A power distribution network multi-objective optimization scheduling method and system based on carbon emission reduction

Abstract

The application belongs to the technical field of new energy, and particularly relates to a power distribution network multi-objective optimization scheduling method and system based on carbon emission reduction, which obtains electric vehicle information and real-time information of charging piles in a charging station, constructs a multi-objective optimization scheduling model containing net load variance, system operation total cost, electric vehicle user cost and charging carbon emission cost according to the information, generates a charging scheduling strategy after solving, fully utilizes a power carbon emission factor to design a low-carbon target of electric vehicle charging, constructs a multi-objective optimization scheduling model, sets a constraint condition of the multi-objective optimization scheduling model, guides electric vehicle owners to participate in power grid scheduling, and when a peak-valley period price difference is large, the electric vehicle owners are more willing to charge in a load valley period and a period with low electricity price, charging cost expenditure can be reduced, peak load can be effectively filled, and the scheduling pressure of the power grid can be relieved.

Description

Technical Field

[0001] This invention belongs to the field of new energy technology, specifically relating to a multi-objective optimization scheduling method and system for power distribution networks based on carbon emission reduction. Background Technology

[0002] The orderly integration of large-scale electric vehicles (EVs) into active distribution networks has become a mainstream trend driving energy transition and achieving green development. This shift aims to reduce dependence on traditional fossil fuels and optimize the energy structure by increasing the penetration rate of EVs. Therefore, researching how to efficiently and safely integrate large-scale EVs into active distribution networks with a high proportion of renewable energy has become a hot topic of common concern in academia and industry. However, the direct integration of large-scale, disorderly EV loads into the grid can not only cause safety hazards such as voltage fluctuations and overloads, but also pose unprecedented challenges to power system dispatching decisions. Currently, most domestic and international research on this issue focuses on minimizing the charging costs of the power system. While this has positive implications for improving economic efficiency, few studies have comprehensively considered the effective absorption of renewable energy, orderly charging strategies for EVs, and the resulting indirect carbon emissions. This means that balancing the multi-dimensional goals of environmental protection, efficient energy utilization, and the safe and stable operation of the power grid while pursuing economic benefits has become a critical issue that urgently needs to be addressed. Summary of the Invention

[0003] The purpose of this invention is to provide a multi-objective optimization scheduling method and system for power distribution networks based on carbon emission reduction, so as to solve the problem that the existing technology cannot simultaneously meet the requirements of new energy consumption, orderly charging of electric vehicles and indirect carbon emissions.

[0004] To achieve the above objectives, the present invention employs the following technical solution: Firstly, this application discloses a multi-objective optimal scheduling method for distribution networks based on carbon emission reduction, including: Obtain information on electric vehicles charging at charging stations; obtain real-time information on charging piles; Based on electric vehicle information and real-time charging pile information, a multi-objective optimization scheduling model is constructed, which includes net load variance, total system operating cost, electric vehicle user cost, and charging carbon emission cost. Solve the multi-objective optimization scheduling model to generate a charging scheduling strategy.

[0005] Preferably, the electric vehicle information includes: the electric vehicle's access time, current SOC, estimated disconnection time, low SOC, and high SOC; The real-time information of the charging piles includes: real-time charging load, wind power, photovoltaic power, and energy storage.

[0006] Preferably, the multi-objective optimization scheduling model is specifically represented by the following formula: Net load variance:

[0007]

[0008] Total system operating cost:

[0009] Electric vehicle user costs:

[0010] Carbon emission costs of charging:

[0011] In the formula, This represents the net load variance. The scheduling period; This represents the actual power grid load. This represents the actual average load of the power grid. The power output of electric vehicles connected to the grid; This is the predicted value for wind power output; Forecast value of photovoltaic power output; This refers to the grid-connected power of the energy storage power station; The operating cost of thermal power units; The operating costs of electric vehicle stations; The cost of wind curtailment for wind power and solar power; Cost of system backup capacity; for Charging costs for users of electric vehicles; and These are the times when the nth electric vehicle starts charging and the times when charging ends, respectively. The unit price for charging electric vehicles during time period t; The variable is 0-1. When the electric vehicle is not charging, the value is 0, and when it is charging, the value is 1. The dynamic electricity carbon emission factor for each time slot of the charging station in the area.

