Power distribution network energy storage optimization configuration method and system

Abstract

The invention discloses a power distribution network energy storage optimization configuration method and system, and the method comprises the steps: enabling an electric vehicle to serve as a flexible resource, carrying out the aggregation to form a virtual energy storage unit in a power distribution network, constructing an energy storage optimization configuration model considering the flexible aggregation of the electric vehicle, and solving the energy storage optimization configuration model to obtain an energy storage optimization configuration scheme. According to the method, based on the characteristics of the novel load, the potential of replacing part of entity energy storage functions by the virtual energy storage technology is fully utilized, the investment of the power distribution network is reduced, and the economy of the power distribution network is improved. According to the method, the response characteristics of the electric vehicle accessing the power grid are considered, and the constraint conditions in the constructed energy storage optimization configuration model comprise the electric vehicle power constraint considering the electric vehicle aggregation capability, so that the method adapts to the electric vehicle data flexibility distribution characteristics, and the model adaptability, flexibility and stability are enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network optimization, and in particular relates to a distribution network energy storage optimization configuration method and system. Background Art

[0002] In traditional distribution network planning, the distribution system connects the transmission network and end users, and the grid is mainly responsible for providing users with the required electricity in a stable manner. However, under the "dual carbon" goal, new elements such as distributed photovoltaics and electric vehicles are widely connected to the distribution system. Compared with the traditional distribution network, it has the characteristics of large randomness, strong volatility and uncontrollable output, which makes the source-load characteristics of the distribution network more complex, and puts higher requirements on the safe and stable operation of the distribution network.

[0003] With its flexibility of bidirectional power regulation, energy storage systems break the limitation of "generating and using" electricity in power systems, and have a significant impact on enhancing the power supply security of distribution networks, improving the utilization efficiency of clean energy, and expanding the carrying capacity for diversified electricity demand. In recent years, the capacity and configuration optimization of energy storage in distribution networks has become a research hotspot. In existing research, it is usually focused on the fields of energy storage scheduling and operation optimization. Some studies also focus on how energy storage can help achieve peak load shaving and valley filling under time-of-use electricity price policies. Most of the current research focuses on traditional residential or commercial electricity loads, and the research on the characteristics of new loads such as electric vehicles is not in-depth enough. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention proposes a distribution network energy storage optimization configuration method and system.

[0005] The technical solution of the present invention is as follows:

[0006] A method for optimizing energy storage configuration of a distribution network, wherein a plurality of electric vehicles are arranged in an area where the distribution network is located, comprising:

[0007] The electric vehicles are aggregated as flexible resources to form virtual energy storage units in the distribution network, and an energy storage optimization configuration model considering the flexibility aggregation of electric vehicles is constructed. The energy storage optimization configuration model uses conventional energy storage configuration capacity as a decision variable, and takes the lowest total cost over the entire life cycle of energy storage as an objective function, and takes electric vehicle power constraints, system power balance constraints, transformer reverse load rate constraints, and energy storage charging and discharging constraints that take into account the aggregation capacity of electric vehicles as constraints;

[0008] The constructed energy storage optimization configuration model is solved to obtain the energy storage optimization configuration plan.

[0009] Furthermore, the objective function of the energy storage optimization configuration model is expressed as:

[0010] Cmin =C in +C o +C ev -C pv -C pc

[0011] in,

[0012] C in =C unit *C bat

[0013] C o =a*C in *n

[0014] C ev =C aev *C evper

[0015] C pv =C apv *C pvper

[0016] C pc =C apc *(P price -V price )

[0017] In the formula, C in is the initial investment of energy storage; C o is the operation and maintenance cost of energy storage; C ev Aggregation compensation for electric vehicles; C pv To reduce the benefits of abandoned light; C pc The benefits of energy storage for peak shifting and valley filling; C unit is the unit investment of energy storage; C bat is the energy storage capacity; a is the proportion of annual operation and maintenance costs to initial investment; n is the energy storage life, in years; C aev Aggregate electricity for electric vehicles; C evper C is the electricity aggregation fee per kWh; apv To reduce the amount of abandoned photovoltaic power; C pvper is the photovoltaic grid-connected electricity price; C apc Energy storage participates in peak load reduction and valley filling; P price is the peak electricity price; V price The valley electricity price.

