CN117096864A

CN117096864A - Game optimization scheduling method for regional comprehensive energy system-main power distribution network

Info

Publication number: CN117096864A
Application number: CN202311068171.3A
Authority: CN
Inventors: 李昊泉; 徐懂理; 高瑞阳; 钱俊杰; 徐北硕; 朱凌锋
Original assignee: Nanjing Institute of Technology
Current assignee: Nanjing Institute of Technology
Priority date: 2023-08-23
Filing date: 2023-08-23
Publication date: 2023-11-21

Abstract

The invention discloses a game optimization scheduling method of a regional comprehensive energy system-main power distribution network, which comprises the following steps: building a RIES structure, connecting the RISE structure AND an upper power grid into an active power distribution network AND designed based on an IEEE9 node system through an electrical interface, AND building a RIES low-carbon model; obtaining actual carbon emission and carbon emission quota based on a life cycle evaluation method LCA, and establishing a reward and punishment ladder-type carbon transaction cost model; establishing an objective function and constraint conditions with minimum comprehensive cost before ADN days of an active power distribution network; establishing a RIES low-carbon model day-ahead dispatching total cost objective function and constraint conditions; and taking the ADN of the main distribution network as a game leader, taking the RIES low-carbon model as a follower, and constructing a master-slave game model to obtain the optimal interactive electric energy price, unit output and electric load. The invention can realize low-carbon economic optimization of RIES and ADN.

Description

Game optimization scheduling method for regional comprehensive energy system-main power distribution network

Technical Field

The invention relates to the technical field of comprehensive energy system optimal operation scheduling, in particular to a regional comprehensive energy system-main power distribution network game optimal scheduling method.

Background

Since the industrial revolution, low-carbon emission reduction has become an important goal for the reform of the world energy industry due to a plurality of environmental problems caused by the emission of greenhouse gases in large quantities.

In this context, the regional integrated energy systems (Regional Integrated Energy System, RIES) with the ability to co-schedule multiple energy sources access the active distribution network (Active Distribution Network, ADN) via electrical interfaces, is an important development of future energy systems, relative to traditional energy systems operating independently planned. How to realize the cooperative scheduling of RIES and ADN, and to utilize active management to mine low-carbon potential, thereby improving the economic benefit of the system is one of the important problems to be solved.

Considerable research is currently underway on modeling the equipment of RIES and on economic low-carbon optimization. Such as: establishing an optimized dispatching model taking Power to gas (P2G) and multi-element energy storage as cores, and analyzing a micro-grid system dispatching typical user side neutralization energy system for natural gas, electric Power and electric heating functions; establishing a low-carbon comprehensive energy system P2G plant station optimization model containing a carbon trapping technology, and realizing low-carbon economic optimization under electric bidirectional coupling; analyzing the energy consumption characteristics of the users and the interaction behaviors with the power supply enterprises by depending on the power big data of the park, evaluating the life cycle stages of the users, effectively dividing the life cycle stages of the users of the park, and accurately grasping the power consumption behaviors and the interaction behavior change trend of the users in each stage; dividing the comprehensive energy microgrid and the power distribution network into a plurality of benefit bodies in a interconnecting mode of connecting wires through distributed modeling, introducing a target cascading analysis method to refine benefit games between the power distribution network and the microgrid, and realizing parallel solving of a plurality of microgrids; and a plurality of comprehensive energy micro-grid collaborative management models under the same power distribution network are established by combining the master-slave game with the collaborative game. However, in the existing research, most of objective functions of optimized scheduling are economical, low-carbon benefits brought by RIES access to a power distribution network are not reflected, and in the scheduling result, the RIES has strong electricity purchasing dependence on ADN, so that the carbon emission effect is obvious, and the requirements of low-carbon emission reduction cannot be met.

Disclosure of Invention

Based on the background, the invention provides a game optimization scheduling method of a regional comprehensive energy system-main power distribution network, which aims to solve the problems of benefit conflict and self carbon emission generated by interaction of the regional comprehensive energy system and the power distribution network, and the regional comprehensive energy system-main power distribution network game optimization scheduling method is used for building a coupling side dynamic carbon cycle by coupling a cogeneration unit, a carbon capture unit and an electric conversion unit, and building load flexible scheduling resources by utilizing comprehensive demand response; introducing a ladder carbon transaction system, realizing accurate measurement of carbon emission in the whole life cycle of RIES by using an LCA method, constructing a master-slave game model taking the ADN as a leader and the RIES as a follower by taking economy and low carbon as targets, and completing solving of a Stackelberg equilibrium solution by using a genetic algorithm nested Cplex solver to obtain an ADN reasonable pricing strategy and realize low-carbon economy optimization of the RIES and the ADN.

In order to achieve the technical purpose, the invention adopts the following technical scheme: a game optimization scheduling method for a regional comprehensive energy system-main power distribution network specifically comprises the following steps:

step 1, building RIES structures according to a supply side, a coupling side AND a demand side, connecting the RISE structures AND an upper power grid into an active power distribution network AND designed based on an IEEE9 node system through an electrical interface, AND building a RIES low-carbon model according to the built RIES structures;

Step 2, calculating actual carbon emission of the RIES low-carbon model and the ADN of the active power distribution network respectively based on a life cycle evaluation method LCA, determining carbon emission limits of the RIES low-carbon model and the ADN of the active power distribution network by adopting gratuitous distribution and based on a datum line method, and establishing a reward and punishment ladder-type carbon transaction cost model through the actual carbon emission and the carbon emission limits;

step 3, comprehensively considering a reward and punishment ladder-type carbon transaction cost model, electricity purchasing cost, electricity selling income, network loss cost and equipment operation and maintenance cost of the ADN of the active power distribution network, and establishing an objective function and constraint conditions with minimum comprehensive cost before the ADN of the active power distribution network; the reward and punishment step-type carbon transaction cost model, the energy purchasing cost, the energy selling cost, the equipment operation and maintenance cost and the demand response cost of the RIES low-carbon model are comprehensively considered, and a daily scheduling total cost objective function and constraint conditions of the RIES low-carbon model are established;

and 4, taking the ADN of the main distribution network as a game leader, taking the RIES low-carbon model as a follower, and constructing a master-slave game model to obtain the optimal interactive electric energy price, unit output and electric load.

Further, the RIES structure specifically comprises: the supply side consists of a power distribution network, wind power, photovoltaic and natural gas networks, the coupling side consists of an electric boiler, an electric energy storage device, a CCS carbon trapping device, a P2G electric conversion device and a cogeneration unit, and the demand side consists of an electric load and a thermal load; the power distribution network, the wind power and the photovoltaic provide electric energy required by the electric boiler and the electric energy storage device to the coupling side, and directly supply the electric energy to an electric load on the demand side; the natural gas net directly conveys natural gas to the hot spot CO-production unit so as to generate electric energy, capture heat energy and discharge CO by the micro-combustion engine and the bromine cooling machine in the hot spot CO-production unit ₂ CO exhausted by cogeneration unit ₂ Will be captured by the CCS carbon capture device and will capture CO ₂ Is supplied to a P2G electric conversion device, and the P2G electric conversion device captures CO ₂ With H obtained by electrolysis of water ₂ Methane is generated by the reaction, and natural gas is replenished to the cogeneration unit through a natural gas pipeline; the cogeneration unit supplies electric energy to the electric load at the demand side, when the electric energy supplied by the cogeneration unit is insufficient to support the electric load at the demand side, the demand side takes electricity from the electric energy storage device, and if the electric energy is insufficient to support the required electric load at the momentThe power distribution network, the wind power and the photovoltaic place are purchased with electricity when the demand is met; the heat load on the demand side is provided by a cogeneration unit and an electric boiler.

