Scheduling method and device for carbon emission flow in electric power system and computer equipment

Abstract

The invention relates to a scheduling method and device for carbon emission flow in an electric power system and computer equipment. The method comprises the following steps: according to electricity consumption information of a target electric power system, other load power, energy storage carbon emission information of energy storage equipment, cluster operation power, charging and discharging power, carbon transaction information, a charging mode selection model and cluster carbon emission information of an electric vehicle cluster, equipment operation power of power generation equipment, operation cost information and power generation carbon emission information, calculating a power generation carbon emission information of the target electric power system; constructing a system operation cost function, a system operation constraint and a carbon emission flow model of the target power system; according to the system operation cost function, the system operation constraint and the carbon emission flow model, determining a first carbon emission flow of power generation equipment in the target power system and a second carbon emission flow of the electric vehicle; and according to the first carbon emission flow and the second carbon emission flow, carbon emission flows of the power generation equipment and the electric vehicle are scheduled. By adopting the method, the carbon emission of the power system can be accurately and effectively scheduled.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a method, device and computer equipment for scheduling carbon emission flows in a power system. Background Art

[0002] With the large-scale deployment of renewable energy represented by wind and solar power and its integration into the grid for power generation, the penetration rate of green renewable energy is getting higher and higher; on the other hand, the number of electric vehicles has grown explosively, and the imbalance problem between the source and load sides of the power grid has become prominent. Various types of energy storage equipment are urgently needed to participate in grid dispatching to ensure the safe and reliable operation of the new power system.

[0003] At present, the research on carbon emissions of power systems has gradually focused on the transfer of carbon emissions from the source side from the power generation side to the load side. However, due to the diversity and uncertainty of power system loads, the access of different loads will cause the carbon emission intensity of the power grid to change. The existing carbon emission research methods cannot achieve accurate and effective scheduling of carbon emissions of power systems. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device and computer equipment for scheduling carbon emission flows in power systems to address the above-mentioned technical problems, which can achieve accurate and effective scheduling of carbon emissions in power systems.

[0005] In a first aspect, the present application provides a method for scheduling carbon emission flows in a power system, comprising:

[0006] Constructing a system operation cost function of the target power system according to the power consumption information of the target power system, the equipment operation power and operation cost information of the power generation equipment in the target power system, and the cluster operation power and carbon trading information of the electric vehicle cluster within the coverage area of ​​the target power system;

[0007] According to the operating power of the equipment, other load powers of other electrical equipment in the target power system except the electric vehicle cluster, and the charging and discharging power and charging mode selection model of the electric vehicle cluster, the system operating constraints of the target power system are constructed;

[0008] Constructing a carbon emission flow model of the target power system according to the operating power of the device, the operating power of the cluster, the energy storage carbon emission information of the energy storage device in the target power system, the power generation carbon emission information of the power generation device, and the cluster carbon emission information of the electric vehicle cluster;

[0009] The carbon emission flows of power generation equipment and electric vehicles in the target power system are scheduled according to the system operation cost function, the system operation constraints and the carbon emission flow model.

[0010] In one embodiment, scheduling the carbon emission flows of power generation equipment and electric vehicles in the target power system according to the system operation cost function, the system operation constraints and the carbon emission flow model includes:

[0011] Determining a first carbon emission flow of the power generation equipment and a second carbon emission flow of each electric vehicle according to the system operation cost function, the system operation constraints and the carbon emission flow model;

[0012] The carbon emission flows of the power generation equipment and each electric vehicle in the target power system are scheduled according to the first carbon emission flow and each of the second carbon emission flows.

[0013] In one embodiment, based on the power consumption information of the target power system, the equipment operating power and operating cost information of the power generation equipment in the target power system, and the cluster operating power and carbon trading information of the electric vehicle cluster within the coverage area of ​​the target power system, the system operating cost function of the target power system is constructed, including:

[0014] Constructing a carbon trading cost function according to the cluster operating power and the carbon trading information; wherein the carbon trading information includes carbon potential information and carbon price information;

[0015] Constructing a new energy power abandonment step penalty cost function according to the power abandonment penalty cost coefficient, power abandonment load and power abandonment step penalty price information in the power consumption information;

[0016] According to the operating cost information, construct a unit output cost function; wherein the operating cost information includes operating cost and startup cost;

[0017] The sum of the carbon trading cost function, the new energy power abandonment step penalty cost function and the unit output cost function is used as the system operation cost function of the target power system.

[0018] In one embodiment, the charging mode selection model is constructed in the following manner:

[0019] Determine the charging emergency value of the electric vehicle cluster according to the charging time of each electric vehicle in the electric vehicle cluster, and the grid connection information, grid-offline information and charging behavior information of each electric vehicle during the charging time;

[0020] A charging mode selection model for the electric vehicle cluster is constructed according to the charging urgency value and the charging and discharging power.

[0021] In one of the embodiments, the system operation constraints include charging and discharging constraints and charging mode selection constraints of the electric vehicle cluster, power system balance constraints of the target power system, and output constraints of the power generation equipment;

[0022] Accordingly, the system operation constraints of the target power system are constructed according to the operating power of the equipment, other load powers of other electrical equipment in the target power system except the electric vehicle cluster, and the charging and discharging power and charging mode selection model of the electric vehicle cluster, including:

[0023] Constructing the charging and discharging constraints according to the charging and discharging power and the grid-connected information and off-grid information of each electric vehicle in the electric vehicle cluster;

[0024] Constructing the charging mode selection constraint according to the charging mode selection model of the electric vehicle cluster and the charging and discharging power;

[0025] Constructing the power system balance constraint according to the equipment operating power, the charging and discharging power and the other load power;

[0026] The output constraint is constructed according to the operating power of the device.

[0027] In one embodiment, the carbon emission flow model includes a carbon emission responsibility model and a carbon emission quantity model;

[0028] The carbon emission flow model of the target power system is constructed according to the device operating power, the cluster operating power, the energy storage carbon emission information of the energy storage device in the target power system, the power generation carbon emission information of the power generation device, and the cluster carbon emission information of the electric vehicle cluster, including:

[0029] Determining the carbon potential of the target power system according to the power generation carbon emission information;

[0030] constructing the carbon emission responsibility model according to the carbon potential, the energy storage carbon emission information and the cluster carbon emission information;

[0031] The carbon emission model is constructed according to the carbon potential and the equipment operating power.

[0032] In one embodiment, constructing the carbon emission responsibility model according to the carbon potential, the energy storage carbon emission information and the cluster carbon emission information includes:

[0033] According to the carbon potential, determining the carbon emission factor of the bidirectional charging and discharging equipment in the target power system during discharge; wherein the bidirectional charging and discharging equipment includes energy storage equipment and electric vehicle clusters;

[0034] Determining a discharge carbon flow rate of the bidirectional charge-discharge device during discharge according to the carbon emission factor, the energy storage carbon emission information and the cluster carbon emission information;

[0035] The carbon emission responsibility model is constructed according to the discharge carbon flow rate and the charging carbon flow rate of the bidirectional charge and discharge device during charging.

