CN113222465A - Comprehensive energy system optimization operation method considering carbon-green certificate transaction mechanism - Google Patents

Comprehensive energy system optimization operation method considering carbon-green certificate transaction mechanism Download PDF

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CN113222465A
CN113222465A CN202110606087.7A CN202110606087A CN113222465A CN 113222465 A CN113222465 A CN 113222465A CN 202110606087 A CN202110606087 A CN 202110606087A CN 113222465 A CN113222465 A CN 113222465A
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骆钊
贾芸睿
秦景辉
梁俊宇
刘泓志
王菁慧
耿家璐
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Kunming University of Science and Technology
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Abstract

The invention belongs to the technical field of the electric power market; the technical problem to be solved is as follows: the method is based on a CET mechanism and a GCT mechanism, an IES low-carbon economic dispatching model considering the CET mechanism and the green GCT mechanism is established, special points of the CET mechanism and the green GCT mechanism are comprehensively considered, results of different dispatching models are compared, and influences of different carbon trading prices and green certificate prices on system operation cost are analyzed; the technical scheme is as follows: the operation method comprises the following steps: step S1), researching the carbon-green certificate transaction concept, analyzing the carbon emission right and green certificate transaction mechanism and feasibility of implementing the mechanism in the IES; step S2), establishing a carbon-green certificate cost model and considering a carbon emission right and green certificate trading market value risk model; step S3) an IES optimization model for carbon-green license transactions is established and example analysis is performed.

Description

Comprehensive energy system optimization operation method considering carbon-green certificate transaction mechanism
Technical Field
The invention relates to an optimized operation method of a comprehensive energy system considering a carbon-green certificate transaction mechanism, belongs to the technical field of power markets, and particularly relates to an optimized operation method of a comprehensive energy system considering a carbon-green certificate transaction mechanism.
Background
The IES coordinates and optimizes links of energy generation, transmission, distribution, conversion, storage, consumption and the like, integrates various energy sources such as cold, heat, electricity, natural gas and the like in an area, realizes coordinated planning and optimized operation among various energy systems, and finally forms integration of energy generation, supply and marketing, so that the IES is one of important technical means for realizing the emission reduction target, however, the existing IES scheduling model only considers the overall economic cost of the system and needs to introduce a carbon emission right and a green certificate transaction mechanism.
Renewable energy quota system (RPS) and GCT mechanisms provide new approaches to increase Renewable energy power generation consumption and reduce carbon emissions. Compared with the traditional electricity price subsidy policy, the RPS and GCT mechanisms are another choice, and the essence is that the direct financial subsidy mode of the renewable energy government is gradually transited to the market subsidy mode. At present, renewable energy sources are more and more occupied in an integrated energy system, the comprehensive energy system has the basic condition of GCT, but the consumption rate of renewable energy power generation is very low, and a series of policies including RPS and GCT mechanisms are correspondingly issued by governments aiming at the problem. CET is an effective means for considering both electricity economy and low-carbon environmental protection, can effectively reduce the carbon emission of an electric power system, is a supplement of a GCT mechanism, and can effectively improve the consumption rate of renewable energy power generation and reduce the total cost of carbon emission and system operation by introducing the two mechanisms into IES.
Disclosure of Invention
The invention overcomes the defects of the prior art, and solves the technical problems that: the method is based on a CET mechanism and a GCT mechanism, an IES low-carbon economic dispatching model considering the CET mechanism and the green GCT mechanism is established, special points of the CET mechanism and the green GCT mechanism are comprehensively considered, results of different dispatching models are compared, and influences of different carbon trading prices and green certificate prices on system operation cost are analyzed.
In order to solve the technical problems, the invention adopts the technical scheme that: an integrated energy system optimization operation method considering a carbon-green certificate transaction mechanism comprises the following steps:
step S1), researching the carbon-green certificate transaction concept, analyzing the carbon emission right and green certificate transaction mechanism and feasibility of implementing the mechanism in the IES;
step S2), establishing a carbon-green certificate cost model and considering a carbon emission right and green certificate trading market value risk model;
step S3), an IES optimization model is established, and the carbon-green certificate cost model in the step S2) and the risk model considering the carbon emission right and the green certificate trading market value are introduced into the IES optimization model for simulation verification.
Said step S1) is to study the concept of carbon-green certificate transaction, analyze the carbon emission rights and the green certificate transaction mechanism and the feasibility of implementing the mechanism in the IES:
step S11): mechanism of CET
The CET is a trading mechanism for controlling the carbon emission by establishing a legal carbon emission right identification mechanism and allowing the legal carbon emission right identification mechanism to be bought and sold, under the mechanism, the carbon emission becomes a commodity which can be traded freely, an enterprise is allowed to trade the carbon emission right in the enterprise on the premise of not breaking through the CET regulation, a government or a supervision department aims at controlling the total carbon emission, a carbon emission quota is distributed to the enterprise containing a carbon emission source, the enterprise makes and adjusts a production plan according to the distribution quota, if the carbon emission generated in the process is higher than the distribution quota, the carbon emission needs to be purchased from a carbon trading market, and if the carbon emission is lower than the quota, the redundant carbon emission can be sold to obtain corresponding benefits;
step S12): mechanism of GCT
The green certificate is a certificate issued by a renewable energy power generator, and proves that a part of electric power of the power generator comes from renewable energy, the certificate also represents a certain amount of green electric quantity, and has certain timeliness, so that the price of the green certificate is determined by a short-term supply-demand relationship, and a green certificate trading system is a matched measure for ensuring the effective implementation of a renewable energy quota system, so that each responsible body trades in a high-efficiency and flexible manner, and aims to gradually transition renewable energy power generation from a government direct compensation mode to a market subsidy mode by implementing the renewable energy quota system and a GCT policy;
step S13): carbon-green certificate combined trading market
In order to improve the CET market and the GCT mechanism and improve the flexibility of market trading, a carbon-green certificate joint trading market frame is designed, and the trading process is as follows:
step S131), the IES applies for the administration supervision department, participates in green certificate transaction after the supervision department verifies the qualification, and the supervision department distributes different carbon emission and renewable energy electric quantity quotas for each IES;
step S132), the IES meeting the quota requirement sells the green certificate and the carbon emission on a green certificate transaction platform and a carbon emission right transaction platform, and the IES or other organizations not meeting the quota requirement can purchase on the platforms, otherwise, the IES is punished.
