Virtual power plant real-time simulation and transaction decision-making system based on digital twinning

Abstract

The invention discloses a virtual power plant real-time simulation and transaction decision-making system based on digital twinning, and the system comprises a data obtaining module which is used for obtaining the related data of a virtual power plant in real time; the electricity price comparison module is used for carrying out dynamic priority comparison on the electricity price according to the related data of the virtual power plant and generating a power supply calling strategy; the electric quantity distribution module is used for hierarchically matching the electric quantity according to the payment willingness of the load side and triggering secondary clearing when distribution is insufficient; the energy storage scheduling module is used for executing a charging and discharging strategy based on an electricity price threshold value and a supply and demand gap, and maximizing interest arbitrage income; and the simulation verification module is used for performing power grid power flow analysis and security constraint verification on the clearing result. According to the system, the power resource allocation economy is improved through dynamic electricity price priority comparison; based on the payment willingness of the load side, carrying out layered metering and triggering secondary clearing, and optimizing the fairness and rationality of electric quantity distribution; according to the energy storage intelligent charging and discharging strategy, the operation risk is effectively avoided while the benefit arbitrage income is maximized.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, and in particular to a real-time simulation and trading decision-making system for virtual power plants based on digital twins. Background Technology

[0002] Against the backdrop of energy transition and deepening power system reform, distributed energy (such as photovoltaic, wind power, and energy storage) is developing rapidly on a large scale. Virtual power plants (VPPs), as the core form for aggregating distributed resources and participating in the interaction between the power market and the grid, have become crucial for improving the flexibility and economy of the power system. Digital twin technology, with its accurate mapping and real-time simulation of physical systems, provides support for virtual power plants to integrate diverse data and simulate operating scenarios. Under this trend, building a real-time simulation and trading decision-making system for virtual power plants based on digital twins, enabling coordinated and optimized scheduling of power generation, grid, load, and storage, dynamic response to electricity prices, and efficient clearing of electricity transactions, is of great significance for adapting to new power system forms, exploring the value of distributed resources, and promoting healthy interaction in the power market. It is one of the core technological directions for driving the evolution of the power system towards intelligence and refinement.

[0003] Despite existing explorations in virtual power plant technologies, key bottlenecks remain: First, insufficient clearing efficiency and price response. Existing systems largely rely on static rules for allocating power resources, failing to fully consider dynamic price differences between the generation and grid sides. This leads to untimely access to low-priced power sources and an inability to respond quickly to grid redundancy or shortages, impacting the economic efficiency of power resource allocation. Second, deficiencies in peak-valley regulation and energy storage coordination. The system doesn't deeply explore elastic demand on the load side, resulting in power allocation not accurately matching user payment intentions. Furthermore, energy storage charging and discharging strategies lack coordination with peak-valley pricing and supply-demand gaps, making it difficult to maximize arbitrage profits and optimize grid peak-valley regulation. Third, the lack of a closed-loop verification mechanism for security constraints. Many clearing schemes lack digital twin simulation to verify safety indicators such as grid power flow, line capacity, and voltage deviation, easily leading to operational risks. The absence of a verification-feedback-optimization closed-loop mechanism results in a disconnect between theoretical decisions and physical system operation. These issues limit the adaptability of virtual power plants to complex power scenarios, necessitating the construction of an integrated system encompassing dynamic price response, elastic power allocation, intelligent energy storage scheduling, and digital twin simulation verification to overcome these technical challenges. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the aforementioned existing problems, this invention is proposed. Therefore, this invention provides a real-time simulation and trading decision-making system for virtual power plants based on digital twins, addressing the issues of low clearing efficiency and insufficient price response in existing virtual power plant technologies.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a real-time simulation and trading decision-making system for virtual power plants based on digital twins, comprising:

[0008] The data acquisition module is used to acquire relevant data from the virtual power plant in real time.

[0009] The electricity price comparison module is used to dynamically prioritize electricity prices based on relevant data from virtual power plants and generate power dispatch strategies.

[0010] The power allocation module is used to match power in layers according to the payment willingness of the load side, and trigger a secondary clearing when the allocation is insufficient;

[0011] The energy storage scheduling module is used to execute charging and discharging strategies based on electricity price thresholds and supply-demand gaps to maximize arbitrage profits.

[0012] As a preferred embodiment of the real-time simulation and trading decision-making system for virtual power plants based on digital twins described in this invention, it further includes:

[0013] The simulation verification module is used to perform power flow analysis and security constraint verification on the clearing results.

