A distributed energy storage virtualization sharing system scheduling method based on target cascading method

Abstract

The application discloses a kind of distributed energy storage virtualization sharing system scheduling method based on target cascade method, comprising:1. the operation model of including including distributed energy storage's producer and consumer community, the operation model of producer and consumer community of virtual energy storage rental;2. the service life model of generator set is established, so as to utilize the interactive power equivalence calculation strategy of distributed energy storage-virtual energy storage, the operation model of operator is established;3. the three-layer optimization model of distributed energy storage virtualization sharing system is established;4. three-layer optimization model is solved based on target cascade method, and the scheduling scheme of distributed energy storage virtualization sharing system is solved.The application introduces the method of distributed energy storage-virtual energy storage interactive power equivalence, and the three-layer optimization model of distributed energy storage virtualization sharing system is solved by combining distributed target cascade optimization method, on the basis of guaranteeing the safe operation boundary of power network and equipment, effectively reduce the system carbon emission and improve the utilization of distributed energy storage.

Description

Technical Field

[0001] This invention relates to the field of power system operation and control technology, specifically to a dispatching method for a distributed energy storage virtualization sharing system based on the target cascading method. This method is applicable to distribution network environments with multiple producers and consumers, enabling large-scale aggregation and privacy-preserving coordinated control of distributed energy storage. Background Technology

[0002] With the rapid development of distributed renewable energy, distributed energy storage systems have become a key technology for improving energy flexibility and economy on the user side. However, due to the difficulty for users to accurately predict the energy storage scale required for long-term operation, the utilization rate of existing distributed energy storage resources is insufficient. Currently, most research focuses on sharing distributed energy storage resources to achieve dynamic allocation and spatiotemporal reuse of energy storage capacity, thereby improving the utilization rate and return on investment of energy storage equipment, and enabling the provision of energy storage services to diverse load users at a lower cost. However, the promotion and application of traditional energy storage sharing models in distributed energy storage systems still faces significant challenges. On the one hand, it is difficult to maximize the flexibility and adjustment potential inherent in massive, dispersed distributed energy storage, making it impossible to accurately match the capacity sharing intentions of distributed energy storage system owners with the energy storage needs of users without distributed energy storage systems. On the other hand, facing multiple types of distributed energy storage entities and diversified energy storage leasing demands, existing models also lack flexible and fair energy storage capacity pricing mechanisms and revenue distribution systems.

[0003] To address this, existing research has introduced the concept of virtualized energy storage. This involves owners of dispersed, heterogeneous distributed energy storage resources sharing their redundant resources with a single energy storage service provider. The service provider then uses collaborative scheduling and control technologies to integrate these dispersed resources into a unified virtualized energy storage capacity unit, enabling the provision of flexibly configurable virtualized energy storage capacity leasing services to different users. Virtual energy storage users reduce energy costs by leasing virtual energy storage on demand. However, the virtual energy storage model relies on efficient collaborative control of massive distributed energy storage systems. Existing research lacks effective scheduling methods to balance the resource allocation of distributed energy storage, severely limiting the application potential and practical deployment feasibility of providing virtual energy storage leasing services through distributed energy storage aggregation. Summary of the Invention

[0004] To overcome the shortcomings of the existing technologies, this invention proposes a scheduling method for a distributed energy storage virtualization sharing system based on the target cascading method. The aim is to eliminate the disturbance of cross-node energy transmission to the power generation output by the coordinated optimization and equivalent power mapping of distributed energy storage and virtual energy storage without disclosing sensitive user data. This will enable the large-scale and efficient utilization of distributed energy storage systems while ensuring the safety, feasibility and stability of the power grid, and help achieve the "dual carbon" target of the new power system.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The present invention discloses a scheduling method for a distributed energy storage virtualization sharing system based on the target cascading method, characterized in that the distributed energy storage virtualization sharing system comprises: A prosumer community with distributed energy storage A community of prosumers renting virtual energy storage, one operator, and one generator set are involved in the scheduling method, which is performed according to the following steps: Step 1: Construct an operational model for a prosumer community that includes distributed energy storage and an operational model for a prosumer community that leases virtual energy storage; Step 2: Establish a lifespan model for the generator set, and then use the interactive power equivalence calculation strategy of distributed energy storage-virtual energy storage to establish an operation model for the operator. Step 3: Establish a three-layer optimization model for the distributed energy storage virtualization sharing system; Step 4: Solve the three-layer optimization model based on the objective cascade method to obtain the scheduling scheme of the distributed energy storage virtualization sharing system, including: the scheduling actions of the prosumer community with distributed energy storage, operators, and prosumer communities leasing virtual energy storage, as well as the upper limit of capacity and power of each leased virtual energy storage.