[0036] In one embodiment, determining a first carbon emission flow of a power generation device and a second carbon emission flow of an electric vehicle in the target power system according to the system operation cost function, the system operation constraint and the carbon emission flow model comprises:

[0037] Taking the minimization of the system operation cost function as the goal and the system operation constraint as the constraint condition, solving the system operation cost function to obtain the power dispatch strategy of the target power system;

[0038] Based on the power dispatching strategy, the carbon emission flow model is solved to obtain a first carbon emission flow of power generation equipment and a second carbon emission flow of electric vehicles in the target power system.

[0039] In a second aspect, the present application also provides a scheduling device for carbon emission flow in a power system, comprising:

[0040] A first construction module is used to construct a system operation cost function of the target power system according to the power consumption information of the target power system, the equipment operation power and operation cost information of the power generation equipment in the target power system, and the cluster operation power and carbon trading information of the electric vehicle cluster within the coverage area of ​​the target power system;

[0041] A second construction module is used to construct a system operation constraint of the target power system according to the operating power of the equipment, other load powers of other electrical equipment in the target power system except the electric vehicle cluster, and the charging and discharging power and charging mode selection model of the electric vehicle cluster;

[0042] A third construction module is used to construct a carbon emission flow model of the target power system according to the operating power of the device, the operating power of the cluster, the energy storage carbon emission information of the energy storage device in the target power system, the power generation carbon emission information of the power generation equipment, and the cluster carbon emission information of the electric vehicle cluster;

[0043] A scheduling module is used to schedule the carbon emission flows of the power generation equipment and each electric vehicle in the target power system according to the system operation cost function, the system operation constraints and the carbon emission flow model.

[0044] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0045] Constructing a system operation cost function of the target power system according to the power consumption information of the target power system, the equipment operation power and operation cost information of the power generation equipment in the target power system, and the cluster operation power and carbon trading information of the electric vehicle cluster within the coverage area of ​​the target power system;

[0046] According to the operating power of the equipment, other load powers of other electrical equipment in the target power system except the electric vehicle cluster, and the charging and discharging power and charging mode selection model of the electric vehicle cluster, the system operating constraints of the target power system are constructed;

[0047] Constructing a carbon emission flow model of the target power system according to the operating power of the device, the operating power of the cluster, the energy storage carbon emission information of the energy storage device in the target power system, the power generation carbon emission information of the power generation device, and the cluster carbon emission information of the electric vehicle cluster;

[0048] The carbon emission flows of power generation equipment and electric vehicles in the target power system are scheduled according to the system operation cost function, the system operation constraints and the carbon emission flow model.

[0049] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0050] Constructing a system operation cost function of the target power system according to the power consumption information of the target power system, the equipment operation power and operation cost information of the power generation equipment in the target power system, and the cluster operation power and carbon trading information of the electric vehicle cluster within the coverage area of ​​the target power system;

[0051] According to the operating power of the equipment, other load powers of other electrical equipment in the target power system except the electric vehicle cluster, and the charging and discharging power and charging mode selection model of the electric vehicle cluster, the system operating constraints of the target power system are constructed;

[0052] Constructing a carbon emission flow model of the target power system according to the operating power of the device, the operating power of the cluster, the energy storage carbon emission information of the energy storage device in the target power system, the power generation carbon emission information of the power generation device, and the cluster carbon emission information of the electric vehicle cluster;

[0053] The carbon emission flows of power generation equipment and electric vehicles in the target power system are scheduled according to the system operation cost function, the system operation constraints and the carbon emission flow model.

[0054] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0055] Constructing a system operation cost function of the target power system according to the power consumption information of the target power system, the equipment operation power and operation cost information of the power generation equipment in the target power system, and the cluster operation power and carbon trading information of the electric vehicle cluster within the coverage area of ​​the target power system;

[0056] According to the operating power of the equipment, other load powers of other electrical equipment in the target power system except the electric vehicle cluster, and the charging and discharging power and charging mode selection model of the electric vehicle cluster, the system operating constraints of the target power system are constructed;

[0057] Constructing a carbon emission flow model of the target power system according to the operating power of the device, the operating power of the cluster, the energy storage carbon emission information of the energy storage device in the target power system, the power generation carbon emission information of the power generation device, and the cluster carbon emission information of the electric vehicle cluster;

[0058] The carbon emission flows of power generation equipment and electric vehicles in the target power system are scheduled according to the system operation cost function, the system operation constraints and the carbon emission flow model.

[0059] The above-mentioned method, device and computer equipment for scheduling carbon emission flow in the power system improve the generalization ability of the constructed system operation cost function, system operation constraints and carbon emission flow model by comprehensively considering the carbon trading information, cluster operating power, charging and discharging power and cluster carbon emission information of the electric vehicle cluster, and are suitable for electric vehicle groups of different sizes and types; at the same time, the system operation cost function can consider the carbon trading cost function and the new energy abandonment ladder penalty cost function, fully considering the intermittent and uncertain nature of new energy, and ensuring the accuracy of the constructed system operation cost function; further, considering the different charging needs of electric vehicles, more accurately reflecting the impact of electric vehicle charging on the carbon emissions of the target power system. In addition, the above-mentioned method ensures the accuracy and effectiveness of carbon emission flow scheduling based on the system operation cost function, system operation constraints and carbon emission flow model. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 A schematic diagram of a flow chart of a method for scheduling a carbon emission flow in a power system in one embodiment;

[0062] Figure 2 A schematic diagram of a process for scheduling carbon emission flows of power generation equipment and electric vehicles in one embodiment;

[0063] Figure 3 A schematic diagram of a process for constructing a system operation cost function in one embodiment;

[0064] Figure 4 A schematic diagram of a process for constructing a charging mode selection model in one embodiment;

[0065] Figure 5 A schematic diagram of a process for constructing system operation constraints in an embodiment;

[0066] Figure 6 A schematic diagram of a bidirectional system constructed in one embodiment;

[0067] Figure 7 A schematic diagram of a process for constructing a carbon emission flow model in one embodiment;

[0068] Figure 8 A schematic diagram of a process for constructing a carbon emission responsibility model in one embodiment;

[0069] Fig. 9 A schematic flow chart of a method for scheduling carbon emission flows in a power system in another embodiment;

[0070] Fig.10 A structural block diagram of a scheduling device for a carbon emission flow in a power system in one embodiment;

[0071] Fig.11 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with 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.

[0073] The method for scheduling carbon emission flows in a power system provided in an embodiment of the present application can be applied to an application environment in which the scheduling of carbon emission flows in a power system is optimized. The method for scheduling carbon emission flows in a power system provided in an embodiment of the present application can be executed by a computer device, which can be a server or a terminal with powerful computing capabilities.

[0074] In one embodiment, Figure 1 As shown, a method for scheduling carbon emission flow in a power system is provided, and the method is applied to a server as an example for explanation, and specifically includes the following steps:

[0075] S101, constructing a system operating cost function of the target power system based on the power consumption information of the target power system, the equipment operating power and operating cost information of the power generation equipment in the target power system, and the cluster operating power and carbon trading information of the electric vehicle cluster within the coverage area of ​​the target power system.