In step S2), a carbon-green certificate cost model is established, and a carbon emission right and green certificate trading market value risk model is considered:
step S21) CET cost
Determining the portion of the uncompensated carbon emission in the IES by adopting a reference line method and a pre-distribution method, wherein the quota amount is the sum of quotas of various units owned by the IES, the carbon emission source in the IES mainly comprises a micro-combustion engine and a gas-fired boiler, and the portion of the uncompensated carbon emission is determined by an equation (1):
CL＝Ce+Ch
Figure BDA0003094146720000031
Figure BDA0003094146720000032
in the formula: cLIs the carbon emission fraction; ceCarbon quota for micro gas turbine; chCarbon quota for gas boiler;
Figure BDA0003094146720000033
the generated power of the micro-combustion engine; b isgIs micro-combustion engine CO2Emission basis, unit: tCO2/MWh；FeThe coefficient is corrected by a unit cooling mode, the water cooling is 1, and the air cooling is 1.05; frThe heat supply correction coefficient of the micro-combustion engine is 1-0.22 x alphaGT(thermoelectric ratio); ffA unit load (output) coefficient correction coefficient;
Figure BDA0003094146720000034
outputting power for the gas boiler; b ishSupplying heat to CO for gas-fired boiler2Carbon emission baseline, unit: tCO2/GJ；
The actual carbon emission of the IES is determined by the output of the micro-combustion engine and the gas boiler, as shown in equation (2):
Figure BDA0003094146720000035
in the formula: a is1,b1,c1Calculating coefficients for carbon emission of the micro-gas turbine set; a is2，b2，c2Is the carbon emission coefficient of the gas boiler;
during solving, the carbon emission of the system is subjected to piecewise linearization treatment, a scheduling model of each interval is changed into a mixed integer linear programming problem, the piecewise linearization principle of the micro gas turbine composition function is used for reference, and piecewise linearization models are adopted to describe and optimize the nonlinear electro-thermal coupling curves of the micro gas turbine and the gas turbine so as to rapidly solve and apply;
the cost calculation model of the step-type transaction is as follows:
Figure BDA0003094146720000041
in the formula:
Figure BDA0003094146720000042
cost for integrated energy system carbon trading; lambda is the CET price on the market; d is the length of the carbon emission interval; sigma is the price increase amplitude of each step CET;
the CET price increases by σ λ for every step up; cP<CLWhen the temperature of the water is higher than the set temperature,
Figure BDA0003094146720000043
will be negative, indicating that CET brings a benefit;
step S22) GCT model
GCT revenue is divided into two cases: firstly, the system consumes the renewable energy generated energy which is smaller than the daily quota value, needs to purchase green certificates, and accepts punishment if the generated energy does not reach the standard; secondly, the consumption of the renewable energy power generation amount is larger than the daily quota system, and the green sale is paid, and the specific model is shown as the formula (4):
Figure BDA0003094146720000044
in the formula:
Figure BDA0003094146720000045
representing a purchase or sale of a green certificate price; presDaily quota amount for renewable energy; pwThe actual consumption is; cpIs a penalty coefficient;
step S23) carbon-Green license transaction Risk measurement
Introducing a Cvar method to quantify the market risk degree of both CET and GCT, wherein Cvar is a risk analysis method based on risk value improvement and is used for representing the condition mean value of loss exceeding VaR under a certain straight line interval level;
market risk utility of carbon trader i
Figure BDA0003094146720000046
Represents:
Figure BDA0003094146720000051
in the formula: f (x)iζ) is a constructed auxiliary function; x is the number ofiTrading volume for a carbon trading party; the optimization result of ζ is defined as the VaR value of the carbon trading cost;
Figure BDA0003094146720000052
is the confidence level;
Figure BDA0003094146720000053
is (K)p1，Kp2，…，KpH) The method is characterized in that a carbon trading party i trades a comprehensive index under H historical data;
the overall risk to the market for g carbon traders is expressed as follows:
Figure BDA0003094146720000054
the market risk metric constraints of carbon traders in participation in trading are as follows:
1) and (4) risk value constraint:
Figure BDA0003094146720000055
2) risk value non-negative constraint:
0≤Sz，z＝1，2，…H (8)
in the formula: the risk value of the carbon trader i in scenario z must not be greater than the limit S allowed in the marketz；
Similarly, the green certificate transaction risk metric value is expressed as follows:
Figure BDA0003094146720000056
in the formula: f (y)iγ) is a constructed auxiliary function, yiThe optimization result of gamma is defined as VaR value of green certificate trade cost, beta is confidence coefficient,
Figure BDA0003094146720000057
is (e)p1，ep2，…，epH) Is a comprehensive index of green certificate transaction of a transaction party i under H historical data;
the overall risk of the trading market for n green-letter traders is expressed as follows:
Figure BDA0003094146720000058
the green trading party and the trading market risk measure are constrained as follows:
1) and (4) risk value constraint:
Figure BDA0003094146720000059
2) risk value non-negative constraint:
0≤Sz，z＝1，2，…H (12)
in the formula: the risk value of the green certificate transaction party i in the scene z must not be greater than the limit value S allowed in the marketz。
Step S3), an IES optimization model is established, and the carbon-green certificate cost model in the step S2) and the risk model considering the carbon emission right and the green certificate trading market value are introduced into the IES optimization model for simulation verification: step S31) objective function
For the grid-connected operation IES containing the CET and the GCT, considering quota requirements and existing market risks, on one hand, the IES must consume the renewable energy electric quantity which is not less than the quota quantity; on the other hand, the carbon emission of the system does not exceed the quota standard, and the minimum of the purchase electricity, gas cost, carbon transaction cost, green certificate transaction cost and the operation and maintenance cost of other equipment in the system is taken as an objective function:
Figure BDA0003094146720000061
in the formula: c is the system operating cost, CeFor electricity purchase charge, CgasFor purchase of natural gas, CesFor operating and maintaining the electricity storage apparatus, ChsFor the operating and maintenance costs of the thermal storage equipment in the system,
Figure BDA0003094146720000062
carbon trading costs for the IES to consider market risk,
Figure BDA0003094146720000063
green certificate revenue for market risk consideration;
the electricity purchasing cost formula is as follows:
Figure BDA0003094146720000064
in the formula:
Figure BDA0003094146720000065
for the electricity purchase price of the system at time t,
Figure BDA0003094146720000066
exchanging power between the park comprehensive energy system and the power grid at the time t;
the gas cost formula is:
Figure BDA0003094146720000067
in the formula: c. CgasThe unit heat value price of the natural gas,
Figure BDA0003094146720000068
the generated power of the micro combustion engine at the time t is shown,
Figure BDA0003094146720000069
indicating the heat production power, eta, of the gas-fired boilerg，ηbRespectively the efficiency of the micro-gas turbine and the gas boiler;
assuming that the use cost of the single charge and discharge of the electric storage equipment is the same, the purchase cost is CpurchaseThe number of times of use is M under the condition of no damagecyclesThen its cost per full charge and discharge crComprises the following steps:
Figure BDA00030941467200000610
the operation and maintenance cost formula of the power storage device is as follows:
Figure BDA0003094146720000071
in the formula: ccapacityAs the capacity of the electric storage device,
Figure BDA0003094146720000072
the charge-discharge power of the electrical storage device at time t; the operation and maintenance cost formula of the heat storage equipment is as follows:
Figure BDA0003094146720000073
in the formula:
Figure BDA0003094146720000074
the charging and discharging power of the heat storage equipment is in a time interval t;
step S32) constraint conditions
1) The lowest selling price of the green certificate market is the current value of the fund subsidy of the electricity price of the renewable energy source and the additional fund, the highest selling price is not higher than the difference value between the electricity price of the renewable energy source for surfing the internet and the electricity price of the micro-combustion engine post, and the lowest and highest selling price limits of the green certificate market are as follows:
Figure BDA0003094146720000075
Figure BDA0003094146720000076
in the formula:
Figure BDA0003094146720000077
the upper and lower price limits of green certificate; siThe internet-surfing electricity price is the ith green power certificate; c is the price of electricity for the post of the micro-combustion engine; r isiThe rate of conversion of the ith green electric energy is reduced; h isiSubsidizing a settlement period for the ith green energy price additional fund; diA payment period is postponed for the subsidy amount of the additional fund for the electricity price of the ith green electric energy;
2) green certificate quota constraint:
Figure BDA0003094146720000078
in the formula: g is a quantization coefficient which represents the quantity of green certificates which can be obtained by green electric energy of a production unit; alpha is alphaiThe renewable power generation ratio of the ith power generation enterprise in a given time is obtained; etaiInitially distributing electric quantity for the ith power generation enterprise; ggreThe number of green certificates; piActual power generation amount for the ith power generation equipment; pi0Initially distributing electric quantity for the ith power generation enterprise;
3) electrical bus balance constraint:
Figure BDA0003094146720000079
in the formula:
Figure BDA0003094146720000081
indicating light at time tThe generated power of the volt;
Figure BDA0003094146720000082
representing the power generation output of the fan at the time t;
Figure BDA0003094146720000083
represents the discharge power of the electric storage device at time t;
Figure BDA0003094146720000084
is an electrical load;
Figure BDA0003094146720000085
the power of the electric refrigerator at the moment t is represented;
Figure BDA0003094146720000086
represents the charging power of the electrical storage device at time t;
4) and (3) hot water bus balance constraint:
Figure BDA0003094146720000087
in the formula: etawhWhich represents the efficiency of the waste heat boiler,
Figure BDA0003094146720000088
representing the power of the waste heat boiler at the moment t;
Figure BDA0003094146720000089
representing the thermal load power at the time t;
5) cold load power balance constraint:
Figure BDA00030941467200000810
in the formula: etaecRepresenting the refrigeration coefficient of the electric refrigerator;
Figure BDA00030941467200000811
representing the power of the electric refrigerator at time t; etaacExpressing the refrigeration coefficient of the adsorption refrigerator;
Figure BDA00030941467200000812
represents the power of the adsorption refrigerator at time t;
Figure BDA00030941467200000813
representing the cold load power at the moment t;
6) steam bus balance constraint:
Figure BDA00030941467200000814
in the formula:
Figure BDA00030941467200000815
representing the heat production power of the gas boiler;
Figure BDA00030941467200000816
representing the power of the heat exchange equipment at the moment t;
7) balance constraint of natural gas flow:
Figure BDA00030941467200000817
in the formula:
Figure BDA00030941467200000818
representing the natural gas quantity of the gas source injection node n;
Figure BDA00030941467200000819
represents the injection and output quantity of the gas storage tank in the period t;
Figure BDA00030941467200000820
representing the natural gas load of a node n at the time t;
Figure BDA00030941467200000821
representing the outflow and inflow between the nodes n-m at the time t;
8) natural gas pipeline restraint:
Figure BDA00030941467200000822
in the formula:
Figure BDA00030941467200000823
representing the pipeline flow between nodes n-m at the time t; snmIs a constant related to the pipeline parameters and gas density;
Figure BDA00030941467200000824
pressure values for nodes n and m;
9) natural gas-to-heat conversion constraints:
Figure BDA00030941467200000825
in the formula:
Figure BDA0003094146720000091
representing natural gas power; hgasIs the heat value of natural gas;
Figure BDA0003094146720000092
represents the natural gas flow rate;
10) gas turbine thermoelectric balance constraint:
Figure BDA0003094146720000093
in the formula:
Figure BDA0003094146720000094
is a system alphagRepresenting the heat-to-electricity ratio of the gas turbine;
Figure BDA0003094146720000095
representing the output of the gas turbine at time t;
11) electric storage device operation constraint:
Figure BDA0003094146720000096
in the formula:
Figure BDA0003094146720000097
representing the upper and lower limits of the charging power of the storage battery;
Figure BDA0003094146720000098
representing the upper and lower limits of the discharge power of the storage battery;
Figure BDA0003094146720000099
marking bits for the charging and discharging states of the storage battery, wherein when the bits are 0, the charging and discharging are stopped, and when the bits are 1, the charging and discharging are carried out;
Figure BDA00030941467200000910
representing the electric energy stored by the storage battery at the moment t; sigmaesThe self-discharge rate of the storage battery; etaes，c，ηes，dRespectively the charging efficiency and the discharging efficiency of the storage battery;
Figure BDA00030941467200000911
representing the upper and lower limits of the energy storage of the storage battery;
12) gas turbine operating constraints:
Figure BDA00030941467200000912
in the formula:
Figure BDA00030941467200000913
representing the fuel consumption input by the gas turbine at the time t;
Figure BDA00030941467200000914
marking the position for starting and stopping the gas turbine, wherein the marking indicates stopping when the marking position is 0, and indicates starting up when the marking position is 1;
Figure BDA00030941467200000915
representing the upper and lower limits of gas turbine power;
13) and (3) power purchasing power constraint:
Figure BDA00030941467200000916
in the formula:
Figure BDA00030941467200000917
the upper limit value of the power purchasing from the system to the power grid;
14) the operation of the heat storage equipment is restricted:
Figure BDA00030941467200000918
in the formula:
Figure BDA0003094146720000101
upper and lower limits of the charging power of the heat storage equipment;
Figure BDA0003094146720000102
the upper and lower limits of the heat release power of the heat storage equipment;
Figure BDA0003094146720000103
marking bits for the charging and discharging states of the heat storage equipment, wherein the variables are 0-1;
Figure BDA0003094146720000104
electric energy stored for the heat storage equipment at the moment t; sigmahsThe self-heat release rate of the heat storage device; etahs,c、ηhs,dThe heat charging efficiency and the heat discharging efficiency of the heat storage equipment are respectively;
Figure BDA0003094146720000105
upper and lower limits of heat storage power for the heat storage equipment;
15) photovoltaic and wind power output restraint:
Figure BDA0003094146720000106
in the formula:
Figure BDA0003094146720000107
and
Figure BDA0003094146720000108
respectively representing the predicted output of the wind turbine generator and the photovoltaic generator;
16) the energy conversion device restrains:
an electric refrigerator:
Figure BDA0003094146720000109
adsorption type refrigerator:
Figure BDA00030941467200001010
electric heating device:
Figure BDA00030941467200001011
heat exchanger, exhaust-heat boiler:
Figure BDA00030941467200001012
in the formula:
Figure BDA00030941467200001013
electric power, COP, consumed for the electric refrigerator at time tecIn order to be the refrigeration coefficient thereof,
Figure BDA00030941467200001014
is the converted power;
Figure BDA00030941467200001015
for the electric power consumed by the adsorption refrigerator at time t,
Figure BDA00030941467200001016
the power after conversion of the waste heat boiler, omega, the distribution coefficient of the waste heat flue gas and COPacThe amount of the refrigerant, which is the refrigeration coefficient,
Figure BDA00030941467200001017
for its converted power;
Figure BDA00030941467200001018
electric power, COP, consumed for the thermal conversion device at time thxIn order to obtain a high heating coefficient,
Figure BDA00030941467200001019
is the converted power;
17) and (3) output constraint of the energy conversion device:
Figure BDA00030941467200001020
Figure BDA00030941467200001021
Figure BDA00030941467200001022
Figure BDA00030941467200001023
in the formula:
Figure BDA0003094146720000111
the minimum and maximum force output values of the energy conversion device are respectively;
step S33) example analysis
Establishing an IES optimization model containing carbon-green certificate transactions, and introducing the carbon-green certificate cost model in the step S2) and a risk model considering carbon emission rights and green certificate transaction market value into the IES optimization model for simulation verification by means of YALMIP and CPLEX.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides an optimized operation method of a comprehensive energy system considering a carbon-green certificate transaction mechanism, which obtains the following conclusion: (1) the CET and GCT mechanisms realize energy conservation and emission reduction in a marketization mode, so that each participant preferentially considers consumption of renewable energy electric quantity for realizing self benefit maximization, and the economic efficiency and the feasibility are good; (2) comprehensively considering the influence of the price change of the carbon-green certificate on the system operation cost in a certain range, when the price of the carbon-green certificate is increased, the system operation cost is reduced, otherwise, the system cost is increased; (3) the CET and GCT mechanisms are implemented in IES, mutual incentive and complementary characteristics of the CET and the GCT are fully considered, the national 'double-carbon' strategic target is met, and the economic and environmental superiority of the model is proved by the algorithm.
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The present invention will be described in further detail with reference to the accompanying drawings;
FIG. 1 is a schematic flow diagram of the process of the present invention;
figure 2 is a block diagram of the IES;
FIG. 3 is the effect of CET price on system cost;
FIG. 4 is a gas turbine plant capacity event;
FIG. 5 is green certificate-the effect of natural gas price on system cost;
FIG. 6 is the impact of carbon-natural gas price on system cost;
FIG. 7 is the cooling, heating and power load real-time power;
FIG. 8 is a wind-photovoltaic real-time output;
fig. 9 is time-of-use electricity prices.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention; all other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention relates to an optimized operation method of a comprehensive energy system considering a carbon-green certificate transaction mechanism, which comprises the following steps of:
step S1), researching the carbon-green certificate transaction concept, analyzing the carbon emission right and green certificate transaction mechanism and feasibility of implementing the mechanism in the IES;
step S2), establishing a carbon-green certificate cost model and considering a carbon emission right and green certificate trading market value risk model;
step S3), an IES optimization model is established, and the carbon-green certificate cost model in the step S2) and the risk model considering the carbon emission right and the green certificate trading market value are introduced into the IES optimization model for simulation verification.
Said step S1) is to study the concept of carbon-green certificate transaction, analyze the carbon emission rights and the green certificate transaction mechanism and the feasibility of implementing the mechanism in the IES:
step S11): mechanism of CET
The CET is a trading mechanism for controlling the carbon emission by establishing a legal carbon emission right identification mechanism and allowing the legal carbon emission right identification mechanism to be bought and sold, under the mechanism, the carbon emission becomes a commodity which can be traded freely, an enterprise is allowed to trade the carbon emission right in the enterprise on the premise of not breaking through the CET regulation, a government or a supervision department aims at controlling the total carbon emission, a carbon emission quota is distributed to the enterprise containing a carbon emission source, the enterprise makes and adjusts a production plan according to the distribution quota, if the carbon emission generated in the process is higher than the distribution quota, the carbon emission needs to be purchased from a carbon trading market, and if the carbon emission is lower than the quota, the redundant carbon emission can be sold to obtain corresponding benefits;
step S12): mechanism of GCT
The green certificate is a certificate issued by a renewable energy power generator, and proves that a part of electric power of the power generator comes from renewable energy, the certificate also represents a certain amount of green electric quantity, and has certain timeliness, so that the price of the green certificate is determined by a short-term supply-demand relationship, and a green certificate trading system is a matched measure for ensuring the effective implementation of a renewable energy quota system, so that each responsible body trades in a high-efficiency and flexible manner, and aims to gradually transition renewable energy power generation from a government direct compensation mode to a market subsidy mode by implementing the renewable energy quota system and a GCT policy;
step S13): carbon-green certificate combined trading market
In order to improve the CET market and the GCT mechanism and improve the flexibility of market trading, a carbon-green certificate joint trading market frame is designed, and the trading process is as follows:
step S131), the IES applies for the administration supervision department, participates in green certificate transaction after the supervision department verifies the qualification, and the supervision department distributes different carbon emission and renewable energy electric quantity quotas for each IES;
step S132), the IES meeting the quota requirement sells the green certificate and the carbon emission on a green certificate transaction platform and a carbon emission right transaction platform, and the IES or other organizations not meeting the quota requirement can purchase on the platforms, otherwise, the IES is punished.