[0006] The scheduling method for a distributed energy storage virtualization sharing system based on the target cascading method described in this invention is characterized in that step one includes: Step 1.1: Use equation (1) to establish any... Individual consumer community Demand response model at any time: (1) In equation (1), It is the first Individual consumer community Load following demand response at any given moment; It is the first Individual consumer community The original load at any given moment; , They are the first Individual consumer community The amount of load decrease and load increase at any given time; The number of prosumer communities that include distributed energy storage; The number of prosumer communities renting virtual energy storage; Step 1.2: Establish the constraints of the demand response model using equations (2)-(6): (2) (3) (4) (5) (6) In equations (2)-(6), It is the first Individual consumer community Maximum adjustable load at any given time; The power factor of the demand response. Total time; Step 1.3: Use equation (7) to establish the first... Individual consumer community A time-based electricity efficiency model: (7) In equation (7), , These are the quadratic and linear weighting coefficients for the electricity consumption benefits of producer-consumer communities, respectively. For the first Individual consumer community The electricity efficiency value at any given time; It is the change in time, that is, the time difference between two moments; Step 1.4: Use equation (8) to establish the first... Individual consumers Carbon emission benefit loss model at time: (8) In equation (8), For the first Individual consumers The carbon emission benefit loss value at any given time. For the first Individual consumer community Photovoltaic power generation at any given time; For the first Individual consumer community The interactive power of distributed energy storage or virtual energy storage at any given time; Weighting for carbon emission benefits; For the first Individual consumer community The nodal carbon emission coefficient at any given time; Step 1.5: Using equations (9) and (10), we obtain the first... Benefits of renting out distributed energy storage in a prosumer community that includes distributed energy storage and the The loss of benefits for individual prosumer communities renting virtual energy storage. : (9) (10) In equations (9)-(10), For the first The benefit weighting of renting out distributed energy storage power in a prosumer community containing distributed energy production and storage. For the operator to the Distributed energy storage dispatch power of a producer-consumer community containing distributed energy production and storage; For the first The maximum capacity of virtual energy storage for a prosumer community that rents virtual energy storage; For the first The upper limit of virtual energy storage power for a pro-consumer community that rents virtual energy storage; , The first The community of prosumers renting virtual energy storage issues the capacity cap weight and power cap weight of virtual energy storage. Step 1.6: Use equation (11) to obtain the first... Energy storage usage cost loss in a prosumer community containing distributed energy storage : (11) In equation (11), This represents the loss coefficient for the benefits of distributed energy storage. For the first A prosumer community that includes distributed energy storage Interactive power of distributed energy storage at any given time; Step 1.7: Using equations (12) and (13), establish the operational efficiency models of prosumer communities with distributed energy storage and prosumer communities with leased virtual energy storage: (12) (13) In equations (12)-(13), For the first The comprehensive benefits of distributed energy storage and generation. For the first The comprehensive benefits of a community of prosumers that rents virtual energy storage; For the first A prosumer community with distributed energy storage The electricity efficiency value at any given time; For the first A community of prosumers renting virtual energy storage. The electricity efficiency value at any given time; No. A community of prosumers renting virtual energy storage. The carbon emission benefit loss value at any given moment; For the first A prosumer community with distributed energy storage The carbon emission benefit loss value at any given moment; Step 1.8: Use equations (14)-(21) to establish the first... Energy constraints of distributed and virtual energy storage in individual consumer communities: (14) (15) (16) (17) (18) (19) (20) (twenty one) In equations (14)-(21), For the first Distributed energy storage or virtual energy storage in individual consumer communities Energy level at any given moment; For the first Distributed energy storage or virtual energy storage in individual consumer communities Energy level at any moment For the first The upper limit of energy storage capacity for individual consumer communities; For the first Distributed energy storage or virtual energy storage in individual consumer communities The charging power at any given time; For the first Distributed energy storage or virtual energy storage in individual consumer communities Discharge power at any given moment; For the first The energy level of distributed or virtual energy storage at the last moment in a consumer community; For the first The initial energy level of distributed or virtual energy storage in a consumer community; For the first Individual consumer communities in distributed energy storage or virtual energy storage The upper limit of interaction power at any given time.

[0007] Furthermore, step two includes: Step 2.1: With the goal of minimizing the total losses of all generator sets, establish a lifespan model for the generator sets using equation (22): (twenty two) In equation (22), This is the sum of all losses across all generator sets. The expected internal depreciation rate; It is the first g The maximum power output of a generator set during its service life. It is the first g One generator set Electricity generation at any given moment; For the first g The service life of each generator set; This is a conversion factor for the useful life; This represents the total number of generator sets. Step 2.2: Establish constraints for the service life model using equations (23)-(24): (twenty three) (twenty four) In equations (23)-(24), and The first g Minimum and maximum generating power of each generator set; For the first g The ramp power coefficient of each generator set; It is the first g One generator set Power generation at any given moment Step 2.3: Calculate the first step using equation (25). A prosumer community with distributed energy storage Interaction power at time : (25) In equation (25) is No. A prosumer community with distributed energy storage Photovoltaic power generation at any given moment; It is the first A prosumer community with distributed energy storage Load following demand response at any given moment; Step 2.4: Calculate the first step using equation (26). A community of prosumers renting virtual energy storage. Actual interaction power at time : (26) In equation (26) is No. A community of prosumers renting virtual energy storage. Photovoltaic power generation at any given moment; It is the first A community of prosumers using virtual energy storage Load following demand response at any given moment; Step 2.5: Interactive power of prosumer communities with distributed energy storage Interaction power with virtual energy storage prosumer community Power flow calculations were performed on the lifetime model to obtain the first... g One generator set Output value at any moment ; Step 2.6: Reset the first step using formula (27). A prosumer community with distributed energy storage Interaction power at time : (27) Step 2.7: Interaction power of the prosumer community based on the reconfigured distributed energy storage Interaction power with generator set The difference between the sum of the dispatched power of distributed energy storage by operators to prosumer communities that include distributed energy storage and the sum of the dispatched power of virtual energy storage by prosumer communities that lease virtual energy storage. With the goal of minimizing power, a distributed energy storage-virtual energy storage power equivalent model is constructed using equation (28), and power flow calculations are performed to obtain the first... A community of prosumers renting virtual energy storage. Equivalent energy storage interaction power at any time ; (28) Step 2.8: Establish the operator's benefit model using equations (29)-(30): (29) (30) In equations (29)-(30), Fixed system losses for operators. The difference in carbon emission losses between a prosumer community with distributed energy storage and a prosumer community that leases virtual energy storage; For the first A community of prosumers renting virtual energy storage. The nodal carbon emission coefficient at any given time; For the first A prosumer community with distributed energy storage The nodal carbon emission coefficient at any given time; For the first A community of prosumers renting virtual energy storage. Virtual energy storage interaction power at any given moment.

