CN114676886A - Energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward and punishment stepped carbon transaction - Google Patents

Energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward and punishment stepped carbon transaction Download PDF

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CN114676886A
CN114676886A CN202210212801.9A CN202210212801A CN114676886A CN 114676886 A CN114676886 A CN 114676886A CN 202210212801 A CN202210212801 A CN 202210212801A CN 114676886 A CN114676886 A CN 114676886A
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程杉
刘延光
王瑞
李沣洋
贺彩
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Abstract

An energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward and punishment stepped carbon transaction comprises the following steps: modeling a gas turbine, a gas boiler, an electric refrigerator, an absorption refrigerator and a storage battery which are contained in the energy hub structure, and reflecting the relation between input power and output power; establishing a comprehensive demand response model, including user cold load demand modeling, user heat load demand modeling and user electric load demand response; establishing a master-slave game low-carbon model, so that EHOs and users participating in the game interaction pursue the optimal benefits under respective operation constraint conditions; and solving the master-slave game low-carbon model through a differential evolution algorithm and a CPLEX solver. The invention can effectively give consideration to the benefits of both parties, fully exert the demand response potential of the user and realize EH economic and low-carbon operation.

Description

Energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward and punishment stepped carbon transaction
Technical Field
The invention relates to the technical field of comprehensive energy system scheduling, in particular to an energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward and punishment ladder carbon transaction.
Background
With the gradual reformation and opening of the national energy market, the search of a safe, efficient, low-carbon and clean energy operation mode becomes a hot spot of the current research. The comprehensive energy system is an efficient, clean and multi-energy coupled energy management system. The energy hub plays an important role in the research of the integrated energy system IES as a future high-efficiency energy form. Comprehensive demand response is the expansion and extension of traditional power demand response, and plays a key role in the optimized operation of the energy hub EH. The establishment of an IDR model which can consider various environmental disturbance factors and reflect the actual energy utilization requirements is a difficult problem to be solved urgently at present.
The reform of the energy market enables a large number of emerging subjects to rush into the market for intense competition, and the application of the game theory can well handle the benefit conflict among different subjects. Carbon trading is considered to be one of the effective means to improve the environmental benefits of the system and to be economically compatible. At present, most researches only introduce carbon transaction cost into IES operation cost, and the full play of energy conservation and emission reduction capability of demand side resources is one of key problems to be solved.
Disclosure of Invention
The invention provides an energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward and punishment stepped carbon transaction, which can effectively give consideration to benefits of both parties, fully exert the demand response potential of a user and realize EH economy and low-carbon operation.
The technical scheme adopted by the invention is as follows:
an energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward and punishment stepped carbon transaction comprises the following steps of:
step 1: modeling a gas turbine, a gas boiler, an electric refrigerator, an absorption refrigerator and a storage battery contained in the energy hub structure, and reflecting the relation between input power and output power;
step 2: and establishing a comprehensive demand response model, including user cold load demand modeling, user heat load demand modeling and user electricity load demand response.
And step 3: and establishing a master-slave game low-carbon model, so that energy hub operators and users participating in the game interaction pursue the optimal benefits under respective operation constraint conditions.
And 4, step 4: and solving the master-slave game low-carbon model through a differential evolution algorithm and a CPLEX solver.
In the step 1, the step of processing the raw material,
1) the gas turbine model is as follows:
electric power output by gas turbine GT
Figure BDA0003532540570000021
And the consumed gas power
Figure BDA0003532540570000022
The relationship is as follows:
Figure BDA0003532540570000023
in the formula:
Figure BDA0003532540570000024
marking bits for the start and stop states of the GT; a. b is a burnup coefficient, and c is a GT start-stop cost coefficient.
In order to accurately reflect the actual operation condition of the GT and facilitate quick calculation, the formula (1) is subjected to three-segment linearization, and the 3-segment slopes after segmentation are respectively:
Figure BDA0003532540570000025
In the formula: and, represents the GT electric power curve parameter after segmentation, which is the upper and lower limits of GT output electric power, so the formula (1) can be rewritten as:
Figure BDA0003532540570000026
when the GT operates, the discharged high-temperature flue gas generates heat through a waste heat boiler WHB, and the heating characteristic model is as follows:
Figure BDA0003532540570000027
Figure BDA0003532540570000028
in the formula:
Figure BDA0003532540570000029
and
Figure BDA00035325405700000210
respectively representing the thermal power output by GT and WHB; lambda [ alpha ]GTAnd λWHBRespectively representing the electric heat power ratio and the heat recovery efficiency of the gas turbine output.
2) The gas boiler model is as follows:
the gas boiler GB generates heat by burning natural gas and outputs heat power Ht GBWith input of breathing power Gt GBThe relationship of (1) is:
Figure BDA00035325405700000211
in the formula: etaGBThe heat production efficiency of GB.
3) The electric refrigerator and the absorption refrigerator model are as follows:
output cold power Q of electric refrigerator AC (air conditioner), absorption refrigerator AR (air regenerator)t AC、Qt ARRespectively as follows:
Figure BDA0003532540570000031
in the formula: etaAR、ηACThe refrigeration efficiency of AR and AC is shown; pt ACAnd Ht ARRespectively representing the input electrical power of the AC and the input thermal power of the AR.
4) The storage battery model is as follows:
the energy storage capacity of the storage battery BT before and after charging and discharging needs to meet the following constraints:
Figure BDA0003532540570000032
in the formula:
Figure BDA0003532540570000033
indicating the capacity state of BT at time t; h isBT.chr、hBT.disRespectively, charge and discharge efficiencies of BT.
Figure BDA0003532540570000034
Shows the capacity state at time t-1 of BT,
Figure BDA0003532540570000035
Indicates the charging power at time t,
Figure BDA0003532540570000036
The discharge power at time t, Δ t, and the time interval,
Figure BDA0003532540570000037
indicates the lower limit of the BT capacity state,
Figure BDA0003532540570000038
Indicating the capacity state at time t,
Figure BDA0003532540570000039
Indicating the BT capacity state upper limit.
In addition, BT also needs to satisfy charging and discharging frequency constraints and mutual exclusion constraints:
Figure BDA00035325405700000310
Figure BDA00035325405700000311
a BT discharge power flag bit representing time t,
Figure BDA00035325405700000312
A BT charging power zone bit representing the time t;
Figure BDA00035325405700000313
t represents a time interval;
in the step 2:
1): the modeling of the user cold load demand is specifically as follows:
the building refrigeration equipment is designed to continuously operate within the service time, and the indoor heat quantity change delta L in the period of t is determined according to the law of conservation of energycEqual to the refrigerating capacity Lt cHeat absorption capacity L of buildingBThe difference is obtained, and the building thermal balance equation is obtained:
Figure BDA00035325405700000314
in the formula: rhoAirIs the air density; cAirIs the air specific heat capacity;
Figure BDA00035325405700000315
is the rate of change of indoor temperature; vBIs the building volume.