[0014] The data acquisition module acquires real-time power flow topology data of the power grid and calculates line load rate and voltage deviation parameters.

[0015] Based on the line load rate and voltage deviation, verify whether the clearing results meet the safety constraints.

[0016] If the clearing result does not meet the safety constraints, the feedback is sent to the electricity price comparison module and the power allocation module to adjust the power dispatch strategy and load allocation scheme until the safety constraints are met.

[0017] As a preferred embodiment of the virtual power plant real-time simulation and transaction decision-making system based on digital twins described in this invention, the virtual power plant related data includes generation-side declaration data, load-side declaration data, real-time electricity price of the power grid, and energy storage status data.

[0018] As a preferred embodiment of the virtual power plant real-time simulation and transaction decision-making system based on digital twins described in this invention, the electricity price comparison module is used to dynamically prioritize electricity prices based on relevant data from the virtual power plant and generate a power dispatch strategy, including:

[0019] The generation price in the data submitted by the generation side is compared with the real-time electricity price of the main power grid. When the generation price is lower than or equal to the real-time electricity price of the main power grid, the generation side's electricity is used first. When the generation price is higher than the real-time electricity price of the main power grid, the grid's electricity is used first.

[0020] If the total declared electricity volume from the power generation side meets the load demand, it will be allocated according to the power generation price from low to high. If the total declared electricity volume from the power generation side does not meet the load demand, the shortfall will be supplemented by the power grid.

[0021] As a preferred embodiment of the virtual power plant real-time simulation and transaction decision-making system based on digital twins described in this invention, the power allocation module is used to hierarchically match power according to the load side's willingness to pay, including:

[0022] The system receives the electricity consumption and corresponding maximum acceptable electricity price data from the load declaration module, sorts users in descending order of their maximum acceptable electricity price, and allocates electricity to users in this order, prioritizing users with a high willingness to pay.

[0023] During allocation, the remaining power is checked. If the remaining power is insufficient, a secondary clearing is triggered and the allocation scheme is updated. If the remaining power is sufficient, the final allocation scheme is generated directly.

[0024] As a preferred embodiment of the virtual power plant real-time simulation and trading decision-making system based on digital twins described in this invention, the energy storage scheduling module is used to execute charging and discharging strategies based on electricity price thresholds and supply-demand gaps to maximize arbitrage profits, including:

[0025] Obtain real-time electricity prices and generation-load imbalance;

[0026] When the real-time electricity price charging threshold is met and power generation is in excess, a charging operation is performed, and the charging electricity price and charging amount are recorded.

[0027] When the real-time electricity price discharge threshold is met but power generation is insufficient, a discharge operation is performed, and the discharge price and discharge amount are recorded.

[0028] Construct an objective function, calculate the objective function, and continuously optimize the timing and amount of charging and discharging to maximize arbitrage profits.

[0029] As a preferred embodiment of the real-time simulation and trading decision-making system for virtual power plants based on digital twins described in this invention, the objective function is expressed as:

[0030] R = π(P) discharge ·E discharge —P charge ·E charge )

[0031] Among them, P discharge For the discharge electricity price, Edischarge P represents the discharge capacity. charge For charging electricity price, E charge This represents the charging capacity.

[0032] Secondly, this invention provides a method for a real-time simulation and trading decision-making system for virtual power plants based on digital twins, including:

[0033] Real-time acquisition of data related to virtual power plants;

[0034] Based on data from virtual power plants, electricity prices are dynamically prioritized and a power dispatch strategy is generated.

[0035] Electricity is allocated in tiers based on the load side's willingness to pay, and a secondary clearing is triggered when the allocation is insufficient.

[0036] Execute charging and discharging strategies based on electricity price thresholds and supply-demand gaps to maximize arbitrage profits;

[0037] Power flow analysis and security constraint verification were performed on the clearing results.

[0038] Thirdly, the present invention provides a computing device, comprising:

[0039] Memory and processor;

[0040] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the real-time simulation and transaction decision-making system for the virtual power plant based on digital twins.

[0041] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the aforementioned real-time simulation and trading decision-making system for a virtual power plant based on digital twins.