[0076] Among them, electricity consumption information is information reflecting the electricity consumption of each electrical equipment in the target power system; the electric vehicle (EV) cluster is a cluster composed of all electric vehicles in the target power system; the cluster operating power is the operating power of all electric vehicles in the electric vehicle cluster; the equipment operating power is the operating power of the power generation equipment in the target power system; the operating cost information of the power generation equipment is information reflecting the cost incurred during the operation and startup of the power generation equipment. In this embodiment of the application, the operating cost information includes but is not limited to the operating cost and startup cost; the carbon trading information is information characterizing the carbon trading of the electric vehicle cluster in the target power system during operation. In this embodiment of the application, the carbon trading information includes but is not limited to carbon potential information and carbon price information.

[0077] Among them, the system operation cost function is a function that characterizes the operation cost of the target power system. In the embodiment of the present application, the system operation cost function at least includes a carbon trading cost function, a new energy curtailment step penalty cost function, and a unit output cost function. The carbon trading cost function is a function that characterizes the cost incurred by carbon trading in the target power system; the new energy curtailment step penalty cost function is a function that reflects the penalty imposed on the power equipment in the target power system when it fails to reach or exceed a specific new energy consumption target; the unit output cost function is a function that reflects the cost incurred by the output of each unit in the target power system.

[0078] Optionally, the system operation cost function of the target power system can be constructed according to the construction rules of the system operation cost function, based on the power consumption information of the target power system, the cluster operating power of the electric vehicle cluster, the equipment operating power of the power generation equipment, the operating cost information of the power generation equipment and the carbon trading information of the electric vehicle cluster.

[0079] S102, constructing system operation constraints of the target power system according to the equipment operation power, other load powers of other power-consuming equipment in the target power system except the electric vehicle cluster, and the charging and discharging power and charging mode selection model of the electric vehicle cluster.

[0080] Among them, other load power is the operating power of other electrical equipment in the target power system except the electric vehicle cluster; the charging mode selection model is a model for selecting a charging mode for the electric vehicle according to the charging needs of the electric vehicle. In the embodiment of the present application, the charging mode includes but is not limited to a fast charging mode and a slow charging mode.

[0081] Among them, the system operation constraints are the constraints that the target power system needs to meet during operation. In the embodiment of the present application, the system operation constraints include but are not limited to the electric vehicle cluster charging and discharging constraints, the charging mode selection constraints, the power system balance constraints and the output constraints of the power generation equipment. The electric vehicle cluster charging and discharging constraints are the constraints that the electric vehicle cluster needs to meet during the charging and discharging process; the charging mode selection constraints are the constraints that the electric vehicle cluster needs to meet when selecting the charging mode; the power system balance constraints are the power balance constraints that the target power system needs to meet during operation; the output constraints of the power generation equipment are the constraints that the power generation equipment needs to meet during the processing process.

[0082] Optionally, rules can be constructed based on the system operation constraints of the target power system, and a model can be selected based on the equipment operating power, the charging and discharging power of the electric vehicle cluster, other load powers, and the charging mode of the electric vehicle cluster to construct the system operation constraints of the target power system.

[0083] S103, constructing a carbon emission flow model of the target power system according to the equipment operating power, the cluster operating power, the energy storage carbon emission information of the energy storage equipment in the target power system, the power generation carbon emission information of the power generation equipment, and the cluster carbon emission information of the electric vehicle cluster.

[0084] Among them, the energy storage carbon emission information characterizes the carbon emissions of energy storage equipment during the charging and discharging process; the power generation carbon emission information characterizes the carbon emissions of power generation equipment during the power generation process; and the cluster carbon emission information characterizes the carbon emissions of electric vehicle clusters during the charging and discharging process.

[0085] Among them, the carbon emission flow model is a model that simulates the carbon emissions of the target power system during operation. In the embodiment of the present application, the carbon emission flow model includes but is not limited to a carbon emission responsibility model and a carbon emission model. The carbon emission responsibility model is a model that characterizes the responsibilities of each device in the target power system during the carbon emission process; the carbon emission model is a model that characterizes the carbon emissions of each device.

[0086] Optionally, the carbon emission flow model of the target power system can be constructed according to the construction rules of the carbon emission flow model of the target power system, based on the energy storage carbon emission information of the energy storage equipment in the target power system, the power generation carbon emission information of the power generation equipment, the cluster carbon emission information of the electric vehicle cluster, the equipment operating power and the cluster operating power.

[0087] S104, scheduling the carbon emission flows of power generation equipment and electric vehicles in the target power system according to the system operation cost function, system operation constraints and the carbon emission flow model.

[0088] Optionally, computing software can be used to jointly solve the system operating cost function, system operating constraints and carbon emission flow model, and the power generation equipment and electric vehicles can be scheduled based on the carbon emission flow scheduling strategy of the power generation equipment in the target power system and the carbon emission flow scheduling strategy of each electric vehicle.

[0089] In the above-mentioned method for dispatching carbon emission flows in the power system, by comprehensively considering the carbon trading information, cluster operating power, charging and discharging power and cluster carbon emission information of the electric vehicle cluster, the generalization ability of the constructed system operation cost function, system operation constraints and carbon emission flow model is improved, and it is suitable for electric vehicle groups of different sizes and types; at the same time, the system operation cost function can consider the carbon trading cost function and the new energy abandonment ladder penalty cost function, fully considering the intermittent and uncertain nature of new energy, and ensuring the accuracy of the constructed system operation cost function; further, considering the different charging needs of electric vehicles, more accurately reflecting the impact of electric vehicle charging on the carbon emissions of the target power system. In addition, the above-mentioned method ensures the accuracy and effectiveness of carbon emission flow dispatching according to the system operation cost function, system operation constraints and carbon emission flow model.

[0090] Optionally, in one embodiment, Figure 2 As shown, a method for scheduling the carbon emission flows of power generation equipment and electric vehicles in a target power system is provided, which specifically includes the following steps:

[0091] S201, determining a first carbon emission flow of power generation equipment and a second carbon emission flow of electric vehicles in a target power system according to a system operation cost function, system operation constraints and a carbon emission flow model.

[0092] Among them, the first carbon emission flow is the optimal carbon emission flow of the power generation equipment in the future period; the second carbon emission flow is the optimal carbon emission flow of the electric vehicle in the future period.

[0093] Optionally, the system operation cost function can be solved with the minimum of the system operation cost function as the goal and the system operation constraints as the constraints to obtain the power dispatch strategy of the target power system; based on the power dispatch strategy, the carbon emission flow model is solved to obtain the first carbon emission flow of the power generation equipment in the target power system and the second carbon emission flow of the electric vehicle.

[0094] It should be noted that if the first carbon emission flow and the second carbon emission flow do not meet the iteration requirements, the system operation cost function and the system operation constraints need to be updated according to the first carbon emission flow and the second carbon emission flow, and the system operation cost function is continued to be minimized as the goal and the system operation constraints are used as constraints to solve the system operation cost function and obtain the power dispatching strategy of the target power system; based on the power dispatching strategy, the carbon emission flow model is solved to obtain the first carbon emission flow of the power generation equipment in the target power system and the second carbon emission flow of the electric vehicle, until the iteration end conditions are met.

[0095] S202: Scheduling the carbon emission flows of power generation equipment and electric vehicles in the target power system according to the first carbon emission flow and the second carbon emission flow.

