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

Abstract

The invention belongs to the technical field of new energy, and particularly relates to a power distribution network multi-target optimization scheduling method and system based on carbon emission reduction. According to the information, a multi-target optimization scheduling model including the net load variance, the total system operation cost, the electric vehicle user cost and the charging carbon emission cost is constructed, a charging scheduling strategy is generated after solving, a low-carbon target of electric vehicle charging is designed by fully utilizing an electric power carbon emission factor, the multi-target optimization scheduling model is constructed, and the charging scheduling strategy is optimized. Constraint conditions of a multi-target optimization scheduling model are set, an electric vehicle owner is guided to participate in power grid scheduling, when the electricity price difference in the peak-valley period is large, the electric vehicle owner is more willing to charge in the load valley period and the period with the low electricity price, the charging expense can be reduced, peak clipping and valley filling can be effectively achieved, and the scheduling pressure of a power grid is relieved.

Description

Technical Field

[0001] The present invention belongs to the field of new energy technology, and specifically relates to a multi-objective optimization scheduling method and system for a distribution network based on carbon emission reduction. Background Art

[0002] With the clear proposal of the "dual carbon" (i.e., carbon peak and carbon neutrality) goals, the orderly access of large-scale electric vehicles (EVs) to active distribution networks has become the mainstream trend to promote energy transformation and achieve green development. This transformation aims to reduce dependence on traditional fossil fuels and promote the optimization of energy structure by increasing the penetration rate of electric vehicles. Therefore, studying how large-scale electric vehicles can efficiently and safely access active distribution networks with a high proportion of new energy penetration has become a hot topic of common concern in academia and industry. However, the direct access of large-scale disordered electric vehicle loads to the power grid may not only cause safety hazards such as voltage fluctuations and overloads, but also bring unprecedented challenges to the dispatching decisions of the power system. At present, most of the research on this issue at home and abroad focuses on how to minimize the charging cost of the power system in terms of optimization methods. Although this has positive significance for improving economic benefits, few studies can comprehensively consider the effective absorption of new energy, the orderly charging strategy of electric vehicles, and the resulting indirect carbon emissions. This means that while pursuing economic benefits, how to balance the multi-dimensional goals of environmental protection, efficient energy utilization, and safe and stable operation of the power grid has become a key issue that needs to be solved urgently. Summary of the invention

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

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present application discloses a multi-objective optimization scheduling method for a distribution network based on carbon emission reduction, comprising: Obtain vehicle information of electric vehicles charging in charging stations; obtain real-time information of charging piles; According to the real-time information of electric vehicles and charging piles, a multi-objective optimization scheduling model including net load variance, total system operation cost, electric vehicle user cost and charging carbon emission cost is constructed; Solve the multi-objective optimization scheduling model and generate the charging scheduling strategy.

[0005] Preferably, the electric vehicle vehicle information includes: the access time, current SOC, expected off-grid time, low SOC and high SOC of the electric vehicle; The real-time information of the charging pile includes: real-time charging load, wind power, photovoltaic power and energy storage.

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

[0007]

[0008] Total system operating cost:

[0009] Electric vehicle user costs:

[0010] Charging Carbon Emission Cost:

[0011] In the formula, is the net load variance; is the scheduling period; is the actual grid load; is the actual average load of the power grid; is the power of electric vehicles connected to the grid; is the predicted value of wind power; is the predicted value of photovoltaic output; The grid-connected power of the energy storage power station; is the operating cost of thermal power units; The operating costs of the electric vehicle station; The cost of wind power and photovoltaic power curtailment; is the system's spare capacity cost; for Charging fees for electric vehicle users; and are the times when the nth electric vehicle starts charging and ends charging respectively; is the charging unit price of electric vehicles in period t; It is a 0-1 variable, the corresponding electric vehicle takes the value of 0 when not charging and takes the value of 1 when charging; It is 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 is , Operating costs of electric vehicle stations , wind power and photovoltaic wind curtailment costs and the system's spare capacity cost The specific calculation is as follows: Operating costs of thermal power units:

[0013] Operating costs of an electric vehicle station:

[0014] The cost of curtailment of wind power and photovoltaic power:

[0015] The system's spare capacity cost:

[0016] In the formula, is the total number of thermal power units participating in the dispatch; , , are the energy consumption characteristic parameters of the i-th thermal power unit; is the output of the i-th thermal power unit in period t; is the start / stop status of the i-th thermal power unit at time t, = 1 is the power-on state, = 0 means shutdown state; is the start-up and shutdown cost of thermal power units; is the number of electric vehicle stations; Cost of running the electric vehicle station; is the capacity of the electric vehicle station; Charging power for electric vehicle stations; Discharge power for electric vehicle stations; is the electricity price per unit of electricity; The loss cost of energy storage equipment; is the wind abandonment penalty coefficient, is the penalty coefficient for abandoning light; The grid-connected power of wind power; is the photovoltaic grid-connected power; is the load forecast error; is the wind farm output prediction error; is the photovoltaic output prediction error; is the positive reserve capacity of the electric vehicle station; is the negative reserve capacity of the electric vehicle station; is the system spare capacity cost coefficient.

[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 constraints:

[0023] Thermal power unit constraints: preset the upper and lower output limits, ramp constraints, minimum allowable start and stop time constraints, and spinning reserve constraints of thermal power units according to the actual application parameters of thermal power units; Energy storage device constraints: preset energy storage output constraints, energy storage device state of charge non-overcharge constraints, and energy storage device state of charge non-discharge constraints according to the actual performance of the energy storage device; In the formula, is the output of the i-th thermal power unit in period t; is the predicted value of wind power; is the predicted value of photovoltaic output; The grid-connected power of the energy storage power station; is the actual grid load; is the power of electric vehicles connected to the grid; is the number of cars parked at the electric vehicle station during period t; The maximum capacity to discharge an electric vehicle; Maximum capacity for charging electric vehicles; , They are the upper and lower limit parameters of battery capacity respectively; The upper limit of the battery's storage capacity; is the total number of electric vehicles that can participate in the power system dispatch; is the battery capacity of electric vehicles; For electric vehicles Power consumption at each moment; For electric vehicles Power consumption at each moment; The charging and discharging efficiency of electric vehicles; is the probability that an electric vehicle enters the electric vehicle station and stops driving during period t; The power consumed by the electric vehicle for every 1 km traveled; is the driving speed of the electric vehicle; is the maximum load power of the transformer; For charging network; It is a 0-1 variable, the corresponding electric vehicle takes the value of 0 when not charging and takes the value of 1 when charging; is the charging rate capacity; and They are the time when the nth electric car starts charging and ends charging respectively.

[0024] In a second aspect, the present application discloses a distribution network multi-objective optimization dispatching system based on carbon emission reduction, comprising: A data acquisition unit, used to acquire vehicle information of electric vehicles charged in a charging station and real-time information of charging piles; A model building unit, used to build a multi-objective optimization scheduling model including net load variance, total system operation cost, electric vehicle user cost and charging carbon emission cost according to electric vehicle information and real-time information of charging piles; The model solving unit is used to solve the multi-objective optimization scheduling model and generate the 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 obtain vehicle information of electric vehicles charged in the charging station, specifically including: the access time of the electric vehicle, the current SOC, the expected off-grid time, the low SOC and the high SOC; and transmit the above electric vehicle information to the model building unit; the charging pile obtains its own real-time information to obtain the real-time information of the charging pile, specifically including: real-time charging load, wind power, photovoltaic and energy storage; and transmits the above real-time information of the charging pile to the model building unit.

