Frequency regulation control method based on load-side resource coordination for carbon emissions

Abstract

This application provides a frequency regulation control method based on carbon emission-based load-side resource coordination. The method includes: constructing a temperature-controlled load frequency regulation model to simulate the changes in the up- and down-regulation capacity of the temperature-controlled load with varying operating states; constructing an electric vehicle load frequency regulation model to simulate the changes in the up- and down-regulation capacity of electric vehicles with varying states of charge; constructing an optimization model considering carbon emissions to simulate changes in the total cost of grid frequency regulation and power generation; constructing constraint sets corresponding to the above models; and, under the constraints of the constraint sets, solving the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model using a commercial solver with the objective function of minimizing the total operating cost of the models, to obtain the operating decisions of the temperature-controlled load and electric vehicles, and their responses participating in frequency regulation control. This method solves the problem of high carbon emissions caused by reserving backup capacity in generator sets to cope with system frequency regulation in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of power system frequency regulation technology, and more specifically, to a frequency regulation control method, frequency regulation control device, computer-readable storage medium, processor, and power dispatching system based on load-side resource coordination for carbon emission control. Background Technology

[0002] Against the backdrop of current global climate change and the energy crisis, the low-carbon operation of power systems is receiving increasing attention. Traditional power systems typically rely on fossil fuels such as coal, oil, and natural gas, which produce large amounts of carbon dioxide during combustion, thus polluting the environment. Therefore, reducing carbon emissions from power systems has become an urgent task in mitigating climate change. To promote the low-carbon operation of power systems, clean new energy sources such as wind, solar, and tidal power have been introduced into power systems on a large scale. This has not only improved the ecological environment but also brought many challenges to the safe and stable operation of power systems due to the uncertainty and intermittency of new energy power generation. One major challenge is frequency stability. The frequency of a power system reflects the balance of active power; frequency fluctuations can jeopardize the safety and stability of the power system.

[0003] Traditional power system frequency regulation primarily relies on generator sets, which is suitable for situations where renewable energy generation is relatively small. However, as the installed capacity of renewable energy sources gradually increases, traditional generator sets, limited by their ramp-up rates, cannot meet the system's frequency regulation demands, thus jeopardizing the system's stable operation. Therefore, it is necessary to explore resources on the demand side of the power system, such as temperature-controlled loads and electric vehicle loads, to maintain frequency stability in power systems with high renewable energy penetration. Traditional generator sets maintain a certain amount of reserve capacity during operation to meet system frequency regulation needs, resulting in high carbon emissions. Utilizing various load-side resources rationally in frequency regulation can reduce the spinning reserve capacity of generator sets, thereby reducing carbon emissions. Summary of the Invention

[0004] The main objective of this application is to provide a frequency regulation control method, frequency regulation control device, computer-readable storage medium, processor, and power dispatching system based on load-side resource coordination for carbon emissions, so as to at least solve the problem of high carbon emissions caused by reserving backup capacity of generator sets to cope with system frequency regulation in the prior art.

[0005] To achieve the above objectives, according to one aspect of this application, a frequency regulation control method based on carbon emission load-side resource coordination is provided. This method is applied to a power frequency regulation system with multiple load-side resources. The power frequency regulation system includes a dispatch center, load aggregators, and load-side resources. The dispatch center controls the load aggregators through automatic generation control, and the load aggregators control the load-side resources according to the control instructions from the dispatch center. The load-side resources are load-controllable devices at the end of the power grid. The method includes: constructing a temperature-controlled load frequency regulation model to simulate the changing trends of the up-frequency regulation capacity and down-frequency regulation capacity of the temperature-controlled load with the operating state of the temperature-controlled load; constructing an electric vehicle load frequency regulation model to simulate the changing trends of the up-frequency regulation capacity and down-frequency regulation capacity of electric vehicles with the state of charge of the electric vehicles; and constructing a carbon emission cost model based on the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model to simulate the total cost of power grid frequency regulation as the power frequency regulation system meets the requirements of the temperature-controlled load and the power supply... The trend of power generation change under the demand of electric vehicles; constructing a first constraint group, a second constraint group, and a third constraint group. The first constraint group is used to limit the operating parameters of the temperature-controlled load frequency regulation model, and the first constraint group includes at least temperature-controlled load power constraints and temperature-controlled load operating state constraints. The second constraint group is used to limit the operating parameters of the electric vehicle load frequency regulation model, and the second constraint group includes at least state of charge constraints, charging and discharging power constraints, and operating state constraints. The third constraint group is used to limit the operating parameters of each node in the power frequency regulation system, and the third constraint group includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints. Under the constraints of the first constraint group, the second constraint group, and the third constraint group, the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model are solved using a commercial solver with the goal of minimizing the output value of the carbon emission cost model, to obtain the operating parameters of the temperature-controlled load and the electric vehicle. Frequency regulation control commands are generated based on the operating parameters. The frequency regulation control commands are sent to the load aggregator to control the operation of the temperature-controlled load and the electric vehicle.

[0006] Optionally, a frequency regulation model for temperature-controlled loads is constructed, including: constructing a first objective formula. The first target formula is used to simulate the trend of indoor temperature change with the heating / cooling capacity of the temperature control load: where, and Let be the indoor temperatures at times t+1 and t. and T t out Let Q be the outdoor temperature at times t+1 and t. t For the heating / cooling capacity of the temperature-controlled load, in Q tWhen Q > 0, the temperature-controlled load performs cooling. t Heating is achieved using a temperature control load of <0, where C is the equivalent specific heat capacity of the room, R is the equivalent thermal resistance of the room, and Δt is the time interval between t+1 and t; a second objective formula is constructed. The second target formula is used to simulate the variation trend of heating / cooling capacity of temperature-controlled load with temperature-controlled load power, where i represents the load aggregator, j represents the load-side resources, and the load-side resources include at least the temperature-controlled load, Q i,j,t η represents the cooling / heating capacity of the j-th temperature-controlled load at time t, where η is the load aggregator i. i,j Let be the energy efficiency conversion coefficient of the j-th temperature-controlled load of load aggregator i; Let the power of the j-th temperature-controlled load of load aggregator i at time t; construct the third objective formula. The third objective formula is used to simulate the variation trend of the polymerization temperature-controlled load of the load aggregator with the power of the temperature-controlled load, wherein... The polymerization temperature control load at time t is the polymerization quotient i of the load. Let i be the set of temperature-controlled loads with load aggregator i; construct the fourth objective formula. and the fifth objective formula The fourth objective formula is used to simulate the changing trend of the upward frequency modulation capacity of the temperature-controlled load with the aggregated temperature-controlled load, and the fifth objective formula is used to simulate the changing trend of the downward frequency modulation capacity of the temperature-controlled load with the operating state of the temperature-controlled load, wherein, The rated power of the j-th temperature-controlled load is given by the load aggregator i. and The upper frequency regulation capacity and the lower frequency regulation capacity are respectively the temperature-controlled load; the temperature-controlled load frequency regulation model is obtained by combining the first objective formula, the second objective formula, the third objective formula, the fourth objective formula and the fifth objective formula.

[0007] Optionally, a load frequency regulation model for electric vehicles is constructed, including: constructing a sixth objective formula. The sixth objective formula is used to simulate the trend of the state of charge (SOC) of an electric vehicle changing with charging and discharging power, where SOC... i,j,t+1 and SOC i,j,t Let be the state of charge of the j-th electric vehicle of the load aggregator i at times t+1 and t, respectively. and Let η be the charging and discharging power of the j-th electric vehicle at time t, where η is the load aggregator i. ch and η dis E represents the charging and discharging efficiency, respectively. i,j Represents the capacity of the j-th electric vehicle of the load aggregator i; construct the seventh objective formula. The seventh objective formula is used to simulate the trend of the aggregated electric vehicle load of the load aggregator changing with the charging and discharging power of the electric vehicle, wherein, Let i be the aggregated electric vehicle load at time t. For the set of electric vehicle loads of the load aggregator i; construct the eighth objective formula. and the ninth objective formula The eighth objective formula is used to simulate the changing trend of the up-frequency regulation capacity of the electric vehicle with the aggregated temperature control load, and the ninth objective formula is used to simulate the changing trend of the down-frequency regulation capacity of the electric vehicle with the operating state of the temperature control load, wherein, and These refer to the up-frequency modulation capacity and the down-frequency modulation capacity of the electric vehicle, respectively. and These represent the maximum values ​​of the charging and discharging power, respectively. By combining the sixth, seventh, eighth, and ninth objective formulas, the electric vehicle load frequency regulation model is obtained.

[0008] Optionally, a carbon emission cost model is constructed based on the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model, including: constructing a tenth objective formula. The tenth objective formula is used to simulate the changing trend of active power at nodes in the power frequency regulation system with aggregated temperature-controlled load and aggregated electric vehicle load, where E N Let pf be the set of nodes in the power grid. ki,t Let pf be the active power flowing from node k to node i at time t. ih,t Let be the active power flowing from node i to node h at time t. Let be the active power of the generator at node i at time t. Let be the active base load of node i at time t. The frequency modulation power change command for node i at time t; Let i be the remaining capacity at node i where frequency modulation failed at time t; construct the eleventh objective formula. Twelfth Target Formula And the thirteenth objective formula The eleventh objective formula is used to simulate the trend of power generation cost as a function of active power; the twelfth objective formula is used to simulate the trend of carbon emission cost as a function of active power; and the thirteenth objective formula is used to simulate the trend of frequency regulation failure cost as a function of the remaining capacity after frequency regulation failure, wherein C gen For the aforementioned power generation cost, C carbon For the carbon emission cost, C fail For the frequency modulation failure cost, a i and bi c is a preset coefficient for the power generation cost. tax Taxes levied per unit of carbon emissions. Let c be the carbon emission factor of the generator at node i. fail The cost of frequency regulation failure per unit flux is defined; a fourteenth objective formula is constructed to simulate the trend of the total grid frequency regulation cost as a function of the generation cost, the carbon emission cost, and the frequency regulation failure cost: C total =C gen +C carbon +C fail Among them, C total The total cost of frequency regulation of the power grid is given by the formula; the carbon emission cost model is obtained by combining the tenth objective formula, the eleventh objective formula, the twelfth objective formula, the thirteenth objective formula, and the fourteenth objective formula.

[0009] Optionally, a first constraint group is constructed, including: constructing operating state constraints for each temperature-controlled load to obtain a first target constraint. in, Let s be the power of the j-th temperature-controlled load of the load aggregator i at time t. i,j,t and s i,j,t-1 These represent the operating states of the j-th temperature-controlled load of the load aggregator i at times t and t-1, respectively. and Let be the minimum and maximum allowable indoor temperatures for the j-th temperature-controlled load, respectively, where . and satisfy: Where, δ i,j This represents the allowable temperature deviation of the j-th temperature-controlled load of the load aggregator i. Set the temperature for the user; construct the power constraints of the temperature-controlled loads corresponding to each temperature-controlled load to obtain the second objective constraint: The first constraint group is constructed based on the first objective constraint and the second objective constraint.

[0010] Optionally, a second set of constraints is constructed, including: constructing the state of charge constraints corresponding to each electric vehicle to obtain a third objective constraint. in, and Let the maximum and minimum state of charge (SOC) of the j-th electric vehicle be the load aggregator i; construct the charging and discharging power constraints corresponding to each electric vehicle to obtain the fourth objective constraint: Where, η ch and η disThe charging and discharging efficiencies are respectively used; the operating state constraints corresponding to each electric vehicle are constructed to obtain the fifth objective constraint: in, and The charging and discharging states of the charging load of the electric vehicle are represented by a binary variable; the second constraint set is constructed based on the third objective constraint, the fourth objective constraint, and the fifth objective constraint.

[0011] Optionally, a third set of constraints is constructed, including: constructing the reactive power balance constraints corresponding to the power frequency regulation system to obtain a sixth objective constraint: Among them, qf ih,t Let be the reactive power flowing from node i to node h at time t. Let be the reactive power of the generator at node i at time t. To represent the reactive power base load at node i at time t; construct the generator generation constraints corresponding to the power frequency regulation system to obtain the seventh objective constraint: in, and These are the maximum and minimum active power of the generator at node i, respectively; and These are the maximum and minimum reactive power of the generator at node i, respectively; Represents the maximum ramp rate of the generator at node i; construct the power flow calculation constraints corresponding to the power frequency regulation system to obtain the eighth objective constraint: Among them, V i,t Let r be the square of the voltage magnitude at node i at time t. ih Indicates the line resistance ih, x ih Indicates the line reactance ih, V i max and V i min This represents the maximum and minimum squared values ​​of the voltage magnitude at node i. The maximum apparent power of line ih is represented (without error); the third constraint group is constructed based on the sixth objective constraint, the seventh objective constraint, and the eighth objective constraint.

[0012] According to another aspect of this application, a frequency regulation control device based on carbon emission load-side resource coordination is provided. This device is applied to a power frequency regulation system with multiple load-side resources. The power frequency regulation system includes a dispatch center, load aggregators, and load-side resources. The dispatch center controls the load aggregators through automatic generation control, and the load aggregators control the load-side resources according to the control instructions from the dispatch center. The load-side resources are load-controllable devices at the end of the power grid. The device includes: a first construction unit for constructing a temperature-controlled load frequency regulation model, which simulates the changing trends of the up-frequency regulation capacity and down-frequency regulation capacity of the temperature-controlled load with the operating state of the temperature-controlled load; a second construction unit for constructing an electric vehicle load frequency regulation model, which simulates the changing trends of the up-frequency regulation capacity and down-frequency regulation capacity of the electric vehicle with the state of charge of the electric vehicle; and a third construction unit for constructing a carbon emission cost model based on the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model, which simulates the total cost of power grid frequency regulation as the power frequency regulation system meets the requirements of the temperature-controlled load and the load-side resource coordination. The power generation trend under the demand of electric vehicles; the fourth construction unit, used to construct a first constraint group, a second constraint group and a third constraint group. The first constraint group is used to limit the operating parameters of the temperature-controlled load frequency regulation model. The first constraint group includes at least temperature-controlled load power constraints and temperature-controlled load operating state constraints. The second constraint group is used to limit the operating parameters of the electric vehicle load frequency regulation model. The second constraint group includes at least state of charge constraints, charging and discharging power constraints and operating state constraints. The third constraint group is used to limit the operating parameters of each node in the power frequency regulation system. The third constraint group includes at least reactive power balance constraints, generator power generation constraints and power flow calculation constraints. The calculation unit is used to solve the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model using a commercial solver under the constraints of the first constraint group, the second constraint group and the third constraint group, with minimizing the output value of the carbon emission cost model as the objective function, to obtain the operating parameters of the temperature-controlled load and the electric vehicle, generate frequency regulation control commands based on the operating parameters, and send the frequency regulation control commands to the load aggregator to control the operation of the temperature-controlled load and the electric vehicle.

[0013] According to another aspect of this application, a computer-readable storage medium is provided, characterized in that the computer-readable storage medium includes a stored program, wherein the program, when executed, controls any of the methods described in the device where the computer-readable storage medium is located.

[0014] According to another aspect of this application, a power dispatching system is provided, characterized in that it includes: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include methods for performing any one of the methods described.

[0015] Applying the technical solution of this application, a frequency regulation control method based on carbon emission load-side resource coordination is applied to a power frequency regulation system with multiple load-side resources. The power frequency regulation system includes a dispatch center, load aggregators, and load-side resources. The dispatch center controls the load aggregators through automatic generation control, and the load aggregators control the load-side resources according to the control instructions from the dispatch center. The load-side resources are controllable load devices at the end of the power grid. First, a temperature-controlled load frequency regulation model is constructed to simulate the changing trends of the up-regulation capacity and down-regulation capacity of the temperature-controlled load with its operating state. Then, an electric vehicle load frequency regulation model is constructed to simulate the changing trends of the up-regulation capacity and down-regulation capacity of electric vehicles with their state of charge. Finally, a carbon emission cost model is constructed based on the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model. This carbon emission cost model simulates the total cost of power grid frequency regulation as the power frequency regulation system meets the needs of the temperature-controlled load and electric vehicles. The trend of power generation change is observed; then, a first constraint group, a second constraint group, and a third constraint group are constructed. The first constraint group is used to limit the operating parameters of the temperature-controlled load frequency regulation model, and includes at least temperature-controlled load power constraints and temperature-controlled load operating state constraints. The second constraint group is used to limit the operating parameters of the electric vehicle load frequency regulation model, and includes at least state-of-charge constraints, charging and discharging power constraints, and operating state constraints. The third constraint group is used to limit the operating parameters of each node in the power frequency regulation system, and includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints. Then, under the constraints of the first, second, and third constraint groups, the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model are solved using a commercial solver with the objective function of minimizing the output value of the carbon emission cost model to obtain the operating parameters of the temperature-controlled load and the electric vehicle. Frequency regulation control commands are generated based on the operating parameters. Finally, the frequency regulation control commands are sent to the load aggregator to control the operation of the temperature-controlled load and the electric vehicle. This application incorporates various load-side resources, such as air conditioners and electric vehicles, into grid frequency regulation control to reduce generator spinning reserve capacity and thus carbon emissions. Numerical models are established and simulated for the load-side resources, and a cost numerical model is constructed by incorporating carbon emissions and generator power generation. To minimize the cost numerical model, an optimization problem is solved using a commercial solver to obtain control commands, which are then sent to the corresponding load-side resources. This method solves the problem of high carbon emissions resulting from reserving backup capacity in generator sets to cope with system frequency regulation in existing technologies. Attached Figure Description

[0016] Figure 1A hardware structure block diagram of a mobile terminal for a frequency modulation control method based on load-side resource coordination for carbon emissions, provided in an embodiment of this application, is shown.

[0017] Figure 2 A schematic flowchart of a frequency regulation control method based on load-side resource coordination for carbon emissions, according to an embodiment of this application, is shown.

[0018] Figure 3 A structural block diagram of a frequency modulation control device based on carbon emission load-side resource coordination provided according to an embodiment of this application is shown.

[0019] The above figures include the following reference numerals:

[0020] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] As described in the background section, traditional generator sets in the prior art have a portion of reserve capacity during operation to meet the needs of system frequency regulation, resulting in high carbon emissions. To address the issue of high carbon emissions caused by reserving reserve capacity in generator sets to meet system frequency regulation, embodiments of this application provide a frequency regulation control method, frequency regulation control device, computer-readable storage medium, processor, and power dispatching system based on carbon emission-based load-side resource coordination.

[0025] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0026] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a frequency modulation control method based on load-side resource coordination for carbon emission control, according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0027] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the device information display method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0028] This embodiment provides a frequency regulation control method for load-side resource coordination based on carbon emissions, which runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than that shown here.

[0029] Figure 2 This is a flowchart of a frequency regulation control method based on load-side resource coordination for carbon emissions, according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0030] Step S201: Construct a temperature-controlled load frequency regulation model. The above temperature-controlled load frequency regulation model is used to simulate the changing trends of the upper and lower frequency regulation capacities of the temperature-controlled load with the operating status of the temperature-controlled load.

[0031] Specifically, the aforementioned temperature-controlled load refers to electrical appliances such as air conditioners that regulate indoor temperature. The frequency modulation model of the aforementioned temperature-controlled load simulates the changing trends of the upper and lower frequency modulation capacities of the temperature-controlled load as the operating state of the temperature-controlled load, and utilizes the thermal inertia of the temperature-controlled load to participate in auxiliary frequency modulation.

[0032] Step S202: Construct an electric vehicle load frequency regulation model. The electric vehicle load frequency regulation model is used to simulate the changing trends of the up-frequency regulation capacity and down-frequency regulation capacity of the electric vehicle with the state of charge of the electric vehicle.

[0033] Specifically, the above-mentioned electric vehicle load frequency regulation model is used to simulate the changing trends of the up-frequency regulation capacity and down-frequency regulation capacity of electric vehicles with the state of charge of the electric vehicles. This allows the electric vehicle load to choose to charge or discharge according to the frequency regulation requirements to assist in frequency regulation. The electric vehicle can not only be charged from the grid, but also discharge to the grid using a bidirectional charging mode.

[0034] Step S203: Construct a carbon emission cost model based on the above temperature-controlled load frequency regulation model and the above electric vehicle load frequency regulation model. The carbon emission cost model is used to simulate the trend of the total grid frequency regulation cost as the power frequency regulation system generates electricity while meeting the needs of the above temperature-controlled load and electric vehicles.

[0035] Specifically, a carbon emission cost model is constructed based on the above-mentioned temperature-controlled load frequency regulation model and the above-mentioned electric vehicle load frequency regulation model. Based on the trends of power generation cost and carbon emission cost in the above-mentioned power frequency regulation system with the change of active power at nodes, the trend of frequency regulation failure cost with the change of the remaining capacity after frequency regulation failure, and the trend of the total power grid frequency regulation cost with the change of the above-mentioned power generation cost, the above-mentioned carbon emission cost, and the above-mentioned frequency regulation failure cost, a carbon emission cost model is constructed together.

[0036] Step S204: Construct a first constraint group, a second constraint group, and a third constraint group. The first constraint group is used to limit the operating parameters of the temperature-controlled load frequency regulation model. The first constraint group includes at least temperature-controlled load power constraints and temperature-controlled load operating state constraints. The second constraint group is used to limit the operating parameters of the electric vehicle load frequency regulation model. The second constraint group includes at least state of charge constraints, charging and discharging power constraints, and operating state constraints. The third constraint group is used to limit the operating parameters of each node in the power frequency regulation system. The third constraint group includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints.

[0037] Specifically, the aforementioned temperature-controlled load power constraint is used to limit the output power of the aforementioned temperature-controlled load, and the aforementioned temperature-controlled load operating state constraint is used to limit the operating mode of the aforementioned temperature-controlled load, so that it remains within the temperature range set by the user and continuously switches between on and off states. The aforementioned temperature-controlled load can only be in one of the charging state, discharging state, and idle state within a certain period of time, and can switch between different states.

[0038] Similarly, electric vehicle power constraints are used to limit the charging and discharging power of electric vehicles, state of charge constraints are used to limit the battery capacity of electric vehicles, and electric vehicle operating state constraints are used to limit the charging and discharging states of electric vehicles. The above-mentioned electric vehicle load can only be in one of the charging, discharging, and idle states within a certain period of time, and transitions between different states are possible.

[0039] Step S205: Under the constraints of the first constraint group, the second constraint group and the third constraint group, the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model are solved by a commercial solver with the objective function of minimizing the output value of the carbon emission cost model to obtain the operating parameters of the temperature-controlled load and the electric vehicle, and frequency regulation control commands are generated based on the operating parameters.

[0040] Specifically, within the first constraint group (i.e., the temperature-controlled load operates within the user-defined operating range), the second constraint group (i.e., the range corresponding to the electric vehicle's state of charge, charging / discharging power, and operating state), and the third constraint group (i.e., the range of reactive power balance, generator power, and power flow calculations of the power frequency regulation system), the objective function is to minimize the aforementioned carbon emission cost. The carbon emission cost model is directly solved using a commercial solver to obtain the operating parameters of the temperature-controlled load and the electric vehicle, and frequency regulation control commands are generated based on the operating parameters.

[0041] Step S206: Send the above-mentioned frequency modulation control command to the above-mentioned load aggregator to control the temperature-controlled load and the operation of the electric vehicle;

[0042] Specifically, the power system's dispatch center sends frequency regulation commands to load aggregators through automatic generation control, thereby controlling various load-side resources.

[0043] In this embodiment, firstly, a temperature-controlled load frequency regulation model is constructed to simulate the changing trends of the upper and lower frequency regulation capacities of the temperature-controlled load with its operating state. Then, an electric vehicle load frequency regulation model is constructed to simulate the changing trends of the upper and lower frequency regulation capacities of electric vehicles with their state of charge. Next, a carbon emission cost model is constructed based on the temperature-controlled load and electric vehicle load frequency regulation models to simulate the changing trends of the total grid frequency regulation cost with the power generation of the power frequency regulation system while meeting the needs of the temperature-controlled load and electric vehicles. Finally, a first constraint group, a second constraint group, and a third constraint group are constructed. The first constraint group is used to limit the operating parameters of the temperature-controlled load frequency regulation model, and includes at least the temperature-controlled load power. The system imposes constraints on the operating parameters of the temperature-controlled load and the electric vehicle load frequency regulation model. The second constraint group, which includes at least state-of-charge constraints, charging / discharging power constraints, and operating state constraints, is used to limit the operating parameters of each node in the power frequency regulation system. The third constraint group, which includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints, is used to limit the operating parameters of the temperature-controlled load and the electric vehicle load frequency regulation model under the constraints of the first, second, and third constraint groups, with the goal of minimizing the output value of the carbon emission cost model. This is achieved by solving the temperature-controlled load and electric vehicle frequency regulation model using a commercial solver. Based on these operating parameters, frequency regulation control commands are generated. Finally, the frequency regulation control commands are sent to the load aggregator to control the operation of the temperature-controlled load and the electric vehicle. This application incorporates various load-side resources, such as air conditioners and electric vehicles, into grid frequency regulation control to reduce generator spinning reserve capacity and thus carbon emissions. Numerical models are established and simulated for the load-side resources, and a cost numerical model is constructed by incorporating carbon emissions and generator power generation. To minimize the cost numerical model, an optimization problem is solved using a commercial solver to obtain control commands, which are then sent to the corresponding load-side resources. This method solves the problem of high carbon emissions resulting from reserving backup capacity in generator sets to cope with system frequency regulation in existing technologies.

[0044] In order to construct a frequency regulation model for a temperature-controlled load, in one optional implementation, step S201 above includes:

[0045] Step S2011, construct the first target formula The first objective formula mentioned above is used to simulate the trend of indoor temperature change with the heating / cooling capacity of the temperature control load, where, and T t in Let be the indoor temperatures at times t+1 and t. and T t out Let Q be the outdoor temperature at times t+1 and t. t For the heating / cooling capacity of the temperature-controlled load, in Q t When Q > 0, the temperature-controlled load performs cooling. t <0 temperature control load for heating, C is the equivalent specific heat capacity of the room, R is the equivalent thermal resistance of the room, and Δt is the time interval between t+1 and t.

[0046] Furthermore, the modeling of temperature control loads typically employs a first-order equivalent thermal parameter model to describe changes in indoor temperature. The formula for the equivalent thermal parameter model is as follows:

[0047]

[0048] Where C is the equivalent heat capacity, R is the equivalent thermal resistance, and T is the equivalent thermal resistance. t in and T t out Let Q represent the indoor and outdoor ambient temperatures at time t, respectively. t Let Q be the cooling / heating capacity of the air conditioner at time t. t When Q > 0, the air conditioner is in cooling mode. t When the temperature is below 0, the air conditioner is in heating mode.

[0049] Discretize the above formula to construct the first objective formula.

[0050] Step S2012, construct the second objective formula The second objective formula described above is used to simulate the variation trend of heating / cooling capacity of temperature-controlled load with temperature-controlled load power, where i represents the load aggregator, j represents the load-side resources, and the load-side resources include at least the temperature-controlled load, Q. i,j,t η represents the cooling / heating capacity of the j-th temperature-controlled load at time t, where η is the load aggregator i. i,j Let be the energy efficiency conversion coefficient of the j-th temperature-controlled load of load aggregator i; Let be the power of the j-th temperature-controlled load of load aggregator i at time t;

[0051] Specifically, the second objective formula is constructed based on the relationship between the air conditioner's cooling capacity and its output power.

[0052] Step S2013, construct the third objective formula The third objective formula described above is used to simulate the variation trend of the polymerization temperature control load of the aforementioned load polymerizer with the aforementioned temperature control load power, wherein, For the above-mentioned load aggregator i at time t, the above-mentioned polymerization temperature control load, For the set of temperature-controlled loads of load aggregator i;

[0053] Specifically, the above-mentioned aggregated temperature control loads are obtained by aggregating the temperature control load power corresponding to each temperature control load based on the aggregator.

[0054] Step S2014, construct the fourth objective formula and the fifth objective formula The fourth objective formula described above is used to simulate the changing trend of the upward frequency regulation capacity of the temperature-controlled load with the aforementioned aggregated temperature-controlled load. The fifth objective formula described above is used to simulate the changing trend of the downward frequency regulation capacity of the temperature-controlled load with the changing operating state of the temperature-controlled load. The rated power of the j-th temperature-controlled load is given by the load aggregator i above. and These refer to the aforementioned upper frequency regulation capacity and the aforementioned lower frequency regulation capacity of the temperature-controlled load, respectively.

[0055] Specifically, based on the fourth objective formula mentioned above, the up-frequency regulation capacity of the temperature-controlled load participating in the system frequency regulation can be simulated. and the aforementioned down-modulation capacity The relationship between the polymerizer's polymerization temperature control load and the polymerizer's output.

[0056] Step S2015: Combine the above first objective formula, the above second objective formula, the above third objective formula, the above fourth objective formula and the above fifth objective formula to obtain the above temperature control load frequency regulation model.

[0057] Specifically, by establishing the aforementioned temperature-controlled load frequency regulation model, the thermal inertia of the aforementioned temperature-controlled load can be used to participate in auxiliary frequency regulation.

[0058] To construct a load frequency regulation model for electric vehicles, in one optional implementation, step S202 includes:

[0059] Step S2021, construct the sixth objective formula The sixth objective formula mentioned above is used to simulate the trend of the state of charge (SOC) of an electric vehicle changing with charging and discharging power, where SOC... i,j,t+1 and SOC i,j,t Let be the state of charge of the j-th electric vehicle of the above load aggregator i at times t+1 and t, respectively. and Let η be the charging and discharging power of the j-th electric vehicle at time t, representing the load aggregator i mentioned above. ch and η dis E represents the charging and discharging efficiency, respectively. i,j This represents the capacity of the j-th electric vehicle in the above load aggregator i;

[0060] Specifically, this embodiment of the invention mainly considers the electric vehicle load in automatic power generation control mode, dividing the electric vehicle load into charging state, discharging state, and idle state. Furthermore, an electric vehicle can only be in one state at a time, and transitions between different states are possible. Based on this, the sixth objective formula is constructed based on the correlation between the electric vehicle's state of charge and its charging / discharging power.

[0061] Step S2022, construct the seventh objective formula The seventh objective formula mentioned above is used to simulate the trend of the aggregated electric vehicle load of the above load aggregator changing with the charging and discharging power of the electric vehicle, wherein, Let i be the aggregated electric vehicle load at time t. Let i be the set of electric vehicle loads of the aforementioned load aggregator i;

[0062] Specifically, the charging and discharging power of each electric vehicle is aggregated based on the aggregator to obtain the above-mentioned aggregated electric vehicle load.

[0063] Step S2023, construct the eighth objective formula and the ninth objective formula The eighth objective formula described above is used to simulate the changing trend of the up-frequency regulation capacity of the electric vehicle with the aforementioned aggregated temperature control load, and the ninth objective formula described above is used to simulate the changing trend of the down-frequency regulation capacity of the electric vehicle with the operating state of the temperature control load, wherein, and These refer to the aforementioned up-frequency modulation capacity and the aforementioned down-frequency modulation capacity of electric vehicles, respectively. and These represent the maximum charging and discharging power, respectively.

[0064] Specifically, based on the eighth objective formula mentioned above, the up-frequency regulation capacity of electric vehicle load participating in frequency regulation can be simulated. and the aforementioned down-modulation capacity The correlation between the aggregated electric vehicle load and the aggregator.

[0065] Step S2024: Combine the above sixth objective formula, the above seventh objective formula, the above eighth objective formula, and the above ninth objective formula to obtain the above electric vehicle load frequency regulation model.

[0066] Specifically, the above-mentioned electric vehicle load frequency regulation model is constructed based on the above-mentioned sixth objective formula, the above-mentioned seventh objective formula, the above-mentioned eighth objective formula and the above-mentioned ninth objective formula.

[0067] In order to construct a carbon emission cost model, in one optional implementation, step S203 includes:

[0068] Step S2031, construct the tenth objective formula The tenth objective formula mentioned above is used to simulate the changing trend of active power at nodes in the aforementioned power frequency regulation system with the aggregated temperature-controlled load and aggregated electric vehicle load, where E N Let pf be the set of nodes in the power grid. ki,t Let pf be the active power flowing from node k to node i at time t. ih,t Let be the active power flowing from node i to node h at time t. Let be the active power of the generator at node i at time t. Let be the active base load of node i at time t. This is the frequency modulation power change command for node i at time t. The remaining capacity at node i at time t is where the frequency modulation failed.

[0069] Specifically, based on the impact of temperature-controlled load regulation and electric vehicle regulation on the power generation of the power grid, the active power balance formula of each node of the power grid is constructed, and the tenth objective formula is obtained.

[0070] Step S2032, construct the eleventh objective formula Twelfth Target Formula And the thirteenth objective formula The eleventh objective formula above is used to simulate the trend of power generation cost changing with the aforementioned active power; the twelfth objective formula above is used to simulate the trend of carbon emission cost changing with the aforementioned active power; and the thirteenth objective formula above is used to simulate the trend of frequency regulation failure cost changing with the aforementioned remaining capacity after frequency regulation failure, where C gen For the aforementioned power generation costs, C carbon For the aforementioned carbon emission costs, C fail For the aforementioned frequency modulation failure cost, a i and b i c is the preset coefficient for the above power generation cost. tax Taxes levied per unit of carbon emissions. Let c be the carbon emission factor of the generator at node i. fail The cost of frequency modulation failure per unit flux;

[0071] Specifically, this application simplifies the cost of the power frequency regulation system into three parts: power generation cost, carbon emission cost, and frequency regulation failure penalty cost. Based on the correlation between the above costs and active power and fertility capacity, corresponding formulas are constructed to simulate the above correlation, resulting in the above eleventh objective formula, the twelfth objective formula, and the above thirteenth objective formula.

[0072] Step S2033, construct the fourteenth objective formula C total =Cgen +C carbon +C fail The fourteenth objective formula mentioned above is used to simulate the changing trend of the total grid frequency regulation cost with respect to the generation cost, carbon emission cost, and frequency regulation failure cost, where C total The total cost of the aforementioned power grid frequency regulation;

[0073] Specifically, based on the correlation between the total cost of power frequency regulation and the aforementioned power generation cost, carbon emission cost, and frequency regulation failure cost, the above-mentioned fourteenth objective formula is constructed.

[0074] Step S2034: Combine the above tenth objective formula, the above eleventh objective formula, the above twelfth objective formula, the above thirteenth objective formula, and the above fourteenth objective formula to obtain the above carbon emission cost model.

[0075] Specifically, the carbon emission cost model is constructed based on the above tenth, eleventh, twelfth, thirteenth and fourteenth objective formulas.

[0076] In order to construct the first set of constraints, in one optional implementation, step S204 includes:

[0077] Step S20411: Construct the above-mentioned operating state constraints for each temperature-controlled load to obtain the first objective constraint:

[0078]

[0079] in, Let s be the power of the j-th temperature-controlled load of the above load aggregator i at time t. i,j,t and s i,j,t-1 These represent the operating states of the j-th temperature-controlled load of the above load aggregator i at times t and t-1, respectively. and Let be the minimum and maximum allowable indoor temperatures for the j-th temperature-controlled load, respectively, of the above load aggregator i. and They respectively satisfy:

[0080]

[0081] Where, δ i,j This represents the allowable temperature deviation of the j-th temperature-controlled load of the above load aggregator i. Set the temperature for the user;

[0082] Specifically, the operating status of the temperature control load is correlated with the above parameters based on the indoor temperature, the user-set temperature, the cooling capacity of the temperature control load, and the heating / cooling power of the temperature control load, thus obtaining the first objective constraint.

[0083] Furthermore, due to the aforementioned nonlinear constraints, to facilitate the solution, this application converts the nonlinear constraints into linear constraints, specifically including:

[0084] Introduce 3 auxiliary binary variables This is used as an indicator variable to represent indoor temperature. The relationship between the auxiliary variable and temperature is established as follows:

[0085]

[0086] Here, M represents a very large number used to relax the constraints.

[0087] Therefore, the first objective constraint mentioned above is transformed as follows:

[0088]

[0089] Step S20412: Construct the power constraints of the temperature-controlled loads corresponding to each temperature-controlled load to obtain the second objective constraint:

[0090]

[0091] Specifically, the second objective constraint mentioned above is used to limit the output power of the temperature-controlled load between the upper and lower limits.

[0092] Step S20413: Construct the first constraint group based on the first objective constraint and the second objective constraint.

[0093] Specifically, the first constraint group is constructed by combining the first objective constraint and the second objective constraint.

[0094] In order to construct the second set of constraints, in one optional implementation, step S204 includes:

[0095] Step S20421: Construct the above-mentioned state-of-charge constraints for each electric vehicle to obtain the third objective constraint:

[0096]

[0097] in, and Let i be the maximum and minimum state of charge of the j-th electric vehicle in the above load aggregator i;

[0098] Specifically, the aforementioned third objective constraint is used to limit the state of charge of the on-board battery of the electric vehicle to prevent overcharging or over-discharging of the on-board battery.

[0099] Step S20422: Construct the charging and discharging power constraints for each electric vehicle as described above, and obtain the fourth objective constraint:

[0100]

[0101] Where, η ch and η dis These represent the charging and discharging efficiencies, respectively.

[0102] Specifically, the fourth objective constraint mentioned above is used to limit the charging and discharging power of the electric vehicle to be less than the rated power.

[0103] Step S20423: Construct the above-mentioned operating state constraints for each electric vehicle to obtain the fifth objective constraint:

[0104]

[0105] in, and The charging and discharging states of the charging load of the aforementioned electric vehicles are represented by two variables.

[0106] Specifically, and The charging and discharging state of the electric vehicle charging load is represented by a binary variable (1 for charging / discharging, 0 for not charging / discharging).

[0107] Step S20424: Construct the second constraint group based on the third, fourth and fifth objective constraints.

[0108] Specifically, the second constraint group is constructed by combining the third, fourth, and fifth objective constraints mentioned above.

[0109] In order to construct the third constraint group, in one optional implementation, step S204 includes:

[0110] Step S20431: Construct the reactive power balance constraints corresponding to the above power frequency regulation system to obtain the sixth objective constraint:

[0111]

[0112] Among them, qf ih,t Let be the reactive power flowing from node i to node h at time t. Let be the reactive power of the generator at node i at time t. This represents the reactive base load at node i at time t.

[0113] Specifically, each node of the aforementioned power grid needs to satisfy both active power balance and reactive power balance. Based on this, the sixth objective constraint is constructed based on the reactive power balance of each node of the power grid.

[0114] Step S20432: Construct the generator generation constraints corresponding to the above power frequency regulation system to obtain the seventh objective constraint:

[0115]

[0116] in, and These are the maximum and minimum active power of the generator at node i, respectively; and Let be the maximum and minimum reactive power of the generator at node i, respectively. This represents the maximum gradeability of the generator at node i.

[0117] Specifically, the seventh objective constraint is obtained by constraining the generator operating status of each node in the aforementioned power grid.

[0118] Step S20433: Construct the power flow calculation constraints corresponding to the above power frequency regulation system to obtain the eighth objective constraint:

[0119]

[0120] Among them, V i,t Let r be the square of the voltage magnitude at node i at time t. ih x represents the resistance of line ih. ih V represents the reactance of line ih. i max and V i min This represents the maximum and minimum squared values ​​of the voltage magnitude at node i. This indicates the maximum apparent power of line ih (correct).

[0121] Specifically, the power flow equations of the power system are represented by a linearized Distflow model. Without considering network losses, the above formula is obtained. Furthermore, node voltage and power flow constraints are constructed based on the power flow equations to obtain the above eighth objective constraint.

[0122] Step S20434: Construct the third constraint group based on the sixth, seventh and eighth objective constraints.

[0123] Specifically, the third constraint group is constructed by combining the sixth, seventh, and eighth objective constraints mentioned above.

[0124] In the above embodiments, the frequency regulation command instructs load aggregators on the power they need to increase or decrease at a specific time. Using our proposed optimization model, under the overall AGC frequency regulation command, the frequency regulation power that each load aggregator should undertake can be accurately determined based on its current state and capacity. This method enables more efficient allocation of frequency regulation responsibilities, reduces reliance on traditional generator sets, and thus reduces generator spinning reserve capacity. Ultimately, this not only improves the frequency regulation efficiency of the power system but also reduces overall system carbon emissions, promoting the low-carbon economic operation of the power system.

[0125] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0126] This application also provides a frequency regulation control device for load-side resource coordination based on carbon emissions. It should be noted that this frequency regulation control device can be used to execute the frequency regulation control method for load-side resource coordination based on carbon emissions provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0127] The following describes the frequency regulation control device based on carbon emission load-side resource coordination provided in the embodiments of this application.

[0128] Figure 3 This is a structural block diagram of a frequency regulation control device based on carbon emission-based load-side resource coordination, according to an embodiment of this application. Figure 3 As shown, the device includes:

[0129] The first building unit 10 is used to build a temperature-controlled load frequency regulation model. The temperature-controlled load frequency regulation model is used to simulate the changing trends of the upper and lower frequency regulation capacity of the temperature-controlled load with the operating status of the temperature-controlled load.

[0130] Specifically, the aforementioned temperature-controlled load refers to electrical appliances such as air conditioners that regulate indoor temperature. The frequency modulation model of the aforementioned temperature-controlled load simulates the changing trends of the upper and lower frequency modulation capacities of the temperature-controlled load as the operating state of the temperature-controlled load, and utilizes the thermal inertia of the temperature-controlled load to participate in auxiliary frequency modulation.

[0131] The second building unit 20 is used to build an electric vehicle load frequency regulation model. The electric vehicle load frequency regulation model is used to simulate the trend of the up-frequency regulation capacity and down-frequency regulation capacity of the electric vehicle as the state of charge of the electric vehicle changes.

[0132] Specifically, the above-mentioned electric vehicle load frequency regulation model is used to simulate the changing trends of the up-frequency regulation capacity and down-frequency regulation capacity of electric vehicles with the state of charge of the electric vehicles. This allows the electric vehicle load to choose to charge or discharge according to the frequency regulation requirements to assist in frequency regulation. The electric vehicle can not only be charged from the grid, but also discharge to the grid using a bidirectional charging mode.

[0133] The third building unit 30 is used to build a carbon emission cost model based on the above temperature-controlled load frequency regulation model and the above electric vehicle load frequency regulation model. The carbon emission cost model is used to simulate the trend of the total grid frequency regulation cost as the power frequency regulation system generates electricity while meeting the needs of the above temperature-controlled load and electric vehicles.

[0134] Specifically, a carbon emission cost model is constructed based on the above-mentioned temperature-controlled load frequency regulation model and the above-mentioned electric vehicle load frequency regulation model. Based on the trends of power generation cost and carbon emission cost in the above-mentioned power frequency regulation system with the change of active power at nodes, the trend of frequency regulation failure cost with the change of the remaining capacity after frequency regulation failure, and the trend of the total power grid frequency regulation cost with the change of the above-mentioned power generation cost, the above-mentioned carbon emission cost, and the above-mentioned frequency regulation failure cost, a carbon emission cost model is constructed together.

[0135] The fourth construction unit 40 is used to construct a first constraint group, a second constraint group, and a third constraint group. The first constraint group is used to limit the operating parameters of the temperature-controlled load frequency regulation model. The first constraint group includes at least temperature-controlled load power constraints and temperature-controlled load operating state constraints. The second constraint group is used to limit the operating parameters of the electric vehicle load frequency regulation model. The second constraint group includes at least state of charge constraints, charging and discharging power constraints, and operating state constraints. The third constraint group is used to limit the operating parameters of each node in the power frequency regulation system. The third constraint group includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints.

[0136] Specifically, the aforementioned temperature-controlled load power constraint is used to limit the output power of the aforementioned temperature-controlled load, and the aforementioned temperature-controlled load operating state constraint is used to limit the operating mode of the aforementioned temperature-controlled load, so that it remains within the temperature range set by the user and continuously switches between on and off states. The aforementioned electric vehicle load can only be in one of the charging, discharging, and idle states within a certain time period, and can switch between different states.

[0137] The calculation unit 50 is used to solve the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model using a commercial solver under the constraints of the first constraint group, the second constraint group and the third constraint group, with the objective function of minimizing the output value of the carbon emission cost model, to obtain the operating parameters of the temperature-controlled load and the electric vehicle, and generate frequency regulation control commands based on the operating parameters.

[0138] Specifically, within the first constraint group (i.e., the temperature-controlled load operates within the user-defined operating range), the second constraint group (i.e., the range corresponding to the electric vehicle's state of charge, charging / discharging power, and operating state), and the third constraint group (i.e., the range of reactive power balance, generator power, and power flow calculations of the power frequency regulation system), the objective function is to minimize the aforementioned carbon emission cost. The carbon emission cost model is directly solved using a commercial solver to obtain the operating parameters of the temperature-controlled load and the electric vehicle, and frequency regulation control commands are generated based on the operating parameters.

[0139] The transmitting unit 60 sends the aforementioned frequency modulation control command to the aforementioned load aggregator to control the temperature-controlled load and the operation of the electric vehicle;

[0140] Specifically, the power system's dispatch center sends frequency regulation commands to load aggregators through automatic generation control, thereby controlling various load-side resources.

[0141] In this embodiment, the first construction unit constructs a temperature-controlled load frequency regulation model, which simulates the changing trends of the upper and lower frequency regulation capacities of the temperature-controlled load with the operating state of the temperature-controlled load; the second construction unit constructs an electric vehicle load frequency regulation model, which simulates the changing trends of the upper and lower frequency regulation capacities of the electric vehicle with the changing state of charge of the electric vehicle; the third construction unit constructs a carbon emission cost model based on the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model, which simulates the changing trend of the total grid frequency regulation cost with the power generation of the power frequency regulation system under the condition of meeting the needs of the temperature-controlled load and electric vehicles; the fourth construction unit constructs a first constraint group, a second constraint group, and a third constraint group, whereby the first constraint group is used to limit the operating parameters of the temperature-controlled load frequency regulation model, and the first constraint group includes at least temperature... The first constraint group and the second constraint group are used to limit the operating parameters of the electric vehicle load frequency regulation model. The second constraint group includes at least state of charge constraints, charging and discharging power constraints, and operating state constraints. The third constraint group is used to limit the operating parameters of each node in the power frequency regulation system. The third constraint group includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints. Under the constraints of the first constraint group, the second constraint group, and the third constraint group, the calculation unit solves the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model using a commercial solver with the objective function of minimizing the output value of the carbon emission cost model. The calculation unit obtains the operating parameters of the temperature-controlled load and the electric vehicle, and generates frequency regulation control commands based on the operating parameters. The sending unit sends the frequency regulation control commands to the load aggregator to control the operation of the temperature-controlled load and the electric vehicle. This application incorporates various load-side resources, such as air conditioners and electric vehicles, into grid frequency regulation control to reduce generator spinning reserve capacity and thus carbon emissions. Numerical models are established and simulated for the load-side resources, and a cost numerical model is constructed by incorporating carbon emissions and generator power generation. To minimize the cost numerical model, an optimization problem is solved using a commercial solver to obtain control commands, which are then sent to the corresponding load-side resources. This method solves the problem of high carbon emissions resulting from reserving backup capacity in generator sets to cope with system frequency regulation in existing technologies.

[0142] The aforementioned frequency regulation control device for load-side resource coordination based on carbon emissions includes a processor and a memory. The first, second, third, and fourth building units, as well as the computing unit, are all stored as program units in the memory. The processor executes these program units to achieve the corresponding functions. All of the above modules reside in the same processor; alternatively, the modules may be located in different processors in any combination.

[0143] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and adjusting kernel parameters can improve the efficiency of frequency regulation control for load-side resource coordination based on carbon emissions.

[0144] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0145] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the aforementioned frequency modulation control method based on carbon emission load-side resource coordination.

[0146] Specifically, frequency regulation control methods based on load-side resource coordination for carbon emissions include:

[0147] Step S201: Construct a temperature-controlled load frequency regulation model. The above temperature-controlled load frequency regulation model is used to simulate the changing trends of the upper and lower frequency regulation capacities of the temperature-controlled load with the operating status of the temperature-controlled load.

[0148] Step S202: Construct an electric vehicle load frequency regulation model. The electric vehicle load frequency regulation model is used to simulate the changing trends of the up-frequency regulation capacity and down-frequency regulation capacity of the electric vehicle with the state of charge of the electric vehicle.

[0149] Step S203: Construct a carbon emission cost model based on the above temperature-controlled load frequency regulation model and the above electric vehicle load frequency regulation model. The carbon emission cost model is used to simulate the trend of the total grid frequency regulation cost as the power frequency regulation system generates electricity while meeting the needs of the above temperature-controlled load and electric vehicles.

[0150] Step S204: Construct a first constraint group, a second constraint group, and a third constraint group. The first constraint group is used to limit the operating parameters of the temperature-controlled load frequency regulation model. The first constraint group includes at least temperature-controlled load power constraints and temperature-controlled load operating state constraints. The second constraint group is used to limit the operating parameters of the electric vehicle load frequency regulation model. The second constraint group includes at least state of charge constraints, charging and discharging power constraints, and operating state constraints. The third constraint group is used to limit the operating parameters of each node in the power frequency regulation system. The third constraint group includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints.

[0151] Step S205: Under the constraints of the first constraint group, the second constraint group and the third constraint group, the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model are solved by a commercial solver with the objective function of minimizing the output value of the carbon emission cost model to obtain the operating parameters of the temperature-controlled load and the electric vehicle, and frequency regulation control commands are generated based on the operating parameters.

[0152] Step S206: Send the above-mentioned frequency modulation control command to the above-mentioned load aggregator to control the temperature-controlled load and the operation of the electric vehicle;

[0153] Specifically, the power system's dispatch center sends frequency regulation commands to load aggregators through automatic generation control, thereby controlling various load-side resources.

[0154] This invention provides a processor for running a program, wherein the program executes the aforementioned frequency regulation control method based on load-side resource coordination for carbon emissions.

[0155] Specifically, frequency regulation control methods based on load-side resource coordination for carbon emissions include:

[0156] Step S201: Construct a temperature-controlled load frequency regulation model. The above temperature-controlled load frequency regulation model is used to simulate the changing trends of the upper and lower frequency regulation capacities of the temperature-controlled load with the operating status of the temperature-controlled load.

[0157] Step S202: Construct an electric vehicle load frequency regulation model. The electric vehicle load frequency regulation model is used to simulate the changing trends of the up-frequency regulation capacity and down-frequency regulation capacity of the electric vehicle with the state of charge of the electric vehicle.

[0158] Step S203: Construct a carbon emission cost model based on the above temperature-controlled load frequency regulation model and the above electric vehicle load frequency regulation model. The carbon emission cost model is used to simulate the trend of the total grid frequency regulation cost as the power frequency regulation system generates electricity while meeting the needs of the above temperature-controlled load and electric vehicles.

[0159] Step S204: Construct a first constraint group, a second constraint group, and a third constraint group. The first constraint group is used to limit the operating parameters of the temperature-controlled load frequency regulation model. The first constraint group includes at least temperature-controlled load power constraints and temperature-controlled load operating state constraints. The second constraint group is used to limit the operating parameters of the electric vehicle load frequency regulation model. The second constraint group includes at least state of charge constraints, charging and discharging power constraints, and operating state constraints. The third constraint group is used to limit the operating parameters of each node in the power frequency regulation system. The third constraint group includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints.

[0160] Step S205: Under the constraints of the first constraint group, the second constraint group and the third constraint group, the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model are solved by a commercial solver with the objective function of minimizing the output value of the carbon emission cost model to obtain the operating parameters of the temperature-controlled load and the electric vehicle, and frequency regulation control commands are generated based on the operating parameters.

[0161] Step S206: Send the frequency modulation control command to the load aggregator to control the temperature-controlled load and the operation of the electric vehicle.

[0162] This invention provides a power dispatching system, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0163] Step S201: Construct a temperature-controlled load frequency regulation model. The above temperature-controlled load frequency regulation model is used to simulate the changing trends of the upper and lower frequency regulation capacities of the temperature-controlled load with the operating status of the temperature-controlled load.

[0164] Step S202: Construct an electric vehicle load frequency regulation model. The electric vehicle load frequency regulation model is used to simulate the changing trends of the up-frequency regulation capacity and down-frequency regulation capacity of the electric vehicle with the state of charge of the electric vehicle.

[0165] Step S203: Construct a carbon emission cost model based on the above temperature-controlled load frequency regulation model and the above electric vehicle load frequency regulation model. The carbon emission cost model is used to simulate the trend of the total grid frequency regulation cost as the power frequency regulation system generates electricity while meeting the needs of the above temperature-controlled load and electric vehicles.

[0166] Step S204: Construct a first constraint group, a second constraint group, and a third constraint group. The first constraint group is used to limit the operating parameters of the temperature-controlled load frequency regulation model. The first constraint group includes at least temperature-controlled load power constraints and temperature-controlled load operating state constraints. The second constraint group is used to limit the operating parameters of the electric vehicle load frequency regulation model. The second constraint group includes at least state of charge constraints, charging and discharging power constraints, and operating state constraints. The third constraint group is used to limit the operating parameters of each node in the power frequency regulation system. The third constraint group includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints.

[0167] Step S205: Under the constraints of the first constraint group, the second constraint group and the third constraint group, the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model are solved by a commercial solver with the objective function of minimizing the output value of the carbon emission cost model to obtain the operating parameters of the temperature-controlled load and the electric vehicle, and frequency regulation control commands are generated based on the operating parameters.

[0168] Step S206: Send the frequency modulation control command to the load aggregator to control the temperature-controlled load and the operation of the electric vehicle.

[0169] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0170] Step S201: Construct a temperature-controlled load frequency regulation model. The above temperature-controlled load frequency regulation model is used to simulate the changing trends of the upper and lower frequency regulation capacities of the temperature-controlled load with the operating status of the temperature-controlled load.

[0171] Step S202: Construct an electric vehicle load frequency regulation model. The electric vehicle load frequency regulation model is used to simulate the changing trends of the up-frequency regulation capacity and down-frequency regulation capacity of the electric vehicle with the state of charge of the electric vehicle.

[0172] Step S203: Construct a carbon emission cost model based on the above temperature-controlled load frequency regulation model and the above electric vehicle load frequency regulation model. The carbon emission cost model is used to simulate the trend of the total grid frequency regulation cost as the power frequency regulation system generates electricity while meeting the needs of the above temperature-controlled load and electric vehicles.

[0173] Step S204: Construct a first constraint group, a second constraint group, and a third constraint group. The first constraint group is used to limit the operating parameters of the temperature-controlled load frequency regulation model. The first constraint group includes at least temperature-controlled load power constraints and temperature-controlled load operating state constraints. The second constraint group is used to limit the operating parameters of the electric vehicle load frequency regulation model. The second constraint group includes at least state of charge constraints, charging and discharging power constraints, and operating state constraints. The third constraint group is used to limit the operating parameters of each node in the power frequency regulation system. The third constraint group includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints.

[0174] Step S205: Under the constraints of the first constraint group, the second constraint group and the third constraint group, the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model are solved by a commercial solver with the objective function of minimizing the output value of the carbon emission cost model to obtain the operating parameters of the temperature-controlled load and the electric vehicle, and frequency regulation control commands are generated based on the operating parameters.

[0175] Step S206: Send the frequency modulation control command to the load aggregator to control the temperature-controlled load and the operation of the electric vehicle.

[0176] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0177] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0178] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0179] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0180] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0181] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0182] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0183] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0184] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0185] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0186] 1) This application discloses a frequency regulation control method based on carbon emission-based load-side resource coordination. First, a temperature-controlled load frequency regulation model is constructed to simulate the changing trends of the upper and lower frequency regulation capacities of the temperature-controlled load with its operating state. Then, an electric vehicle load frequency regulation model is constructed to simulate the changing trends of the upper and lower frequency regulation capacities of electric vehicles with their state of charge. Next, a carbon emission cost model is constructed based on the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model. This carbon emission cost model simulates the changing trend of the total grid frequency regulation cost with the power generation of the power frequency regulation system while meeting the needs of the temperature-controlled load and electric vehicles. Finally, a first constraint group, a second constraint group, and a third constraint group are constructed. The first constraint group is used to limit the operating parameters of the temperature-controlled load frequency regulation model. The first constraint group includes at least temperature-controlled load power constraints and temperature-controlled load operating state constraints. The second constraint group is used to limit the operating parameters of the electric vehicle load frequency regulation model. The second constraint group includes at least state-of-charge constraints, charging and discharging power constraints, and operating state constraints. The third constraint group is used to limit the operating parameters of each node in the power frequency regulation system. The third constraint group includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints. Then, under the constraints of the first, second, and third constraint groups, the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model are solved using a commercial solver with the objective function of minimizing the output value of the carbon emission cost model to obtain the operating parameters of the temperature-controlled load and the electric vehicle. Frequency regulation control commands are generated based on the operating parameters. Finally, the frequency regulation control commands are sent to the load aggregator to control the operation of the temperature-controlled load and the electric vehicle. This application incorporates various load-side resources, such as air conditioners and electric vehicles, into grid frequency regulation control to reduce generator spinning reserve capacity and thus carbon emissions. Numerical models are established and simulated for the load-side resources, and a cost numerical model is constructed by incorporating carbon emissions and generator power generation. To minimize the cost numerical model, an optimization problem is solved using a commercial solver to obtain control commands, which are then sent to the corresponding load-side resources. This method solves the problem of high carbon emissions resulting from reserving backup capacity in generator sets to cope with system frequency regulation in existing technologies.

[0187] 2) A frequency regulation control device based on carbon emission load-side resource coordination according to this application comprises: a first building unit constructing a temperature-controlled load frequency regulation model, which simulates the changing trends of the up-regulation capacity and down-regulation capacity of the temperature-controlled load with the operating state of the temperature-controlled load; a second building unit constructing an electric vehicle load frequency regulation model, which simulates the changing trends of the up-regulation capacity and down-regulation capacity of the electric vehicle with the changing trends of the electric vehicle's state of charge; a third building unit constructing a carbon emission cost model based on the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model, which simulates the changing trend of the total grid frequency regulation cost with the power generation of the power frequency regulation system under the condition of meeting the needs of the temperature-controlled load and the electric vehicle; and a fourth building unit constructing a first constraint group, a second constraint group, and a third constraint group, wherein the first constraint group is used to limit the operating parameters of the temperature-controlled load frequency regulation model. The first constraint group includes at least temperature-controlled load power constraints and temperature-controlled load operating state constraints. The second constraint group is used to limit the operating parameters of the electric vehicle load frequency regulation model. The second constraint group includes at least state-of-charge constraints, charging and discharging power constraints, and operating state constraints. The third constraint group is used to limit the operating parameters of each node in the power frequency regulation system. The third constraint group includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints. Under the constraints of the first, second, and third constraint groups, the calculation unit solves the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model using a commercial solver, with the goal of minimizing the output value of the carbon emission cost model, to obtain the operating parameters of the temperature-controlled load and the electric vehicle. Based on the operating parameters, a frequency regulation control command is generated. The sending unit sends the frequency regulation control command to the load aggregator to control the operation of the temperature-controlled load and the electric vehicle. This application incorporates various load-side resources, such as air conditioners and electric vehicles, into grid frequency regulation control to reduce generator spinning reserve capacity and thus carbon emissions. Numerical models are established and simulated for the load-side resources, and a cost numerical model is constructed by incorporating carbon emissions and generator power generation. To minimize the cost numerical model, an optimization problem is solved using a commercial solver to obtain control commands, which are then sent to the corresponding load-side resources. This method solves the problem of high carbon emissions resulting from reserving backup capacity in generator sets to cope with system frequency regulation in existing technologies.

[0188] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A frequency regulation control method based on load-side resource coordination for carbon emissions, characterized in that, This method is applied to a power frequency regulation system with multiple load-side resources. The power frequency regulation system includes a dispatch center, load aggregators, and load-side resources. The dispatch center controls the load aggregators through automatic generation control. The load aggregators control the load-side resources according to the control instructions from the dispatch center. The load-side resources are load-controllable devices at the end of the power grid. The method includes: A frequency regulation model for temperature-controlled load is constructed. The frequency regulation model for temperature-controlled load is used to simulate the changing trends of the up-frequency regulation capacity and down-frequency regulation capacity of temperature-controlled load with the operating state of temperature-controlled load. An electric vehicle load frequency regulation model is constructed, which is used to simulate the changing trends of the up-frequency regulation capacity and down-frequency regulation capacity of the electric vehicle with the state of charge of the electric vehicle. A carbon emission cost model is constructed based on the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model. The carbon emission cost model is used to simulate the trend of the total grid frequency regulation cost as the power generation of the power frequency regulation system changes under the condition that the temperature-controlled load and the electric vehicle demand are met. A first constraint group, a second constraint group, and a third constraint group are constructed. The first constraint group is used to limit the operating parameters of the temperature-controlled load frequency regulation model. The first constraint group includes at least temperature-controlled load power constraints and temperature-controlled load operating state constraints. The second constraint group is used to limit the operating parameters of the electric vehicle load frequency regulation model. The second constraint group includes at least state of charge constraints, charging and discharging power constraints, and operating state constraints. The third constraint group is used to limit the operating parameters of each node in the power frequency regulation system. The third constraint group includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints. Under the constraints of the first constraint group, the second constraint group, and the third constraint group, the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model are solved by a commercial solver with the objective function of minimizing the output value of the carbon emission cost model to obtain the operating parameters of the temperature-controlled load and the electric vehicle, and frequency regulation control commands are generated based on the operating parameters. The frequency modulation control command is sent to the load aggregator to control the temperature-controlled load and the operation of the electric vehicle.

2. The method according to claim 1, characterized in that, Constructing a frequency regulation model for temperature-controlled loads includes: Constructing the first objective formula The first target formula is used to simulate the trend of indoor temperature change with the heating / cooling capacity of the temperature-controlled load, wherein, and Let be the indoor temperatures at times t+1 and t. and Let Q be the outdoor temperature at times t+1 and t. t For the heating / cooling capacity of the temperature-controlled load, in Q t When Q > 0, the temperature-controlled load performs cooling. t <0 temperature control load for heating, C is the equivalent specific heat capacity of the room, R is the equivalent thermal resistance of the room, and Δt is the time interval between t+1 and t. Constructing the second objective formula The second target formula is used to simulate the variation trend of heating / cooling capacity of temperature-controlled load with temperature-controlled load power, where i represents the load aggregator, j represents the load-side resources, and the load-side resources include at least the temperature-controlled load, Q i,j,t η represents the cooling / heating capacity of the j-th temperature-controlled load at time t, where η is the load aggregator i. i,j Let be the energy efficiency conversion coefficient of the j-th temperature-controlled load of load aggregator i; Let be the power of the j-th temperature-controlled load of load aggregator i at time t; Constructing the third objective formula The third objective formula is used to simulate the variation trend of the polymerization temperature-controlled load of the load aggregator with the power of the temperature-controlled load, wherein... The polymerization temperature control load at time t is the polymerization quotient i of the load. For the set of temperature-controlled loads of load aggregator i; Constructing the fourth objective formula and the fifth objective formula The fourth objective formula is used to simulate the changing trend of the upward frequency modulation capacity of the temperature-controlled load with the aggregated temperature-controlled load, and the fifth objective formula is used to simulate the changing trend of the downward frequency modulation capacity of the temperature-controlled load with the operating state of the temperature-controlled load, wherein, The rated power of the j-th temperature-controlled load is given by the load aggregator i. and These refer to the upper frequency modulation capacity and the lower frequency modulation capacity of the temperature-controlled load, respectively. The temperature-controlled load frequency regulation model is obtained by combining the first objective formula, the second objective formula, the third objective formula, the fourth objective formula, and the fifth objective formula.

3. The method according to claim 2, characterized in that, Constructing a load frequency regulation model for electric vehicles, including: Constructing the sixth objective formula The sixth objective formula is used to simulate the trend of the state of charge (SOC) of an electric vehicle changing with charging and discharging power, where SOC... i,j,t+1 and SOC i,j,t Let be the state of charge of the j-th electric vehicle of the load aggregator i at times t+1 and t, respectively. and Let η be the charging and discharging power of the j-th electric vehicle at time t, where η is the load aggregator i. ch and η dis E represents the charging and discharging efficiency, respectively. i,j This represents the capacity of the j-th electric vehicle in the load aggregator i; Constructing the seventh objective formula The seventh objective formula is used to simulate the trend of the aggregated electric vehicle load of the load aggregator changing with the charging and discharging power of the electric vehicle, wherein, Let i be the aggregated electric vehicle load at time t. The set of electric vehicle loads for the load aggregator i; Construct the eighth objective formula. and the ninth objective formula The eighth objective formula is used to simulate the changing trend of the up-frequency regulation capacity of the electric vehicle with the aggregated temperature control load, and the ninth objective formula is used to simulate the changing trend of the down-frequency regulation capacity of the electric vehicle with the operating state of the temperature control load, wherein, and These refer to the up-frequency modulation capacity and the down-frequency modulation capacity of the electric vehicle, respectively. and These represent the maximum charging and discharging power, respectively. By combining the sixth objective formula, the seventh objective formula, the eighth objective formula, and the ninth objective formula, the electric vehicle load frequency regulation model is obtained.

4. The method according to claim 3, characterized in that, A carbon emission cost model is constructed based on the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model, including: Constructing the tenth objective formula The tenth objective formula is used to simulate the changing trend of active power at nodes in the power frequency regulation system with aggregated temperature-controlled load and aggregated electric vehicle load, where E N Let pf be the set of nodes in the power grid. ki,t Let pf be the active power flowing from node k to node i at time t. ih,t Let be the active power flowing from node i to node h at time t. Let be the active power of the generator at node i at time t. Let be the active base load of node i at time t. The frequency modulation power change command for node i at time t; The remaining capacity at node i at time t is where the frequency modulation failed. Constructing the Eleventh Objective Formula Twelfth Target Formula And the thirteenth objective formula The eleventh objective formula is used to simulate the trend of power generation cost as a function of active power; the twelfth objective formula is used to simulate the trend of carbon emission cost as a function of active power; and the thirteenth objective formula is used to simulate the trend of frequency regulation failure cost as a function of the remaining capacity after frequency regulation failure, wherein C gen For the aforementioned power generation cost, C carbon For the carbon emission cost, C fail For the frequency modulation failure cost, a i and b i c is a preset coefficient for the power generation cost. tax The tax amount per unit of carbon emissions, c fail The cost of frequency modulation failure per unit flux. Let be the carbon emission factor of the generator at node i; Construct the fourteenth objective formula C total =C gen +C carbon +C fail The fourteenth objective formula is used to simulate the changing trend of the total grid frequency regulation cost with the generation cost, the carbon emission cost, and the frequency regulation failure cost, where C total The total cost of frequency regulation for the power grid; The carbon emission cost model is obtained by combining the tenth, eleventh, twelfth, thirteenth, and fourteenth objective formulas.

5. The method according to any one of claims 1 to 4, characterized in that, Construct the first constraint group, including: Construct the operating state constraints for each temperature-controlled load to obtain the first objective constraint: in, Let s be the power of the j-th temperature-controlled load of the load aggregator i at time t. i,j,t and s i,j,t-1 These represent the operating states of the j-th temperature-controlled load of the load aggregator i at times t and t-1, respectively. and They respectively satisfy: Where, δ i,j This represents the allowable temperature deviation of the j-th temperature-controlled load of the load aggregator i. Set the temperature for the user; Construct the power constraints of the temperature-controlled loads corresponding to each temperature-controlled load to obtain the second objective constraint: The first constraint group is constructed based on the first objective constraint and the second objective constraint.

6. The method according to claim 3, characterized in that, Construct a second set of constraints, including: Construct the charge state constraints for each electric vehicle to obtain the third objective constraint. in, and Let i be the maximum and minimum values ​​of the state of charge of the j-th electric vehicle of the load aggregator i; The charging and discharging power constraints for each electric vehicle are constructed to obtain the fourth objective constraint. Where, η ch and η dis These are the efficiency factors for charging and discharging, respectively. The operating state constraints corresponding to each electric vehicle are constructed to obtain the fifth objective constraint. in, and The charging and discharging states of the charging load of the electric vehicle are represented by a binary variable. The second constraint group is constructed based on the third objective constraint, the fourth objective constraint, and the fifth objective constraint.

7. The method according to claim 4, characterized in that, Construct a third set of constraints, including: Construct the reactive power balance constraints corresponding to the power frequency regulation system to obtain the sixth objective constraint. Among them, qf ki,t Let qf be the reactive power flowing from node k to node i at time t. ih,t Let be the reactive power flowing from node i to node h at time t. Let be the reactive power of the generator at node i at time t. This represents the reactive base load at node i at time t. Construct the generator generation constraints corresponding to the power frequency regulation system to obtain the seventh objective constraint. in, and These are the maximum and minimum active power of the generator at node i, respectively; and These are the maximum and minimum reactive power of the generator at node i, respectively; This represents the maximum gradeability of the generator at node i; Construct the power flow calculation constraints corresponding to the power frequency regulation system to obtain the eighth objective constraint. Among them, V i,t V is the square of the voltage magnitude at node i at time t. h,t r represents the square of the voltage magnitude at node h at time t. ih x represents the resistance of line ih. ih V represents the reactance of line ih. i max and V i min This represents the maximum and minimum squared values ​​of the voltage magnitude at node i. This represents the maximum apparent power of line ih; The third constraint group is constructed based on the sixth objective constraint, the seventh objective constraint, and the eighth objective constraint.

8. A frequency regulation control device for load-side resource coordination based on carbon emissions, characterized in that, This device is applied to a power frequency regulation system with multiple load-side resources. The power frequency regulation system includes a dispatch center, load aggregators, and load-side resources. The dispatch center controls the load aggregators through automatic generation control. The load aggregators control the load-side resources according to the control commands from the dispatch center. The load-side resources are controllable load devices at the end of the power grid. The device includes: The first building unit is used to build a temperature-controlled load frequency regulation model, which is used to simulate the changing trends of the up-frequency regulation capacity and down-frequency regulation capacity of the temperature-controlled load with the operating state of the temperature-controlled load. The second construction unit is used to construct an electric vehicle load frequency regulation model, which is used to simulate the changing trends of the electric vehicle's up-frequency regulation capacity and down-frequency regulation capacity with the electric vehicle's state of charge. The third construction unit is used to construct a carbon emission cost model based on the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model. The carbon emission cost model is used to simulate the trend of the total grid frequency regulation cost as the power generation of the power frequency regulation system changes under the condition of meeting the needs of the temperature-controlled load and electric vehicles. The fourth construction unit is used to construct a first constraint group, a second constraint group, and a third constraint group. The first constraint group is used to limit the operating parameters of the temperature-controlled load frequency regulation model. The first constraint group includes at least temperature-controlled load power constraints and temperature-controlled load operating state constraints. The second constraint group is used to limit the operating parameters of the electric vehicle load frequency regulation model. The second constraint group includes at least state of charge constraints, charging and discharging power constraints, and operating state constraints. The third constraint group is used to limit the operating parameters of each node in the power frequency regulation system. The third constraint group includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints. The calculation unit is used to solve the temperature-controlled load frequency regulation model and the electric vehicle load frequency regulation model using a commercial solver under the constraints of the first constraint group, the second constraint group and the third constraint group, with the objective function of minimizing the output value of the carbon emission cost model, to obtain the operating parameters of the temperature-controlled load and the electric vehicle, and generate frequency regulation control commands based on the operating parameters. The transmitting unit is used to send the frequency modulation control command to the load aggregator to control the temperature-controlled load and the operation of the electric vehicle.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 7.

10. A power dispatching system, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising methods for performing any one of claims 1 to 7.

Images