CN113592365A - Energy optimization scheduling method and system considering carbon emission and green electricity consumption - Google Patents

Energy optimization scheduling method and system considering carbon emission and green electricity consumption Download PDF

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CN113592365A
CN113592365A CN202111005567.4A CN202111005567A CN113592365A CN 113592365 A CN113592365 A CN 113592365A CN 202111005567 A CN202111005567 A CN 202111005567A CN 113592365 A CN113592365 A CN 113592365A
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李锋
陆仕荣
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Chongqing Institute of Green and Intelligent Technology of CAS
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Abstract

The invention provides an energy optimization scheduling method and system considering carbon emission and green electricity consumption, which comprises the steps of constructing an output model of energy supply equipment according to the running characteristics of the energy supply equipment in a park and a carbon emission right; configuring carbon emission rights and green electricity consumption indexes for different user types, and creating energy demand constraints of output models of energy supply equipment according to different user types; according to the energy demand constraint and the energy prices and powers of the current park energy suppliers and other park energy suppliers, an outer energy supplier optimized scheduling model is constructed by taking the optimal income of the energy suppliers as a target, and the outer energy supplier optimized scheduling model is solved through an optimization algorithm to obtain an energy optimal scheduling scheme; according to the invention, the carbon emission right and the green electricity consumption are fully considered, and the accuracy and the applicability of energy optimization scheduling are improved.

Description

Energy optimization scheduling method and system considering carbon emission and green electricity consumption
Technical Field
The invention relates to the field of new energy application, in particular to an energy optimization scheduling method and system considering carbon emission and green electricity consumption.
Background
The traditional single energy system is limited to single energy forms such as electricity, gas and heat, cannot give full play to advantage complementation among energy sources, and cannot effectively coordinate comprehensive utilization of multiple energy sources, renewable energy consumption and the like. With the rapid development of economy, energy and environmental problems are increasingly prominent, and how to realize the clean and efficient utilization of multiple energy sources becomes a key point of research in recent years, and the concepts of energy internet and comprehensive energy systems are generated at the same time.
Many experts and scholars have conducted a great deal of research on electricity-heat-gas comprehensive energy systems in certain parks (such as residential parks, industrial parks, etc.) and parks, and have achieved remarkable results. But the capacity of a single campus is limited and the types of users in a single campus are relatively single. The green electricity consumption index and the carbon emission rate have increasingly more influence on the energy demand of different users in the garden, and the status of the user side on the optimal scheduling of the comprehensive energy system in the garden or a plurality of parks in the area is also increasingly important. In the existing optimal scheduling scheme of the comprehensive energy of the park, the optimal configuration of a regional multi-park comprehensive energy system considering the green electricity requirements of different users does not exist, and the important role played by the users in the system optimal scheduling of the energy Internet and the comprehensive energy system can not be effectively reflected.
Disclosure of Invention
In view of the problems in the prior art, the invention provides an energy optimization scheduling method and system considering carbon emission and green electricity consumption, and mainly solves the problems that the green electricity consumption of users cannot be accurately and effectively reflected by the traditional energy scheduling, and the applicability is poor.
In order to achieve the above and other objects, the present invention adopts the following technical solutions.
An energy optimization scheduling method considering carbon emission and green electricity consumption comprises the following steps:
constructing a power output model of the energy supply equipment according to the operating characteristics of the energy supply equipment in the park and the carbon emission right; configuring carbon emission rights and green electricity consumption indexes for different user types, and creating energy demand constraints of output models of energy supply equipment according to different user types; wherein, the functional device output model comprises: the system comprises a gas turbine output model, a boiler equipment output model, a new energy power generation equipment output model, an electric heating equipment output model, an electric refrigerating equipment output model and an electric hydrogen production equipment output model; the user types include: high energy consumption industrial users, general industrial and commercial users, and residential users;
and constructing an outer energy service provider optimized dispatching model by taking the optimal income of the energy provider as a target according to the energy demand constraint and the energy prices and powers of the current park energy provider and other park energy providers, and solving the outer energy service provider optimized dispatching model through an optimization algorithm to obtain an energy optimal dispatching scheme.
Optionally, the gas turbine output model is:
VGT≤δ(Clim_GT+Clim_GT_buy)
CGT＝aClim_GT_buy
in the formula: vGTNatural gas consumption for gas turbines; is the carbon emission coefficient, Clim_GTTo allocate carbon emission limits; cGTRepresents the extra carbon cost for the energy supplier to generate electricity using the gas turbine; clim_GT_buyRepresents the extra carbon emission rights purchased by the energy supplier to ensure normal production;
the boiler equipment comprises a gas boiler, and the output model of the boiler equipment is as follows:
VGB≤δ(Clim_GB+Clim_GB_buy)
CGB＝aClim_GB_buy
wherein, VGBConsuming power for gas boiler natural gas; clim_GBA carbon emission allowance assigned to the gas boiler plant; cGBRepresents the extra carbon cost for the supplier to supply heat with the gas boiler; clim_GB_buyRepresents the extra carbon emission rights purchased by the energy supplier to ensure normal production heating;
the new energy power generation equipment comprises solar power generation equipment and wind power generation equipment, and the output model of the new energy power generation equipment is as follows:
CPV_WT＝aδPV_WT(PWT+PPV)Δt
wherein, CPV_WTIncome from new energy generation, deltaPV_WTA carbon emission factor;
the electric heating equipment comprises electric boiler equipment, and the output model of the electric heating equipment is as follows:
CEB＝aδEBPEB
wherein, PEBConsuming electrical energy for the electrical heating device; cEBAdditional carbon cost, δ, of using electrical heating equipment for non-green electricityEBEnergy consumption equivalent carbon emission factors for the electric heating equipment;
the output model of the electric refrigeration equipment is as follows:
CHP＝aδHPPHP
wherein, PHPConsuming electrical energy for the electrical refrigeration equipment; cHPThe extra carbon cost, δ, of using electric refrigeration equipment for non-green electricityHPEnergy consumption equivalent carbon emission factors for the electric refrigeration equipment;
the output model of the electrical hydrogen production equipment is as follows:
CSOEC＝δSOECPSOEC
wherein, PSOECElectrical energy consumed by the electrolytic cell; cSOECThe additional carbon cost of using electrical hydrogen production equipment for non-green electricity; deltaSOECThe unit of the electric hydrogen production equipment consumes energy and carbon cost.