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

[0027]

[0028] Total system operating cost:

[0029]

[0030]

[0031]

[0032]

[0033] Electric vehicle user costs:

[0034] Charging Carbon Emission Cost:

[0035] In the formula, is the net load variance; is the scheduling period; is the actual grid load; is the actual average load of the power grid; is the power of electric vehicles connected to the grid; is the predicted value of wind power; is the predicted value of photovoltaic output; The grid-connected power of the energy storage power station; is the operating cost of thermal power units; The operating costs of the electric vehicle station; The cost of wind power and photovoltaic power curtailment; is the system's spare capacity cost; for Charging fees for electric vehicle users; and are the times when the nth electric vehicle starts charging and ends charging respectively; is the charging unit price of electric vehicles in period t; It is a 0-1 variable, the corresponding electric vehicle takes the value of 0 when not charging and takes the value of 1 when charging; The dynamic electricity carbon emission factor for each time slot of the charging station in the area; is the total number of thermal power units participating in the dispatch; , , are the energy consumption characteristic parameters of the i-th thermal power unit; is the output of the i-th thermal power unit in period t; is the start / stop status of the i-th thermal power unit at time t, = 1 is the power-on state, = 0 means shutdown state; is the start-up and shutdown cost of thermal power units; is the number of electric vehicle stations; Cost of running the electric vehicle station; is the capacity of the electric vehicle station; Charging power for electric vehicle stations; Discharge power for electric vehicle stations; is the electricity price per unit of electricity; The loss cost of energy storage equipment; is the wind abandonment penalty coefficient, is the penalty coefficient for abandoning light; The grid-connected power of wind power; is the photovoltaic grid-connected power; is the load forecast error; is the wind farm output prediction error; is the photovoltaic output prediction error; is the positive reserve capacity of the electric vehicle station; is the negative reserve capacity of the electric vehicle station; is the system spare capacity cost coefficient.

[0036] In a third aspect, the present application discloses an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above-mentioned methods for multi-objective optimization and scheduling of distribution networks based on carbon emission reduction when executing the computer program.

[0037] In a fourth aspect, the present application discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the multi-objective optimization scheduling method for distribution network based on carbon emission reduction as described above.

[0038] Compared with the prior art, the present invention has the following beneficial effects: This application makes full use of the carbon emission factor of electricity to design a low-carbon target for electric vehicle charging, and uses the net load variance, total system operating cost, electric vehicle user cost, and charging carbon emission cost as the objective function to establish an orderly charging multi-objective optimization scheduling model, and establishes constraints for the multi-objective optimization scheduling model to study the orderly 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 hours is large, electric vehicle owners are more willing to charge during low load and low electricity price hours, which can reduce charging expenses and effectively realize peak load shaving and valley filling, alleviating the dispatch pressure of the grid.

[0040] (2) Compared with disorderly charging, orderly charging in different scenarios can effectively save users’ charging costs and smooth the load curve. At the same time, it can also significantly reduce the carbon emissions of electric vehicle charging, thus achieving mutual benefit and win-win results with the participation of multiple subjects. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 It is a schematic diagram of the method flow of the present invention; Figure 2 This is a diagram of an orderly charging scene of a charging station according to an embodiment of the present invention; Figure 3 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0044] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

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

[0047] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which 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 is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0049] The present invention is further described in detail below in conjunction with the accompanying drawings: See also Figure 1 The present application discloses a multi-objective optimization scheduling method for a distribution network based on carbon emission reduction, comprising: S1: Obtain vehicle information of electric vehicles charging in the charging station and real-time information of charging piles; S2: Based on the real-time information of electric vehicles and charging piles, a multi-objective optimization scheduling model is constructed, which includes net load variance, total system operation cost, electric vehicle user cost and charging carbon emission cost; S3: Solve the multi-objective optimization scheduling model and generate the charging scheduling strategy.

[0050] This application makes full use of the carbon emission factor of electricity to design the low-carbon target of electric vehicle charging, and establishes a multi-objective optimization scheduling model for orderly charging with the net load variance, total system operation cost, electric vehicle user cost, and charging carbon emission cost as the objective function, and sets up the constraints of the multi-objective optimization scheduling model to study the orderly charging scheduling and optimization strategy of electric vehicles. By guiding electric vehicle owners to participate in grid scheduling, when the price difference between peak and valley periods is large, electric vehicle owners are more willing to charge during low load periods and periods with lower electricity prices, which can reduce charging expenses, and can effectively achieve peak shaving and valley filling, alleviating the scheduling pressure of the grid.