[0012] Preferably, the operating cost of the thermal power unit Operating costs of electric vehicle stations Wind curtailment costs of wind power and solar power And the cost of system backup capacity The following formula is used for calculation: Operating costs of thermal power units:

[0013] Operating costs of electric vehicle stations:

[0014] Wind curtailment costs for wind and solar power:

[0015] System backup capacity cost:

[0016] In the formula, The total number of thermal power units participating in the dispatch; , , These are the energy consumption characteristic parameters of the i-th thermal power unit; The output of the i-th thermal power unit during time period t; Let represent the start-up and shutdown status of the i-th thermal power unit at time t. = 1 indicates the device is powered on. = 0 indicates a stopped state; The start-up and shutdown costs of thermal power units; Number of electric vehicle stations; For the operating costs of electric vehicle stations; For electric vehicle station capacity; Charging power for electric vehicle stations; The discharge power of the electric vehicle station; Electricity price per unit of electricity; Cost of energy storage equipment losses; For wind curtailment penalty coefficient, This is the penalty coefficient for discarded light; This refers to the grid-connected power of wind power. This refers to the grid-connected power of photovoltaic power. This refers to load forecasting error; For wind farm output prediction error; For photovoltaic power output prediction error; This is the positive standby capacity for electric vehicle stations; For the negative backup capacity of electric vehicle stations; This is the cost coefficient for system backup capacity.

[0017] Preferably, the solution of the multi-objective optimization scheduling model includes the following constraints: Power balance constraints:

[0018] Charge and discharge constraints:

[0019] Electric vehicle battery capacity constraints:

[0020] Energy constraints:

[0021] Transformer capacity constraints:

[0022] Charging rate constraint:

[0023] Thermal power unit constraints: Based on the actual application parameters of thermal power units, preset the upper and lower limits of output, ramping constraints, minimum allowable start-up and shutdown time constraints, and rotating standby constraints of thermal power units. Energy storage device constraints: Based on the actual performance of the energy storage device, preset constraints on energy storage output, non-overcharging of the energy storage device under state of charge, and non-discharging of the energy storage device under state of charge are set. In the formula, The output of the i-th thermal power unit during time period t; This is the predicted value for wind power output; Forecast value of photovoltaic power output; This refers to the grid-connected power of the energy storage power station; This represents the actual power grid load. The power output of electric vehicles connected to the grid; Let be the number of cars parked at the electric vehicle station during time period t; The maximum discharge capacity of an electric vehicle; Maximum charging capacity for electric vehicles; , These are the upper and lower limits of the battery capacity, respectively. This is the upper limit of the battery's storage capacity; The total number of electric vehicles that can participate in power system dispatch; For electric vehicle battery capacity; For electric vehicles Power consumption at any given moment; For electric vehicles Power consumption at any given moment; For the charging and discharging efficiency of electric vehicles; Let t be the probability that an electric vehicle enters the electric vehicle station and stops during time period t; The power consumed by an electric vehicle per kilometer traveled; The driving speed of the electric vehicle; This represents the maximum load power of the transformer. For charging network; The variable is 0-1. When the electric vehicle is not charging, the value is 0, and when it is charging, the value is 1. The charging rate is the capacity. and These are the times when the nth electric vehicle starts charging and the times when charging ends, respectively.

[0024] Secondly, this application discloses a multi-objective optimization scheduling system for power distribution networks based on carbon emission reduction, comprising: The data acquisition unit is used to acquire vehicle information of electric vehicles charging in the charging station and real-time information of charging piles; The model building unit is used to build a multi-objective optimization scheduling model based on electric vehicle information and real-time charging pile information, including net load variance, total system operating cost, electric vehicle user cost and charging carbon emission cost. The model solving unit is used to solve the multi-objective optimization scheduling model and generate a charging scheduling strategy.