Further, the RIES low-carbon model consists of total power generation of the combined heat and power generation unit at the coupling side, a response electric load model and an interruptible heat load model;

the calculation process of the total power of the combined heat and power unit at the coupling side is as follows:

wherein P is _e,t And P _h,t Respectively the total power generation and the heat generation power of the cogeneration unit at the time t, P _echp,t Representing the electric energy supplied by the cogeneration unit to the power grid at the time t, and P _ep2g,t The electric energy of the P2G electric conversion device is supplied to the cogeneration unit at the time t, and P _ep2g,t ＝P _gs,t And (5) theta, pgs, wherein t is the natural gas output value of the P2G electric conversion device at the moment t, and theta is the conversion efficiency of the P2G electric conversion device; peccs, t represents the electric energy supplied by the cogeneration unit to the CCS carbon capture device at the time t, P _eccs,t ＝C _cc,t /δ，C _cc T represents CO captured by CCS carbon capture device at t moment ₂ Quantity, δ represents carbon capture efficiency; p (P) _e,min And P _e,max Respectively represents the upper limit and the lower limit of the electric output force, k of the cogeneration unit _v1 And k _v2 Respectively representing the corresponding cogeneration unit electrothermal conversion coefficient, k when the output electric power is minimum and maximum _m Representing power coefficient, P of cogeneration unit under back pressure working condition _h0 Representing the corresponding output thermal power when the output electrical power of the cogeneration unit is minimum;

the response electrical load model is as follows:

wherein P is _l0,i And P _l,i Electrical loads before and after demand response, ρ _ii For period iSelf elastic coefficient ρ _ij For the mutual elasticity coefficient of period i to period j, c _i For the real-time electricity price of period i, deltac _i Delta P is the change of electricity price in the i period _l,i Interrupting the load amount, ζ, for the i-period electrical load _DR A interruptible load scaling factor for the consumer electrical load;

the interruptible thermal load model is:

wherein P is _lh0,t And P _lh,t The heat load before and after the demand response is respectively,and->Is the indoor and outdoor temperature of the building, S is the indoor area of the building, epsilon _h Is the indoor heat loss under the temperature difference of unit building area, C _air Specific heat capacity per building area +.>Delta t is time variable and delta P is the variation of indoor temperature _h,t Gamma, the interruptible amount of thermal load for the user _DR An interruptible scaling factor for the user's thermal load, < >>And->Upper and lower limits for the temperature the user is in the comfort range.

Further, the establishment process of the reward and punishment ladder-type carbon transaction cost model is as follows:

wherein F is _c,i Carbon representing RIES low-carbon model or active distribution network ADNTransaction cost, C is carbon transaction price, h is carbon emission interval length, alpha is rewarding coefficient, beta is punishment coefficient, C _c,i Representing carbon emission limits, C, of RIES low-carbon models or active distribution networks ADN _p,i Representing the actual carbon emissions of the RIES low-carbon model or the active distribution network ADN.

Further, when i represents the RIES low carbon model, the actual carbon emission C of the RIES low carbon model _p,i Specifically denoted as C _p,R ：

C _p,R ＝(e _g,gr P _g,gr +e _t,N P _t,N +e _g,N P _g,N +e _R,ch P _R,ch )Δt-C _cc (5)

Wherein P is _g,gr Representing the output power of the wind-solar operation link, e _g,gr Representing the carbon emission coefficient of the wind-solar operation link, e _g,gr ＝je _o E, j is the energy consumption calculation coefficient corresponding to the unit electric quantity, E _o Carbon emission intensity for maintenance or consumable replacement, E is energy intensity for maintenance or consumable replacement; p (P) _t,N Representing the output power of the coupling device transportation link e _t,N Representing the carbon emission coefficient of the coupling device transportation link e _t,N ＝Q _N ν _t D _t e _N ，Q _N Representing the total amount of transport energy, v _t D, corresponding to the energy consumption intensity of the fuel for the selected transportation mode _t E is the total transportation distance of the fire coal _N Representing the carbon emission coefficient of energy transportation required by the corresponding coupling equipment; p (P) _g,N Indicating the output power of the coupling device using link e _g,N Representing the carbon emission coefficient of the coupling equipment using link e _g,N ＝ε _N e _c,N ，ε _N Representing the unit energy conversion coefficient of the coupling equipment, e _c,N Representing the carbon emission coefficient of the energy conversion process of the coupling equipment; p (P) _R,ch Representing the total power of electricity purchase of RIES low-carbon model, e _R,ch Representing the carbon emission coefficient of unit electricity purchasing of the RIES low-carbon model; Δt represents the carbon emission time interval, C _cc Indicating the total amount of carbon dioxide captured by CCS;

carbon credit C of RIES low carbon model _c,i Is specifically shown asC _c,R ：

C _c,R ＝χ _h (P _h +P _EB +ψP _e )+χ _e (P _WP +P _PV )+χ _ch,e P _R,ch +χ _ch,g G _gas (6)

Wherein P is _e And P _h Respectively representing the electric power and the heat-generating power of the cogeneration unit, P _EB The heat power output by the electric boiler is represented by χh, the carbon emission limit allocated to the unit heat power is represented by χh, the conversion coefficient for converting the generated energy of the cogeneration unit into heat supply is represented by ψ, and χ _e Representing the carbon emission allowance distributed to the unit power generation, P _WP And P _PV Respectively represent the electric power and χ of the fan and the photovoltaic output in the RIES low-carbon model _ch,e Represents the input carbon emission quota, χ of the power grid _ch,g Represents the carbon emission quota of natural gas input, G _gas The total amount of purchased air was calculated as RIES.

Further, when i represents an active distribution network ADN, the actual carbon emission C of the active distribution network ADN _p,i Specifically denoted as C _p,A ：C _p,A ＝(e _p,c P _p,c +e _t,c P _t,c +e _g,c P _g,c +e _p,g P _p,g +e _t,g P _t,g +e _A,ch P _A,ch )Δt (7)

Wherein P is _p,c For the output power of coal mining links e _p,c E is the carbon emission coefficient of the coal exploitation link _p,c ＝e _m Q _p ，e _m Unit carbon emission coefficient of mining link, Q _p The raw coal exploitation amount is; p (P) _t,c E is the output power of the coal transportation link _t,c E is the carbon emission coefficient of the coal transportation link _t,c ＝υ _t e _t Q _t D _t ，υ _t E, corresponding to the energy consumption intensity of the fuel for the selected transportation mode _t Carbon emission coefficient, Q, of fuel used for transportation _t For the total transportation amount of fire coal D _t Is the total transportation distance of the fire coal; p (P) _g,c Output power of coal using link e _g,c Carbon emission for coal burning linkCoefficient e _g,c ＝N _g e _g ，N _g E, for standard coal consumption of power supply _g Carbon emission coefficient per ton of coal; p (P) _p,g E is the output power of the natural gas exploitation link _p,g Carbon emission coefficient e for natural gas exploitation _p,g ＝β _p,g e _gas ，β _p,g E is the natural gas exploitation overflow rate _gas The carbon emission intensity of the natural gas; p (P) _t,g E is the output power of the natural gas transportation link _t,g Is the carbon emission coefficient of the natural gas transportation link, e for natural gas weighting using pipeline transportation _p 、e _LNG Carbon emission coefficients corresponding to pipeline transportation and LNG liquefied natural gas transportation respectively; p (P) _A,ch E is the total power of electricity purchase of ADN _A,ch The carbon emission coefficient is purchased for the unit of ADN, and delta t is the carbon emission time interval;

carbon emission credit C of ADN of active power distribution network _c,i Specifically denoted as C _c,A The method comprises the following steps:

C _c,A ＝χ _e (P _WPS +P _CFPP )+χ _ch,e P _A,ch (8)

wherein χ is _e Carbon emission allowance distributed for unit power generation, P _WPS And P _CFPP Respectively represents the electric power output by an ADN fan and the thermal power unit, χ _ch,e Carbon emission quota is input for the grid.