The IES system meeting the quota requirement sells green certificates and carbon emission, not only responds to the national policy requirement, but also reduces the operation cost of the IES, and has higher economy and feasibility.
In the step S2), a carbon-green certificate cost model is established and a carbon emission right and green certificate trading market value risk model is considered:
step S21) CET cost
Carbon trading is in a fully developed stage in china. At present, the carbon emission right distribution method at home and abroad mainly comprises 3 modes of free distribution, auction distribution and free + auction mixed distribution, and the free distribution method is mainly adopted at home. The invention adopts a reference line method and a pre-distribution method to determine the free carbon emission share in IES, the quota amount is the sum of quotas of various units owned by the IES, the carbon emission source in the IES mainly comprises a micro-combustion engine and a gas boiler, and the free carbon emission share is determined by the formula (1):
CL＝Ce+Ch
Figure BDA0003094146720000131
Figure BDA0003094146720000132
in the formula: cLIs the carbon emission fraction; ceCarbon quota for micro gas turbine; chCarbon quota for gas boiler;
Figure BDA0003094146720000133
the generated power of the micro-combustion engine; b isgIs micro-combustion engine CO2Emission basis, unit: tCO2/MWh；FeThe coefficient is corrected by a unit cooling mode, the water cooling is 1, and the air cooling is 1.05; frThe heat supply correction coefficient of the micro-combustion engine is 1-0.22 x alphaGT(thermoelectric ratio); ffA unit load (output) coefficient correction coefficient;
Figure BDA0003094146720000134
outputting power for the gas boiler; b ishSupplying heat to CO for gas-fired boiler2Carbon emission baseline, unit: tCO2/GJ；
The actual carbon emission of the IES is determined by the output of the micro-combustion engine and the gas boiler, as shown in equation (2):
Figure BDA0003094146720000135
in the formula: a is1,b1,c1Calculating coefficients for carbon emission of the micro-gas turbine set; a is2,b2,c2Is the carbon emission coefficient of the gas boiler;
during solving, the carbon emission of the system is subjected to piecewise linearization processing, a scheduling model of each interval is changed into a Mixed Integer linear Programming Problem (MINLP), and for Mixed Integer linear Programming, an intelligent algorithm is mostly adopted for solving, but the Mixed Integer linear Programming is easy to fall into local optimization and consumes a long time. The invention uses the principle of the piecewise linearization micro-gas turbine composition function for reference, and adopts piecewise linearization model description and optimization to the nonlinear electro-thermal coupling curve of the micro-gas turbine and the gas turbine so as to rapidly solve and apply;
the cost calculation model of the step-type transaction is as follows:
Figure BDA0003094146720000141
in the formula:
Figure BDA0003094146720000142
cost for integrated energy system carbon trading; lambda is the CET price on the market; d is the length of the carbon emission interval; sigma is the price increase amplitude of each step CET;
the CET price increases by σ λ for every step up; cP<CLWhen the temperature of the water is higher than the set temperature,
Figure BDA0003094146720000143
will be negative, indicating that CET brings a benefit;
step S22) GCT model
GCT revenue is divided into two cases: firstly, the system consumes the renewable energy generated energy which is smaller than the daily quota value, needs to purchase green certificates, and accepts punishment if the generated energy does not reach the standard; secondly, the consumption of the renewable energy power generation amount is larger than the daily quota system, and the green sale is paid, and the specific model is shown as the formula (4):
Figure BDA0003094146720000144
in the formula:
Figure BDA0003094146720000145
representing a purchase or sale of a green certificate price; presDaily quota amount for renewable energy; pwThe actual consumption is; cpIs a penalty coefficient;
step S23) carbon-Green license transaction Risk measurement
The invention introduces a Cvar (condition Value at Risk) method to quantify the market risk degree of both CET and GCT, and the condition risk Value (Cvar) is a risk analysis method based on risk Value improvement and is used for representing the condition mean Value of loss exceeding the VaR under a certain linear interval level;
market risk utility of carbon trader i
Figure BDA0003094146720000151
Represents:
Figure BDA0003094146720000152
in the formula: f (x)iζ) is a constructed auxiliary function; x is the number ofiTrading volume for a carbon trading party; the optimization result of ζ is defined as the VaR value of the carbon trading cost;
Figure BDA0003094146720000153
is the confidence level;
Figure BDA0003094146720000154
is (K)p1，Kp2，…，KpH) The method is characterized in that a carbon trading party i trades a comprehensive index under H historical data; historical data can be obtained by applying to a transaction supervision department, and weights of all regions are different for actual market-invested operation data;
the overall risk to the market for g carbon traders is expressed as follows:
Figure BDA0003094146720000155
the market risk metric constraints of carbon traders in participation in trading are as follows:
1) and (4) risk value constraint:
Figure BDA0003094146720000159
2) risk value non-negative constraint:
0≤Sz，z＝1，2，…H (8)
in the formula: the risk value of the carbon trader i in scenario z must not be greater than the limit S allowed in the marketz；
Similarly, the green certificate transaction risk metric value is expressed as follows:
Figure BDA0003094146720000156
in the formula: f (y)iγ) is a constructed auxiliary function, yiThe optimization result of gamma is defined as VaR value of green certificate trade cost, beta is confidence coefficient,
Figure BDA0003094146720000157
is (e)p1，ep2，…，epH) The green certificate trading method is characterized in that a green certificate trading party i trades comprehensive indexes under H pieces of historical data, the historical data can be applied to a trading supervision department, and values of different regions are different for actual market-invested operation data;
the overall risk of the trading market for n green-letter traders is expressed as follows:
Figure BDA0003094146720000158
the green trading party and the trading market risk measure are constrained as follows:
1) and (4) risk value constraint:
Figure BDA0003094146720000161
2) risk value non-negative constraint:
0≤Sz,z＝1,2,…H (12)
in the formula: the risk value of the green certificate transaction party i in the scene z must not be greater than the limit value S allowed in the marketz。
The step S3) is used for establishing an IES optimization model, and introducing the carbon-green certificate cost model in the step S2) and a risk model for considering carbon emission rights and green certificate trading market values into the IES optimization model for simulation verification;
the IES architecture diagram of the energy hub based study of the present invention is shown in fig. 2, where the energy input includes grid power, micro gas turbine, wind, photovoltaic and natural gas; the energy conversion equipment comprises a gas boiler, an electric refrigerator and a waste heat recovery device; the energy storage device is a storage battery, a heat storage device and a gas storage device, a detailed model of IES (intelligent electronic equipment) optimization scheduling of carbon-green certificate transaction is specifically introduced below, and the economy of the model in different cases is compared:
step S31) objective function
For the grid-connected operation IES containing the CET and the GCT, considering quota requirements and existing market risks, on one hand, the IES must consume the renewable energy electric quantity which is not less than the quota quantity; on the other hand, the carbon emission of the system does not exceed the quota standard, and the minimum of the purchase electricity, gas cost, carbon transaction cost, green certificate transaction cost and the operation and maintenance cost of other equipment in the system is taken as an objective function:
Figure BDA0003094146720000162
in the formula: c is the system operating cost, CeFor electricity purchase charge, CgasFor purchase of natural gas, CesFor operating and maintaining the electricity storage apparatus, ChsFor the operating and maintenance costs of the thermal storage equipment in the system,
Figure BDA0003094146720000163
carbon trading costs for the IES to consider market risk,
Figure BDA0003094146720000164
green certificate revenue for market risk consideration;
the electricity purchasing cost formula is as follows:
Figure BDA0003094146720000165
in the formula:
Figure BDA0003094146720000166
for the electricity purchase price of the system at time t,
Figure BDA0003094146720000167
exchanging power between the park comprehensive energy system and the power grid at the time t;
the gas cost formula is:
Figure BDA0003094146720000171
in the formula: c. CgasThe unit heat value price of the natural gas,
Figure BDA0003094146720000172
the generated power of the micro combustion engine at the time t is shown,
Figure BDA0003094146720000173
indicating the heat production power, eta, of the gas-fired boilerg，ηbRespectively the efficiency of the micro-gas turbine and the gas boiler;
assuming that the use cost of the single charge and discharge of the electric storage equipment is the same, the purchase cost is CpurchaseThe number of times of use is M under the condition of no damagecyclesThen its cost per full charge and discharge crComprises the following steps:
Figure BDA0003094146720000174
the operation and maintenance cost formula of the power storage device is as follows:
Figure BDA0003094146720000175
in the formula: ccapacityAs the capacity of the electric storage device,
Figure BDA0003094146720000176