[0025] Preferably, the data acquisition unit includes an electric vehicle aggregator and a charging pile; the electric vehicle aggregator is used to acquire information about electric vehicles charging in the charging station, specifically including: the electric vehicle's access time, current SOC, estimated off-grid time, low SOC, and high SOC; and transmits the above electric vehicle information to the model building unit; the charging pile acquires its own real-time information to obtain real-time charging pile information, specifically including: real-time charging load, wind power, photovoltaic, and energy storage; and transmits the above charging pile real-time information to the model building unit.

[0026] Preferably, the multi-objective optimization scheduling model is specifically represented by the following formula: Net load variance:

[0027]

[0028] Total system operating cost:

[0029]

[0030]

[0031]

[0032]

[0033] Electric vehicle user costs:

[0034] Carbon emission costs of charging:

[0035] In the formula, This represents the net load variance. The scheduling period; This represents the actual power grid load. This represents the actual average load of the power grid. The power output of electric vehicles connected to the grid; This is the predicted value for wind power output; Forecast value of photovoltaic power output; This refers to the grid-connected power of the energy storage power station; The operating cost of thermal power units; The operating costs of electric vehicle stations; The cost of wind curtailment for wind power and solar power; Cost of system backup capacity; for Charging costs for users of electric vehicles; and These are the times when the nth electric vehicle starts charging and the times when charging ends, respectively. The unit price for charging electric vehicles during time period t; The variable is 0-1. The value is 0 for electric vehicles that are not charging and 1 for those that are charging. The dynamic electricity carbon emission factor for each time slot of the charging station in the transformer area; The total number of thermal power units participating in the dispatch; , , These are the energy consumption characteristic parameters of the i-th thermal power unit; The output of the i-th thermal power unit during time period t; Let represent the start-up and shutdown status of the i-th thermal power unit at time t. = 1 indicates the device is powered on. = 0 indicates a stopped state; The start-up and shutdown costs of thermal power units; Number of electric vehicle stations; For the operating costs of electric vehicle stations; For electric vehicle station capacity; Charging power for electric vehicle stations; The discharge power of the electric vehicle station; Electricity price per unit of electricity; Cost of energy storage equipment losses; For wind curtailment penalty coefficient, This is the penalty coefficient for discarded light; This refers to the grid-connected power of wind power. This refers to the grid-connected power of photovoltaic power. This refers to load forecasting error; For wind farm output prediction error; For photovoltaic power output prediction error; This is the positive standby capacity for electric vehicle stations; For the negative backup capacity of electric vehicle stations; This is the cost coefficient for system backup capacity.

[0036] Thirdly, this application discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the multi-objective optimization scheduling method for distribution networks based on carbon emission reduction as described in any of the preceding claims.

[0037] Fourthly, this application discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the multi-objective optimization scheduling method for power distribution networks based on carbon emission reduction as described in any of the preceding claims.

[0038] Compared with the prior art, the present invention has the following beneficial effects: This application fully utilizes the carbon emission factor of electricity to design the low-carbon target of electric vehicle charging, and establishes an ordered charging multi-objective optimization scheduling model with net load variance, total system operating cost, electric vehicle user cost, and charging carbon emission cost as objective functions. It also sets constraints on the multi-objective optimization scheduling model to study the ordered charging scheduling and optimization strategy of electric vehicles.

[0039] (1) By guiding electric vehicle owners to participate in grid dispatch, when the price difference between peak and valley periods is large, electric vehicle owners are more willing to charge during periods of low load and low electricity price, which can reduce charging expenses and effectively achieve peak shaving and valley filling, thus alleviating the dispatch pressure of the grid.

[0040] (2) Compared with disordered charging, orderly charging in different scenarios can effectively save users' charging costs, smooth the load curve, and significantly reduce the carbon emissions of electric vehicle charging, achieving a win-win situation with the participation of multiple stakeholders. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a diagram illustrating an orderly charging scenario at a charging station, as described in an embodiment of the present invention. Figure 3 This is a system block diagram of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0044] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0045] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0046] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0047] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0048] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0049] The present invention will now be described in further detail with reference to the accompanying drawings: See Figure 1 This application discloses a multi-objective optimization scheduling method for distribution networks based on carbon emission reduction, including: S1: Obtain vehicle information and real-time information of charging piles for electric vehicles charging at the charging station; S2: Based on electric vehicle information and real-time charging pile information, construct a multi-objective optimization scheduling model that includes net load variance, total system operating cost, electric vehicle user cost, and charging carbon emission cost; S3: Solve the multi-objective optimization scheduling model to generate a charging scheduling strategy.