[0008] Furthermore, step three includes: Step 3.1: With the goal of maximizing the energy efficiency of each prosumer community renting virtual energy storage, and using the energy load power of the prosumer community renting virtual energy storage, the capacity limit and power limit of the rented virtual energy storage, and the virtual energy storage interaction power as decision variables, the upper-level optimization model of the three-layer optimization model is constructed using equation (31): (31) Step 3.2: With the goal of maximizing the operator's operational efficiency, and using the energy storage scheduling power and virtual energy interaction power of the producer-consumer community containing distributed energy storage as decision variables, construct the middle-level optimization model of the three-layer optimization model using equation (32): (32) Step 3.3: With the goal of maximizing the energy efficiency of each prosumer community containing distributed shared energy storage, and using the energy load power, distributed energy storage interaction power, and distributed energy storage resource interaction power of the prosumer community containing distributed energy storage as decision variables, the lower-level optimization model of the three-layer optimization model is constructed using equation (33): (33) Furthermore, step four: Step 4.1: Using the upper-level optimization model, middle-level optimization model, and lower-level optimization model described in the solver, obtain the first... Energy efficiency value of individual prosumers renting virtual energy storage Operators A prosumer community with distributed energy storage Energy storage dispatch power at any time Operators Individual prosumers renting virtual energy storage Energy storage interaction power at any time The operational efficiency value of operators , No. Energy storage efficiency value of a prosumer community containing distributed energy storage ; Step 4.2, Define the current iteration number as... and initialize The maximum number of iterations is ; Define and initialize the first In the nth iteration A prosumer community with distributed energy storage The coefficient of the Lagrange penalty function at time t is , No. In the nth iteration A prosumer community with distributed energy storage The Lagrange penalty function multipliers at time t are respectively , No. In the nth iteration A community of prosumers renting virtual energy storage. The coefficient of the Lagrange penalty function at time t is , No. In the nth iteration A community of prosumers renting virtual energy storage. The Lagrange penalty function multiplier at time t is ; The iteration step size for the prosumer community with distributed energy storage is set to... ; The iteration step size for setting up a prosumer community for renting virtual energy storage is... ; The convergence error of the producer-consumer community consensus constraint with distributed energy storage is set to be... ; The objective function convergence error of a prosumer community including distributed energy storage is set as follows: ; The convergence error of setting the producer-consumer community consensus constraint for rented virtual energy storage is: ; The objective function convergence error of the prosumer community for renting virtual energy storage is set as follows: ; Initialize the first In the next iteration, the operator... A prosumer community with distributed energy storage Energy storage dispatch power at any time Initialization In the nth iteration Individual prosumers renting virtual energy storage Energy storage interaction power at any time Initialization In the next iteration, the operator... Energy storage benefits of a prosumer community with distributed energy storage The operational efficiency value of operators Initialization In the nth iteration Energy efficiency for prosumers who rent virtual energy storage ; Step 4.3: Calculate the first step using equations (34)-(35). In the nth iteration The prosumer community for rented virtual energy storage augments the Lagrangian function. , obtained the In the nth iteration A community of prosumers renting virtual energy storage. Interactive power of virtual energy storage at any time : (34) (35) In equations (34)-(35), For the first in the upper-level optimization model A community of prosumers renting virtual energy storage augments the Lagrange function suffix term; For the upper-level optimization model in the first... In the nth iteration User benefits of a community of prosumers renting virtual energy storage; Step 4.4: Calculate the first step using equations (36) and (37) respectively. In the nth iteration An augmented Lagrangian function for a prosumer community containing distributed energy storage. , obtained the In the next iteration, the operator... A prosumer community with distributed energy storage Real-time energy storage dispatch power : (36) (37) In equations (36)-(37), For the lower-level optimization model, the first An augmented Lagrange function suffix term for a prosumer community containing distributed energy storage; For the lower-level optimization model in the first In the nth iteration User benefits of a producer-consumer community that includes distributed energy storage; Step 4.5: Calculate the first step using equations (38)-(40). The operator's augmented Lagrangian function in the next iteration , obtained the In the nth iteration A prosumer community with distributed energy storage Energy storage dispatch power at any time and the In the nth iteration A community of prosumers renting virtual energy storage. Virtual energy storage interaction power at any time : (38) (39) (40) In equations (38)-(40), In the mid-level optimization model, the first An augmented Lagrange function suffix term for a prosumer community containing distributed energy storage; In the mid-level optimization model, the first An augmented Lagrange function suffix term is added to a community of prosumers who rent virtualized energy storage. Step 4.6: Using equations (41) and (42), obtain the first... In the nth iteration A community of prosumers renting virtual energy storage. Lagrange penalty function coefficients at time t Sum of multipliers : (41) (42) Step 4.7: Using equations (43)-(44), obtain the first... In the first iteration A community of prosumers renting virtual energy storage. Lagrange penalty function coefficients at time t Sum of multipliers : (43) (44) Step 4.8: Construct convergence conditions using equations (45)-(48): (45) (46) (47) (48) Step 4.9, if satisfied If all of equations (45)-(48) are true, then the calculation stops, and the scheduling scheme of the distributed energy storage virtualization sharing system under the k-th iteration is output, including: the scheduling power of the energy storage system by the prosumer community containing distributed energy storage, the operator, and the prosumer community leasing virtual energy storage under the k-th iteration, as well as the upper limit of capacity and power of each leased virtual energy storage under the k-th iteration; let Assign to , Assign to , Assign to ; Assign to , Assign to , Assign to Then return to step 4.3 and execute sequentially.

[0009] The present invention provides an electronic device, including a memory and a processor, characterized in that the memory is used to store a program that supports the processor in executing the distributed energy storage virtualization sharing system scheduling method based on the target cascading method, and the processor is configured to execute the program stored in the memory.

[0010] The present invention discloses a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, performs the steps of the aforementioned scheduling method for a distributed energy storage virtualization sharing system based on the target cascading method.

[0011] Compared with existing technologies, the beneficial effects of this invention are reflected in: 1. This invention addresses the energy storage needs of various prosumer communities. Based on the consideration of system carbon emission losses, it establishes an energy efficiency model for prosumer communities that includes distributed energy storage and a prosumer energy efficiency model that includes virtual energy storage leasing. This provides a unified and calculable decision-making basis for carrying out collaborative optimization of distributed energy storage, promotes the efficient utilization of distributed energy storage, and supports low-carbon and safe operation.

[0012] 2. This invention addresses the issue of power output fluctuations on the generation side caused by energy transmission through the power grid at the user side. It proposes a distributed energy storage-virtual energy storage interactive power equivalence method, employs a two-stage optimal power flow calculation method to avoid passive deviations in generator output values, and verifies voltage, line current, and power balance to ensure the safety and feasibility of the entire network.