[0018] Furthermore, the electric vehicle power constraint taking into account the electric vehicle aggregation capability is

[0019]

[0020] In the formula, and are the lower power limit and the upper power limit of the electric vehicle aggregation downward adjustment capability, respectively; P t eva The real-time power exchanged between electric vehicles and the power grid.

[0021] Furthermore, the power lower limit and power cap The method to obtain is as follows:

[0022] Real-time SOC based on electric vehicles before traveling 0,t The battery capacity D and the battery energy consumption per unit distance C are obtained by sampling the probability distribution of battery capacity, battery energy consumption per unit distance and daily travel distance using the Monte Carlo method. e , daily travel distance d, to build a real-time initial SOC value model when electric vehicles are connected to the grid;

[0023] The expression of the real-time initial SOC value model when the electric vehicle enters the grid is:

[0024]

[0025] Based on the real-time initial SOC value model of the electric vehicle when it enters the network, the real-time exchange power P that meets the travel demand is calculated. t ;

[0026] The electric vehicle start travel time t obtained by sampling the probability distribution of the start travel time and the end travel time of the electric vehicle using the Monte Carlo method d and the end travel time t of the electric vehicle s As the real-time exchange power P t Scope restrictions:

[0027] Based on the real-time exchange power and its range restriction conditions, the exchangeable power P of the electric vehicle monomer is calculated. t ;

[0028] The lower power limit of the electric vehicle's aggregate downregulation capability is calculated as follows: and represents the power ceiling of the EV aggregate upregulation capability

[0029]

[0030] Where n t is the number of electric vehicles connected to the grid in real time; P j,t is the exchangeable power of electric vehicle j at time t.

[0031] Furthermore, the real-time exchange power P t The method to obtain is as follows:

[0032] The Monte Carlo method is used to sample the probability distribution of energy demand to determine the SOC value of electric vehicle travel demand SOC d ;

[0033] The real-time exchange power P t The expression is:

[0034]

[0035] in,

[0036] In the formula, SOC t-Δt and SOC t are the SOC values ​​at time t-Δt and time t respectively; D t is the battery capacity involved in the reverse power supply process; Δt is the reverse power supply time; η d is the discharge efficiency.

[0037] Furthermore, the exchangeable power P of the electric vehicle monomer t The expression is:

[0038] P t =-P t ,t∈[t s ,t d ]

[0039] P t =0,

[0040] Where P t is the exchangeable power; is the upper limit of the exchangeable power; P t is the lower limit of the exchangeable power.

[0041] Furthermore, the expression of the system power balance constraint is:

[0042] P t up =P t pv -P t bat -P t lo +P t eva

[0043] Where P t up is the power supplied by the transformer, P t pv is the photovoltaic output, P t bat is the energy storage power, Pt lo is the load power, P t eva The power of electric vehicles;

[0044] The expression of the transformer reverse load rate constraint is:

[0045] P t up ≤T cap *cosφ*0.8

[0046] Where, T cap is the transformer capacity; cosφ is the power factor;

[0047] The expression of the energy storage charge and discharge constraint is:

[0048] -P t batmax ≤P t bat ≤P t batmax

[0049] Where P t batmax It is the maximum charge and discharge power of the energy storage battery.