[0051] In some embodiments, the electric vehicle vehicle information includes: the access time, current SOC, expected off-grid time, low SOC and high SOC of the electric vehicle; The real-time information of the charging pile 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] Charging Carbon Emission Cost:

[0057] In the formula, is the net load variance; is the scheduling period; is the actual grid load; is the actual average load of the power grid; is the power of electric vehicles connected to the grid; is the predicted value of wind power; is the predicted value of photovoltaic output; The grid-connected power of the energy storage power station; is the operating cost of thermal power units; The operating costs of the electric vehicle station; The cost of wind power and photovoltaic power curtailment; is the system's spare capacity cost; for Charging fees for electric vehicle users; and are the times when the nth electric vehicle starts charging and ends charging respectively; is the charging unit price of electric vehicles in period t; It is a 0-1 variable, the corresponding electric vehicle takes the value of 0 when not charging and takes the value of 1 when charging; It is the dynamic electricity carbon emission factor for each time slot of the charging station in the area.

[0058] Further preferably, the operating cost of the thermal power unit is , Operating costs of electric vehicle stations , wind power and photovoltaic wind curtailment costs and the system's spare capacity cost The specific calculation is as follows: Operating costs of thermal power units:

[0059] Operating costs of an electric vehicle station:

[0060] The cost of curtailment of wind power and photovoltaic power:

[0061] The system's spare capacity cost:

[0062] Where N is the total number of thermal power units participating in the dispatch; , , are the energy consumption characteristic parameters of the i-th thermal power unit; is the output of the i-th thermal power unit in period t; is the start / stop status of the i-th thermal power unit at time t, = 1 is the power-on state, = 0 means shutdown state; is the start-up and shutdown cost of thermal power units; is the number of electric vehicle stations; Cost of running the electric vehicle station; is the capacity of the electric vehicle station; , Charge and discharge power for electric vehicle stations; is the electricity price per unit of electricity; The loss cost of energy storage equipment; , is the penalty coefficient for abandoning wind and solar power; , The grid-connected power of wind power and photovoltaic power; is the load forecast error; is the wind farm output prediction error; is the photovoltaic output prediction error; , are the positive and negative reserve capacities of the EV station, respectively; is the system spare capacity cost coefficient.

[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 constraints:

[0069] Thermal power unit constraints: preset the upper and lower output limits, ramp constraints, minimum allowable start and stop time constraints, and spinning reserve constraints of thermal power units according to the actual application parameters of thermal power units; Energy storage device constraints: preset energy storage output constraints, energy storage device state of charge non-overcharge constraints, and energy storage device state of charge non-discharge constraints according to the actual performance of the energy storage device; In the formula, is the output of the i-th thermal power unit in period t; is the predicted value of wind power; is the predicted value of photovoltaic output; The grid-connected power of the energy storage power station; is the actual grid load; is the power of electric vehicles connected to the grid; is the number of cars parked at the electric vehicle station during period t; The maximum capacity to discharge an electric vehicle; Maximum capacity for charging electric vehicles; , They are the upper and lower limit parameters of battery capacity respectively; The upper limit of the battery's storage capacity; is the total number of electric vehicles that can participate in the power system dispatch; is the battery capacity of electric vehicles; For electric vehicles Power consumption at each moment; For electric vehicles Power consumption at each moment; The charging and discharging efficiency of electric vehicles; is the probability that an electric vehicle enters the electric vehicle station and stops driving during period t; The power consumed by the electric vehicle for every 1 km traveled; is the driving speed of the electric vehicle; is the maximum load power of the transformer; For charging network; It is a 0-1 variable, the corresponding electric vehicle takes the value of 0 when not charging and takes the value of 1 when charging; is the charging rate capacity; and They are the time when the nth electric car starts charging and ends charging respectively.

