711117990000111
in the formula, pxFor unit output power costs of different energy devices, cbatReplacement of cost for electrical energy storage, Qess,maxThe total charge and discharge amount of the storage battery in the whole life cycle is obtained.
The economy of low-temperature hot water mass storage is poor, and the heat load of plant space, boiler backwater heating and the like are not considered, so that the supply is set to be larger than the demand in constraint, the energy supply and demand balance on each bus needs to be ensured during operation, and each bus constraint is as follows:
Figure BDA0002711117990000112
in addition, the energy equipment needs to meet the maximum minimum power and the constraint of climbing slope during operation:
Figure BDA0002711117990000113
in the formula, Px,min、Px,maxRespectively minimum maximum operating power, D, of different energy devicesx、BxRespectively the downward and upward climbing rates of different energy devices.
Based on linear modeling, the method belongs to a mixed integer linear programming problem and can be solved by adopting a commercial solver Gurobi. An energy flow mathematical model and constraint of the comprehensive energy system are constructed based on a Matlab platform and a Yalmip tool box, a day is divided into 96 time nodes in the simulation process, and a scheduling plan of each device is formulated with the aim of lowest daily operation cost.
In the solving process, because the constraint definitions of a plurality of devices of the same type are the same, the solver has certain randomness when selecting the output devices, and in order to make the selection of the devices have distinction degree, the operation and maintenance costs of the devices of the same type are processed, namely the operation costs of the devices of the same type are sequentially increased, so that the devices with the front serial numbers are preferentially selected. Where x represents the device type, i is the device number, erFor cost increase, the cost increase only plays a distinguishing role, so the defined numerical value is extremely small, the influence on the final solution result is negligible, and the value in the text is 10-4。
px,i+1＝px·(1+i·er)
According to the embodiment of the invention, an energy coupling model and an energy grade conversion model are established according to different energy grades by utilizing an energy gradient utilization principle and an electric heating coupling gradient utilization principle, energy supply and demand models of different buses are established through the energy coupling model and the energy grade conversion model, and an optimization model for energy gradient utilization is further established. The invention has the advantages that the data is easy to obtain, the data is closer to the actual situation, the energy efficiency utilization rate of the comprehensive energy system can be enhanced, the electric heating microgrid is taken as the typical form of the comprehensive energy system, the characteristics of different energy forms can be utilized, the complementation and the cooperative optimization of the multi-energy advantages are realized, the comprehensive benefits of the energy system in the links of production, transmission and distribution, utilization, circulation and the like are improved, the clean development of energy sources can be effectively promoted, the high-efficiency utilization of the energy sources and the construction of energy conservation and emission reduction.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (6)