Further, the objective function with the minimum comprehensive cost before the ADN day of the active power distribution network is as follows:

wherein T is the number of scheduling hours before day, F _A,ch Mu, the purchase cost of ADN of the active distribution network _A,ch1 、μ _A,ch2 ADN to RIES low-carbon module of active distribution network respectivelyThe unit electricity purchasing price of the upper power grid,and->Purchasing electric power from the power distribution network to the RIES low-carbon model and the upper power grid for the t-th period; f (F) _A,loss Loss cost for active distribution network ADN, < ->ADN loss of active distribution network for t-th period>The electricity selling price of the ADN of the active power distribution network to other connected load units is set for the t-th period; f (F) _A,f The equipment operation maintenance cost of the ADN of the active power distribution network is Z is the number of equipment in the ADN of the active power distribution network, < ->Outputting power for the z-th device in the ADN of the active power distribution network in the t-th period, b _A,z A maintenance cost coefficient for a z-th device in the ADN of the active power distribution network; f (F) _c,A For the carbon transaction cost of the ADN of the active power distribution network, F _A,sell For selling electricity income of ADN of active distribution network, +.>Price per unit of electricity selling for the t-th time period ADN to RIES low-carbon model,/day>And->The method comprises the steps that electricity is sold to RIES and other loads connected by an active power distribution network ADN in a t-th period respectively;

the constraint conditions include: the method comprises the steps of electric energy balance constraint of an active power distribution network ADN, interactive power capacity constraint of the active power distribution network ADN and time-of-use purchase electricity price constraint;

the energy balance constraint of the active distribution network ADN is expressed as:

P _WPS +P _CFPP +P _A,up +P _Ach,R ＝P _A +P _As.R (10)

wherein P is _A,up Purchasing electric power to an upper power grid for active power distribution network ADN, P _Ach,R The power purchase power P of the active distribution network ADN to RIES low-carbon model _A Selling electric power to other loads connected to active distribution network ADN, P _As,R The electricity selling power from the ADN of the active distribution network to the RIES low-carbon model is realized;

the interactive power capacity constraint and the time-sharing purchase electricity price constraint of the active distribution network ADN are expressed as follows:

wherein P is _R,s Represents the selling power of the RIES low-carbon model to the ADN of the active distribution network,representing maximum interaction power of ADN and RIES low-carbon model of active power distribution network, < >>And the power price of the upper power grid is represented.

Further, the RIES low-carbon model day-ahead scheduling total cost objective function is:

Wherein F is _R,ch Is the purchase energy cost of RIES low-carbon model, F _R,sell Energy selling cost for RIES low-carbon model, F _R,f The running maintenance cost of the equipment for the RIES low-carbon model is F _DR The cost of demand response for the rias low-carbon model,price of unit electricity selling and heat selling energy source, mu, of RIES low-carbon model in the t-th period _R,ch For natural gas purchase unit price, G _gas Is the natural of RIESGas purchase amount, M is total number of devices contained in RIES low-carbon model, b _R,m Maintaining a cost factor for the operation of the mth device in the RIES low-carbon model, +.>For the mth device in RIES low-carbon model to output power in the t-th period,/>And->The unit compensation cost of the electric and thermal interruptible loads is respectively;

the constraint conditions include: the power balance constraint of the RIES low-carbon model and the operation constraint of a cogeneration unit-CCS carbon capture device-P2G electric conversion device;

the power balance constraint of the RIES low carbon model is expressed as:

wherein P is _ES,c 、P _ES,f Charging and discharging power of the electric energy storage respectively; p (P) _EB,e Input electric power for the electric boiler;

the operation constraint of the cogeneration unit-CCS carbon capture device-P2G electric conversion device is expressed as follows:

wherein,for the wind-solar pre-measurement in t period, eta _EB Indicating the heat production efficiency of the electric boiler, +. >Respectively represents the upper limit and the lower limit of the heat generation amount of the electric boiler, W _ES,t Representing the storage capacity, sigma, of an electrical energy storage device during a period t _ES Representing the loss rate of stored energy，η _c 、η _f Indicating the charge-discharge energy efficiency of the energy storage device, +.>For energy storage device energy capacity, SOC ^min 、SOC ^max Is the minimum and maximum charge state of energy storage.

Further, step 4 comprises the following sub-steps:

step 4.1, initializing population quantity by taking the interactive electricity prices of the ADN and RIES low-carbon models of the main distribution network formulated by game leaders as parameters, and setting the maximum iteration times;

step 4.2, the game leader issues the formulated strategy of the interactive electricity prices of the ADN and RIES low-carbon models of the main distribution network to the follower, and the RIES low-carbon models are solved by using a Cplex solver according to the daily scheduling total cost objective function and constraint conditions of the established RIES low-carbon models, so that a demand response strategy, a multi-element energy price and interactive electricity energy are obtained and fed back to the game leader at the upper layer;

step 4.3, the game leader combines and establishes an objective function with the minimum daily total cost of the active power distribution network ADN and constraint conditions according to the feedback result of the follower to calculate the daily total cost of the current active power distribution network ADN

Step 4.4, carrying out mutation and crossover operation on parameters of the initial population according to a genetic algorithm to form a new population, and repeating the steps 4.2-4.3 to obtain the comprehensive cost of the active power distribution network ADN of the new population before the day

Step 4.5, ifKeep->Corresponding population parameters; otherwise, keep->Corresponding population parameters;

and 4.6, repeating the steps 4.4-4.5 until the maximum iteration times are reached, and outputting the optimal interactive electricity price, unit output and electric load of the low-carbon models of the main distribution network ADN and the RIES.

Compared with the prior art, the invention has the following beneficial effects:

(1) Performing low-carbon transformation on the combined heat and power generation unit which is in key coupling with the RIES low-carbon model, and considering comprehensive demand response, realizing complementary joint debugging of the combined heat and power generation unit and the combined heat and power generation unit, and effectively improving the RIES energy supply structure and the flexibility of a load curve;

(2) The effective metering of carbon emission is realized by an LCA method, and the high carbon emission behavior of RIES is effectively restrained by using a ladder carbon transaction system, so that the dependence on ADN electricity purchasing is reduced;

(3) And (3) taking ADN active scheduling capability into consideration, establishing an ADN-RIES master-slave game model to obtain a reasonable pricing strategy, realizing effective integration of ADN on flexible resources, ensuring benefits of all the main bodies, and obtaining multi-main-body benefit optimization.

In conclusion, the invention relates to a regional comprehensive energy system-main distribution network game optimization scheduling method considering life cycle theory, which fills up the research blank in solving the problems of benefit conflict and carbon emission between the comprehensive energy system and the distribution network. The new research method can provide reference for similar problems, promote the sustainable development of the comprehensive energy system and provide a new idea for transformation of the energy system. The method can help policy makers and energy planners to better design and implement carbon emission reduction policies, and promote clean energy use and carbon emission control. Simulation results show that the game double-side can continuously optimize the energy strategy, and low-carbon and economic operation is realized. This provides practical guidelines for actual energy system operation and management, helping energy enterprises and government agencies to better optimize energy configuration and carbon emission management.

Drawings

FIG. 1 is a schematic diagram of an ADN network of a main distribution network with RIES structure according to the present invention;

FIG. 2 is a diagram of the structure of the RIES low carbon model of the present invention;

FIG. 3 is a diagram of a model architecture of the CHP-CCS-P2G unit model of the present invention;

FIG. 4 is a schematic diagram of the CHP-CCS-P2G working area of the present invention;

FIG. 5 is a diagram of an ADN-RIES master-slave gaming framework of the present invention;

FIG. 6 is a flow chart of an ADN-RIES master-slave gaming solution of the present invention;

FIG. 7 is a plot of RIES predicted quantity data of the present invention;

FIG. 8 is a graph of ADN predicted quantity data for the present invention;

FIG. 9 is a graph of ADN pricing result data for the present invention;

FIG. 10 is a graph of the electrical balance results of ADN of the present invention;

FIG. 11 is a graph of RIES electrical balance results data of the present invention;

FIG. 12 is a graph of RIES thermal equilibrium result data of the present invention;

FIG. 13 is a graph of RIES carbon quota and carbon emission metering data of the present invention.

