Virtual power plant optimal scheduling method considering load demand response under stepped carbon transaction mechanism

Abstract

The invention relates to a virtual power plant optimal scheduling method considering load demand response under a stepped carbon transaction mechanism, belongs to the technical field of power system operation and control, and provides a virtual power plant optimal scheduling method integrating stepped carbon transaction LTCT and load demand response DR in a virtual power plant aiming at the defects of a traditional power generation mode and the problem of energy shortage. A virtual power plant model containing wind power, a gas-fired boiler, cogeneration equipment and the like is constructed, and optimal scheduling is realized by taking total cost minimization as a target and combining constraint conditions such as electric power, thermal power and natural gas balance; a price-based DR model including a reducible load and a transferable load and an LTCT model are also established, and the effectiveness of the model is verified through example simulation; according to the method, the operation potential of the VPP in a low-carbon economic environment is effectively verified, the flexibility of a power system is remarkably improved, the comprehensive operation cost and the carbon emission are reduced, and the method has important significance in promoting low-carbon economic development and is beneficial to promoting low-carbon economic and social development.

Description

Technical Field

[0001] The present invention relates to the field of power system operation and control technology, and in particular to a virtual power plant optimization scheduling method considering load demand response under a stepped carbon trading mechanism. Background Art

[0002] As the global energy shortage crisis and environmental pollution become increasingly serious, the high pollution and high energy consumption of traditional power generation methods have gradually become prominent and have become a bottleneck restricting sustainable development. In order to promote the transformation of energy structure, vigorously developing clean and low-cost new energy such as wind power and solar energy has become the consensus and direction of action of the international community. In this context, the transmission mode of the power system has also gradually changed from the traditional "power generation, transmission, distribution, and use" to a more flexible and efficient "source, network, load, and storage" new system.

[0003] As an innovative power system operation mode, Virtual Power Plant (VPP) achieves efficient energy utilization and optimized scheduling by integrating various distributed energy resources (including traditional energy and new energy) in the local power system, which can not only improve the flexibility and reliability of the power system, but also bring significant economic and environmental benefits. In recent years, research and practice on VPP have increased, especially in power capacity configuration, internal optimization scheduling strategy and coordinated operation with the power grid.

[0004] In the field of VPP research, optimal dispatch considering the environment and benefits has become a hot topic. For example, the article "Economic Optimization Dispatch of Virtual Power Plant Considering Environmental Benefits" in Volume 3, Issue 5 of New Energy Progress constructed a VPP economic optimization dispatch model including wind power generation, photovoltaic power generation, small hydropower and micro gas turbine power generation, and fully considered the impact of environmental benefits on VPP operation; in addition, "Stochastic Dispatch Optimization Model of Wind, Solar, Fuel and Storage Integrated Virtual Power Plant Considering Uncertainty and Demand Response" in Volume 41, Issue 11 of Power System Technology further introduced the conditional risk value theory and confidence method to describe the uncertainty of VPP operation, and considered the impact of demand response (DR, Demand Response) on VPP optimal dispatch.

[0005] However, although some research has achieved certain results in the optimization and dispatch of VPP, most of the research still focuses on the optimization and dispatch of power resources. There is still a lack of research on the combination of carbon trading mechanism (especially the tiered carbon trading LTCT mechanism) and real-time regulation of load demand response (DR). With the gradual establishment and improvement of the carbon trading market and the high penetration rate of new energy, how to fully consider the impact of the carbon trading mechanism in the optimization and dispatch of VPP, and how to use the DR mechanism to further reduce carbon emissions and economic dispatch costs, have become key issues that need to be solved.

[0006] For example, the article "Optimal Dispatch Strategy of Virtual Power Plant with Demand Response and Electricity Carbon Trading Considering Multiple Uncertainties" in Volume 43, Issue 5 of Electric Power Automation Equipment comprehensively considered electricity carbon trading and incentive-based demand response mechanisms, but did not deeply explore the VPP optimal dispatch problem under the tiered carbon trading mechanism. In addition, the existing literature focuses on a single form of demand response (such as price-based demand response or incentive-based demand response), but lacks in-depth research and application of interchangeable demand response (such as the interchangeability between electrical load and thermal load).