[0096] In the embodiment of the present application, by determining the first carbon emission flow of the power generation equipment and the second carbon emission flow of the electric vehicle in the target power system, the accurate scheduling quantity of the power generation equipment and each electric vehicle is clarified, thereby ensuring the accuracy of the carbon emission flow scheduling process.

[0097] Optionally, in one embodiment, Figure 3 As shown, a method for constructing a system operation cost function of a target power system is provided, which specifically includes the following steps:

[0098] S301, constructing a carbon trading cost function according to cluster operation power and carbon trading information.

[0099] Among them, carbon trading information at least includes carbon potential information and carbon price information.

[0100] Optionally, according to the cluster operating power and carbon trading information, the process of constructing the carbon trading cost function can be expressed by the following formula (1):

[0101] (1)

[0102] in, is the power of the EV cluster node, e is the carbon potential of the EV cluster node, is the EV cluster node carbon price, is the carbon emission step penalty price growth rate, and G is the power step penalty interval. In the process of step-type carbon trading penalties, as the output increases, the penalty cost of the corresponding interval increases. The greater the price growth rate, the higher the price increase in each interval, but the higher the total cost; the larger the penalty interval, the larger the span of each carbon trading penalty level, and both should be preset according to the actual situation of the regional power grid.

[0103] S302, constructing a new energy power abandonment step penalty cost function according to the power abandonment penalty cost coefficient, power abandonment load and power abandonment step penalty price information in the power consumption information.

[0104] Among them, the abandoned power load represents the abandoned wind and solar power amount; the abandoned power ladder penalty price information includes but is not limited to the abandoned power ladder penalty price growth rate of new energy power.

[0105] Optionally, in order to further promote the system to absorb new energy, a new energy curtailment ladder penalty mechanism is introduced. When a specific new energy absorption target is not reached or exceeded, different levels of penalty costs are imposed on it. Specifically, according to the curtailment penalty cost coefficient, curtailment load and curtailment ladder penalty price information in the electricity consumption information, the process of constructing a new energy curtailment ladder penalty cost function can be expressed by the following formula (2):

[0106] (2)

[0107] in, is the penalty cost coefficient for power curtailment, To abandon the amount of wind and solar power, is the growth rate of the step penalty price for new energy power abandonment, and D is the step penalty interval for new energy power abandonment. The same is true for the step penalty price growth rate and step penalty interval for carbon emissions mentioned above.

[0108] S303: constructing a unit output cost function according to the operation cost information.

[0109] The operating cost information includes at least operating cost and startup cost.

[0110] Optionally, based on the operating cost information, the process of constructing the unit output cost function can be expressed by the following formula (3):

[0111] (3)

[0112] in, , are the operating cost and startup cost of unit i in period t, It represents the output of unit i in period t, where the unit operating cost is a multi-segment linear function related to the output ranges reported by the unit and the corresponding energy prices.

[0113] S304, taking the sum of the carbon trading cost function, the new energy power abandonment step penalty cost function and the unit output cost function as the system operation cost function of the target power system.

[0114] Optionally, the sum of the carbon trading cost function, the renewable energy power abandonment step penalty cost function and the unit output cost function can be used as the system operation cost function of the target power system.

[0115] In this embodiment, by introducing the carbon trading cost function, the new energy power abandonment step penalty cost function and the unit output cost function, a method for constructing the system operation cost function of the target power system is provided, thereby ensuring the comprehensiveness and effectiveness of the constructed system operation cost function of the target power system.

[0116] Optionally, EV has two common charging modes, namely fast charging mode and slow charging mode. Although fast charging provides a shorter charging time, frequent fast charging may accelerate the battery life decay, and fast charging systems usually generate large battery losses and heat when charging the battery. The additional loss will reduce the charging efficiency of the battery, so the fast charging mode is usually used in certain emergency situations. Therefore, a charging emergency value is defined in the embodiment of the present application as a standard for whether the electric vehicle adopts a fast charging mode or a slow charging mode, as follows:

[0117] (4)

[0118] in, For the How long does it take to charge an EV? Slow charging power for EV, and are the charging and discharging efficiencies, Representative The lower limit of SOC (state of charge) when an EV ends its charging behavior. is the SOC of the i-th EV when it is connected to the grid, is the battery capacity of the EV, For the EVs in The charging power after the charging mode is selected at any time. For the EVs in The discharge power at the moment, is the time step. When IND<1, it means An electric car needs emergency charging; when IND>1, it means the There is no need for emergency charging for EVs.

[0119] Furthermore, according to the above-mentioned charging urgency value of individual EVs, the charging selection model of electric vehicles can be obtained as follows:

[0120] (5)

[0121] On this basis, if Figure 4 As shown, a method for constructing a charging mode selection model for an electric vehicle cluster is provided, which specifically includes the following steps:

[0122] S401, determining a charging emergency value of the electric vehicle cluster according to the charging time of each electric vehicle in the electric vehicle cluster, and the grid connection information, grid-offline information and charging behavior information of each electric vehicle during the charging time.

[0123] Optionally, according to the calculation method of the charging emergency value of the above-mentioned individual EV, a Boolean variable u is introduced (u=1 for grid connection, u=0 for off-grid connection), and the above-mentioned individual EV charging selection standard is expanded to the EV cluster charging selection standard. Specifically, according to the charging time of each electric vehicle in the electric vehicle cluster, and the grid connection information, off-grid information and charging behavior information of each electric vehicle during the charging time, the process of determining the charging emergency value of the electric vehicle cluster is as follows:

[0124] (6)

[0125] in, Charging time for electric vehicle clusters, and are the total charge and discharge powers that can be dispatched by cluster v at time t. is the step power generated when EVs in cluster v are connected to or off the grid at time t, For the EV is not connected to the grid at time t, otherwise EVs connected to the grid, and Respectively The battery level of the EV when it is connected and when it is away. <1, it means that the electric vehicle cluster needs emergency charging; when When >1, it means that the EV cluster has no need for emergency charging.

[0126] S402: constructing a charging mode selection model for the electric vehicle cluster according to the charging urgency value and the charging and discharging power of the electric vehicle cluster.

[0127] Optionally, the process of constructing a charging mode selection model for an electric vehicle cluster according to the charging urgency value and the charging and discharging power of the electric vehicle cluster is as follows:

[0128] (7)

[0129] In this embodiment, a charging urgency value is introduced, and a charging mode selection model that can accurately select a charging mode for an electric vehicle cluster is constructed according to the charging urgency value.

[0130] Optionally, due to the limited individual EV load storage capacity, and the large number of variables that will be introduced when large-scale EVs are connected to the grid, it is easy to cause calculation pressure problems. At the same time, the prediction of individual EVs is highly random and cannot meet the threshold of flexible grid dispatch. Therefore, building an EV cluster load storage model is very important for the flexible grid dispatch of EVs. After EV is connected to the grid, it can participate in the grid dispatch plan with the help of vehicle-grid interaction technology, provided that the individual needs of EVs are met. Individual EVs are divided into two modes: fast charging and slow charging. In the embodiment of the present application, a charging mode selection standard is used to determine the fast or slow charging mode of the electric vehicle. The individual EV charging and discharging model is as follows:

[0131] (8)

[0132] (9)

[0133] in, is the charging power of the i-th EV after the charging mode is selected at time t, is the discharge power of the i-th EV at time t, and are the state of charge (SOC) of the i-th EV when it is off-grid and on-grid, is the battery capacity of the EV, and , are the grid-connected period and the time of access and departure of the i-th EV, and is the rated charging and discharging power of the i-th EV, is the SOC of the i-th EV at the t-th moment, and are the charging and discharging efficiencies, and are the maximum SOC and minimum SOC of the i-th EV, is the time step.

