New energy access virtual power plant multi-time scale coordination control method and system

Abstract

The invention discloses a multi-time-scale coordination control method and system for a virtual power plant with new energy access, and aims to solve the challenge of the fluctuation of new energy on the stability of a power grid. The method comprises the following steps: in a day-ahead scheduling stage, constructing a baseline scheduling plan based on natural conditions and historical loads, and allocating energy storage node demands through a block chain; in the intra-day rolling optimization stage, the plan is dynamically adjusted through short-term prediction, and energy storage requirements are redistributed; in the construction stage, a digital twinborn technology is adopted to simulate dynamic interaction between new energy and bus nodes and energy storage nodes; in the real-time control stage, energy storage demand sharing is realized in combination with model prediction control and a block chain. The system comprises a baseline scheduling module, an intraday optimization module, a virtual power plant construction module and a real-time control module. According to the method, through multi-time scale cooperation, block chain distributed management and total element simulation, the power balance capability is remarkably improved, the adaptability to the uncertainty of natural conditions is enhanced, and the operation efficiency and transparency are improved.

Description

Technical Field

[0001] The present invention belongs to the field of new energy power generation, and particularly relates to a multi-time scale coordinated control method and system for a virtual power plant with new energy access. Background Art

[0002] With the transformation of the global energy structure, the proportion of renewable energy (such as wind energy and solar energy) in the power system has increased significantly. However, wind power and photovoltaic power generation have significant randomness, volatility, and weather dependence. For example, sudden changes in wind speed or light intensity may cause a large change in output power in a short period of time. This instability poses severe challenges to the power balance, frequency stability, and voltage distribution of the power grid. Especially in the scenario of high proportion of new energy access, the scheduling mode of traditional centralized power generation systems has been difficult to meet the requirements.

[0003] As an emerging technology, a virtual power plant (VPP) integrates distributed power sources (sources), power grids (nets), loads (loads), and energy storage systems (storages) to achieve flexible scheduling and collaborative optimization of resources, and is considered an effective solution to address the challenges of new energy access. A virtual power plant can virtually integrate dispersed power generation units, controllable loads, and energy storage devices into a whole and participate in the operation of the power grid through intelligent control. However, the existing virtual power plant control methods still have the following problems when facing new energy fluctuations: Limitations of single-time scale control: Traditional methods mostly adopt single-time scale scheduling (such as day-ahead planning or real-time control), which are difficult to take into account the long-term prediction deviation of new energy (such as weather changes) and short-term instantaneous fluctuations (such as sudden drops in wind speed), resulting in difficulty in simultaneously optimizing power balance and stability.

[0004] Insufficient dynamic interaction modeling: The dynamic interaction between sources, nets, loads, and storages is complex. Existing technologies often rely on static assumptions or simplified models and fail to fully reflect the real-time impact of new energy fluctuations on the power flow distribution of the power grid and the response of energy storage, limiting the control accuracy.

[0005] Limited real-time optimization ability: Traditional control strategies (such as rule-based scheduling) are difficult to achieve global optimization when facing the rapid changes of new energy, while existing optimization algorithms (such as linear programming) have high computational complexity and are difficult to meet the real-time requirements.

[0006] Inefficient allocation of energy storage resources: Distributed energy storage systems play a key buffering role in virtual power plants. However, existing methods mostly rely on centralized scheduling and lack an efficient distributed cooperation mechanism, resulting in low utilization rate of energy storage resources and inability to quickly respond to local power shortages. Summary of the Invention

[0007] In view of this, the present invention provides a multi-time scale coordinated control method for a virtual power plant with new energy access. The virtual power plant includes a plurality of system nodes, and the system nodes include energy storage nodes and bus nodes. Step 1, in the day-ahead scheduling stage, based on natural conditions and historical load demands, construct a baseline scheduling plan for the virtual power plant, and allocate the demands of the energy storage nodes through the blockchain to ensure power balance and adapt to the natural conditions. Step 2, in the intraday rolling optimization stage, utilize the short-term prediction data of the natural conditions to dynamically adjust the scheduling plan, and reallocate the demands of the energy storage nodes through the blockchain. Step 3, in the virtual power plant construction stage, adopt digital twin technology to construct a real-time virtual mirror of the virtual power plant, and simulate the dynamic interaction between the new energy generation units in the virtual power plant and the charging and discharging of the bus nodes, grid load demands, and energy storage nodes through full-element simulation. Step 4, in the real-time control stage, combine the model predictive control instructions, and based on the baseline scheduling plan and the adjusted scheduling plan, realize the demand sharing of the energy storage nodes through the blockchain.