[0007] In summary, although the existing technology has made some progress in VPP optimization scheduling, it still has deficiencies in considering the combination of the step-by-step carbon trading mechanism and the real-time regulation of load demand response. Therefore, the present invention proposes a virtual power plant optimization scheduling method considering load demand response under the step-by-step carbon trading mechanism, aiming to further enhance the operation potential and comprehensive benefits of VPP in a low-carbon economic environment by finely modeling the carbon trading mechanism and demand response mechanism. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a virtual power plant optimization scheduling method that takes into account load demand response under a stepped carbon trading mechanism, and to further enhance the operating potential and comprehensive benefits of VPP in a low-carbon economic environment by finely modeling the carbon trading mechanism and demand response mechanism.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is a virtual power plant optimization scheduling method considering load demand response under a stepped carbon trading mechanism, and the strategy includes the following steps: Step 1: Establish a virtual power plant and market environment that considers ladder-type carbon trading (LTCT) and load demand response (DR). The energy supply equipment units in the market environment convert heat and electricity and store energy. The virtual power plant interacts with the power grid as an independent entity. Step 2: A virtual power plant dispatch model based on LTCT and DR takes minimizing the total cost as the objective function, where the total cost includes the electricity purchase cost, gas purchase cost and equipment operation and maintenance cost; Step 3: Determine the operating constraints of the virtual power plant considering LTCT and DR, including wind power generation constraints, power balance constraints, thermal power balance constraints, natural gas balance constraints, and combined heat and power comprehensive constraints; Step 4: Establish a DR model, which includes price-based DR and replaceable DR, where the price-based DR further includes a curtailable load (CL) model and a shiftable load (SL) model; Step 5: Establish the LTCT model, including the carbon emission quota model and the actual carbon emission model. Calculate the actual carbon emission volume in the carbon market based on the carbon emission quota and the actual carbon emission volume, and then build the LTCT model. Step 6: Use example simulation to verify the effectiveness of this method to ensure that the virtual power plant has good operating potential and economic benefits in a low-carbon economic environment.

[0010] In the preferred scheme, the market environment energy supply unit in Step 1 includes wind power, upper-level gas grid, upper-level power grid, terminal energy consumption unit and carbon trading market unit; the virtual power plant includes coupled equipment units of combined heat and power (CHP, Combined Heat and Power), gas boiler (GB, Gas Boiler), gas turbine (GT, Gas Turbine) and electric boiler (EB, Electric Boiler).

[0011] In a preferred solution, the calculation method of the objective function in Step 2 includes: (1) in, represents the total operating cost; T is the operating cycle, for t During this period, electricity is purchased from the upper power grid. For price, t Purchase electricity during time periods, is the gas purchase volume during period t, is the unit natural gas price; i The values ​​1, 2, ..., 6 represent wind power, gas boiler GB, waste heat recovery (HR, Heat Recovery,), combined heat and power unit (CHP), heat storage tank (HST, Heat Storage Tank,), and energy storage (ES, Energy Storage,) respectively; for equipment i, represents the operation and maintenance ratio, is the output power.

[0012] In the preferred solution, the wind power generation constraint condition of the virtual power plant operation constraint condition considering LTCT and DR in Step 3 is expressed as: (2) in, and They are t The actual output and predicted output power of wind power in the period; The power balance constraint is expressed as: (3) in, For hot price t The electrical power consumed during the period, For the thermoelectric coupling unit t The electrical power generated during the period, , for t The charge and discharge capacity of the battery, for t The amount of electric load demand response during the time period; , , After considering demand response t Changes in load that can be reduced, load that can be moved, and load that can be interchanged during a period of time; The thermal power balance constraint is expressed as: (4) in, is the heat output of the gas boiler during period t; , They are t The heat generation of the heat storage tank HST and the combined heat and power unit CHP during the period, , are the discharge and charge power of the heat storage tank during period t, for t The heat load demand response amount for each period; for t The amount of heat load change that can be interchanged during time periods; The natural gas balance constraint is expressed as: (5) in, , are the gas consumption of CHP and GB in period t respectively.

[0013] The comprehensive constraints of CHP cogeneration are expressed as: (6) in, is the electrical load of the gas turbine; is the heat output of the low-temperature waste heat generator set during period t, is the percentage of waste heat generated by GT during period t that is sent to HST for heat generation, is the heat transfer efficiency of HST. and It is the efficiency of GT's gas turbine power generation and gas heating. is the calorific value of natural gas, equal to 9.88kW•h / m 3 .

[0014] In a preferred solution, the price-based DR in Step 4 includes a curtailable load CL model and a movable load SL model, wherein: The curtailable load CL model is expressed as: (7) Among them, after considering demand response t The load variation during a period is defined as , is the initial load, After DR j The difference in electricity cost between time periods, for j The initial price of electricity for the time period; Among them, after considering demand response t The load reduction period is modified as follows: : (8) in, for t The initial load that can be reduced during the period, is the demand elasticity of the CL price matrix, for j The price of electricity during the time period; The movable load SL model is expressed as: (9) in, is the initial movable load in period t, is the SL price elasticity of the demand matrix.

[0015] In the preferred solution, the replaceable demand response DR in Step 4 is expressed as: (10) in, and are the amounts of interchangeable thermal and electrical loads, respectively, is the substitution coefficient of electricity and heat, and are the calorific value per unit electrical power and per unit heat, respectively. and are the energy consumption of electrical energy and thermal energy, respectively, and the negative sign in the formula means that an increase in replaceable thermal load corresponds to a decrease in replaceable electrical load; The number of replaceable loads (RL, Replaceable Load,) that need to be considered for the replaceable demand response is: (11) in, and are the minimum and maximum amounts of replaceable electrical loads, and are the minimum and maximum amounts of replaceable heat load, respectively.