[0050] This application fully utilizes the carbon emission factor of electricity to design low-carbon objectives for electric vehicle charging. It establishes an ordered charging multi-objective optimization scheduling model using net load variance, total system operating cost, electric vehicle user cost, and charging carbon emission cost as objective functions. Constraints are set for the multi-objective optimization scheduling model to study ordered charging scheduling and optimization strategies for electric vehicles. By guiding electric vehicle owners to participate in grid dispatch, when the peak-valley electricity price difference is large, electric vehicle owners are more willing to charge during off-peak periods and when electricity prices are lower, which can reduce charging costs and effectively achieve peak shaving and valley filling, alleviating grid dispatch pressure.

[0051] In some embodiments, the electric vehicle information includes: electric vehicle access time, current SOC, estimated off-grid time, low SOC, and high SOC; The real-time information of the charging piles includes: real-time charging load, wind power, photovoltaic power, and energy storage.

[0052] In some embodiments, the multi-objective optimization scheduling model is specifically represented by the following formula: Net load variance:

[0053]

[0054] Total system operating cost:

[0055] Electric vehicle user costs:

[0056] Carbon emission costs of charging:

[0057] In the formula, This represents the net load variance. The scheduling period; This represents the actual power grid load. This represents the actual average load of the power grid. The power output of electric vehicles connected to the grid; This is the predicted value for wind power output; Forecast value of photovoltaic power output; This refers to the grid-connected power of the energy storage power station; The operating cost of thermal power units; The operating costs of electric vehicle stations; The cost of wind curtailment for wind power and solar power; Cost of system backup capacity; for Charging costs for users of electric vehicles; and These are the times when the nth electric vehicle starts charging and the times when charging ends, respectively. The unit price for charging electric vehicles during time period t; The variable is 0-1. The value is 0 for electric vehicles that are not charging and 1 for those that are charging. The dynamic electricity carbon emission factor for each time slot of the charging station in the area.

[0058] More preferably, the operating cost of the thermal power unit Operating costs of electric vehicle stations Wind curtailment costs for wind and solar power And the cost of system backup capacity The following formula is used for calculation: Operating costs of thermal power units:

[0059] Operating costs of electric vehicle stations:

[0060] Wind curtailment costs for wind and solar power:

[0061] System backup capacity cost:

[0062] In the formula, N is the total number of thermal power units participating in the dispatch; , , These are the energy consumption characteristic parameters of the i-th thermal power unit; The output of the i-th thermal power unit during time period t; Let represent the start-up and shutdown status of the i-th thermal power unit at time t. = 1 indicates the device is powered on. = 0 indicates a stopped state; The start-up and shutdown costs of thermal power units; Number of electric vehicle stations; For the operating costs of electric vehicle stations; For electric vehicle station capacity; , Power for charging and discharging electric vehicles at the station; Electricity price per unit of electricity; Cost of energy storage equipment losses; , The penalty coefficients for wind and solar power curtailment; , For wind power and solar power grid connection power; This refers to load forecasting error; For wind farm output prediction error; For photovoltaic power output prediction error; , These are the positive and negative reserve capacities of the electric vehicle depot, respectively. This is the cost coefficient for system backup capacity.