The main factors influencing the heat absorption of the building are: heat quantity L transferred from building external wall and external windowWall、LWinIndoor heat source L generated by building absorbing heat such as indoor illumination and human body heat dissipationInAnd solar radiationHeat L generated by the jetSThus, LBCan be expressed as:
Figure BDA0003532540570000041
in the formula: l isBRepresenting the heat absorption of the building;
Figure BDA0003532540570000042
respectively representing the heat transfer coefficients of the outer wall, the outer window and the outdoor of the building when the building faces j;
Figure BDA0003532540570000043
The areas of the outer wall and the outer window of the building when the building orientation is j are respectively; j represents the building orientation;
Figure BDA0003532540570000044
the heat transfer coefficients of the building outer wall, the outer window and the outdoor are respectively;
Figure BDA0003532540570000045
the areas of the building outer wall and the outer window are respectively; t is a unit ofIn、TOutIndoor and outdoor temperatures respectively; i is solar radiation power; s, C are shading coefficient and heat gain factor of the outer window;
combining the equations (11) and (12), and through the differentiation process, obtaining the discretized building thermal balance equation:
Figure BDA0003532540570000046
the relation between the indoor temperature and the refrigerating power can be obtained by the formula (11), and in order to guarantee the comfort of a user, the room temperature meets the upper and lower limits and the room temperature fluctuation constraint:
Figure BDA0003532540570000047
Figure BDA0003532540570000048
represents the indoor temperature at time t;
Figure BDA0003532540570000049
Figure BDA00035325405700000410
in the formula:
Figure BDA00035325405700000411
respectively an upper limit and a lower limit of indoor temperature acceptable by a user,
Figure BDA00035325405700000412
is set to the optimum room temperature
Figure BDA00035325405700000413
Respectively representing the upper limit and the lower limit of the relative value of the room temperature fluctuation;
2): the user thermal load demand modeling is specifically as follows:
the relationship between the water supply temperature and the heat load is described by a hot water storage model:
Figure BDA0003532540570000051
in the formula: chIs the specific heat capacity of water; t ishAnd TC,hRespectively indicating the water storage temperature and the temperature of cold water entering the water storage tank to replace consumed hot water; vhAnd
Figure BDA0003532540570000052
respectively representing the total amount of stored water and the total amount of cold water for replacing consumed hot water;
Figure BDA0003532540570000053
representing the energy required to supply the hot water.
Figure BDA0003532540570000054
Represents the indoor temperature at time t + 1;
for guaranteeing user comfort, the water temperature should satisfy upper and lower limit restraint and water temperature fluctuation restraint:
Figure BDA0003532540570000055
Figure BDA0003532540570000056
represents the water temperature at time t;
Figure BDA0003532540570000057
Figure BDA0003532540570000058
in the formula:
Figure BDA0003532540570000059
respectively an upper limit and a lower limit of indoor temperature acceptable by a user,
Figure BDA00035325405700000510
is the set optimum water temperature.
Figure BDA00035325405700000511
Respectively representing the upper limit and the lower limit of the water temperature fluctuation relative value;
3): the customer electrical load demand response is modeled as follows:
the user electric load comprises a fixed electric load and a transferable electric load, and the transferable electric load refers to that a user transfers according to the electricity price information and the user demand and adjusts the electricity utilization strategy under the condition of not influencing the comfort level of the user. Time settingTransferable load L within segment tt e,kThe expressions (A) are shown in formulas (21) to (22).
Figure BDA00035325405700000512
Figure BDA00035325405700000513
In the formula:
Figure BDA00035325405700000514
indicating the transferable electric load power before the ith user is not regulated by the electricity price IDR;
Figure BDA00035325405700000515
and
Figure BDA00035325405700000516
the power of the electric load transferred into and out of the ith user after being regulated by the electricity price IDR, and M is the number of users participating in response
Figure BDA00035325405700000517
The electric load power is converted into and out after being regulated by the electricity price IDR;
in the step 3, the master-slave game low-carbon model comprises the following steps:
1): the EHO carbon emission amount is allocated as follows:
a baseline method is adopted to determine the uncompensated carbon emission quota of the EHO, and the carbon emission quota allocation in the EH comprises CCHP, GB and a conventional unit. Converting the CCHP power generation into equivalent heat productivity and distributing carbon quota:
Ep＝EGrid+EGB+ECCHP (23)；
EGrid＝δePbuy (24)；
Figure BDA0003532540570000061
Figure BDA0003532540570000062
In the formula: eGrid、EGBAnd ECCHPPurchasing power for an external power grid, and the emission quotas of the gratuitous carbon of GB and CCHP respectively; epAllotment for total system carbon emissions;
Figure BDA0003532540570000063
power purchased from an external grid for the EHO;
Figure BDA0003532540570000064
representing a conversion coefficient; deltae、δhThe carbon emission allocation coefficient of unit electric quantity and heat is respectively 0.728t/(MWh) and 0.102 t/(GJ).
Figure BDA0003532540570000065
GB output thermal power at time t,
Figure BDA0003532540570000066
An output electric power representing the time GT,
Figure BDA0003532540570000067
the output thermal power of WHB at time t,
Figure BDA0003532540570000068
Represents the output cold power of AR at time t;
2): the reward and punishment step type carbon transaction cost calculation model specifically comprises the following steps:
the constructed reward and punishment stepped carbon transaction cost model is as shown in formula (27), when the carbon emission is smaller than the free carbon quota, the energy supply enterprise can sell the redundant carbon emission quota and obtain a part of reward subsidies, otherwise, the insufficient carbon emission right needs to be purchased. The larger the carbon emission, the higher the corresponding carbon trade price.
Figure BDA0003532540570000069
In the formula: eco2Carbon transaction costs incurred for EHO, EcIs the actual total carbon emissions of the EHO, and c is the unit carbon transaction price; λ and μ represent a reward coefficient and a penalty coefficient, respectively, and h represents a carbon emission interval length. Wherein the actual carbon emission of the conventional generator set is 1.08 t/(MWh); the actual carbon emissions of CCHP and GB were 0.065 t/GJ. E pRepresenting the total carbon emission allowance of the IES;
3): the energy hub operator model is as follows:
the EHO represents an energy hub operator, and adjusts the output of the energy coupling equipment and the internal energy price in the EH according to the user energy utilization strategy so as to maximize the EHO net profit as an objective function:
maxEEHO＝Esale-Ebuy-Eco2-EK (28)；
Figure BDA0003532540570000071
Figure BDA0003532540570000072
Figure BDA0003532540570000073
in the formula: i belongs to { E, c, h }, EsaleAn energy sale revenue for EHO; ebuyThe purchase cost of electricity and gas of the EHO; eco2And EKCarbon transaction cost and equipment operation and maintenance cost respectively borne by the EHO; lambda [ alpha ]tiThe price of the ith energy source to the consumer for EHO,
Figure BDA0003532540570000074
is negative for corresponding userLoading;
Figure BDA0003532540570000075
respectively the purchase and sale prices of the EHO to the external power grid,
Figure BDA0003532540570000076
the power is corresponding power for purchasing and selling electricity respectively; lambda [ alpha ]gasIn order to be the price of the natural gas,
Figure BDA0003532540570000077
natural gas power consumed by GT and GB respectively; kiThe unit operating maintenance cost for the equipment;
Figure BDA0003532540570000078
indicating the output power of each device. Δ t represents a time interval;
in the optimal scheduling of the EHO, not only the supply and demand balance of various energy sources in the EH and the upper and lower limit constraints of each energy device need to be considered, but also the constraint of the internal energy price needs to be considered:
Figure BDA0003532540570000079
in the formula: lambda [ alpha ]ti,minAnd λti,maxThe upper and lower limit values of the price for selling the ith energy to the user are respectively for the EHO.
Figure BDA00035325405700000710
Representing the ith energy price at time t.
4) The user model is as follows:
The objective function for the user is the sum of the cost of energy purchase and the cost of discomfort. Assuming that all users in the EH can accept a certain degree of discomfort variation, the objective function is:
minEUser＝CUser+UUser (33)；
in the formula: cUserCost for the user to purchase energy; u shapeUserFor the user discomfort cost, the expressions are:
Figure BDA00035325405700000711
Figure BDA0003532540570000081
in the formula: i belongs to { e, c, h }, gammaiTransferring or reducing the discomfort coefficient of the ith energy corresponding to the user, and reflecting the demand preference of the user on the energy; lambdatiThe price of consuming the ith energy for the user at the moment t;
Figure BDA0003532540570000082
the most comfortable load demand for the user;
Figure BDA0003532540570000083
actual load after performing IDR for the user; delta LtiIndicating the amount of load change before and after the user performs IDR.
For transferable loads, the following constraints need to be satisfied:
Figure BDA0003532540570000084
Figure BDA0003532540570000085
in the formula:
Figure BDA0003532540570000086
an upper limit value indicating a load shifting amount,
Figure BDA0003532540570000087
representing the total amount of transferable load of the load,
Figure BDA0003532540570000088
representing the energy usage load of the i energy source prior to the demand response,
Figure BDA0003532540570000089
represents the energy utilization load of the ith energy source at the moment t,
Figure BDA00035325405700000810
representing the variation of the ith load at the time T, wherein delta T represents a time interval, and T represents 24 hours a day;
the step 4 comprises the following steps:
step 4.1: initializing a population, and enabling the iteration number k to be 0;
step 4.2: if k is less than or equal to kmaxIf not, making k equal to k + 1;
Step 4.3: the EHO sends the internal price to the user;
step 4.4: a user calls a CPLEX to optimize the load;
step 4.5: the user sends the optimized load to the EHO;
step 4.6: solving an objective function E by the EHO;
step 4.7: carrying out mutation operation;
step 4.8: a crossover operation is performed. Generating offspring
Figure BDA00035325405700000811
Calculating the child E ', if E > E', then order
Figure BDA00035325405700000812
And k is k +1. if not, then let
Figure BDA00035325405700000813
And k is k +1, go back to step 4.2.
The invention relates to an energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward and punishment stepped carbon transaction, which has the following technical effects:
1): the refined building IDR model comprehensively considers various heat disturbance factors such as heat storage capacity, outdoor/indoor temperature, solar radiation and the like, considers the room temperature fluctuation of the user caused by IDR participation, can more accurately describe the energy utilization characteristic and the schedulable characteristic of the user in the actual life, and fully exerts the response flexibility of the demand side resource.
2): according to the invention, a reward and punishment step-type carbon transaction mechanism is introduced into a supply and demand game model, and the influence of unit carbon transaction price and different reward coefficients on EH optimal scheduling is analyzed. Simulation results show that the model not only can effectively reduce the carbon emission of the system, but also can give consideration to the benefits of both parties, and realizes the win-win of EH economy and environmental protection.
Drawings
Fig. 1 is a schematic view of an EH structure.
Fig. 2 is a piecewise linearized GT burn-up graph.
Fig. 3 is a schematic diagram of a building heat transfer process.
Fig. 4 is a diagram of a master-slave gaming framework.
Fig. 5 is a flowchart of the Stackelberg game solving process.
Fig. 6 is a graph of load and new energy predictions.
FIG. 7(a) is a graph of predicted heat source data for a building in the day ahead;
fig. 7(b) is a graph of outdoor temperature versus day-ahead predicted light intensity.
FIG. 8(a) is a diagram of a residential building refrigeration scheme;
FIG. 8(b) is a diagram of an office building refrigeration scheme;
FIG. 8(c) is a diagram of a cooling scheme for an apartment building;
fig. 8(d) is a diagram of a cooling scheme of a shopping mall.
Fig. 9 is a diagram of a domestic hot water optimization scheme.
FIG. 10(a) is a graph of the electrical load optimization results;
fig. 10(b) is a graph showing the heat load optimization result.
Fig. 11(a) is a diagram of an optimization result of the electric energy scheduling device;
FIG. 11(b) is a diagram of the optimization results of the thermal energy scheduling device;
fig. 11(c) is a diagram of the optimization result of the cold energy scheduling device.
FIG. 12 is a graph of the effect of carbon trading price on carbon emissions.
FIG. 13 is a graph of the effect of different reward factors on carbon trading cost.
Detailed Description
An energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward and punishment stepped carbon transaction comprises the following steps: establishing a refined comprehensive demand response model considering various heat disturbance factors, and integrating a building heat transfer model and a domestic hot water storage model into a building EH model according to the flexibility characteristics and response capacity of three loads of electricity, heat and cold. And establishing a room temperature fluctuation constraint model to ensure the comfort of the user. And a reward and punishment step type carbon transaction mechanism is established, the carbon emission of the EHO is limited, and the green regulation capability of a user is exerted. Based on the Stackelberg game theory, a master-slave game model of an energy hub operator and a user is constructed, a reward and punishment step-type carbon transaction mechanism is introduced into the game model, the carbon emission of EHO is limited, and the green regulation capability of the user is exerted. And solving the extracted model by adopting a differential evolution algorithm combined with a CPLEX tool box. Simulation results show that the method can effectively give consideration to benefits of both parties, fully exerts the demand response potential of users, and realizes EH economy and low-carbon operation. The method comprises the following steps:
Step 1: modeling a gas turbine, a gas boiler, an electric refrigerator, an absorption refrigerator and a storage battery which are contained in the EH structure, and reflecting the relation between input power and output power;
and 2, step: and establishing a comprehensive demand response model, including user cold load demand modeling, user heat load demand modeling and user electricity load demand response.
And 3, step 3: and establishing a master-slave game low-carbon model, so that the EHO and the users participating in the game interaction pursue the optimal benefits under respective operation constraint conditions.
And 4, step 4: and solving the proposed game model through a differential evolution algorithm and a CPLEX solver.
And 5: and (4) carrying out example analysis by considering the actual situation, and verifying the correctness of the proposed scheme and model.
1. The energy hub structure model is described as follows:
the EH configuration studied by the present invention is shown in fig. 1. The new energy equipment is a wind turbine generator and a photovoltaic generator; the energy supply equipment comprises a gas turbine and a gas boiler; the energy conversion equipment comprises a waste heat boiler, an electric refrigerator and an absorption refrigerator; the energy storage device is a storage battery.
1) The gas turbine model is described as follows:
electric power output from GT
Figure BDA0003532540570000101
And the consumed gas power
Figure BDA0003532540570000102
The relationship is as follows:
Figure BDA0003532540570000103
in the formula:
Figure BDA0003532540570000104
Marking a start-stop state of the GT; a. b is a burnup coefficient, and c is a GT start-stop cost coefficient.
In order to accurately reflect the actual operation condition of the GT and facilitate quick calculation, the invention performs three-segment linearization processing on formula (1), as shown in fig. 2. The slopes of the segmented 3 segments are respectively as follows:
Figure BDA0003532540570000105
in the formula: and, represents the GT electric power curve parameters after segmentation, which are the upper and lower limits of the GT output electric power, so the formula (1) can be rewritten as follows:
Figure BDA0003532540570000111
when the GT operates, the discharged high-temperature flue gas generates heat through the WHB, and the heating characteristic model is as follows:
Figure BDA0003532540570000112
Figure BDA0003532540570000113
in the formula:
Figure BDA0003532540570000114
and
Figure BDA0003532540570000115
respectively representing the thermal power output by GT and WHB; lambda [ alpha ]GTAnd λWHBRespectively representing the electric heat power ratio and the heat recovery efficiency of the gas turbine output.
2) The gas boiler model is described as follows:
GB generates heat by burning natural gas and outputs heat power Ht GBWith input of breathing power Gt GBThe relationship of (1) is:
Figure BDA0003532540570000116
in the formula: etaGBThe heat production efficiency of GB.
3) The electric refrigerator and the absorption refrigerator are described in model as follows:
AC. Output cold power Q of ARt AC、Qt ARRespectively as follows:
Figure BDA0003532540570000117
in the formula: etaAR、ηACThe refrigeration efficiency of AR and AC is shown; pt ACAnd Ht ARRespectively representing the input electrical power of the AC and the input thermal power of the AR.
4) The battery model is described as follows:
the energy storage capacity before and after BT charge and discharge needs to meet the following constraints:
Figure BDA0003532540570000118
In the formula: SOC (system on chip)t xThe capacity state of BT; h is a total ofBT.chr、hBT.disRespectively, charge and discharge efficiencies of BT. In addition, BT also needs to satisfy charging and discharging frequency constraints and mutual exclusion constraints:
Figure BDA0003532540570000119
Figure BDA00035325405700001110
2. the integrated demand response model is described as follows:
by means of fine modeling of user load requirements, the energy utilization strategy is adjusted in the temperature comfort level and the room temperature fluctuation range, and the EH operation economy and the environmental protection performance can be effectively improved.
1) The user cooling load demand modeling is described as follows:
assuming that the building refrigeration equipment continuously operates in the service time, the indoor heat quantity change quantity delta L in the period of t is determined according to the energy conservation theoremcEqual to the refrigerating capacity Lt cHeat absorption capacity L of buildingBThe difference, from which the building thermal balance equation can be derived:
Figure BDA0003532540570000121
in the formula: rhoAirIs the air density; cAirIs the air specific heat capacity;
Figure BDA0003532540570000122
is the rate of change of indoor temperature; vBIs the building volume.
Fig. 3 is a process of building heat transfer. The main factors influencing the heat absorption of the building are: heat quantity L transferred from building external wall and external windowWall、 LWinReason for buildingIndoor heat source L for absorbing heat generated by indoor illumination, human body heat dissipation and the likeInAnd heat L generated by solar radiationSThus L isBCan be expressed as:
Figure BDA0003532540570000123
in the formula: j represents the building orientation; kWall and kWin are respectively the heat transfer coefficients of the building outer wall, the outer window and the outdoor; fWall and fWin are the areas of the outer wall and the outer window of the building respectively; TIn and TOut are indoor and outdoor temperatures respectively; i is solar radiation power; s, C are shading coefficient and heat gain factor of the outer window;
Combining the equation (11) and the equation (12), and performing a differencing process to obtain a discretized building thermal balance equation:
Figure BDA0003532540570000124
the relation between the indoor temperature and the refrigerating power can be obtained by the formula (11), and in order to guarantee the comfort of a user, the room temperature meets the upper limit and the lower limit and the room temperature fluctuation constraint:
Figure BDA0003532540570000125
Figure BDA0003532540570000126
Figure BDA0003532540570000131
in the formula:
Figure BDA0003532540570000132
respectively an upper limit and a lower limit of indoor temperature acceptable by a user,
Figure BDA0003532540570000133
is set to be the optimum room temperature. 2) The user thermal load demand modeling is described as follows:
the relationship between the water supply temperature and the heat load is described by a hot water storage model:
Figure BDA0003532540570000134
in the formula: chIs the specific heat capacity of water; t ishAnd Tt C,hRespectively indicating the water storage temperature and the temperature of cold water entering the water storage tank to replace consumed hot water; vhAnd Vt C,hRespectively representing the total amount of stored water and the total amount of cold water for replacing consumed hot water; l isthRepresenting the energy required to supply the hot water.
For guaranteeing the comfort of the user, the water temperature should satisfy the upper and lower limit constraint and the water temperature fluctuation constraint:
Figure BDA0003532540570000135
Figure BDA0003532540570000136
Figure BDA0003532540570000137
in the formula:
Figure BDA0003532540570000138
respectively an upper limit and a lower limit of indoor temperature acceptable by a user,
Figure BDA0003532540570000139
is the set optimum water temperature. 3) The user electrical load demand modeling is described as follows:
the user electric load comprises a fixed electric load and a transferable electric load, and the transferable electric load refers to the user according to the information of the electricity price and the user The demand shifts, adjusts the power consumption strategy under the condition that does not influence self comfort level. Transferable load L within a set time period tt e,kThe expressions (A) are shown in formulas (21) to (22).
Figure BDA00035325405700001310
Figure BDA00035325405700001311
In the formula:
Figure BDA00035325405700001312
indicating the transferable electric load power before the ith user is not regulated by the electricity price IDR;
Figure BDA00035325405700001313
and
Figure BDA00035325405700001314
the power of the electric load transferred into and out of the ith user after being adjusted by the electricity price IDR, and M is the number of users participating in response.
3. Establishing a master-slave game low-carbon model:
the master-slave gaming framework of the present invention is shown in fig. 4. The market main bodies participating in the game interaction are EHO and users, and the two parties respectively pursue the optimal benefits under respective operation constraint conditions.
1): the EHO carbon emission quota allocation is described as follows:
the invention adopts a reference line method to determine the uncompensated carbon emission quota of the EHO, and the carbon emission quota allocation in the EH is considered to mainly comprise CCHP, GB and a conventional unit. Converting the CCHP power generation into equivalent heat productivity and distributing carbon quota:
Ep＝EGrid+EGB+ECCHP (23)；
EGrid＝δePbuy (24)；
Figure BDA0003532540570000141
Figure BDA0003532540570000142
in the formula: eGrid、EGBAnd ECCHPPurchasing electricity for an external power grid, and the emission quotas of the uncompensated carbon of GB and CCHP respectively; epAllotment of total system carbon emissions;
Figure BDA0003532540570000143
power purchased from an external power grid for the EHO;
Figure BDA0003532540570000144
representing a conversion coefficient; deltae、δhThe carbon emission allocation coefficient of unit electric quantity and heat is respectively 0.728t/(MWh) and 0.102 t/(GJ).
2): the rewarding and punishing stepped carbon transaction cost calculation model is described as follows:
the reward and punishment stepped carbon transaction cost model constructed by the method is shown as a formula (27), when the carbon emission is smaller than the free carbon quota, an energy supply enterprise can sell redundant carbon emission quotas and obtain a part of reward subsidies, otherwise, insufficient carbon emission rights need to be purchased. The larger the interval of carbon emission, the higher the corresponding carbon trade price.
Figure BDA0003532540570000145
In the formula: eco2Carbon transaction costs incurred by EHO, EcIs the actual total carbon emissions of the EHO, and c is the unit carbon transaction price; λ and μ represent a reward coefficient and a penalty coefficient, respectively, and h represents a carbon emission interval length. The actual carbon emission of the conventional generator set is 1.08 t/(MWh); the actual carbon emissions of CCHP and GB were 0.065 t/GJ.
3): the energy hub operator model is described as follows:
the EHO adjusts the output of the energy coupling equipment in the EH and the price of internal energy according to the user energy strategy, and maximizes the EHO net profit as an objective function:
maxEEHO＝Esale-Ebuy-Eco2-EK (28)；
Figure BDA0003532540570000151
Figure BDA0003532540570000152
Figure BDA0003532540570000153
in the formula: i belongs to { E, c, h }, EsaleIs an energy sale benefit of EHO; ebuyThe cost of electricity and gas purchase of EHO; eco2And EKCarbon transaction cost and equipment operation and maintenance cost respectively borne by the EHO; lambda [ alpha ]tiThe price of the ith energy source to the consumer for EHO,
Figure BDA00035325405700001511
Is the corresponding user load; lambdatbGrid/λtsGridPurchase/sell electricity price, P, for EHO to external power gridtb/Grid/PtsGridFor corresponding power purchased/sold; lambdagasIn order to be the price of the natural gas,
Figure BDA0003532540570000154
natural gas power consumed for GT/GB; k isiThe unit operating maintenance cost for the equipment;
Figure BDA0003532540570000155
indicating the output power of each device.
The EHO not only needs to consider the supply and demand balance of various energy sources in the EH and the upper and lower limit constraints of each energy device, but also needs to consider the constraints of the internal energy price:
Figure BDA0003532540570000156
in the formula: lambda [ alpha ]ti,minAnd λti,maxThe upper and lower price limits for the ith energy source are sold to the consumer for EHO, respectively.
4): the user model is described as follows:
the objective function for the user is the sum of the cost of energy purchase and the cost of discomfort. Assuming that users in the EH can all accept a certain degree of discomfort variation, the objective function is:
minEUser＝CUser+UUser (33)；
in the formula: cUserThe cost of energy purchase for the user; u shapeUserFor the user discomfort cost, the expressions are respectively:
Figure BDA0003532540570000157
Figure BDA0003532540570000158
in the formula: i belongs to { e, c, h }, gammaiTransferring or reducing the discomfort coefficient of the ith energy corresponding to the user, and reflecting the demand preference of the user on the energy; lambda [ alpha ]tiThe price of consuming the ith energy for the user at the moment t;
Figure BDA0003532540570000159
the most comfortable load demand for the user;
Figure BDA00035325405700001510
actual load after performing IDR for the user; delta LtiIndicating the amount of load change before and after the user performs IDR.
For transferable loads, the following constraints need to be satisfied:
Figure BDA0003532540570000161
Figure BDA0003532540570000162
in the formula:
Figure BDA0003532540570000163
upper limit value, W, representing load transferabilitytiAnd represents the total transferable load of the load.
4. The model is solved by adopting a differential evolution algorithm and combining cplex software, the solving process is shown in figure 5, and the steps are as follows:
step 4.1: initializing a population, and enabling the iteration times k to be 0;
and 4.2: if k is less than or equal to kmaxIf not, making k equal to k + 1;
step 4.3: the EHO sends the internal price to the user;
step 4.4: a user calls a CPLEX to optimize the load;
step 4.5: the user sends the optimized load to the EHO;
step 4.6: solving an objective function E by the EHO;
step 4.7: carrying out mutation operation;
step 4.8: a crossover operation is performed. Generating offspring
Figure BDA0003532540570000164
Calculating the child E ', if E > E', then order
Figure BDA0003532540570000165
And k is k +1. if not, then let
Figure BDA0003532540570000166
And k is k +1, go back to step 4.2.
In the above steps, E is an objective function, k is the number of iterations, λtiIs EHO selling the price of the ith energy source to the user.
5. Example analysis and verification:
1) the basic data are as follows:
an example simulation analysis is carried out by taking a certain commercial building EH as an example. The typical daily predicted load and the predicted output of the new energy of the user are shown in fig. 6, and the relevant parameters of the building are shown in table 1; the equipment and related constraint parameters in EH are shown in table 2; the purchase and sale prices of EHO to the grid are shown in table 3. The price of the natural gas is 3.24 yuan/m 3; the upper and lower limit of the heat and cold price of EHO is 0.15,0.4 ]Yuan; the heat source, outdoor temperature and solar radiation curves in the building are shown in fig. 7(a) and 7 (b); the optimal room temperature of the user is set to be 22.5 ℃, the optimal water temperature is set to be 70 ℃, and the discomfort coefficient gamma of the user to electricity, heat/cold energye、γc/h0.008 and 0.016 respectively; the carbon transaction price was 0.268 yuan/kg, μ was 0.2.
The refrigeration open time of 4 commercial buildings studied by the present invention is as follows. Building A: a residential building with refrigeration times of 00:00-09:00 and 18:00-23: 00; building B: in an office building, the refrigeration time is 08:00-20: 00; and (3) building C: apartment, the refrigeration time is all day; and (5) building D: the refrigerating time of the shopping mall is 10:00-22: 00.
TABLE 1 construction parameters
Table 1Parameters of the buildings
Figure BDA0003532540570000171
TABLE 2 energy hub parameters
Table 2Parameters of the EH
Figure BDA0003532540570000172
TABLE 3 price for electricity purchase
Table 3Prices for the purchase and sale of electricity
Figure BDA0003532540570000173
2) Comparative analysis of different protocols:
the invention sets four schemes to compare and analyze with the invention scheme:
the first scheme is as follows: in the carbon transaction mode, the supply and demand game model without considering the IDR model and the carbon transaction cost;
scheme II: under the carbon transaction mode, a simplified IDR model and a supply and demand game model without considering the carbon transaction cost are considered;
the third scheme is as follows: a simplified IDR model and a supply-demand game model of traditional carbon transaction cost are considered;
and the scheme is as follows: consideration is given to the refined IDR model and the traditional carbon transaction cost.
The results of the five protocol comparisons are shown in table 4.
Compared with the scheme 1 and the scheme 2, the energy cost for the user and the carbon emission of the system in the scheme 2 are respectively reduced by 3.81 percent and 4.76 percent, because the user can reasonably adjust the load demand according to the price signal after considering the IDR strategy, the load peak-valley difference of the user is effectively smoothed, and the energy cost and the carbon emission caused by outsourcing electric power are reduced. But the net profit of EHO decreased by 2.84% due to the user shifting and cutting part of the load.
Comparing scheme 2 and scheme 3, the user energy cost and system carbon emission are respectively reduced by 1.40% and 5.98% in scheme 3, and the EHO net profit is increased by 3.43%. Therefore, after the carbon trading mechanism is considered in the optimization model, the EHO can reasonably adjust the output of the equipment, reduce the carbon emission caused by outsourcing power, and obtain carbon benefits in the carbon trading market because the total carbon emission of the system is lower than the freely distributed carbon quota. In addition, the user can also profit from the adjustment of the EHO.
Comparing scheme 3 with scheme 4, the user energy cost and system carbon emissions decreased by 1.59% and 1.79%, respectively, in scheme 4. The reason is that when the refined IDR model is adopted, factors such as temperature comfort level and room temperature fluctuation are considered, the energy utilization condition of the user under the actual condition can be reflected, so that the user can participate in IDR response more actively, and the energy utilization comfort level of the user is ensured.
Comparing scheme 4 with the strategy of the present invention, the energy cost for users and the carbon emission of the system are respectively reduced by 0.88% and 2.32%, and the EHO net profit is increased by 2.01%. The method is characterized in that after the stepped carbon trading cost and the reward coefficient are introduced, the EHO can sell redundant carbon quota to obtain carbon profit and also can obtain certain reward profit, so that the EHO is further stimulated to increase the output of each device and reduce the purchased electric quantity, and the total carbon emission of the system is effectively reduced.
Table 4 comparative results under different protocols
Table 4 Comparison results under different schemes
Figure BDA0003532540570000181
3) And (3) analyzing a scheduling result:
the effect of the flexible cooling load on the EH was first analyzed. Assuming that when no flexible cooling load is introduced, the temperature of the building is kept constant at 22.5 ℃ in the respective open time of the building, and the non-open time is not required, and the cooling load at the moment is taken as the original cooling load; when a flexible cooling load is introduced, the temperature inside the building may fluctuate during its open time, when the cooling load takes it as the actual cooling load. As can be seen from the cooling results of the buildings shown in fig. 8(a) -8 (d), the cooling load will increase rapidly during the first time period when the buildings are open to meet the indoor room temperature demand, and the cooling load will change substantially with the change of the outside temperature and the solar radiation during the subsequent time period to maintain the indoor temperature.
In addition, the construction cooling capacity is related to the electricity price after the flexible cooling load is introduced. For a residential building, the actual refrigeration load is also larger when the cooling price is lower at 00: 00-7: 00, namely the peak cooling load is transferred to the valley cooling load. Therefore, the comfort level of a user can be guaranteed, and heat can be stored in advance to reduce the load demand at the peak value cold price; because the opening time of the office building and the market is at the energy price and the outdoor temperature is higher at 08: 00-20: 00, the cold load requirement and the energy cost can be reduced by properly increasing the indoor temperature; apartments that are open throughout the day may also improve system economics by cutting or shifting portions of the cooling load.
Fig. 9 shows the scheduling result of domestic hot water in the day ahead, similar to the refrigeration result of a building, the system can reduce the energy cost by reducing the water temperature, transferring and reducing part of the heat load, such as 06: 00-10: 00 and 17: 00-20: 00 time periods, the heat load is transferred and reduced for reducing the cost for users, and the hot water in the water storage tank is additionally heated in 15: 00-16: 00 time periods to meet the hot water requirement in the future time period with higher heat price.
Fig. 10(a) and 10(b) are graphs of internal energy price and user energy strategy results formulated by EHO after game balance optimization. As can be seen from fig. 10(a), the energy selling price established by the EHO is within the time-of-use electricity price of the external power grid, and the fluctuation trend of the electricity price coincides with the time-of-use electricity price, so as to provide the user with an energy selling price that is more favorable than the power grid, and to promote the load shifting and reduction of the user. When the electricity price is higher at 08: 00-12: 00 and 16: 00-22: 00, the user transfers the electricity load in the time periods to the time periods of 00: 00-7: 00 and 23: 00-24: 00 with lower electricity price, so that the energy purchasing cost per se is reduced.
Also, the optimization results of the heat load and the cold load are shown in fig. 10(b), and in order to simplify the model, the heat price and the cold price according to the present invention are optimized using the same variables, so that the corresponding price variation trend is similar to the variation trend of the total amount of the heat load and the cold load, similar to the analysis of fig. 10(a), and the optimization results of the heat load and the cold load are shown in fig. 10 (b). In order to simplify the model, the heat selling price and the cold selling price are optimized by adopting the same variable, so that the corresponding price variation trend is similar to the variation trend of the total amount of the cold load and the heat load. As can be seen from 10(b), the heat and cold loads are high during the periods of 12:00-15:00 and 17:00-20:00, and the heat/cold prices are high, so that the reduction is different, and the periods of 10:00-11:00 and 21:00-24:00 are shifted. Presenting the advantage of good peak clipping and valley filling.
The device scheduling result after the Stackelberg game is optimized is shown in fig. 11(a) to 11 (c). Considering environmental protection, EHO preferentially consumes renewable energy sources PV and WT. First, for 00: 00-6: in the period of 00, the electricity price is in the valley section, the GT stops starting at the moment, and the EHO mainly meets the electricity load of the user through WT electricity generation and outsourcing electricity; the heat load is satisfied by GB heat generation; the cooling load is satisfied by AC refrigeration. For the time period of 07: 00-12: 00, the PV starts to output power, at the moment, the electric load is mainly provided by GT, PV and WT, the EHO is used for profit, the GT outputs more power, and abundant electric quantity can be provided for AC or sold to an external power grid; the heat load is satisfied by WHB and GB, and the abundant heat is refrigerated by AR and combined with AC to satisfy the cold load demand. In the flat period of the electricity price of 13:00-18:00, because the heat and cold loads are higher at the moment, in order to utilize the waste heat of the power generation to meet the heat load, the GT output is higher, and the abundant electric load is stored by the BT so as to deal with the next electric load peak period. During the peak time period of the electricity price of 19:00-22:00, the working condition is similar to that of 08: 00-12: 00, but due to the lack of PV power generation, insufficient electricity needs to be supplemented by outsourcing electricity and BT. During the flat period of 23:00-24:00 electricity price, various loads are gradually reduced, the GT power generation amount is also gradually reduced, the WHB basically meets the requirement of heat load, and then rich heat is provided for AR for refrigeration.
Fig. 12 shows the influence of the carbon transaction price change on the carbon emission of the system in scenario 4 and the scenario of the present invention. As can be seen from fig. 12, as the carbon unit transaction price increases, the carbon transaction cost increases in proportion to the total cost, and the system increases the constraint on the carbon emission amount, so that the carbon emission amount gradually decreases. In addition, the carbon emission amount under the strategy of the invention is lower than that of the scenario 4, because the reward and punishment step carbon transaction mechanism has the emission reduction advantage compared with the traditional carbon transaction mechanism, and the carbon emission amount of the system can be better limited.
Transaction price variance. As can be seen from fig. 13, when the system carbon transaction cost is greater than 0, i.e., the EHO needs to bear the carbon transaction fee, the reward factor has no effect on the carbon transaction cost. In contrast, when the EHO starts to obtain the carbon transaction income, the larger the reward coefficient is, the more the carbon transaction income is, i.e., the more significant the system carbon emission is reduced, because the carbon transaction income obtained by the EHO is increased, the CCHP unit and GB output with lower carbon emission are increased, and the outsourcing electric quantity is further reduced. In addition, when the carbon unit transaction price is increased to about 450 yuan/t, the carbon transaction cost descending trend becomes slow, the CCHP unit basically reaches a constant value, and if the carbon unit transaction price is continuously increased, the carbon emission of the system is not obviously reduced. The invention provides an EH master-slave game optimization strategy considering an IDR model and a reward and punishment ladder type carbon transaction mechanism based on the Stackelberg game theory. The detailed IDR model of the building comprehensively considers various heat disturbance factors such as heat storage capacity, outdoor/indoor temperature, solar radiation and the like, considers the room temperature fluctuation caused by participation of IDR of a user, can more accurately describe the energy utilization characteristic and the scheduling characteristic of the user in the actual life, and fully exerts the response flexibility of resources on the demand side. A reward and punishment step-type carbon transaction mechanism is introduced into the supply and demand game model, and the influence of unit carbon transaction price and different reward coefficients on EH optimization scheduling is analyzed. The result shows that the model not only can effectively reduce the carbon emission of the system, but also can give consideration to the benefits of both parties, and realizes the win-win of EH economy and environmental protection.

Claims (6)

1. An energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward and punishment stepped carbon transaction is characterized by comprising the following steps of:
step 1: modeling a gas turbine, a gas boiler, an electric refrigerator, an absorption refrigerator and a storage battery which are contained in the energy hub structure, and reflecting the relation between input power and output power;
and 2, step: establishing a comprehensive demand response model, including user cold load demand modeling, user heat load demand modeling and user electric load demand response;
and 3, step 3: establishing a master-slave game low-carbon model, so that energy hub operators and users participating in the game interaction pursue the optimal benefits under respective operation constraint conditions;
and 4, step 4: and solving the master-slave game low-carbon model through a differential evolution algorithm and a CPLEX solver.
2. The energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward punishment stepped carbon transaction according to claim 1, characterized in that: in the step (1), the step (2),
1) the gas turbine model is as follows:
electric power output by gas turbine GT
Figure FDA0003532540560000011
And the consumed gas power
Figure FDA0003532540560000012
The relationship is as follows:
Figure FDA0003532540560000013
in the formula:
Figure FDA0003532540560000014
marking bits for the start and stop states of the GT; a. b is a burnup coefficient, and c is a GT start-stop cost coefficient;
In order to accurately reflect the actual operation condition of the GT, three-segment linearization processing is performed on the equation (1), and the 3 segmented slopes are respectively:
Figure FDA0003532540560000015
in the formula: d is a radical of1、d2、d3、d4Represents the GT power curve parameter after segmentation, d1、d4Since GT outputs the upper and lower limits of electric power, equation (1) can be rewritten as follows:
Figure FDA0003532540560000016
when the GT operates, the discharged high-temperature flue gas generates heat through a waste heat boiler WHB, and the heating characteristic model is as follows:
Figure FDA0003532540560000017
Figure FDA0003532540560000021
in the formula:
Figure FDA0003532540560000022
and
Figure FDA0003532540560000023
respectively representing the thermal power output by GT and WHB; lambda [ alpha ]GTAnd λWHBRespectively representing the electric heat power ratio and the heat recovery efficiency output by the gas turbine;
2) the gas boiler model is as follows:
the gas boiler GB generates heat by burning natural gas and outputs heat power Ht GBWith input of breathing power Gt GBThe relationship of (1) is:
Figure FDA0003532540560000024
in the formula: etaGBHeat generation efficiency of GB;
3) the electric refrigerator and the absorption refrigerator model are as follows:
output cold power Q of electric refrigerator AC, absorption refrigerator ARt AC、Qt ARRespectively as follows:
Figure FDA0003532540560000025
in the formula: etaAR、ηACThe refrigeration efficiency of AR and AC is shown; pt ACAnd Ht ARRespectively representing input electric power of AC and input thermal power of AR;
4) the storage battery model is as follows:
the energy storage capacity of the storage battery BT before and after charging and discharging needs to meet the following constraints:
Figure FDA0003532540560000026
in the formula:
Figure FDA0003532540560000027
indicating the capacity state of BT at time t; h isBT.chr、hBT.disCharge and discharge efficiencies of BT, respectively;
Figure FDA0003532540560000028
Shows the capacity state at time t-1 of BT,
Figure FDA0003532540560000029
Indicates the charging power at time t,
Figure FDA00035325405600000210
The discharge power at time t, Δ t, the time interval,
Figure FDA00035325405600000211
indicates the lower limit of the BT capacity state,
Figure FDA00035325405600000212
Indicating the capacity state at time t,
Figure FDA00035325405600000213
Represents the upper limit of the capacity state of BT;
in addition, BT also satisfies charge and discharge frequency constraints and mutual exclusion constraints:
Figure FDA00035325405600000214
Figure FDA00035325405600000215
a BT discharge power flag bit representing time t,
Figure FDA00035325405600000216
A BT charging power zone bit representing the time t;
Figure FDA00035325405600000217
t represents a time interval.
3. The energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward punishment stepped carbon transaction according to claim 1, characterized in that: in the step 2, in the step of processing,
1): the modeling of the user cold load demand is specifically as follows:
the building refrigeration equipment is designed to continuously operate within the service time, and the indoor heat quantity change delta L in the period of t is determined according to the law of conservation of energycEqual to the refrigerating capacity Lt cHeat absorption capacity L of buildingBThe difference is obtained, and the building thermal balance equation is obtained:
Figure FDA0003532540560000031
in the formula: rhoAirIs the air density; cAirIs the air specific heat capacity;
Figure FDA0003532540560000032
is the rate of change of indoor temperature; vBIs the building volume;
the main factors affecting the heat absorption of buildings include: heat quantity L transferred from building external wall and external windowWall、LWinIndoor heat source L generated by building absorbing heat such as indoor illumination and human body heat dissipation InAnd generation of solar radiationHeat quantity of (L)SThus, LBCan be expressed as:
Figure FDA0003532540560000033
in the formula: l is a radical of an alcoholBExpressing the heat absorption capacity of the building;
Figure FDA0003532540560000034
the heat transfer coefficients of the outer wall, the outer window and the outdoor of the building when the building faces j are respectively;
Figure FDA0003532540560000035
the areas of the outer wall and the outer window of the building when the building orientation is j are respectively; j represents the building orientation; k is a radical ofWall、kWinThe heat transfer coefficients of the building outer wall, the outer window and the outdoor are respectively; f. ofWall、fWinThe areas of the building outer wall and the outer window are respectively; t isIn、TOutIndoor and outdoor temperatures respectively; i is solar radiation power; s, C are shading coefficient and heat gain factor of the outer window;
combining the formula (11) and the formula (12), and obtaining a discretized building thermal balance equation through differential processing:
Figure FDA0003532540560000036
the relation between the indoor temperature and the refrigerating power can be obtained by the formula (11), and in order to guarantee the comfort of a user, the room temperature meets the upper and lower limits and the room temperature fluctuation constraint:
Figure FDA0003532540560000037
Figure FDA0003532540560000038
represents the indoor temperature at time t;
Figure FDA0003532540560000041
Figure FDA0003532540560000042
in the formula:
Figure FDA0003532540560000043
respectively an upper limit and a lower limit of indoor temperature acceptable by a user,
Figure FDA0003532540560000044
is set to the optimum room temperature
Figure FDA0003532540560000045
Respectively representing the upper limit and the lower limit of the relative value of the room temperature fluctuation;
2): the user thermal load demand modeling is specifically as follows:
the relationship between the water supply temperature and the heat load is described by a hot water storage model:
Figure FDA0003532540560000046
in the formula: chIs the specific heat capacity of water; t is hAnd TC,hRespectively indicating the temperature of the stored water and the temperature of cold water entering the water storage tank to replace consumed hot water; vhAnd
Figure FDA0003532540560000047
respectively representing the total amount of stored water and the total amount of cold water for replacing consumed hot water;
Figure FDA0003532540560000048
represents the energy required to supply hot water;
Figure FDA0003532540560000049
represents the indoor temperature at time t + 1;
the water temperature satisfies the upper and lower limit constraint and the water temperature fluctuation constraint:
Figure FDA00035325405600000410
Figure FDA00035325405600000411
represents the water temperature at time t;
Figure FDA00035325405600000412
Figure FDA00035325405600000413
in the formula:
Figure FDA00035325405600000414
respectively an upper limit and a lower limit of indoor temperature acceptable by a user,
Figure FDA00035325405600000415
setting the optimum water temperature;
Figure FDA00035325405600000416
respectively representing the upper limit and the lower limit of the relative value of the water temperature fluctuation;
3): the customer electrical load demand response is modeled as follows:
the user electric load comprises a fixed electric load and a transferable electric load, and the transferable electric load refers to that a user transfers according to the electricity price information and the user requirement and adjusts the electricity utilization strategy under the condition of not influencing the comfort level of the user; transferable load L within a set time period tt e,kAre represented by the formulae (21) to (22)) Shown;
Figure FDA0003532540560000051
Figure FDA0003532540560000052
in the formula:
Figure FDA0003532540560000053
indicating the transferable electric load power before the ith user is not regulated by the electricity price IDR;
Figure FDA0003532540560000054
and
Figure FDA0003532540560000055
respectively the power of the electric load transferred into and out of the ith user after being regulated by the electricity price IDR, M is the number of users participating in response,
Figure FDA0003532540560000056
the electric load power is transferred into and out after being regulated by the electricity price IDR.
4. The energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward punishment stepped carbon transaction according to claim 1, characterized in that: in the step 3, the master-slave game low-carbon model comprises the following steps:
1): the EHO carbon emission amount is allocated as follows:
determining a gratuitous carbon emission quota of the EHO by adopting a reference line method, wherein the carbon emission quota in the EH comprises CCHP, GB and a conventional unit; converting the CCHP power generation into equivalent heat productivity and distributing carbon quota:
Ep＝EGrid+EGB+ECCHP (23)；
EGrid＝δePbuy (24)；
Figure FDA00035325405600000514
Figure FDA0003532540560000057
in the formula: eGrid、EGBAnd ECCHPPurchasing electricity for an external power grid, and the emission quotas of the uncompensated carbon of GB and CCHP respectively; epAllotment for total system carbon emissions;
Figure FDA0003532540560000058
power purchased from an external power grid for the EHO;
Figure FDA0003532540560000059
representing a conversion coefficient; deltae、δhCarbon emission allocation coefficient of unit electric quantity and heat;
Figure FDA00035325405600000510
GB output thermal power at time t,
Figure FDA00035325405600000511
An output electric power representing the time GT,
Figure FDA00035325405600000512
The output thermal power at the time WHB,
Figure FDA00035325405600000513
Represents the output cold power of AR at time t;
2): the reward and punishment step type carbon transaction cost calculation model specifically comprises the following steps:
the constructed reward and punishment stepped carbon transaction cost model is shown as a formula (27), when the carbon emission is smaller than the free carbon quota, an energy supply enterprise can sell redundant carbon emission quota and obtain a part of reward subsidies, otherwise, insufficient carbon emission rights need to be purchased; the larger the carbon emission is, the higher the corresponding carbon transaction price is;
Figure FDA0003532540560000061
In the formula: eco2Carbon transaction costs incurred for EHO, EcIs the actual total carbon emissions of the EHO, and c is the unit carbon transaction price; lambda and mu respectively represent an incentive coefficient and a penalty coefficient, and h represents the length of a carbon emission interval; epRepresents the total carbon emission quota of the IES;
3): the energy hub operator model is as follows:
EHO represents an energy hub operator, and adjusts the output of energy coupling equipment and the internal energy price in EH according to the user energy strategy to maximize the EHO net profit as an objective function:
maxEEHO＝Esale-Ebuy-Eco2-EK (28)；
Figure FDA0003532540560000062
Figure FDA0003532540560000063
Figure FDA0003532540560000064
in the formula: i belongs to { E, c, h }, EsaleIs an energy sale benefit of EHO; ebuyThe cost of electricity and gas purchase of EHO; eco2And EKCarbon transaction cost and equipment operation and maintenance cost respectively borne by the EHO; lambda [ alpha ]tiThe price of the ith energy source to the consumer for EHO,
Figure FDA0003532540560000065
is the corresponding user load;
Figure FDA0003532540560000066
respectively the purchase and sale prices of the EHO to the external power grid,
Figure FDA0003532540560000067
the power is corresponding power for purchasing and selling electricity respectively; lambda [ alpha ]gasIn order to be the price of the natural gas,
Figure FDA0003532540560000068
natural gas power consumed by GT and GB respectively; kiThe unit operating maintenance cost for the equipment;
Figure FDA0003532540560000069
representing the output power of each device; Δ t represents a time interval;
in the optimal scheduling of the EHO, not only the supply and demand balance of various energy sources in the EH and the upper and lower limit constraints of each energy device need to be considered, but also the constraint of the internal energy price needs to be considered:
Figure FDA0003532540560000071
In the formula: lambda [ alpha ]ti,minAnd λti,maxUpper and lower limit values of price for selling the ith energy to the user for the EHO respectively;
Figure FDA0003532540560000072
the ith energy price at the moment t;
4) the user model is as follows:
the objective function of the user is the sum of the energy purchasing cost and the discomfort degree cost; assuming that users in the EH can all accept a certain degree of discomfort variation, the objective function is:
minEUser＝CUser+UUser (33)；
in the formula: cUserThe cost of energy purchase for the user; u shapeUserFor the user discomfort cost, the expressions are respectively:
Figure FDA0003532540560000073
Figure FDA0003532540560000074
in the formula: i belongs to { e, c, h }, gammaiTransferring or reducing the discomfort coefficient of the ith energy corresponding to the user, and reflecting the demand preference of the user on the energy; lambda [ alpha ]tiThe price of consuming the ith energy for the user at the moment t;
Figure FDA0003532540560000075
the most comfortable load demand for the user;
Figure FDA0003532540560000076
actual load after performing IDR for the user; delta LtiRepresenting the amount of load change before and after the user performs IDR;
for transferable loads, the following constraints need to be satisfied:
Figure FDA0003532540560000077
Figure FDA0003532540560000078
in the formula:
Figure FDA0003532540560000079
an upper limit value indicating a load shifting amount,
Figure FDA00035325405600000710
representing the total amount of transferable load of the load,
Figure FDA00035325405600000711
representing the energy usage load of the i energy source prior to the demand response,
Figure FDA00035325405600000712
represents the energy utilization load of the ith energy source at the moment t,
Figure FDA00035325405600000713
the variation of the ith load at time T is shown, Δ T represents the time interval, and T represents 24 hours a day.
5. The energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward punishment stepped carbon transaction according to claim 1, characterized in that: the step 4 comprises the following steps:
step 4.1: initializing a population, and enabling the iteration number k to be 0;
step 4.2: if k is less than or equal to kmaxIf not, making k equal to k + 1;
step 4.3: the EHO sends the internal price to the user;
step 4.4: a user calls a CPLEX to optimize the load;
step 4.5: the user sends the optimized load to the EHO;
step 4.6: solving an objective function E by the EHO;
step 4.7: carrying out mutation operation;
step 4.8: performing cross operation; generating offspring
Figure FDA0003532540560000081
Calculating the child E ', if E > E', then order
Figure FDA0003532540560000082
And k is k +1. if not, then let
Figure FDA0003532540560000083
And k is k +1, go back to step 4.2.
6. A reward and punishment ladder type carbon transaction cost model is characterized in that the model is as follows:
Figure FDA0003532540560000084
in the formula: eco2Carbon transaction costs incurred for EHO, EcIs the actual total carbon emissions of the EHO, and c is the unit carbon transaction price; lambda and mu respectively represent an incentive coefficient and a penalty coefficient, and h represents the length of a carbon emission interval; epRepresenting the total carbon emission quota of the IES.
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