[0134] Since the complementary conditions of EV charging and discharging power are redundant during EV individual charging and discharging, that is, It must be 0. The EV grid connection time There are differences, and the premise of using Minkowski is that all variables satisfy the same definition domain, so it is necessary to calculate the individual EV grid connection time. The same time scheduling domain T is used. Therefore, the definition domain of EV individuals in the EV cluster is expanded, and a Boolean variable u (1 for grid connection, otherwise for off-grid connection) is introduced to constrain grid connection. By transforming, we can obtain an individual EV charging and discharging model within the scheduling cycle time domain T, as follows:

[0135] (10)

[0136] in, , If the i-th EV is not connected to the grid at time t, otherwise the i-th EV is connected to the grid, is the battery capacity of the i-th EV at time t, that is , and are the minimum and maximum safe power boundaries of the i-th EV respectively.

[0137] The Minkowski method is used to obtain the upper and lower boundaries of the EV cluster dispatching capacity for the above EV charging and discharging power boundaries and battery safety power boundaries, as follows:

[0138] (11)

[0139] in, , and are the maximum dispatchable charging and discharging power of EV cluster v at time t, is the set of the number of EV individuals in cluster v, and are the maximum and minimum energy storage capacities that can be dispatched by the EV cluster v at time t, respectively.

[0140] Considering that the step power of the EV cluster occurs when it is connected to the grid or off the grid, that is, the EV is already in the grid-connected state, When EV is connected to the grid, ;When EV is off the grid, Due to the Minkowski and necessary conditions, the above three time periods need to be integrated into a time scheduling domain T. Boolean variables are used to replace whether the EV is connected to the grid or off-grid, that is, the time t when the EV is connected to the grid is replaced by Equivalent; the time t after EV leaves the grid is given by Therefore, in order to solve the instantaneous step power phenomenon, the dynamic energy storage power of the EV cluster is corrected, and the dynamic step deviation power can be obtained as follows:

[0141] (12)

[0142] in, , is the step power generated when EVs in cluster v are connected to or off the grid at time t, and are the battery capacities of the i-th EV when it connects and leaves, respectively.

[0143] Optionally, the system operation constraints include charging and discharging constraints and charging mode selection constraints of the electric vehicle cluster, power system balance constraints of the target power system, and output constraints of the power generation equipment; in one embodiment, Figure 5 As shown, a method for constructing system operation constraints of a target power system is provided, which specifically includes the following steps:

[0144] S501, constructing charging and discharging constraints according to the charging and discharging power and the grid-connected information and off-grid information of each electric vehicle in the electric vehicle cluster.

[0145] In summary, based on Minkowski and mapping the multi-dimensional decision variables of all EV individuals into single-dimensional decision variables, and calling their dynamic energy balance capabilities when participating in the regulation of the power grid system the load-storage dispatchability of the EV cluster, the EV variables are integrated, which essentially maps all EV variables into multi-dimensional space, and finally forms the EV cluster charge and discharge constraints and battery power constraints. Based on this, according to the charge and discharge power, the grid-connected information and off-grid information of the electric vehicle cluster, the process of constructing the charge and discharge constraints of the electric vehicle cluster is as follows:

[0146] (13)

[0147] in, , and are the total charge and discharge power that can be dispatched by cluster v at time t, is the total energy storage capacity that can be dispatched by cluster v at time t.

[0148] S502: construct a charging mode selection constraint according to the charging mode selection model and the charging and discharging power of the electric vehicle cluster.

[0149] Optionally, the charging mode selection model of the electric vehicle cluster in the above embodiment, such as the above formula (7), is substituted with the charging and discharging power of the electric vehicle cluster to obtain the charging mode selection constraint.

[0150] S503: construct a power system balance constraint according to the equipment operating power, charging and discharging power and other load powers.

[0151] Among them, the power system balance constraints include but are not limited to system power balance constraints, system positive reserve capacity constraints, system negative reserve capacity constraints, line transmission capacity constraints, balance node constraints, node power balance constraints and power balance constraints.

[0152] Optionally, the system power balance constraint can be expressed by the following formula (14):

[0153] (14)

[0154] Among them, j is the unit; is the power generation capacity of the thermal power unit at time t, is the wind power at time t, is the photovoltaic power generation power at time t, is the purchased electric power at time t, is the power of other loads at time t, is the charging power of the electric vehicle at time t (if it is the discharging power, it will take a negative value). The units of the above variables are all MW.

[0155] The system positive reserve capacity constraint can be expressed by the following formula (15):

[0156] (15)

[0157] in, represents the start and stop status of unit j in period t, Indicates that the unit is shut down. Indicates that the unit is turned on, J is the total number of units, is the maximum output of unit j in period t (usually rated capacity), is the system load during period t, which has deducted the net power supplied by the interconnection line.

[0158] The system negative reserve capacity constraint can be expressed by the following formula (16):

[0159] (16)

[0160] in, is the minimum output of unit j in period t, is the system negative reserve capacity requirement for period t.

[0161] The line transmission capacity constraint can be expressed by the following formula (17):

[0162] (17)

[0163] in, is the active power flow of the line at time t, , It is the upper and lower limits of line transmission power.

[0164] The equilibrium node constraint can be expressed by the following formula (18):

[0165] (18)

[0166] in, is the phase angle of the balanced node voltage at time t.

[0167] The node power balance constraint can be expressed by the following formula (19):

[0168] (19)

[0169] in, is the set of thermal power units at node n, is the set of wind turbines at node n, is the set of photovoltaic units at node n, is the set of lines connected to node n, is the power load of node n at time t.

[0170] The power balance constraint can be expressed by the following formula (20):

[0171] (20)

[0172] Where V is the total number of clusters, For the purchased power, is the input power, is the output power, Charging power for EV clusters, Discharge power for the EV cluster.

[0173] S504: Establish output constraints according to the operating power of the equipment.

[0174] Among them, the output constraints of power generation equipment include thermal power unit output constraints, wind power output constraints and photovoltaic output constraints.

[0175] Optionally, the output constraint of the thermal power unit can be expressed by the following formula (21):

[0176] (twenty one)

[0177] in, , are the maximum and minimum output values ​​of the i-th thermal power unit respectively.

[0178] The wind power output constraint can be expressed by the following formula (22):

[0179] (twenty two)

[0180] in, It is the maximum output of the wind turbine.

[0181] The photovoltaic output constraint can be expressed by the following formula (23):

[0182] (twenty three)

[0183] in, It is the maximum output of the photovoltaic unit.