Detailed Description

The technical scheme of the invention is further explained below with reference to the accompanying drawings.

The invention provides a game optimization scheduling method of a regional comprehensive energy system-main power distribution network, which aims to solve the problems of benefit conflict and self carbon emission generated by interaction of the regional comprehensive energy system and the active power distribution network. The method specifically comprises the following steps:

step 1, building RIES structures according to a supply side, a coupling side AND a demand side, as shown in fig. 1, connecting the RISE structures AND an upper power grid into an active power distribution network AND designed based on an IEEE9 node system through an electrical interface, AND building a RIES low-carbon model according to the built RIES structures.

As shown in fig. 2, the structure of the rias in the present invention is specifically: the supply side consists of a power distribution network, wind power, photovoltaic and natural gas networks, the coupling side consists of an electric boiler, an electric energy storage device, a CCS carbon trapping device, a P2G electric conversion device and a cogeneration unit, and the demand side consists of an electric load and a thermal load; wherein, the distribution network, wind power and photovoltaic provide electric energy required by the electric boiler and the electric energy storage device to the coupling side and provide electricity to the requirement sideThe load directly supplies electric energy; the natural gas net directly conveys natural gas to the hot spot CO-production unit so as to generate electric energy, capture heat energy and discharge CO by the micro-combustion engine and the bromine cooling machine in the hot spot CO-production unit ₂ CO exhausted by cogeneration unit ₂ Will be captured by the CCS carbon capture device and will capture CO ₂ Is supplied to a P2G electric conversion device, and the P2G electric conversion device captures CO ₂ With H obtained by electrolysis of water ₂ The methane is generated by the reaction, and natural gas is supplemented to the cogeneration unit again through a natural gas pipeline, so that the dynamic utilization and the non-forced emission of carbon are realized; the heat and power cogeneration unit supplies electric energy to the electric load on the demand side, when the electric energy supplied by the heat and power cogeneration unit is insufficient to support the electric load demand on the demand side, the demand side takes electricity from the electric energy storage device, and if the electric energy is insufficient to support the electric load demand at the moment, electricity is purchased from the power distribution network, wind power and photovoltaic places; the heat load on the demand side is provided by a cogeneration unit and an electric boiler.

The RIES low-carbon model consists of total power generation of the combined heat and power generation unit at the coupling side, a response electric load model and an interruptible heat load model;

as shown in fig. 3, the coupling side cogeneration unit is a key coupling device of the RIES low-carbon model, and is a low-carbon operation model with the cogeneration unit as a core, and the cogeneration unit is operated in combination with the P2G electricity-to-gas device and the CCS carbon capture device, and the operation energy consumption is provided by the cogeneration unit. Cogeneration units typically operate in a "hot-fix" mode, with an operating area such as the ABCD area of fig. 4. The electric energy produced by the cogeneration unit is divided into three parts, the unit power adjusting capability is improved, and the unit works in an ABEFG area, so that the calculation process of the total power of the cogeneration unit at the coupling side is as follows:

wherein P is _e,t And P _h,t Respectively the total power generation and the heat generation power of the cogeneration unit at the time t, P _echp,t Indicating that the cogeneration unit supplies power at the time tElectric energy to the grid, P _ep2g,t The electric energy which represents that the cogeneration unit supplies the P2G electric energy to the gas conversion device at the t moment, the P2G electric energy conversion device can convert the electric energy into natural gas with lower storage cost, the natural gas conversion device is an effective way for reducing the phenomena of wind abandon and light abandon, the working process of the P2G electric energy conversion device comprises two stages, and the first stage generates H by electrolyzed water ₂ Second stage H ₂ With CO captured by CCS carbon capture devices ₂ The methane produced by synthesis can be supplied to the cogeneration unit again through a natural gas pipeline to realize the internal carbon circulation of the cogeneration unit, the P2G electric gas conversion device and the CCS carbon trapping device, so that the coupling relation between the output natural gas of the P2G electric gas conversion device and the electric energy supplied by CHP is as follows: p (P) _ep2g,t ＝P _gs,t /θ，P _gs,t The natural gas output value of the P2G electric gas conversion device at the t moment is theta, and the conversion efficiency of the P2G electric gas conversion device is theta; p (P) _eccs,t The electric energy of the CCS carbon trapping device is supplied to the cogeneration unit at the time t, the CCS carbon trapping device adopting the post-combustion trapping technology is used for trapping carbon dioxide generated after the cogeneration unit burns fuel, and the rich liquid is used for absorbing CO in the flue gas ₂ CO is separated by chemical chain separation ₂ The carbon capture amount of the CCS carbon capture device is directly proportional to the energy consumption of the CCS carbon capture device, and the relation is as follows: p (P) _eccs,t ＝C _cc,t /δ，C _cc,t Representing CO captured by CCS carbon capture device at time t ₂ Quantity, δ represents carbon capture efficiency; p (P) _e,min And P _e,max Respectively represents the upper limit and the lower limit of the electric output force, k of the cogeneration unit _v1 And k _v2 Respectively representing the corresponding cogeneration unit electrothermal conversion coefficient, k when the output electric power is minimum and maximum _m Representing power coefficient, P of cogeneration unit under back pressure working condition _h0 Representing the corresponding output thermal power when the output electrical power of the cogeneration unit is minimum;

the carbon emission of the system can be reduced after the coupling side cogeneration unit is coupled with the CCS carbon capture device-P2G electric conversion device, however, during the peak load period, the cogeneration unit faces huge energy supply pressure, the CCS capture energy consumption cannot be timely supplied, and namely the high carbon emission behavior in the peak load period cannot be effectively relieved. Therefore, the invention adds demand response, adjusts the load demand through price signals or direct control protocol, considers releasing time-sharing price signals and sets interruptible load as a flexible adjustment means for RIES electric load. Thus, the response electrical load model at period i is:

wherein P is _l0,i And P _l,i Electrical loads before and after demand response, ρ _ii Is the self-elasticity coefficient of the i period ρ _ij For the mutual elasticity coefficient of period i to period j, c _i For the real-time electricity price of period i, deltac _i Delta P is the change of electricity price in the i period _l,i Interrupting the load amount, ζ, for the i-period electrical load _DR A interruptible load scaling factor for the consumer electrical load;

the thermal load response has time delay, is not suitable for guiding by price signals, and considers the thermal load to have perception ambiguity, so the interruptible load is used as a flexible adjustment means for RIES thermal load, and the quantitative relation between the thermal load demand and the temperature is added for considering the energy comfort of users Calculating an interruptible thermal load model:

wherein P is _lh0,t And P _lh,t The heat load before and after the demand response is respectively,and->Is the indoor and outdoor temperature of the building, S is the indoor area of the building, epsilon _h Is the indoor heat loss under the temperature difference of unit building area, C _air Is the specific heat capacity per unit building area,/>delta t is time variable and delta P is the variation of indoor temperature _h,t Gamma, the interruptible amount of thermal load for the user _DR An interruptible scaling factor for the user's thermal load, < >>And->Upper and lower limits for the temperature the user is in the comfort range.

