Load scheduling strategy determination method and device and computer equipment

Abstract

The invention relates to a load scheduling strategy determination method and device and computer equipment. The method comprises the following steps: acquiring equipment parameter information of power generation equipment in a power system in a target time period and load demand information of each load in an area to which the power system belongs in the target time period; based on a first scheduling prediction model, determining scheduling output information of the power generation equipment in a future time period according to the equipment parameter information and the load demand information; and determining a load scheduling strategy of each load in the power system in the future time period based on the second scheduling prediction model according to the electricity transaction information and the scheduling output information of the power system in the future time period and the carbon emission task information of each load. The method can ensure that the determined load scheduling strategy can effectively reduce the carbon emission.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and particularly to a method, device, and computer equipment for determining a load scheduling strategy. Background Art

[0002] With the aggravation of environmental problems such as global warming, reducing the use of fossil fuels and increasing the consumption of clean energy are important issues that need to be solved for the survival and development of human society. As a key part of energy system energy conservation and emission reduction, improving the economy and low-carbon performance of the system has become one of the necessary measures to reduce carbon emissions. Reasonable scheduling of the load on the user side is conducive to exerting the potential of energy conservation and carbon emission reduction.

[0003] However, most of the current related research explores the low-carbon potential from the perspective of the source side, only limited to the analysis of carbon emission flows. By combining carbon emission flows with the power grid, the real-time flow of carbon emissions from the power generation side to the demand side in the power grid is analyzed, resulting in the determined load scheduling strategy being unable to effectively reduce carbon emissions. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method, device, and computer equipment for determining a load scheduling strategy, which can ensure that the determined load scheduling strategy can effectively reduce carbon emissions.

[0005] In a first aspect, the present application provides a method for determining a load scheduling strategy, including:

[0006] Obtain the equipment parameter information of the power generation equipment in the power system during the target period and the load demand information of each load in the area where the power system is located during the target period;

[0007] Based on the first scheduling prediction model, determine the scheduling output information of the power generation equipment in the future period according to the equipment parameter information and the load demand information; wherein, the first scheduling prediction model includes a first objective function and a first constraint function, the first objective function is used to describe the transaction cost of the power system participating in carbon trading, and the first constraint function is used to constrain the system steady state of the power system;

[0008] Based on the second scheduling prediction model, determine the load scheduling strategy of each load in the power system in the future period according to the electricity trading information, scheduling output information, and carbon emission task information of each load in the power system in the future period; wherein, the second scheduling prediction model includes a second objective function and a second constraint function, the second objective function is used to describe the response cost of each load to carbon emission scheduling, and the second constraint function is used to constrain the power balance of each load.

[0009] In a second aspect, the present application further provides a device for determining a load scheduling strategy, including:

[0010] An information acquisition module for acquiring the equipment parameter information of power generation equipment in the power system during the target period and the load demand information of each load in the area where the power system is located during the target period;

[0011] A scheduling determination module for determining the scheduling output information of power generation equipment in the future period based on the first scheduling prediction model according to the equipment parameter information and the load demand information; wherein, the first scheduling prediction model includes a first objective function and a first constraint function, the first objective function is used to describe the trading cost of the power system participating in carbon trading, and the first constraint function is used to constrain the system steady state of the power system;

[0012] A strategy determination module for determining the load scheduling strategy of each load in the power system in the future period based on the second scheduling prediction model according to the electricity trading information, the scheduling output information and the carbon emission task information of each load in the power system in the future period; wherein, the second scheduling prediction model includes a second objective function and a second constraint function, the second objective function is used to describe the response cost of each load to carbon emission scheduling, and the second constraint function is used to constrain the power balance of each load.

[0013] In a third aspect, the present application also provides a computer device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps involved in the first aspect are implemented.

[0014] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps involved in the first aspect are implemented.

[0015] In a fifth aspect, the present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps involved in the first aspect are implemented.

[0016] The above load scheduling strategy determination method, device, and computer device introduce a first scheduling prediction model. Since the first objective function in the first prediction model describes the trading cost of the power system participating in carbon trading, it is equivalent to tracking the carbon emission flow on the power system side. At the same time, by combining the first constraint function, the system steady state of the power system is constrained, ensuring that the first scheduling prediction model can accurately describe the process of the power system conducting carbon trading with the outside. Furthermore, by comprehensively considering the equipment parameter information and load demand information, the accuracy of the scheduling output information of the power generation equipment in the future period determined based on the first scheduling prediction model is ensured. In addition, a second scheduling prediction model is further introduced. Since the second objective function in the second prediction model describes the response cost of each load responding to carbon emission scheduling, it is equivalent to tracking the carbon emission flow on the load side. At the same time, by combining the second constraint function to constrain the power balance of each load, it is ensured that the obtained second scheduling prediction model can accurately describe the process of the load side conducting carbon trading. Further, by combining the carbon emission task information of each load, the carbon emission responsibility of the power system is transferred from the power generation side to the load side for attribution, ensuring that the load scheduling strategy of each load in the power system in the future period determined based on the second scheduling prediction model can effectively reduce carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 It is a schematic flowchart of the load scheduling strategy determination method in an embodiment;

[0019] Figure 2 It is a schematic flowchart of determining the load scheduling strategy in an embodiment;

[0020] Figure 3 It is a schematic flowchart of determining the carbon emissions of each load in the future period in an embodiment;

[0021] Figure 4 It is a schematic flowchart of obtaining the carbon emission task information of each load in an embodiment;

[0022] Figure 5 It is an improved IEEE14 node power grid coupling topology structure diagram in an embodiment;

[0023] Figure 6 It is a schematic diagram of the carbon emission quota of each LA in an embodiment;

[0024] Figure 7 Carbon potential distribution diagrams of each node at different times in an embodiment;

[0025] Figure 8A Schematic diagram of the average carbon potential magnitudes of each LA in an embodiment;

[0026] Figure 8B Schematic diagram of the load variation of LA1 before and after demand response in an embodiment;

[0027] Figure 8C Schematic diagram of the load variation of LA2 before and after demand response in an embodiment;

[0028] Figure 8D Schematic diagram of the load variation of LA3 before and after demand response in an embodiment;

[0029] Figure 9 Schematic flow diagram of a method for determining a load scheduling strategy in another embodiment;

[0030] Figure 10 Schematic block diagram of a device for determining a load scheduling strategy in an embodiment;

[0031] Figure 11 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0032] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0033] The load scheduling strategy determination method provided by the embodiments of the present application can be applied to an application environment as shown in Figure 1 . The load scheduling strategy determination method provided by the embodiments of the present application can be executed by a computer device, and the computer device can be a server or a terminal with powerful computing capabilities.

[0034] In an exemplary embodiment, as shown in Figure 1 , a load scheduling strategy determination method is provided. Taking the application of this method to a server as an example, the method specifically includes the following steps:

[0035] S101, obtain the device parameter information of the power generation equipment in the power system during the target period and the load demand information of each load in the area to which the power system belongs during the target period.