Optionally, configuring carbon emission rights and green electricity consumption indexes for different user types, and creating energy demand constraints of output models of energy supply devices according to different user types, including:
according to the carbon emission right and the renewable energy power consumption responsibility index of the high-energy-consumption industrial user, creating an energy demand constraint of the high-energy-consumption industrial user, wherein the energy demand constraint is expressed as follows:
Cem,user1＝δ1Puser1
Puser1＝Puser1_PV_WT_buy+Puser1_PV_WT+Puser1_buy
0≤Cem,user1≤Cem,user1_lim+Cem,user1_buy
0≤εPuser1≤Puser1_PV_WT_buy+Puser1_PV_WT
Cuser1＝aCem,user1_buy+bPuser1_PV_WT_buy+cPuser1_buy+d
wherein the carbon emissions are evaluated on the basis of the total electricity consumption of the high-energy-consuming industrial users, Cem,user1Represents the carbon emissions of said high energy consuming industrial user; delta1Represents a carbon emission conversion factor; puser1Representing the total electric energy consumption of the high-energy-consumption industrial users; puser1_PV_WT_buyRepresenting green electric energy purchased by the high-energy-consumption industrial user from an integrated energy service provider; puser1_PV_WTRepresenting the green electric energy generated by the distributed photovoltaic and wind power of the high-energy-consumption industrial users; puser1_buyRepresents conventional electrical energy purchased by the high energy consuming industrial user; cem,user1_limRepresenting carbon emission rights assigned to the high energy consuming industrial user; cem,user1_buyRepresenting the carbon emission right purchased by the high-energy-consumption industrial user from the comprehensive energy service provider, and representing that the surplus carbon emission right is traded with the energy service provider when the value is negative; epsilon represents the renewable energy power consumption responsibility weight of the high energy consumption industrial user; cuser1Represents the total cost of energy purchase produced by the high energy consuming industrial user; aCem,user1_buyRepresents a cost of purchasing carbon emissions rights; bPuser1_PV_WT_buyRepresents the cost of purchasing green electricity; cP (personal computer)user1_buyRepresents the cost of purchasing traditional electrical energy; d represents other costs;
the energy demand constraints of the general industrial and commercial users are as follows:
Cem,user2＝δ2Puser2
Puser2＝Puser2_PV_WT+Puser2_PV_WT_buy
0≤Cem,user2≤Cem,user2_lim+Cem,user2_buy
0≤εPuser2≤Puser2_PV_WT+Puser2_PV_WT_buy
Cuser2＝aCem,user2_buy+bPuser2_PV_WT_buy+c
wherein, Cem,user2Represents the general industrial and commercial user carbon emission; delta2Representing the carbon emission conversion coefficient of the general industrial and commercial users; puser2Representing the total electric energy consumption of the general industrial and commercial users; puser2_PV_WT_buyThe green electric energy which is purchased from the comprehensive energy service provider by the general industrial and commercial user is represented, the value is 0 or a negative value, and the green electric energy is not purchased from the comprehensive energy service provider or surplus green electric power is sold to the comprehensive energy service provider; puser2_PV_WTGreen electric energy representing the self distributed photovoltaic and wind power generation of the general industrial and commercial users; cem,user2_limRepresenting carbon emission rights assigned to the general industrial and commercial user; cem,user2_buyThe carbon emission right purchased from the general industrial and commercial user to the comprehensive energy service provider is represented, and when the value is negative, the surplus carbon emission right is represented to be traded with the energy service provider; epsilon represents the renewable energy power consumption responsibility weight of the general industrial and commercial users; cuser2Representing the total cost of the general industrial and commercial user production energy purchase; aCem,user2_buyRepresents a cost of purchasing carbon emissions rights; bPuser2_PV_WT_buyRepresents the cost of purchasing green electricity; c represents other costs;
the energy demand constraint of the residential user is as follows:
Puser3＝Puser3_PV_WT+Puser3_PV_WT_buy
Cuser3＝aPuser3_PV_WT_buy+b
not considering the carbon emission right and the green electricity consumption index of the residential user, wherein Puser3Representing the total power consumption of the residential user; puser3_PV_WT_buyThe method comprises the steps that green electric energy purchased from a comprehensive energy service provider by a resident user is represented, if the value is positive, the distributed energy of the resident user cannot meet the self demand, the energy is required to be purchased from the energy service provider, if the value is zero, the energy is not purchased from the comprehensive energy service provider, and if the value is negative, the surplus green electric power of the resident user is sold to the comprehensive energy service provider; puser3_PV_WTRepresenting the green electric energy generated by the distributed photovoltaic and wind power of the resident users; cuser3To representThe total cost of energy purchase of the resident users; aPuser3_PV_WT_buyRepresenting the cost of purchasing green electricity, and representing the surplus energy profit when the value is negative; b represents other energy costs.
Optionally, according to the energy demand constraint and the energy prices and powers of the current park energy provider and other park energy providers, with the optimal income of the energy provider as a target, an outer energy provider optimized scheduling model is constructed, and the outer energy provider optimized scheduling model is solved through an optimization algorithm to obtain an energy optimal scheduling scheme, including:
setting comprehensive energy service providers of all parks in the area to perform external energy trade through information sharing, and constructing an outer-layer energy service provider optimized scheduling model; each regional park service provider carries out optimization regulation and control by taking the benefit maximization of the regional park service provider as a target, and the optimization target function is as follows:
Figure BDA0003236936930000051
wherein the content of the first and second substances,
Figure BDA0003236936930000052
Figure BDA0003236936930000053
Figure BDA0003236936930000054
Figure BDA0003236936930000055
Figure BDA0003236936930000056
Figure BDA0003236936930000057
wherein N is a regional park set and indicates that N parks of the regional comprehensive energy network participate in energy trading; f is the total income of the park comprehensive energy service provider;
Figure BDA0003236936930000058
the internal electricity selling income of the comprehensive energy service provider in the time period t park is obtained;
Figure BDA0003236936930000059
the electricity selling income is obtained for the comprehensive energy service business in the time period t park;
Figure BDA00032369369300000510
the comprehensive energy service provider sells electricity to the power grid for the time period t park;
Figure BDA00032369369300000511
purchasing energy cost from energy suppliers in the park for the time period t;
Figure BDA00032369369300000512
the energy interaction cost of the time period t and other parks in the area is shown;
Figure BDA00032369369300000513
the electricity purchasing cost from the power grid is achieved for the comprehensive energy service provider in the park at the time t;
Figure BDA00032369369300000514
respectively the unit electric energy and heat energy interaction costs of the energy service provider and other regional park service providers; pin、HinThe interaction quantity of electric energy and heat energy is provided for the energy service provider and other regional park service providers;
Figure BDA00032369369300000515
the energy selling or purchasing cost of the energy service provider from the power grid unit is t;
Figure BDA00032369369300000516
the energy service provider and the power grid interact for the time t;
Figure BDA00032369369300000517
the unit electric energy and heat energy costs purchased by an energy service provider and an energy provider in the park at the time t are respectively;
Figure BDA00032369369300000518
electric energy and heat energy purchased by energy service providers in the park for the time period t;
Figure BDA00032369369300000519
the price of unit electric energy and heat energy sold to the user by the energy service provider for the time t; Δ t is the time span.