[0042] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention constructs a system integrating data acquisition, electricity price comparison, power allocation, energy storage scheduling, and digital twin simulation verification. The system accurately calls upon low-priced power sources through dynamic electricity price priority comparison, improving the economic efficiency of power resource allocation and avoiding inefficient responses to power grid redundancy and shortages. Based on load-side payment willingness, it hierarchically allocates power and triggers secondary clearing, deeply mining elastic demand and optimizing the fairness and rationality of power allocation. Combined with intelligent charging and discharging strategies for energy storage based on electricity price thresholds and supply-demand gaps, it maximizes arbitrage profits while assisting in peak-valley regulation of the power grid. Digital twin simulation provides security verification and closed-loop feedback for the clearing scheme, effectively mitigating operational risks and ensuring the stability of the physical system. The overall system establishes a collaborative link between power generation, grid, load, and storage, adapting to new power system configurations and enhancing the intelligence, precision scheduling level, and resource value mining capabilities of virtual power plants. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a schematic diagram of a real-time simulation and transaction decision-making system for a virtual power plant based on digital twins, as described in one embodiment of the present invention.

[0045] Figure 2 This is a schematic diagram of the clearing result process of a real-time simulation and trading decision system for virtual power plants based on digital twins, as described in one embodiment of the present invention.

[0046] Figure 3 This is a schematic diagram of the charging and discharging rules of the energy storage scheduling module in a real-time simulation and trading decision-making system for a virtual power plant based on digital twins, as described in one embodiment of the present invention.

[0047] Figure 4 This is a schematic diagram illustrating the simulation verification results of a real-time simulation and trading decision-making system for a virtual power plant based on digital twins, as described in one embodiment of the present invention. Detailed Implementation

[0048] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0049] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0051] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0052] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the system or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0053] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0054] Example 1

[0055] Reference Figure 1 As one embodiment of the present invention, a real-time simulation and trading decision-making system for a virtual power plant based on digital twins is provided, comprising:

[0056] The data acquisition module is used to acquire relevant data from the virtual power plant in real time.

[0057] The electricity price comparison module is used to dynamically prioritize electricity prices based on relevant data from virtual power plants and generate power dispatch strategies.

[0058] The power allocation module is used to match power in layers according to the payment willingness of the load side, and trigger a secondary clearing when the allocation is insufficient;

[0059] The energy storage dispatch module is used to execute charging and discharging strategies based on electricity price thresholds and supply-demand gaps to maximize arbitrage profits;

[0060] The simulation verification module is used to perform power flow analysis and security constraint verification on the clearing results.

[0061] In this embodiment of the invention, the virtual power plant related data includes generation-side declaration data, load-side declaration data, real-time electricity price of the main power grid, and energy storage status data.

[0062] Preferably, the electricity price comparison module is used to dynamically prioritize electricity prices based on relevant data from virtual power plants and generate power dispatch strategies, including:

[0063] The generation price in the data submitted by the generation side is compared with the real-time electricity price of the main power grid. When the generation price is lower than or equal to the real-time electricity price of the main power grid, the generation side's electricity is used first. When the generation price is higher than the real-time electricity price of the main power grid, the grid's electricity is used first.

[0064] For example, dynamic priority comparison is represented as follows:

[0065]

[0066] If the total declared electricity volume from the power generation side meets the load demand, it will be allocated according to the power generation price from low to high. If the total declared electricity volume from the power generation side does not meet the load demand, the shortfall will be supplemented by the power grid.

[0067] It should be noted that this invention dynamically compares the power generation price in the power generation side's declaration data with the real-time electricity price of the main grid. It prioritizes the use of power generation power with a price lower than or equal to the real-time electricity price of the main grid, and prioritizes the use of grid power when the price is higher. When the total declared power volume of the power generation side meets the load demand, it calls power from low to high according to the price. If there is a shortage, the grid will supplement the power supply. This electricity price comparison module can realize the priority call of low-cost power sources, improve the utilization rate of low-priced power sources on the power generation side, optimize the allocation of power resources, reduce electricity costs, and at the same time ensure the stability and reliability of power supply. It effectively solves the problem of insufficient collaborative dispatch optimization caused by the lack of price response mechanism in traditional power trading systems, and improves the economy and flexibility of the power system.

[0068] Preferably, the power allocation module is used to tiered match power consumption based on the load side's willingness to pay, including:

[0069] The system receives the electricity consumption and corresponding maximum acceptable electricity price data from the load declaration module, sorts users according to their maximum acceptable electricity price from highest to lowest, and allocates electricity to users in this order, prioritizing users with a high willingness to pay. The flowchart for obtaining the clearing results is as follows: Figure 2 As shown;

[0070] During allocation, the remaining power is checked. If the remaining power is insufficient, a secondary clearing is triggered and the allocation scheme is updated. If the remaining power is sufficient, the final allocation scheme is generated directly.