[0008] Specifically, in step 1, in the day-ahead scheduling stage, predict the output of wind power and photovoltaic power according to the natural conditions. The natural conditions include changes in wind speed, sunlight, and cloud cover. The power balance satisfies the following formula:

[0009] Where: represents the wind power output, affected by the wind speed and its standard deviation of random disturbance ; represents the photovoltaic power output, affected by the light intensity and its standard deviation of random disturbance ; represents the net power contribution of the distributed energy storage nodes; where, represents the charging power of the th energy storage node at time , which is allocated by the blockchain smart contract in response to new energy surplus; represents the th energy storage node at time discharge power, which is allocated by the blockchain smart contract to make up for the shortage of new energy; represents the controllable load, affected by the natural power generation fluctuation ; represents the grid loss, related to the voltage of the bus node and the phase angle Dynamic correlation; is the total length of the scheduling period, reflecting the time cumulative effect.

[0010] In particular, in step 2, the intra-day rolling optimization stage includes: updating the new energy output prediction according to the short-term changes of the natural conditions; reallocating the charging and discharging demands of the energy storage nodes through the smart contract of the blockchain to cope with the fluctuations of natural power generation.

[0011] In particular, constructing a real-time virtual mirror of the virtual power plant using digital twin technology includes: capturing the changes of the natural conditions using real-time sensor data and updating the real-time virtual mirror of the virtual power plant; establishing a model of the actual operating state of the virtual power plant.

[0012] In particular, in step 3, simulating the dynamic interaction between the new energy generation units, bus nodes, grid load demands, and charging and discharging of energy storage nodes in the virtual power plant includes: inputting the disturbances of sudden wind speed drop or sudden light change in real time through the digital twin model, and calculating the impact of the new energy output change on the power injection of the bus nodes and the charging and discharging demands of the energy storage nodes; based on the power injection adjusted by the new energy fluctuations and the response of the energy storage nodes, using the Newton-Raphson method to iteratively solve the voltage amplitude and phase angle of each system node, determine the line power flow and losses, and ensure that the voltage and frequency meet the stability constraints, so as to realize the calculation of the power grid power flow distribution.

[0013] In particular, the calculation of the power grid power flow distribution further includes: where the power of the system nodes satisfies the following power flow equation:

[0014] , where represents the active power of the system node at time , which is directly affected by the changes in wind power or photovoltaic output, representing the net power injected or absorbed by the system node; represents the voltage amplitude of the system node at time , reflecting the power grid voltage distribution, which is dynamically regulated by the power injection of new energy and the energy storage nodes; represents the voltage amplitude of the system node at time , which is connected to the system node through a line; represents the conductance between the system node and , representing the real part of the power grid admittance matrix and reflecting the resistance characteristics of the line; represents the system node and The susceptance between them, representing the imaginary part of the grid admittance matrix, reflects the reactance characteristics of the line; among which, represents the system node and at time The phase angle difference affects the distribution of power flow and is driven by new energy fluctuations and load changes; represents the total number of system nodes, indicating the number of nodes within the simulation scope.

[0015] In particular, after the calculation of the grid power flow distribution, it further includes: Analyze the power flow calculation results to check whether the node voltages are within the safe range. Analyze the power flow calculation results to check whether the node voltages are within the safe range.

[0016] In particular, in step 4, the optimization process of the model predictive control includes: defining an objective function through the following formula to minimize the power deviation caused by natural power generation fluctuations and imposing constraint conditions to cope with the instantaneous changes of natural power generation; the objective function reflects the comprehensive optimization of power balance, frequency, and voltage under the uncertainty of natural power generation;

[0017] , where is the predicted power deviation, reflecting the uncertainty of natural power generation, represents the objective function value, reflecting the total cost of the optimization process, which needs to be minimized; represents the prediction window length, indicating the number of time steps for prediction; represents at time The predicted time The power deviation, reflecting the difference between new energy output and load demand, is affected by wind power and photovoltaic fluctuations; represents at time The predicted time The frequency deviation, reflecting the system frequency stability; represents at time The predicted time The average voltage deviation of the bus nodes, representing the stability of the grid voltage distribution; , representing at time The predicted time The control input vector; represents the power deviation matrix, represents the frequency deviation matrix, represents the weight matrix of the voltage deviation, represents the weight matrix of the control input, represents the weighted squared norm, indicating the weighted squared deviation of the corresponding variable.

[0018] In particular, the realization of demand sharing of the energy storage nodes through the blockchain includes: recording the real-time status and demands of each energy storage node through a distributed ledger to cope with the randomness of natural power generation; using smart contracts to automatically match the supply and demand of the energy storage nodes and trigger dispatching instructions for the distributed energy storage nodes to smooth fluctuations; ensuring that the total energy storage power meets the system demands.