the charge-discharge power of the electrical storage device at time t; the operation and maintenance cost formula of the heat storage equipment is as follows:
Figure BDA0003094146720000177
in the formula:
Figure BDA0003094146720000178
the charging and discharging power of the heat storage equipment is in a time interval t;
step S32) constraint conditions
1) The lowest selling price of the green certificate market is the current value of the fund subsidy of the electricity price of the renewable energy source and the additional fund, the highest selling price is not higher than the difference value between the electricity price of the renewable energy source for surfing the internet and the electricity price of the micro-combustion engine post, and the lowest and highest selling price limits of the green certificate market are as follows:
Figure BDA0003094146720000179
Figure BDA00030941467200001710
in the formula:
Figure BDA00030941467200001711
the upper and lower price limits of green certificate; siThe internet-surfing electricity price is the ith green power certificate; c is the price of electricity for the post of the micro-combustion engine; r isiThe rate of conversion of the ith green electric energy is reduced; h isiSubsidizing a settlement period for the ith green energy price additional fund; diIs the ith green electric energy source electricityA payment period is postponed by subsidy amount of additional funds;
2) green certificate quota constraint:
Figure BDA0003094146720000181
in the formula: g is a quantization coefficient which represents the quantity of green certificates which can be obtained by green electric energy of a production unit; alpha is alphaiThe renewable power generation ratio of the ith power generation enterprise in a given time is obtained; etaiInitially distributing electric quantity for the ith power generation enterprise; ggreThe number of green certificates; piActual power generation amount for the ith power generation equipment; pi0Initially distributing electric quantity for the ith power generation enterprise;
3) electrical bus balance constraint:
Figure BDA0003094146720000182
in the formula:
Figure BDA0003094146720000183
representing the generated power of the photovoltaic at the time t;
Figure BDA0003094146720000184
representing the power generation output of the fan at the time t;
Figure BDA0003094146720000185
represents the discharge power of the electric storage device at time t;
Figure BDA0003094146720000186
is an electrical load;
Figure BDA0003094146720000187
the power of the electric refrigerator at the moment t is represented;
Figure BDA0003094146720000188
represents the charging power of the electrical storage device at time t;
4) and (3) hot water bus balance constraint:
Figure BDA0003094146720000189
in the formula: etawhWhich represents the efficiency of the waste heat boiler,
Figure BDA00030941467200001810
representing the power of the waste heat boiler at the moment t;
Figure BDA00030941467200001811
representing the thermal load power at the time t;
5) cold load power balance constraint:
Figure BDA00030941467200001812
in the formula: etaecRepresenting the refrigeration coefficient of the electric refrigerator;
Figure BDA00030941467200001813
representing the power of the electric refrigerator at time t; etaacExpressing the refrigeration coefficient of the adsorption refrigerator;
Figure BDA00030941467200001814
represents the power of the adsorption refrigerator at time t;
Figure BDA00030941467200001815
representing the cold load power at the moment t;
6) steam bus balance constraint:
Figure BDA00030941467200001816
in the formula:
Figure BDA00030941467200001817
representing the heat production power of the gas boiler;
Figure BDA00030941467200001818
representing the power of the heat exchange equipment at the moment t;
7) balance constraint of natural gas flow:
Figure BDA00030941467200001819
in the formula:
Figure BDA0003094146720000191
representing the natural gas quantity of the gas source injection node n;
Figure BDA0003094146720000192
represents the injection and output quantity of the gas storage tank in the period t;
Figure BDA0003094146720000193
representing the natural gas load of a node n at the time t;
Figure BDA0003094146720000194
representing the outflow and inflow between the nodes n-m at the time t;
8) natural gas pipeline restraint:
Figure BDA0003094146720000195
in the formula:
Figure BDA0003094146720000196
representing the pipeline flow between nodes n-m at the time t; snmIs a constant related to the pipeline parameters and gas density;
Figure BDA0003094146720000197
pressure values for nodes n and m;
9) natural gas-to-heat conversion constraints:
Figure BDA0003094146720000198
in the formula:
Figure BDA0003094146720000199
representing natural gas power; hgasIs the heat value of natural gas;
Figure BDA00030941467200001910
represents the natural gas flow rate;
10) gas turbine thermoelectric balance constraint:
Figure BDA00030941467200001911
in the formula:
Figure BDA00030941467200001912
is a system alphagRepresenting the heat-to-electricity ratio of the gas turbine;
Figure BDA00030941467200001913
representing the output of the gas turbine at time t;
11) electric storage device operation constraint:
Figure BDA00030941467200001914
in the formula:
Figure BDA00030941467200001915
representing the upper and lower limits of the charging power of the storage battery;
Figure BDA00030941467200001916
representing the upper and lower limits of the discharge power of the storage battery;
Figure BDA00030941467200001917
marking bits for the charging and discharging states of the storage battery, wherein when the bits are 0, the charging and discharging are stopped, and when the bits are 1, the charging and discharging are carried out;
Figure BDA00030941467200001918
representing the electric energy stored by the storage battery at the moment t; sigmaesThe self-discharge rate of the storage battery; etaes,c，ηes,dRespectively the charging efficiency and the discharging efficiency of the storage battery;
Figure BDA00030941467200001919
representing the upper and lower limits of the energy storage of the storage battery;
12) gas turbine operating constraints:
Figure BDA00030941467200001920
in the formula:
Figure BDA00030941467200001921
representing the fuel consumption input by the gas turbine at the time t;
Figure BDA00030941467200001922
marking the position for starting and stopping the gas turbine, wherein the marking indicates stopping when the marking position is 0, and indicates starting up when the marking position is 1;
Figure BDA0003094146720000201
representing the upper and lower limits of gas turbine power;
13) and (3) power purchasing power constraint:
Figure BDA0003094146720000202
in the formula:
Figure BDA0003094146720000203
the upper limit value of the power purchasing from the system to the power grid;
14) the operation of the heat storage equipment is restricted:
Figure BDA0003094146720000204
in the formula：
Figure BDA0003094146720000205
Upper and lower limits of the charging power of the heat storage equipment;
Figure BDA0003094146720000206
the upper and lower limits of the heat release power of the heat storage equipment;
Figure BDA0003094146720000207
marking bits for the charging and discharging states of the heat storage equipment, wherein the variables are 0-1;
Figure BDA0003094146720000208
electric energy stored for the heat storage equipment at the moment t; sigmahsThe self-heat release rate of the heat storage device; etahs,c、ηhs,dThe heat charging efficiency and the heat discharging efficiency of the heat storage equipment are respectively;
Figure BDA0003094146720000209
upper and lower limits of heat storage power for the heat storage equipment;
15) photovoltaic and wind power output restraint:
Figure BDA00030941467200002010
in the formula:
Figure BDA00030941467200002011
and
Figure BDA00030941467200002012
respectively representing the predicted output of the wind turbine generator and the photovoltaic generator;
16) the energy conversion device restrains:
an electric refrigerator:
Figure BDA00030941467200002013
adsorption type refrigerator:
Figure BDA00030941467200002014
electric heating device:
Figure BDA00030941467200002015
heat exchanger, exhaust-heat boiler:
Figure BDA00030941467200002016
in the formula:
Figure BDA0003094146720000211
electric power, COP, consumed for the electric refrigerator at time tecIn order to be the refrigeration coefficient thereof,
Figure BDA0003094146720000212
is the converted power;
Figure BDA0003094146720000213
for the electric power consumed by the adsorption refrigerator at time t,
Figure BDA0003094146720000214
the power after conversion of the waste heat boiler, omega, the distribution coefficient of the waste heat flue gas and COPacThe amount of the refrigerant, which is the refrigeration coefficient,
Figure BDA0003094146720000215
for its converted power;
Figure BDA0003094146720000216
electric power, COP, consumed for the thermal conversion device at time thxIn order to obtain a high heating coefficient,
Figure BDA0003094146720000217
is the converted power;
17) and (3) output constraint of the energy conversion device:
Figure BDA0003094146720000218
Figure BDA0003094146720000219
Figure BDA00030941467200002110
Figure BDA00030941467200002111
in the formula:
Figure BDA00030941467200002112
the minimum and maximum force output values of the energy conversion device are respectively;
step S33) example analysis
1) Establishing an IES optimization model of carbon-green certificate containing transaction;
2) introducing the carbon-green certificate cost model of the step S2) and a trading market value risk model considering carbon emission rights and green certificates into an IES optimization model;
3) after the model is subjected to piecewise linearization, a mixed integer linear programming problem is solved, and simulation verification is performed by adopting YALMIP and CPLEX;
4) typical days in spring, summer, autumn and winter are selected for analysis and comparison, and 4 scenes are established to analyze the economy of the IES optimization model of the carbon-green certificate transaction mechanism.