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

[0071] Step 2: The control center then combines the real-time charging load, wind power, photovoltaic, energy storage and other information, standardizes the four objective functions of net load variance, total system operation cost, electric vehicle user cost, and charging carbon emission cost, and transforms the multi-objective problem into a single-objective optimization problem according to equal weight factors. After solving the problem, the charging power is dispatched and allocated to the vehicles in an orderly manner.

[0072] Step 3: In the orderly charging process, the EV aggregator is an interface entity responsible for matching the decentralized energy resources of EVs, collecting charging vehicle information, and delivering scheduling strategies.

[0073] Step 4: To meet the travel needs of users and make the total load curve of the power grid smoother, the aggregator will transfer part of the charging load to the low-consumption period. Figure 2 shown.

[0074] In some embodiments, the present application is based on the wind-solar-thermal-storage joint scheduling model, and considers the regulation capacity of electric vehicles to establish a multi-objective charging optimization scheduling model that considers carbon emission reduction and includes electric vehicles. The multi-objective charging optimization scheduling model is represented by the following objective function: 1) Minimum net load variance (1) (2) Where: is the net load variance; For the scheduling cycle, the time interval is set to 15 minutes, and the whole day is divided into 96 time periods; is the actual grid load; is the actual average load of the power grid; is the power of electric vehicles connected to the grid; is the predicted value of wind power; is the predicted value of photovoltaic output; is the grid-connected power of the energy storage power station.

[0075] 2) Minimum total system operating cost (3) Operating costs of thermal power units: (4) Where: N is the total number of thermal power units participating in the dispatch; , , are the energy consumption characteristic parameters of the i-th thermal power unit; is the output of the i-th thermal power unit in period t; is the start / stop status of the i-th thermal power unit at time t, = 1 is the power-on state, = 0 means shutdown state; is the start-up and shutdown cost of thermal power units.

[0076] Operating costs of an electric vehicle station: (5) Where: is the number of electric vehicle stations; Cost of running the electric vehicle station; The capacity of the electric vehicle station; , Charging and discharging power for electric vehicle stations; is the electricity price per unit of electricity; The loss cost of energy storage equipment.

[0077] The cost of curtailment of wind power and photovoltaic power: (6) Where: , is the penalty coefficient for abandoning wind and solar power; , It is the grid-connected power of wind power and photovoltaic power.

[0078] The system's spare capacity cost: (7) Where: is the load forecast error; is the wind farm output prediction error; is the photovoltaic output prediction error; , are the positive and negative reserve capacities of the EV station, respectively; is the system spare capacity cost coefficient.

[0079] 3) Electric vehicle user costs are minimal (8) Where: Charging costs for users of N electric vehicles; and are the times when the nth electric vehicle starts charging and ends charging respectively; is the charging unit price of electric vehicles in period t; It is a 0-1 variable, the corresponding electric vehicle takes the value of 0 when not charging and takes the value of 1 when charging.

[0080] 4) Charging has the lowest carbon emission cost (9) Where: is the dynamic electricity carbon emission factor of each time slot of the charging station in the area. To minimize the carbon emissions during the charging process of electric vehicles, the charging time should be scheduled during the period with low carbon intensity of the grid.

[0081] The constraints to be solved are: 1) Power balance constraints: (10) 2) Constraints on thermal power units: upper and lower output limits of thermal power units, ramp constraints, minimum allowable start and stop time constraints of thermal power units, and spinning reserve constraints of thermal power units. 3) Output constraints of 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 wind farms and photovoltaic panels.

[0082] 4) Energy storage device constraints: Energy storage output constraints and the charge state of the energy storage device are to ensure that the energy storage will not be overcharged or over-discharged.