[0184] In this embodiment, by introducing electric vehicle cluster charging and discharging constraints, electric vehicle cluster charging mode selection constraints, power system balance constraints and power generation equipment output constraints, a method for constructing system operation constraints of a target power system is provided, thereby ensuring the accuracy of the constructed system operation constraints.

[0185] Optional, the energy storage system in the power system mainly includes electrochemical energy storage, pumped storage, flywheel energy storage, etc.; bidirectional loads mainly include electric vehicles, rail transit electric locomotives, etc. Both (hereinafter referred to as bidirectional systems) are systems with rapid power changes, and the physical principles of each system are different, but from the perspective of charging, discharging, and electricity consumption, the energy storage system and the bidirectional load can be unified into the same physical model. Figure 6 As shown, the bidirectional system constructed by the embodiment of the present application; from Figure 6 It can be seen that the unified power model of energy storage and bidirectional load is studied based on the point of common coupling (PCC) of the power grid. The energy storage equipment and electric vehicle cluster are uniformly referred to as bidirectional electric devices to form a bidirectional system. Without loss of generality, the following two settings are made:

[0186] 1) The power loss and load power during the charging and discharging process are not differentiated and are uniformly regarded as active power.

[0187] 2) Divide the charging process into lossless charging and power consumption stages, and establish the principle of storage before use. Figure 5 As shown, it is a schematic diagram of a bidirectional system constructed in an embodiment of the present application; when charging, S1 and S2 are closed first to store energy, and then S2 is disconnected and S3 is closed to use energy. However, the order of the two processes is virtual, and they actually occur at the same time.

[0188] Based on the above setting, in one embodiment, Figure 7 As shown, a method for constructing a carbon emission flow model of a target power system is provided, which specifically includes the following steps:

[0189] S701, determining the carbon potential of the target power system according to the carbon emission information of power generation.

[0190] Optionally, the carbon potential of node n is determined by the carbon emissions generated by the connected generator and the carbon emissions flowing into the remaining nodes. Specifically, according to the carbon emission information of power generation, the process of determining the carbon potential of the target power system can be expressed by the following formula (24):

[0191] (twenty four)

[0192] in, , are the inflow branches and generator sets connected to node n, is the carbon flow density of branch i in time period t, is the carbon emission intensity of generator set j in period t, is the electric power passing through branch i, is the electrical output of generator set j.

[0193] S702, constructing a carbon emission responsibility model based on carbon potential, energy storage carbon emission information and cluster carbon emission information.

[0194] Optionally, the key to establishing a carbon emission flow model is to calculate the carbon emission factor when they return power to the grid (i.e., the ratio of the branch carbon flow rate to the active power flow). The carbon emission flow model without carbon emission responsibility is based on the following settings: the electric vehicle is charged from zero initial state to 100% and then discharged to zero state, and the carbon emission responsibility transferred from the external power system to the electric vehicle is 0 (i.e., the carbon flow flowing into the public connection point of the grid in the above process ).

[0195] S703, constructing a carbon emission model according to the carbon potential and the equipment operating power.

[0196] Optionally, based on the carbon potential and equipment operating power, the process of constructing a carbon emission model can be expressed by the following formula (25):

[0197] (25)

[0198] Where T represents the total number of time periods considered, is the carbon emissions at time t, is the carbon potential of node n (the load node is the node carbon potential, the unit node is the carbon emission intensity), is the unit output at time t.

[0199] In this embodiment, by introducing a carbon emission responsibility model and a carbon emission amount model, a method for constructing a carbon emission flow model of a target power system is provided, thereby ensuring the accuracy and comprehensiveness of the constructed carbon emission flow model of the target power system.

[0200] Optionally, in one embodiment, Figure 8 As shown, a method for constructing a carbon emission responsibility model is provided, which specifically includes the following steps:

[0201] S801, determining the carbon emission factor of the bidirectional charging and discharging equipment in the target power system during discharge according to the carbon potential.

[0202] Among them, bidirectional charging and discharging equipment includes energy storage equipment and electric vehicle clusters.

[0203] Optional, assuming The system is in charging state at all times. At this moment, the system starts to enter the discharge state. The carbon emission factor during discharge can be described by the following formula (26):

[0204] (26)

[0205] in, is the carbon emission factor during discharge, is the instantaneous carbon flow rate of charging, which is calculated by the external power grid of PCC. The maximum amount of electrical energy that the system can discharge to the PCC starting from the start of the discharge state.

[0206] S802, determining a discharge carbon flow rate of the bidirectional charge-discharge device during discharge according to the carbon emission factor, the energy storage carbon emission information and the cluster carbon emission information.

[0207] Optionally, the process of determining the discharge carbon flow rate of the bidirectional charging and discharging device during discharge according to the carbon emission factor, the energy storage carbon emission information and the cluster carbon emission information can be expressed by the following formula (27):

[0208] (27)

[0209] in, is the carbon flow rate when the bidirectional charge and discharge device discharges to the PCC.

[0210] S803: construct a carbon emission responsibility model according to the discharge carbon flow rate and the charging carbon flow rate of the bidirectional charging and discharging device during charging.

[0211] Optionally, based on the discharge carbon flow rate and the charging carbon flow rate of the bidirectional charging and discharging device during charging, the carbon emission responsibility model can be expressed according to the following formula (28):

[0212] (28)

[0213] in, is the carbon emission factor during discharge, is the instantaneous carbon flow rate of charging, which is calculated by the external power grid of PCC. The maximum amount of electrical energy that the system can discharge to the PCC starting from the start of the discharge state. is the carbon flow rate when discharging to PCC, It is the carbon flow into the PCC point, that is, the carbon emission responsibility of the two-way system.

[0214] In this embodiment, by introducing the carbon emission factor and the discharge carbon flow rate, a method for constructing the carbon emission responsibility model is provided, thereby ensuring the accuracy and effectiveness of the constructed carbon emission responsibility model.

[0215] Fig. 9 FIG. 1 is a flow chart of a method for scheduling carbon emission flows in a power system in another embodiment. Based on the above embodiment, this embodiment provides an optional example of a method for scheduling carbon emission flows in a power system. Fig. 9 The specific implementation process is as follows:

[0216] S901, constructing a carbon trading cost function according to cluster operation power and carbon trading information.

[0217] Among them, carbon trading information at least includes carbon potential information and carbon price information.

[0218] S902, constructing a new energy power abandonment step penalty cost function according to the power abandonment penalty cost coefficient, power abandonment load and power abandonment step penalty price information in the power consumption information.

[0219] S903: Construct a unit output cost function based on the operation cost information.

[0220] The operating cost information includes at least operating cost and startup cost.

[0221] S904, taking the sum of the carbon trading cost function, the new energy power abandonment step penalty cost function and the unit output cost function as the system operation cost function of the target power system.

[0222] S905 , constructing a charging and discharging constraint in a system operation constraint according to the charging and discharging power and the grid-connected information and off-grid information of each electric vehicle in the electric vehicle cluster.

[0223] S906: Construct a charging mode selection constraint in the system operation constraint according to the charging mode selection model and the charging and discharging power of the electric vehicle cluster.