Optionally, the setting of the energy external transaction by the integrated energy service provider of each park in the area through information sharing includes:
creating external trading power demand constraints and energy supply constraints for the campus and other camps in the campus, expressed as maximum sales or purchase revenue objective function for the campus and other camps
Figure BDA0003236936930000061
Wherein the content of the first and second substances,
Figure BDA0003236936930000062
Figure BDA0003236936930000063
Figure BDA0003236936930000064
Figure BDA0003236936930000065
Figure BDA0003236936930000066
Figure BDA0003236936930000067
wherein, Pout，HoutRespectively carrying out external transaction on electric energy and heat energy, including transaction with other regional energy service providers and energy transaction with a network;
Figure BDA0003236936930000068
representing the thermal energy purchased by the energy service from the grid unit for time period t.
Optionally, the optimization algorithm comprises a particle swarm algorithm or an ant colony algorithm.
An energy optimization scheduling system considering carbon emission and green electricity consumption, comprising:
the model and constraint creation module is used for constructing a power output model of the energy supply equipment according to the operating characteristics of the energy supply equipment in the park and the carbon emission right; configuring carbon emission rights and green electricity consumption indexes for different user types, and creating energy demand constraints of output models of energy supply equipment according to different user types; wherein, the functional device output model comprises: the system comprises a gas turbine output model, a boiler equipment output model, a new energy power generation equipment output model, an electric heating equipment output model, an electric refrigerating equipment output model and an electric hydrogen production equipment output model; the user types include: high energy consumption industrial users, general industrial and commercial users, and residential users;
and the optimization calculation module is used for constructing an outer energy service provider optimization scheduling model by taking the optimal income of the energy provider as a target according to the energy demand constraint and the energy prices and powers of the current park energy provider and other park energy providers, and solving the outer energy service provider optimization scheduling model through an optimization algorithm to obtain an energy optimal scheduling scheme.
As described above, the energy optimization scheduling method and system considering carbon emission and green power consumption according to the present invention have the following advantages.
The comprehensive energy system established by the invention considers the carbon emission index factor of the energy industry, adds the carbon transaction into the production energy of power generation enterprises or users, and perfects the traditional equivalent model;
users in the traditional model are divided into high-energy-consumption industrial users, general industrial and commercial users and residential users, so that the carbon emission rights and green electricity consumption requirements of different users are fully reflected, and the important position of the users in a comprehensive energy system is highlighted;
through cooperation and transaction among energy service providers, energy interaction among multiple regional garden intervals is promoted, and the energy utilization efficiency of the comprehensive energy system of the garden is further improved.
Drawings
FIG. 1 is a topological diagram of an integrated energy system for a single campus in accordance with the present invention.
FIG. 2 is a topological diagram of the regional multi-park integrated energy system of the present invention.
FIG. 3 is a flow chart of the optimal scheduling solution of the regional multi-park integrated energy system of the present invention.
FIG. 4 is a flow chart of solving particle swarm optimization used in the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
The regional multi-park comprehensive energy system optimization scheduling method considering carbon emission and green electricity requirements of different users adds carbon emission rights into an equivalent model, divides users into high-energy-consumption industrial users, general industrial and commercial users and residential users, and constructs a regional multi-park comprehensive energy system optimization scheduling model by taking an energy service provider as a button and taking energy price and power provided by the service provider as links. The model comprises an inner-layer single-park comprehensive energy network system and an outer-layer regional multi-park energy service provider cooperation transaction model.
In the model solving process, firstly, different users propose energy demand according to own carbon emission weight, energy demand and current energy price; then, the optimal scheduling model of the inner layer of the park performs optimal scheduling on the energy output of the energy supplier, and simultaneously returns the output result to the energy service provider; and then the energy service provider integrally plans the energy output condition and the green electricity consumption condition of the energy provider in the park and the energy prices and power of the energy providers in other parks, carries out regional multi-park coordination solving through the outer layer optimization scheduling model, obtains a regional multi-park comprehensive energy system optimization scheduling scheme, and finally determines the energy prices of all parks.
The invention discloses an energy optimization scheduling method considering carbon emission and green electricity consumption, which comprises the following steps:
1) constructing a power output model of the energy supply equipment according to the operating characteristics of the energy supply equipment in the park and the carbon emission right; configuring carbon emission rights and green electricity consumption indexes for different user types, and creating energy demand constraints of output models of energy supply equipment according to different user types; wherein, the functional device output model comprises: the system comprises a gas turbine output model, a boiler equipment output model, a new energy power generation equipment output model, an electric heating equipment output model, an electric refrigerating equipment output model and an electric hydrogen production equipment output model; the user types include: high energy consumption industrial users, general industrial and commercial users, and residential users;
energy supply equipment include gas turbine, gas boiler, electricity heating equipment, electric refrigeration plant, absorption chiller, electricity hydrogen manufacturing equipment, new forms of energy power generation facility etc. each power consumption equipment considers carbon emission right, and new forms of energy power generation facility considers carbon income, the user considers green electricity consumption index and carbon income according to the type difference, specific equivalent model is as follows:
(1.1) wherein the gas turbine generates high-temperature gas to expand and do work through the mixed combustion of compressed air and natural gas so as to generate electric energy, the high-temperature flue gas after combustion meets the cold and hot load requirements of users through an absorption refrigerator or a heat exchanger, and the equivalent mathematical expression of the gas turbine output model is as follows:
GGT,t＝VGT,t
PGT,t＝GGT,t*ηGT
Figure BDA0003236936930000081
PGT,t,min≤PGT,t≤PGT,t,max
VGT≤δ(Clim_GT+Clim_GT_buy)
CGT＝aClim_GT_buy
in the formula: gGT,tConsumption of input power, V, of natural gas for gas turbinesGT,tNatural gas consumption for gas turbines; hgasIs the heat value of natural gas; pGT,tFor the power, eta, of the gas turbineGTEfficiency of power supply to the gas turbine; qWHB,tIs the heat recovery power, eta of the waste heat boilerWHBThe heat recovery efficiency of the waste heat boiler; pGT,t,maxIs the upper limit, P, of the electrical output of the gas turbineGT,t,minA lower limit for the electrical output power of the gas turbine; is the carbon emission coefficient, Clim_GTTo allocate carbon emission limits. CGTRepresents the extra carbon cost for the supplier to generate electricity using the gas turbine; clim_GT_buyRepresents the extra carbon emission rights purchased by the supplier to ensure normal production;
(1.2) the refrigeration power of the absorption chiller is related to the heat energy discharged from the gas turbine, so that the carbon emission power is not separately restricted, and the mathematical model is as follows
Qac＝QpCOPac
Wherein Q isacThe refrigeration power of the absorption refrigerator; qpConsuming thermal power for the absorption chiller; COPacThe conversion efficiency of the absorption refrigerator is obtained.