[0083] 5) Restrictions on electric vehicles: Charge and discharge constraints: (11) Where: is the number of cars parked at the electric vehicle station during period t; , are the maximum charging and discharging capacities of electric vehicles respectively.

[0084] Electric vehicle battery capacity constraints: In order to prevent electric vehicles from overcharging and over-discharging and extend the service life of the battery, upper and lower limits of the battery storage energy should be set.

[0085] (12) Where: , They are the upper and lower limit parameters of battery capacity respectively; is the upper limit of the storage capacity of the battery; is the total number of electric vehicles that can participate in the power system dispatch; The battery capacity of electric vehicles.

[0086] Energy Constraints: (13) Where: The charging and discharging efficiency of electric vehicles; is the probability that an electric vehicle enters the electric vehicle station and stops driving during period t; The power consumed by the electric vehicle for every 1 km traveled; is the driving speed of the electric vehicle.

[0087] Transformer capacity constraints: (14) Where: is the maximum load power of the transformer.

[0088] Charging rate constraints: (15) Where: The former ensures that electric vehicles comply with the charging network during the entire charging process The latter ensures that the electric vehicle complies with the charging rate capacity of the charger throughout the charging process. limit.

[0089] See also Figure 3 The present application also discloses a distribution network multi-objective optimization dispatching system based on carbon emission reduction, including: A data acquisition unit, used to acquire vehicle information of electric vehicles charged in a charging station and real-time information of charging piles; A model building unit, used to build a multi-objective optimization scheduling model including net load variance, total system operation cost, electric vehicle user cost and charging carbon emission cost according to electric vehicle information and real-time information of charging piles; The model solving unit is used to solve the multi-objective optimization scheduling model and generate the 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 obtain vehicle information of electric vehicles charged in the charging station, specifically including: the access time of the electric vehicle, the current SOC, the expected off-grid time, the low SOC and the high SOC; and transmit the above electric vehicle information to the model building unit; the charging pile obtains its own real-time information to obtain the real-time information of the charging pile, specifically including: real-time charging load, wind power, photovoltaics and energy storage; and transmits the above real-time information of the charging pile 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] Charging Carbon Emission Cost:

[0100] In the formula, is the net load variance; is the scheduling period; is the actual grid load; is the actual average load of the power grid; is the power of electric vehicles connected to the grid; is the predicted value of wind power; is the predicted value of photovoltaic output; The grid-connected power of the energy storage power station; is the operating cost of thermal power units; The operating costs of the electric vehicle station; The cost of wind power and photovoltaic power curtailment; is the system's spare capacity cost; for Charging fees for electric vehicle users; and are the times when the nth electric vehicle starts charging and ends charging respectively; is the charging unit price of electric vehicles in period t; It is a 0-1 variable, the corresponding electric vehicle takes the value of 0 when not charging and takes the value of 1 when charging; The dynamic electricity carbon emission factor for each time slot of the charging station in the area; is the total number of thermal power units participating in the dispatch; , , are the energy consumption characteristic parameters of the i-th thermal power unit; is the output of the i-th thermal power unit in period t; is the start / stop status of the i-th thermal power unit at time t, = 1 is the power-on state, = 0 means shutdown state; is the start-up and shutdown cost of thermal power units; is the number of electric vehicle stations; Cost of running the electric vehicle station; is the capacity of the electric vehicle station; , Charge and discharge power for electric vehicle stations; is the electricity price per unit of electricity; The loss cost of energy storage equipment; is the wind abandonment penalty coefficient; is the penalty coefficient for abandoning light; , The grid-connected power of wind power and photovoltaic power; is the load forecast error; is the wind farm output prediction error; is the photovoltaic output prediction error; , are the positive and negative reserve capacities of the EV station, respectively; is the system spare capacity cost coefficient.

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

[0102] The present invention also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the multi-objective optimization scheduling method for distribution network based on carbon emission reduction described above are implemented.