[0019] The present invention also discloses a multi-time scale coordinated control system for a virtual power plant with new energy access. The virtual power plant includes a plurality of system nodes, and the system nodes include energy storage nodes and bus nodes, and it includes: A baseline dispatching plan construction module, configured to construct a baseline dispatching plan for the virtual power plant based on natural conditions and historical load demands during the day-ahead dispatching stage, and allocate the demands of the energy storage nodes through the blockchain to ensure power balance and adapt to the natural conditions; An intra-day rolling optimization module, configured to dynamically adjust the dispatching plan by using the short-term prediction data of the natural conditions during the intra-day rolling optimization stage, and re-allocate the demands of the energy storage nodes through the blockchain; A virtual power plant construction stage module, configured to construct a real-time virtual mirror of the virtual power plant by using digital twin technology during the virtual power plant construction stage, and simulate the dynamic interaction between the new energy power generation units in the virtual power plant and the bus nodes, grid load demands, and charge and discharge of the energy storage nodes through full-element simulation; A real-time control module, configured to realize demand sharing of the energy storage nodes through the blockchain during the real-time control stage by combining model predictive control instructions based on the baseline dispatching plan and the adjusted dispatching plan.

[0020] Beneficial effects: Through the technical solution of the present invention, the power balance ability of the virtual power plant in the scenario of high proportion of new energy access can be significantly improved. The present invention effectively copes with the random fluctuations of wind power and photovoltaic output caused by natural conditions (such as sudden drops in wind speed and sudden changes in light) through multi-time scale coordinated control (day-ahead dispatching, intra-day rolling optimization, and real-time control), combined with blockchain allocation of energy storage node demands and digital twin simulation technology, ensuring that the system power deviation is controlled within ±1 MW, which is significantly better than traditional single-time scale dispatching methods.

[0021] Through the technical solution of the present invention, the voltage and frequency stability of the power grid can be improved. The present invention uses a digital twin model to real-time simulate the dynamic interaction between new energy, bus nodes, and energy storage nodes, and accurately calculates the voltage amplitude and phase angle of system nodes through the Newton-Raphson method, keeping the bus node voltage within the range of 0.95 - 1.05 p.u. and the frequency deviation less than 0.05 Hz, thereby ensuring the safe operation of the power grid under high proportion of new energy grid connection.

[0022] Through the technical solution of the present invention, the utilization efficiency of distributed energy storage resources can be optimized. The present invention dynamically allocates the charging and discharging requirements of energy storage nodes through blockchain smart contracts, and optimizes the energy storage state in combination with model predictive control (MPC), achieving an energy utilization rate of more than 90% for energy storage nodes, reducing the waste of energy storage capacity by about 20% compared with the traditional fixed scheduling method, and improving the economy and sustainability of the virtual power plant.

[0023] Through the technical solution of the present invention, the adaptability of the virtual power plant to the uncertainty of natural conditions can be enhanced. The present invention captures short-term disturbances of natural conditions such as wind speed and light in real time through short-term prediction updates and full-factor simulations, and quickly adjusts the response strategies of energy storage nodes and controllable loads, enabling the system to maintain power balance even when the new energy output suddenly changes (such as a reduction of 14 MW), with an adaptability improvement of about 30% compared with traditional prediction methods.

[0024] Through the technical solution of the present invention, the transparency and efficiency of the virtual power plant operation can be achieved. The present invention uses blockchain technology to record the real-time status and requirements of energy storage nodes, automatically matches energy storage supply and demand through a distributed ledger and smart contracts, reduces the manual intervention time by about 50%, and at the same time ensures the transparency and traceability of dispatching instructions, significantly improving the operation efficiency and reliability of the virtual power plant compared with the centralized control system. Description of the Drawings

[0025] Figure 1 It is a multi-time scale coordinated control method for a virtual power plant with new energy access proposed in the present invention; Figure 2 It is a multi-time scale coordinated control system for a virtual power plant with new energy access proposed in the present invention. Detailed Embodiments

[0026] The following takes embodiments in conjunction with the drawings and describes the present invention in detail.

[0027] The present invention provides a multi-time scale coordinated control method for a virtual power plant with new energy access, as Figure 1 shown. The virtual power plant includes multiple system nodes, and the system nodes include energy storage nodes and bus nodes. In a regional power grid with a high proportion of new energy access, the virtual power plant (VPP) integrates distributed wind power, photovoltaic power generation, energy storage systems, and controllable loads, with a total installed capacity of 100 MW, of which wind power accounts for 40 MW, photovoltaic power accounts for 30 MW, the energy storage system accounts for 20 MW, and the controllable load accounts for 10 MW. The power grid is connected to the virtual power plant through 10 main bus nodes, and the energy storage system is distributed on 5 energy storage nodes. This embodiment shows how to achieve power balance and system stability under new energy fluctuations through a multi-time scale coordinated control method.