(1.3) the boiler equipment is a gas boiler, converts chemical energy into heat energy, acts as a standby heat source when the comprehensive energy gas turbine can not meet the heat load demand of a user, adds carbon emission right constraint, shows that the heat output of the gas boiler is not only limited by the rated heat power limit of the gas boiler equipment, but also limited by the carbon emission amount distributed to the gas boiler equipment, and the specific equivalent mathematical model is as follows:
QGB,t＝ηGBVGB,t
0≤VGB,t≤VGB,t,max
VGB≤δ(Clim_GB+Clim_GB_buy)
CGB＝aClim_GB_buy
wherein Q isGB,tOutputting thermal power for the gas boiler; etaGBThe conversion efficiency of the gas boiler is obtained; vGB,tConsuming power for gas boiler natural gas; vGB,t,maxThe maximum air inlet power of the gas boiler; clim_GBIs the carbon emission limit allocated to the gas boiler plant.
CGBRepresents the extra carbon cost for the supplier to supply heat with the gas boiler; clim_GB_buyRepresents the extra carbon emissions purchased by the supplier to ensure normal production heating;
(1.4) the mathematical expression of the equivalent model of the new energy power generation (green power) equipment is as follows:
(1.4.1) for photovoltaic power generation equipment, the output power mainly depends on the intensity of solar radiation, and the modeling process is as follows:
the distribution of the solar radiation intensity approximates to a Beta distribution with a distribution function of:
Figure BDA0003236936930000091
wherein G istIs the illumination intensity at the time t (W/m 2); gmaxThe maximum illumination intensity in the time period; b (alpha, Beta) is a Beta function, and alpha and Beta are shape parameters of a distribution function.
The photovoltaic power generation output mainly depends on the illumination intensity, and is influenced by the area of a battery plate, the temperature and the like, and the output power of the photovoltaic power generation can be estimated by the following formula:
Ppv＝A{PscGt[1+k(Tc-Tr)]/Gsc}ηPV
a is the area of the photovoltaic array; etaPVThe photovoltaic conversion efficiency of the photovoltaic module is obtained; pscThe maximum output power of the photovoltaic power generation system under standard conditions (the illumination intensity is 1000W/m2, the ambient temperature is 25 ℃); k is a temperature coefficient, and is-0.47%/K; gscThe illumination intensity under the standard condition is 1000W/m 2; t isrFor reference temperature, 25 ℃ is generally adopted; t iscThe calculation formula of the working temperature of the photovoltaic cell assembly is as follows:
Tc＝Ta+30Gt/1000
Tais ambient temperature
(1.4.2) for the wind power generation equipment, the output power of the wind power generation equipment mainly depends on the wind speed, the calculation of the wind speed is generally carried out by using a Weber distribution, and the mathematical expression of the wind power generation equipment is as follows:
Figure BDA0003236936930000101
wherein v is the wind speed; λ is a proportional parameter; k is a shape parameter
The wind power generation has three typical wind speed values, i.e. cut-in wind speed vinRated wind speed vrAnd cut-out wind speed voutWhen the output of the wind generating set is modeled in a digital mode, the power curve needs to be approximately simulated, and when the rated power of the fan is usedA rate of Pr,WTThe equivalent numerical model is as follows:
Figure BDA0003236936930000102
for a new energy generation (green electricity) plant, considering its carbon yield, it is shown as follows:
CPV_WT＝aδPV_WT(PWT+PPV)Δt
CPV_WTrevenue for new energy generation (green electricity), deltaPV_WTA carbon emission factor.
(1.5) the electric heating equipment, namely the electric boiler equipment, wherein the mathematical expression of the output model of the electric heating equipment is as follows:
QEB＝ηEBPEB
PEB_min≤PEB≤PEB_max
CEB＝aδEBPEB
wherein, PEB、ηEBRespectively consuming electric energy and converting efficiency for the electric boiler heating equipment; cEBAdditional carbon costs, delta, generated by using electric boiler plants for non-green electricityEBThe carbon emission factor is equivalent to the energy consumption of the electric boiler.
(1.6) the electric refrigeration equipment utilizes electric energy to do work to transfer heat, in recent years, a Heat Pump (HP) attracts wide attention, can be directly installed on a user side of an integrated energy system to achieve the effects of heating in winter and cooling in summer, and can meet the requirements of cold and hot loads together with other energy supply equipment in the system, and the output model of the electric refrigeration equipment is as follows:
QHP＝ηHPPHP
PHP_min≤PHP≤PHP_max
CHP＝aδHPPHP
wherein, PHP、ηHPRespectively consuming electric energy and converting efficiency for the electric refrigeration equipment; cHPProduced using electric refrigeration equipment for non-green electricityExtra carbon cost, δHPThe carbon emission factor is equivalent to the energy consumption of the electric refrigeration equipment.
(1.7) the electrical hydrogen production equipment uses electrical energy to electrolyze water to generate hydrogen and oxygen through an electrolytic cell (SOEC), and the mathematical expression of the output model of the electrical hydrogen production equipment is as follows:
Figure BDA0003236936930000111
CSOEC＝δSOECPSOEC
wherein, gSOECThe amount of hydrogen produced by the electrolytic cell in the current time interval;
Figure BDA0003236936930000112
is a low calorific value of hydrogen; pSOECThe electric energy consumed by the electrolytic cell in the current time interval; etaSOECThe electrolysis efficiency of the electrolytic cell; cSOECThe additional carbon cost of using electrical hydrogen production equipment for non-green electricity; deltaSOECEnergy consumption and carbon cost of unit electric hydrogen production equipment; according to the current practical production and application conditions, in order to ensure the maximum benefit, the hydrogen production by green electricity is only carried out when surplus green electricity exists.
(1.8) because the energy consumption factors to be considered by different users are different, and the influence on the optimization scheduling of the comprehensive energy system is different, the traditional user model is divided into high-energy-consumption industrial users, general industrial and commercial users and residential users, and different energy constraints are set.