[0103] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented 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] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions 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 rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does 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 optimization scheduling method for distribution network based on carbon emission reduction, characterized in that: include: Obtain vehicle information of electric vehicles charging in charging stations and real-time information of charging piles; According to the real-time information of electric vehicles and charging piles, a multi-objective optimization scheduling model including net load variance, total system operation cost, electric vehicle user cost and charging carbon emission cost is constructed; Solve the multi-objective optimization scheduling model and generate the charging scheduling strategy.

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

3. A multi-objective optimization scheduling method for distribution network based on carbon emission reduction according to claim 1, characterized in that: The multi-objective optimization scheduling model is specifically expressed by the following formula: Net load variance: Total system operating cost: Electric vehicle user costs: Charging Carbon Emission Cost: In the formula, is the net load variance; is the scheduling period; is the actual grid load; is the actual average load of the power grid; is the power of electric vehicles connected to the grid; is the predicted value of wind power; is the predicted value of photovoltaic output; The grid-connected power of the energy storage power station; is the operating cost of thermal power units; The operating costs of the electric vehicle station; The cost of curtailment of wind power and photovoltaic power; is the system's spare capacity cost; for Charging fees for electric vehicle users; and are the times when the nth electric vehicle starts charging and ends charging respectively; is the charging unit price of electric vehicles in period t; It is a 0-1 variable, the corresponding electric vehicle takes the value of 0 when not charging and takes the value of 1 when charging; It is the dynamic electricity carbon emission factor for each time slot of the charging station in the area.

4. A multi-objective optimization scheduling method for distribution network based on carbon emission reduction according to claim 3, characterized in that: The operating cost of the thermal power unit , Operating costs of electric vehicle stations , wind power and photovoltaic wind curtailment costs and the system's spare capacity cost The specific calculation is as follows: Operating costs of thermal power units: Operating costs of an electric vehicle station: The cost of curtailment of wind power and photovoltaic power: The system's spare capacity cost: In the formula, is the total number of thermal power units participating in the dispatch; , , are the energy consumption characteristic parameters of the i-th thermal power unit; is the output of the i-th thermal power unit in period t; is the start and stop status of the i-th thermal power unit at time t, = 1 is the power-on state, = 0 means shutdown state; is the start-up and shutdown cost of thermal power units; is the number of electric vehicle stations; Cost of running the electric vehicle station; is the capacity of the electric vehicle station; Charging power for electric vehicle stations; Discharge power for electric vehicle stations; is the electricity price per unit of electricity; The loss cost of energy storage equipment; is the wind abandonment penalty coefficient, is the penalty coefficient for abandoning light; The grid-connected power of wind power; is the photovoltaic grid-connected power; is the load forecast error; is the wind farm output prediction error; is the photovoltaic output prediction error; is the positive reserve capacity of the electric vehicle station; is the negative reserve capacity of the electric vehicle station; is the system spare capacity cost coefficient.

5. A multi-objective optimization scheduling method for distribution network based on carbon emission reduction according to claim 1, characterized in that: The multi-objective optimization scheduling model is solved, including the following constraints: Power balance constraints: Charge and discharge constraints: Electric vehicle battery capacity constraints: Energy Constraints: Transformer capacity constraints: Charging rate constraints: Thermal power unit constraints: preset the upper and lower output limits, ramp constraints, minimum allowable start and stop time constraints, and spinning reserve constraints of thermal power units according to the actual application parameters of thermal power units; Energy storage device constraints: preset energy storage output constraints, energy storage device state of charge non-overcharge constraints, and energy storage device state of charge non-discharge constraints according to the actual performance of the energy storage device; In the formula, is the output of the i-th thermal power unit in period t; is the predicted value of wind power; is the predicted value of photovoltaic output; The grid-connected power of the energy storage power station; is the actual grid load; is the power of electric vehicles connected to the grid; is the number of cars parked at the electric vehicle station during period t; The maximum capacity to discharge the electric vehicle; Maximum capacity for charging electric vehicles; , They are the upper and lower limit parameters of battery capacity respectively; The upper limit of the battery's storage capacity; is the total number of electric vehicles that can participate in the power system dispatch; is the battery capacity of electric vehicles; For electric vehicles Power consumption at each moment; For electric vehicles Power consumption at each moment; The charging and discharging efficiency of electric vehicles; is the probability that an electric vehicle enters the electric vehicle station and stops driving during period t; The power consumed by the electric vehicle for every 1 km traveled; is the driving speed of the electric vehicle; is the maximum load power of the transformer; For charging network; It is a 0-1 variable, the corresponding electric vehicle takes the value of 0 when not charging and takes the value of 1 when charging; is the charging rate capacity; and They are the time when the nth electric car starts charging and ends charging respectively.