[0016] In a preferred solution, the LTCT model in Step 5 includes a carbon emission quota model and an actual carbon emission model, wherein: The carbon emission quota model is expressed as: (12) in, , , , They are the carbon emission quotas for VPP, upstream power grid, cogeneration unit and gas boiler; is the electric energy purchased by the upper power grid during period t; T is the dispatching period , They are the carbon emission quotas corresponding to the consumption of coal-fired units and natural gas units respectively; is the electric energy output by the cogeneration unit during period t; is the heat energy output by the cogeneration unit during period t; is the heat energy output by the gas boiler during period t; The actual carbon emission model is expressed as: (13) in, is the actual carbon emissions of the VPP; Actual carbon emissions for electricity purchased from upstream grid; is the actual total carbon emissions of CHP and GB; is the equivalent output of CHP and GB at time t. and They are the calculation coefficient sets for coal-fired units and natural gas equipment respectively.

[0017] In the preferred solution, in Step 5, the calculation method of the carbon emissions actually traded in the carbon market can be obtained based on the carbon emission quota and the actual carbon emissions as follows: (14) in, The carbon emissions actually traded in the carbon market.

[0018] In the preferred solution, the LTCT model in Step 5 is finally expressed as: (15) in, It is the basic price of the carbon trading market; is the length of the carbon emission interval; is the growth rate of carbon emission price.

[0019] The virtual power plant optimization scheduling method considering load demand response under the stepped carbon trading mechanism provided by the present invention has the following beneficial effects: 1. This invention conducts a refined modeling of the carbon trading mechanism and proposes a VPP optimal scheduling method considering the electric-thermal load demand response (DR). By integrating demand response in the virtual power plant model, part of the load can be transferred from high electricity price periods to low electricity price periods, reducing economic losses and replacing heat and electricity on the user side to a certain extent. 2. The VPP scheduling of the present invention and considering both LTCT and DR can enhance the operation flexibility of the power supply side and the load side, so that the comprehensive operation cost and carbon emission reduction effect of the VPP can obtain higher benefits; 3. The scheduling method of the present invention fully exploits the flexible resources of electric-thermal coupling, which can effectively verify the operation potential of VPP in a low-carbon environment and is conducive to promoting low-carbon economic and social development; 4. The present invention conducts a step-by-step modeling of carbon trading, which significantly reduces the total carbon emissions and provides practical support for the green development concept of energy conservation and emission reduction; 5. The present invention considers in detail the virtual power plant market environment of tiered carbon trading and demand response, and fully explains the virtual power plant market operating environment, including wind power, gas grid, power grid and terminal energy consumption units and carbon trading market of upper-level energy supply, which conforms to the actual market operating working environment; 6. The present invention considers in detail the virtual power plant as an independent entity to interact with the electricity-heat network, which has practical reference significance for the virtual power plant operation model in the actual market; 7. The present invention has a more sophisticated consideration for demand response, and establishes two models of curtailable load and transferable load, which can reflect the flexibility of loads that need to be responded to, while the patent only considers demand response regulation under conventional electrical and thermal loads; 8. The objective function of the present invention takes into account the coupling of electricity, heat and carbon, which is more in line with the operation rules of virtual power plants that consider demand response under the ladder carbon trading, is consistent with the realization of low-carbon emission reduction, and helps achieve the goal of low-carbon economy; 9. The present invention incorporates demand response into the VPP model, which can transfer part of the load from the high electricity price period to the low price period, reduce losses, and to some extent replace the combined cooling, heating and power supply on the user side; 10. The present invention takes into account the virtual grid dispatching of LTCT and DR, which can enhance the flexibility of the grid source and load ends, and improve the comprehensive operation cost and emission reduction benefits of the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further described below in conjunction with the accompanying drawings and implementation examples: Figure 1 It is a schematic diagram of a virtual power plant operation framework considering load demand response under the stepped carbon trading mechanism of Example 2 of the present invention; Figure 2 This is a graph showing changes in electricity and heat prices considering demand response in Example 2 of the present invention; Figure 3 This is a curve diagram of electric load variation before and after demand response in Example 2 of the present invention; Figure 4 This is a heat load variation curve diagram before and after demand response according to Example 2 of the present invention. DETAILED DESCRIPTION