(1.8.1) for a high energy consuming industrial user, there may be a carbon emission right requirement in addition to the regular energy requirement, i.e. additional carbon emission right needs to be purchased from an integrated energy service provider for normal production; in addition, the power consumption of the power generation system has a renewable energy power consumption responsibility weight index, namely the proportion of green power (new energy power generation) in the total power consumed by the power generation system needs to reach a specified responsibility weight index. According to the carbon emission right and the renewable energy power consumption responsibility index of the high-energy-consumption industrial user, creating an energy demand constraint of the high-energy-consumption industrial user, which can be expressed as:
Cem,user1＝δ1Puser1
Puser1＝Puser1_PV_WT_buy+Puser1_PV_WT+Puser1_buy
0≤Cem,user1≤Cem,user1_lim+Cem,user1_buy
0≤εPuser1≤Puser1_PV_WT_buy+Puser1_PV_WT
Cuser1＝aCem,user1_buy+bPuser1_PV_WT_buy+cPuser1_buy+d
the carbon emission of high-energy-consumption industrial users is related to the production process, and the model estimates the carbon emission by using the total power consumption of enterprises, wherein Cem,user1Represents the carbon emissions of the high energy consuming industrial user; delta1Represents a carbon emission conversion factor; puser1Representing the total electric energy consumption of the high-energy-consumption industrial user; puser1_PV_WT_buyRepresenting green electric energy purchased by the high-energy-consumption industrial user from an integrated energy service provider; puser1_PV_WTRepresenting the green electric energy generated by the distributed photovoltaic and wind power of the high-energy-consumption industrial user; puser1_buyRepresents the conventional electrical energy purchased by the high energy consuming industrial user; cem,user1_limRepresenting carbon emission rights assigned to the high energy consuming industrial user; cem,user1_buyRepresenting the carbon emission right purchased by the high-energy-consumption industrial user from the comprehensive energy service provider, and representing that the surplus carbon emission right is traded with the energy service provider when the value is negative; epsilon represents the renewable energy power consumption responsibility weight of the high energy consumption industrial user; cuser1Represents the total cost of the energy purchase produced by the high energy consuming industrial user; aCem,user1_buyRepresents a cost of purchasing carbon emissions rights; bPuser1_PV_WT_buyRepresents the cost of purchasing green electricity; cP (personal computer)user1_buyRepresents the cost of purchasing traditional electrical energy; and d represents other costs.
(1.8.2) for general industrial and commercial users, due to the relatively low energy consumption level and the large application of distributed energy, the spontaneous green power can basically meet the electric energy requirement, and the full green power is realized, and the energy requirement constraint of the general industrial and commercial users can be expressed as follows:
Cem,user2＝δ2Puser2
Puser2＝Puser2_PV_WT+Puser2_PV_WT_buy
0≤Cem,user2≤Cem,user2_lim+Cem,user2_buy
0≤εPuser2≤Puser2_PV_WT+Puser2_PV_WT_buy
Cuser2＝aCem,user2_buy+bPuser2_PV_WT_buy+c
the carbon emission of general industrial and commercial users is related to the production process, and the model estimates the carbon emission by using the total power consumption of enterprises, wherein Cem,user2Represents the carbon emission of the general industrial and commercial users; delta2Representing the carbon emission conversion coefficient thereof; puser2Representing the total electric energy consumption of the general industrial and commercial users; puser2_PV_WT_buyThe general industrial and commercial user purchases green electric energy from the comprehensive energy service provider, and the value is 0 or negative value according to the user type, so that the general industrial and commercial user does not purchase the green electric energy from the comprehensive energy service provider or sell surplus green electric energy to the comprehensive energy service provider; puser2_PV_WTRepresenting the green electric energy generated by the distributed photovoltaic and wind power of the high-energy-consumption industrial user; cem,user2_limIndicating the carbon emission rights assigned to the general industrial and commercial user; cem,user2_buyThe carbon emission right purchased from the general industrial and commercial user to the comprehensive energy service provider is represented, and when the value is negative, the surplus carbon emission right is represented to be traded with the energy service provider; epsilon represents the renewable energy power consumption responsibility weight of the general industrial and commercial users; cuser2Represents the total cost of the production energy purchase of the general industrial and commercial users; aCem,user2_buyRepresents a cost of purchasing carbon emissions rights; bPuser2_PV_WT_buyRepresents the cost of purchasing green electricity; and c represents other costs.
(1.8.3) for the residential users, the carbon emission right and the green electricity consumption index are not considered, and due to the condition difference, the difference of the residential users' own distributed energy is large, the bidirectional flow of electric energy exists between the residential users and the energy service providers, the residential surplus green electric energy can be traded with the energy service providers, and the energy demand constraint of the residential users can be expressed as:
Puser3＝Puser3_PV_WT+Puser3_PV_WT_buy
Cuser3＝aPuser3_PV_WT_buy+b
not considering the carbon emission right and the green electricity consumption index of the residential user, wherein Puser3Representing the total electric energy consumption of the resident user; puser3_PV_WT_buyThe method represents green electric energy purchased by the resident user from the comprehensive energy service provider, and the values are different according to different user types: if the value is positive, the resident user self distributed energy cannot meet self requirements and needs to purchase energy from the energy service provider, if the value is zero, the resident user does not purchase energy from the comprehensive energy service provider, and if the value is negative, the resident user self surplus green power is sold to the comprehensive energy service provider; puser3_PV_WTRepresenting the green electric energy generated by the distributed photovoltaic and wind power of the resident user; cuser3Representing the total cost of the energy purchase of the resident user; aPuser3_PV_WT_buyRepresenting the cost of purchasing green electricity, and representing the surplus energy profit when the value is negative; b represents other energy costs.
2) Constructing an energy supply network structure model of the single-park comprehensive energy system;
an energy supply network structure model of a single-park comprehensive energy system is constructed, as shown in the attached drawing 1, the comprehensive energy system network comprises four kinds of energy of cold-heat-electricity-hydrogen, and relates to equipment and technologies such as a gas turbine, a gas boiler, new energy power generation, absorption refrigeration, electric hydrogen production, electric heating, electric refrigeration and the like, and energy demand constraints of comprehensive energy suppliers and service providers and different energy demands of high-energy-consumption industrial users, general industrial and commercial users and residential users can be comprehensively considered.
3) An energy service provider is used as a button, and energy price and power provided by the service provider are used as links to construct an outer-layer energy service provider optimized scheduling model;
and (3) constructing an outer energy service provider optimized dispatching model, as shown in the attached figure 2, taking each energy service provider in the park as a representative of the energy in the corresponding park, comprehensively summarizing the energy supply and demand conditions in the park, making energy transaction prices, and performing cooperative transaction with other energy service providers to form a regional multi-park comprehensive energy network.
4) And initializing an energy supply network model of the integrated energy system in each single park according to a preset value, carrying out optimized scheduling on the integrated energy system of the inner single park, optimizing the benefit of an energy supplier in the park by using an objective function of the optimized scheduling model of the inner park, and feeding back the energy price and the energy supply quantity to an outer energy supplier.
Figure BDA0003236936930000141
Figure BDA0003236936930000142
Figure BDA0003236936930000143
Figure BDA0003236936930000144
F is the total cost of the park comprehensive energy supplier;
Figure BDA0003236936930000145
external gas purchase cost;
Figure BDA0003236936930000146
the unit price of the outsourcing gas is; pgas,tThe gas amount is purchased;
Figure BDA0003236936930000147
operating and maintaining costs for energy suppliers;
Figure BDA0003236936930000148
each unit of gas turbine and gas boilerThe operating and maintenance costs of the output; pt own、
Figure BDA0003236936930000149
Electric power and thermal power of a gas turbine and a gas boiler of an energy supplier in a time period t respectively;
Figure BDA00032369369300001410
the carbon cost for the production and operation of suppliers specifically comprises the carbon cost generated by the production of gas turbines, gas boilers, new energy power generation, electric heating and electric refrigeration equipment.