[0063] In some embodiments, solving the multi-objective optimization scheduling model includes the following constraints: Power balance constraints:

[0064] Charge and discharge constraints:

[0065] Electric vehicle battery capacity constraints:

[0066] Energy constraints:

[0067] Transformer capacity constraints:

[0068] Charging rate constraint:

[0069] Thermal power unit constraints: Based on the actual application parameters of thermal power units, preset the upper and lower limits of output, ramping constraints, minimum allowable start-up and shutdown time constraints, and rotating standby constraints of thermal power units. Energy storage device constraints: Based on the actual performance of the energy storage device, preset constraints on energy storage output, non-overcharging of the energy storage device under state of charge, and non-discharging of the energy storage device under state of charge are set. In the formula, The output of the i-th thermal power unit during time period t; This is the predicted value for wind power output; Forecast value of photovoltaic power output; This refers to the grid-connected power of the energy storage power station; This represents the actual power grid load. The power output of electric vehicles connected to the grid; Let be the number of cars parked at the electric vehicle station during time period t; The maximum discharge capacity of an electric vehicle; Maximum charging capacity for electric vehicles; , These are the upper and lower limits of the battery capacity, respectively. This is the upper limit of the battery's storage capacity; The total number of electric vehicles that can participate in power system dispatch; For electric vehicle battery capacity; For electric vehicles Power consumption at any given moment; For electric vehicles Power consumption at any given moment; For the charging and discharging efficiency of electric vehicles; Let t be the probability that an electric vehicle enters the electric vehicle station and stops during time period t; The power consumed by an electric vehicle per kilometer traveled; The driving speed of the electric vehicle; This represents the maximum load power of the transformer. For charging network; The variable is 0-1. When the electric vehicle is not charging, the value is 0, and when it is charging, the value is 1. The charging rate is the capacity. and These are the times when the nth electric vehicle starts charging and the times when charging ends, respectively.

[0070] In some embodiments, a multi-objective optimization scheduling method for distribution networks based on carbon emission reduction includes the following steps: Step 1: When an electric vehicle enters the charging station and connects to the charging pile to start charging, the charging station's EV aggregator transmits information such as the electric vehicle's access time, current SOC, expected off-grid time, low SOC, and high SOC to the station's control center.

[0071] Step 2: The control center then combines real-time charging load, wind power, photovoltaic, and energy storage information to standardize the four objective functions: net load variance, total system operating cost, electric vehicle user cost, and charging carbon emission cost. After standardizing these functions, the multi-objective problem is transformed into a single-objective optimization problem with equal weighting factors. After solving the problem, the charging power is scheduled and allocated to the vehicles in an orderly manner.

[0072] Step 3: During the orderly charging process, the electric vehicle aggregator is an interface entity responsible for matching the dispersed energy resources of electric vehicles, collecting charging vehicle information, and transmitting scheduling strategies.

[0073] Step 4: To smooth the overall grid load curve while meeting user travel needs, the aggregator will shift some charging load to off-peak periods. A schematic diagram of the orderly charging scheduling scenario at charging stations is shown below. Figure 2 As shown.

[0074] In some embodiments, this application establishes a multi-objective charging optimization scheduling model that considers carbon emission reduction and includes electric vehicles, based on a wind-solar-thermal-storage joint scheduling model and taking into account the regulation capability of electric vehicles. The multi-objective charging optimization scheduling model is represented by the following objective function: 1) Minimum net load variance (1) (2) In the formula: This represents the net load variance. The scheduling cycle is set to 15 minutes, and the day is divided into 96 time periods. This represents the actual power grid load. This represents the actual average load of the power grid. The power output of electric vehicles connected to the grid; This is the predicted value for wind power output; Forecast value of photovoltaic power output; This refers to the grid connection power of the energy storage power station.

[0075] 2) Minimize total system operating cost (3) Operating costs of thermal power units: (4) In the formula: N is the total number of thermal power units participating in the dispatch; , , These are the energy consumption characteristic parameters of the i-th thermal power unit; The output of the i-th thermal power unit during time period t; Let represent the start-up and shutdown status of the i-th thermal power unit at time t. = 1 indicates the device is powered on. = 0 indicates a stopped state; This refers to the start-up and shutdown costs of thermal power units.

[0076] Operating costs of electric vehicle stations: (5) In the formula: The number of electric vehicle stations; For the operating costs of electric vehicle stations; For electric vehicle station capacity; , The charging and discharging power of electric vehicle stations; Electricity price per unit of electricity; Cost of energy storage equipment losses.

[0077] Wind curtailment costs for wind and solar power: (6) In the formula: , The penalty coefficients for wind and solar power curtailment; , This refers to the grid connection power of wind and solar power.