[0050] A distribution network energy storage optimization configuration system, wherein a plurality of electric vehicles are arranged in an area where the distribution network is located, comprises a model building module and a model solving module, wherein:

[0051] The model building module is used to aggregate the electric vehicles as flexible resources to form virtual energy storage units in the distribution network, and to build an energy storage optimization configuration model that takes into account the flexibility aggregation of electric vehicles. The energy storage optimization configuration model uses conventional energy storage configuration capacity as a decision variable, and takes the lowest total cost over the entire life cycle of energy storage as an objective function, and takes electric vehicle power constraints, system power balance constraints, transformer reverse load rate constraints, and energy storage charging and discharging constraints that take into account the aggregation capacity of electric vehicles as constraints.

[0052] The model solving module is used to solve the constructed energy storage optimization configuration model to obtain the energy storage optimization configuration solution.

[0053] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0054] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention proposes a method and system for optimizing energy storage configuration in a distribution network. The method aggregates electric vehicles as flexible resources to form virtual energy storage units in the distribution network, constructs an energy storage optimization configuration model that considers the flexibility aggregation of electric vehicles, and obtains an energy storage optimization configuration scheme by solving the energy storage optimization configuration model. The method is based on the characteristics of new loads, fully utilizes the potential of virtual energy storage technology to replace part of the physical energy storage functions, reduces distribution network investment and improves the economy of the distribution network.

[0057] The method of the present invention takes into account the response characteristics of electric vehicles connected to the power grid. The constraints in the constructed energy storage optimization configuration model include electric vehicle power constraints that take into account the aggregation capacity of electric vehicles, thereby adapting to the flexibility distribution characteristics of electric vehicle data to enhance the adaptability, flexibility and stability of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of a flow chart of a method for optimizing energy storage configuration in a distribution network in an embodiment;

[0059] Figure 2 Response characteristics of electric vehicles connected to the power grid in the embodiment. DETAILED DESCRIPTION

[0060] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0061] Embodiment 1:

[0062] The present invention provides a method for optimizing energy storage configuration of a distribution network. A distribution network is located in an area where a plurality of electric vehicles are arranged, such as Figure 1 As shown, including:

[0063] Electric vehicles are aggregated as flexible resources to form virtual energy storage units in the distribution network, and an energy storage optimization configuration model considering the flexibility aggregation of electric vehicles is constructed. The energy storage optimization configuration model uses the conventional energy storage configuration capacity as the decision variable, and takes the lowest total cost over the entire life cycle of energy storage as the objective function, and takes the electric vehicle power constraint that takes into account the aggregation capacity of electric vehicles, the system power balance constraint, the transformer reverse load rate constraint, and the energy storage charging and discharging constraint as the constraint conditions;

[0064] The constructed energy storage optimization configuration model is solved to obtain the energy storage optimization configuration plan.

[0065] Embodiment 2:

[0066] This embodiment is further designed on the basis of the first embodiment in that the expression of the objective function of the energy storage optimization configuration model in this embodiment is:

[0067] C min =C in +C o +C ev -C pv -C pc

[0068] in,

[0069] C in =C unit *C bat

[0070] C o =a*C in *n

[0071] C ev =C aev *C evper

[0072] C pv =C apv *C pvper

[0073] C pc =C apc *(P price -V price )

[0074] In the formula, C in is the initial investment of energy storage; C o is the operation and maintenance cost of energy storage; C ev Aggregation compensation for electric vehicles; C pv To reduce the benefits of abandoned light; C pc The benefits of energy storage for peak shifting and valley filling; C unit is the unit investment of energy storage; C bat is the energy storage capacity; a is the proportion of annual operation and maintenance costs to initial investment; n is the energy storage life, in years; C aev Aggregate electricity for electric vehicles; C evper C is the electricity aggregation fee per kWh; apv To reduce the amount of abandoned photovoltaic power; C pvper is the photovoltaic grid-connected electricity price; C apc Energy storage participates in peak load reduction and valley filling; P price is the peak electricity price; V price The valley electricity price.