[0013] 3. This invention addresses the data privacy and security issues in distributed energy storage virtualization and sharing by using the objective cascade method to solve the problem. Without disclosing sensitive data, it effectively balances the performance differences of different physical energy storage systems and achieves distributed collaborative optimization across nodes and multiple entities. Attached Figure Description

[0014] Figure 1 A flowchart illustrating the decision-making process of a distributed energy storage virtualization shared scheduling system; Detailed Implementation

[0015] In this embodiment, addressing issues such as multi-entity privacy protection, power output fluctuations caused by cross-node energy transmission, and the difficulty in ensuring the coordination and consistency of different types of distributed energy storage, a scheduling method for a distributed energy storage virtualization sharing system based on the objective cascading method is proposed. This method includes: 1. Constructing an operational model comprising a prosumer community containing distributed energy storage and a prosumer community renting virtual energy storage; 2. Establishing a generator set lifespan model, thereby utilizing the power equivalence calculation strategy of distributed energy storage-virtual energy storage interaction to establish an operator's operational model; 3. Establishing a three-layer optimization model for the distributed energy storage virtualization sharing system; 4. Solving the three-layer optimization model based on the objective cascading method to obtain a scheduling scheme for the distributed energy storage virtualization sharing system. Specifically, as... Figure 1 As shown, the method is performed according to the following steps: Step 1: Construct an operational model for a prosumer community that includes distributed energy storage and an operational model for a prosumer community that leases virtual energy storage: Step 1.1: Use equation (1) to establish any... Individual consumer community Demand response model at any time: (1) In equation (1), It is the first Individual consumer community Load following demand response at any given moment; It is the first Individual consumer community The original load at any given moment; , They are the first Individual consumer community The amount of load decrease and load increase at any given time; The number of prosumer communities that include distributed energy storage; The number of consumer communities renting virtual energy storage.

[0016] Step 1.2: During the demand response process, the demand response amounts for various load types are subject to certain limits, and the total daily load amount must remain constant before and after the demand response for each type of load. Therefore, the constraints of the demand response model are established using equations (2)-(6): (2) (3) (4) (5) (6) In equations (2)-(6), It is the first Individual consumer community Maximum adjustable load at any given time; The power factor of the demand response. For the total time.

[0017] Step 1.3: In economics, the utility function is often used to quantify the degree of satisfaction consumers obtain when consuming a given combination of goods. It can simulate users' requirements for comfort during demand response, avoiding large deviations in energy consumption behavior. Using equation (7), the first... Individual consumer community A time-based electricity efficiency model: (7) In equation (7), , These are the quadratic and linear weighting coefficients for the electricity consumption benefits of producer-consumer communities, respectively. For the first Individual consumer community The electricity efficiency value at any given time; It represents the change in time, that is, the time difference between two moments.

[0018] Step 1.4: Based on the node carbon emission intensity information, the load side assumes the corresponding carbon emission responsibility of the power purchase station, and at the same time optimizes its own power consumption plan to maximize the user utility function. The first step is to establish the first step using equation (8). Individual consumers Carbon emission benefit loss model at time: (8) In equation (8), For the first Individual consumers The carbon emission benefit loss value at any given time. For the first Individual consumer community Photovoltaic power generation at any given time; For the first Individual consumer community The interactive power of distributed energy storage or virtual energy storage at any given time; Weighting for carbon emission benefits; For the first Individual consumer community The node carbon emission coefficient at any given time.

[0019] Step 1.5: Using equations (9) and (10), we obtain the first... Benefits of renting out distributed energy storage in a prosumer community that includes distributed energy storage and the The loss of benefits for individual prosumer communities renting virtual energy storage. : (9) (10) In equations (9)-(10), For the first The benefit weighting of renting out distributed energy storage power in a prosumer community containing distributed energy production and storage. For the operator to the Distributed energy storage dispatch power of a producer-consumer community containing distributed energy production and storage; For the first The maximum capacity of virtual energy storage for a prosumer community that rents virtual energy storage; For the first The upper limit of virtual energy storage power for a pro-consumer community that rents virtual energy storage; , The first A community of prosumers renting virtual energy storage will determine the weights of the virtual energy storage capacity cap and the power cap.

[0020] Step 1.6: Since the physical distributed energy storage usage loss is affected by the charging and discharging interaction power, the first step is obtained using equation (11). Energy storage usage cost loss in a prosumer community containing distributed energy storage : (11) In equation (11), This represents the loss coefficient for the benefits of distributed energy storage. For the first A prosumer community that includes distributed energy storage Interactive power of distributed energy storage at any given time.

[0021] Step 1.7: Prosumer communities with distributed energy storage improve their efficiency by leasing out a portion of their energy storage capacity; prosumer communities leasing virtual energy storage adjust their virtual energy storage charging and discharging strategies based on their own energy needs to optimize energy efficiency. Therefore, using equations (12)-(13), we establish the operational efficiency models for prosumer communities with distributed energy storage and for prosumer communities leasing virtual energy storage: (12) (13) In equations (12)-(13), For the first The comprehensive benefits of distributed energy storage and generation. For the first The comprehensive benefits of a community of prosumers that rents virtual energy storage; For the first A prosumer community with distributed energy storage The electricity efficiency value at any given time; For the first A community of prosumers renting virtual energy storage. The electricity efficiency value at any given time; No. A community of prosumers renting virtual energy storage. The carbon emission benefit loss value at any given moment; For the first A prosumer community with distributed energy storage The carbon emission benefit loss value at any given moment.

[0022] Step 1.8: Use equations (14)-(21) to establish the first... Energy constraints of distributed and virtual energy storage in individual consumer communities: (14) (15) (16) (17) (18) (19) (20) (twenty one) In equations (14)-(21), For the first Distributed energy storage or virtual energy storage in individual consumer communities Energy level at any given moment; For the first Distributed energy storage or virtual energy storage in individual consumer communities Energy level at any moment For the first The upper limit of energy storage capacity for individual consumer communities; For the first Distributed energy storage or virtual energy storage in individual consumer communities The charging power at any given time; For the first Distributed energy storage or virtual energy storage in individual consumer communities Discharge power at any given moment; For the first The energy level of distributed or virtual energy storage at the last moment in a consumer community; For the first The initial energy level of distributed or virtual energy storage in a consumer community; For the first Individual consumer communities in distributed energy storage or virtual energy storage The upper limit of interaction power at any given time.