[0078] System backup capacity cost: (7) In the formula: This refers to load forecasting error; For wind farm output prediction error; For photovoltaic power output prediction error; , These represent the positive and negative reserve capacities of the electric vehicle depot, respectively. This is the cost coefficient for system backup capacity.

[0079] 3) Electric vehicle users have the lowest cost. (8) In the formula: The charging cost for users of N electric vehicles; and These are the times when the nth electric vehicle starts charging and the times when charging ends, respectively. The unit price for charging electric vehicles during time period t; The variable is 0-1. The value is 0 for electric vehicles that are not charging and 1 for those that are charging.

[0080] 4) Minimal carbon emission cost for charging (9) In the formula: This refers to the dynamic electricity carbon emission factor for each time slot at the charging station in the power grid area. To minimize carbon emissions during electric vehicle charging, charging times should be scheduled during periods of lower grid carbon intensity.

[0081] The constraints for solving are: 1) Power balance constraints: (10) 2) Constraints of thermal power units: upper and lower limits of output of thermal power units, ramping constraints, minimum allowable start-up and shutdown time constraints of thermal power units, and rotating reserve constraints of thermal power units. 3) Output constraints for wind turbines and photovoltaic units: The actual output of wind turbines and photovoltaic units should be less than or equal to the maximum output of the wind farm and photovoltaic panels.

[0082] 4) Constraints on energy storage devices: Constraints on energy storage output and state of charge of energy storage devices to ensure that energy storage is not overcharged or over-discharged.

[0083] 5) Constraints related to electric vehicles: Charge and discharge constraints: (11) In the formula: Let be the number of cars parked at the electric vehicle station during time period t; , These represent the maximum charging and discharging capacity of electric vehicles, respectively.

[0084] Electric vehicle battery capacity constraints: To prevent electric vehicles from being overcharged and over-discharged and to extend the battery's lifespan, upper and lower limits should be set for the energy stored in the battery.

[0085] (12) In the formula: , These are the upper and lower limits of the battery capacity, respectively. This is the upper limit of the battery's storage capacity; The total number of electric vehicles that can participate in power system dispatch; This refers to the battery capacity of electric vehicles.

[0086] Energy constraints: (13) In the formula: For the charging and discharging efficiency of electric vehicles; Let t be the probability that an electric vehicle enters the electric vehicle station and stops during time period t; The power consumed by an electric vehicle per kilometer traveled; This refers to the driving speed of the electric vehicle.

[0087] Transformer capacity constraints: (14) In the formula: This represents the maximum load power of the transformer.

[0088] Charging rate constraint: (15) In the formula: the former ensures that the electric vehicle adheres to the charging network throughout the entire charging process. The latter ensures that the electric vehicle adheres to the charger's charging rate capacity throughout the charging process. limit.

[0089] See Figure 3 This application also discloses a multi-objective optimization scheduling system for distribution networks based on carbon emission reduction, comprising: The data acquisition unit is used to acquire information on electric vehicles charging in the charging station and real-time information on charging piles. The model building unit is used to build a multi-objective optimization scheduling model based on electric vehicle information and real-time charging pile information, including net load variance, total system operating cost, electric vehicle user cost and charging carbon emission cost. The model solving unit is used to solve the multi-objective optimization scheduling model and generate a charging scheduling strategy.

[0090] In some embodiments, the data acquisition unit includes an electric vehicle aggregator and a charging pile; the electric vehicle aggregator is used to acquire information about electric vehicles charging in the charging station, specifically including: the electric vehicle's access time, current SOC, estimated off-grid time, low SOC, and high SOC; and transmits the above electric vehicle information to the model building unit; the charging pile acquires its own real-time information to obtain real-time charging pile information, specifically including: real-time charging load, wind power, photovoltaic, and energy storage; and transmits the above charging pile real-time information to the model building unit.