And 2, dividing the life cycle of each energy flow into three links of production, transportation and use based on a life cycle evaluation method LCA, respectively calculating the actual carbon emission of the RIES low-carbon model and the active power distribution network ADN, determining the carbon emission limit of the RIES low-carbon model and the active power distribution network ADN by adopting gratuitous distribution and based on a datum line method, and establishing a reward and punishment stepped carbon transaction cost model through the actual carbon emission and the carbon emission limit. LCA is often used to evaluate the potential environmental impact of a service or product throughout its life, when the evaluation object is CO ₂ In the process, the LCA method can comprehensively track the carbon emission track of each link of the energy chain. The traditional carbon emission measurement only considers the energy use links, ignores the carbon emission of other links, and has various energy types due to the energy conversion complexity in the multi-element energy system, so that clear carbon emission measurement standards cannot be defined. Therefore, the LCA method is applied to divide the life cycle of each energy flow into three links of production, transportation and use, the carbon emission of each energy source in the three links is analyzed based on the RIES structure, and the normalized carbon emission coefficient of each device is calculated.

In order to excite emission reduction, the invention adopts a reward and punishment ladder-type carbon transaction mode, alpha and beta are set as reward and punishment coefficients to realize flexible activation variation of carbon price, thus adopting gratuitous allocation and providing carbon emission quota for the system based on a datum line method, and when the actual carbon emission C of a carbon emission source is _p Exceeding the gratuitous distribution carbon emission credit C _c When the price of the carbon right is required to be purchased in the carbon trade marketAdditional carbon credits; in addition, when the actual carbon emission does not reach the allocated carbon emission allowance, the excessive allowance can be sold in the carbon market, so that a certain benefit is obtained. The establishment process of the winning punishment ladder-type carbon transaction cost model comprises the following steps:

wherein F is _c,i Representing carbon transaction cost of RIES low-carbon model or active power distribution network ADN, C is carbon transaction price, h is carbon emission interval length, alpha represents rewarding coefficient, beta represents punishment coefficient, C _c,i Representing carbon emission limits, C, of RIES low-carbon models or active distribution networks ADN _p,i Representing the actual carbon emissions of the RIES low-carbon model or the active distribution network ADN.

When i represents the RIES low carbon model, the actual carbon emission C of the RIES low carbon model _p,i Specifically denoted as C _p,R ：

carbon credit C of RIES low carbon model _c,i Specifically denoted as C _c,R ：

Wherein P is _e And P _h Respectively representing the electric power and the heat-generating power of the cogeneration unit, P _EB Representing the thermal power, χ, of the output of an electric boiler _h Represents the carbon emission limit allocated to the unit heating power, psi represents the conversion coefficient of converting the generated energy of the cogeneration unit into the heat supply quantity, χ _e Representing the carbon emission allowance distributed to the unit power generation, P _WP And P _PV Respectively represent the electric power and χ of the fan and the photovoltaic output in the RIES low-carbon model _ch,e Represents the input carbon emission quota, χ of the power grid _ch,g Represents the carbon emission quota of natural gas input, G _gas The total amount of purchased air was calculated as RIES.

When i represents an active distribution network ADN, the actual carbon emission C of said active distribution network ADN _p,i Specifically denoted as C _p,A ：

C _p,A ＝(e _p,c P _p,c +e _t,c P _t,c +e _g,c P _g,c +e _p,g P _p,g +e _t,g P _t,g +e _A,ch P _A,ch )Δt (7)

Wherein P is _p,c For the output power of coal mining links e _p,c E is the carbon emission coefficient of the coal exploitation link _p,c ＝e _m Q _p ，e _m Unit carbon row for mining linkPut coefficient, Q _p The raw coal exploitation amount is; p (P) _t,c E is the output power of the coal transportation link _t,c E is the carbon emission coefficient of the coal transportation link _t,c ＝υ _t e _t Q _t D _t ，υ _t E, corresponding to the energy consumption intensity of the fuel for the selected transportation mode _t Carbon emission coefficient, Q, of fuel used for transportation _t For the total transportation amount of fire coal D _t Is the total transportation distance of the fire coal; p (P) _g,c Output power of coal using link e _g,c E is the carbon emission coefficient of the burning link of the fire coal _g,c ＝N _g e _g ，N _g E, for standard coal consumption of power supply _g Carbon emission coefficient per ton of coal; p (P) _p,g E is the output power of the natural gas exploitation link _p,g Carbon emission coefficient e for natural gas exploitation _p,g ＝β _p,g e _gas ，β _p,g E is the natural gas exploitation overflow rate _gas The carbon emission intensity of the natural gas; p (P) _t,g E is the output power of the natural gas transportation link _t,g Is the carbon emission coefficient of the natural gas transportation link, e for natural gas weighting using pipeline transportation _p 、e _LNG Carbon emission coefficients corresponding to pipeline transportation and LNG liquefied natural gas transportation respectively; p (P) _A,ch E is the total power of electricity purchase of ADN _A,ch The carbon emission coefficient is purchased for the unit of ADN, and delta t is the carbon emission time interval;

C _c,A ＝χ _e (P _WPS +P _CFPP )+χ _ch,e P _A,ch (8)

wherein χ is _e Carbon emission allowance distributed for unit power generation, P _WPS And P _CFPP Respectively represents the electric power output by an ADN fan and the thermal power unit, χ _ch,e For electric network transmissionCarbon-in emission quota.

And 3, comprehensively considering a reward and punishment ladder-type carbon transaction cost model, electricity purchasing cost, electricity selling income, network loss cost and equipment operation and maintenance cost of the ADN of the active power distribution network, and establishing an objective function and constraint conditions with minimum comprehensive cost before the ADN of the active power distribution network. Specifically, the active power distribution network model mainly comprises a traditional thermal power generating unit and a wind power generating unit in an ADN, and is connected with the RIES through an electrical interface. As an upper energy supply network of the RIES low-carbon model, ADN can interact with RIES to generate a certain benefit. Considering that the internal unit of the ADN has carbon emission behavior, the ADN should participate in carbon emission trade as a carbon emission main body. Therefore, in order to realize economic and low-carbon operation of the power distribution network, the ADN electricity purchasing cost F should be comprehensively considered _A,ch Income F of electricity selling _A,sell Cost of net loss F _A,loss Cost F of equipment operation and maintenance _A,f And carbon trade cost F _c,A . And obtaining ADN day-ahead comprehensive cost F through comprehensive analysis of each cost _A The minimum is an objective function, constraint conditions are additionally arranged in the follow-up process to ensure the interactive interests of the ADN and the RIES, avoid the condition of the RIES override purchase of electricity, and enable the electricity price determined by the ADN to be positioned in the electricity price interval of the upper-level power grid. The objective function with the minimum comprehensive cost before ADN of the active power distribution network is as follows:

wherein T is the number of scheduling hours before day, F _A,ch Mu, the purchase cost of ADN of the active distribution network _A,ch1 、μ _A,ch2 The price of electricity purchase of the active distribution network ADN to the RIES low-carbon model and the upper power grid unit is respectively,and->Purchasing electric power from the power distribution network to the RIES low-carbon model and the upper power grid for the t-th period; f (F) _A,loss Loss cost for active distribution network ADN, < ->ADN loss of active distribution network for t-th period>The electricity selling price of the ADN of the active power distribution network to other connected load units is set for the t-th period; f (F) _A,f The equipment operation maintenance cost of the ADN of the active power distribution network is Z is the number of equipment in the ADN of the active power distribution network, < ->Outputting power for the z-th device in the ADN of the active power distribution network in the t-th period, b _A,z A maintenance cost coefficient for a z-th device in the ADN of the active power distribution network; f (F) _c,A For the carbon transaction cost of the ADN of the active power distribution network, F _A,sell For selling electricity income of ADN of active distribution network, +.>Price per unit of electricity selling for the t-th time period ADN to RIES low-carbon model,/day>And->The method comprises the steps that electricity is sold to RIES and other loads connected by an active power distribution network ADN in a t-th period respectively;