[0023] Step 2: Establish a lifespan model for the generator set, and then use the interactive power equivalence calculation strategy of distributed energy storage-virtual energy storage to establish an operation model for the operator. Step 2.1: The prosumer community that rents virtual energy storage adjusts the charging and discharging strategy of its virtual energy storage according to its own energy usage requirements to optimize electricity costs. Unlike physical distributed energy storage systems, it only achieves virtual energy dispatch through information layer interaction. The energy demand corresponding to these commands is provided by distributed energy storage dispatch. However, since distributed energy storage and virtual energy storage are geographically separated and connected to different physical nodes of the distribution network, distributed energy storage needs to be transmitted across nodes. This will cause additional network losses due to line resistance, thereby changing the power flow distribution of the distribution network. This loss ultimately requires adjusting the power output to maintain the system power balance. First, with the goal of minimizing the total loss of all generator sets, the lifespan model of the generator sets is established using equation (22): (twenty two) In equation (22), This is the sum of all losses across all generator sets. The expected internal depreciation rate; It is the first g The maximum power output of a generator set during its service life. It is the first g One generator set Electricity generation at any given moment; For the first g The service life of each generator set; This is a conversion factor for the useful life; This represents the total number of generator sets.

[0024] Step 2.2: Establish constraints for the service life model using equations (23)-(24): (twenty three) (twenty four) In equations (23)-(24), and The first g Minimum and maximum generating power of each generator set; For the first g The ramp power coefficient of each generator set; It is the first g One generator set Power generation at any given moment Step 2.3: Calculate the first step using equation (25). A prosumer community with distributed energy storage Interaction power at time : (25) In equation (25) is No. A prosumer community with distributed energy storage Photovoltaic power generation at any given moment; It is the first A prosumer community with distributed energy storage Load following demand response at any given moment; Step 2.4: Calculate the first step using equation (26). A community of prosumers renting virtual energy storage. Actual interaction power at time : (26) In equation (26) is No. A community of prosumers renting virtual energy storage. Photovoltaic power generation at any given moment; It is the first A community of prosumers using virtual energy storage Load following the demand response at any given moment.

[0025] Step 2.5: Interactive power of prosumer communities with distributed energy storage Interaction power with virtual energy storage prosumer community Power flow calculations were performed on the lifetime model to obtain the first... g One generator set Output value at any moment ; Step 2.6: Reset the first step using formula (27). A prosumer community with distributed energy storage Interaction power at time : (27) Step 2.7: Interaction power of the prosumer community based on the reconfigured distributed energy storage Interaction power with generator set The difference between the sum of the dispatched power of distributed energy storage by operators to prosumer communities that include distributed energy storage and the sum of the dispatched power of virtual energy storage by prosumer communities that lease virtual energy storage. With the goal of minimizing power, a distributed energy storage-virtual energy storage power equivalent model is constructed using equation (28), and power flow calculations are performed to obtain the first... A community of prosumers renting virtual energy storage. Equivalent energy storage interaction power at any time ; (28) Step 2.8: Establish the operator's benefit model using equations (29)-(30): (29) (30) In equations (29)-(30), Fixed system losses for operators. The difference in carbon emission losses between a prosumer community with distributed energy storage and a prosumer community that leases virtual energy storage; For the first A community of prosumers renting virtual energy storage. The nodal carbon emission coefficient at any given time; For the first A prosumer community with distributed energy storage The nodal carbon emission coefficient at any given time; For the first A community of prosumers renting virtual energy storage. Virtual energy storage interaction power at any given moment.