Examples analytical examples:
to verify the effectiveness of the proposed optimization model, a regional IES was selected for example analysis, and the equipment capacity and parameters in the example are shown in table 1. In order to measure the long-term benefits of the model provided by the invention, the model is analyzed and compared by selecting typical days in spring, summer, autumn and winter. Setting a day-ahead schedule as 24 time periods, wherein the scheduling time is 1 h; the model is segmented and linearized to form a mixed integer linear programming problem, and can be directly solved by commercial software such as CPLEX, GUROBI, LINGO and the like, and simulation solving is carried out by adopting YALMIP + CPLEX;
considering that the GCT mechanism is in a development stage, the green license price is drawn to be 100 yuan/parent, the carbon transaction price is 0.15 yuan/Kg, and the punishment cost is 3 times of the green license price or the carbon price; the equipment capacity and parameters of the examples are shown in table 1. The cooling, heating and power loads of the system are shown in fig. 7, the photovoltaic and fan scheduling data are shown in fig. 8, and the real-time electricity price data are shown in fig. 9. The price of the natural gas is 0.35 yuan/(kW.h).
TABLE 1 in-System device Capacity and parameters
Table A1 Equipment capacity and parameters in the system
Figure BDA0003094146720000221
(1) IES optimization result analysis
The invention establishes 4 scenarios to analyze the economics of the proposed IES optimization model of the carbon-green certificate trading mechanism: the scenario I is that the IES does not consider carbon emission rights and green certificate transaction; the scene II is that IES only considers CET and does not have GCT; scenario iii considers GCT only for IES, no CET; scenario iv considers both CET and GCT for IES and accounts for the market risk (CvaR) of CET and GCT. The results of the winter calculation are shown in table 3. The results of other season calculations are shown in Table 2.
TABLE 2 results of different seasons
Table A4 Results of calculation examples in different seasons
Figure BDA0003094146720000231
The data of four seasons of spring, summer, autumn and winter are simulated, and the scheduling result is analyzed by taking a typical winter day as an example.
TABLE 3 winter example scheduling results
Table 1 Scheduling results of winter study
Figure BDA0003094146720000232
As can be seen from table 3, the total IES operating costs for scenarios ii, iii, and iv are reduced by 10389.79, 4542.76, and 14932.56 yuan, respectively, as compared to scenario i; under the condition of considering the market risk value, compared with the situation that only GCT is considered in the scene II, the green certificate income is increased by 170.734 yuan, and the total cost is reduced by 12.6%; compared with the scenario III only considering CET, the carbon yield is increased by 160.523 yuan, and the total cost is reduced by 24.8%; scenario iv the system operates with the best overall economy because, given the combined consideration of CET and GCT mechanisms, the system can bring green license and carbon emissions gains by increasing renewable energy consumption. Therefore, the CET and GCT have good promotion effect, and the introduction of the CET and GCT into IES has good economical efficiency and feasibility.
(2) Impact of carbon number and emissions on system cost
Fig. 3 shows the trend of the influence of the stepped carbon transaction price on the operating cost of the IES. As can be seen from fig. 3: when the carbon value is 0.00-0.25 yuan, the outsourcing energy cost and the carbon emission of the system are increased, and the operation cost of the system is increased; when the carbon value is 0.25-0.50 yuan, the purchased energy of the system is converted from electric power to natural gas, and the purchased energy cost of the system is increased; the carbon emission curve of the system rises slowly because the higher carbon value activates the system to limit the carbon emission; the higher the price of CET, the stronger the system constraints the carbon emission, the more CET revenue brought increases, and the system cost decreases.
Fig. 4 shows the output of the gas turbine unit (gas boiler and gas turbine) in different scenarios. In fig. 4, in a scene i, a gas turbine set preferentially generates power and ensures heat supply balance, and the gas turbine set outputs more power; in the scene II, the system improves the consumption of renewable energy sources, the output of the gas turbine unit begins to be reduced, and additional green income is brought; the system in the scene III strengthens the carbon emission constraint, can further reduce the output of the gas turbine set and bring the carbon emission right gain; a scene IV considers the operation conditions of CET and GCT under the condition of IES and market risk, so that on one hand, the system strengthens the constraint on carbon emission; on the other hand, the system increases the consumption of the electric quantity of the renewable energy, the output of the gas turbine unit reaches the minimum, and the environmental protection of the system operation is ensured.
(3) Green certificate-influence of natural gas price on system cost
Fig. 5 shows the trend of the influence of green certificate-natural gas price on the system cost. As can be seen from fig. 5:
1) when the gas purchase price is 0.25-0.35 yuan and the green license price is 50-70 yuan, the operation cost of the system is increased at a high speed, and the system mainly generates electricity by using gas because the lower green license price has slight influence on the operation cost of the system. When the green certificate price is 80-100 yuan, the higher green certificate price stabilizes the gas purchase cost of the system, and the increase rate of the operation cost of the system tends to be smooth;
2) when the gas purchase price of the system is 0.35-0.40 yuan and the green certificate price is 70 yuan, the rising rate of the system cost is high; when the gas purchase price of the system is 0.40-0.45 yuan and the green certificate price is 80 yuan, the rising rate of the system cost is high, which shows that the system cost is very sensitive to the change of the green certificate price;
3) when the gas purchase price is 0.40-0.50 yuan and the green certificate price is 50-70 yuan, the rising rate of the system cost is faster, because the system can cause the rising of the cost under the conditions of higher gas purchase cost and lower green certificate income.
(4) Impact of carbon-natural gas price on system cost
Fig. 6 shows the trend of the influence of carbon-natural gas price on the system cost. As can be seen from fig. 6:
1) changes in natural gas prices can affect the relative weighting of the cost of the CET of the system. When the gas purchase price of the system is unchanged, the cost of the system is reduced along with the increase of the CET price, because when the CET price of the system is higher, the relative weight of the CET is high, and the cost of the system is reduced;
2) when the CET price is not changed, the operating cost of the system increases as the gas purchase price increases. When the gas purchase price is 0.25-0.40 yuan, the trend of the rising of the system cost is fast, which shows that the influence of the natural gas price on the relative weight of the CET of the system is obvious; and when the gas purchase price is 0.40-0.55 yuan, the change trend of the system cost is gentle along with the increase of the unit carbon price, which shows that the influence of the relative weight of the CET and the change of the gas purchase price gradually reaches the balance.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (4)

1. An integrated energy system optimization operation method considering a carbon-green certificate transaction mechanism is characterized by comprising the following steps:
step S1), researching the carbon-green certificate transaction concept, analyzing the carbon emission right and green certificate transaction mechanism and feasibility of implementing the mechanism in the IES;
step S2), establishing a carbon-green certificate cost model and considering a carbon emission right and green certificate trading market value risk model;
step S3), an IES optimization model is established, and the carbon-green certificate cost model of the step S2) and the risk model considering the carbon emission right and the green certificate trading market value are introduced into the IES optimization model for simulation verification.
2. The method of claim 1, wherein the method comprises: said step S1) is to study the concept of carbon-green certificate transaction, analyze the carbon emission rights and the green certificate transaction mechanism and the feasibility of implementing the mechanism in the IES:
step S11): mechanism of CET
The CET is a trading mechanism for controlling the carbon emission by establishing a legal carbon emission right identification mechanism and allowing the legal carbon emission right identification mechanism to be bought and sold, under the mechanism, the carbon emission becomes a commodity which can be traded freely, an enterprise is allowed to trade the carbon emission right in the enterprise on the premise of not breaking through the CET regulation, a government or a supervision department aims at controlling the total carbon emission, a carbon emission quota is distributed to the enterprise containing a carbon emission source, the enterprise makes and adjusts a production plan according to the distribution quota, if the carbon emission generated in the process is higher than the distribution quota, the carbon emission needs to be purchased from a carbon trading market, and if the carbon emission is lower than the quota, the redundant carbon emission can be sold to obtain corresponding benefits;
step S12): mechanism of GCT
The green certificate is a certificate issued by a renewable energy power generator, and proves that a part of electric power of the power generator comes from renewable energy, the certificate also represents a certain amount of green electric quantity, and has certain timeliness, so that the price of the green certificate is determined by a short-term supply-demand relationship, and a green certificate trading system is a matched measure for ensuring the effective implementation of a renewable energy quota system, so that each responsible body trades in a high-efficiency and flexible manner, and aims to gradually transition renewable energy power generation from a government direct compensation mode to a market subsidy mode by implementing the renewable energy quota system and a GCT policy;
step S13): carbon-green certificate combined trading market
In order to improve the CET market and the GCT mechanism and improve the flexibility of market trading, a carbon-green certificate joint trading market frame is designed, and the trading process is as follows:
step S131), the IES applies for the administration supervision department, participates in green certificate transaction after the supervision department verifies the qualification, and the supervision department distributes different carbon emission and renewable energy electric quantity quotas for each IES;
step S132), the IES meeting the quota requirement sells the green certificate and the carbon emission on a green certificate transaction platform and a carbon emission right transaction platform, and the IES or other organizations not meeting the quota requirement can purchase on the platforms, otherwise, the IES is punished.
3. The method of claim 1, wherein the method comprises: in step S2), a carbon-green certificate cost model is established, and a carbon emission right and green certificate trading market value risk model is considered:
step S21) CET cost
Determining the portion of the uncompensated carbon emission in the IES by adopting a reference line method and a pre-distribution method, wherein the quota amount is the sum of quotas of various units owned by the IES, the carbon emission source in the IES mainly comprises a micro-combustion engine and a gas-fired boiler, and the portion of the uncompensated carbon emission is determined by an equation (1):
CL＝Ce+Ch
Figure FDA0003094146710000021
Figure FDA0003094146710000022
in the formula: cLIs the carbon emission fraction; ceCarbon quota for micro gas turbine; chCarbon quota for gas boiler;
Figure FDA0003094146710000023
the generated power of the micro-combustion engine; b isgIs micro-combustion engine CO2Emission basis, unit: tCO2/MWh；FeThe coefficient is corrected by a unit cooling mode, the water cooling is 1, and the air cooling is 1.05; frThe heat supply correction coefficient of the micro-combustion engine is 1-0.22 x alphaGT(thermoelectric ratio); ffA unit load (output) coefficient correction coefficient;
Figure FDA0003094146710000024
outputting power for the gas boiler; b ishSupplying heat to CO for gas-fired boiler2Carbon emission baseline, unit: tCO2/GJ；
The actual carbon emission of the IES is determined by the output of the micro-combustion engine and the gas boiler, as shown in equation (2):
Figure FDA0003094146710000025
in the formula: a is1,b1,c1Calculating coefficients for carbon emission of the micro-gas turbine set; a is2，b2，c2Is the carbon emission coefficient of the gas boiler;
during solving, the carbon emission of the system is subjected to piecewise linearization treatment, a scheduling model of each interval is changed into a mixed integer linear programming problem, the piecewise linearization principle of the micro gas turbine composition function is used for reference, and piecewise linearization models are adopted to describe and optimize the nonlinear electro-thermal coupling curves of the micro gas turbine and the gas turbine so as to rapidly solve and apply;
the cost calculation model of the step-type transaction is as follows:
Figure FDA0003094146710000031
in the formula:
Figure FDA0003094146710000032
cost for integrated energy system carbon trading; lambda is the CET price on the market; d is the length of the carbon emission interval; sigma is the price increase amplitude of each step CET;
the CET price increases by σ λ for every step up; cP<CLWhen the temperature of the water is higher than the set temperature,
Figure FDA0003094146710000033
will be negative, indicating that CET brings a benefit;
step S22) GCT model
GCT revenue is divided into two cases: firstly, the system consumes the renewable energy generated energy which is smaller than the daily quota value, needs to purchase green certificates, and accepts punishment if the generated energy does not reach the standard; secondly, the consumption of the renewable energy power generation amount is larger than the daily quota system, and the green sale is paid, and the specific model is shown as the formula (4):
Figure FDA0003094146710000034
in the formula:
Figure FDA0003094146710000035
representing a purchase or sale of a green certificate price; presDaily quota amount for renewable energy; pwThe actual consumption is; cpIs a penalty coefficient;
step S23) carbon-Green license transaction Risk measurement
Introducing a CVaR method to quantify market risk degree of both CET and GCT, wherein Cvar is a risk analysis method based on risk value improvement and is used for representing a condition mean value of loss exceeding VaR under a certain straight line interval level;
market risk utility of carbon trader i
Figure FDA0003094146710000036
Represents:
Figure FDA0003094146710000041
in the formula: f (x)iζ) is a constructed auxiliary function; x is the number ofiTrading volume for a carbon trading party; the optimization result of ζ is defined as the VaR value of the carbon trading cost;
Figure FDA0003094146710000042
is the confidence level;
Figure FDA0003094146710000043
is (K)p1，Kp2，…，KpH) The method is characterized in that a carbon trading party i trades a comprehensive index under H historical data;
the overall risk to the market for g carbon traders is expressed as follows:
Figure FDA0003094146710000044
the market risk metric constraints of carbon traders in participation in trading are as follows:
1) and (4) risk value constraint:
Figure FDA0003094146710000045
2) risk value non-negative constraint:
0≤Sz，z＝1，2，…H (8)
in the formula: the risk value of the carbon trader i in scenario z must not be greater than the limit S allowed in the marketz；
Similarly, the green certificate transaction risk metric value is expressed as follows:
Figure FDA0003094146710000046
in the formula: f (y)iγ) is a constructed auxiliary function, yiThe optimization result of gamma is defined as VaR value of green certificate trade cost, beta is confidence coefficient,
Figure FDA0003094146710000047
is (e)p1，ep2，…，epH) Is a comprehensive index of green certificate transaction of a transaction party i under H historical data;
the overall risk of the trading market for n green-letter traders is expressed as follows:
Figure FDA0003094146710000048
the green trading party and the trading market risk measure are constrained as follows:
1) and (4) risk value constraint:
Figure FDA0003094146710000049
2) risk value non-negative constraint:
0≤Sz，z＝1，2，…H (12)
in the formula: the risk value of the green certificate transaction party i in the scene z must not be greater than the limit value S allowed in the marketz。
4. The method of claim 1, wherein the method comprises: the step S3) is to establish an IES optimization model containing carbon-green certificate transaction, and introduce a carbon-green certificate cost model, a carbon emission right calculation model and a green certificate transaction market value risk model into the IES optimization model for simulation verification;
step S31) objective function
For the grid-connected operation IES containing the CET and the GCT, considering quota requirements and existing market risks, on one hand, the IES must consume the renewable energy electric quantity which is not less than the quota quantity; on the other hand, the carbon emission of the system does not exceed the quota standard, and the minimum of the purchase electricity, gas cost, carbon transaction cost, green certificate transaction cost and the operation and maintenance cost of other equipment in the system is taken as an objective function:
Figure FDA0003094146710000051
in the formula: c is the system operating cost, CeFor electricity purchase charge, CgasFor purchase of natural gas, CesFor operating and maintaining the electricity storage apparatus, ChsFor the operating and maintenance costs of the thermal storage equipment in the system,
Figure FDA0003094146710000052
carbon trading costs for the IES to consider market risk,
Figure FDA0003094146710000053
green certificate revenue for market risk consideration;
the electricity purchasing cost formula is as follows:
Figure FDA0003094146710000054
in the formula:
Figure FDA0003094146710000055
for the electricity purchase price of the system at time t,
Figure FDA0003094146710000056
exchanging power between the park comprehensive energy system and the power grid at the time t;
the gas cost formula is:
Figure FDA0003094146710000057
in the formula: c. CgasThe unit heat value price of the natural gas,
Figure FDA0003094146710000058
the generated power of the micro combustion engine at the time t is shown,
Figure FDA0003094146710000059
indicating the heat production power, eta, of the gas-fired boilerg，ηbRespectively the efficiency of the micro-gas turbine and the gas boiler;
assuming that the use cost of the single charge and discharge of the electric storage equipment is the same, the purchase cost is CpurchaseThe number of times of use is M under the condition of no damagecyclesThen its cost per full charge and discharge crComprises the following steps:
Figure FDA00030941467100000510
the operation and maintenance cost formula of the power storage device is as follows:
Figure FDA0003094146710000061
in the formula: ccapacityAs the capacity of the electric storage device,
Figure FDA0003094146710000062
the charge-discharge power of the electrical storage device at time t;
the operation and maintenance cost formula of the heat storage equipment is as follows:
Figure FDA0003094146710000063
in the formula:
Figure FDA0003094146710000064
the charging and discharging power of the heat storage equipment is in a time interval t;
step S32) constraint conditions
1) The lowest selling price of the green certificate market is the current value of the fund subsidy of the electricity price of the renewable energy source and the additional fund, the highest selling price is not higher than the difference value between the electricity price of the renewable energy source for surfing the internet and the electricity price of the micro-combustion engine post, and the lowest and highest selling price limits of the green certificate market are as follows:
Figure FDA0003094146710000065
Figure FDA0003094146710000066