[0075] Embodiment three:

[0076] This embodiment is further designed on the basis of the first embodiment in that the electric vehicle power constraint taking into account the electric vehicle aggregation capability in this embodiment is:

[0077]

[0078] In the formula, and are the lower power limit and the upper power limit of the electric vehicle aggregation downward adjustment capability, respectively; P t eva The real-time power exchanged between electric vehicles and the power grid.

[0079] Embodiment 4:

[0080] This embodiment is further designed on the basis of the third embodiment in that the power lower limit in this embodiment is and power cap The method to obtain is as follows:

[0081] Real-time SOC based on electric vehicles before traveling 0,t The battery capacity D and the battery energy consumption per unit distance C are obtained by Monte Carlo method based on the probability distribution of battery capacity, battery energy consumption per unit distance and daily travel distance. e , daily travel distance d, to build a real-time initial SOC value model when electric vehicles are connected to the grid;

[0082] The following is an analysis of the response characteristics of independent electric vehicles during the grid access process. Figure 2 As shown in the figure, the vertical axis is the battery charge state SOC (State of Charge, used to reflect the remaining capacity of the battery, indicating the battery's ability to continue working), the horizontal axis is time, and the shaded area ABCFED is used to illustrate the response characteristics of electric vehicles during the grid access process. SOC , the upper limit of allowed charging and the time t when the electric vehicle is connected to the grid s , time to leave the grid t d As a boundary condition for the response characteristics of independent electric vehicles during grid access; SOC d In order to meet the user's travel needs, the SOC must ensure that the electric vehicle maintains a charge state before leaving the grid. range (i.e. CF segment); SOC t is the real-time initial SOC of the electric vehicle when it enters the grid. s to d During the time period, the charge state SOC of the vertical axis battery is SOC t To SOC dThe range is the effective interval where independent electric vehicles can be aggregated as flexibility resources to form a virtual energy storage unit in the distribution network.

[0083] The expression of the real-time initial SOC value model when the electric vehicle enters the grid is:

[0084]

[0085] Based on the real-time initial SOC value model when the electric vehicle enters the grid, the real-time exchange power P that meets the travel demand is calculated. t ;

[0086] The electric vehicle start travel time t obtained by sampling the probability distribution of the start travel time and the end travel time of the electric vehicle using the Monte Carlo method d , that is, the time when the electric vehicle leaves the grid and the end time of the electric vehicle’s trip t s , that is, the real-time exchange power P when the electric vehicle is connected to the grid t Scope restrictions:

[0087] Based on the real-time exchange power and its range constraints, the exchangeable power P of the electric vehicle monomer is calculated. t ;

[0088] The lower power limit of the electric vehicle's aggregate downregulation capability is calculated as follows: and represents the power ceiling of the EV aggregate upregulation capability

[0089]

[0090]

[0091] Where n t is the number of electric vehicles connected to the grid in real time, n t ≤N0, N0 is the number of electric vehicles in the distribution network area; P j,t is the exchangeable power of electric vehicle j at time t.

[0092] Embodiment five:

[0093] This embodiment is further designed on the basis of the fourth embodiment in that the SOC during charging is constantly changing. In this example, the direction of the electric vehicle supplying power to the grid, that is, the reverse power supply, is assumed to be the positive direction. Therefore, during charging, P t is a negative value, and P t The switching power limit must be met. Real-time switching power P t The expression is:

[0094]

[0095] in,

[0096] In the formula, SOC t-Δt and SOC t are the SOC values ​​at time t-Δt and time t respectively; D t is the battery capacity involved in the reverse power supply process; Δt is the reverse power supply time; SOC d The SOC value of travel demand is determined by sampling based on the probability distribution of energy demand using the Monte Carlo method. d ; η d is the discharge efficiency.

[0097] Embodiment six:

[0098] This embodiment is further designed on the basis of the fifth embodiment in that the exchangeable power P of the electric vehicle monomer in this embodiment is t The expression is:

[0099] P t =-P t ,t∈[t s ,t d ]

[0100] P t =0,

[0101] Where P t is the exchangeable power; is the upper limit of the exchangeable power; P t is the lower limit of the exchangeable power.

[0102] Embodiment seven:

[0103] This embodiment is further designed on the basis of the first embodiment in that the expression of the system power balance constraint in this embodiment is:

[0104] P t up =P t pv -P t bat -P t lo +P t eva

[0105] Where P t up is the power supplied by the transformer, P t pv is the photovoltaic output, P t bat is the energy storage power, Pt lo is the load power, P t eva The power of electric vehicles;

[0106] The expression of transformer reverse load rate constraint is:

[0107] P t up ≤T cap *cosφ*0.8

[0108] Where, T cap is the transformer capacity; cosφ is the power factor, which is 0.95 in this example; the transformer reverse load rate is not greater than 80%.

[0109] The expression of energy storage charge and discharge constraint is:

[0110] -P t batmax ≤P t bat ≤P t batmax

[0111] Where P t batmax It is the maximum charge and discharge power of the energy storage battery.

[0112] Embodiment eight:

[0113] The present invention provides a distribution network energy storage optimization configuration system, in which a plurality of electric vehicles are arranged in the distribution network area, and includes a model building module and a model solving module, wherein:

[0114] A model building module is used to aggregate electric vehicles as flexible resources to form virtual energy storage units in the distribution network, and to build an energy storage optimization configuration model that considers the flexibility aggregation of electric vehicles. The energy storage optimization configuration model uses conventional energy storage configuration capacity as a decision variable, and takes the lowest total cost over the entire life cycle of energy storage as the objective function, and takes electric vehicle power constraints that take into account the aggregation capacity of electric vehicles, system power balance constraints, transformer reverse load rate constraints, and energy storage charging and discharging constraints as constraints;

[0115] The model solving module is used to solve the constructed energy storage optimization configuration model and obtain the energy storage optimization configuration solution.

[0116] Embodiment nine:

[0117] An electronic device of the present invention includes a memory and a processor. The memory stores a computer program. The processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0118] A computer-readable storage medium of the present invention stores a computer program, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0119] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing energy storage configuration of a distribution network, wherein a plurality of electric vehicles are arranged in an area where the distribution network is located, characterized in that: include: The electric vehicles are aggregated as flexible resources to form virtual energy storage units in the distribution network, and an energy storage optimization configuration model considering the flexibility aggregation of electric vehicles is constructed. The energy storage optimization configuration model uses conventional energy storage configuration capacity as a decision variable, and takes the lowest total cost over the entire life cycle of energy storage as an objective function, and takes electric vehicle power constraints, system power balance constraints, transformer reverse load rate constraints, and energy storage charging and discharging constraints that take into account the aggregation capacity of electric vehicles as constraints; The constructed energy storage optimization configuration model is solved to obtain the energy storage optimization configuration plan.

2. The method for optimizing energy storage configuration in a distribution network according to claim 1, characterized in that: The expression of the objective function of the energy storage optimization configuration model is: C min =C in +C o +C ev -C pv -C pc in, C in =C unit *C bat C o =a*C in *n C ev =C aev *C evper C pv =C apv *C pvper C pc =C apc *(P price -V price ) In the formula, C in is the initial investment of energy storage; C o is the operation and maintenance cost of energy storage; C ev Aggregation compensation for electric vehicles; C pv To reduce the benefits of abandoned light; C pc The benefits of energy storage for peak shifting and valley filling; C unit is the unit investment of energy storage; C bat is the energy storage capacity; a is the proportion of annual operation and maintenance costs to initial investment; n is the energy storage life, in years; C aev Aggregate electricity for electric vehicles; C evper C is the electricity aggregation fee per kWh; apv To reduce the amount of abandoned photovoltaic power; C pvper is the photovoltaic grid-connected electricity price; C apc Energy storage participates in peak load reduction and valley filling; P price is the peak electricity price; V price The valley electricity price.

3. The method for optimizing energy storage configuration in a distribution network according to claim 1, characterized in that: The electric vehicle power constraint considering the electric vehicle aggregation capability is In the formula, P t eva and are the lower power limit representing the aggregated down-regulation capability of electric vehicles and the upper power limit representing the aggregated up-regulation capability of electric vehicles, respectively; The real-time power exchanged between electric vehicles and the power grid.

4. The method for optimizing energy storage configuration in a distribution network according to claim 3, characterized in that: The lower power limit and power cap The method to obtain is as follows: Real-time SOC based on electric vehicles before traveling 0,t The battery capacity D and the battery energy consumption per unit distance C are obtained by sampling the probability distribution of battery capacity, battery energy consumption per unit distance and daily travel distance using the Monte Carlo method. e , daily travel distance d, to build a real-time initial SOC value model when electric vehicles are connected to the grid; The expression of the real-time initial SOC value model when the electric vehicle enters the grid is: Based on the real-time initial SOC value model of the electric vehicle when it enters the network, the real-time exchange power P that meets the travel demand is calculated. t ; The electric vehicle start travel time t obtained by sampling the probability distribution of the start travel time and the end travel time of the electric vehicle using the Monte Carlo method d and the end travel time t of the electric vehicle s As the real-time exchange power P t Scope restrictions: Based on the real-time exchange power and its range restriction conditions, the exchangeable power P of the electric vehicle monomer is calculated. t ; The lower power limit of the electric vehicle's aggregate downregulation capability is calculated as follows: and represents the power ceiling of the EV aggregate upregulation capability Where n t is the number of electric vehicles connected to the grid in real time; P j,t is the exchangeable power of electric vehicle j at time t.

5. The method for optimizing energy storage configuration in a distribution network according to claim 4, characterized in that: The real-time exchange power P t The method to obtain is as follows: The Monte Carlo method is used to sample the probability distribution of energy demand to determine the SOC value of electric vehicle travel demand SOC d ; The real-time exchange power P t The expression is: in, In the formula, SOC t-Δt and SOC t are the SOC values ​​at time t-Δt and time t respectively; D t is the battery capacity involved in the reverse power supply process; Δt is the reverse power supply time; η d is the discharge efficiency.

6. The method for optimizing energy storage configuration in a distribution network according to claim 5, characterized in that: The exchangeable power P of the electric vehicle monomer t The expression is: Where P t is the exchangeable power; is the upper limit of the exchangeable power; P t is the lower limit of the exchangeable power.

7. The method for optimizing energy storage configuration in a distribution network according to claim 1, characterized in that: The expression of the system power balance constraint is: In the formula, is the upper power of the transformer, For photovoltaic power output, is the energy storage power, is the load power, The power of electric vehicles; The expression of the transformer reverse load rate constraint is: Where, T cap is the transformer capacity; cosφ is the power factor; The expression of the energy storage charge and discharge constraint is: In the formula, It is the maximum charge and discharge power of the energy storage battery.

8. A distribution network energy storage optimization configuration system, wherein a plurality of electric vehicles are arranged in the area where the distribution network is located, characterized in that: It includes a model building module and a model solving module, among which, The model building module is used to aggregate the electric vehicles as flexible resources to form virtual energy storage units in the distribution network, and to build an energy storage optimization configuration model that takes into account the flexibility aggregation of electric vehicles. The energy storage optimization configuration model uses conventional energy storage configuration capacity as a decision variable, and takes the lowest total cost over the entire life cycle of energy storage as an objective function, and takes electric vehicle power constraints, system power balance constraints, transformer reverse load rate constraints, and energy storage charging and discharging constraints that take into account the aggregation capacity of electric vehicles as constraints. The model solving module is used to solve the constructed energy storage optimization configuration model to obtain the energy storage optimization configuration solution.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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