P _WPS +P _CFPP +P _A,up +P _Ach,R ＝P _A +P _As.R (10)

in order to ensure the interactive benefits of the ADN and the RIES, namely ensure that the power purchased by the RIES is from the ADN, avoid the condition of over-grade power purchase of the RIES, the power price determined by the ADN should be positioned in the upper power grid power price interval, and the following constraint is made on the interactive power capacity and the time-sharing power purchase price:

Comprehensively considering a reward and punishment stepped carbon transaction cost model, energy purchasing cost, energy selling cost, equipment operation and maintenance cost and demand response cost of the RIES low-carbon model, and establishing a daily scheduling total cost objective function and constraint conditions of the RIES low-carbon model; specifically, the RIES day-ahead scheduling model provided by the invention needs to adjust the energy supply strategy of the coupling side equipment and the load side demand response strategy according to the energy interaction price provided by the ADN so as to realize low-carbon and economic operation under a ladder carbon transaction system. Comprehensively consider RIES purchase cost F _R,ch Cost of energy selling F _R,sell Cost F of equipment operation and maintenance _R,f Cost of carbon trade F _c,R And demand response cost F _DR To obtain the minimum RIES day front scheduling total cost F _R And the objective function has various constraint functions with the daily front scheduling model of the active power distribution network, and the running constraint of the RIES internal unit is added for ensuring the rationality of the daily front scheduling model of the RIES. The RIES low-carbon model day-ahead scheduling total cost objective function is:

wherein,F _R,ch is the purchase energy cost of RIES low-carbon model, F _R,sell Energy selling cost for RIES low-carbon model, F _R,f The running maintenance cost of the equipment for the RIES low-carbon model is F _DR The cost of demand response for the rias low-carbon model,price of unit electricity selling and heat selling energy source, mu, of RIES low-carbon model in the t-th period _R,ch For the purchase unit price of natural gas,

G _gas the natural gas purchase amount of RIES, M is the total number of equipment contained in the RIES low-carbon model, b _R,m Maintaining a cost factor for the operation of the mth device within the RIES low carbon model,for the mth device in RIES low-carbon model to output power in the t-th period,/>And->The unit compensation cost of the electric and thermal interruptible loads is respectively;

the constraint conditions include: power balance constraint and cogeneration unit-CCS carbon trapping device of RIES low-carbon model

Operating constraints of the P2G electrical switching apparatus;

the power balance constraint of the RIES low carbon model is expressed as:

wherein,for the wind-solar pre-measurement in t period, eta _EB Indicating the heat production efficiency of the electric boiler, +.>Respectively represents the upper limit and the lower limit of the heat generation amount of the electric boiler, W _ES,t Representing the storage capacity, sigma, of an electrical energy storage device during a period t _ES Represents the loss rate, eta of energy storage _c 、η _f Indicating the charge-discharge energy efficiency of the energy storage device, +.>For energy storage device energy capacity, SOC ^min 、SOC ^max Is the minimum and maximum charge state of energy storage.

Step 4, taking the ADN of the main distribution network as a game leader, taking the RIES low-carbon model as a follower, constructing a master-slave game model, and obtaining the optimal interactive electric energy price, unit output and electric load; ADN in the RIES-ADN master-slave game model constructed by the invention is used as an upper layer leader, the formulated interactive electric energy price is sent to a lower layer follower RIES load and energy supply coupling unit, the lower layer comprehensively considers unit output and demand response information to optimize the interactive electric quantity with the ADN, the upper layer obtains feedback and takes the maximized net profit as a target to optimize the energy price strategy, the existing information successive iterative relationship belongs to a typical master-slave game relationship, and finally the RIES-ADN master-slave game model is solved by a mode of nesting a Cplex solver through a genetic algorithm.

As shown in fig. 5, unlike the conventional energy system, the benefits created by the integrated energy system are mainly to improve the energy utilization efficiency of the multi-energy coupling, so as to reduce the purchase cost from the superior network. Therefore, the benefits of ADN and RIES conflict, and obvious game relation exists. Wherein: the upper layer consists of an active power distribution network ADN, the ADN is used as a leader of games and is also a connecting medium of RIES and a large power grid, and the upper layer bears the responsibility of balancing energy income and ensuring the reliable operation of the power grid; the lower layer consists of a RIES function coupling side and a RIES load side user, and the response of the equipment output and the user side demand response strategy is managed respectively.

In the master-slave game model, ADN is used as a game leader, a series of strategy plans are formulated according to own benefits and strategies in the game process, follow-up strategies are considered, an optimal reaction strategy is selected to achieve maximization of own benefits, the follower on the RIES side is relatively passive, the leader continuously adjusts the strategies according to the reaction and benefits generated by follow-up of the formulated strategies, own benefits are maximized when the master-slave game is balanced. In the RIES-ADN master-slave game model, the goal of ADN is to minimize the total daily cost on the premise of guaranteeing the energy balance of internal nodes, the game shows maximum interactive profit with RIES, and the strategy set is the interactive electric energy price released to RIES. And after the interactive electric energy price of the upper layer is fixed, the interactive electric energy price is transmitted to the lower layer as a constraint condition. In the lower layer, the RIES load side comprehensively considers information such as flexible load proportion, multi-element energy price and the like, adjusts load demands, and synchronizes corresponding demand response strategies to the RIES energy supply coupling side. The RIES energy supply coupling side takes a CHP-CCS-P2G unit as a core, adjusts the output of each unit by taking the minimum running cost and carbon emission cost of each unit as objective functions, and feeds back the interactive electric energy to an upper layer leader.

In summary, the ADN serves as an upper layer leader, the formulated interactive electric energy price is sent to a lower layer follower RIES load and energy supply coupling unit, the unit output and demand response information is comprehensively considered by the lower layer to optimize the interactive electric quantity with the ADN, the upper layer obtains feedback and optimizes the energy price strategy with the aim of maximizing net profit, and the existing information successive iterative relationship belongs to a typical master-slave game relationship.

As shown in FIG. 6, the invention adopts a mode of nesting a Cplex solver by a genetic algorithm to solve a game model, wherein the upper layer adopts the genetic algorithm to update and solve ADN pricing and day-ahead running cost, and the lower layer adopts Cplex to directly solve the internal unit output, demand response strategy and ADN interaction strategy of RIES. The method specifically comprises the following substeps:

Step 4.5, ifKeep->Corresponding population parameters; otherwise, keep->Corresponding population parameters; and 4.6, repeating the steps 4.4-4.5 until the maximum iteration times are reached, and outputting the optimal interactive electricity price, unit output and electric load of the low-carbon models of the main distribution network ADN and the RIES.

Examples

In this embodiment, the master-slave game model is set to have a population individual number of 50, a population individual dimension of 48, a population crossover factor of 0.9, a variation factor of 0.5, and a maximum iteration number of 150. Its maximum crossingThe mutual power is 1000kW. RIES in-coupling device parameters: the P2G conversion efficiency theta is 0.55, the delta CCS carbon capture efficiency is 1.02, the upper limit and the lower limit of the electric energy storage capacity of the electric energy storage equipment are 500kWh and 1800kWh, and the electric energy storage capacity of the initial electric energy storage device is 800kWh; demand response parameters: c (C) _air Is 1 kJ/(kg. DEG C),and18 ℃ and 24 ℃ respectively, and the load subsidy unit price +.>And->0.04 and 0.05, respectively; the flexible electrothermal loads respectively account for 15% of the total electrothermal load; wind-solar prediction and load prediction curves of RIES and ADN are shown in figures 7 and 8.

The carbon emission allowance selections are shown in table 1, with reference to the relevant carbon allowance scheme.