6. A multi-objective optimization dispatching system for distribution network based on carbon emission reduction, characterized in that: include: A data acquisition unit, used to acquire vehicle information of electric vehicles being charged in a charging station; Get real-time information of charging piles; A model building unit, used to build a multi-objective optimization scheduling model including net load variance, total system operation cost, electric vehicle user cost and charging carbon emission cost according to electric vehicle information and real-time information of charging piles; The model solving unit is used to solve the multi-objective optimization scheduling model and generate the charging scheduling strategy.

7. A distribution network multi-objective optimization dispatching system based on carbon emission reduction according to claim 6, characterized in that: The data acquisition unit includes an electric vehicle aggregator and a charging pile; the electric vehicle aggregator is used to obtain vehicle information of electric vehicles charged in the charging station, specifically including: the access time of the electric vehicle, the current SOC, the expected off-grid time, the low SOC and the high SOC; and transmit the above electric vehicle information to the model building unit; the charging pile obtains its own real-time information to obtain the real-time information of the charging pile, specifically including: real-time charging load, wind power, photovoltaic and energy storage; and transmits the above real-time information of the charging pile to the model building unit.

8. A distribution network multi-objective optimization dispatching system based on carbon emission reduction according to claim 6, characterized in that: The multi-objective optimization scheduling model is specifically expressed by the following formula: Net load variance: Total system operating cost: Electric vehicle user costs: Charging Carbon Emission Cost: In the formula, is the net load variance; is the scheduling period; is the actual grid load; is the actual average load of the power grid; is the power of electric vehicles connected to the grid; is the predicted value of wind power; is the predicted value of photovoltaic output; The grid-connected power of the energy storage power station; is the operating cost of thermal power units; The operating costs of the electric vehicle station; The cost of wind power and photovoltaic power curtailment; is the system's spare capacity cost; for Charging fees for electric vehicle users; and are the times when the nth electric vehicle starts charging and ends charging respectively; is the charging unit price of electric vehicles in period t; It is a 0-1 variable, the corresponding electric vehicle takes the value of 0 when not charging and takes the value of 1 when charging; The dynamic electricity carbon emission factor for each time slot of the charging station in the area; is the total number of thermal power units participating in the dispatch; , , are the energy consumption characteristic parameters of the i-th thermal power unit; is the output of the i-th thermal power unit in period t; is the start and stop status of the i-th thermal power unit at time t, = 1 is the power-on state, = 0 means shutdown state; is the start-up and shutdown cost of thermal power units; is the number of electric vehicle stations; Cost of running the electric vehicle station; is the capacity of the electric vehicle station; Charging power for electric vehicle stations; Discharge power for electric vehicle stations; is the electricity price per unit of electricity; The loss cost of energy storage equipment; is the wind abandonment penalty coefficient, is the penalty coefficient for abandoning light; The grid-connected power of wind power; is the photovoltaic grid-connected power; is the load forecast error; is the wind farm output prediction error; is the photovoltaic output prediction error; is the positive reserve capacity of the electric vehicle station; is the negative reserve capacity of the electric vehicle station; is the system spare capacity cost coefficient.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the 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 to 5 when executing the computer program.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the multi-objective optimization scheduling method for distribution network based on carbon emission reduction according to any one of claims 1 to 5.

Images