5) The energy service provider comprehensively manages the energy demand of users in the park, the energy price and power provided by the energy provider and other park energy service providers, constructs an outer energy service provider optimized dispatching model by taking the benefit optimization of the energy service provider as a target, and solves the optimized model by utilizing a particle swarm algorithm to obtain an optimal dispatching scheme.
And (5.1) comprehensive energy service providers of all parks in the area form an outer-layer energy service provider optimized scheduling model through information sharing, energy cooperation and transaction. According to the information provided by the comprehensive energy service provider, the energy demand and the energy price of each park in the regional system are known. And (3) aiming at maximizing the benefits of each park in the area, carrying out single-park energy optimization regulation and control, wherein the optimization objective function is as follows:
Figure BDA0003236936930000151
wherein the content of the first and second substances,
Figure BDA0003236936930000152
Figure BDA0003236936930000153
Figure BDA0003236936930000154
Figure BDA0003236936930000155
Figure BDA0003236936930000156
Figure BDA0003236936930000157
wherein N is a set of regional parks and represents that N parks participate in the transaction in the regional comprehensive energy network; f is the income of the park comprehensive energy service provider;
Figure BDA0003236936930000158
the internal electricity selling income of the comprehensive energy service provider in the time period t park is obtained;
Figure BDA0003236936930000159
the electricity selling income is obtained for the comprehensive energy service business in the time period t park;
Figure BDA00032369369300001510
the comprehensive energy service provider sells electricity to the power grid for the time period t park;
Figure BDA00032369369300001511
purchasing energy cost from energy suppliers in the park for the time period t;
Figure BDA00032369369300001512
the energy interaction cost of the time period t and other parks in the area is shown;
Figure BDA00032369369300001513
the electricity purchasing cost from the power grid is achieved for the comprehensive energy service provider in the park at the time t;
Figure BDA00032369369300001514
respectively serving energy service providers and regional service providers in other parksEnergy and heat energy interaction cost; pin、HinThe interaction quantity of electric energy and heat energy is provided for the energy service provider and other regional park service providers;
Figure BDA00032369369300001515
energy selling (energy purchasing) cost from the power grid unit for the time period t;
Figure BDA00032369369300001516
the energy service provider and the power grid interact for the time t;
Figure BDA00032369369300001517
the unit electric energy and heat energy costs purchased by an energy service provider and an energy provider in the park at the time t are respectively;
Figure BDA00032369369300001518
electric energy and heat energy purchased by energy service providers in the park for the time period t;
Figure BDA0003236936930000161
the price of unit electric energy and heat energy sold to the user by the energy service provider for the time t; Δ t is the time span.
And (5.2) determining the external trading power composition of the park according to the external trading power demand constraint and the energy supply constraint of other parks in the region by using the external trading (selling or purchasing) maximum profit of the park as an objective function through the external trading optimization strategy. The objective function is:
Figure BDA0003236936930000162
wherein the content of the first and second substances,
Figure BDA0003236936930000163
Figure BDA0003236936930000164
Figure BDA0003236936930000165
Figure BDA0003236936930000166
Figure BDA0003236936930000167
Figure BDA0003236936930000168
wherein, Pout，HoutRespectively carrying out external transaction on electric energy and heat energy, including transaction with other regional energy service providers and energy transaction with a network;
Figure BDA0003236936930000169
representing the thermal energy purchased by the energy service from the grid unit for time period t.
And (5.3) solving the optimized dispatching model of the external energy service provider by utilizing a particle swarm algorithm to obtain the optimal dispatching scheme of the comprehensive energy system.
In the optimization solution of the objective function, common optimization algorithms include a Particle Swarm Optimization (PSO), a Genetic Algorithm (GA), an improved non-dominated sorting genetic algorithm (NSGA-II), an ant colony algorithm, and the like, where the particle swarm optimization algorithm has a simple structure, a fast convergence speed, and is easy to implement and intelligent, and can perform optimization solution through iteration according to a multidimensional constraint condition in a calculation process, and the method is widely applied to solving optimization problems of load economic distribution, power grid planning, and the like of a power system.
Particle swarm optimization designs a particle without mass, and the particle has only two properties: speed, which represents how fast the movement is, and position, which represents the direction of the movement. And each particle independently searches an optimal solution in a search space, records the optimal solution as a current individual extremum, shares the individual extremum with other particles in the whole particle swarm, finds the optimal individual extremum as a current global optimal solution of the whole particle swarm, and adjusts the speed and the position of each particle in the particle swarm according to the found current individual extremum and the current global optimal solution shared by the whole particle swarm.
Initially, a group of random particles (random solution) is present, and in each iteration, the particles update their speed and position by tracking extrema (pbest, gbest) according to the following formula, and then find the optimal solution by iteration.
vi＝ω*vi+c1*rand()*(pbesti-xi)+c2*rand()*(gbesti-xi)
xi＝xi+vi
Wherein i is 1,2,3 … … N, N is the total number of the particle groups; omega is an inertia factor and is more than or equal to 0, when the value of omega is larger, the global optimizing capability of the algorithm is strong, the local optimizing capability of the algorithm is weak, and when the value of omega is smaller, the global optimizing capability of the algorithm is weak, and the local optimizing capability of the algorithm is strong; v. ofiIs the velocity of the particle, and vi≤vmax(ii) a rand () is a random number between (0, 1); x is the number ofiThe current position of the particle; c. C1、c2Are learning factors, usually equal and take the value 2;
in the practical application of the particle swarm algorithm, the inertia factor in the formula mostly adopts a dynamic value to adjust the global and local searching capability of the algorithm, and the determining method is as follows:
ω(t)＝(ωint-ωend)(Gk-g)/Gk+ωend
wherein ω isintThe value of the initial inertia factor is 0.9 as a typical value; omegaendThe value of the inertia factor is the value when the iteration reaches the maximum algebra, and the typical value is 0.4; gkIs the maximum number of iterations.
The solving process of the regional multi-park energy dispatching optimization model is shown in the attached drawing 3, and the specific solving process of the particle swarm algorithm is shown in the attached drawing 4.
The embodiment provides an energy optimization scheduling system considering carbon emission and green power consumption, which is used for executing the energy optimization scheduling method considering carbon emission and green power consumption in the foregoing method embodiments. Since the technical principle of the system embodiment is similar to that of the method embodiment, repeated description of the same technical details is omitted.