[0021] The technical solution of the present invention is further described below in conjunction with the embodiments and drawings: Example 1 The optimal dispatching method of virtual power plants considering load demand response under the tiered carbon trading mechanism includes the following steps: Step 1: Establish a virtual power plant and market environment that considers tiered carbon trading (LTCT) and load demand response (DR). The energy supply equipment units in the market environment convert heat and electricity and store energy. The virtual power plant interacts with the power grid as an independent entity. Step 2: A virtual power plant dispatch model based on LTCT and DR takes minimizing the total cost as the objective function, where the total cost includes the electricity purchase cost, gas purchase cost and equipment operation and maintenance cost; Step 3: Determine the operating constraints of the virtual power plant considering LTCT and DR, including wind power generation constraints, power balance constraints, thermal power balance constraints, natural gas balance constraints, and combined heat and power comprehensive constraints; Step 4: Establish a DR model, which includes price-based DR and replaceable DR, wherein the price-based DR further includes a curtailable load CL model and a movable load SL model; Step 5: Establish the LTCT model, including the carbon emission quota model and the actual carbon emission model. Calculate the actual carbon emission volume in the carbon market based on the carbon emission quota and the actual carbon emission volume, and then build the LTCT model. Step 6: Use example simulation to verify the effectiveness of this method to ensure that the virtual power plant has good operating potential and economic benefits in a low-carbon economic environment.

[0022] In this embodiment, the market environment energy supply unit in Step 1 includes wind power, upper-level gas grid, upper-level power grid, terminal energy consumption unit and carbon trading market unit; the virtual power plant includes the coupling equipment units of cogeneration CHP, gas boiler GB, gas turbine GT and electric boiler EB.

[0023] Furthermore, the calculation method of the objective function in Step 2 includes: (1) in, represents the total operating cost; T is the operating cycle, for t During this period, the company purchases electricity from the upper power grid. For price, t Purchase electricity during time periods, is the gas purchase volume during period t, is the unit natural gas price; i The values ​​1, 2, ..., 6 represent wind power, gas boiler GB, waste heat recovery HR, combined heat and power unit CHP, heat storage tank HST, and energy storage ES respectively; for equipment i , represents the operation and maintenance ratio, is the output power.

[0024] Furthermore, the wind power generation constraints considering the virtual power plant operation constraints of LTCT and DR in Step 3 are expressed as: (2) in, and They are t The actual output and predicted output power of wind power in the period; The power balance constraint is expressed as: (3) in, For hot price t The electrical power consumed during the period, For the thermoelectric coupling unit t The electrical power generated during the period, , for tThe charge and discharge capacity of the battery, for t The amount of electric load demand response during the time period; , , After considering demand response t Changes in load that can be reduced, load that can be moved, and load that can be interchanged during a period of time; The thermal power balance constraint is expressed as: (4) in, is the heat output of the gas boiler during period t; , They are t The heat generation of the heat storage tank HST and the combined heat and power unit CHP during the period, , are the discharge and charge power of the heat storage tank during period t, for t The heat load demand response amount for each period; for t The amount of heat load change that can be interchanged during time periods; The natural gas balance constraint is expressed as: (5) in, , are the gas consumption of CHP and GB in period t respectively.

[0025] The comprehensive constraints of CHP cogeneration are expressed as: (6) in, is the electrical load of the gas turbine; is the heat output of the low-temperature waste heat generator set during period t, is the percentage of waste heat generated by GT during period t that is sent to HST for heat generation, is the heat transfer efficiency of HST. and It is the efficiency of GT's gas turbine power generation and gas heating. is the calorific value of natural gas, equal to 9.88kW•h / m 3 .

[0026] Furthermore, the price-based DR in Step 4 includes a curtailable load CL model and a movable load SL model, wherein: The curtailable load CL model is expressed as: (7) Among them, after considering demand response tThe load variation during a period is defined as , is the initial load, After DR j The difference in electricity cost between time periods, for j The initial price of electricity for the time period; Among them, after considering demand response t The load reduction period is modified as follows: : (8) in, for t The initial load that can be reduced during the period, is the demand elasticity of the CL price matrix, for j The price of electricity during the time period; The movable load SL model is expressed as: (9) in, is the initial movable load in period t, is the SL price elasticity of the demand matrix.

[0027] Furthermore, the replaceable demand response DR in Step 4 is expressed as: (10) in, and are the amounts of interchangeable thermal and electrical loads, respectively, is the substitution coefficient of electricity and heat, and are the calorific value per unit electrical power and per unit heat, respectively. and are the energy consumption of electrical energy and thermal energy, respectively, and the negative sign in the formula means that an increase in replaceable thermal load corresponds to a decrease in replaceable electrical load; The number of replaceable load RL limits that need to be considered for the replaceable demand response is: (11) in, and are the minimum and maximum amounts of replaceable electrical loads, and are the minimum and maximum amounts of replaceable heat load, respectively.

[0028] Furthermore, the LTCT model in Step 5 includes a carbon emission quota model and an actual carbon emission model, wherein: The carbon emission quota model is expressed as: (12) in, , , , They are the carbon emission quotas for VPP, upstream power grid, cogeneration units and gas boilers; is the electric energy purchased by the upper power grid during period t; T is the dispatching period , They are the carbon emission quotas corresponding to the consumption of coal-fired units and natural gas units respectively; is the electric energy output by the cogeneration unit during period t; is the heat energy output by the cogeneration unit during period t; is the heat energy output by the gas boiler during period t; The actual carbon emission model is expressed as: (13) in, is the actual carbon emissions of the VPP; Actual carbon emissions for electricity purchased from upstream grid; is the actual total carbon emissions of CHP and GB; is the equivalent output of CHP and GB at time t. and They are the calculation coefficient sets for coal-fired units and natural gas equipment respectively.

[0029] Furthermore, in Step 5, based on the carbon emission quota and the actual carbon emissions, the calculation method of the carbon emissions actually traded in the carbon market can be obtained as follows: (14) in, The carbon emissions actually traded in the carbon market.

[0030] Furthermore, the LTCT model in Step 5 is finally expressed as: (15) in, It is the basic price of the carbon trading market; is the length of the carbon emission interval; is the growth rate of carbon emission price.

[0031] Example 2 In another preferred embodiment, based on the above embodiment 1, Figures 1 to 4 As shown in the figure, the virtual power plant optimization dispatching method considering load demand response under the tiered carbon trading mechanism includes: A virtual power plant and market environment considering LTCT and DR are established. The energy supply units of the virtual power plant market environment considering LTCT and DR include wind power, upper-level gas grid, upper-level power grid, terminal energy consumption unit and carbon trading market unit; the virtual power plant considering LTCT and DR includes coupled equipment units of combined heat and power (CHP), gas boiler (GB), gas turbine (GT) and electric boiler (EB) that can convert heat and electricity to each other, and energy storage equipment units; the virtual power plant conducts market transactions and interactions as an independent entity. The operating framework is as follows: Figure 1 As shown; The virtual power plant dispatch model based on LTCT and DR takes the minimum total cost as the objective function and considers the step-by-step carbon emission strategy to reduce carbon emissions; Determine the operating constraints of the virtual power plant considering LTCT and DR, including wind power generation constraints, power balance constraints, thermal power balance constraints, natural gas balance constraints, and combined heat and power constraints; Establish DR models, including price-based DR and replaceable DR, where price-based DR includes curtailable load (CL) model and shiftable load (SL) model; Based on the objective function and various balance and constraint conditions, the LTCT model is established, including the carbon emission quota model and the actual carbon emission model. According to the carbon emission quota and the actual carbon emissions, the actual carbon emissions traded in the carbon market can be obtained, and then the LTCT model is established; A simulation example is used to verify the effectiveness of the virtual power plant optimization scheduling method considering LTCT and DR to achieve the effect of low-carbon economy.

[0032] Furthermore, the calculation method of the objective function includes: (1) in, represents the total operating cost; T is the operating cycle, for t During this period, electricity is purchased from the upper power grid. For price, t Purchase electricity during time periods, is the gas purchase volume during period t, is the unit natural gas price; i The values ​​1, 2, ..., 6 represent wind power, gas boiler (GB), waste heat recovery (HR), combined heat and power unit (CHP), heat storage tank (HST), and energy storage (ES) respectively; for equipment i , represents the operation and maintenance ratio, is the output power.

[0033] Wind power generation includes real-time output power and predicted output power. Furthermore, wind power generation constraints include: (2) in, and They are t The actual and predicted output power of wind power in the given period.

[0034] Factors affecting power balance include the power generated by wind power, the power purchased by the upper level, the power consumed under the heat price, the power generated by cogeneration, and the power of energy storage charging and discharging. Preferably, the power balance constraints include: (3) in, For the hot price t The electrical power consumed during the period, For the thermoelectric coupling unit t The electrical power generated during the period, , for t The charging and discharging power of the battery is for t The amount of electric load demand response during the time period; , , After considering demand response t The amount of load that can be reduced, the amount of load that can be moved, and the amount of load that can be interchanged in each time period.

[0035] Factors affecting thermal balance include heat generation from gas boilers, heat generation from heat storage tanks, heat generation from combined heat and power generation, and heat generation from energy storage charging and discharging. Furthermore, thermal power balance constraints include: (4) in, is the heat output of the gas boiler during period t; , They are t Heat generation from time-slot heat storage tanks (HST) and combined heat and power (CHP) units, , are the discharge and charge power of the heat storage tank during period t, for t The heat load demand response amount for each period; for t The heat load variation can be interchanged in time periods.

[0036] The units that consume natural gas include gas boilers and cogeneration units. Furthermore, the natural gas balance constraints include: (5).

[0037] Factors that affect CHP include gas turbine electrical load, waste heat generator heat generation, gas turbine power generation and gas heating efficiency. Furthermore, the comprehensive constraints of CHP include: (6) in, is the electrical load of the gas turbine; is the heat output of the low-temperature waste heat generator set during period t, is the percentage of waste heat generated by GT during period t that is sent to HST for heat generation, is the heat transfer efficiency of HST. and It is the efficiency of GT's gas turbine power generation and gas heating. is the calorific value of natural gas, equal to 9.88kW•h / m 3 .