in the formula:
Figure FDA0003094146710000067
syndrome of green colorUpper and lower price limits; siThe internet-surfing electricity price is the ith green power certificate; c is the price of electricity for the post of the micro-combustion engine; r isiThe rate of conversion of the ith green electric energy is reduced; h isiSubsidizing a settlement period for the ith green energy price additional fund; diA payment period is postponed for the subsidy amount of the additional fund for the electricity price of the ith green electric energy;
2) green certificate quota constraint:
Figure FDA0003094146710000068
in the formula: g is a quantization coefficient which represents the quantity of green certificates which can be obtained by green electric energy of a production unit; alpha is alphaiThe renewable power generation ratio of the ith power generation enterprise in a given time is obtained; etaiInitially distributing electric quantity for the ith power generation enterprise; ggreThe number of green certificates; piActual power generation amount for the ith power generation equipment; pi0Initially distributing electric quantity for the ith power generation enterprise;
3) electrical bus balance constraint:
Figure FDA0003094146710000071
in the formula:
Figure FDA0003094146710000072
representing the generated power of the photovoltaic at the time t;
Figure FDA0003094146710000073
representing the power generation output of the fan at the time t;
Figure FDA0003094146710000074
represents the discharge power of the electric storage device at time t;
Figure FDA0003094146710000075
is an electrical load;
Figure FDA0003094146710000076
the power of the electric refrigerator at the moment t is represented;
Figure FDA0003094146710000077
represents the charging power of the electrical storage device at time t;
4) and (3) hot water bus balance constraint:
Figure FDA0003094146710000078
in the formula: etawhWhich represents the efficiency of the waste heat boiler,
Figure FDA0003094146710000079
representing the power of the waste heat boiler at the moment t;
Figure FDA00030941467100000710
representing the thermal load power at the time t;
5) cold load power balance constraint:
Figure FDA00030941467100000711
in the formula: etaecRepresenting the refrigeration coefficient of the electric refrigerator;
Figure FDA00030941467100000712
representing the power of the electric refrigerator at time t; etaacExpressing the refrigeration coefficient of the adsorption refrigerator;
Figure FDA00030941467100000713
represents the power of the adsorption refrigerator at time t;
Figure FDA00030941467100000714
representing the cold load power at the moment t;
6) steam bus balance constraint:
Figure FDA00030941467100000715
in the formula:
Figure FDA00030941467100000716
representing the heat production power of the gas boiler;
Figure FDA00030941467100000717
representing the power of the heat exchange equipment at the moment t;
7) balance constraint of natural gas flow:
Figure FDA00030941467100000718
in the formula:
Figure FDA00030941467100000719
representing the natural gas quantity of the gas source injection node n;
Figure FDA00030941467100000720
represents the injection and output quantity of the gas storage tank in the period t;
Figure FDA00030941467100000721
representing the natural gas load of a node n at the time t;
Figure FDA00030941467100000722
representing the outflow and inflow between the nodes n-m at the time t;
8) natural gas pipeline restraint:
Figure FDA00030941467100000723
in the formula:
Figure FDA00030941467100000724
representing the pipeline flow between nodes n-m at the time t; snmIs a constant related to the pipeline parameters and gas density;
Figure FDA00030941467100000725
pressure values for nodes n and m;
9) natural gas-to-heat conversion constraints:
Figure FDA0003094146710000081
in the formula:
Figure FDA0003094146710000082
representing natural gas power; hgasIs the heat value of natural gas;
Figure FDA0003094146710000083
represents the natural gas flow rate;
10) gas turbine thermoelectric balance constraint:
Figure FDA0003094146710000084
in the formula:
Figure FDA0003094146710000085
is a system alphagRepresenting the heat-to-electricity ratio of the gas turbine;
Figure FDA0003094146710000086
representing the output of the gas turbine at time t;
11) electric storage device operation constraint:
Figure FDA0003094146710000087
in the formula:
Figure FDA0003094146710000088
representing the upper and lower limits of the charging power of the storage battery;
Figure FDA0003094146710000089
representing the upper and lower limits of the discharge power of the storage battery;
Figure FDA00030941467100000810
marking bits for the charging and discharging states of the storage battery, wherein when the bits are 0, the charging and discharging are stopped, and when the bits are 1, the charging and discharging are carried out;
Figure FDA00030941467100000811
representing the electric energy stored by the storage battery at the moment t; sigmaesThe self-discharge rate of the storage battery; etaes，c，ηes,dRespectively the charging efficiency and the discharging efficiency of the storage battery;
Figure FDA00030941467100000812
representing the upper and lower limits of the energy storage of the storage battery;
12) gas turbine operating constraints:
Figure FDA00030941467100000813
Figure FDA00030941467100000814
in the formula:
Figure FDA00030941467100000815
representing the fuel consumption input by the gas turbine at the time t;
Figure FDA00030941467100000816
marking the position for starting and stopping the gas turbine, wherein the marking indicates stopping when the marking position is 0, and indicates starting up when the marking position is 1;
Figure FDA00030941467100000817
representing the upper and lower limits of gas turbine power;
13) and (3) power purchasing power constraint:
Figure FDA00030941467100000818
in the formula:
Figure FDA00030941467100000819
the upper limit value of the power purchasing from the system to the power grid;
14) the operation of the heat storage equipment is restricted:
Figure FDA0003094146710000091
in the formula:
Figure FDA0003094146710000092
upper and lower limits of the charging power of the heat storage equipment;
Figure FDA0003094146710000093
the upper and lower limits of the heat release power of the heat storage equipment;
Figure FDA0003094146710000094
marking bits for the charging and discharging states of the heat storage equipment, wherein the variables are 0-1;
Figure FDA0003094146710000095
electric energy stored for the heat storage equipment at the moment t; sigmahsThe self-heat release rate of the heat storage device; etahs,c、ηhs,dThe heat charging efficiency and the heat discharging efficiency of the heat storage equipment are respectively;
Figure FDA0003094146710000096
upper and lower limits of heat storage power for the heat storage equipment;
15) photovoltaic and wind power output restraint:
Figure FDA0003094146710000097
in the formula:
Figure FDA0003094146710000098
and
Figure FDA0003094146710000099
respectively representing the predicted output of the wind turbine generator and the photovoltaic generator;
16) the energy conversion device restrains:
an electric refrigerator:
Figure FDA00030941467100000910
adsorption type refrigerator:
Figure FDA00030941467100000911
Figure FDA00030941467100000912
electric heating device:
Figure FDA00030941467100000913
heat exchanger, exhaust-heat boiler:
Figure FDA00030941467100000914
in the formula:
Figure FDA00030941467100000915
electric power, COP, consumed for the electric refrigerator at time tecIn order to be the refrigeration coefficient thereof,
Figure FDA00030941467100000916
is the converted power;
Figure FDA00030941467100000917
for the electric power consumed by the adsorption refrigerator at time t,
Figure FDA00030941467100000918
the power after conversion of the waste heat boiler, omega, the distribution coefficient of the waste heat flue gas and COPacThe amount of the refrigerant, which is the refrigeration coefficient,
Figure FDA00030941467100000919
for its converted power;
Figure FDA00030941467100000920
electric power, COP, consumed for the thermal conversion device at time thxIn order to obtain a high heating coefficient,
Figure FDA00030941467100000921
is the converted power;
17) and (3) output constraint of the energy conversion device:
Figure FDA00030941467100000922
Figure FDA0003094146710000101
Figure FDA0003094146710000102
Figure FDA0003094146710000103
in the formula:
Figure FDA0003094146710000104
the minimum and maximum force output values of the energy conversion device are respectively;
step S33) example analysis:
1) establishing an IES optimization model of carbon-green certificate containing transaction;
2) introducing the carbon-green certificate cost model of the step S2) and a trading market value risk model considering carbon emission rights and green certificates into an IES optimization model;
3) after the model is subjected to piecewise linearization, a mixed integer linear programming problem is solved, and simulation verification is performed by adopting YALMIP and CPLEX;
4) typical days in spring, summer, autumn and winter are selected for analysis and comparison, and 4 scenes are established to analyze the economy of the IES optimization model of the carbon-green certificate transaction mechanism.
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