In one embodiment, an energy optimization scheduling system considering carbon emissions and green power consumption includes:
the model and constraint creation module is used for constructing a power output model of the energy supply equipment according to the operating characteristics of the energy supply equipment in the park and the carbon emission right; configuring carbon emission rights and green electricity consumption indexes for different user types, and creating energy demand constraints of output models of energy supply equipment according to different user types; wherein, the functional device output model comprises: the system comprises a gas turbine output model, a boiler equipment output model, a new energy power generation equipment output model, an electric heating equipment output model, an electric refrigerating equipment output model and an electric hydrogen production equipment output model; the user types include: high energy consumption industrial users, general industrial and commercial users, and residential users;
and the optimization calculation module is used for constructing an outer energy service provider optimization scheduling model by taking the optimal income of the energy provider as a target according to the energy demand constraint and the energy prices and powers of the current park energy provider and other park energy providers, and solving the outer energy service provider optimization scheduling model through an optimization algorithm to obtain an energy optimal scheduling scheme.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.

Claims (7)

1. An energy optimization scheduling method considering carbon emission and green electricity consumption is characterized by comprising the following steps:
constructing a power output model of the energy supply equipment according to the operating characteristics of the energy supply equipment in the park and the carbon emission right; configuring carbon emission rights and green electricity consumption indexes for different user types, and creating energy demand constraints of output models of energy supply equipment according to different user types; wherein, the functional device output model comprises: the system comprises a gas turbine output model, a boiler equipment output model, a new energy power generation equipment output model, an electric heating equipment output model, an electric refrigerating equipment output model and an electric hydrogen production equipment output model; the user types include: high energy consumption industrial users, general industrial and commercial users, and residential users;
and constructing an outer energy service provider optimized dispatching model by taking the optimal income of the energy provider as a target according to the energy demand constraint and the energy prices and powers of the current park energy provider and other park energy providers, and solving the outer energy service provider optimized dispatching model through an optimization algorithm to obtain an energy optimal dispatching scheme.
2. The energy optimization scheduling method considering carbon emission and green electricity consumption according to claim 1, wherein the gas turbine output model is:
VGT≤δ(Clim_GT+Clim_GT_buy)
CGT＝aClim_GT_buy
in the formula: vGTNatural gas consumption for gas turbines; is the carbon emission coefficient, Clim_GTTo allocate carbon emission limits; cGTRepresents the extra carbon cost for the energy supplier to generate electricity using the gas turbine; clim_GT_buyRepresents the extra carbon emission rights purchased by the energy supplier to ensure normal production;
the boiler equipment comprises a gas boiler, and the output model of the boiler equipment is as follows:
VGB≤δ(Clim_GB+Clim_GB_buy)
CGB＝aClim_GB_buy
wherein, VGBConsuming power for gas boiler natural gas; clim_GBA carbon emission allowance assigned to the gas boiler plant; cGBRepresents the extra carbon cost for the supplier to supply heat with the gas boiler; clim_GB_buyRepresents the extra carbon emission rights purchased by the energy supplier to ensure normal production heating;
the new energy power generation equipment comprises solar power generation equipment and wind power generation equipment, and the output model of the new energy power generation equipment is as follows:
CPV_WT＝aδPV_WT(PWT+PPV)Δt
wherein, CPV_WTIncome from new energy generation, deltaPV_WTA carbon emission factor;
the electric heating equipment comprises electric boiler equipment, and the output model of the electric heating equipment is as follows:
CEB＝aδEBPEB
wherein, PEBConsuming electrical energy for the electrical heating device; cEBAdditional carbon cost, δ, of using electrical heating equipment for non-green electricityEBEnergy consumption equivalent carbon emission factors for the electric heating equipment;
the output model of the electric refrigeration equipment is as follows:
CHP＝aδHPPHP
wherein, PHPConsuming electrical energy for the electrical refrigeration equipment; cHPThe extra carbon cost, δ, of using electric refrigeration equipment for non-green electricityHPEnergy consumption equivalent carbon emission factors for the electric refrigeration equipment;
the output model of the electrical hydrogen production equipment is as follows:
CSOEC＝δSOECPSOEC
wherein, PSOECElectrical energy consumed by the electrolytic cell; cSOECThe additional carbon cost of using electrical hydrogen production equipment for non-green electricity; deltaSOECThe unit of the electric hydrogen production equipment consumes energy and carbon cost.
3. The energy optimization scheduling method considering carbon emission and green electricity consumption according to claim 1, wherein carbon emission rights and green electricity consumption indexes are configured for different user types, and energy demand constraints of output models of energy supply devices are created according to different user types, and the method comprises the following steps:
according to the carbon emission right and the renewable energy power consumption responsibility index of the high-energy-consumption industrial user, creating an energy demand constraint of the high-energy-consumption industrial user, wherein the energy demand constraint is expressed as follows:
Cem,user1＝δ1Puser1
Puser1＝Puser1_PV_WT_buy+Puser1_PV_WT+Puser1_buy
0≤Cem,user1≤Cem,user1_lim+Cem,user1_buy
0≤εPuser1≤Puser1_PV_WT_buy+Puser1_PV_WT
Cuser1＝aCem,user1_buy+bPuser1_PV_WT_buy+cPuser1_buy+d
wherein the carbon emissions are evaluated on the basis of the total electricity consumption of the high-energy-consuming industrial users, Cem,user1Represents the carbon emissions of said high energy consuming industrial user; delta1Represents a carbon emission conversion factor; puser1Representing the total electric energy consumption of the high-energy-consumption industrial users; puser1_PV_WT_buyRepresenting green electric energy purchased by the high-energy-consumption industrial user from an integrated energy service provider; puser1_PV_WTRepresenting the green electric energy generated by the distributed photovoltaic and wind power of the high-energy-consumption industrial users; puser1_buyRepresents conventional electrical energy purchased by the high energy consuming industrial user; cem,user1_limRepresenting carbon emission rights assigned to the high energy consuming industrial user; cem,user1_buyRepresenting the carbon emission right purchased by the high-energy-consumption industrial user from the comprehensive energy service provider, and representing that the surplus carbon emission right is traded with the energy service provider when the value is negative; epsilon represents the renewable energy power consumption responsibility weight of the high energy consumption industrial user; cuser1Represents the total cost of energy purchase produced by the high energy consuming industrial user; aCem,user1_buyRepresents a cost of purchasing carbon emissions rights; bPuser1_PV_WT_buyRepresents the cost of purchasing green electricity; cP (personal computer)user1_buyRepresents the cost of purchasing traditional electrical energy; d represents other costs;
the energy demand constraints of the general industrial and commercial users are as follows:
Cem,user2＝δ2Puser2
Puser2＝Puser2_PV_WT+Puser2_PV_WT_buy
0≤Cem,user2≤Cem,user2_lim+Cem,user2_buy
0≤εPuser2≤Puser2_PV_WT+Puser2_PV_WT_buy
Cuser2＝aCem,user2_buy+bPuser2_PV_WT_buy+c
