CN113592365B

CN113592365B - Energy optimization scheduling method and system considering carbon emission and green electricity consumption

Info

Publication number: CN113592365B
Application number: CN202111005567.4A
Authority: CN
Inventors: 李锋; 陆仕荣
Original assignee: Chongqing University; Chongqing Institute of Green and Intelligent Technology of CAS
Current assignee: Chongqing University; Chongqing Institute of Green and Intelligent Technology of CAS
Priority date: 2021-08-30
Filing date: 2021-08-30
Publication date: 2022-08-05
Anticipated expiration: 2041-08-30
Also published as: CN113592365A

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 output model of the energy supply equipment 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: large 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:

V _GT ≤δ(C _{lim_GT} +C _{lim_GT_buy} )

C _GT ＝aC _{lim_GT_buy}

in the formula: v _GT Natural gas consumption for gas turbines; delta is the carbon emission coefficient, C _{lim_GT} To allocate carbon emission limits; c _GT Represents the extra carbon cost for the energy supplier to generate electricity using the gas turbine; c _{lim_GT_buy} Represents the extra carbon emission rights purchased by the energy supplier to ensure normal production; a is a carbon unit price;

the boiler equipment comprises a gas boiler, and the output model of the boiler equipment is as follows:

V _GB ≤δ(C _{lim_GB} +C _{lim_GB_buy} )

C _GB ＝aC _{lim_GB_buy}

wherein, V _GB Consuming power for gas boiler natural gas; c _{lim_GB} A carbon emission allowance assigned to the gas boiler plant; c _GB Represents the extra carbon cost for the supplier to supply heat with the gas boiler; c _{lim_GB_buy} Represents 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:

C _{PV_WT} ＝aδ _{PV_WT} (P _WT +P _PV )Δt

wherein, C _{PV_WT} Income from new energy generation, delta _{PV_WT} A carbon emission factor; (ii) a P _WT Outputting power for wind power generation; p _PV Outputting power for photovoltaic power generation; Δ t is the time span;

the electric heating equipment comprises electric boiler equipment, and the output model of the electric heating equipment is as follows:

C _EB ＝aδ _EB P _EB

wherein, P _EB Consuming electrical energy for the electrical heating device; c _EB Additional carbon cost, δ, of using electrical heating equipment for non-green electricity _EB Energy consumption equivalent carbon emission factors for the electric heating equipment;

the output model of the electric refrigeration equipment is as follows:

C _HP ＝aδ _HP P _HP

wherein, P _HP Consuming electrical energy for the electrical refrigeration equipment; c _HP The extra carbon cost, δ, of using electric refrigeration equipment for non-green electricity _HP Energy-consuming equivalent carbon emission factor for electric refrigeration equipment；

The output model of the electrical hydrogen production equipment is as follows:

C _SOEC ＝δ _SOEC P _SOEC

wherein, P _SOEC Electrical energy consumed for the electrolytic cell; c _SOEC The additional carbon cost of using electrical hydrogen production equipment for non-green electricity; delta _SOEC The 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 each energy supply device according to different user types, including:

according to the carbon emission right and the renewable energy power consumption responsibility weight index of the large industrial user, creating an energy demand constraint of the large industrial user, wherein the energy demand constraint is expressed as:

C _em,user1 ＝δ ₁ P _user1

P _user1 ＝P _{user1_PV_WT_buy} +P _{user1_PV_WT} +P _{user1_buy}

0≤C _em,user1 ≤C _{em,user1_lim} +C _{em,user1_buy}

0≤ε ₁ P _user1 ≤P _{user1_PV_WT_buy} +P _{user1_PV_WT}

C _user1 ＝aC _{em,user1_buy} +bP _{user1_PV_WT_buy} +cP _{user1_buy} +d

wherein the carbon emissions, C, are estimated from the total electricity consumption of large industrial users _em,user1 Represents the carbon emissions of the large industrial user; delta. for the preparation of a coating ₁ Represents a carbon emission conversion factor; p _user1 Representing the total electric energy consumption of the large industrial user; p _{user1_PV_WT_buy} Representing green electric energy purchased by the large industrial user from an integrated energy service provider;