[0026] Step 3: Establish a three-layer optimization model for the distributed energy storage virtualization sharing system; Step 3.1: There are multi-level strong couplings between objectives and constraints in the prosumer community containing distributed energy storage, operators, and prosumer communities leasing virtual energy storage. There are also issues related to user data privacy protection requirements and the consistency and coordination between virtual energy storage demand and the available capacity of distributed energy storage. Therefore, it is necessary to establish a three-layer optimization model for the distributed energy storage virtualization sharing system. Taking the maximization of energy consumption efficiency of each prosumer community leasing virtual energy storage as the objective, and using the energy load power of the prosumer community leasing virtual energy storage, the capacity limit and power limit of the leased virtual energy storage, and the virtual energy storage interaction power as decision variables, the upper-level optimization model of the three-layer optimization model is constructed using equation (31): (31) Step 3.2: With the goal of maximizing the operator's operational efficiency, and using the energy storage scheduling power and virtual energy interaction power of the producer-consumer community containing distributed energy storage as decision variables, construct the middle-level optimization model of the three-layer optimization model using equation (32): (32) Step 3.3: With the goal of maximizing the energy efficiency of each prosumer community containing distributed shared energy storage, and using the energy load power, distributed energy storage interaction power, and distributed energy storage resource interaction power of the prosumer community containing distributed energy storage as decision variables, the lower-level optimization model of the three-layer optimization model is constructed using equation (34): (33) Step 4: Solve the three-layer optimization model based on the objective cascade method to obtain the scheduling scheme of the distributed energy storage virtualization sharing system, including: the scheduling actions of the prosumer community with distributed energy storage, the operator, and the prosumer community leasing virtual energy storage, as well as the upper limit of capacity and power of each leased virtual energy storage. Step 4.1: Use the solver to calculate the upper-level optimization model, middle-level optimization model, and lower-level optimization model from steps 3.1-3.3, and obtain the... Energy efficiency value of individual prosumers renting virtual energy storage Operators A prosumer community with distributed energy storage Energy storage dispatch power at any time Operators Individual prosumers renting virtual energy storage Energy storage interaction power at any time The operational efficiency value of operators , No. Energy storage efficiency value of a prosumer community containing distributed energy storage ; Step 4.2: The objective cascading method is suitable for solving hierarchical, distributed decision-making and coordination problems. This is completely consistent with the distributed energy storage virtualization sharing system proposed in Step 3. Furthermore, the objective cascading method can perform localized optimization at each level and can perform calculations without disclosing sensitive local information. In this method, the data exchanged between levels consists only of parameters of distributed energy storage and virtual energy storage. The current iteration number is defined as... and initialize The maximum number of iterations is ; Define and initialize the first In the nth iteration A prosumer community with distributed energy storage The coefficient of the Lagrange penalty function at time t is , No. In the nth iteration A prosumer community with distributed energy storage The Lagrange penalty function multipliers at time t are respectively , No. In the nth iteration A community of prosumers renting virtual energy storage. The coefficient of the Lagrange penalty function at time t is , No. In the nth iteration A community of prosumers renting virtual energy storage. The Lagrange penalty function multiplier at time t is ; The iteration step size for the prosumer community with distributed energy storage is set to... ; The iteration step size for setting up a prosumer community for renting virtual energy storage is... ; The convergence error of the producer-consumer community consensus constraint with distributed energy storage is set to be... ; The objective function convergence error of a prosumer community including distributed energy storage is set as follows: ; The convergence error of setting the producer-consumer community consensus constraint for rented virtual energy storage is: ; The objective function convergence error of the prosumer community for renting virtual energy storage is set as follows: ; Initialize the first In the next iteration, the operator... A prosumer community with distributed energy storage Energy storage dispatch power at any time Initialization In the nth iteration Individual prosumers renting virtual energy storage Energy storage interaction power at any time Initialization In the next iteration, the operator... Energy storage benefits of a prosumer community with distributed energy storage The operational efficiency value of operators Initialization In the nth iteration Energy efficiency for prosumers who rent virtual energy storage ; Step 4.3: After relaxing the coupling constraints between the upper, middle, and lower level systems using the augmented Lagrange penalty function, only the local constraints and local decision variables that need to be satisfied in the regional variables remain in the different level systems, thus decoupling the systems between different levels. The calculation of the first... During the iteration The prosumer community for rented virtual energy storage augments the Lagrangian function. , obtained the In the nth iteration A community of prosumers renting virtual energy storage. Interactive power of virtual energy storage at any moment : (34) (35) In equations (34)-(35), For the first in the upper-level optimization model A community of prosumers renting virtual energy storage augments the Lagrange function suffix term; For the upper-level optimization model in the first... In the nth iteration User benefits of a community of prosumers renting virtual energy storage; Step 4.4: Calculate the first step using equations (36) and (37) respectively. In the nth iteration An augmented Lagrangian function for a prosumer community containing distributed energy storage. , obtained the In the next iteration, the operator... A prosumer community with distributed energy storage Real-time energy storage dispatch power : (36) (37) In equations (36)-(37), For the lower-level optimization model, the first An augmented Lagrange function suffix term for a prosumer community containing distributed energy storage; For the lower-level optimization model in the first In the nth iteration User benefits of a producer-consumer community that includes distributed energy storage; Step 4.5: Calculate the first step using equations (38)-(40). The operator's augmented Lagrangian function in the next iteration , obtained the In the nth iteration A prosumer community with distributed energy storage Energy storage dispatch power at any time and the In the nth iteration A community of prosumers renting virtual energy storage. Virtual energy storage interaction power at any time : (38) (39) (40) In equations (38)-(40), In the mid-level optimization model, the first An augmented Lagrange function suffix term for a prosumer community containing distributed energy storage; In the mid-level optimization model, the first An augmented Lagrange function suffix term is added to a community of prosumers who rent virtualized energy storage. Step 4.6: Using equations (41) and (42), obtain the first... In the nth iteration A community of prosumers renting virtual energy storage. Lagrange penalty function coefficients at time t Sum of multipliers : (41) (42) Step 4.7: Using equations (43)-(44), obtain the first... In the first iteration A community of prosumers renting virtual energy storage. Lagrange penalty function coefficients at time t Sum of multipliers : (43) (44) Step 4.8: Construct convergence conditions using equations (45)-(48): (45) (46) (47) (48) Step 4.9, if satisfied If all of equations (45)-(48) are true, then the calculation stops, and the scheduling scheme of the distributed energy storage virtualization sharing system under the k-th iteration is output, including: the scheduling power of the energy storage system by the prosumer community containing distributed energy storage, the operator, and the prosumer community leasing virtual energy storage under the k-th iteration, as well as the upper limit of capacity and power of each leased virtual energy storage under the k-th iteration; let Assign to , Assign to , Assign to ; Assign to , Assign to , Assign to Then return to step 4.3 and execute sequentially.

[0027] In summary, this invention proposes a scheduling method for a distributed energy storage virtualization sharing system based on the objective cascading method. This method aims to provide the optimal decision-making scheme for the distributed energy storage virtualization sharing system by constructing a three-layer optimization model consisting of a "prosumer community containing distributed energy storage – operator – prosumer community leasing virtual energy storage," and combining the power equivalence method for distributed energy storage-virtual energy storage node interaction with the objective cascading method. This method not only absorbs the impact of network losses and ensures power flow and constraint feasibility while maintaining the reference output of generator units, but also improves energy storage utilization, smooths load fluctuations, and enhances system resilience. It has strong scalability and is compatible with different types of distributed energy storage devices.

[0028] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described comprehensive assessment method for the acceptance capacity of electric vehicles in the power distribution network. The processor is configured to execute the program stored in the memory.

[0029] In this embodiment, a computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of a comprehensive assessment method for the acceptance capacity of electric vehicles in a power distribution network.