wherein, Cem,user2Represents the general industrial and commercial user carbon emission; delta2Representing the carbon emission conversion coefficient of the general industrial and commercial users; puser2Representing the total electric energy consumption of the general industrial and commercial users; puser2_PV_WT_buyThe green electric energy which is purchased from the comprehensive energy service provider by the general industrial and commercial user is represented, the value is 0 or a negative value, and the green electric energy is not purchased from the comprehensive energy service provider or surplus green electric power is sold to the comprehensive energy service provider; puser2_PV_WTGreen electric energy representing the self distributed photovoltaic and wind power generation of the general industrial and commercial users; cem,user2_limRepresenting carbon emission rights assigned to the general industrial and commercial user; cem,user2_buyThe carbon emission right purchased from the general industrial and commercial user to the comprehensive energy service provider is represented, and when the value is negative, the surplus carbon emission right is represented to be traded with the energy service provider; epsilon represents the renewable energy power consumption responsibility weight of the general industrial and commercial users; cuser2Representing the total cost of the general industrial and commercial user production energy purchase; aCem,user2_buyRepresents a cost of purchasing carbon emissions rights; bPuser2_PV_WT_buyRepresents the cost of purchasing green electricity; c represents other costs;
the energy demand constraint of the residential user is as follows:
Puser3＝Puser3_PV_WT+Puser3_PV_WT_buy
Cuser3＝aPuser3_PV_WT_buy+b
not considering the carbon emission right and the green electricity consumption index of the residential user, wherein Puser3Representing the total power consumption of the residential user; puser3_PV_WT_buyThe method comprises the steps that green electric energy purchased from a comprehensive energy service provider by a resident user is represented, if the value is positive, the distributed energy of the resident user cannot meet the self demand, the energy is required to be purchased from the energy service provider, if the value is zero, the energy is not purchased from the comprehensive energy service provider, and if the value is negative, the surplus green electric power of the resident user is sold to the comprehensive energy service provider; puser3_PV_WTRepresenting the green electric energy generated by the distributed photovoltaic and wind power of the resident users; cuser3Representing the total cost of energy purchase by the residential user; aPuser3_PV_WT_buyRepresenting the cost of purchasing green electricity, and representing the surplus energy profit when the value is negative; b represents other energy costs.
4. The energy optimization scheduling method considering carbon emission and green electricity consumption according to claim 1, wherein an outer energy service provider optimization scheduling model is constructed according to the energy demand constraint and energy prices and powers of current park energy providers and other park energy providers, with the goal of optimal income of energy providers, and the outer energy service provider optimization scheduling model is solved through an optimization algorithm to obtain an energy optimal scheduling scheme, comprising:
setting comprehensive energy service providers of all parks in the area to perform external energy trade through information sharing, and constructing an outer-layer energy service provider optimized scheduling model; each regional park service provider carries out optimization regulation and control by taking the benefit maximization of the regional park service provider as a target, and the optimization target function is as follows:
Figure FDA0003236936920000041
wherein the content of the first and second substances,
Figure FDA0003236936920000042
Figure FDA0003236936920000043
Figure FDA0003236936920000044
Figure FDA0003236936920000045
Figure FDA0003236936920000046
Figure FDA0003236936920000047
wherein N is a regional park set and indicates that N parks of the regional comprehensive energy network participate in energy trading; f is the total income of the park comprehensive energy service provider;
Figure FDA0003236936920000048
the internal electricity selling income of the comprehensive energy service provider in the time period t park is obtained;
Figure FDA0003236936920000049
the electricity selling income is obtained for the comprehensive energy service business in the time period t park;
Figure FDA00032369369200000410
the comprehensive energy service provider sells electricity to the power grid for the time period t park;
Figure FDA00032369369200000411
purchasing energy cost from energy suppliers in the park for the time period t;
Figure FDA0003236936920000051
the energy interaction cost of the time period t and other parks in the area is shown;
Figure FDA0003236936920000052
the electricity purchasing cost from the power grid is achieved for the comprehensive energy service provider in the park at the time t;
Figure FDA0003236936920000053
respectively the unit electric energy and heat energy interaction costs of the energy service provider and other regional park service providers; pin、HinThe interaction quantity of electric energy and heat energy is provided for the energy service provider and other regional park service providers;
Figure FDA0003236936920000054
the energy selling or purchasing cost of the energy service provider from the power grid unit is t;
Figure FDA0003236936920000055
the energy service provider and the power grid interact for the time t;
Figure FDA0003236936920000056
the unit electric energy and heat energy costs purchased by an energy service provider and an energy provider in the park at the time t are respectively;
Figure FDA0003236936920000057
electric energy and heat energy purchased by energy service providers in the park for the time period t;
Figure FDA0003236936920000058
the price of unit electric energy and heat energy sold to the user by the energy service provider for the time t; Δ t is the time span.
5. The energy optimization scheduling method considering carbon emission and green electricity consumption according to claim 4, wherein the energy external trade is performed by the comprehensive energy service providers of the parks in the set area through information sharing, and comprises the following steps:
creating external trading power demand constraints and energy supply constraints for the campus and other camps in the campus, expressed as maximum sales or purchase revenue objective function for the campus and other camps
Figure FDA0003236936920000059
Wherein the content of the first and second substances,
Figure FDA00032369369200000510
Figure FDA00032369369200000511
Figure FDA00032369369200000512
Figure FDA00032369369200000513
Figure FDA00032369369200000514
Figure FDA00032369369200000515
wherein, Pout，HoutRespectively for external transactions of electric energy, heat energy, including transactions with other regional energy facilitators and with the networkInter-energy trading;
Figure FDA00032369369200000516
representing the thermal energy purchased by the energy service from the grid unit for time period t.
6. The energy optimization scheduling method considering carbon emission and green power consumption according to claim 1, wherein the optimization algorithm comprises a particle swarm algorithm or an ant colony algorithm.
7. An energy optimization scheduling system considering carbon emission and green electricity consumption, comprising:
the model and constraint creation module is used for constructing a power output model of the energy supply equipment according to the operating characteristics of the energy supply equipment in the park and the carbon emission right; configuring carbon emission rights and green electricity consumption indexes for different user types, and creating energy demand constraints of output models of energy supply equipment according to different user types; wherein, the functional device output model comprises: the system comprises a gas turbine output model, a boiler equipment output model, a new energy power generation equipment output model, an electric heating equipment output model, an electric refrigerating equipment output model and an electric hydrogen production equipment output model; the user types include: high energy consumption industrial users, general industrial and commercial users, and residential users;
and the optimization calculation module is used for constructing an outer energy service provider optimization scheduling model by taking the optimal income of the energy provider as a target according to the energy demand constraint and the energy prices and powers of the current park energy provider and other park energy providers, and solving the outer energy service provider optimization scheduling model through an optimization algorithm to obtain an energy optimal scheduling scheme.
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