green electric energy representing self distributed photovoltaic and wind power generation of the large industrial users; p _{user1_buy} Represents conventional electrical energy purchased by the large industrial user; c _{em,user1_lim} Representing carbon emission rights assigned to the large industrial user; c _{em,user1_buy} Representing the carbon emission rights purchased by the large industrial user from the comprehensive energy service provider, and when the value is negative, representing that the surplus carbon emission rights are traded with the energy service provider; epsilon ₁ Representing a renewable energy power consumption liability weight for the large industrial user; c _user1 Represents the total cost of production energy purchase for the large industrial user; aC _{em,user1_buy} Represents a cost of purchasing carbon emissions rights; bP _{user1_PV_WT_buy} Represents the cost of purchasing green electricity; cP (personal computer) _{user1_buy} Represents the cost of purchasing traditional electrical energy; d represents other costs for the large industrial user to purchase energy costs;

the energy demand constraints of the general industrial and commercial users are as follows:

C _em,user2 ＝δ ₂ P _user2

P _user2 ＝P _{user2_PV_WT_buy} +P _{user2_PV_WT}

0≤C _em,user2 ≤C _{em,user2_lim} +C _{em,user2_buy}

0≤ε ₂ P _user2 ≤P _{user2_PV_WT_buy} +P _{user2_PV_WT}

C _user2 ＝aC _{em,user2_buy} +bP _{user2_PV_WT_buy} +c

wherein, C _em,user2 Represents the general industrial and commercial user carbon emission; delta ₂ Representing the carbon emission conversion coefficient of the general industrial and commercial users; p _user2 Representing the total electric energy consumption of the general industrial and commercial users; p _{user2_PV_WT_buy} The 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; p _{user2_PV_WT} Green electric energy representing the self distributed photovoltaic and wind power generation of the general industrial and commercial users; c _{em,user2_lim} Representing carbon emission rights assigned to the general industrial and commercial user; c _{em,user2_buy} Represents the carbon purchased from the general industrial and commercial users to the comprehensive energy service providerThe emission right indicates that the surplus carbon emission right trades with the energy service provider when the value is negative; epsilon ₂ A renewable energy power consumption responsibility weight representing the general industrial and commercial user; c _user2 Representing the total cost of the general industrial and commercial user production energy purchase; aC _{em,user2_buy} Represents a cost of purchasing carbon emissions rights; bP _{user2_PV_WT_buy} Represents the cost of purchasing green electricity; c represents other costs for the general industry and commerce users to purchase energy;

the energy demand constraint of the residential user is as follows:

P _user3 ＝P _{user3_PV_WT} +P _{user3_PV_WT_buy}

C _user3 ＝aP _{user3_PV_WT_buy} +b

not considering the carbon emission right and the green electricity consumption index of the residential user, wherein P _user3 Representing the total power consumption of the residential user; p _{user3_PV_WT_buy} The 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; p _{user3_PV_WT} Representing the green electric energy generated by the distributed photovoltaic and wind power of the resident users; c _user3 Representing the total cost of energy purchase by the residential user; aP _{user3_PV_WT_buy} Representing the cost of purchasing green electricity, and representing the surplus energy profit when the value is negative; b represents other energy cost of the residential user.

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:

wherein the content of the first and second substances,

wherein N is a regional park set, which indicates that N parks of the regional comprehensive energy network participate in energy trading, and N is the number of the parks; f is the total income of the park comprehensive energy service provider;

the electricity selling income of the comprehensive energy service provider of the ith park is obtained for the time period t;

for a time period t of the ith campusThe electricity selling income among the comprehensive energy service providers;

the comprehensive energy service provider of the ith park sells electricity to the power grid for the time t;

purchasing energy costs from the ith campus energy supplier for time period t;

the energy interaction cost of the ith park and other parks in the area is t;

the electricity purchasing cost from the power grid for the ith park comprehensive energy service provider in the time period t;

the unit electric energy income for the ith energy service provider and the nth park service provider,

The unit heat energy profit for the ith energy service provider and the nth park service provider,

The cost for the interaction of the energy service provider and the service providers of other parks in the area,

Heat energy interaction cost is provided for the ith energy service provider and the nth park service provider in the region; p _in Electric energy interaction quantity H for ith energy service provider and nth regional park service provider _in The heat energy interaction amount is provided for the ith energy service provider and the nth park service provider in the region;

the energy sale cost of the ith park energy service provider from the power grid unit,