Claims

1. A scheduling method for a distributed energy storage virtualization sharing system based on the target cascading method, characterized in that, The distributed energy storage virtualization sharing system includes: A prosumer community with distributed energy storage A community of prosumers renting virtual energy storage, one operator, and one generator set are involved in the scheduling method, which is performed according to the following steps: Step 1: Construct an operational model for a prosumer community that includes distributed energy storage and an operational model for a prosumer community that leases virtual energy storage; Step 1.1: Use equation (1) to establish any... Individual consumer community Demand response model at any time: (1) In equation (1), It is the first Individual consumer community Load following demand response at any given moment; It is the first Individual consumer community The original load at any given moment; , They are the first Individual consumer community The amount of load decrease and load increase at any given time; The number of prosumer communities that include distributed energy storage; The number of prosumer communities renting virtual energy storage; Step 1.2: Establish the constraints of the demand response model using equations (2)-(6): (2) (3) (4) (5) (6) In equations (2)-(6), It is the first Individual consumer community Maximum adjustable load at any given time; The power factor of the demand response. Total time; Step 1.3: Use equation (7) to establish the first... Individual consumer community A time-based electricity efficiency model: (7) In equation (7), , These are the quadratic and linear weighting coefficients for the electricity consumption benefits of producer-consumer communities, respectively. For the first Individual consumer community The electricity efficiency value at any given time; It is the change in time, that is, the time difference between two moments; Step 1.4: Use equation (8) to establish the first... Individual consumers Carbon emission benefit loss model at time: (8) In equation (8), For the first Individual consumers The carbon emission benefit loss value at any given time. For the first Individual consumer community Photovoltaic power generation at any given time; For the first Individual consumer community The interactive power of distributed energy storage or virtual energy storage at any given time; Weighting for carbon emission benefits; For the first Individual consumer community The nodal carbon emission coefficient at any given time; Step 1.5: Using equations (9) and (10), we obtain the first... Benefits of renting out distributed energy storage in a prosumer community that includes distributed energy storage and the The loss of benefits for individual prosumer communities renting virtual energy storage. : (9) (10) In equations (9)-(10), For the first The benefit weighting of renting out distributed energy storage power in a prosumer community containing distributed energy production and storage. For the operator to the Distributed energy storage dispatch power of a producer-consumer community containing distributed energy production and storage; For the first The maximum capacity of virtual energy storage for a prosumer community that rents virtual energy storage; For the first The upper limit of virtual energy storage power for a pro-consumer community that rents virtual energy storage; , The first The community of prosumers renting virtual energy storage issues the capacity cap weight and power cap weight of virtual energy storage. Step 1.6: Use equation (11) to obtain the first... Energy storage usage cost loss in a prosumer community containing distributed energy storage : (11) In equation (11), This represents the loss coefficient for the benefits of distributed energy storage. For the first A prosumer community that includes distributed energy storage Interactive power of distributed energy storage at any given time; Step 1.7: Using equations (12) and (13), establish the operational efficiency models of prosumer communities with distributed energy storage and prosumer communities with leased virtual energy storage: (12) (13) In equations (12)-(13), For the first The comprehensive benefits of distributed energy storage and generation. For the first The comprehensive benefits of a community of prosumers that rents virtual energy storage; For the first A prosumer community with distributed energy storage The electricity efficiency value at any given time; For the first A community of prosumers renting virtual energy storage. The electricity efficiency value at any given time; No. A community of prosumers renting virtual energy storage. The carbon emission benefit loss value at any given moment; For the first A prosumer community with distributed energy storage The carbon emission benefit loss value at any given moment; Step 1.8: Use equations (14)-(21) to establish the first... Energy constraints of distributed and virtual energy storage in individual consumer communities: (14) (15) (16) (17) (18) (19) (20) (21) In equations (14)-(21), For the first Distributed energy storage or virtual energy storage in individual consumer communities Energy level at any given moment; For the first Distributed energy storage or virtual energy storage in individual consumer communities Energy level at any moment For the first The upper limit of energy storage capacity for individual consumer communities; For the first Distributed energy storage or virtual energy storage in individual consumer communities The charging power at any given moment; For the first Distributed energy storage or virtual energy storage in individual consumer communities Discharge power at any given moment; For the first The energy level of distributed or virtual energy storage at the last moment in a consumer community; For the first The initial energy level of distributed or virtual energy storage in a consumer community; For the first Individual consumer communities in distributed energy storage or virtual energy storage The upper limit of interaction power at any given time; Step 2: Establish a lifespan model for the generator set, and then use the interactive power equivalence calculation strategy of distributed energy storage-virtual energy storage to establish an operation model for the operator. Step 2.1: With the goal of minimizing the total losses of all generator sets, establish a lifespan model for the generator sets using equation (22): (22) In equation (22), This is the sum of all losses across all generator sets. The expected internal depreciation rate; It is the maximum power generation of the g-th generator unit during its service life cycle. Is the g-th generator unit in Electricity generation at any given moment; The usable lifespan of the g-th generator unit; This is a conversion factor for the useful life; This represents the total number of generator sets. Step 2.2: Establish constraints for the service life model using equations (23)-(24): (23) (24) In equations (23)-(24), and These are the minimum and maximum generating power of the g-th generator unit, respectively; Let g be the ramp power coefficient of the g-th generator unit; Is the g-th generator unit in Power generation at any given moment Step 2.3: Calculate the first step using equation (25). A prosumer community with distributed energy storage Interaction power at time : (25) In equation (25) is No. A prosumer community with distributed energy storage Photovoltaic power generation at any given moment; It is the first A prosumer community with distributed energy storage Load following demand response at any given moment; Step 2.4: Calculate the first step using equation (26). A community of prosumers renting virtual energy storage. Actual interaction power at time : (26) In equation (26) is No. A community of prosumers renting virtual energy storage. Photovoltaic power generation at any given moment; It is the first A community of prosumers using virtual energy storage Load following demand response at any given moment; Step 2.5: Interactive power of prosumer communities with distributed energy storage Interaction power with virtual energy storage prosumer community Power flow calculations are performed on the service life model to obtain the value of the g-th generator unit at the end of its service life. Output value at any moment ; Step 2.6: Reset the first step using formula (28). A prosumer community with distributed energy storage Interaction power at time : (28) Step 2.7: Interaction power of the prosumer community based on the reconfigured distributed energy storage Interaction power with generator set The difference between the sum of the dispatched power of distributed energy storage by operators to prosumer communities that include distributed energy storage and the sum of the dispatched power of virtual energy storage by prosumer communities that lease virtual energy storage. With the goal of minimizing power, a distributed energy storage-virtual energy storage power equivalent model is constructed using equation (29), and power flow calculations are performed to obtain the first... A community of prosumers renting virtual energy storage. Equivalent energy storage interaction power at any time ; (29) Step 2.8: Establish the operator's benefit model using equations (30) and (31): (30) (31) In equation (15), Fixed system losses for operators. The difference in carbon emission losses between a prosumer community with distributed energy storage and a prosumer community that leases virtual energy storage; For the first A community of prosumers renting virtual energy storage. The nodal carbon emission coefficient at any given time; For the first A prosumer community with distributed energy storage The nodal carbon emission coefficient at any given time; For the first A community of prosumers renting virtual energy storage. Virtual energy storage interaction power at any given moment; Step 3: Establish a three-layer optimization model for the distributed energy storage virtualization sharing system; Step 3.1: With the goal of maximizing the energy efficiency of each prosumer community renting virtual energy storage, and using the energy load power of the prosumer community renting virtual energy storage, the capacity limit and power limit of the rented virtual energy storage, and the virtual energy storage interaction power as decision variables, the upper-level optimization model of the three-layer optimization model is constructed using equation (32): (32) Step 3.2: With the goal of maximizing the operator's operational efficiency, and using the energy storage scheduling power and virtual energy interaction power of the producer-consumer community containing distributed energy storage as decision variables, construct the middle-level optimization model of the three-layer optimization model using equation (33): (33) Step 3.3: With the goal of maximizing the energy efficiency of each prosumer community containing distributed shared energy storage, and using the energy load power, distributed energy storage interaction power, and distributed energy storage resource interaction power of the prosumer community containing distributed energy storage as decision variables, the lower-level optimization model of the three-layer optimization model is constructed using equation (34): (34) Step 4: Solve the three-layer optimization model based on the objective cascade method to obtain the scheduling scheme of the distributed energy storage virtualization sharing system, including: the scheduling actions of the prosumer community with distributed energy storage, operators, and prosumer communities leasing virtual energy storage, as well as the upper limit of capacity and power of each leased virtual energy storage.