[0224] Optionally, the charging urgency value of the electric vehicle cluster is determined based on the charging time of each electric vehicle in the electric vehicle cluster, as well as the grid-connected information, off-grid information and charging behavior information of each electric vehicle during the charging time; and a charging mode selection model for the electric vehicle cluster is constructed based on the charging urgency value and the charging and discharging power of the electric vehicle cluster.

[0225] S907: construct a power system balance constraint in the system operation constraint according to the equipment operation power, the charging and discharging power and other load powers.

[0226] S908: Construct output constraints in system operation constraints according to the equipment operation power.

[0227] S909, determining the carbon potential of the target power system according to the power generation carbon emission information.

[0228] S910, constructing a carbon emission responsibility model in the carbon emission flow model according to the carbon potential, the energy storage carbon emission information and the cluster carbon emission information.

[0229] Optionally, based on the carbon potential, determine the carbon emission factor of the bidirectional charging and discharging equipment in the target power system during discharge; wherein the bidirectional charging and discharging equipment includes energy storage equipment and electric vehicle clusters; determine the discharge carbon flow rate of the bidirectional charging and discharging equipment during discharge based on the carbon emission factor, energy storage carbon emission information and cluster carbon emission information; construct a carbon emission responsibility model based on the discharge carbon flow rate and the charging carbon flow rate of the bidirectional charging and discharging equipment during charging.

[0230] S911, construct a carbon emission model in the carbon emission flow model based on the carbon potential and equipment operating power.

[0231] S912, determining a first carbon emission flow of power generation equipment and a second carbon emission flow of electric vehicles in the target power system according to the system operation cost function, the system operation constraints and the carbon emission flow model.

[0232] Optionally, with the minimization of the system operation cost function as the goal and the system operation constraints as constraints, the system operation cost function is solved to obtain the power dispatch strategy of the target power system; based on the power dispatch strategy, the carbon emission flow model is solved to obtain the first carbon emission flow of the power generation equipment in the target power system and the second carbon emission flow of the electric vehicle.

[0233] S913: Scheduling the carbon emission flows of power generation equipment and electric vehicles in the target power system according to the first carbon emission flow and the second carbon emission flow.

[0234] The specific process of S901-S913 above can refer to the description of the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0235] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, 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 can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0236] Based on the same inventive concept, the embodiment of the present application also provides a scheduling device for carbon emission flows in a power system for implementing the above-mentioned scheduling method for carbon emission flows in a power system. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of the scheduling device for carbon emission flows in a power system provided below can refer to the above-mentioned limitations on the scheduling method for carbon emission flows in a power system, and will not be repeated here.

[0237] In an exemplary embodiment, Fig.10 As shown, a scheduling device 100 for carbon emission flow in a power system is provided, comprising: a first building module 10, a second building module 20, a third building module 30 and a scheduling module 40, wherein:

[0238] The first construction module 10 is used to construct a system operating cost function of the target power system based on the power consumption information of the target power system, the equipment operating power and operating cost information of the power generation equipment in the target power system, and the cluster operating power and carbon trading information of the electric vehicle cluster within the coverage area of ​​the target power system.

[0239] The second construction module 20 is used to construct the system operation constraints of the target power system according to the equipment operation power, other load powers of other electrical equipment in the target power system except the electric vehicle cluster, and the charging and discharging power and charging mode selection model of the electric vehicle cluster.

[0240] The third construction module 30 is used to construct a carbon emission flow model of the target power system based on the equipment operating power, the cluster operating power, the energy storage carbon emission information of the energy storage equipment in the target power system, the power generation carbon emission information of the power generation equipment, and the cluster carbon emission information of the electric vehicle cluster.

[0241] The scheduling module 40 is used to schedule the carbon emission flows of the power generation equipment and each electric vehicle in the target power system according to the system operation cost function, the system operation constraints and the carbon emission flow model.

[0242] In one embodiment, the scheduling module 40 includes:

[0243] The first determination unit is used to determine the first carbon emission flow of the power generation equipment and the second carbon emission flow of each electric vehicle according to the system operation cost function, the system operation constraint and the carbon emission flow model.

[0244] The first determining unit is used to schedule the carbon emission flows of the power generation equipment and each electric vehicle in the target power system according to the first carbon emission flow and each second carbon emission flow.

[0245] In one embodiment, the scheduling unit is specifically used for:

[0246] Taking the minimization of the system operation cost function as the goal and the system operation constraints as the constraints, the system operation cost function is solved to obtain the power dispatch strategy of the target power system; based on the power dispatch strategy, the carbon emission flow model is solved to obtain the first carbon emission flow of the power generation equipment in the target power system and the second carbon emission flow of the electric vehicle.

[0247] In one embodiment, the first building module 10 is specifically used for:

[0248] A carbon trading cost function is constructed based on the cluster operating power and carbon trading information, wherein the carbon trading information includes carbon potential information and carbon price information; a new energy power abandonment ladder penalty cost function is constructed based on the power abandonment penalty cost coefficient, power abandonment load and power abandonment ladder penalty price information in the electricity consumption information; a unit output cost function is constructed based on the operating cost information; wherein the operating cost information includes operating cost and start-up cost; the sum of the carbon trading cost function, the new energy power abandonment ladder penalty cost function and the unit output cost function is used as the system operating cost function of the target power system.

[0249] In one embodiment, the second building module 20 is specifically used for:

[0250] According to the charging time of each electric vehicle in the electric vehicle cluster, as well as the grid-connected information, off-grid information and charging behavior information of each electric vehicle during the charging time, the charging urgency value of the electric vehicle cluster is determined; according to the charging urgency value and the charging and discharging power, a charging mode selection model for the electric vehicle cluster is constructed.

[0251] In one embodiment, the system operation constraints include the charging and discharging constraints and the charging mode selection constraints of the electric vehicle cluster, the power system balance constraints of the target power system, and the output constraints of the power generation equipment; the second building module 20 is also used for:

[0252] Based on the charging and discharging power, as well as the grid-connected and off-grid information of each electric vehicle in the electric vehicle cluster, the charging and discharging constraints are constructed; based on the charging mode selection model and charging and discharging power of the electric vehicle cluster, the charging mode selection constraints are constructed; based on the equipment operating power, charging and discharging power and other load powers, the power system balance constraints are constructed; based on the equipment operating power, the output constraints are constructed.

[0253] In one embodiment, the carbon emission flow model includes a carbon emission responsibility model and a carbon emission amount model; the third building block 30 includes:

[0254] The second determination unit is used to determine the carbon potential of the target power system according to the power generation carbon emission information.

[0255] The first construction unit is used to construct a carbon emission responsibility model according to carbon potential, energy storage carbon emission information and cluster carbon emission information.

[0256] The second construction unit is used to construct a carbon emission model according to the carbon potential and the equipment operating power.

[0257] In one embodiment, the first building block is specifically used for:

[0258] According to the carbon potential, the carbon emission factor of the bidirectional charging and discharging equipment in the target power system during discharge is determined; wherein the bidirectional charging and discharging equipment includes energy storage equipment and electric vehicle clusters; according to the carbon emission factor, energy storage carbon emission information and cluster carbon emission information, the discharge carbon flow rate of the bidirectional charging and discharging equipment during discharge is determined; according to the discharge carbon flow rate and the charging carbon flow rate of the bidirectional charging and discharging equipment during charging, a carbon emission responsibility model is constructed.