Purchasing energy cost from a power grid unit for the ith park energy service provider in the time period t;

the electric energy interaction quantity between the energy service provider of the ith park and the power grid is t;

the unit electric energy cost purchased by the ith park energy service provider and the own park energy provider in the time period t,

The unit heat energy cost purchased by the ith park energy service provider and the energy supplier of the park at the time t;

the electric energy purchased by the energy service provider of the park for the ith time period t through the energy provider of the park,

The heat energy purchased by the energy service provider of the ith park in the time period t through the energy provider of the park;

the unit price of electricity sold to the user by the ith campus energy service provider for the time period t,

The unit heat energy price sold to the user by the ith park energy service provider for the time period 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

Wherein the content of the first and second substances,

wherein, P _out ，H _out Respectively carrying out external transaction on electric energy and heat energy, including transaction with other regional energy service providers and energy transaction with a network;

representing the thermal energy purchased by the ith campus energy service provider 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 output model of the energy supply equipment 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: large 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 large 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 the 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 large 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 self carbon emission rights, 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 output model of the energy supply equipment 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: large 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:

G _GT,t ＝V _GT,t

P _GT,t ＝G _GT,t *η _GT

P _GT,t,min ≤P _GT,t ≤P _GT,t,max

V _GT ≤δ(C _{lim_GT} +C _{lim_GT_buy} )

C _GT ＝aC _{lim_GT_buy}

in the formula: g _GT,t Consumption of input power, V, of natural gas for gas turbines _GT,t Natural gas consumption for gas turbines; h _gas Is the heat value of natural gas; p _GT,t For the power, eta, of the gas turbine _GT Efficiency of power supply to the gas turbine; q _WHB,t Is the heat recovery power, eta of the waste heat boiler _WHB The heat recovery efficiency of the waste heat boiler; p _GT,t,max Is the upper limit, P, of the electrical output of the gas turbine _GT,t,min A lower limit for the electrical output power of the gas turbine; is the carbon emission coefficient, C _{lim_GT} To allocate carbon emission limits. C _GT Represents the extra carbon cost for the supplier to generate electricity using the gas turbine; c _{lim_GT_buy} Represents 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

Q _ac ＝Q _p COP _ac

Wherein Q is _ac The refrigeration power of the absorption refrigerator; q _p Consuming thermal power for the absorption chiller; COP (coefficient of Performance) _ac The 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:

Q _GB,t ＝η _GB V _GB,t

0≤V _GB,t ≤V _GB,t,max

V _GB ≤δ(C _{lim_GB} +C _{lim_GB_buy} )

C _GB ＝aC _{lim_GB_buy}

wherein Q is _GB,t Outputting thermal power for the gas boiler; eta _GB The conversion efficiency of the gas boiler is obtained; v _GB,t Consuming power for gas boiler natural gas; v _GB,t,max The maximum air inlet power of the gas boiler; c _{lim_GB} Is the carbon emission limit allocated to the gas boiler plant.

C _GB Represents the extra carbon cost for the supplier to supply heat with the gas boiler; c _{lim_GB_buy} Represents the extra carbon emissions purchased by the supplier to ensure normal production heating; a is a carbon unit price.

(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:

wherein G is _t Is the illumination intensity at the time t (W/m 2); g _max The 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:

P _pv ＝A{P _sc G _t [1+k(T _c -T _r )]/G _sc }η _PV

a is the area of the photovoltaic array; eta _PV The photovoltaic conversion efficiency of the photovoltaic module is obtained; p _sc The 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; g _sc The illumination intensity under the standard condition is 1000W/m 2; t is _r For reference temperature, 25 ℃ is generally adopted;T _c the calculation formula of the working temperature of the photovoltaic cell assembly is as follows:

T _c ＝T _a +30G _t /1000

T _a is 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:

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 v _in Rated wind speed v _r And cut-out wind speed v _out When 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 a fan is P _r,WT The equivalent numerical model is as follows:

for a new energy generation (green electricity) plant, considering its carbon yield, it is shown as follows:

C _{PV_WT} ＝aδ _{PV_WT} (P _WT +P _PV )Δt

C _{PV_WT} revenue for new energy generation (green electricity), delta _{PV_WT} A 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:

Q _EB ＝η _EB P _EB

P _{EB_min} ≤P _EB ≤P _{EB_max}

C _EB ＝aδ _EB P _EB

wherein, P _EB 、η _EB Are respectively electric boilersThe heating equipment consumes electric energy and has conversion efficiency; c _EB Additional carbon costs, delta, generated by using electric boiler plants for non-green electricity _EB The 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:

Q _HP ＝η _HP P _HP

P _{HP_min} ≤P _HP ≤P _{HP_max}

C _HP ＝aδ _HP P _HP

wherein, P _HP 、η _HP Respectively consuming electric energy and converting efficiency for the electric refrigeration equipment; c _HP The extra carbon cost, δ, of using electric refrigeration equipment for non-green electricity _HP The 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:

C _SOEC ＝δ _SOEC P _SOEC

wherein, g _SOEC The amount of hydrogen produced by the electrolytic cell in the current time interval;

is a low calorific value of hydrogen; p _SOEC The electric energy consumed by the electrolytic cell in the current time interval; eta _SOEC The electrolysis efficiency of the electrolytic cell; c _SOEC The additional carbon cost of using electrical hydrogen production equipment for non-green electricity; delta _SOEC Energy-consuming carbon for unit electric hydrogen production equipmentCost; 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 which need 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 large-scale industrial users, general industrial and commercial users and residential users, and different energy constraints are set.

(1.8.1) for large industrial users, there may be a carbon emission right requirement in addition to the regular energy requirement, i.e. additional carbon emission rights need to be purchased from the comprehensive 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 large industrial user, creating the energy demand constraint of the large industrial user, which can be expressed as:

C _em,user1 ＝δ ₁ P _user1

P _user1 ＝P _{user1_PV_WT_buy} +P _{user1_PV_WT} +P _{user1_buy}

0≤C _em,user1 ≤C _{em,user1_lim} +C _{em,user1_buy}

0≤ε ₁ P _user1 ≤P _{user1_PV_WT_buy} +P _{user1_PV_WT}

C _user1 ＝aC _{em,user1_buy} +bP _{user1_PV_WT_buy} +cP _{user1_buy} +d

the carbon emission of large industrial users is related to the production process, and the model estimates the carbon emission by using the total power consumption of enterprises, wherein C _em,user1 Represents the carbon emissions of the large industrial user; delta ₁ Represents a carbon emission conversion factor; p _user1 Representing the total power consumption of the large industrial user; p _{user1_PV_WT_buy} Representing green electric energy purchased by the large industrial user from the comprehensive energy service provider; p _{user1_PV_WT} Represents the distributed photovoltaic and wind power generation of the large industrial userGreen electric energy is generated; p _{user1_buy} Represents the conventional power purchased by the large industrial user; c _{em,user1_lim} Indicating carbon emission rights assigned to the large industrial user; c _{em,user1_buy} Representing the carbon emission right purchased by the large 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 ₁ Representing a renewable energy power consumption responsibility weight for the large industrial user; c _user1 Represents the total cost of the production energy purchase of the large industrial user; aC _{em,user1_buy} Represents a cost of purchasing carbon emissions rights; bP _{user1_PV_WT_buy} Represents the cost of purchasing green electricity; cP (personal computer) _{user1_buy} Represents the cost of purchasing traditional electrical energy; d represents the additional cost of energy expenditure for a large industrial user to purchase.

(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:

C _em,user2 ＝δ ₂ P _user2

P _user2 ＝P _{user2_PV_WT_buy} +P _{user2_PV_WT}

0≤C _em,user2 ≤C _{em,user2_lim} +C _{em,user2_buy}

0≤ε ₂ P _user2 ≤P _{user2_PV_WT_buy} +P _{user2_PV_WT}

C _user2 ＝aC _{em,user2_buy} +bP _{user2_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 C _em,user2 Represents the carbon emission of the general industrial and commercial users; delta ₂ Representing the carbon emission conversion coefficient thereof; p _user2 Representing the total electric energy consumption of the general industrial and commercial users; p _{user2_PV_WT_buy} The general industrial and commercial user is represented with the green electric energy purchased from the comprehensive energy service provider, and the value is generally 0 or negative according to the user type, which means that the general industrial and commercial user does not take the comprehensive energy serviceBuying green electric energy or selling surplus green electric energy to a comprehensive energy service provider by a service provider; p _{user2_PV_WT} Representing the green electric energy generated by the distributed photovoltaic and wind power of the large industrial user; c _{em,user2_lim} Indicating the carbon emission rights assigned to the general industrial and commercial user; c _{em,user2_buy} The 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 ₂ Representing a renewable energy power consumption responsibility weight of the general industrial and commercial user; c _user2 Represents the total cost of the production energy purchase of the general industrial and commercial users; aC _{em,user2_buy} Represents a cost of purchasing carbon emissions rights; bP _{user2_PV_WT_buy} Represents the cost of purchasing green electricity; and c represents other costs for the general industry and commerce users to purchase energy.

(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:

P _user3 ＝P _{user3_PV_WT} +P _{user3_PV_WT_buy}

C _user3 ＝aP _{user3_PV_WT_buy} +b

not considering the carbon emission right and the green electricity consumption index of the residential user, wherein P _user3 Representing the total electric energy consumption of the resident user; p _{user3_PV_WT_buy} The 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; p _{user3_PV_WT} Representing the green electric energy generated by the distributed photovoltaic and wind power of the resident user; c _user3 Indicating the total cost of energy purchase of the resident user；aP _{user3_PV_WT_buy} Representing the cost of purchasing green electricity, and representing the surplus energy profit when the value is negative; b represents other energy cost of the residential user.

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 integrated energy system is constructed, as shown in the attached drawing 1, the integrated energy system network comprises four kinds of energy sources of cold, heat, electricity and hydrogen, and relates to equipment and technologies such as a gas turbine, a gas boiler, new energy power generation, absorption refrigeration, electricity hydrogen production, electricity heating, electricity refrigeration and the like, and energy demand constraints of integrated energy suppliers and service providers and different energy demands of large-scale 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.

F is the total cost of the park comprehensive energy supplier;

cost of purchasing gas for the outside;

the unit price of the purchased gas; p _gas,t The gas amount is purchased;

operating and maintaining costs for energy suppliers;

the operation and maintenance costs of each unit of output of the gas turbine and the gas boiler are respectively;

electric power and thermal power of a gas turbine and a gas boiler of an energy supplier in a time period t respectively;

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:

wherein the content of the first and second substances,

the electricity selling income of the integrated energy service business of the ith park is obtained for the time period t;

(ii) purchase energy costs from the ith campus energy provider for time period t;

the energy interaction cost of the ith park and other parks in the area is t;

the unit electric energy income for the ith energy service provider and the nth regional park service provider,

Heat energy interaction cost is provided for the ith energy service provider and the nth park service provider in the region; p _in Electric energy interactive quantity H for ith energy service provider and nth regional park service provider _in The heat energy interaction amount is provided for the ith energy service provider and the nth park service provider in the region;

time t the ith campusThe energy service provider can sell energy cost from the power grid unit,

The unit heat energy cost purchased by the ith park energy service provider and the park energy supplier in the time period t;

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:

wherein the content of the first and second substances,

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 the 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 shared current global optimal solution of 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 extreme values (pbest, gbest) according to the following formula, and then find the optimal solution by iteration.

v _i ＝ω*v _i +c ₁ *rand()*(pbest _i -x _i )+c ₂ *rand()*(gbest _i -x _i )

x _i ＝x _i +v _i

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. of _i Is the velocity of the particle, and v _i ≤v _max (ii) a rand () is a random number between (0, 1); x is the number of _i Is the current position of the particle; c. C ₁ 、c ₂ Are 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) ＝(ω _int -ω _end )(G _k -g)/G _k +ω _end

wherein ω is _int The value of the initial inertia factor is 0.9 as a typical value; omega _end The value of the inertia factor is the value when the iteration reaches the maximum algebra, and the typical value is 0.4; g _k Is 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 method comprises the following steps of (1) generating a new energy power generation system by using 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: large industrial users, general industrial and commercial users, and residential users;

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

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 output model of the energy supply equipment 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: large industrial users, general industrial and commercial users, and residential users; the gas turbine output model is as follows:

V _GT ≤δ(C _{lim_GT} +C _{lim_GT_buy} )