2. The scheduling method for a distributed energy storage virtualization sharing system based on the target cascading method according to claim 1, characterized in that, Step four: Step 4.1: Using the upper-level optimization model, middle-level optimization model, and lower-level optimization model described in the solver, obtain the first... Energy efficiency value of individual prosumers renting virtual energy storage Operators A prosumer community with distributed energy storage Energy storage dispatch power at any time Operators Individual prosumers renting virtual energy storage Energy storage interaction power at any time The operational efficiency value of operators , No. Energy storage efficiency value of a prosumer community containing distributed energy storage ; Step 4.2: Define the current iteration number as... and initialize The maximum number of iterations is ; Define and initialize the first In the nth iteration A prosumer community with distributed energy storage The coefficient of the Lagrange penalty function at time t is , No. In the nth iteration A prosumer community with distributed energy storage The Lagrange penalty function multipliers at time t are respectively , No. In the nth iteration A community of prosumers renting virtual energy storage. The coefficient of the Lagrange penalty function at time t is , No. In the nth iteration A community of prosumers renting virtual energy storage. The Lagrange penalty function multiplier at time t is ; The iteration step size for the prosumer community with distributed energy storage is set to... ; The iteration step size for setting up a prosumer community for renting virtual energy storage is... ; The convergence error of the producer-consumer community consensus constraint with distributed energy storage is set to be... ; The objective function convergence error of a prosumer community including distributed energy storage is set as follows: ; The convergence error of setting the producer-consumer community consensus constraint for rented virtual energy storage is: ; The objective function convergence error of the prosumer community for renting virtual energy storage is set as follows: ; Initialize the first In the next iteration, the operator... A prosumer community with distributed energy storage Energy storage dispatch power at any time Initialization In the nth iteration Individual prosumers renting virtual energy storage Energy storage interaction power at any time Initialization In the next iteration, the operator... Energy storage benefits of a prosumer community with distributed energy storage The operational efficiency value of operators Initialization In the nth iteration Energy efficiency for prosumers who rent virtual energy storage ; Step 4.3: Calculate the first step using equations (35)-(36). In the nth iteration The prosumer community for rented virtual energy storage augments the Lagrangian function. , obtained the In the nth iteration A community of prosumers renting virtual energy storage. Interactive power of virtual energy storage at any time : (35) (36) In equations (35)-(36), For the first in the upper-level optimization model A community of prosumers renting virtual energy storage augments the Lagrange function suffix term; For the upper-level optimization model in the first... In the nth iteration User benefits of a community of prosumers renting virtual energy storage; Step 4.4: Calculate the first step using equations (37) and (38) respectively. In the nth iteration An augmented Lagrangian function for a prosumer community containing distributed energy storage. , obtained the In the next iteration, the operator... A prosumer community with distributed energy storage Real-time energy storage dispatch power : (37) (38) In equations (37)-(38), For the lower-level optimization model, the first An augmented Lagrange function suffix term for a prosumer community containing distributed energy storage; For the lower-level optimization model in the first In the nth iteration User benefits of a producer-consumer community that includes distributed energy storage; Step 4.5: Calculate the first step using equations (39)-(41). The operator's augmented Lagrangian function in the next iteration , obtained the In the nth iteration A prosumer community with distributed energy storage Energy storage dispatch power at any time and the In the nth iteration A community of prosumers renting virtual energy storage. Virtual energy storage interaction power at any time : (39) (40) (41) In equations (39)-(41), In the mid-level optimization model, the first An augmented Lagrange function suffix term for a prosumer community containing distributed energy storage; In the mid-level optimization model, the first An augmented Lagrange function suffix term is added to a community of prosumers who rent virtualized energy storage. Step 4.6: Using equations (42) and (43), obtain the first... In the nth iteration A community of prosumers renting virtual energy storage. Lagrange penalty function coefficients at time t Sum of multipliers : (42) (43) Step 4.7: Using equations (44)-(45), obtain the first... In the first iteration A community of prosumers renting virtual energy storage. Lagrange penalty function coefficients at time t Sum of multipliers : (44) (45) Step 4.8: Construct convergence conditions using equations (46)-(49): (46) (47) (48) (49) Step 4.9, if satisfied If all of equations (46)-(49) are true, then stop the calculation and output the scheduling scheme of the distributed energy storage virtualization sharing system under the k-th iteration, including: the scheduling power of the energy storage system of the producer-consumer community with distributed energy storage, the operator and the producer-consumer community renting virtual energy storage under the k-th iteration, and the upper limit of capacity and power of each rented virtual energy storage under the k-th iteration. make Assign to , Assign to , Assign to ; Assign to , Assign to , Assign to Then return to step 4.3 and execute sequentially.

3. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports the processor in executing the distributed energy storage virtualization sharing system scheduling method based on the target cascading method as described in any one of claims 1-2, and the processor is configured to execute the program stored in the memory.

4. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is run by the processor, it executes the steps of the scheduling method for a distributed energy storage virtualization sharing system based on the target cascading method as described in any one of claims 1-2.

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