Table 1 carbon emission quota for each energy source

Energy type	Carbon emission quota (g/kWh)
		Coal power (Main net)	798
CHP	424
		New energy power generation (wind and light)	78
Electric boiler	152
		Electric energy storage	0

The following is a master-slave game result analysis of this embodiment:

the ADN pricing result shown in fig. 9 is obtained based on the regional comprehensive energy system-main distribution network game optimization scheduling method. In order to avoid the phenomenon of over-level electricity purchase, the benefits of the two main bodies of ADN and RIES are ensured, the price of the ADN is always between the price of an upper-level power grid, and the price of electricity selling is higher than the price of electricity purchase. With reference to fig. 7, 8 and 9, the ADN pricing trend is affected by the self electrical load curve, and the purpose of the ADN pricing trend is to stimulate the rias to participate in ADN peak shaving scheduling, improve ADN new energy consumption and improve peak shaving margin.

In order to verify the effectiveness of the RIES-ADN master-slave game economic low-carbon optimization model, the following three scenes are set for comparison analysis:

Scene 1: RIES off-grid operation;

scene 2: RIES grid-connected operation, the interactive electricity price is fixed electricity price;

scene 3: RIES grid-connected operation, the interactive electricity price is the electricity price after game;

the results of the comparison of the three scenarios are shown in table 2.

Table 2 comparison results of three scenarios

RIES in scene 1 is in island operation, no power exchange is performed with ADN, internal load is supported by wind-light units, CHP, EB and the like, translation of electricity supply and demand in time dimension is realized by utilizing energy storage equipment, electric energy smooth output characteristics of the wind-light units are improved, and utilization rate of internal equipment of the system is higher.

RIES in scene 2 runs in parallel with ADN, and is coupled with ADN at any time during running, so that real-time power exchange can be performed with ADN. The interactive electricity prices of ADN and RIES are divided into 3 prices of peak/flat/valley according to time interval. According to table 2, compared with scenario 1, the rias of scenario 2 can flexibly select peak shaving power sources by coupling with ADN, thereby reducing the natural gas purchasing cost by 8.52%, smoothing the energy supply curve, and reducing the demand response cost by 14.28%. And the RIES can purchase electricity to the ADN in the electricity-down period to charge the energy storage device, and release the electricity in the energy storage device at the electricity-peak time, so that the flexible margin of the energy storage device is improved, and the dispatching pressure of the ADN can be relieved to a certain extent.

In contrast to scenario 2, the interactive power rates in scenario 3 are established after active gaming. FIGS. 10-12 are ADN and RIES master-slave gaming scheduling results for scenario 3, and FIG. 13 is a carbon quota and carbon emissions metering scenario for RIES carbon transactions.

Combining ADN pricing results and two-body scheduling results can be found: ADN dynamically adjusts pricing policies to get higher yields, rias actively responds to ADN scheduling, optimizes self-coupling benefits and effectively improves flexible margins. The specific expression is as follows: 2-4, the ADN new energy output is excessive and the electricity price is at the valley time, and the RIES has higher heat load demand, so that the ADN valley filling scheduling is actively responded; 8-9, the ADN faces peak load, the RIES actively responds to the ADB load demand to supply power to the ADN, and the energy supply pressure is relieved; the new RIES energy source output is excessive at 12-15, the acquisition electricity price is reduced by ADN, and the excessive output is consumed for RIES while the benefit is ensured; 19-24, wherein ADN is still at peak-load at 19, ADN reduces energy supply to the rias, the rias enforces a demand response strategy and releases the energy storage, avoiding impact from peak load; and the ADN fan is vigorous in output at 20-24 hours, and electricity is sold to the RIES to slow down the energy supply pressure.

The response behavior of the RIES can be verified, and the pricing strategy of the ADN effectively stimulates the interactive behavior of the RIES on the premise of ensuring the benefit of the ADN, and simultaneously improves the scheduling margin of the ADN and the digestion capability of new energy.

In the carbon emission part, the new energy source is vigorous in output and sufficient in carbon quota in the process of 6-17, and meanwhile, a cleaner electric boiler is selected for supplying heat and charging energy for avoiding excessive carbon emission and absorbing new energy sources; 18-24, the RIES power supply pressure is too high, a cogeneration unit is selected to replace an electric boiler, multi-energy output is carried out, and meanwhile, a distribution network with high carbon emission is selected to purchase electricity for selling so as to relieve the energy supply pressure.

It can be seen that under the influence of the ladder carbon transaction regime, except when the peak load pressure is encountered at 19-24 hours and the carbon quota is sufficient at 2-4 hours, RIES selects to purchase electricity to ADN, and the rest has no excessive electricity purchasing phenomenon. Therefore, the LCA method based on the life cycle evaluation method is used for tracing the carbon emission behavior, so that the electricity purchasing dependence of RIES on ADN can be reduced to a certain extent, and the low-carbon benefit of the model provided by the invention is also verified.

In summary, the invention relates to a regional comprehensive energy system-main distribution network game optimization scheduling method considering life cycle theory, which introduces an LCA method under a ladder carbon transaction system and carries out low-carbonization modification on a RIES coupling side, so as to flexibly activate RIES load side resources by comprehensive demand response, and a master-slave game model taking ADN as a leader and RIES as a follower is constructed by considering ADN active scheduling characteristics, thereby realizing ordered interaction between ADN and RIES and low-carbon economic operation.

Although the present invention has been described above with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments, which are illustrative and not restrictive, and other forms may be made by those skilled in the art without departing from the spirit of the present invention, which are all within the protection of the present invention.

Claims

1. A game optimization scheduling method of a regional comprehensive energy system-main power distribution network is characterized by comprising the following steps:

2. The regional integrated energy system-main power distribution network game optimization scheduling method according to claim 1, wherein the RIES structure is specifically: the supply side consists of a power distribution network, wind power, photovoltaic and natural gas networks, the coupling side consists of an electric boiler, an electric energy storage device, a CCS carbon trapping device, a P2G electric conversion device and a cogeneration unit, and the demand side consists of an electric load and a thermal load; the power distribution network, the wind power and the photovoltaic provide electric energy required by the electric boiler and the electric energy storage device to the coupling side, and directly supply the electric energy to an electric load on the demand side; the natural gas net directly conveys natural gas to the hot spot CO-production unit so as to generate electric energy, capture heat energy and discharge CO by the micro-combustion engine and the bromine cooling machine in the hot spot CO-production unit ₂ CO exhausted by cogeneration unit ₂ Will be captured by the CCS carbon capture device and will capture CO ₂ Is supplied to a P2G electric conversion device, and the P2G electric conversion device captures CO ₂ Obtained by electrolysis of waterH of (2) ₂ Methane is generated by the reaction, and natural gas is replenished to the cogeneration unit through a natural gas pipeline; the heat and power cogeneration unit supplies electric energy to the electric load on the demand side, when the electric energy supplied by the heat and power cogeneration unit is insufficient to support the electric load demand on the demand side, the demand side takes electricity from the electric energy storage device, and if the electric energy is insufficient to support the electric load demand at the moment, electricity is purchased from the power distribution network, wind power and photovoltaic places; the heat load on the demand side is provided by a cogeneration unit and an electric boiler.