[0038] Demand response includes curtailable load and shiftable load. Further, price-based DR includes curtailable load model (CL model) and shiftable load (SL model), where: The curtailable load model includes: (7) Among them, after considering demand response t The load reduction period is modified as follows:

[0039] (8) The transferable load model includes: (9) Among them, after considering demand response t The load variation during a period is defined as , is the initial load, After DR j The difference in electricity cost between time periods, for j The initial price of electricity for the time period; for t The initial load that can be reduced during the period, is the demand elasticity of the CL price matrix, for j The price of electricity during the time period; is the initial movable load in period t, is the SL price elasticity of the demand matrix.

[0040] According to the model, the demand response of electric load and heat load can be switched. Further, the switchable demand response includes: (10) in, and are the amounts of interchangeable thermal and electrical loads, respectively. is the substitution coefficient for electricity and heat. and They are the calorific value per unit electrical power and per unit heat respectively. and are the energy consumption of electrical energy and thermal energy, respectively; the negative sign in the formula means that an increase in replaceable thermal load corresponds to a decrease in replaceable electrical load.

[0041] There are upper and lower limits for switchable demand response. Furthermore, the limit of replaceable load (RL) that switchable demand response needs to consider is: (11) in, and are the minimum and maximum amounts of replaceable electrical load, respectively. and are the minimum and maximum amounts of replaceable heat load, respectively.

[0042] Before modeling the tiered carbon trading, the carbon quota and actual carbon emissions are first modeled, and then the carbon emission quota model and the actual carbon emission model are modeled, where: The carbon emission quota model includes: (12) The actual carbon emission model includes: (13) in, , , , They are the carbon emission quotas for VPP, upstream power grid, cogeneration unit and gas boiler; is the electric energy purchased by the upper power grid during period t; T is the dispatching period , They are the carbon emission quotas corresponding to the consumption of coal-fired units and natural gas units respectively; is the electric energy output by the cogeneration unit during period t; is the heat energy output by the cogeneration unit during period t; is the heat energy output by the gas boiler during period t; is the actual carbon emissions of the VPP; Actual carbon emissions for electricity purchased from upstream grid; is the actual total carbon emissions of CHP and GB; is the equivalent output of CHP and GB at time t. (a1, b1, c1) and (a2, b2, c2) are the calculation coefficient sets of coal-fired units and natural gas equipment, respectively.

[0043] Based on carbon emission quotas and actual carbon emissions, the calculation methods for carbon emissions actually participating in carbon market transactions include: (14) in, The carbon emissions actually traded in the carbon market.

[0044] The ladder carbon trading has a certain carbon emission interval length and tan emission price growth rate. Furthermore, the LTCT model includes: (15) in, It is the basic price of the carbon trading market; is the length of the carbon emission interval; is the growth rate of carbon emission price.

[0045] The results are as follows Figure 2 and Figure 3 It shows that 1:00~7:00 is the valley electricity price period. During the period of 1:00~7:00, the DR mode transfers the power load of the peak period to the period of 1:00~7:00, about 200kWh / h; 8:00~9:00 is the shoulder price period, receiving about 100kWh of peak power load per hour; the peak power price period is 10:00~12:00. During this period, the power load is reduced by about 15kWh per hour; 13:00~19:00 and 23:00~24:00 are the normal power price periods; the peak load of the DR model during this period is about 80kWh per hour.

[0046] Similarly, the heat load of VPP after loading DR changes as follows Figure 2 and Figure 4 shown.

[0047] In summary, this paper proposes an optimal dispatch model for electric-thermal VPP considering step-by-step carbon trading and demand response, solves the problem of optimal dispatch for low-carbon economy, and draws the following conclusions: (1) Integrating DR into the VPP model can shift part of the load from high electricity price periods to low electricity price periods, reduce energy losses, and replace heat and electricity on the user side to a certain extent; (2) Considering LTCT and DR in VPP scheduling can enhance the operational flexibility on the source side and the load side, reduce the comprehensive operating cost of VPP and increase the carbon emission reduction effect, thereby obtaining higher benefits.

[0048] The design of the invention is in line with practical logic, has a scientific method, is highly practical, has a wide range of applications, has good practical value, and plays a good environmental effect and economic benefit.

[0049] In summary, the virtual power plant optimization scheduling method considering load demand response under the stepped carbon trading mechanism provided by the present invention solves the problem of how to efficiently utilize clean and low-cost new energy sources such as wind energy and solar energy, and optimize the operation and scheduling of the power system under the background of increasingly serious energy shortages and environmental pollution, when the drawbacks of traditional power generation methods are prominent; the present invention introduces the stepped carbon trading mechanism into the optimization scheduling of virtual power plants (VPPs) for the first time. This mechanism not only considers the real-time adjustment of carbon emissions, but also manages carbon emissions more finely through a stepped pricing strategy, thereby reducing carbon emissions while improving economic benefits; the present invention combines the electric-thermal load demand response (DR) mechanism with virtual power plant scheduling. This comprehensive The combined demand response model not only considers the impact of electricity price changes on electricity load, but also considers the regulatory effect of thermal energy substitution on electricity load and thermal load, thereby increasing the flexibility and economy of scheduling; the virtual power plant of the present invention includes a variety of energy coupling equipment units such as combined heat and power (CHP), gas boiler (GB), gas turbine (GT) and electric boiler (EB), which can convert heat and electricity to each other, making the virtual power plant more efficient and flexible in energy utilization; the present invention establishes detailed operating constraints including wind power generation constraints, power balance constraints, thermal power balance constraints, natural gas balance constraints and CHP comprehensive constraints to ensure the stability and safety of the virtual power plant in the process of optimized scheduling; the present invention The invention innovatively combines the step-by-step carbon trading mechanism with the demand response mechanism, and realizes the optimal scheduling of virtual power plants in a low-carbon economic environment by adjusting carbon emissions and electric-thermal loads in real time. This combination not only improves the economic benefits of virtual power plants, but also significantly reduces carbon emissions. The invention proposes a replaceable load (RL) model, which allows substitution between electricity and thermal energy, increasing the flexibility of virtual power plants in the scheduling process. This model not only considers load reduction and transfer, but also load replacement, so that virtual power plants can better adapt to changes in market demand and carbon trading policies. The invention takes the minimum total cost as the objective function, and considers the coupling effects of electricity, heat and carbon at the same time, and constructs a multi-objective optimization scheduling model. This model can achieve the reduction of carbon emissions while ensuring economic benefits, which is in line with the development trend of low-carbon economy; the present invention establishes a detailed market environment model including wind power, upper-level gas grid, upper-level power grid, terminal energy consumption unit and carbon trading market unit, which provides a more realistic market environment for the optimal scheduling of virtual power plants. This modeling method not only improves the accuracy of scheduling, but also enhances the practicality of scheduling; in addition, the present invention can also introduce intelligent prediction algorithms to accurately predict wind power, load and market prices, and further optimize the scheduling strategy; by integrating advanced cloud computing and big data technologies, real-time analysis and processing of data are realized, laying a solid foundation for the intelligent operation of virtual power plants;At the same time, the system of this solution supports dynamic adjustment strategies, which can respond quickly to emergencies and ensure the balance between stable power supply and carbon emission control. The solution can also integrate blockchain technology to ensure the transparency and immutability of carbon trading data, promote market fairness and trust, and lead virtual power plants towards a smarter and low-carbon future, contributing to the construction of a green and efficient energy ecosystem. ;

Claims

1. A virtual power plant optimization scheduling method considering load demand response under a stepped carbon trading mechanism, characterized in that: The strategy includes the following steps: Step 1: Establish a virtual power plant and market environment that considers tiered carbon trading (LTCT) and load demand response (DR). The energy supply equipment units in the market environment convert heat and electricity and store energy. The virtual power plant interacts with the power grid as an independent entity. Step 2: A virtual power plant dispatch model based on LTCT and DR takes minimizing the total cost as the objective function, where the total cost includes the electricity purchase cost, gas purchase cost and equipment operation and maintenance cost; Step 3: Determine the operating constraints of the virtual power plant considering LTCT and DR, including wind power generation constraints, power balance constraints, thermal power balance constraints, natural gas balance constraints, and combined heat and power comprehensive constraints; Step 4: Establish a DR model, which includes price-based DR and replaceable DR, wherein the price-based DR further includes a curtailable load CL model and a movable load SL model; Step 5: Establish the LTCT model, including the carbon emission quota model and the actual carbon emission model. Calculate the actual carbon emission volume in the carbon market based on the carbon emission quota and the actual carbon emission volume, and then build the LTCT model. Step 6: Use example simulation to verify the effectiveness of this method to ensure that the virtual power plant has good operating potential and economic benefits in a low-carbon economic environment.

2. The virtual power plant optimization scheduling method considering load demand response under the tiered carbon trading mechanism according to claim 1 is characterized by: The market environment energy supply unit in Step 1 includes wind power, upper-level gas grid, upper-level power grid, terminal energy consumption unit and carbon trading market unit; the virtual power plant includes the coupling equipment units of cogeneration CHP, gas boiler GB, gas turbine GT and electric boiler EB.

3. The virtual power plant optimization scheduling method considering load demand response under the tiered carbon trading mechanism according to claim 2 is characterized in that: The calculation method of the objective function in Step 2 includes: （1） in, represents the total operating cost; T is the operating cycle, for t During this period, electricity is purchased from the upper power grid. For price, t Purchase electricity during time periods, is the gas purchase volume during period t, is the unit natural gas price; i The values ​​1, 2, ..., 6 represent wind power, gas boiler GB, waste heat recovery HR, combined heat and power unit CHP, heat storage tank HST, and energy storage ES respectively; for equipment i , represents the operation and maintenance ratio, is the output power.