[0036] Among them, the target time period can be any preset time period, specifically, it can be any time period within the current time period or any time period within the historical time period. The device parameter information characterizes the operating conditions of the power generation devices in the power system. The load demand information characterizes the load demand conditions of all loads in the area to which the power system belongs during the target time period; in the embodiments of the present application, the loads in the area to which the power system belongs include, but are not limited to, electric vehicles, curtailable loads (CL), and transferable loads (TL).

[0037] Optionally, the device parameter information of the power generation devices in the power system during the target time period and the load demand information of each load in the area to which the power system belongs during the target time period can be obtained through the official platform of the power system.

[0038] S102. Based on the first scheduling prediction model, determine the scheduling output information of the power generation devices in the future time period according to the device parameter information and the load demand information.

[0039] Among them, the first scheduling prediction model is used to predict the scheduling output of the power generation devices; in the embodiments of the present application, the first scheduling prediction model includes a first objective function and a first constraint function. The first objective function is used to describe the trading cost of the power system participating in carbon trading, and the first constraint function is used to constrain the system steady state of the power system.

[0040] In the embodiments of the present application, the first scheduling prediction model can be regarded as an upper-layer model, which describes the process of the grid operator of the power system trading with the external carbon market. In this process, with the goal of minimizing the trading cost of the power system participating in carbon trading, the output plans of each unit of the power system are adjusted. Among them, the first objective function includes, but is not limited to, the thermal power coal consumption cost function, the wind power generation cost function, and the power system carbon trading cost function. The first constraint function includes the thermal power unit constraint function, the wind power unit constraint function, the transmission capacity constraint function, and the system power balance constraint function. The thermal power coal consumption cost function describes the coal consumption cost of the thermal power generation equipment in the power system for power generation; the wind power generation cost function describes the cost of the wind turbine for power generation; the power system carbon trading cost function describes the cost generated by the load aggregator to which the power system belongs trading carbon with the external carbon market.

[0041] Optionally, the first objective function can be represented by the sum of the thermal power coal consumption cost function, the wind power generation cost function, and the power system carbon trading cost function. Specifically, the first objective function can be represented by the following formula (1):

[0042] (1)

[0043] (2)

[0044] (3)

[0045] (4)

[0046] Among them, is the first objective function; is the thermal power coal consumption cost function; is the wind power generation cost function; is the power system carbon trading cost function; is the total duration of participation in response; is the number of wind power scenarios; is the number of generator units; , and is the coal consumption cost coefficient of the nth thermal power unit; is the nth thermal power unit output at time t; is the number of wind turbine units; is the wind power generation cost coefficient; is the mth wind turbine unit actual output at time t; is the probability of the mth wind power scenario; is the wind power curtailment penalty coefficient; is the mth wind power scenario mth wind turbine unit predicted output at time t; and are the carbon emission coefficient and the initial carbon quota coefficient corresponding to the unit power generation of the generator unit respectively; is the time variation.

[0047] Furthermore, the first constraint function includes a thermal power unit constraint function, a wind turbine unit constraint function, a transmission capacity constraint function, and a system power balance constraint function. Among them, the thermal power unit constraint function includes a thermal power unit output constraint function, a thermal power unit ramping constraint function, and a thermal power unit start-stop constraint. The thermal power unit output constraint function can be expressed by the following formula (5):

[0048] (5)

[0049] Among them, and are the minimum and maximum outputs of the nth thermal power unit respectively; is the Output of a thermal power unit.

[0050] The ramp rate constraint function of a thermal power unit can be expressed by the following formulas (6)-(7):

[0051] (6)

[0052] (7)

[0053] Where, is the output of the th thermal power unit at time; and are the maximum ramp-up rate and maximum ramp-down rate of the th thermal power unit respectively.

[0054] The start-stop constraint of a thermal power unit can be expressed by the following formulas (8)-(9):

[0055] (8)

[0056] (9)

[0057] Where, and are the running time and shutdown time of the th thermal power unit at time respectively; and are the shortest running time and shortest shutdown time of the th thermal power unit respectively; and are the start-stop status of the th thermal power unit at time and time respectively.

[0058] Furthermore, the wind turbine constraint function can be expressed by the following formula (10):

[0059] (10)

[0060] Where, is the maximum output of the th wind turbine; is the output of the th wind turbine at time.

[0061] The transmission capacity constraint function can be expressed by the following formula (11):

[0062] (11)

[0063] Among them, is the active power flow of the line at time . and are respectively the lower and upper limits of the transmission power between lines .

[0064] The system power balance constraint function includes the slack node constraint function, the node power balance constraint function, and the line power flow equality constraint. Among them, the slack node constraint function can be expressed by the following formula (12):

[0065] (12)

[0066] Among them, is the voltage phase angle of the slack node at time

[0067] The node power balance constraint function can be expressed by the following formula (13):

[0068] (13)

[0069] Among them, is the set of thermal power units at node ; is the set of wind power units at node ; is the set of lines connected to node ; is the power load of node at time

[0070] The line power flow equality constraint can be expressed by the following formulas (14)-(15):

[0071] (14)

[0072] (15)

[0073] Among them, is the reactance of line ; and are respectively the voltage phase angles of node and node at time

[0074] Therefore, in the embodiments of the present application, the first objective function and the first constraint function can be updated according to the device parameter information and the load demand information to obtain the updated first objective function and the updated first constraint function. Taking the minimum function value of the updated first objective function as the objective and the updated first constraint function as the constraint condition, the updated first objective function is solved to obtain the scheduling output information of the power generation equipment in the power system in the future time period. That is, the device parameter information and the load demand information can be substituted into the above formulas (1)-(15) to update the above formulas (1)-(15). Further, taking the updated formula (1) as the objective function and the updated formulas (1)-(15) as the constraint conditions, the objective function is solved to obtain the scheduling output information of the power generation equipment in the power system in the future time period.

[0075] S103. Based on the second scheduling prediction model, according to the electricity trading information, the scheduling output information, and the carbon emission task information of each load in the power system in the future time period, determine the load scheduling strategy of each load in the power system in the future time period.

[0076] Among them, the second scheduling prediction model includes a second objective function and a second constraint function. The second objective function is used to describe the response cost of each load to the carbon emission scheduling, and the second constraint function is used to constrain the power balance of each load. The carbon emission task information of the load characterizes the carbon emission responsibility borne by the load in the process of responding to the carbon emission scheduling.

[0077] Optionally, in the embodiments of the present application, the second scheduling prediction model is used to describe that the load responds to the node carbon potential signal of the first scheduling prediction model and adjusts the flexible load power consumption strategy to reduce the carbon emissions and the total cost of the load aggregator. In addition, the second objective function includes a power purchase cost function, a load carbon trading cost function, a demand response reward cost function, and an electric vehicle discharge reward cost function; the second constraint function includes a power balance constraint function, an electric vehicle charge and discharge constraint function, and an electric vehicle battery power constraint function.

[0078] It should be noted that based on the user's power consumption demand, the load aggregator guides the user to participate in the low-carbon response through price incentives, and the load aggregator gives the user a certain degree of economic subsidy, including the cost of purchasing electricity from the superior (i.e., the power purchase cost), the carbon trading cost (i.e., the load carbon trading cost), the TL demand response subsidy cost (i.e., the demand response reward cost), and the electric vehicle discharge subsidy cost (i.e., the electric vehicle discharge reward cost).

[0079] Optionally, the second objective function can be expressed by the following formula (16):

[0080] (16)

[0081] (17)

[0082] (18)

[0083] (19)

[0084] Wherein, is the second objective function; is the electricity purchase cost function; is the load carbon trading cost function; is the demand response reward cost function; is the electric vehicle discharging reward cost function; is the electricity purchase power of the load aggregator from the grid operator at time is the time-of-use electricity purchase price; is the unit CL compensation coefficient; and are respectively the transfer-in compensation coefficient and the transfer-out compensation coefficient of unit TL; is the electric vehicle discharging subsidy coefficient; and are respectively the TL transfer-in power and the TL transfer-out power.

[0085] In addition, a step carbon price interval method is adopted to construct the load carbon trading cost function. Specifically, the step interval can be divided into , , and , corresponding to 4 carbon price intervals of free, low price, medium price and high price. On this basis, the step carbon price emission cost can be expressed by the following formula (20):

[0086] (20)

[0087] Wherein, is the step carbon price emission cost; is the carbon trading benchmark price, and are both carbon trading step electricity prices, with the unit of yuan / t; is the total carbon emission of the load node per unit time.

[0088] Furthermore, the load carbon trading cost function is calculated according to the step carbon price emission cost, which can be specifically expressed by the following formula (21):

[0089] (21)

[0090] Furthermore, the second constraint function includes a power balance constraint function, an electric vehicle charging and discharging constraint function, and an electric vehicle battery power constraint function. Among them, the power balance constraint function can be expressed by the following formula (22):

[0091] (22)

[0092] Among them, is the load demand power.

[0093] The electric vehicle charging and discharging constraint function can be expressed by the following formulas (23)-(25):

[0094] (23)

[0095] (24)

[0096] (25)

[0097] Among them, and are the charging state and discharging state of the rd electric vehicle at the th moment respectively; when the electric vehicle is charging, is 1, and when the electric vehicle is discharging, is 1; and are the maximum charging power and maximum discharging power of the electric vehicle respectively; and are the charging power and discharging power of the th electric vehicle at the th moment respectively.

[0098] The electric vehicle battery power constraint function can be expressed by the following formulas (26)-(28):

[0099] (26)

[0100] (27)

[0101] (28)

[0102] Among them, and are the initial arrival electric energy and target electric energy of the th electric vehicle respectively; and are the access time and departure time of the electric vehicle respectively; and are the charging efficiency and discharging efficiency of the electric vehicle respectively; and are respectively the th electric vehicle's SOC at time and time ; is the rated capacity of the electric vehicle's battery; and are respectively the upper limit value and the lower limit value of the electric vehicle's battery SOC.

[0103] On this basis, according to the electricity trading information, dispatching output information of the power system and the carbon emission task information of each load in the future period, the second objective function and the second constraint function can be updated to obtain the updated second objective function and the updated second constraint function; aiming at minimizing the function value of the updated second objective function and taking the updated second constraint function as the constraint condition, the updated second objective function is solved to obtain the load dispatching strategy of each load in the power system in the future period.

[0104] It should be noted that in order to ensure the accuracy of the load dispatching strategy obtained by solving based on the first dispatching prediction model and the second dispatching prediction model, after obtaining the load dispatching strategy through the second dispatching prediction model, it is also possible to verify whether the obtained load dispatching strategy meets the convergence condition. If it does not meet, the load dispatching strategy needs to be used as the new load demand information and return to execute the step of determining the dispatching output information of the power generation equipment in the future period based on the first dispatching prediction model according to the equipment parameter information and the load demand information until the obtained load dispatching strategy meets the convergence condition. Therefore, according to the load dispatching strategy and the load demand information, it can be determined whether the load dispatching strategy meets the load demand information; if it does not meet, the load dispatching strategy is used as the new load demand information and return to execute the operation of determining the dispatching output information of the power generation equipment in the future period based on the first dispatching prediction model according to the equipment parameter information and the load demand information. In the embodiments of the present application, since the load dispatching strategy also reflects the load demand situation of each load, the convergence condition can be: whether the error between the load dispatching strategy and the load demand information of each load is less than a preset threshold. If it is less, it means that the load dispatching strategy meets the convergence condition.

[0105] In the above method for determining the load scheduling strategy, by introducing the first scheduling prediction model, since the first objective function in the first prediction model describes the trading cost of the power system participating in carbon trading, it is equivalent to tracking the carbon emission flow on the power system side. At the same time, by combining the first constraint function, the system steady state of the power system is constrained, ensuring that the first scheduling prediction model can accurately describe the process of the power system conducting carbon trading with the outside; and further comprehensively considering the equipment parameter information and load demand information, ensuring the accuracy of the scheduling output information of the power generation equipment in the future period determined based on the first scheduling prediction model; in addition, the second scheduling prediction model is further introduced. Since the second objective function in the second prediction model describes the response cost of each load responding to carbon emission scheduling, it is equivalent to tracking the carbon emission flow on the load side. At the same time, by combining the second constraint function to constrain the power balance of each load, ensuring that the obtained second scheduling prediction model can accurately describe the process of carbon trading on the load side, and further combining the carbon emission task information of each load, the carbon emission responsibility of the power system is transferred from the power generation side to the load side for attribution, ensuring that the load scheduling strategy of each load in the power system in the future period determined based on the second scheduling prediction model can effectively reduce carbon emissions.

[0106] Optionally, in an exemplary embodiment, as Figure 2 shown, a method for determining the load scheduling strategy of each load in the power system in the future period is provided to refine the above S103, which specifically includes the following steps:

[0107] S201. Determine the carbon emissions of each load in the future period according to the scheduling output information of the power generation equipment in the power system.

[0108] Optionally, based on a pre-set calculation method for carbon emissions, combined with the scheduling output information of the power generation equipment in the power system, the carbon emissions of each load in the future period can be calculated.

[0109] S202. Update the second objective function and the second constraint function according to the electricity trading information, the carbon emissions of each load, and the carbon emission task information of the power system in the future period, to obtain the updated second objective function and the updated second constraint function.

[0110] Optionally, the electricity trading information, the carbon emissions of each load, and the carbon emission task information of the power system in the future period can be substituted into the second objective function and the second constraint function to update the second objective function and the second constraint function, to obtain the updated second objective function and the updated second constraint function.

[0111] S203. Taking the minimum function value of the updated second objective function as the goal and using the updated second constraint function as the constraint condition, solve the updated second objective function to obtain the load scheduling strategy of each load in the power system for future time periods.

[0112] Optionally, a preset optimization algorithm can be used. Taking the minimum function value of the updated second objective function as the goal and using the updated second constraint function as the constraint condition, iteratively solve the updated second objective function to obtain the load scheduling strategy of each load in the power system for future time periods.

[0113] In this embodiment, by introducing the carbon emission, it is equivalent to comprehensively considering the carbon emission flow on the load side. Combining the carbon emissions of each load, update the second objective function and the second constraint function, ensuring that taking the minimum function value of the updated second objective function as the goal and using the updated second constraint function as the constraint condition, the accuracy of the load scheduling strategy of each load in the power system obtained by solving the updated second objective function for future time periods.

[0114] Optionally, in one embodiment, as Figure 3 shown, a method for determining the carbon emissions of each load in future time periods is provided to refine S201 in the above embodiment, which specifically includes the following steps:

[0115] S301. According to the dispatching output information of the power generation equipment in the power system, determine the carbon flow rate and active power flow flowing into each load in the future time period.

[0116] Among them, the carbon flow rate represents the carbon emissions corresponding to the energy flow passing through the network node or branch per unit time. The active power flow represents the sum of the active power flows flowing into the load.

[0117] Optionally, the active power flow can be calculated by the sum of the active powers of each branch flowing into the node where the load is located and the output of the power generation equipment connected to the load. Among them, the sum of the active powers of each branch flowing into the node where the load is located and the output of the power generation equipment connected to the load can be determined according to the dispatching output information of the power generation equipment in the power system. Specifically, the active power can be represented by the following formula (29):

[0118] (29)

[0119] Among them, represents the active power flow flowing into the node where the load is located; is the active power of branch ; represents the set of branches where the active power flow flows into the node where the load is located; is the output of the generator connected.

[0120] In addition, the carbon flow rate can be expressed by the following formula (30):

[0121] (30)

[0122] wherein, is the carbon emission inflow; represents time. The carbon emission inflow can be calculated from the dispatching output information of the power generation equipment in the power system.

[0123] S302. For any load, determine the carbon flow density of the load in the future period according to the ratio between the carbon flow rate flowing into the load and the active power flow in the future period.

[0124] Among them, the carbon flow density represents the carbon emission per unit of electricity, including three concepts: the carbon emission intensity of the generator, the branch carbon flow density, and the node carbon potential, all in units of t / (MWh). The carbon emission intensity of the generator represents the real-time carbon emission intensity of the power plant based on its power generation characteristics.

[0125] Optionally, the carbon flow density can be expressed by the ratio between the carbon flow rate flowing into the load and the active power flow in the future period. Specifically, it can be expressed by the following formula (31):

[0126] (31)

[0127] wherein, represents the carbon flow density.

[0128] S303. Determine the carbon potential of the load in the power system in the future period according to the carbon flow density of the load in the future period and the dispatching output information of the power generation equipment in the power system.

[0129] Among them, the carbon potential represents the equivalent carbon emission generated on the power generation side when the load at the node consumes unit electricity.

[0130] Optionally, the carbon potential is equal to the weighted average of the carbon flow densities of all branches flowing into the node with respect to the active power flow. Specifically, the carbon potential can be expressed by the following formula (32):

[0131] (32)

[0132] wherein, is the carbon potential of the node where the load is located ; is the carbon emission intensity of the power generation equipment , which can be calculated from the dispatching output information of the power generation equipment; is the branch The injection power can be calculated from the dispatching output information of the power generation equipment; is the node where the power generation equipment The generated power can be calculated from the dispatching output information of the power generation equipment. is the carbon flow density of the injection node; is the number of injection power branches connected to this node.

[0133] S304. Determine the carbon emissions of the load in the future period according to the product of the carbon potential and the load amount of the load in the future period.

[0134] Optionally, the process of determining the carbon emissions of the load in the future period according to the product of the carbon potential and the load amount of the load in the future period can be expressed by the following formula (33):

[0135] (33)

[0136] where represents the carbon emissions of the node where the load is located and is the load amount.

[0137] In this embodiment, by introducing the carbon flow rate and active power flow of each load and obtaining the carbon potential of the load in the power system in the future period, the influence of the load dispatching on the carbon emissions on the power system side is comprehensively considered, ensuring the accuracy of the determined carbon emissions of the load in the future period.

[0138] It should be noted that, in order to accurately track the carbon flow, the calculation methods of the carbon emissions of different types of loads are different. In the embodiments of the present application, flexible loads can also be guided to respond to carbon emission dispatching through incentive contracts. In the embodiments of the present application, a flexible load carbon emission model based on the carbon potential of the load node can be constructed to obtain the carbon emissions of the flexible load after demand response. In the embodiments of the present application, the flexible loads are divided into electric vehicles, curtailable loads, and shiftable loads. The flexible load carbon emission model guided by the node carbon potential can be as follows.

[0139] After the electric vehicle signs an agreement with the load aggregator, the electric vehicle assigns the right of charge and discharge to the load aggregator, and the load aggregator can freely dispatch the authorized electric vehicles within the agreement time and limiting conditions. In the embodiments of the present application, the Monte Carlo method is used to simulate the uncertainties of the daily driving mileage, access time, departure time, initial access power, and target power of the electric vehicle. The carbon emissions of the electric vehicle are the carbon emissions generated by the electric vehicle charging at time t minus the carbon emissions reduced by discharging, which can be specifically expressed by the following formulas (34)-(35):

[0140] (34)

[0141] (35)

[0142] Among them, is the sum of the charging and discharging amounts of all connected electric vehicles at time is the node carbon potential of node ; is the number of electric vehicles; and are respectively the charging power and discharging power of the th electric vehicle at time is the carbon emission of the electric vehicle at time

[0143] The incentive contract stipulates the load response amount, load response compensation cost, and response time length of the load that users can curtail and transfer. The load that can be curtailed refers to the load that cannot be transferred but can be curtailed by a certain proportion within a certain period. The operation period of the load that can be transferred is relatively flexible. Under the condition of ensuring that its cumulative operation duration remains unchanged, interruption is allowed and the interruption duration is not fixed, and the total amount of load transferred in and out remains unchanged.

[0144] The carbon emissions after demand response of the load that can be curtailed and the load that can be transferred are:

[0145] (36)

[0146] (37)

[0147] Among them, and are respectively the power transferred in and out of the load that can be transferred at time is the amount of load curtailed at time is the carbon potential of node after participating in demand response at time is the 0-1 state variable of the load that can be curtailed at time is the upper limit value of the load that can be curtailed; and are respectively the start time and end time of the load curtailment response; is the upper limit of the load curtailment response period; and are respectively The transferable load can enter the response state and exit the response state, which are represented by 0-1; and are the upper limit of the transfer-in power and the upper limit of the transfer-out power of the transferable load respectively; and are the start time and the end time of the transfer-in of the transferable load respectively; and are the start time and the end time of the transfer-out of the transferable load respectively.

[0148] Combining the above formula (37) and formula (36), the actual carbon emissions of the load aggregator can be obtained, that is, the sum of the initial load, the transferable load, the response amount of the curtailment load and the carbon emissions generated by the charging and discharging of electric vehicles, which is:

[0149] (38)

[0150] Among them, is the initial load at time

[0151] According to the theory of consumer psychology, in order to change the user's charging load and habitual power consumption mode, price-based demand response formulates a reasonable electricity price to guide the user load distribution. Taking the electricity load as an example, through the demand elasticity matrix represents the impact of electricity price changes on the electricity load, which is specifically as follows:

[0152] (39)

[0153] (40)

[0154] (41)

[0155] Among them, the electricity load and the user's electricity value the values at time and are from time to time and are represented by the change amounts respectively, and the ratio between the two is the load change rate and the electricity price change rate at time and are represented respectively; the demand elasticity matrix the main diagonal is the self-elasticity coefficient, and the sub-diagonal is the cross-elasticity coefficient. In the embodiments of the present application, they are respectively taken as -0.2 and 0.033; the total load remains unchanged before and after the demand response, is the load prediction value at time is the pre - load prediction value before demand response; is the entire scheduling period.

[0156] Optionally, in one embodiment, as Figure 4 shown, a method for obtaining carbon emission task information of each load in a power system is provided, which specifically includes the following steps:

[0157] S401. Determine the participation probability of each load in responding to the carbon emission scheduling of the power system according to the number of loads in the power system.

[0158] It should be noted that the carbon emission flow theory traces the carbon responsibility on the source side to the load power consumption side for attribution. However, due to the influence of problems such as the differences in the positions of each node in the power system, the power consumption levels, and grid congestion, the carbon emission responsibilities corresponding to each node are different, and the carbon emission responsibility costs borne are also different. In order to enable the load - side users to fairly and reasonably share the carbon emission responsibilities of each load node and solve the deficiencies of the traditional stepped carbon price calculation method with a fixed interval, in the embodiments of the present application, the Shapley value allocation method is adopted.

[0159] Optionally, in the embodiments of the present application, after different loads participate in responding to the carbon emission scheduling of the power system, they form a sub - coalition. The participation probability of a load in responding to the carbon emission scheduling of the power system can be expressed by the following formula (42):

[0160] (42)

[0161] Where, is the participation probability of the load in responding to the carbon emission scheduling of the power system; is the number of all loads; is the number of loads that respond to the carbon emission scheduling of the power system.

[0162] S402. Determine the carbon emission task information of each load according to the participation probability corresponding to each load.

[0163] In the embodiments of the present application, the carbon emission task information of each load includes the carbon emission responsibility of the load.

[0164] Optionally, for the problem of how to allocate the carbon emission responsibility on the load side, the Shapley value sharing method is adopted, which considers the marginal effect of each sub - coalition on the overall coalition. According to the Shapley value allocation principle, the carbon emission responsibilities of each sub - coalition are calculated from the average value of their marginal effects. Specifically, the process of determining the carbon emission task information of each load according to the participation probability corresponding to each load can be expressed by the following formula (43):

[0165] (43)

[0166] Among them, represents the carbon emission responsibility corresponding to the load after sharing; is a sub - coalition composed of other loads that are loads not included; is the new coalition formed by incorporating the load into the sub - coalition ; is the marginal impact generated when the load joins the sub - coalition . represents the marginal impact generated when the load joins the sub - coalition .

[0167] It should be noted that the carbon emission responsibilities borne by the members of the sub - coalition should be within a certain range, not greater than the maximum value of the marginal effects of the members of the sub - coalition , nor less than the minimum value of its marginal effects . That is to say:

[0168] (44)

[0169] In this embodiment, combining the participation probabilities of each load in the carbon emission scheduling of the power system is equivalent to considering the contributions of each load's participation in carbon emission scheduling to the power system, and combining the participation probabilities to reasonably allocate the carbon responsibilities of each load node. That is to say, it ensures the accuracy of the carbon emission task information of each determined load.

[0170] In one embodiment, to more intuitively describe the load scheduling strategy determination method provided in this application, an improved Institute of Electrical and Electronics Engineers (IEEE) 14 - node power grid coupled with typical residential, industrial, and commercial load aggregators (Load Aggregator, LA) is used for analysis, and its topological structure is as Figure 5 shown. In the figure: Industrial LA1, residential LA2, and commercial LA3 are divided to include different regional nodes (1 - 14). Set units G1, G2, and G4 as thermal power units. Among them, industrial LA1 is coupled with residential LA2 and commercial LA3 respectively, and there is also a coupling relationship between residential LA2 and commercial LA3. The relevant parameters are shown in Table 1, and the time - of - use electricity purchase prices are shown in Table 2.

[0171] Table 1 Relevant parameters of thermal power units

[0172]

[0173] Table 2 Time - of - use electricity purchase prices

[0174]

[0175] The unit G3 is a wind power generating unit, and the unit G5 is a hydraulic power generating unit. The Monte Carlo method generates 1000 wind power scenarios and 4 scenarios after curtailment. The probabilities of each scenario are: 0.281, 0.239, 0.221, 0.259; the cost coefficient of wind power generation is 60 yuan / MW, the penalty coefficient for wind power curtailment is 250 yuan / MW, and the carbon emission coefficient of wind power is 0.043 t / MW. Set the carbon emission quota coefficient per unit of electricity purchased by LA to 0.728 t / MW, the carbon trading benchmark price to 290 yuan / t, and the price growth coefficient to 0.25. Assume that all electric vehicles are of the same model, and the relevant parameters of the electric vehicles are shown in Table 3. Set the number of electric vehicles in each LA to 1000, 1500, and 600 respectively. The TL and CL incentive contract parameters are shown in Tables 4 and 5. Set the scheduling period to 24 h, Δt = 1 h, and use MATLAB to call Cplex for solution.

[0176] Table 3 Relevant parameters of electric vehicles

[0177]

[0178] Table 4 TL incentive contract parameters

[0179]

[0180] Table 5 CL incentive contract parameters

[0181]

[0182] On this basis, to verify the effectiveness of the load scheduling strategy determination method provided by this application, analyze the situation where the node carbon potential and time-of-use electricity price jointly guide the flexible loads of each LA to perform demand response. The following 5 scenarios are set up:

[0183] Scenario 1: Considering the fixed electricity price, the load does not participate in the optimal scheduling, and carbon trading is not considered.

[0184] Scenario 2: Considering the two-layer optimal scheduling strategy of guiding the flexible load adjustment by the time-of-use electricity price, and carbon trading is not considered.

[0185] Scenario 3: Considering the time-of-use electricity price and stepped carbon trading, and the flexible load does not participate in the optimal scheduling.

[0186] Scenario 4: Considering the two-layer optimal scheduling strategy of guiding the flexible load adjustment by the fixed electricity price, stepped carbon trading and node carbon potential.

[0187] Scenario 5: Considering the two-layer optimal scheduling strategy of guiding the flexible load adjustment by the time-of-use electricity price, stepped carbon trading and node carbon potential.

[0188] Using the Shapley value method to calculate the carbon emissions of each LA. It can be seen from the improved IEEE 14-node system topology diagram that there are 3 LAs in the system, which are divided into regional LA1, LA2, and LA3 respectively. Therefore, N = {LA1, LA2, LA3}, and there are 6 non-empty sub-coalitions in the grand coalition, namely {LA1}, {LA2}, {LA3}, {LA1, LA2}, {LA1, LA3}, and {LA2, LA3}. Taking Scenario 5, the two-layer optimal scheduling strategy considering time-of-use electricity price, ladder-type carbon trading, and node carbon potential to guide flexible load adjustment as an example, at t = 1, the carbon emission results of each sub-coalition are shown in Table 6.

[0189] Table 6 Comparison of carbon emissions and costs of each LA under different scenarios

[0190]

[0191] Taking the sub-coalition LA1 as an example, when t = 1 h, the carbon emissions of the sub-coalition {LA1} consist of four parts, namely {LA1}, {LA1, LA2} - {LA2}, {LA1, LA3} - {LA3} and {LA1, LA2, LA3} - {LA1, LA2}. Among them, the minimum value is 175.9, and the maximum value is 240.7. According to formula (35), it can be calculated that . According to formula (20), when t = 1 h, the carbon emission quota intervals of LA1 are [0, 175.9], [175.9, 219.5], [219.5, 240.7], and [240.7, ∞]. The carbon emission quotas of each LA for 24 h are as shown in Figure 6 ; among them Figure 6 (a) is the carbon emission quota of LA1, Figure 6 (b) is the carbon emission quota of LA2, Figure 6 (c) is the carbon emission quota of LA3.

[0192] Furthermore, the carbon emissions and costs of each LA under five scenarios are compared in Tables 7 - 9. When time - of - use electricity price or nodal carbon potential - guided demand response is not considered in Scenarios 1 and 3, the carbon emissions are the highest, and the total cost of Scenario 3 is the highest. In Scenario 2, the time - of - use electricity price is used to guide flexible loads for demand response. In Scenario 4, the nodal carbon potential is used to guide the load side for demand response. After the response in both scenarios, the carbon emissions are reduced. Scenario 5 combines Scenario 2 and Scenario 4, and simultaneously considers the time - of - use electricity price and nodal carbon potential to guide the LA for demand response. After the response, compared with Scenario 1, the electricity purchase cost of the LA decreases, and the total cost increases. The main increase is the carbon trading cost. The carbon emissions of each LA are reduced by 58.87, 37.69, and 23.69 t respectively, taking into account both the low - carbon and economic characteristics of the LA.

[0193] Table 7 Carbon Emissions and Costs of LA1 under Different Scenarios

[0194]

[0195] Table 8 Carbon Emissions and Costs of LA2 under Different Scenarios

[0196]

[0197] Table 9 Carbon Emissions and Costs of LA3 under Different Scenarios

[0198]

[0199] In addition, after incorporating carbon trading into Scenario 5 based on Scenario 2, under the influence of time - of - use electricity price and nodal carbon potential, the carbon trading cost and electricity purchase cost of each LA decrease, and the carbon emissions are significantly reduced, by 20.81, 5.73, and 0.3 t respectively. This shows that the time - of - use electricity price and nodal carbon potential used in this paper to guide flexible loads can effectively improve the low - carbon and economic characteristics of the system.

[0200] By comparing Scenario 4 and Scenario 5, it can be clearly seen that compared with the fixed electricity price, after considering the time - of - use electricity price in Scenario 5, the response degree of flexible loads participating in demand response is improved. The carbon emissions of each LA are reduced by 22.93, 5.95, and 3.06 t respectively compared with Scenario 4, and the carbon trading cost also decreases accordingly. However, due to the limitations of the EV access time and departure time, in order to meet the charging demand of electric vehicles, charging may occur during the peak period of the time - of - use electricity price, resulting in an increase in the electricity purchase cost of Scenario 5 compared with Scenario 4.

[0201] Therefore, the load scheduling strategy determination method proposed in this application reduces the dependence on the source side by increasing the response volume of flexible loads, and reduces more carbon emissions, thus improving the low - carbon characteristics of the system.

[0202] Furthermore, the nodal carbon potential can reflect the carbon emission levels of each node in the power system. Figure 7Carbon potential distribution diagrams of each node at different times. Node 1 is connected to a thermal power unit, and the carbon potential is at a relatively high level; for Nodes 2 and 6, the carbon potentials are both higher than the carbon emission factors of the generators at these nodes because the unit at Node 1 injects power into Nodes 2 and 6 through the line; Node 8 is a clean energy unit and there is no injection from other units, so the carbon potential is 0. The higher the carbon potential of a node, the more electric energy flows into it from coal-fired units and the less from clean energy units in the power source. Therefore, guiding electric vehicles to charge at nodes with lower carbon potential in the area is beneficial to achieving system carbon emission reduction. The carbon emissions generated by the basic load of each node are mainly concentrated in Nodes 2 to 4 because these nodes are relatively close to Unit 1, which has the largest power generation capacity and the highest carbon emission factor among the units. So, the carbon emissions from the unit account for a large proportion. The higher the carbon potential and the greater the carbon emission of a node, the greater the carbon flow rate of the branch near Unit 1.

[0203] Figure 8A This is the average carbon potential of each LA. It can be seen that the carbon potential trends are basically the same, and there is a sudden increase in carbon potential during the period from 16:00 to 23:00. This is because this time period is the peak load period, and the clean energy output of LA equipment decreases, mainly from thermal power units. The carbon emission intensity of the units is much greater than that of gas-fired units and the carbon emission intensity of clean energy output. Therefore, the carbon potential increases during this period. The average carbon potential of LA1 is greater than that of LA2 and LA3, which is also a phenomenon caused by the relatively high load carbon emissions in LA1, an industrial area. Therefore, the carbon emission flow model can accurately reflect the carbon emission situation of the system and trace the carbon emission footprint. The load changes of LA1, LA2, and LA3 before and after demand response are shown in Figure 8B 、 8C and 8D respectively. The load before response includes the original load curve and the unordered charging load of electric vehicles. Since no optimized scheduling was carried out, electric vehicles started charging immediately after being connected, forming an obvious load peak, and the peak-to-valley difference of the original load was large. After LA conducts incentive scheduling on flexible loads, electric vehicles perform orderly charging and discharging scheduling, load reduction and transfer, shifting the load during the peak electricity consumption period to the low electricity consumption period, reducing the electricity consumption pressure during the peak load period.