3. The regional comprehensive energy system-main power distribution network game optimization scheduling method according to claim 2, wherein the RIES low-carbon model consists of total power generation power, a response electric load model and an interruptible heat load model of a coupling side combined heat and power generation unit;

wherein P is _e,t And P _h,t Respectively the total power generation and the heat generation power of the cogeneration unit at the time t, P _echp,t Representing the electric energy supplied by the cogeneration unit to the power grid at the time t, and P _ep2g,t The electric energy of the P2G electric conversion device is supplied to the cogeneration unit at the time t, and P _ep2g,t ＝P _gs,t /θ，P _gs,t The natural gas output value of the P2G electric gas conversion device at the t moment is theta, and the conversion efficiency of the P2G electric gas conversion device is theta; p (P) _eccs,t Represents the electric energy supplied by the cogeneration unit to the CCS carbon trapping device at the time t, P _eccs,t ＝C _cc,t /δ，C _cc,t Representing CO captured by CCS carbon capture device at time t ₂ Quantity, δ represents carbon capture efficiency; p (P) _e,min And P _e,max Respectively represents the upper limit and the lower limit of the electric output force, k of the cogeneration unit _v1 And k _v2 Respectively representing the corresponding cogeneration unit electrothermal conversion coefficient, k when the output electric power is minimum and maximum _m Representing power coefficient, P of cogeneration unit under back pressure working condition _h0 Representing the corresponding output thermal power when the output electrical power of the cogeneration unit is minimum;

The response electrical load model is as follows:

the interruptible thermal load model is:

wherein P is _lh0,t And P _lh,t The heat load before and after the demand response is respectively,and->Is the indoor and outdoor temperature of the building, S is the indoor area of the building, epsilon _h Is the indoor heat loss under the temperature difference of unit building area, C _air Specific heat capacity per building area +.>Delta t is time variable and delta P is the variation of indoor temperature _h,t Gamma, the interruptible amount of thermal load for the user _DR As an interruptible scaling factor for the user's thermal load,/>and->Upper and lower limits for the temperature the user is in the comfort range.

4. The regional comprehensive energy system-main power distribution network game optimization scheduling method according to claim 3, wherein the establishment process of the reward and punishment ladder-type carbon transaction cost model is as follows:

5. The method for optimizing and scheduling a regional integrated energy system-main power distribution network game according to claim 4, wherein when i represents a RIES low carbon model, the actual carbon emission C of the RIES low carbon model _p,i Specifically denoted as C _p,R ：

Wherein P is _g,gr Representing the output power of the wind-solar operation link, e _g,gr Representing the carbon emission coefficient of the wind-solar operation link, e _g,gr ＝je _o E, j is the energy consumption calculation coefficient corresponding to the unit electric quantity, E _o For the intensity of carbon emissions for service or consumable replacement,e is the energy intensity of maintenance or consumable replacement; p (P) _t,N Representing the output power of the coupling device transportation link e _t,N Representing the carbon emission coefficient of the coupling device transportation link e _t,N ＝Q _N v _t D _t e _N ，Q _N Representing the total amount of transport energy, v _t D, corresponding to the energy consumption intensity of the fuel for the selected transportation mode _t E is the total transportation distance of the fire coal _N Representing the carbon emission coefficient of energy transportation required by the corresponding coupling equipment; p (P) _g,N Indicating the output power of the coupling device using link e _g,N Representing the carbon emission coefficient of the coupling equipment using link e _g,N ＝ε _N e _c,N ，ε _N Representing the unit energy conversion coefficient of the coupling equipment, e _c,N Representing the carbon emission coefficient of the energy conversion process of the coupling equipment; p (P) _R,ch Representing the total power of electricity purchase of RIES low-carbon model, e _R,ch Representing the carbon emission coefficient of unit electricity purchasing of the RIES low-carbon model; Δt represents the carbon emission time interval, C _cc Indicating the total amount of carbon dioxide captured by CCS;

6. The method for optimizing and scheduling a regional integrated energy system-main distribution network game according to claim 4, wherein when i represents an active distribution network ADN, the actual carbon emission amount C of the active distribution network ADN is _p,i Specifically denoted as C _p, A：

Wherein P is _p,c For the output power of coal mining links e _p,c E is the carbon emission coefficient of the coal exploitation link _p,c ＝e _m Q _p ，e _m Unit carbon emission coefficient of mining link, Q _p The raw coal exploitation amount is; p (P) _t,c E is the output power of the coal transportation link _t,c E is the carbon emission coefficient of the coal transportation link _t,c ＝υ _t e _t Q _t D _t ，υ _t E, corresponding to the energy consumption intensity of the fuel for the selected transportation mode _t Carbon emission coefficient, Q, of fuel used for transportation _t For the total transportation amount of fire coal D _t Is the total transportation distance of the fire coal; p (P) _g,c Output power of coal using link e _g,c E is the carbon emission coefficient of the burning link of the fire coal _g,c ＝N _g e _g ，N _g E, for standard coal consumption of power supply _g Carbon emission coefficient per ton of coal; p (P) _p,g E is the output power of the natural gas exploitation link _p,g Carbon emission coefficient e for natural gas exploitation _p,g ＝β _p,g e _gas ，β _p,g E is the natural gas exploitation overflow rate _gas The carbon emission intensity of the natural gas; p (P) _t,g E is the output power of the natural gas transportation link _t,g Is the carbon emission coefficient of the natural gas transportation link, e for natural gas weighting using pipeline transportation _p 、e _LNG Carbon emission coefficients corresponding to pipeline transportation and LNG liquefied natural gas transportation respectively; p (P) _A,ch E is the total power of electricity purchase of ADN _A,ch The carbon emission coefficient is purchased for the unit of ADN, and delta t is the carbon emission time interval;

C _c,A ＝χ _e (P _WPS +P _CFPP )+χ _ch,e P _A,ch (8)

7. The method for optimizing and scheduling the regional integrated energy system-main distribution network game according to claim 6, wherein the objective function with the minimum integrated cost before ADN days of the active distribution network is as follows:

wherein T is the number of scheduling hours before day, F _A,ch Mu, the purchase cost of ADN of the active distribution network _A,ch1 、μ _A,ch2 The price of electricity purchase of the active distribution network ADN to the RIES low-carbon model and the upper power grid unit is respectively,and->Purchasing electric power from the power distribution network to the RIES low-carbon model and the upper power grid for the t-th period; f (F) _A,loss Loss cost for active distribution network ADN, < ->ADN loss of active distribution network for t-th period>The electricity selling price of the ADN of the active power distribution network to other connected load units is set for the t-th period; f (F) _A,f The equipment operation maintenance cost of the ADN of the active power distribution network is Z is the number of equipment in the ADN of the active power distribution network, < ->Outputting power for the z-th device in the ADN of the active power distribution network in the t-th period, b _A,z A maintenance cost coefficient for a z-th device in the ADN of the active power distribution network; f (F) _c,A For the carbon transaction cost of the ADN of the active power distribution network, F _A,sell For selling electricity income of ADN of active distribution network, +.>Price per unit of electricity selling for the t-th time period ADN to RIES low-carbon model,/day >And->The method comprises the steps that electricity is sold to RIES and other loads connected by an active power distribution network ADN in a t-th period respectively;

P _WPS +P _CFPP +P _A,up +P _Ach,R ＝P _A +P _As.R (10)

wherein P is _A,up Purchasing electric power to an upper power grid for active power distribution network ADN, P _Ach,R ADN to RIES for active distribution networkPurchase power of low-carbon model, P _A Selling electric power to other loads connected to active distribution network ADN, P _As,R The electricity selling power from the ADN of the active distribution network to the RIES low-carbon model is realized;

8. The method for optimizing and scheduling the regional integrated energy system-main power distribution network game according to claim 5, wherein the RIES low-carbon model daily scheduling total cost objective function is as follows:

wherein F is _R,ch Is the purchase energy cost of RIES low-carbon model, F _R,sell Energy selling cost for RIES low-carbon model, F _R,f The running maintenance cost of the equipment for the RIES low-carbon model is F _DR The cost of demand response for the rias low-carbon model,price of unit electricity selling and heat selling energy source, mu, of RIES low-carbon model in the t-th period _R,ch For natural gas purchase unit price, G _gas The natural gas purchase amount of RIES, M is the total number of equipment contained in the RIES low-carbon model, b _R,m Maintaining a cost factor for the operation of the mth device in the RIES low-carbon model, +.>For the mth device in RIES low-carbon model to output power in the t-th period,/>And->The unit compensation cost of the electric and thermal interruptible loads is respectively;

the power balance constraint of the RIES low carbon model is expressed as:

9. The method for optimizing and scheduling the regional integrated energy system-main power distribution network game according to claim 1, wherein the step 4 comprises the following sub-steps: