CN114662330A - Comprehensive energy system model construction method considering carbon transaction mechanism and demand response - Google Patents

Comprehensive energy system model construction method considering carbon transaction mechanism and demand response Download PDF

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CN114662330A
CN114662330A CN202210346213.4A CN202210346213A CN114662330A CN 114662330 A CN114662330 A CN 114662330A CN 202210346213 A CN202210346213 A CN 202210346213A CN 114662330 A CN114662330 A CN 114662330A
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王俐英
董厚琦
宋美琴
曾鸣
王鑫
郭珂
李函奇
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North China Electric Power University
Marketing Service Center of State Grid Shandong Electric Power Co Ltd
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Abstract

The invention discloses a method for constructing a comprehensive energy system model considering a carbon transaction mechanism and demand response, which comprises the following steps: acquiring basic parameters, wherein the basic parameters comprise stepped carbon transaction price and demand response parameters; establishing a first model and a second model for scheduling the comprehensive energy system by using an information gap decision theory; substituting the basic parameters into the first model, solving the first model by taking the highest daily operating profit of an operator as a target, and outputting a scheme of the output of each unit in the comprehensive energy system; and substituting the basic parameters into the second model, solving the second model by taking the lowest daily performance cost of the user as a target, and outputting demand response parameters. Therefore, a step-type carbon transaction mechanism and a demand response are introduced into the comprehensive energy system at the energy supply side, low-carbon environment-friendly operation of the comprehensive energy system is achieved, energy supply is guaranteed, a user adjusts own energy utilization requirements according to the demand response compensation price and the cold, hot and electricity prices at the energy demand side, and own energy utilization cost is reduced.

Description

Comprehensive energy system model construction method considering carbon transaction mechanism and demand response
Technical Field
The invention relates to the field of power systems, in particular to a method, a device and computing equipment for building a comprehensive energy system model considering a carbon transaction mechanism and demand response.
Background
In recent years, it has become an important means to improve the comprehensive utilization efficiency of Energy and promote the consumption of new Energy by constructing an Integrated Energy System (IES) for overall planning and coordinated scheduling of various types of Energy such as electricity, gas, heat, and cold.
With the development of the power market and the carbon market, Demand Response (DR) is considered in the IES scheduling optimization, load transfer and reduction are performed by using DR, energy supply and Demand balance can be guaranteed, energy economy of users can be improved, and a carbon trading mechanism is introduced into the IES scheduling, so that low-carbon economic operation of the IES becomes more important. However, most of the prior art focuses on introducing a single carbon transaction mechanism, price-type DR or incentive-type DR into the IES operation scheduling, which makes the IES scheduling less than ideal, and increases the investment cost on the energy supply side and the electricity consumption cost on the demand side.
In addition, uncertainty of wind power photovoltaic output of the energy supply side and electricity, heat and cold multiple loads of the demand side in the IES is also an important influence factor influencing investment cost of the energy supply side and electricity utilization cost of the demand side. When the realized prediction error falls within the maximum fluctuation range of the uncertainty variable, the Information Gap Decision Theory (IGDT) ensures that the objective function meets the predetermined target, and is a feasible method for relieving the IES uncertainty. In the existing research, some researches of the IES scheduling model based on the IGDT are included, for example, an independent microgrid operation planning model based on the IGDT is proposed on the basis of considering the uncertainty of renewable energy output and load. As another example, using IGDT theory, an IES scheduling model is constructed that considers multiple energy requirements and power rate uncertainty from the perspective of risk neutrality, risk aversion, and risk seeking. However, the above studies of the IGDT-based IES scheduling model do not consider the demand response and carbon transaction mechanism, so that the IES scheduling is not ideal, and the investment cost on the energy supply side and the electricity consumption cost on the demand side are increased. The carbon trading is based on the fact that the carbon emission exceeds the carbon quota distributed by the state without compensation, and the excess carbon emission needs to be funded by a user for carbon neutralization.
Disclosure of Invention
To this end, the present invention provides an integrated energy system model building method, apparatus and computing device that accounts for carbon trading mechanisms and demand response in an effort to solve, or at least alleviate, the problems identified above.
According to one aspect of the invention, a method for building an integrated energy system model considering carbon transaction mechanism and demand response is provided, which is suitable for being executed in a computing device, and comprises the following steps: acquiring basic parameters, wherein the basic parameters comprise stepped carbon transaction prices and demand response parameters, and the demand response parameters comprise transferable demand response and reducible demand response; establishment of accounting and carbon transaction mechanism by using information gap decision theory andthe method comprises the steps that a demand response comprehensive energy system schedules a first model and a second model, wherein the first model comprises a first objective function and a first constraint condition, and the second model comprises a second objective function and a second constraint condition; substituting the basic parameters into the first model, solving the first model by taking the highest daily operating profit of an operator as a target, and outputting a scheme of the output of each unit in the comprehensive energy system; substituting the basic parameters into the second model, solving the second model by taking the lowest daily energy cost of the user as a target, and outputting transferable demand response and reducible demand response; wherein the first objective function is:
Figure BDA0003576600360000021
in the formula, RsellThe energy sale profit is expressed and,
Figure BDA0003576600360000022
representing the cost of purchasing energy of electricity and natural gas to the upper power and gas grids, CopeRepresents the equipment operation and maintenance cost, CdrWhich represents the cost of the demand response and,
Figure BDA0003576600360000023
represents a carbon transaction cost; wherein the second objective function is:
Figure BDA0003576600360000024
in the formula (I), the compound is shown in the specification,
Figure BDA0003576600360000025
represents the cost of the energy use by the user,
Figure BDA0003576600360000026
representing the demand response cost, R, of a user adjusting a power plan to participate in demand responsedrRepresenting the user's participation in the subsidy for demand response acquisition.
Optionally, the first objective function comprises:
Figure BDA0003576600360000027
Figure BDA0003576600360000028
Figure BDA0003576600360000029
Figure BDA00035766003600000210
Figure BDA0003576600360000031
Figure BDA0003576600360000032
Figure BDA0003576600360000033
in the formula (I), the compound is shown in the specification,
Figure BDA0003576600360000034
respectively representing the prices for buying electricity and natural gas to the upper level power and gas grids,
Figure BDA0003576600360000035
Qg,trespectively representing the quantity of electricity purchased from the power grid and the quantity of natural gas purchased from the gas grid at the moment t, cgb、cchp、chp、cer、cac、cwt、cpv、cees、ctes、ccesRespectively represents the unit output operation and maintenance costs of a gas boiler, cogeneration, a heat pump, an electric refrigerator, a refrigerator, distributed wind power, distributed photovoltaic, electric energy storage equipment, heat energy storage equipment and cold energy storage equipment,
Figure BDA0003576600360000036
represents the thermal power output by the gas boiler at time t,
Figure BDA0003576600360000037
respectively the electric power and the thermal power output by the cogeneration unit at the moment t,
Figure BDA0003576600360000038
Figure BDA0003576600360000039
respectively showing the thermal power output by the heat pump, the cold power output by the electric refrigerator and the cold power output by the absorption refrigerator at the time t,
Figure BDA00035766003600000310
respectively representing the output of the wind turbine generator and the photovoltaic generator,
Figure BDA00035766003600000311
Figure BDA00035766003600000312
for the time t the electrical energy storage discharge power and the charging power,
Figure BDA00035766003600000313
for the heat release power and the heat storage power of the thermal energy storage device at time t,
Figure BDA00035766003600000314
respectively representing the cold discharge power and the cold storage power of the cold energy storage device at time t, CdrWhich represents the cost of the demand response and,
Figure BDA00035766003600000315
indicating that the user's demand response is rolling out of load,
Figure BDA00035766003600000316
indicating that the demand response of the user is load shedding,
Figure BDA00035766003600000317
respectively represents the electricity price, the heat price and the cold price provided by the user of the comprehensive energy system,
Figure BDA00035766003600000318
representing the electrical load demand of the user at time t,
Figure BDA00035766003600000319
indicating that the demand response of the user is shifted to load,
Figure BDA00035766003600000320
representing the thermal load demand of the user at time t,
Figure BDA00035766003600000321
indicating the cooling load demand of the user at time t,
Figure BDA00035766003600000322
indicating carbon transaction price, CE1Represents total carbon emission, CE, of the integrated energy system2Represents the total carbon emission quota of the integrated energy system,
Figure BDA00035766003600000323
respectively representing the electric energy and the heat energy generated by the CHP unit,
Figure BDA00035766003600000324
which represents the thermal energy generated by the GB,
Figure BDA00035766003600000325
represents the electric energy purchased by the integrated energy system to the upper-level power grid and air grid,
Figure BDA00035766003600000326
respectively representing the carbon emission intensity of the unit generating power of the CHP unit and the carbon emission intensity, xi, of the unit heating power of the CHP unitgb、
Figure BDA00035766003600000327
Respectively representing the carbon emission intensity of GB unit active power output and the carbon emission intensity of the distribution network unit active power output.
Optionally, the second objective function comprises:
Figure BDA00035766003600000328
Figure BDA0003576600360000041
Rdr＝Cdr
in the formula (I), the compound is shown in the specification,
Figure BDA0003576600360000042
indicating the energy use cost, R, of the usersellThe energy sale profit is expressed and,
Figure BDA0003576600360000043
indicating the demand response cost of the user in adjusting the electricity usage plan to participate in the demand response,
Figure BDA0003576600360000044
representing the user's demand response load, a, b representing the user's response demand cost factor, RdrSubsidies representing user participation in demand response procurement, CdrAnd (4) showing.
Optionally, the first constraint comprises: one or more of power balance constraints, equipment output and ramp constraints, tie-line constraints, energy storage constraints, demand response constraints, and other constraints, the second constraint comprising: the demand response constraint.
Optionally, the power balance constraints comprise:
Figure BDA0003576600360000045
in the formula (I), the compound is shown in the specification,
Figure BDA0003576600360000046
respectively representing the electrical load, the cold load and the heat load demand of the user at the moment t,
Figure BDA0003576600360000047
a distribution coefficient representing the direct supply of the electrical energy produced to the electrical load,
Figure BDA0003576600360000048
representing the distribution coefficient of the produced electrical energy directly supplied to the electrical refrigerator,
Figure BDA0003576600360000049
representing the distribution coefficient of the produced electrical energy directly to the heat pump,
Figure BDA00035766003600000410
a distribution coefficient representing the direct supply of thermal energy produced to the thermal load,
Figure BDA00035766003600000411
representing the distribution coefficient of the thermal energy produced directly to the absorption chiller,
Figure BDA00035766003600000412
respectively representing the electrical and thermal energy provided by the integrated energy system operator.
Optionally, the device force and hill climbing constraints comprise:
Figure BDA00035766003600000413
in the formula (I), the compound is shown in the specification,
Figure BDA00035766003600000414
representing the contribution of the device m at time t,
Figure BDA00035766003600000415
the maximum output of the device m is represented,
Figure BDA00035766003600000416
representing the contribution of device m at time t +1,
Figure BDA00035766003600000417
represents the maximum ramp power of devices m, including GB, CHP, HP, ER, AC, WT, PV, EES, TES, and CES.
Optionally, the tie line constraint comprises:
Figure BDA0003576600360000051
in the formula (I), the compound is shown in the specification,
Figure BDA0003576600360000052
it is shown that,
Figure BDA0003576600360000053
representing the maximum amount of power purchased to the grid.
Optionally, the energy storage device constraint comprises:
Figure BDA0003576600360000054
in the formula (I), the compound is shown in the specification,
Figure BDA0003576600360000055
respectively representing the charging state and the discharging state of the energy storage device s,
Figure BDA0003576600360000056
respectively represents the charging and discharging power of the energy storage device s at the moment t,
Figure BDA0003576600360000057
representing the maximum charge/discharge power of the energy storage device s,
Figure BDA0003576600360000058
respectively representing the minimum and maximum capacity of the energy storage device s,
Figure BDA0003576600360000059
representing the capacity of the energy storage device s at time t,
Figure BDA00035766003600000510
respectively representing the capacity of the energy storage device with t-1 and t-24 h.
Optionally, the demand response constraints comprise:
Figure BDA00035766003600000511
in the formula (I), the compound is shown in the specification,
Figure BDA00035766003600000512
indicating that the user's demand response is rolling out of load,
Figure BDA00035766003600000513
indicating that the demand response of the user is shifted to load,
Figure BDA00035766003600000514
indicating the maximum transferable load of the user,
Figure BDA00035766003600000515
and (4) showing.
Optionally, the other constraints include:
Figure BDA00035766003600000516
Figure BDA00035766003600000517
Figure BDA00035766003600000518
Figure BDA00035766003600000519
Figure BDA00035766003600000520
Figure BDA0003576600360000061
Figure BDA0003576600360000062
Figure BDA0003576600360000063
Figure BDA0003576600360000064
Figure BDA0003576600360000065
in the formula, FlowerAn objective function representing the underlying model,
Figure BDA0003576600360000066
indicating that the user's demand response is rolling out of load,
Figure BDA0003576600360000067
the price of the electricity sold by the operator of the comprehensive energy system at the moment t is shown, a and b both show the cost coefficient of the response demand of the user,
Figure BDA0003576600360000068
is represented by CdrIt is shown that,
Figure BDA0003576600360000069
the dual-mode variable is represented by a dual-mode variable,
Figure BDA00035766003600000610
indicating that the demand response of the user is shifted to load,
Figure BDA00035766003600000611
for the introduction of the auxiliary boolean variable, M represents a constant,
Figure BDA00035766003600000612
indicating the maximum transferable load of the user,
Figure BDA00035766003600000613
and (4) showing.
Optionally, the step of solving the model comprises: equivalently converting the lower-layer optimization problem under the Countak condition, and further converting the double-layer model into a nonlinear single-layer model;
and converting the nonlinear single-layer model into a linear single-layer model, and solving the single-layer linear model.
Optionally, the basic parameters include basic parameters of a first model and basic parameters of a second model, and the basic parameters of the first model include: one or more of the price of selling electric energy to a user, the price of selling hot energy to the user, the price of selling cold energy to the user, the price of buying electric power and natural gas to a power grid and a gas grid, the demand response compensation price to the user, the transferable demand response and reducible demand response fed back by the second model, the operation and maintenance cost of the distributed wind turbine, the operation and maintenance cost of the distributed photovoltaic generator, the operation and maintenance cost of a gas boiler, the operation and maintenance cost of a cogeneration unit, the operation and maintenance cost of a heat pump, the operation and maintenance cost of an electric refrigerator, the operation and maintenance cost of an absorption refrigerator, the operation and maintenance cost of an electric energy storage device, the operation and maintenance cost of a hot energy storage device, the operation and maintenance cost of a cold energy storage device and the stepped carbon trading price, and the basic parameters of the second model comprise: one or more of a selling price of the electric energy source fed back to the second model by the first model, a selling price of the thermal energy source fed back to the second model by the first model, a selling price of the cold energy source fed back to the second model by the first model, a demand response compensation price, and a cost coefficient of participation of the user in the demand response.
Optionally, the carbon trading mechanism comprises:
Figure BDA00035766003600000614
in the formula (I), the compound is shown in the specification,
Figure BDA0003576600360000071
representation, CE1Represents total carbon emission, CE, of the integrated energy system2Representing the total amount of carbon emission quota of the integrated energy system.
According to an aspect of the present invention, there is provided an integrated energy system model building apparatus considering carbon transaction mechanism and demand response, adapted to be executed in a computing device, comprising: the parameter acquisition module is suitable for acquiring basic parameters, the basic parameters comprise stepped carbon transaction prices and demand response parameters, and the demand response parameters comprise transferable demand response and reducible demand response; the model building unit is suitable for building a first model and a second model of the comprehensive energy system scheduling considering the carbon transaction mechanism and the demand response by using an information gap decision theory, the first model comprises a first objective function and a first constraint condition, and the second model comprises a second objective function and a second constraint condition; the model solving unit is suitable for substituting the basic parameters into the first model, solving the first model by taking the highest daily operating profit of an operator as a target and outputting a scheme of output of each unit in the comprehensive energy system, and substituting the basic parameters into the second model, solving the second model by taking the lowest daily energy cost of a user as a target, and outputting transferable demand response and reducible demand response;
wherein the first objective function is:
Figure BDA0003576600360000072
in the formula, RsellThe energy sales proceeds are represented and,
Figure BDA0003576600360000073
representing the cost of purchasing energy of electricity and natural gas to the upper power and gas grids, CopeRepresents the equipment operation and maintenance cost, CdrWhich represents the cost of the demand response and,
Figure BDA0003576600360000074
represents a carbon transaction cost; wherein the second objective function is:
Figure BDA0003576600360000075
in the formula (I), the compound is shown in the specification,
Figure BDA0003576600360000076
represents the cost of the energy use by the user,
Figure BDA0003576600360000077
representing the demand response cost, R, of a user adjusting a power plan to participate in demand responsedrRepresenting the participation of the user in the subsidy obtained by the demand response.
According to an aspect of the invention, there is provided a computing device comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions comprising instructions for performing the method as described above.
According to an aspect of the present invention, there is provided a readable storage medium storing program instructions which, when read and executed by a computing device, cause the computing device to perform the method as described above.
According to the technical scheme, the invention provides a method for constructing the comprehensive energy system model considering the carbon transaction mechanism and the demand response, and the model comprises a first model and a second model. In the first model, the highest daily operating profit of an operator is taken as a target, a stepped carbon trading mechanism and demand response are introduced into a centralized optimization scheduling objective function of the comprehensive energy system, the output scheme of each unit in the comprehensive energy system is determined, and the demand response compensation price and the cold, hot and electricity prices are sent to comprehensive energy users. In the second model, the lowest daily energy cost of the user is taken as a target, and the demand response compensation price and the price of cold, heat and electricity are considered when the energy is used, so that transferable demand response quantity and reducible demand response quantity are output.
A step-type carbon transaction mechanism and demand response are introduced into the comprehensive energy system, so that low-carbon and environment-friendly operation of the comprehensive energy system is realized. On the energy supply side, an integrated energy system operator purchases energy for an upper-level power grid and an air grid, energy utilization requirements of users are met by optimizing energy production, conversion coupling and output of storage equipment inside the integrated energy system operation and guiding the users to participate in demand response by adopting a demand response compensation mechanism, and energy supply is guaranteed. On the energy demand side, the comprehensive energy user adjusts the self energy demand according to the demand response compensation price and the cold, hot and electricity price given by the demand response compensation price, so that the self energy consumption cost is reduced, and the balance of energy supply and demand and the safe and stable operation of the system are guaranteed.
In addition, after a step-type carbon transaction mechanism is introduced into the model, the total carbon emission of the system is further effectively reduced, and the requirement of the current Chinese double-carbon target is met. On the other hand, the comprehensive energy system is based on the principles of system energy conversion coupling and cascade utilization and utilizes DR, so that the carbon emission of the system is obviously smaller than the distributed carbon emission quota, and the residual carbon emission right is traded, thereby increasing the operating economic benefit of the comprehensive energy system.
Drawings
Fig. 1 shows a schematic diagram of a stepped carbon transaction mechanism according to one embodiment of the invention;
FIG. 2 shows a schematic diagram of an integrated energy system according to an embodiment of the invention;
FIG. 3 illustrates a block diagram of a computing device 300, according to one embodiment of the invention;
FIG. 4 illustrates a flow diagram of a method 400 of building an integrated energy system model that accounts for carbon trading mechanisms and demand response, according to one embodiment of the invention;
FIG. 5 is a schematic diagram illustrating an integrated energy system model framework according to one embodiment of the invention;
fig. 6 shows a block diagram illustrating an integrated energy system model building apparatus 600 that accounts for carbon trading mechanisms and demand response, according to one embodiment of the invention;
FIG. 7 shows a schematic of the day-ahead prediction curves for wind power, photovoltaic, electrical load, thermal load, and cold load;
FIG. 8 shows a schematic diagram of electricity purchase price, electricity sales price, heat purchase price, natural gas price, cold purchase price;
FIG. 9 shows a schematic diagram of the electrical power balance of a conventional strategy in a deterministic scenario;
FIG. 10 shows a schematic diagram of thermal power balancing for a conventional strategy in a deterministic scenario;
FIG. 11 shows a schematic diagram of cold power balancing for a conventional strategy in a deterministic scenario;
FIG. 12 shows a schematic diagram of the electrical power balance of a risk avoidance maneuver under an uncertainty scenario;
FIG. 13 shows a schematic diagram of thermal power balancing for a risk avoidance maneuver under an uncertainty scenario;
FIG. 14 shows a diagram of cold power balancing for a risk avoidance maneuver under an uncertainty scenario.
Detailed Description
The invention provides a method for constructing a comprehensive energy system scheduling model considering a carbon transaction mechanism and demand response, and further can be a method for constructing a campus-level comprehensive energy system scheduling model considering the carbon transaction mechanism and the demand response. On the energy supply side, an integrated energy system operator purchases energy for an upper-level power grid and an air grid, energy utilization requirements of users are met by optimizing energy production, conversion coupling and output of energy storage equipment in the integrated energy system and guiding the users to participate in demand response by adopting an incentive subsidy mechanism, energy supply is guaranteed, and carbon neutralization cost is minimized according to a stepped carbon trading mechanism. On the energy demand side, the comprehensive energy user adjusts the self energy demand according to the incentive subsidy price and the cold, hot and electricity price given by the comprehensive energy system operator, so that the self energy consumption cost is reduced, and the energy supply and demand balance and the safe and stable operation of the system are guaranteed.
Under the carbon trading mechanism, carbon emission is a commodity which can be freely traded according to the difference between the carbon emission amount allocated by the government and the actual carbon emission amount. In the power industry, at present, China mainly adopts a reference line method to determine the uncompensated carbon emission quota of the system. For the comprehensive energy system aiming at improving the comprehensive utilization efficiency of energy and the consumption rate of new energy, the carbon emission sources include outsourcing Power, Combined Heat and Power (CHP) units and Gas boilers (Gas Boiler, GB). In order to simplify the model, the embodiment assumes that the carbon emission of each unit is proportional to the output thereof, and when the actual carbon emission of the system exceeds the initial allocation amount, the excess carbon emission amount is purchased according to a stepped price, so the carbon transaction mechanism can also be called a stepped carbon transaction mechanism.
The stepped carbon trading mechanism is to perform stepped price buying/selling for the exceeded carbon credit when the actual carbon emissions of the system exceed/fall below the initial allocation amount, the stepped carbon emission price being as shown in fig. 1. The specific expression is as follows:
Figure BDA0003576600360000101
in the formula (I), the compound is shown in the specification,
Figure BDA0003576600360000102
indicating carbon transaction price, CE1Represents total carbon emission, CE, of the integrated energy system2Representing the total amount of carbon emission quota of the integrated energy system.
Demand Response (DR), which is short for power Demand Response, means that when the price of the wholesale market of power is increased or the reliability of the system is threatened, after a power consumer receives a direct compensation notification of an inductive reduction load sent by a power supplier or a power price increase signal, the power consumer changes the inherent conventional power mode to reduce the power load in a certain period of time, so that the stability of the power grid is guaranteed, and the short-term behavior of power price increase is suppressed. The present invention primarily considers price-based demand response.
Fig. 2 shows a schematic diagram of a campus-type integrated energy system according to an embodiment of the present invention. As shown in fig. 2, the park-type integrated energy system includes three parts, which are: energy suppliers, campus integrated energy system operators, and integrated energy users.
The operators of the park integrated Energy system include Energy conversion devices, which include CHP units, GB, distributed Wind Turbine (WT) units, Photovoltaic (PV) units, Electric Refrigerators (ER), Cold Energy Storages (CES), Thermal Energy Storages (TES), Electric Energy Storages (EES), Absorption refrigerators (AC), Heat pumps (Heat Pump, HP), and the like, where CES, TES, and EES are collectively referred to as Energy Storage Systems (ESS).
The energy suppliers comprise power grid companies and natural gas companies. The park integrated energy system operator purchases energy from a power grid company or a natural gas company of an energy supplier, converts the purchased energy through a distributed wind turbine generator, a distributed photovoltaic generator, a gas boiler, a heat pump, an electric refrigerator and the like to obtain electric energy, heat energy and cold energy, respectively stores the electric energy, the heat energy and the cold energy to an electric energy storage device, a heat energy storage device and a cold energy storage device, and sends an excitation subsidy mechanism to an integrated energy user. And the comprehensive energy user adjusts the self energy demand according to the incentive subsidy price given by the comprehensive energy system operator, and acquires corresponding energy from each energy storage device.
The model for the energy supplier to generate direct electrical and thermal energy is as follows:
Figure BDA0003576600360000103
in the formula, Ee、EhRespectively provides the comprehensive energy system operator with electric energy and heat energy through energy production equipment,
Figure BDA0003576600360000111
Qgrespectively for the electric energy and the natural gas purchased by the comprehensive energy system operator to the upper-level power grid and the gas grid,
Figure BDA0003576600360000112
the WT and PV forces are the respective forces,
Figure BDA0003576600360000113
efficiency, η, of CHP units outputting electric and thermal energy, respectivelygbIn order for the GB to output the efficiency of the thermal energy,
Figure BDA0003576600360000114
and respectively supplying natural gas to the CHP unit and the GB distribution coefficient.
The energy production equipment model, the energy coupling equipment model and the energy storage equipment model are respectively as follows:
the energy production equipment model is as follows:
Figure BDA0003576600360000115
the energy coupling equipment model is as follows:
Figure BDA0003576600360000116
the energy storage equipment model is as follows:
Figure BDA0003576600360000117
in the formula (I), the compound is shown in the specification,
Figure BDA0003576600360000118
respectively the electric energy and the heat energy generated by the CHP unit,
Figure BDA0003576600360000119
for thermal energy generated by GB, QgNatural gas purchased from the gas grid for an integrated energy system operator，
Figure BDA00035766003600001110
The distribution coefficients of the CHP unit and the GB are respectively supplied for natural gas,
Figure BDA00035766003600001111
efficiency, η, of CHP units outputting electric and thermal energy, respectivelygbIn order for the GB to output the efficiency of the thermal energy,
Figure BDA00035766003600001112
heat energy for HP output, EeProvides the comprehensive energy system operator with electric energy provided by the energy production equipment,
Figure BDA00035766003600001113
distribution coefficients, eta, for direct supply of HP, ER and AC, respectively, for electric energy produced by an integrated energy systemhpEfficiency of heat output for HP [. eta. ]er、ηacThe efficiency of the output cold power for ER and AC respectively,
Figure BDA00035766003600001114
is the capacity of the ESS at time t and time t-1, respectivelysFor the self-loss rate of the ESS,
Figure BDA00035766003600001115
for the stored energy power of the ESS at the time t,
Figure BDA00035766003600001116
the charging power and discharging power of the ESS are separated,
Figure BDA00035766003600001117
let us the discharge power of the ESS at the time t, Δ t is the energy storage or discharge time scale of the ESS, and s represents the ESS category, including EES, TES and CES.
The invention provides a construction method of a comprehensive energy system scheduling model considering a carbon transaction mechanism and demand response, which is suitable for being executed in computing equipment. FIG. 3 shows a block diagram of a computing device 300, according to one embodiment of the invention. A block diagram of a computing device 300 as shown in fig. 3, in a basic configuration 302, the computing device 200 typically includes a system memory 306 and one or more processors 304. A memory bus 308 may be used for communication between the processor 304 and the system memory 306.
Depending on the desired configuration, the processor 304 may be any type of processing, including but not limited to: a microprocessor (μ P), a microcontroller (μ C), a Digital Signal Processor (DSP), or any combination thereof. The processor 204 may include one or more levels of cache, such as a level one cache 310 and a level two cache 312, a processor core 314, and registers 316. The example processor core 314 may include an Arithmetic Logic Unit (ALU), a Floating Point Unit (FPU), a digital signal processing core (DSP core), or any combination thereof. The example memory controller 318 may be used with the processor 304, or in some implementations the memory controller 318 may be an internal part of the processor 304.
Depending on the desired configuration, system memory 306 may be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 306 may include an operating system 320, one or more applications 322, and program data 324. In some embodiments, application 322 may be arranged to operate with program data 324 on an operating system.
The computing device 300 also includes a storage device 332, the storage device 332 including removable storage 336 and non-removable storage 338, the removable storage 336 and the non-removable storage 338 each connected to the storage interface bus 334. In the present invention, the data related to each event occurring during the execution of the program and the time information indicating the occurrence of each event may be stored in the storage device 332, and the operating system 320 is adapted to manage the storage device 332. The storage device 332 may be a magnetic disk.
The computing device 300 may also include an interface bus 340 that facilitates communication from various interface devices (e.g., output devices 342, peripheral interfaces 344, and communication devices 346) to the basic configuration 302 via the bus/interface controller 330. The example output device 342 includes an image processing unit 348 and an audio processing unit 350. They may be configured to facilitate communications with various external devices, such as a display or speakers, via one or more a/V ports 352. Example peripheral interfaces 344 may include a serial interface controller 354 and a parallel interface controller 356, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 358. An example communication device 346 can include a network controller 360, which can be arranged to facilitate communications with one or more other computing devices 362 over a network communication link via one or more communication ports 364.
A network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, in a modulated data signal, such as a carrier wave or other transport mechanism, and may include any information delivery media. A "modulated data signal" may be a signal that has one or more of its data set or its changes made in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or private-wired network, and various wireless media such as acoustic, Radio Frequency (RF), microwave, Infrared (IR), or other wireless media. The term computer readable media as used herein may include both storage media and communication media.
Computing device 300 may be implemented as a server, such as a file server, a database server, an application server, a WEB server, etc., or as part of a small-form factor portable (or mobile) electronic device, such as a cellular telephone, a Personal Digital Assistant (PDA), a personal media player device, a wireless WEB-watch device, a personal headset device, an application specific device, or a hybrid device that include any of the above functions. Computing device 300 may also be implemented as a personal computer including both desktop and notebook computer configurations. In some embodiments, the computing device 300 is configured to perform a method 400 in accordance with the present invention.
The invention takes a campus-level comprehensive energy system considering a carbon transaction mechanism and demand response as an example for research, and the basic structure of the campus-level comprehensive energy system is shown in fig. 2.
Fig. 4 illustrates a schematic diagram of a method 400 of building an integrated energy system model that accounts for carbon trading mechanisms and demand response, suitable for execution resident in the computing device 300 shown in fig. 3, in accordance with one embodiment of the present invention. As shown in FIG. 4, the method 400 includes steps S410 to S430
In step S410, basic parameters are acquired. The underlying data is input data as a model. The basic parameters of the first model and the basic parameters of the second model, the basic parameters of the first model comprise: one or more of a price of selling the electric energy to the user, a price of selling the hot energy to the user, a price of selling the cold energy to the user, a price of buying the electric power and the natural gas to the power grid and the gas grid, a demand response compensation price to the user, a transferable demand response and reducible demand response fed back by the second model, an operation and maintenance cost of the distributed wind turbine, an operation and maintenance cost of the distributed photovoltaic turbine, an operation and maintenance cost of the gas boiler, an operation and maintenance cost of the cogeneration turbine, an operation and maintenance cost of the heat pump, an operation and maintenance cost of the electric refrigerator, an operation and maintenance cost of the absorption refrigerator, an operation and maintenance cost of the electric energy storage device, an operation and maintenance cost of the heat energy storage device, an operation and maintenance cost of the cold energy storage device, and a stepped carbon trading price.
The basic parameters of the second model include: one or more of a selling price of the electric energy source fed back to the second model by the first model, a selling price of the thermal energy source fed back to the second model by the first model, a selling price of the cold energy source fed back to the second model by the first model, a demand response compensation price, and a cost coefficient of participation of the user in the demand response.
Subsequently, in step S420, an integrated energy system scheduling model considering the carbon trading mechanism and the demand response is established by using the information gap decision theory, and the model includes an objective function and a constraint condition.
Fig. 5 is a schematic diagram of an integrated energy system model framework, and as can be seen from fig. 5, the model in the present invention includes a first model (i.e., an upper layer model) and a second model (i.e., a lower layer model), the first model is applied to an operator of energy supply, and the first model corresponds to a first objective function and a first constraint condition. A second model is applied to the user on the energy demand side, the second model corresponding to a second objective function and a second constraint. The first objective function and the second objective function are collectively called an objective function, and the first constraint and the second constraint are collectively called a constraint.
The first model targets daily operating margins to be the highest. The first model is an optimized scheduling model based on (information gap determination-mapping theory, IGDT) conventional strategies of an integrated energy system operator under a deterministic model and risk avoidance strategies considering uncertainty. IGDT deals with the adverse effects of uncertain events, namely Risk avoidance maneuver (rask Averse Strategy, RAS), by defining a robust function. A comprehensive energy system scheduling model considering a carbon trading mechanism and demand response is established by using an information gap decision theory, and the maximum uncertainty of the uncertain quantity is sought under the condition that the optimization target is within an acceptable range. The first model includes:
when the uncertainty of the parameters is not considered, the first model is:
Figure BDA0003576600360000141
in the formula, C represents a decision target, x represents a decision variable, d represents an uncertainty input parameter, f (x, d) represents, g (x, d) represents, and h (x, d) represents.
When considering the uncertainty of the parameters, the first model is:
uncertainty:
Figure BDA0003576600360000151
a first objective function:
Figure BDA0003576600360000152
and others:
Figure BDA0003576600360000153
wherein, alpha is the uncertainty,
Figure BDA0003576600360000154
respectively represents the weight coefficients of the photovoltaic output of the wind power output, the electric load demand, the heat load demand and the cold load demand under the risk avoidance strategy,
Figure BDA0003576600360000155
are respectively provided with
Figure BDA0003576600360000156
Representing the uncertainty of the wind power output photovoltaic output, the electric load demand, the heat load demand and the cold load demand under the risk avoidance strategy,
Figure BDA0003576600360000157
represents the daily operating profit, delta, of the operator of the integrated energy system under the risk avoidance strategy1Representing a robust model target bias factor, RPIESORepresents the daily operating profit of the comprehensive energy system operator under the conventional strategy,
Figure BDA0003576600360000158
respectively representing output predicted values of the distributed wind turbine generator and the photovoltaic generator after the uncertainty degree is considered,
Figure BDA0003576600360000159
respectively representing the output predicted values of the distributed wind generating set and the photovoltaic generating set under the condition of not considering the uncertainty,
Figure BDA00035766003600001510
the present disclosure shows predicted values of electrical, thermal, and cold loads after consideration of uncertainty，Le、Lh、LcRespectively representing predicted values of electrical, thermal and cooling loads without taking uncertainty into account, RsellThe energy sale profit is expressed and,
Figure BDA00035766003600001511
representing the cost of purchasing power and natural gas to the upper power and gas grids, CopeRepresents the equipment operation and maintenance cost, CdrWhich represents the cost of the demand response and,
Figure BDA00035766003600001512
representing the carbon transaction cost.
The first objective function specifically includes:
Figure BDA00035766003600001513
Figure BDA0003576600360000161
Figure BDA0003576600360000162
Figure BDA0003576600360000163
Figure BDA0003576600360000164
Figure BDA0003576600360000165
Figure BDA0003576600360000166
in the formula (I), the compound is shown in the specification,
Figure BDA0003576600360000167
respectively representing the prices for buying electricity and natural gas to the upper level power and gas grids,
Figure BDA0003576600360000168
Qg,trespectively representing the quantity of electricity purchased from the power grid and the quantity of natural gas purchased from the gas grid at time t, cgb、cchp、chp、cer、cac、cwt、cpv、cees、ctes、ccesRespectively represents the unit output operation and maintenance costs of a gas boiler, cogeneration, a heat pump, an electric refrigerator, a refrigerator, distributed wind power, distributed photovoltaic, electric energy storage equipment, heat energy storage equipment and cold energy storage equipment,
Figure BDA0003576600360000169
represents the thermal power output by the gas boiler at time t,
Figure BDA00035766003600001610
respectively the electric power and the thermal power output by the cogeneration unit at the moment t,
Figure BDA00035766003600001611
respectively showing the thermal power output by the heat pump, the cold power output by the electric refrigerator and the cold power output by the absorption refrigerator at the time t,
Figure BDA00035766003600001612
respectively representing the output of the wind turbine generator and the photovoltaic generator,
Figure BDA00035766003600001613
for the time t the electrical energy storage discharge power and the charging power,
Figure BDA00035766003600001614
for the heat release power and the heat storage power of the thermal energy storage device at time t,
Figure BDA00035766003600001615
respectively representing the cold discharge power and the cold storage power of the cold energy storage device at time t, CdrWhich represents the cost of the demand response and,
Figure BDA00035766003600001616
indicating that the user's demand response is rolling out of load,
Figure BDA00035766003600001617
indicating that the demand response of the user is load shedding,
Figure BDA00035766003600001618
respectively represents the electricity price, the heat price and the cold price provided by the user of the comprehensive energy system,
Figure BDA00035766003600001619
representing the electrical load demand of the user at time t,
Figure BDA00035766003600001620
indicating that the demand response of the user is shifted to load,
Figure BDA00035766003600001621
representing the thermal load demand of the user at time t,
Figure BDA00035766003600001622
indicating the cooling load demand of the user at time t,
Figure BDA00035766003600001623
indicating carbon transaction price, CE1Represents total carbon emission, CE, of the integrated energy system2Represents the total carbon emission quota of the integrated energy system,
Figure BDA00035766003600001624
respectively representing the electric energy and the heat energy generated by the CHP unit,
Figure BDA00035766003600001625
indicates GB productThe heat energy generated by the heat energy generation device,
Figure BDA00035766003600001626
represents the electric energy purchased by the integrated energy system to the upper-level power grid and air grid,
Figure BDA0003576600360000171
respectively representing the carbon emission intensity of the unit generating power of the CHP unit and the carbon emission intensity, xi, of the unit heating power of the CHP unitgb、
Figure BDA0003576600360000172
Respectively representing the carbon emission intensity of GB unit active power output and the carbon emission intensity of distribution network unit active power output.
The first constraint corresponding to the first objective function includes: one or more of power balance constraints, equipment force and grade constraints, tie line constraints, energy storage constraints, demand response constraints, and other constraints.
1) The power balance constraints are:
Figure BDA0003576600360000173
in the formula (I), the compound is shown in the specification,
Figure BDA0003576600360000174
respectively representing the electrical load, the cold load and the heat load demand of the user at the moment t,
Figure BDA0003576600360000175
a distribution coefficient representing the direct supply of the electrical energy produced to the electrical load,
Figure BDA0003576600360000176
representing the distribution coefficient of the produced electrical energy directly supplied to the electrical refrigerator,
Figure BDA0003576600360000177
representing the distribution coefficient of the produced electrical energy directly to the heat pump,
Figure BDA0003576600360000178
a distribution coefficient representing the direct supply of thermal energy produced to the thermal load,
Figure BDA0003576600360000179
representing the distribution coefficient of the thermal energy produced directly to the absorption chiller,
Figure BDA00035766003600001710
respectively representing the electrical and thermal energy provided by the integrated energy system operator.
2) The equipment output and climbing constraints are as follows:
Figure BDA00035766003600001711
in the formula (I), the compound is shown in the specification,
Figure BDA00035766003600001712
representing the contribution of the device m at time t,
Figure BDA00035766003600001713
the maximum output of the device m is represented,
Figure BDA00035766003600001714
representing the contribution of device m at time t +1,
Figure BDA00035766003600001715
represents the maximum ramp power of devices m, including GB, CHP, HP, ER, AC, WT, PV, EES, TES, and CES.
3) The tie line constraint is:
Figure BDA00035766003600001716
in the formula (I), the compound is shown in the specification,
Figure BDA00035766003600001717
it is shown that,
Figure BDA00035766003600001718
representing the maximum amount of power purchased to the grid.
4) The energy storage device is constrained as follows:
Figure BDA0003576600360000181
in the formula (I), the compound is shown in the specification,
Figure BDA0003576600360000182
respectively representing the charging state and the discharging state of the energy storage device s,
Figure BDA0003576600360000183
respectively represents the charging power and the discharging power of the energy storage device s at the moment t,
Figure BDA0003576600360000184
representing the maximum charge/discharge power of the energy storage device s,
Figure BDA0003576600360000185
respectively representing the minimum and maximum capacity of the energy storage device s,
Figure BDA0003576600360000186
representing the capacity of the energy storage device s at time t,
Figure BDA0003576600360000187
respectively representing the capacity of the energy storage device with t-1 and t-24 h.
5) The demand response constraints are:
Figure BDA0003576600360000188
in the formula (I), the compound is shown in the specification,
Figure BDA0003576600360000189
indicating that the user's demand response is rolling out of load,
Figure BDA00035766003600001810
indicating that the demand response of the user is shifted to load,
Figure BDA00035766003600001811
indicating the maximum transferable load of the user,
Figure BDA00035766003600001812
and (4) showing.
6) Other constraints include:
Figure BDA00035766003600001813
Figure BDA00035766003600001814
Figure BDA00035766003600001815
Figure BDA00035766003600001816
Figure BDA00035766003600001817
Figure BDA00035766003600001818
Figure BDA00035766003600001819
Figure BDA00035766003600001820
Figure BDA0003576600360000191
Figure BDA0003576600360000192
in the formula, FlowerAn objective function representing the underlying model,
Figure BDA0003576600360000193
indicating that the user's demand response is rolling out of load,
Figure BDA0003576600360000194
the price of the electricity sold by the operator of the comprehensive energy system at the moment t is shown, a and b both show the cost coefficient of the response demand of the user,
Figure BDA0003576600360000195
is represented by CdrIt is shown that the process of the present invention,
Figure BDA0003576600360000196
the dual-mode variable is represented by a dual-mode variable,
Figure BDA0003576600360000197
indicating that the demand response of the user is shifted to load,
Figure BDA0003576600360000198
for the introduced auxiliary boolean variables (for linearized expressions), integer variables of 0-1, M represents a constant with a large value,
Figure BDA0003576600360000199
indicating the maximum transferable load of the user,
Figure BDA00035766003600001910
and (4) showing.
The second model aims at the lowest daily energy cost of the user, and the objective function is as follows:
Figure BDA00035766003600001911
Figure BDA00035766003600001912
Figure BDA00035766003600001913
Rdr＝Cdr
in the formula (I), the compound is shown in the specification,
Figure BDA00035766003600001914
represents the cost of the energy use by the user,
Figure BDA00035766003600001915
representing the demand response cost, R, of a user adjusting a power plan to participate in demand responsedrRepresenting the participation of the user in the subsidy of demand response acquisition,
Figure BDA00035766003600001916
indicating the energy use cost, R, of the usersellThe energy sales proceeds are represented and,
Figure BDA00035766003600001917
indicating the demand response cost of the user in adjusting the electricity usage plan to participate in the demand response,
Figure BDA00035766003600001918
representing the user's demand response load, a, b representing the user's response demand cost factor, RdrSubsidies representing user participation in demand response procurement, CdrAnd (4) showing.
The second constraint corresponding to the second objective function is the demand response constraint 5), as described above, which is not described herein again.
Then, in step S430, the basic parameters are substituted into the model, and the model is solved with the goals of highest daily operating profit of the operator and lowest daily energy cost of the user, the first model outputs a scheme of outputting power of each unit in the integrated energy system, and the units in the integrated energy system include a distributed wind turbine, a distributed photovoltaic unit, a gas boiler, a cogeneration unit, a heat pump, an electric refrigerator, an absorption refrigerator, an electric energy storage device, a heat energy storage device and a cold energy storage device. The second model outputs a transferable demand response and a reducible demand response.
It should be understood that there are many ways to solve the model, and the present invention is not limited to the specific implementation, and all ways to solve the model are within the scope of the present invention.
It should be noted that, the purpose of establishing the double-layer model in the invention is to consider the operating economy of the two benefit agents respectively, but the established double-layer model has a coupling relation and has nonlinear constraints, and is difficult to solve directly. In order to effectively solve the model, in one embodiment of the invention, the two-layer model is equivalently converted into a single-layer optimization model which is easier to solve, and then the single-layer optimization model is quickly solved. The method for equivalently converting the two-layer model into the single-layer optimization model which is easy to solve and the method for rapidly solving the single-layer optimization model can be set according to the actual application scene, and the method is not limited by the invention.
For example, the two-layer model is converted into the single-layer model under the kurosh-Kuhn-Tucker (KKT) condition, the nonlinear expression in the single-layer model is converted into the linear expression through the large M (M is a larger integer) method, that is, the nonlinear single-layer model is converted into the linear single-layer model through the large M method, and the linear single-layer model is solved by calling a CPLEX solver of Matlab. The process of converting a two-layer model into a single-layer model under the KKT condition, converting a nonlinear expression in the single-layer model into a linear expression by a large M (M is a larger integer) method, and solving the linear single-layer model by a CPLEX solver is known in the art, and is not described herein again, but is within the protection scope of the present invention.
Fig. 6 illustrates a block diagram of an apparatus 600 for building an integrated energy system model that accounts for carbon trading mechanisms and demand response, according to an embodiment of the invention, where the apparatus 600 may reside in a computing device 300, the model including a first model and a second model, the first model being a model suitable for economy, the second model being a model suitable for renewable energy utilization; the first model corresponds to a first objective function, and the second model corresponds to a second objective function; the first objective function and the second objective function are collectively called objective functions; the device comprises:
as shown in fig. 6, the apparatus 600 includes: an acquisition parameter unit 610, a model construction unit 620, and a model solution unit 630.
The parameter obtaining unit 610 is adapted to obtain basic parameters, where the basic parameters include a stepped carbon transaction price and demand response parameters, and the demand response parameters include a transferable demand response and a reducible demand response.
The model building unit 620 is adapted to build a first model and a second model of the integrated energy system scheduling considering the carbon trading mechanism and the demand response by using the information gap decision theory, wherein the first model includes a first objective function and a first constraint condition, and the second model includes a second objective function and a second constraint condition.
The model solving unit 630 is adapted to substitute the basic parameters into the first model, solve the first model with the goal of highest daily operating profit of the operator, and output the output scheme of each unit in the integrated energy system, and is further adapted to substitute the basic parameters into the second model, solve the second model with the goal of lowest daily energy cost of the user, and output transferable demand response and reducible demand response.
Wherein the first objective function is:
Figure BDA0003576600360000211
in the formula, RsellThe energy sales proceeds are represented and,
Figure BDA0003576600360000212
indicating power upCost of purchasing power and natural gas from the grid and the gas grid, CopeRepresents the equipment operation and maintenance cost, CdrWhich represents the cost of the demand response and,
Figure BDA0003576600360000213
representing the carbon transaction cost.
Wherein the second objective function is:
Figure BDA0003576600360000214
in the formula (I), the compound is shown in the specification,
Figure BDA0003576600360000215
represents the cost of the energy use by the user,
Figure BDA0003576600360000216
representing the demand response cost, R, of a user adjusting a power plan to participate in demand responsedrRepresenting the user's participation in the subsidy for demand response acquisition.
It should be noted that the operation principle of the apparatus 600 for constructing an integrated energy system dispatching model considering a carbon transaction mechanism and a demand response is similar to that of the method 400 for constructing an integrated energy system model considering a carbon transaction mechanism and a demand response, and reference may be made to the description of the method 400 for relevant points, which is not described herein again.
Specific cases are adopted to verify that the comprehensive energy system scheduling model of the carbon transaction mechanism and the demand response constructed by the invention is subjected to numerical example simulation.
The method selects the actual engineering data of a typical industrial park in North China, and carries out example simulation by taking 24h a day as a scheduling period and 1h as a step length. Fig. 7 is a schematic diagram of a day-ahead prediction curve of wind power, photovoltaic, electric load, heat load and cold load, and fig. 8 is a power purchase price, an electric power sale price, a heat purchase price, a natural gas price and a cold purchase price. Wherein the purchase price of the natural gas is 0.25 yuan/kWh, the lower calorific value of the natural gas is 9.7kWh/m3, and the upper limit of the power purchased from the PIESO to the power grid is 1500 kW.
Table 1 shows the energy storage device parameters, and table 2 shows the parameters of other devices. Because the distributed wind power and photovoltaic power generation output is greatly influenced by natural environment factors, the prediction of the distributed wind power and photovoltaic power generation output is relatively difficult, and the load prediction is relatively more accurate. Therefore, the trapezoidal membership parameter can be set according to the above features as shown in table 3, with a confidence level of 0.9. Assume that the campus user response cost factor a is 0.001, b is 1, and c is 0.2.
TABLE 1 energy storage System parameters
Figure BDA0003576600360000221
TABLE 2 other plant parameters
Figure BDA0003576600360000222
TABLE 3 trapezoidal membership parameter
Figure BDA0003576600360000223
And, in the risk avoidance maneuver, order
Figure BDA0003576600360000224
The robust model target bias factor is set to 3%.
And inputting the corresponding parameters into the first model and the second model to obtain the risk evasion strategy in an uncertain scene, the conventional strategy in a deterministic scene, the day-ahead output result of each unit, the economic benefit and the environmental benefit. In the following contents, the day-ahead output results, economic benefits and environmental benefits of each unit of the conventional strategy in a deterministic scene and the risk evasion strategy in an uncertain scene are compared. The conventional strategy and the day-ahead output result, the economic benefit and the environmental benefit of each unit in the deterministic scene are shown in figures 9 to 11, and the risk avoiding strategy and the day-ahead output result, the economic benefit and the environmental benefit of each unit in the uncertain scene are shown in figures 12 to 14.
1. Day-ahead output result, economic benefit and environmental benefit of each unit of conventional strategy in deterministic scene
According to the equipment scheduling plan shown in fig. 9 to 11, under the conventional strategy that uncertainty of output of distributed wind power and photovoltaic wind power and uncertainty of demand of electricity, heat and cold loads are not considered, the obtained daily operation income of an operator of the comprehensive energy system is 34858.61 yuan, the energy cost of a user is 24815.94 yuan, the total carbon emission amount of the system is 95.7% of carbon emission quota, 4.3% is reduced, and carbon transaction income is 21531.36 yuan.
The power consumption requirement is mainly met by a wind turbine generator, a photovoltaic generator and a CHP generator. As can be seen from fig. 6 to 8, the user increases the power load at the power consumption valley time of 1:00-9:00, 13:00-15:00 and the like, and decreases the power load at the power consumption valley time of 10:00-11:00, 16:00-18:00 and the like, and actively responds to DR incentive mechanisms such as time-of-use power price and subsidy price and the like given by the comprehensive energy system operator, so that the effect of peak clipping and valley filling is achieved while the energy consumption cost of the user is reduced. The electric energy storage unit is charged in the time interval of 16:00-18:00, discharged in the time interval of 20:00-24:00, and the HP unit is powered in the time intervals of 1:00-11:00 and 19:00-24: 00.
The heat consumption requirements of users and the AC unit are mainly met by the CHP and HP units, and the heat power generated by the CHP unit is mainly used for meeting the heat consumption requirements of the AC unit in the heat consumption valley period of 12:00-19: 00. The cold demand of a user is mainly met by the ER unit and the AC unit, the AC unit mainly converts certain heat load into cold load in a period of 12:00-17:00 when the heat load demand of the user is low, and the ER unit mainly generates corresponding cold load by consuming electric power in other periods. Therefore, the cascade utilization of energy is realized by utilizing the time complementary characteristics of various loads such as electricity, heat and cold of park users and various energy conversion coupling units in the PIES, so that the comprehensive utilization efficiency of the energy is improved.
2. Risk avoidance strategy day-ahead output result, economic benefit and environmental benefit of each unit under uncertain scene
The critical scheduling income of the operator of the comprehensive energy system obtained by the IGDT robust model is 33812.85 yuan, the uncertainty of wind power, the uncertainty of photovoltaic output, the uncertainty of electric load demand, the uncertainty of heat load demand and the uncertainty of cold load demand, and the comprehensive uncertainty of the comprehensive energy system is 0.14. It is shown that when the fluctuation of the actual values of the wind power and photovoltaic output and the actual values of the electric load, the heat load and the cold load demand relative to the predicted values is within 12.1%, 23.4%, 11.1%, 6% and 10%, the total scheduling revenue of the comprehensive energy system operator is not less than 33812.85 yuan.
The capacity plans for each unit are shown in figures 12-14. As can be seen from fig. 12 to 14, the IGDT robust scheduling plan is more conservative than the initial contribution plan, and more enables the CCHP unit with stable contribution characteristics to contribute to the uncertainty, thereby resulting in a reduction in scheduling revenue. The electric power and the thermal power output by the CHP unit, the electric power output by the electric energy storage unit and the DR quantity of a user are increased, so that the carbon emission of PIES is increased, the carbon emission right capable of trading and the corresponding carbon trading income are reduced, and the DR subsidy cost is increased, so that the total daily operation income of an integrated energy system operator is reduced.
From the above, the invention provides a stochastic optimization scheduling model based on IGDT and considering carbon trading mechanism and demand response for a campus comprehensive energy system considering carbon trading mechanism and demand response. By researching a scheduling plan of an integrated energy system operator under a conventional strategy in a deterministic scene and a scheduling plan under a risk avoiding strategy in an uncertain scene, the influence of risk awareness and carbon transaction price on daily operation profit of the integrated energy system operator is analyzed, and the main conclusion is as follows:
under a risk avoidance strategy, in order to deal with uncertainty of wind power and photovoltaic output at the source side and multiple loads of electricity, heat and cold at the demand side, an integrated energy system operator tends to meet the electricity and heat demands of users through a CHP unit with a stable output characteristic, and call more DR loads to ensure energy supply and demand balance. In addition, after a step-type carbon transaction mechanism is introduced into the model, the total carbon emission of the system is further effectively reduced, and the requirement of the current Chinese double-carbon target is met. On the other hand, the comprehensive energy system is based on the principles of system energy conversion coupling and cascade utilization and utilizes DR, so that the carbon emission of the system is obviously smaller than the distributed carbon emission quota, and the residual carbon emission right is traded, thereby increasing the operating economic benefit of the comprehensive energy system.

Claims (10)

1. A method of building an integrated energy system model that accounts for carbon trading mechanisms and demand response, adapted to be executed in a computing device, the method comprising:
obtaining basic parameters, wherein the basic parameters comprise a stepped carbon transaction price and demand response parameters, and the demand response parameters comprise transferable demand response and reducible demand response;
establishing a first model and a second model of the integrated energy system dispatching considering a carbon transaction mechanism and demand response by using an information gap decision theory, wherein the first model comprises a first objective function and a first constraint condition, and the second model comprises a second objective function and a second constraint condition;
substituting the basic parameters into the first model, solving the first model by taking the highest daily operating profit of an operator as a target, and outputting a scheme of the output of each unit in the comprehensive energy system;
substituting the basic parameters into the second model, solving the second model by taking the lowest daily energy cost of the user as a target, and outputting transferable demand response and reducible demand response;
wherein the first objective function is:
Figure FDA0003576600350000011
in the formula, RsellThe energy sales proceeds are represented and,
Figure FDA0003576600350000012
representing the cost of purchasing energy of electricity and natural gas to the upper power and gas grids, CopeRepresents the equipment operation and maintenance cost, CdrWhich represents the cost of the demand response and,
Figure FDA0003576600350000013
represents a carbon transaction cost;
wherein the second objective function is:
Figure FDA0003576600350000014
in the formula (I), the compound is shown in the specification,
Figure FDA0003576600350000015
represents the cost of the energy use by the user,
Figure FDA0003576600350000016
representing demand response costs, R, of a user adjusting a power usage plan for participation in demand responsesdrRepresenting the participation of the user in the subsidy obtained by the demand response.
2. The method of claim 1, wherein the first objective function comprises:
Figure FDA0003576600350000017
Figure FDA0003576600350000018
Figure FDA0003576600350000019
Figure FDA00035766003500000110
Figure FDA0003576600350000021
Figure FDA0003576600350000022
Figure FDA0003576600350000023
in the formula (I), the compound is shown in the specification,
Figure FDA0003576600350000024
respectively representing the prices for buying electricity and natural gas to the upper level power and gas grids,
Figure FDA0003576600350000025
Qg,trespectively representing the quantity of electricity purchased from the power grid and the quantity of natural gas purchased from the gas grid at the moment t, cgb、cchp、chp、cer、cac、cwt、cpv、cees、ctes、ccesRespectively represents the unit output operation and maintenance costs of a gas boiler, cogeneration, a heat pump, an electric refrigerator, a refrigerator, distributed wind power, distributed photovoltaic, electric energy storage equipment, heat energy storage equipment and cold energy storage equipment,
Figure FDA0003576600350000026
represents the thermal power output by the gas boiler at time t,
Figure FDA0003576600350000027
respectively the electric power and the thermal power output by the combined heat and power generation unit at the moment t,
Figure FDA0003576600350000028
Figure FDA0003576600350000029
respectively showing the thermal power output by the heat pump, the cold power output by the electric refrigerator and the cold power output by the absorption refrigerator at the time t,
Figure FDA00035766003500000210
respectively representing the output of the wind turbine generator and the photovoltaic generator,
Figure FDA00035766003500000211
Figure FDA00035766003500000212
for the time t the electrical energy storage discharge power and the charging power,
Figure FDA00035766003500000213
for the heat release power and the heat storage power of the thermal energy storage device at time t,
Figure FDA00035766003500000214
respectively representing the cold discharge power and the cold storage power of the cold energy storage device at time t, CdrWhich represents the cost of the demand response and,
Figure FDA00035766003500000215
indicating that the user's demand response is rolling out of load,
Figure FDA00035766003500000216
indicating that the demand response of the user is load shedding,
Figure FDA00035766003500000217
respectively represents the electricity price, the heat price and the cold price provided by the user of the comprehensive energy system,
Figure FDA00035766003500000218
representing the electrical load demand of the user at time t,
Figure FDA00035766003500000219
indicating that the demand response of the user is shifted to load,
Figure FDA00035766003500000220
to representThe thermal load demand of the user at time t,
Figure FDA00035766003500000221
indicating the cooling load demand of the user at time t,
Figure FDA00035766003500000222
indicating carbon transaction price, CE1Represents total carbon emission, CE, of the integrated energy system2Represents the total carbon emission quota of the integrated energy system,
Figure FDA00035766003500000223
respectively representing the electrical and thermal energy generated by the CHP plant,
Figure FDA00035766003500000224
the heat energy generated by the GB is expressed,
Figure FDA00035766003500000225
represents the electric energy purchased by the integrated energy system to the upper-level power grid and air grid,
Figure FDA00035766003500000226
respectively representing the carbon emission intensity of the unit generating power of the CHP unit and the carbon emission intensity, xi, of the unit heating power of the CHP unitgb、
Figure FDA00035766003500000227
Respectively representing the carbon emission intensity of GB unit active power output and the carbon emission intensity of distribution network unit active power output.
3. The method of claim 1 or 2, wherein the second objective function comprises:
Figure FDA00035766003500000228
Figure FDA00035766003500000229
Rdr＝Cdr
in the formula (I), the compound is shown in the specification,
Figure FDA0003576600350000031
represents the energy use cost, R, of the usersellThe energy sales proceeds are represented and,
Figure FDA0003576600350000032
indicating the demand response cost of the user in adjusting the electricity usage plan to participate in the demand response,
Figure FDA0003576600350000033
representing the user's demand response rolling load, a, b representing the user's response demand cost factor, RdrSubsidies representing participation of the user in demand response procurement, CdrAnd (4) showing.
4. A method as claimed in any one of claims 1 to 3, wherein the first constraint comprises: one or more of power balance constraints, equipment output and ramp constraints, tie-line constraints, energy storage constraints, demand response constraints, and other constraints, the second constraint comprising: the demand response constraint.
5. The method of claim 4, wherein the power balance constraint comprises:
Figure FDA0003576600350000034
in the formula (I), the compound is shown in the specification,
Figure FDA0003576600350000035
respectively representing the electrical load, the cold load and the heat load demand of the user at the moment t,
Figure FDA0003576600350000036
a distribution coefficient representing the direct supply of the electrical energy produced to the electrical load,
Figure FDA0003576600350000037
representing the distribution coefficient of the produced electrical energy directly supplied to the electrical refrigerator,
Figure FDA0003576600350000038
representing the distribution coefficient of the produced electrical energy directly to the heat pump,
Figure FDA0003576600350000039
a distribution coefficient representing the direct supply of thermal energy produced to the thermal load,
Figure FDA00035766003500000310
representing the distribution coefficient of the thermal energy produced directly to the absorption chiller,
Figure FDA00035766003500000311
respectively representing the electrical and thermal energy provided by the integrated energy system operator.
6. The method of claim 4 or 5, wherein the device output and hill climbing constraints comprise:
Figure FDA00035766003500000312
in the formula (I), the compound is shown in the specification,
Figure FDA00035766003500000313
representing the contribution of the device m at time t,
Figure FDA00035766003500000314
the maximum output of the device m is represented,
Figure FDA00035766003500000315
representing the contribution of device m at time t +1,
Figure FDA00035766003500000316
represents the maximum ramp power of the devices m, including GB, CHP, HP, ER, AC, WT, PV, EES, TES and CES.
7. The method of any of claims 4 to 6, wherein the tie line constraint comprises:
Figure FDA00035766003500000317
in the formula (I), the compound is shown in the specification,
Figure FDA0003576600350000041
it is shown that,
Figure FDA0003576600350000042
representing the maximum amount of power purchased from the grid.
8. An integrated energy system model building apparatus that accounts for carbon trading mechanisms and demand responses, adapted to be executed in a computing device, comprising:
the parameter obtaining module is suitable for obtaining basic parameters, wherein the basic parameters comprise stepped carbon transaction prices and demand response parameters, and the demand response parameters comprise transferable demand response quantity and reducible demand response quantity;
the model construction unit is suitable for establishing a first model and a second model for scheduling the integrated energy system considering the carbon transaction mechanism and the demand response by using an information gap decision theory, wherein the first model comprises a first objective function and a first constraint condition, and the second model comprises a second objective function and a second constraint condition;
the model solving unit is suitable for substituting the basic parameters into the first model, solving the first model by taking the highest daily operating profit of an operator as a target and outputting a scheme of output of each unit in the integrated energy system, and is also suitable for substituting the basic parameters into the second model, solving the second model by taking the lowest daily energy cost of a user as a target, and outputting transferable demand response quantity and reducible demand response quantity;
wherein the first objective function is:
Figure FDA0003576600350000043
in the formula, RsellThe energy sales proceeds are represented and,
Figure FDA0003576600350000044
representing the cost of purchasing energy of electricity and natural gas to the upper power and gas grids, CopeRepresents the equipment operation and maintenance cost, CdrWhich represents the cost of the demand response and,
Figure FDA0003576600350000045
represents a carbon transaction cost;
wherein the second objective function is:
Figure FDA0003576600350000046
in the formula (I), the compound is shown in the specification,
Figure FDA0003576600350000047
represents the cost of the energy use by the user,
Figure FDA0003576600350000048
representing the demand response cost, R, of a user adjusting a power plan to participate in demand responsedrRepresenting the participation of the user in the subsidy obtained by the demand response.
9. A computing device, comprising:
at least one processor; and
a memory storing program instructions configured for execution by the at least one processor, the program instructions comprising instructions for performing the method of any of claims 1-7.
10. A readable storage medium storing program instructions that, when read and executed by a computing device, cause the computing device to perform the method of any of claims 1-7.
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CN115063003A (en) * 2022-06-30 2022-09-16 华北电力大学（保定） Time-interval scheduling method of comprehensive energy system based on multivariate load time sequence analysis
CN115115277A (en) * 2022-08-23 2022-09-27 华北电力大学 Garden comprehensive energy scheduling method and device, computer equipment and storage medium
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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115063003A (en) * 2022-06-30 2022-09-16 华北电力大学（保定） Time-interval scheduling method of comprehensive energy system based on multivariate load time sequence analysis
CN115063003B (en) * 2022-06-30 2023-01-24 华北电力大学（保定） Time-interval scheduling method of comprehensive energy system based on multivariate load time sequence analysis
CN115115277A (en) * 2022-08-23 2022-09-27 华北电力大学 Garden comprehensive energy scheduling method and device, computer equipment and storage medium
CN117436672A (en) * 2023-12-20 2024-01-23 国网湖北省电力有限公司经济技术研究院 Comprehensive energy operation method and system considering equivalent cycle life and temperature control load
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