[0071] Secondary clearing includes: calling on energy storage to discharge and replenish electricity; calling on high-priced electricity from the grid to meet load demand; adjusting the allocation strategy based on real-time electricity prices to optimize resource allocation; redistributing electricity based on newly available electricity from the generation side; dynamically adjusting allocation priorities based on real-time user electricity consumption feedback; adjusting the allocation scheme by comprehensively considering grid security constraints; verifying the feasibility of the secondary clearing scheme through digital twin simulation; optimizing discharge allocation based on the SOC status of energy storage; adjusting power supply allocation based on the real-time generation capacity of the generation side; and optimizing the allocation strategy by referring to historical user electricity consumption data.

[0072] It should be noted that this invention receives the electricity consumption and acceptable maximum electricity price data reported by the load, sorts users according to the acceptable maximum electricity price from high to low, and allocates electricity accordingly, prioritizing users with high willingness to pay. Simultaneously, it checks the remaining electricity during allocation; if insufficient, it triggers a secondary clearing process, including calling upon energy storage for discharge, calling upon high-priced electricity from the grid, and adjusting the allocation based on real-time electricity prices and the available electricity on the generation side. This electricity allocation module enables intelligent hierarchical matching of load-side electricity consumption, improving the rationality and flexibility of electricity allocation, effectively meeting the electricity needs of different users, reducing power shortages for high-priced users and waste of low-priced resources. Furthermore, the secondary clearing mechanism ensures sufficient electricity allocation and optimized resource allocation, while digital twin simulation verification ensures the feasibility of the allocation scheme, thereby improving the economy of the power system and user satisfaction.

[0073] Preferably, the energy storage dispatch module is used to execute charging and discharging strategies based on electricity price thresholds and supply-demand gaps to maximize arbitrage profits, including:

[0074] Obtain real-time electricity prices and generation-load imbalance;

[0075] When the real-time electricity price charging threshold is met and power generation is in excess, a charging operation is performed, and the charging electricity price and charging amount are recorded.

[0076] When the real-time electricity price discharge threshold is met but power generation is insufficient, a discharge operation is performed, and the discharge price and discharge amount are recorded.

[0077] The charging and discharging rules are shown in Figure 3. Figure 3 In the middle, the charging phase (low electricity price period): when the real-time electricity price is at or below the charging threshold, and there is a surplus in power generation (negative supply-demand gap), the system enters the charging zone, such as... Figure 3 During the green fill period, such as from early morning to morning, energy storage often absorbs excess electricity due to overcapacity in photovoltaic and other power generation.

[0078] Discharge Phase (Peak Electricity Price Period): When the real-time electricity price reaches or exceeds the discharge threshold, and the industrial park experiences insufficient power generation (positive supply-demand gap), it enters the discharge phase. Figure 3During the red-filled periods, such as the evening peak electricity consumption period, the energy storage system releases stored electricity because the demand for electricity is high and the power generation is difficult to meet.

[0079] Construct and calculate the objective function, continuously optimize the timing and amount of charging and discharging, and maximize arbitrage profits.

[0080] It should be noted that this invention obtains real-time electricity prices and the generation-load imbalance, performs charging operations when the real-time electricity price is at or below the charging threshold and generation is excessive, and performs discharging operations when the real-time electricity price reaches or above the discharging threshold and generation is insufficient. It also constructs an objective function to continuously optimize the timing and amount of charging and discharging. This energy storage scheduling module can realize automated arbitrage and peak-valley regulation of energy storage side charging and discharging, effectively improve energy storage utilization, increase energy storage arbitrage revenue, improve the peak-valley difference rate of the power grid, optimize the flexibility and economy of the power system, and solve the problem that traditional energy storage scheduling strategies rely on manual experience and cannot dynamically trigger strategies based on electricity price fluctuations and supply-demand gaps.

[0081] In this embodiment of the invention, the objective function is expressed as:

[0082] R = π(P) discharge ·E discharge —P charge ·E charge )

[0083] Among them, P discharge For the discharge electricity price, E discharge P represents the discharge capacity. charge For charging electricity price, E charge This represents the charging capacity.

[0084] Preferably, the simulation verification module is used to perform power flow analysis and security constraint verification on the clearing results, including:

[0085] The data acquisition module acquires real-time power flow topology data of the power grid and calculates line load rate and voltage deviation parameters.

[0086] Based on line load factor and voltage deviation, verify whether the clearing results meet the safety constraints.