[0259] Each module in the above-mentioned dispatching device for carbon emission flow in the power system can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, 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 each of the above modules.

[0260] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.11 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. 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. 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 an external device. 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, a method for scheduling carbon emission flows in a power system is implemented.

[0261] Those skilled in the art will understand that Fig.11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0262] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the method for scheduling carbon emission flows in the power system are implemented.

[0263] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for scheduling carbon emission flows in the power system are implemented.

[0264] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for scheduling carbon emission flows in a power system.

[0265] It should be noted that the data involved in this application (including but not limited to data used for analysis, storage, display, etc.) are all information and data fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0266] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. 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. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0267] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0268] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for scheduling carbon emission flows in a power system, characterized in that: The method comprises: Constructing a system operation cost function of the target power system according to the power consumption information of the target power system, the equipment operation power and operation cost information of the power generation equipment in the target power system, and the cluster operation power and carbon trading information of the electric vehicle cluster within the coverage area of ​​the target power system; According to the operating power of the equipment, other load powers of other electrical equipment in the target power system except the electric vehicle cluster, and the charging and discharging power and charging mode selection model of the electric vehicle cluster, the system operating constraints of the target power system are constructed; Constructing a carbon emission flow model of the target power system according to the operating power of the device, the operating power of the cluster, the energy storage carbon emission information of the energy storage device in the target power system, the power generation carbon emission information of the power generation device, and the cluster carbon emission information of the electric vehicle cluster; The carbon emission flows of power generation equipment and electric vehicles in the target power system are scheduled according to the system operation cost function, the system operation constraints and the carbon emission flow model.

2. The method according to claim 1, characterized in that The step of scheduling the carbon emission flows of the power generation equipment and each electric vehicle in the target power system according to the system operation cost function, the system operation constraints and the carbon emission flow model includes: Determining a first carbon emission flow of the power generation equipment and a second carbon emission flow of each electric vehicle according to the system operation cost function, the system operation constraints and the carbon emission flow model; The carbon emission flows of the power generation equipment and each electric vehicle in the target power system are scheduled according to the first carbon emission flow and each of the second carbon emission flows.

3. The method according to claim 1, characterized in that The system operation cost function of the target power system is constructed based on the power consumption information of the target power system, the equipment operation power and operation cost information of the power generation equipment in the target power system, and the cluster operation power and carbon trading information of the electric vehicle cluster within the coverage area of ​​the target power system, including: Constructing a carbon trading cost function according to the cluster operating power and the carbon trading information; wherein the carbon trading information includes carbon potential information and carbon price information; Constructing a new energy power abandonment step penalty cost function according to the power abandonment penalty cost coefficient, power abandonment load and power abandonment step penalty price information in the power consumption information; According to the operating cost information, construct a unit output cost function; wherein the operating cost information includes operating cost and startup cost; The sum of the carbon trading cost function, the new energy power abandonment step penalty cost function and the unit output cost function is used as the system operation cost function of the target power system.

4. The method according to claim 1, characterized in that: The charging mode selection model is constructed in the following way: Determine the charging emergency value of the electric vehicle cluster according to the charging time of each electric vehicle in the electric vehicle cluster, and the grid connection information, grid-offline information and charging behavior information of each electric vehicle during the charging time; A charging mode selection model for the electric vehicle cluster is constructed according to the charging urgency value and the charging and discharging power.

5. The method according to claim 1, characterized in that The system operation constraints include the charging and discharging constraints and the charging mode selection constraints of the electric vehicle cluster, the power system balance constraints of the target power system, and the output constraints of the power generation equipment; Accordingly, the system operation constraints of the target power system are constructed according to the operating power of the equipment, other load powers of other electrical equipment in the target power system except the electric vehicle cluster, and the charging and discharging power and charging mode selection model of the electric vehicle cluster, including: Constructing the charging and discharging constraints according to the charging and discharging power and the grid-connected information and off-grid information of each electric vehicle in the electric vehicle cluster; Constructing the charging mode selection constraint according to the charging mode selection model of the electric vehicle cluster and the charging and discharging power; Constructing the power system balance constraint according to the equipment operating power, the charging and discharging power and the other load power; The output constraint is constructed according to the operating power of the device.

6. The method according to claim 1, characterized in that The carbon emission flow model includes a carbon emission responsibility model and a carbon emission quantity model; The carbon emission flow model of the target power system is constructed according to the device operating power, the cluster operating power, the energy storage carbon emission information of the energy storage device in the target power system, the power generation carbon emission information of the power generation device, and the cluster carbon emission information of the electric vehicle cluster, including: Determining the carbon potential of the target power system according to the power generation carbon emission information; constructing the carbon emission responsibility model according to the carbon potential, the energy storage carbon emission information and the cluster carbon emission information; The carbon emission model is constructed according to the carbon potential and the equipment operating power.

7. The method according to claim 6, characterized in that The step of constructing the carbon emission responsibility model according to the carbon potential, the energy storage carbon emission information and the cluster carbon emission information includes: According to the carbon potential, determining the carbon emission factor of the bidirectional charging and discharging equipment in the target power system during discharge; wherein the bidirectional charging and discharging equipment includes energy storage equipment and electric vehicle clusters; Determining a discharge carbon flow rate of the bidirectional charge-discharge device during discharge according to the carbon emission factor, the energy storage carbon emission information and the cluster carbon emission information; The carbon emission responsibility model is constructed according to the discharge carbon flow rate and the charging carbon flow rate of the bidirectional charge and discharge device during charging.

8. The method according to claim 2, characterized in that: The determining, according to the system operation cost function, the system operation constraint and the carbon emission flow model, a first carbon emission flow of a power generation device and a second carbon emission flow of an electric vehicle in the target power system comprises: Taking the minimization of the system operation cost function as the goal and the system operation constraint as the constraint condition, solving the system operation cost function to obtain the power dispatch strategy of the target power system; Based on the power dispatching strategy, the carbon emission flow model is solved to obtain a first carbon emission flow of power generation equipment and a second carbon emission flow of electric vehicles in the target power system.

9. A device for dispatching carbon emission flow in a power system, characterized in that: The device comprises: A first construction module is used to construct a system operation cost function of the target power system according to the power consumption information of the target power system, the equipment operation power and operation cost information of the power generation equipment in the target power system, and the cluster operation power and carbon trading information of the electric vehicle cluster within the coverage area of ​​the target power system; A second construction module is used to construct a system operation constraint of the target power system according to the operating power of the equipment, other load powers of other electrical equipment in the target power system except the electric vehicle cluster, and the charging and discharging power and charging mode selection model of the electric vehicle cluster; A third construction module is used to construct a carbon emission flow model of the target power system according to the operating power of the device, the operating power of the cluster, the energy storage carbon emission information of the energy storage device in the target power system, the power generation carbon emission information of the power generation equipment, and the cluster carbon emission information of the electric vehicle cluster; A scheduling module is used to schedule the carbon emission flows of the power generation equipment and each electric vehicle in the target power system according to the system operation cost function, the system operation constraints and the carbon emission flow model.

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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