[0091] In some embodiments, the multi-objective optimization scheduling model is specifically represented by the following formula: Net load variance:

[0092]

[0093] Total system operating cost:

[0094]

[0095]

[0096]

[0097]

[0098] Electric vehicle user costs:

[0099] Carbon emission costs of charging:

[0100] In the formula, This represents the net load variance. The scheduling period; This represents the actual power grid load. This represents the actual average load of the power grid. The power output for electric vehicles connected to the grid; This is the predicted value for wind power output; Forecast value of photovoltaic power output; This refers to the grid-connected power of the energy storage power station; The operating cost of thermal power units; For the operating costs of electric vehicle stations; The cost of wind curtailment for wind power and solar power; Cost of system backup capacity; for Charging costs for users of electric vehicles; and These are the times when the nth electric vehicle starts charging and the times when charging ends, respectively. The unit price for charging electric vehicles during time period t; The variable is 0-1. The value is 0 for electric vehicles that are not charging and 1 for those that are charging. The dynamic electricity carbon emission factor for each time slot of the charging station in the transformer area; The total number of thermal power units participating in the dispatch; , , These are the energy consumption characteristic parameters of the i-th thermal power unit; The output of the i-th thermal power unit during time period t; Let represent the start-up and shutdown status of the i-th thermal power unit at time t. = 1 indicates the device is powered on. = 0 indicates a stopped state; The start-up and shutdown costs of thermal power units; Number of electric vehicle stations; For the operating costs of electric vehicle stations; For electric vehicle station capacity; , Power for charging and discharging electric vehicles at the station; Electricity price per unit of electricity; Cost of energy storage equipment losses; This is the wind curtailment penalty coefficient; This is the penalty coefficient for discarded light; , For wind power and solar power grid connection power; This refers to load forecasting error; For wind farm output prediction error; For photovoltaic power output prediction error; , These are the positive and negative reserve capacities of the electric vehicle depot, respectively. This is the cost coefficient for system backup capacity.

[0101] The present invention also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the multi-objective optimization scheduling method for power distribution networks based on carbon emission reduction as described above.

[0102] The present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the multi-objective optimization scheduling method for distribution networks based on carbon emission reduction described above.

[0103] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0105] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-objective optimal scheduling method for power distribution networks based on carbon emission reduction, characterized in that, include: Obtain information on electric vehicles charging at charging stations and real-time information on charging piles; Based on electric vehicle information and real-time charging pile information, a multi-objective optimization scheduling model is constructed, which includes net load variance, total system operating cost, electric vehicle user cost, and charging carbon emission cost. The multi-objective optimization scheduling model is specifically expressed by the following formula: Net load variance: Total system operating cost: Electric vehicle user costs: Carbon emission costs of charging: Operating costs of thermal power units: Operating costs of electric vehicle stations: Costs of wind and solar curtailment: System backup capacity cost: Solve the multi-objective optimization scheduling model to generate a charging scheduling strategy; In the formula, This represents the net load variance. The scheduling period; This represents the actual power grid load. This represents the actual average load of the power grid. The power output for electric vehicles connected to the grid; This is the predicted value for wind power output; Forecast value of photovoltaic power output; This refers to the grid-connected power of the energy storage power station; The operating cost of thermal power units; For the operating costs of electric vehicle stations; Costs associated with wind and solar power curtailment; Cost of system backup capacity; for Charging costs for users of electric vehicles; and These are the times when the nth electric vehicle starts charging and the times when charging ends, respectively. The unit price for charging electric vehicles during time period t; The variable is 0-1. When the electric vehicle is not charging, the value is 0, and when it is charging, the value is 1. The dynamic electricity carbon emission factor for each time slot of the charging station in the transformer area; The total number of thermal power units participating in the dispatch; , , These are the energy consumption characteristic parameters of the i-th thermal power unit; The output of the i-th thermal power unit during time period t; Let represent the start-up and shutdown status of the i-th thermal power unit at time t. = 1 indicates the device is powered on. = 0 indicates a stopped state; The start-up and shutdown costs of thermal power units; The number of electric vehicle stations; For the operating costs of electric vehicle stations; For electric vehicle station capacity; Charging power for electric vehicle stations; The discharge power of the electric vehicle station; Electricity price per unit of electricity; Cost of energy storage equipment losses; For wind curtailment penalty coefficient, This is the penalty coefficient for discarded light; This refers to the grid-connected power of wind power. This refers to the grid-connected power of photovoltaic power. This refers to load forecasting error; For wind farm output prediction error; For photovoltaic power output prediction error; This is the positive standby capacity for electric vehicle stations; This is the negative backup capacity for electric vehicle stations; This is the cost coefficient for system backup capacity.