[0087] If the clearing result does not meet the safety constraints, the feedback is sent to the electricity price comparison module and the power allocation module to adjust the power dispatch strategy and load allocation scheme until the safety constraints are met.

[0088] Specifically, the simulation verification module first captures the real-time power flow topology of the power grid through the data acquisition interface, including the connection relationship, voltage, line load, and other raw data of the generation nodes M1-M2 and load nodes N3-N6; then it uses the power flow calculation algorithm to calculate the load rate of each line and the voltage deviation of the entire network; at the same time, it retrieves the load prediction curve and compares it with the actual load data, and the results are shown in Figure 4.

[0089] Depend on Figure 4 As can be seen in the power flow topology, line L12 is marked as normal (green line) due to a load factor of 18%, while line L34 is marked as trending towards a red warning line due to a load factor of 95% approaching the threshold. The voltage deviation of +0.8% is within the safe range, but fluctuations need to be monitored. The energy storage SOC is 89%, ensuring regulation capability. Although no severe overload was triggered, the high load on L34 and the 109% overload on line L12 indicate that the clearing scheme has local risks, requiring adjustments to power dispatch and load allocation, such as reducing the power supply to L34 and transferring some load from the N5 / N6 area. The predicted load and the actual load trend are basically consistent, verifying the basic adaptability of the load allocation strategy. However, the actual load is higher than the prediction in some periods, requiring optimization of the prediction model or dynamic adjustment of the allocation.

[0090] The real-time simulation and transaction decision-making method for virtual power plants based on digital twins in this embodiment includes:

[0091] Real-time acquisition of data related to virtual power plants;

[0092] Based on data from virtual power plants, electricity prices are dynamically prioritized and a power dispatch strategy is generated.

[0093] Electricity is allocated in tiers based on the load side's willingness to pay, and a secondary clearing is triggered when the allocation is insufficient.

[0094] Execute charging and discharging strategies based on electricity price thresholds and supply-demand gaps to maximize arbitrage profits;

[0095] Power flow analysis and security constraint verification were performed on the clearing results.

[0096] This embodiment also provides a computing device suitable for a real-time simulation and trading decision-making system for a virtual power plant based on digital twins, including:

[0097] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the real-time simulation and trading decision-making system for a virtual power plant based on digital twins, as proposed in the above embodiments.

[0098] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the real-time simulation and transaction decision system for a virtual power plant based on digital twins as proposed in the above embodiments.

[0099] The storage medium proposed in this embodiment and the implementation of the real-time simulation and transaction decision system for virtual power plants based on digital twins proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0100] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0101] Example 2

[0102] Reference Figure 1 As one embodiment of the present invention, based on the above embodiment, a real-time simulation and transaction decision-making system for virtual power plants based on digital twins is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0103] Implementing this invention in a power system of a certain industrial park, the first step is to acquire real-time data such as generation-side bid prices, load demand, grid electricity prices, and energy storage SOC through a data acquisition module. The generation-side declared electricity volume is 5000 kWh, with bid prices between 0.3 and 0.5 yuan / kWh, while the real-time grid electricity price is 0.5 yuan / kWh. The electricity price comparison module compares the generation bid price with the grid electricity price, prioritizing the use of generation-side power with bid prices ≤ 0.5 yuan / kWh. Any shortfall is supplemented by the grid, increasing the utilization rate of low-priced generation-side power sources by 25%.

[0104] The total electricity consumption declared by the load side is 4500 kWh. The highest acceptable electricity price for users is between 0.4 and 0.7 yuan / kWh. The electricity allocation module sorts users according to their highest acceptable electricity price from highest to lowest and allocates electricity accordingly, prioritizing users with a high willingness to pay. During the allocation process, the remaining electricity is checked, triggering two secondary clearing operations to replenish the electricity by activating energy storage. Ultimately, this reduces the user's electricity cost by 15%.

[0105] The energy storage dispatch module executes charging and discharging strategies based on real-time electricity prices and supply-demand gaps. Charging occurs when the real-time electricity price is ≤0.3 yuan / kWh and power generation is in excess; discharging occurs when the real-time electricity price is ≥0.6 yuan / kWh and power generation is insufficient. This increases energy storage arbitrage profits by 30% and reduces the peak-valley difference rate from 40% to 28%. The simulation verification module uses a digital twin model to simulate the impact of the clearing results on grid power flow, ensuring safety constraints such as line capacity. The clearing calculation time is <1 second / time, achieving efficient source-grid-load-storage coordinated clearing.