C _GT ＝aC _{lim_GT_buy}

in the formula: v _GT Natural gas consumption for gas turbines; is the carbon emission coefficient, C _{lim_GT} To allocate carbon emission limits; c _GT Represents the extra carbon cost for the energy supplier to generate electricity using the gas turbine; c _{lim_GT_buy} Represents the extra carbon emission rights purchased by the energy supplier to ensure normal production; a is a carbon unit price;

V _GB ≤δ(C _{lim_GB} +C _{lim_GB_buy} )

C _GB ＝aC _{lim_GB_buy}

C _{PV_WT} ＝aδ _{PV_WT} (P _WT +P _PV )Δt

wherein, C _{PV_WT} Income from new energy generation, delta _{PV_WT} A carbon emission factor; p _WT Outputting power for wind power generation; p _PV Outputting power for photovoltaic power generation; Δ t is the time span;

C _EB ＝aδ _EB P _EB

the output model of the electric refrigeration equipment is as follows:

C _HP ＝aδ _HP P _HP

wherein, P _HP Consuming electrical energy for the electrical refrigeration equipment; c _HP Additional carbon cost, δ, of using electric refrigeration equipment for non-green electricity _HP Energy consumption equivalent carbon emission factors for the electric refrigeration equipment;

the output model of the electrical hydrogen production equipment is as follows:

C _SOEC ＝δ _SOEC P _SOEC

wherein, P _SOEC Electrical energy consumed by the electrolytic cell; c _SOEC Electric hydrogen production facility for non-green electricityExtra carbon cost for production; delta _SOEC Energy consumption and carbon cost of unit electric hydrogen production equipment;

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 service provider optimized dispatching model is constructed by taking optimal income of the energy suppliers as a target, and the outer energy service provider optimized dispatching model is solved through an optimization algorithm to obtain an energy optimal dispatching scheme, which comprises the following steps: 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:

wherein the content of the first and second substances,

the method comprises the following steps that N is a regional park set, N shows that N parks of a regional comprehensive energy network participate in energy trading, and N is the number of the parks; f is the total income of the park comprehensive energy service provider;

purchasing energy costs from the ith campus energy supplier for time period t;

the energy interaction cost of the ith park and other parks in the area is t;

the energy selling cost of the ith park energy service provider from the power grid unit for the time period t,

For a time period t the ith energy of the parkThe source service provider purchases heat energy through the energy supplier of the park;

The unit price of heat energy sold to the user by the ith campus energy service provider for time period t.

2. 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 weight and the renewable energy power consumption responsibility weight index of the large industrial user, creating an energy demand constraint of the large industrial user, wherein the energy demand constraint is represented as:

C _em,user1 ＝δ ₁ P _user1

P _user1 ＝P _{user1_PV_WT_buy} +P _{user1_PV_WT} +P _{user1_buy}

0≤C _em,user1 ≤C _{em,user1_lim} +C _{em,user1_buy}

0≤ε ₁ P _user1 ≤P _{user1_PV_WT_buy} +P _{user1_PV_WT}

C _user1 ＝aC _{em,user1_buy} +bP _{user1_PV_WT_buy} +cP _{user1_buy} +d

wherein the carbon emissions, C, are evaluated on the basis of the total power consumption of large industrial users _em,user1 Represents the carbon emissions of the large industrial user; delta ₁ Represents a carbon emission conversion factor; p _user1 Representing the total electric energy consumption of the large industrial user; p _{user1_PV_WT_buy} Representing green electric energy purchased by the large industrial user from an integrated energy service provider;

P _{user1_PV_WT} green electric energy representing self distributed photovoltaic and wind power generation of the large industrial users; p _{user1_buy} Represents conventional electrical energy purchased by the large industrial user; c _{em,user1_lim} Representing carbon emission rights assigned to the large industrial user; c _{em,user1_buy} Representing the carbon emission rights purchased by the large industrial user from the comprehensive energy service provider, and when the value is negative, representing that the surplus carbon emission rights are traded with the energy service provider; epsilon ₁ Representing a renewable energy power consumption liability weight for the large industrial user; c _user1 Represents the total cost of production energy purchase for the large industrial user; aC _{em,user1_buy} Represents a cost of purchasing carbon emissions rights; bP _{user1_PV_WT_buy} Represents the cost of purchasing green electricity; cP (personal computer) _{user1_buy} Represents the cost of purchasing traditional electrical energy; d represents other costs for the purchase of energy by large industrial users;