2. The multi-objective optimization scheduling method for distribution networks based on carbon emission reduction according to claim 1, characterized in that, The electric vehicle information includes: electric vehicle access time, current SOC, estimated off-grid time, low SOC, and high SOC; The real-time information of the charging piles includes: real-time charging load, wind power, photovoltaic power, and energy storage.

3. The multi-objective optimization scheduling method for distribution networks based on carbon emission reduction according to claim 1, characterized in that, The solution to the multi-objective optimization scheduling model includes the following constraints: Power balance constraints: Charge and discharge constraints: Electric vehicle battery capacity constraints: Energy constraints: Transformer capacity constraints: Charging rate constraint: Thermal power unit constraints: Based on the actual application parameters of thermal power units, preset the upper and lower limits of output, ramping constraints, minimum allowable start-up and shutdown time constraints, and rotating standby constraints of thermal power units. Energy storage device constraints: Based on the actual performance of the energy storage device, preset constraints on energy storage output, non-overcharging of the energy storage device under state of charge, and non-discharging of the energy storage device under state of charge are set. In the formula, The output of the i-th thermal power unit during time period t; This is the predicted value for wind power output; Forecast value of photovoltaic power output; This refers to the grid-connected power of the energy storage power station; This represents the actual power grid load. The power output for electric vehicles connected to the grid; Let be the number of cars parked at the electric vehicle station during time period t; The maximum discharge capacity of an electric vehicle; Maximum charging capacity for electric vehicles; , These are the upper and lower limits of the battery capacity, respectively. This is the upper limit of the battery's storage capacity; The total number of electric vehicles that can participate in power system dispatch; For electric vehicle battery capacity; For electric vehicles Power consumption at any given moment; For electric vehicles Power consumption at any given moment; For the charging and discharging efficiency of electric vehicles; Let t be the probability that an electric vehicle enters the electric vehicle station and stops during time period t; The power consumed by an electric vehicle per kilometer traveled; The driving speed of the electric vehicle; This represents the maximum load power of the transformer. For charging network; The variable is 0-1. When the electric vehicle is not charging, the value is 0, and when it is charging, the value is 1. The charging rate is the capacity. and These are the times when the nth electric vehicle starts charging and the times when charging ends, respectively.

4. A multi-objective optimization scheduling system for power distribution networks based on carbon emission reduction, characterized in that, The method for implementing the carbon emission reduction-based multi-objective optimization scheduling method for distribution networks as described in any one of claims 1 to 3 includes: The data acquisition unit is used to acquire information about electric vehicles charging in the charging station and to acquire real-time information about charging piles. The model building unit is used to build a multi-objective optimization scheduling model based on electric vehicle information and real-time charging pile information, including net load variance, total system operating cost, electric vehicle user cost and charging carbon emission cost. The model solving unit is used to solve the multi-objective optimization scheduling model and generate a charging scheduling strategy.

5. A multi-objective optimization scheduling system for power distribution networks based on carbon emission reduction according to claim 4, characterized in that, The data acquisition unit includes an electric vehicle aggregator and charging piles. The electric vehicle aggregator is used to acquire information about electric vehicles charging in the charging station, specifically including: the electric vehicle's access time, current SOC, estimated off-grid time, low SOC, and high SOC; and transmits the above electric vehicle information to the model building unit. The charging pile acquires its own real-time information to obtain real-time charging pile information, specifically including: real-time charging load, wind power, photovoltaic, and energy storage; and transmits the above charging pile real-time information to the model building unit.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the multi-objective optimization scheduling method for power distribution networks based on carbon emission reduction as described in any one of claims 1-3.

7. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the multi-objective optimization scheduling method for distribution networks based on carbon emission reduction as described in any one of claims 1-3.

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