4. The virtual power plant optimization scheduling method considering load demand response under the tiered carbon trading mechanism according to claim 3 is characterized in that: The wind power generation constraints of the virtual power plant operation constraints considering LTCT and DR in Step 3 are expressed as: （2） in, and They are t The actual output and predicted output power of wind power in the period; The power balance constraint is expressed as: （3） in, For hot price t The electrical power consumed during the period, For the thermoelectric coupling unit t The electrical power generated during the period, , for t The charge and discharge capacity of the battery, for t The amount of electric load demand response during the time period; , , After considering demand response t Changes in load that can be reduced, load that can be moved, and load that can be interchanged during a period of time; The thermal power balance constraint is expressed as: （4） in, is the heat output of the gas boiler during period t; , They are t The heat generation of the heat storage tank HST and the combined heat and power unit CHP during the period, , are the discharge and charge power of the heat storage tank during period t, for t The heat load demand response amount for each period; for t The amount of heat load change that can be interchanged during time periods; The natural gas balance constraint is expressed as: （5） in, , are the gas consumption of CHP and GB in period t respectively. The comprehensive constraints of CHP cogeneration are expressed as: （6） in, is the electrical load of the gas turbine; is the heat output of the low-temperature waste heat generator set during period t, is the percentage of waste heat generated by GT during period t that is sent to HST for heat generation, is the heat transfer efficiency of HST. and It is the efficiency of GT's gas turbine power generation and gas heating. is the calorific value of natural gas, equal to 9.88kW•h / m 3 .

5. The virtual power plant optimization scheduling method considering load demand response under the tiered carbon trading mechanism according to claim 4 is characterized in that: The price-based DR in Step 4 includes a curtailable load CL model and a movable load SL model, where: The curtailable load CL model is expressed as: （7） Among them, after considering demand response t The load variation during a period is defined as , is the initial load, After DR j The difference in electricity cost between time periods, for j The initial price of electricity for the time period; Among them, after considering demand response t The load reduction period is modified as follows: : （8） in, for t The initial load that can be reduced during the period, is the demand elasticity of the CL price matrix, for j The price of electricity during the time period; The movable load SL model is expressed as: （9） in, is the initial movable load in period t, is the SL price elasticity of the demand matrix.

6. The virtual power plant optimization scheduling method considering load demand response under the tiered carbon trading mechanism according to claim 5 is characterized in that: The replaceable demand response DR in Step 4 is expressed as: （10） in, and are the amounts of interchangeable thermal and electrical loads, respectively, is the substitution coefficient of electricity and heat, and are the calorific value per unit electrical power and per unit heat, respectively. and are the energy consumption of electrical energy and thermal energy, respectively, and the negative sign in the formula means that an increase in replaceable thermal load corresponds to a decrease in replaceable electrical load; The number of replaceable load RL limits that need to be considered for the replaceable demand response is: （11） in, and are the minimum and maximum amounts of replaceable electrical loads, and are the minimum and maximum amounts of replaceable heat load, respectively.

7. The virtual power plant optimization scheduling method considering load demand response under the tiered carbon trading mechanism according to claim 6 is characterized in that: The LTCT model in Step 5 includes a carbon emission quota model and an actual carbon emission model, where: The carbon emission quota model is expressed as: （12） in, , , , They are the carbon emission quotas for VPP, upstream power grid, cogeneration unit and gas boiler; is the electric energy purchased by the upper power grid during period t; T is the dispatching period , They are the carbon emission quotas corresponding to the consumption of coal-fired units and natural gas units respectively; is the electric energy output by the cogeneration unit during period t; is the heat energy output by the cogeneration unit during period t; is the heat energy output by the gas boiler during period t; The actual carbon emission model is expressed as: （13） in, is the actual carbon emissions of the VPP; Actual carbon emissions for electricity purchased from upstream grid; is the actual total carbon emissions of CHP and GB; is the equivalent output of CHP and GB at time t. and They are the calculation coefficient sets for coal-fired units and natural gas equipment respectively.

8. The virtual power plant optimization scheduling method considering load demand response under the tiered carbon trading mechanism according to claim 7 is characterized in that: In Step 5, based on the carbon emission quota and the actual carbon emissions, the calculation method for the carbon emissions actually traded in the carbon market is as follows: （14） in, The carbon emissions actually traded in the carbon market.

9. The virtual power plant optimization scheduling method considering load demand response under the tiered carbon trading mechanism according to claim 8 is characterized in that: The LTCT model in Step 5 is finally expressed as: （15） in, It is the basic price of the carbon trading market; is the length of the carbon emission interval; is the growth rate of carbon emission price.

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