C _em,user2 ＝δ ₂ P _user2

P _user2 ＝P _{user2_PV_WT_buy} +P _{user2_PV_WT}

0≤C _em,user2 ≤C _{em,user2_lim} +C _{em,user2_buy}

0≤ε ₂ P _user2 ≤P _{user2_PV_WT_buy} +P _{user2_PV_WT}

C _user2 ＝aC _{em,user2_buy} +bP _{user2_PV_WT_buy} +c

wherein, C _em,user2 Represents the general industrial and commercial user carbon emission; delta ₂ Representing the carbon emission conversion coefficient of the general industrial and commercial users; p _user2 Representing the total electric energy consumption of the general industrial and commercial users; p _{user2_PV_WT_buy} The 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; p _{user2_PV_WT} Green electric energy representing the self distributed photovoltaic and wind power generation of the general industrial and commercial users; c _{em,user2_lim} Representing carbon emission rights assigned to the general industrial and commercial user; c _{em,user2_buy} The 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 ₂ A renewable energy power consumption responsibility weight representing the general industrial and commercial user; c _user2 Representing the total cost of the general industrial and commercial user production energy purchase; aC _{em,user2_buy} Represents a cost of purchasing carbon emissions rights; bP _{user2_PV_WT_buy} Represents the cost of purchasing green electricity; c represents other costs for the general industry and commerce users to purchase energy;

the energy demand constraint of the residential user is as follows:

P _user3 ＝P _{user3_PV_WT} +P _{user3_PV_WT_buy}

C _user3 ＝aP _{user3_PV_WT_buy} +b

3. The energy optimization scheduling method considering carbon emission and green electricity consumption according to claim 1, 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:

Wherein the content of the first and second substances,

to representTime t thermal energy purchased by the ith campus energy service provider from the grid unit.

4. 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.

5. 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 output model of the energy supply equipment 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: large industrial users, general industrial and commercial users, and residential users; the gas turbine output model is as follows:

V _GT ≤δ(C _{lim_GT} +C _{lim_GT_buy} )

C _GT ＝aC _{lim_GT_buy}

V _GB ≤δ(C _{lim_GB} +C _{lim_GB_buy} )

C _GB ＝aC _{lim_GB_buy}

wherein, V _GB Is natural for gas boilerGas consumption power; c _{lim_GB} A carbon emission allowance assigned to the gas boiler plant; c _GB Represents the extra carbon cost for the supplier to supply heat with the gas boiler; c _{lim_GB_buy} Represents the extra carbon emission rights purchased by the energy supplier to ensure normal production heating;

C _{PV_WT} ＝aδ _{PV_WT} (P _WT +P _PV )Δt

wherein, C _{PV_WT} Revenue of new energy generation, delta _{PV_WT} A carbon emission factor; p is _WT Outputting power for wind power generation; p is _PV Outputting power for photovoltaic power generation; Δ t is the time span;

C _EB ＝aδ _EB P _EB

the output model of the electric refrigeration equipment is as follows:

C _HP ＝aδ _HP P _HP

wherein, P _HP Consuming electrical energy for the electrical refrigeration equipment; c _HP The extra carbon cost, δ, of using electric refrigeration equipment for non-green electricity _HP Energy consumption equivalent carbon emission factors for the electric refrigeration equipment;

the output model of the electrical hydrogen production equipment is as follows:

C _SOEC ＝δ _SOEC P _SOEC

wherein, P _SOEC Electrical energy consumed by the electrolytic cell; c _SOEC The additional carbon cost of using electrical hydrogen production equipment for non-green electricity; delta _SOEC Energy consumption and carbon cost of unit electric hydrogen production equipment;

the optimization calculation module is used for constructing an outer energy service provider optimization scheduling model by taking 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, and the optimization calculation module comprises the following steps: 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:

wherein the content of the first and second substances,

wherein N is a set of regional parks representing the regional integrationThe energy network has N parks participating in energy trading, and N is the number of the parks; f is the total income of the park comprehensive energy service provider;