1. A cascade utilization optimization method for energy coupling of an electric heating microgrid is characterized by comprising the following operations:
s1, establishing an energy coupling model according to different energy source grades by using an electric heating microgrid system;
s2, realizing conversion of energy grade from high to low between electric energy and heat energy and between different grades of heat energy through an electric heat pump, an absorption heat pump and spike heater equipment, and establishing an energy grade conversion model;
s3, analyzing the energy supply and demand conditions on different energy buses from the energy cascade utilization angle by utilizing the energy flow coupling model and the energy grade conversion model, and establishing energy supply and demand models of the different energy buses;
and S4, establishing an optimization model based on energy cascade utilization through an energy cascade utilization principle, and solving the model to obtain each energy optimization scheduling strategy.
2. The cascade utilization optimization method for energy coupling of the electric heating microgrid according to claim 1, wherein the energy coupling model comprises: the system comprises a combined heat and power generation model of a gas turbine, a gas boiler heating model, an electric heat pump and electric refrigeration model, an absorption heat pump and absorption refrigeration model, a peak heater model, a solar photo-thermal equipment model, an electric energy storage model and a cold and hot water energy storage model.
3. The cascade utilization optimization method for energy coupling of the electric heating microgrid according to claim 1, wherein the establishing of the energy grade conversion model specifically comprises:
the multi-energy coupling relation among different heat energy product levels is as follows:
Figure FDA0002711117980000011
wherein x is an energy device type;
Figure FDA0002711117980000012
to output heat energy; cxIs an energy efficiency ratio constant;
Figure FDA0002711117980000013
inputting energy to the driving side;
Figure FDA0002711117980000014
inputting heat energy to the heated side;
Figure FDA0002711117980000015
and
Figure FDA0002711117980000016
respectively the flow rates of working media at a driving side and a heated side; h isin、hheated、houtThe heat energy ratio of the driving side input, the heated side input and the heated side output is respectively baked; h isbaseBaking at normal temperature in water ratio as a reference;
Figure FDA0002711117980000021
in the formula, RxIs the ratio of the heat input at the heated side to the drive side.
4. The cascade utilization optimization method for energy coupling of the electric heating microgrid according to claim 1, wherein the establishing of the energy supply and demand models of different energy buses is specifically as follows:
energy sources on each bus:
Figure FDA0002711117980000022
in the formula:
Figure FDA0002711117980000023
respectively being electric power, steam and high-temperature hot waterEnergy source sum of Chinese hot water, low-temperature hot water and cold water buses;
considering the energy interaction of electricity, cold and hot energy storage and corresponding buses, the energy consumption model of each bus is as follows:
Figure FDA0002711117980000024
in the formula:
Figure FDA0002711117980000025
respectively is the energy consumption sum of electric power, steam, high-temperature hot water, medium-temperature water, low-temperature hot water and cold water buses;
Figure FDA0002711117980000026
Figure FDA0002711117980000027
respectively, electric load, steam load, warm hot water load, medium warm hot water load and cold water load.
5. The cascade utilization optimization method for energy coupling of the electric heating microgrid according to claim 1, wherein the optimization model aims at the lowest daily operation cost, and an objective function is composed of natural gas and electric energy purchase cost and equipment operation cost:
Call＝Cng+Cgnd+Cdevice
the purchase cost of natural gas is as follows:
Figure FDA0002711117980000031
in the formula, i represents the number in the same type of equipment;
the micro-grid and the main power grid are in a grid-connected operation state, electricity is purchased from the main power grid according to the time-of-use electricity price, and the electricity purchasing and selling cost of the power grid is as follows:
Figure FDA0002711117980000032
in the formula (I), the compound is shown in the specification,
Figure FDA0002711117980000033
the price of the time-of-use electricity is the price of the time-of-use electricity;
the equipment running cost can be divided into energy equipment operation and maintenance cost and battery energy storage depreciation cost, and the equipment maintenance cost is defined by equipment unit power cost, and battery energy storage depreciation cost is relevant with the charge-discharge electric quantity, assumes that the electric energy storage is along with the linear depreciation of charge-discharge increase, then equipment running cost is:
Figure FDA0002711117980000034
in the formula, pxFor unit output power costs of different energy devices, cbatReplacement of cost for electrical energy storage, Qess,maxThe total charge and discharge amount of the storage battery in the whole life cycle is obtained.
6. The cascade utilization optimization method for energy coupling of the electric heating microgrid according to claim 1, wherein the operation constraints of the optimization model are as follows:
the constraints of each bus are:
Figure FDA0002711117980000035
in addition, the energy equipment needs to meet the maximum minimum power and the constraint of climbing slope during operation:
Figure FDA0002711117980000041
in the formula, Px,min、Px,maxRespectively minimum maximum operating power, D, of different energy devicesx、BxRespectively the downward and upward climbing rates of different energy devices.
CN202011057064.7A 2020-09-30 2020-09-30 Cascade utilization optimization method for energy coupling of electric heating microgrid Pending CN112202203A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116484999A (en) * 2023-01-16 2023-07-25 华北电力大学 Urban comprehensive energy system scheduling optimization method considering cascade utilization and application thereof

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107832979A (en) * 2017-12-06 2018-03-23 浙江大学 A kind of factory integration energy resource system economic optimization dispatching method for considering cascaded utilization of energy
CN108206543A (en) * 2018-02-05 2018-06-26 东北大学 A kind of energy source router and its running optimizatin method based on energy cascade utilization
CN109327042A (en) * 2018-09-27 2019-02-12 南京邮电大学 A kind of micro-grid multi-energy joint optimal operation method
CN110458353A (en) * 2019-08-08 2019-11-15 上海交通大学 Electric heating combines microgrid energy step optimization method and system
US20190369581A1 (en) * 2017-01-11 2019-12-05 Southeast University Integrated energy system operational optimization method considering thermal inertia of district heating networks and buildings

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190369581A1 (en) * 2017-01-11 2019-12-05 Southeast University Integrated energy system operational optimization method considering thermal inertia of district heating networks and buildings
CN107832979A (en) * 2017-12-06 2018-03-23 浙江大学 A kind of factory integration energy resource system economic optimization dispatching method for considering cascaded utilization of energy
CN108206543A (en) * 2018-02-05 2018-06-26 东北大学 A kind of energy source router and its running optimizatin method based on energy cascade utilization
CN109327042A (en) * 2018-09-27 2019-02-12 南京邮电大学 A kind of micro-grid multi-energy joint optimal operation method
CN110458353A (en) * 2019-08-08 2019-11-15 上海交通大学 Electric heating combines microgrid energy step optimization method and system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
徐航等: "考虑能量梯级利用的工厂综合能源系统多能协同优化", 《电力系统自动化》 *
戴世刚: "电热联合微网协调消纳与梯级优化策略研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116484999A (en) * 2023-01-16 2023-07-25 华北电力大学 Urban comprehensive energy system scheduling optimization method considering cascade utilization and application thereof

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