[0204] To further analyze the impact of the participation ratio of electric vehicles in demand response on the carbon emissions and costs of LA, the response ratios are set to 1.0, 0.8, 0.6, and 0.4 respectively, and the remaining vehicles that do not participate in the response are for unordered charging. The carbon emissions and costs of each LA are shown in the table respectively.

[0205] As can be seen from Tables 10 to 12, as the response ratio of electric vehicles increases, the number of electric vehicles participating in discharging increases, the discharging subsidy cost increases, the overall carbon emissions of LA2 show an upward trend, and the carbon trading cost also increases accordingly, while the electricity purchase cost decreases slightly with the increase in the ratio. Therefore, appropriately increasing the response ratio of electric vehicles is beneficial to improving the economy and low-carbon performance of LA.

[0206] Table 10 Changes in LA1 Carbon Emissions and Costs under Different Response Ratios of Electric Vehicles

[0207]

[0208] Table 11 Changes in LA2 Carbon Emissions and Costs under Different Response Ratios of Electric Vehicles

[0209]

[0210] Table 12 Changes in LA3 Carbon Emissions and Costs under Different Response Ratios of Electric Vehicles

[0211]

[0212] Figure 9 It is a schematic flow chart of a method for determining a load scheduling strategy in another embodiment. On the basis of the above embodiment, this embodiment provides an alternative example of a method for determining a load scheduling strategy. Combining Figure 9 , the specific implementation process is as follows:

[0213] S901. Obtain the equipment parameter information of the power generation equipment in the power system during the target period and the load demand information of each load in the area to which the power system belongs during the target period.

[0214] S902. Update the first objective function and the first constraint function according to the equipment parameter information and the load demand information, and obtain the updated first objective function and the updated first constraint function.

[0215] Among them, the first objective function includes a thermal power coal consumption cost function, a wind power generation cost function, and a power system carbon trading cost function, and the first constraint function includes a thermal power unit constraint function, a wind power unit constraint function, a transmission capacity constraint function, and a system power balance constraint function.

[0216] S903. With the goal of minimizing the function value of the updated first objective function and using the updated first constraint function as the constraint condition, solve the updated first objective function to obtain the scheduling output information of the power generation equipment in the power system during the future period.

[0217] S904. Determine the carbon flow rate and active power flow into each load during the future period according to the scheduling output information of the power generation equipment in the power system.

[0218] S905. Determine the carbon flow density of each load during the future period according to the ratio between the carbon flow rate and the active power flow into each load during the future period.

[0219] S906. Determine the carbon potential of each load in the power system during a future period based on the carbon flow density of each load during the future period and the dispatching output information of the power generation equipment in the power system.

[0220] S907. Determine the carbon emissions of each load during the future period based on the product of the carbon potential and the load amount of each load during the future period.

[0221] S908. Update the second objective function and the second constraint function according to the electricity trading information of the power system during the future period, the carbon emissions of each load, and the carbon emission task information, to obtain the updated second objective function and the updated second constraint function.

[0222] Among them, the second objective function includes a power purchase cost function, a load carbon trading cost function, a demand response reward cost function, and an electric vehicle discharging reward cost function; the second constraint function includes a power balance constraint function, an electric vehicle charging and discharging constraint function, and an electric vehicle battery power constraint function.

[0223] Optionally, determine the participation probability of each load in responding to the carbon emission dispatching of the power system according to the number of loads in the power system; determine the carbon emission task information of each load according to the corresponding participation probability of each load.

[0224] S909. With the goal of minimizing the function value of the updated second objective function and taking the updated second constraint function as the constraint condition, solve the updated second objective function to obtain the load dispatching strategy of each load in the power system during the future period.

[0225] The specific processes of the above S901 - S909 can refer to the descriptions of the above method embodiments, and their implementation principles and technical effects are similar, so they will not be elaborated here.

[0226] It should be understood that although the steps in the flowcharts involved in the above - mentioned embodiments are shown in sequence according to the arrows, these steps do not necessarily need to be executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above - mentioned embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily need to be executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential either, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0227] Based on the same inventive concept, an embodiment of the present application further provides a load scheduling strategy determination device for implementing the load scheduling strategy determination method involved above. The implementation solutions provided by this device to solve problems are similar to those described in the above method. Therefore, the specific limitations in one or more embodiments of the load scheduling strategy determination device provided below can refer to the limitations on the load scheduling strategy determination method in the above text, and will not be repeated here.

[0228] In an exemplary embodiment, as Figure 10 shown, a load scheduling strategy determination device 1000 is provided, including: an information acquisition module 1010, a scheduling determination module 1020, and a strategy determination module 1030, where:

[0229] The information acquisition module 1010 is configured to acquire the device parameter information of the power generation equipment in the power system during the target period and the load demand information of each load in the area to which the power system belongs during the target period.

[0230] The scheduling determination module 1020 is configured to determine the scheduling output information of the power generation equipment in the future period based on the first scheduling prediction model according to the device parameter information and the load demand information; wherein, the first scheduling prediction model includes a first objective function and a first constraint function, the first objective function is used to describe the transaction cost of the power system participating in carbon trading, and the first constraint function is used to constrain the system steady state of the power system.

[0231] The strategy determination module 1030 is configured to determine the load scheduling strategy of each load in the power system in the future period based on the second scheduling prediction model according to the electricity trading information, the scheduling output information, and the carbon emission task information of each load in the power system in the future period; wherein, the second scheduling prediction model includes a second objective function and a second constraint function, the second objective function is used to describe the response cost of each load to respond to carbon emission scheduling, and the second constraint function is used to constrain the power balance of each load.

[0232] In one embodiment, the scheduling determination module 1020 is specifically configured to:

[0233] Update the first objective function and the first constraint function according to the device parameter information and the load demand information to obtain the updated first objective function and the updated first constraint function; aiming at the minimum function value of the updated first objective function and using the updated first constraint function as the constraint condition, solve the updated first objective function to obtain the scheduling output information of the power generation equipment in the future period of the power system.

[0234] In one embodiment, the strategy determination module 1030 includes:

[0235] A data determination unit, configured to determine the carbon emissions of each load in a future period according to the dispatching output information of the power generation equipment in the power system.

[0236] A function update unit, configured to update the second objective function and the second constraint function according to the electricity trading information, the carbon emissions of each load, and the carbon emission task information in the power system in a future period, so as to obtain the updated second objective function and the updated second constraint function.