[0106] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A real-time simulation and trading decision-making system for virtual power plants based on digital twins, characterized in that: include: The data acquisition module is used to acquire relevant data from the virtual power plant in real time. The electricity price comparison module is used to dynamically prioritize electricity prices based on relevant data from virtual power plants and generate power dispatch strategies. The power allocation module is used to match power in layers according to the payment willingness of the load side, and trigger a secondary clearing when the allocation is insufficient; The energy storage scheduling module is used to execute charging and discharging strategies based on electricity price thresholds and supply-demand gaps to maximize arbitrage profits.

2. The real-time simulation and transaction decision-making system for virtual power plants based on digital twins as described in claim 1, characterized in that, Also includes: The simulation verification module is used to perform power flow analysis and security constraint verification on the clearing results. The data acquisition module acquires real-time power flow topology data of the power grid and calculates line load rate and voltage deviation parameters. Based on the line load rate and voltage deviation, verify whether the clearing results meet the safety constraints. If the clearing result does not meet the safety constraints, the feedback is sent to the electricity price comparison module and the power allocation module to adjust the power dispatch strategy and load allocation scheme until the safety constraints are met.

3. The real-time simulation and transaction decision-making system for virtual power plants based on digital twins as described in claim 1 or 2, characterized in that, The data related to virtual power plants includes data submitted by the generation side, data submitted by the load side, real-time electricity prices of the main power grid, and energy storage status data.

4. The real-time simulation and transaction decision-making system for virtual power plants based on digital twins as described in claim 3, characterized in that, The electricity price comparison module is used to dynamically prioritize electricity prices based on data from virtual power plants and generate power dispatch strategies, including: The generation price in the data submitted by the generation side is compared with the real-time electricity price of the main power grid. When the generation price is lower than or equal to the real-time electricity price of the main power grid, the generation side's electricity is used first. When the generation price is higher than the real-time electricity price of the main power grid, the grid's electricity is used first. If the total declared electricity volume from the power generation side meets the load demand, it will be allocated according to the power generation price from low to high. If the total declared electricity volume from the power generation side does not meet the load demand, the shortfall will be supplemented by the power grid.

5. The real-time simulation and transaction decision-making system for virtual power plants based on digital twins as described in claim 4, characterized in that, The power allocation module is used to match power consumption in tiers based on the load side's willingness to pay, including: The system receives the electricity consumption and corresponding maximum acceptable electricity price data from the load declaration module, sorts users in descending order of their maximum acceptable electricity price, and allocates electricity to users in this order, prioritizing users with a high willingness to pay. During allocation, the remaining power is checked. If the remaining power is insufficient, a secondary clearing is triggered and the allocation scheme is updated. If the remaining power is sufficient, the final allocation scheme is generated directly.

6. The real-time simulation and transaction decision-making system for virtual power plants based on digital twins as described in claim 5, characterized in that, The energy storage dispatch module is used to execute charging and discharging strategies based on electricity price thresholds and supply-demand gaps to maximize arbitrage profits, including: Obtain real-time electricity prices and generation-load imbalance; When the real-time electricity price charging threshold is met and power generation is in excess, a charging operation is performed, and the charging electricity price and charging amount are recorded. When the real-time electricity price discharge threshold is met but power generation is insufficient, a discharge operation is performed, and the discharge price and discharge amount are recorded. Construct an objective function, calculate the objective function, and continuously optimize the timing and amount of charging and discharging to maximize arbitrage profits.

7. The real-time simulation and transaction decision-making system for virtual power plants based on digital twins as described in claim 6, characterized in that, The objective function is expressed as: R=π(P discharge ·AND discharge ―P charge ·AND charge ) Among them, P discharge For the discharge electricity price, E discharge P represents the discharge capacity. charge For charging electricity price, E charge This represents the charging capacity.

8. A method for a real-time simulation and trading decision-making system for virtual power plants based on digital twins, characterized in that: include, Real-time acquisition of data related to virtual power plants; Based on data from virtual power plants, electricity prices are dynamically prioritized and a power dispatch strategy is generated. Electricity is allocated in tiers based on the load side's willingness to pay, and a secondary clearing is triggered when the allocation is insufficient. Execute charging and discharging strategies based on electricity price thresholds and supply-demand gaps to maximize arbitrage profits; Power flow analysis and security constraint verification were performed on the clearing results.

9. Electronic devices, including: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the real-time simulation and transaction decision system for virtual power plants based on digital twins as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the real-time simulation and trading decision system for a digital twin-based virtual power plant as described in any one of claims 1 to 7.

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