[0237] A strategy determination unit, configured to solve the updated second objective function with the minimum function value of the updated second objective function as the target and the updated second constraint function as the constraint condition, so as to obtain the load dispatching strategy of each load in the power system in a future period.

[0238] In one embodiment, the data determination unit is specifically configured to:

[0239] According to the dispatching output information of the power generation equipment in the power system, determine the carbon flow rate and active power flow flowing into each load in a future period; for any load, determine the carbon flow density of the load in a future period according to the ratio between the carbon flow rate and the active power flow flowing into the load in a future period; according to the carbon flow density of the load in a future period and the dispatching output information of the power generation equipment in the power system, determine the carbon potential of the load in the power system in a future period; according to the product of the carbon potential of the load in a future period and the load amount, determine the carbon emissions of the load in a future period.

[0240] In one embodiment, the load dispatching strategy determination device 1000 is further configured to:

[0241] Determine the participation probability of each load in responding to the carbon emission dispatching of the power system according to the number of loads in the power system; determine the carbon emission task information of each load according to the corresponding participation probability of each load.

[0242] Each module in the above load dispatching strategy determination device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in the form of hardware or be independent of it, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above respective modules.

[0243] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as Figure 11As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for determining a load scheduling strategy.

[0244] Those skilled in the art can understand that Figure 11 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0245] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, it implements the steps of the method for determining a load scheduling strategy provided in the above embodiment.

[0246] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it implements the steps of the method for determining a load scheduling strategy provided in the above embodiment.

[0247] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, it implements the steps of the method for determining a load scheduling strategy provided in the above embodiment.

[0248] It should be noted that the data involved in this application (including but not limited to data for analysis, stored data, displayed data, etc.) are all information and data that have been fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0249] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc.

[0250] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification. The above embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for determining a load dispatching strategy, characterized in that: The method comprises: Acquire equipment parameter information of power generation equipment in the power system during a target period and load demand information of each load in the area to which the power system belongs during the target period; Based on the first scheduling prediction model, according to the equipment parameter information and the load demand information, determining the scheduling output information of the power generation equipment in the future period; wherein the first scheduling prediction model includes a first objective function and a first constraint function, the first objective function is used to describe the transaction cost of the power system participating in carbon trading, and the first constraint function is used to constrain the system steady state of the power system; Based on the second scheduling prediction model, the load scheduling strategy of each load in the power system in the future time period is determined according to the electricity trading information of the power system in the future time period, the scheduling output information and the carbon emission task information of each load; wherein the second scheduling prediction model includes a second objective function and a second constraint function, the second objective function is used to describe the response cost of each load in response to carbon emission scheduling, and the second constraint function is used to constrain the power balance of each load.

2. The method according to claim 1, characterized in that The determining, based on the first scheduling prediction model and according to the equipment parameter information and the load demand information, the scheduling output information of the power generation equipment in the future period includes: According to the equipment parameter information and the load demand information, the first objective function and the first constraint function are updated to obtain an updated first objective function and an updated first constraint function; Taking the minimum function value of the updated first objective function as the goal and the updated first constraint function as the constraint condition, the updated first objective function is solved to obtain the dispatching output information of the power generation equipment of the power system in the future period.

3. The method according to claim 1, characterized in that The method of determining the load dispatching strategy of each load in the power system in the future period based on the second dispatching prediction model according to the power transaction information of the power system in the future period, the dispatching output information and the carbon emission task information of each load includes: Determining the carbon emissions of each load in the future period according to the dispatch output information of the power generation equipment in the power system; According to the electricity trading information of the power system in the future period, the carbon emissions of each load and the carbon emission task information, the second objective function and the second constraint function are updated to obtain an updated second objective function and an updated second constraint function; Taking the minimum function value of the updated second objective function as the goal and the updated second constraint function as the constraint condition, the updated second objective function is solved to obtain the load dispatch strategy of each load in the power system in the future period.

4. The method according to claim 3, characterized in that The step of determining the carbon emissions of each load in the future period according to the dispatch output information of the power generation equipment in the power system includes: Determine the carbon flow rate and active power flow flowing into each load in a future period according to the dispatch output information of the power generation equipment in the power system; For any load, determining the carbon flow density of the load in the future period according to the ratio between the carbon flow rate flowing into the load in the future period and the active power flow; Determining the carbon potential of the load in the power system in the future period according to the carbon flow density of the load in the future period and the dispatch output information of the power generation equipment in the power system; The carbon emission of the load in the future period is determined according to the product of the carbon potential of the load in the future period and the load amount.

5. The method according to claim 1, characterized in that The carbon emission task information of each load in the power system is obtained by: Determining, according to the number of loads in the power system, a probability of each load participating in responding to the carbon emission dispatch of the power system; According to the participation probability corresponding to each load, the carbon emission task information of each load is determined.

6. The method according to claim 1, characterized in that After determining the load dispatching strategy of each load in the power system in the future period based on the second dispatching prediction model according to the power transaction information of the power system in the future period, the dispatching output information and the carbon emission task information of each load, the method further includes: Determining, according to the load dispatching strategy and the load demand information, whether the load dispatching strategy satisfies the load demand information; If not satisfied, the load dispatching strategy is used as new load demand information, and the operation of determining the dispatching output information of the power generation equipment in the future period based on the first dispatching prediction model and the equipment parameter information and the load demand information is returned.

7. The method according to claim 1, characterized in that The first objective function includes the thermal power coal consumption cost function, the wind power generation cost function and the power system carbon trading cost function, and the first constraint function includes the thermal power unit constraint function, the wind power unit constraint function, the transmission capacity constraint function and the system power balance constraint function.

8. The method according to claim 1, characterized in that The second objective function includes the electricity purchase cost function, the load carbon trading cost function, the demand response reward cost function and the electric vehicle discharge reward cost function; the second constraint function includes the power balance constraint function, the electric vehicle charging and discharging constraint function and the electric vehicle battery power constraint function.

9. A load dispatching strategy determination device, characterized in that: The device comprises: An information acquisition module, used to acquire equipment parameter information of power generation equipment in the power system during a target period and load demand information of each load in the area to which the power system belongs during the target period; A scheduling determination module, configured to determine the scheduling output information of the power generation equipment in a future period based on a first scheduling prediction model, according to the equipment parameter information and the load demand information; wherein the first scheduling prediction model includes a first objective function and a first constraint function, the first objective function is used to describe the transaction cost of the power system participating in carbon trading, and the first constraint function is used to constrain the system steady state of the power system; A strategy determination module is used to determine the load dispatching strategy of each load in the power system in the future time period based on a second dispatching prediction model, according to the electricity trading information of the power system in the future time period, the dispatching output information and the carbon emission task information of each load; wherein the second dispatching prediction model includes a second objective function and a second constraint function, the second objective function is used to describe the response cost of each load in response to carbon emission scheduling, and the second constraint function is used to constrain the power balance of each load.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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