[0028] Step 1, in the day-ahead scheduling stage, based on natural conditions and historical load demands, construct a baseline scheduling plan for the virtual power plant, and allocate the demands of the energy storage nodes through the blockchain to ensure power balance and adapt to the natural conditions; in Step 1, in the day-ahead scheduling stage, predict the output of wind power and photovoltaic power according to the natural conditions, and the natural conditions include changes in wind speed, illumination, and cloud cover. Natural condition prediction: Based on meteorological data, predict the wind speed for the next 24 hours (average , standard deviation ) and illumination intensity (average , standard deviation ). - Historical load demand: Predict the fixed load to be 50MW, and the adjustable range of the controllable load is

[0029] The power balance satisfies the following formula:

[0030] Where: represents the output of wind power, affected by the wind speed and its standard deviation of random disturbance ; represents the output of photovoltaic power, affected by the illumination intensity and its standard deviation of random disturbance ; represents the net power contribution of the distributed energy storage nodes; among them, represents the charging power of the th energy storage node at time , which is allocated by the blockchain smart contract in response to the excess of new energy; represents the discharging power of the th energy storage node at time , which is allocated by the blockchain smart contract to make up for the shortage of new energy; represents the controllable load, affected by the natural power generation fluctuation ; represents the grid loss, dynamically related to the bus node voltage and phase angle ; is the total length of the scheduling period, reflecting the time accumulation effect.

[0031] Calculation result: The average output of wind power is 32MW, the average output of photovoltaic power is 24MW, the net power contribution of the energy storage nodes is -2MW (mainly charging), and the power exchanged with the main grid is -4MW (output to the main power grid), with losses of approximately 1MW. Through the blockchain smart contract, the charging demands of 5 energy storage nodes are allocated as follows: Node 1 charges 2MW, Node 2 charges 1.5MW, and the remaining nodes standby.

[0032] Step 2, in the intraday rolling optimization stage, using the short-term prediction data of the natural conditions, dynamically adjust the scheduling plan, and reallocate the demands of the energy storage nodes through the blockchain; Short-term prediction update: At 10:00 am, it is detected that the wind speed suddenly drops to (perturbation ), the light intensity drops to 600 (perturbation ), and the total new energy output decreases by 14MW.

[0033] In the said Step 2, the intraday rolling optimization stage includes: updating the new energy output prediction according to the short-term changes of the natural conditions; reallocating the charging and discharging demands of the energy storage nodes through the smart contract of the blockchain to cope with the fluctuations of natural power generation; wherein, the reallocation formula for the charging and discharging demands of the energy storage nodes includes:

[0034] represents the th energy storage state of the said energy storage node at the next moment , indicating the remaining power of the energy storage represents the th energy storage state of the energy storage node at the current moment , the initial condition affected by the fluctuations of natural power generation; represents the charging efficiency, that is, the effectiveness of energy conversion during the charging process; represents the th charging power of the said energy storage node at the moment , which is allocated by the blockchain smart contract in response to new energy surplus; represents the time interval of scheduling or control; represents the th discharging power of the said energy storage node at the moment , which is allocated by the blockchain smart contract to make up for the shortage of new energy; represents the discharging efficiency, that is, the effectiveness of energy conversion during the discharging process; the reallocation formula reflects the dynamic energy balance of the energy storage nodes under the fluctuations of natural power generation. In this embodiment, the parameters: Minutes. Adjustment result: Energy storage node 1 discharges 3 MW (initial power 50 MWh, updated to 48.42 MWh), energy storage node 2 discharges 2 MW, and the remaining nodes standby; the controllable load increases by 2 MW, and the main grid supplies 7 MW. The smart contract detects a power gap and automatically triggers a discharge command for the energy storage node to ensure power balance.

[0035] Step 3, in the virtual power plant construction stage, use digital twin technology to construct a real-time virtual mirror of the virtual power plant, and simulate the dynamic interaction between the new energy generation units, the bus nodes, the grid load demand, and the charging and discharging of the energy storage nodes in the virtual power plant through full-factor simulation; Using digital twin technology to construct a real-time virtual mirror of the virtual power plant includes: capturing changes in the natural conditions using real-time sensor data and updating the real-time virtual mirror of the virtual power plant; establishing a model of the actual operating state of the virtual power plant, and the actual operating state of the virtual power plant is achieved through the following expression: , Where: represents the state matrix, represents the input matrix, represents the output matrix, represents the disturbance matrix, represents the noise matrix, represents the non-linear influence matrix; represents the state vector, represents the time, represents the total state of distributed energy storage, which is used to reflect the energy reserve of the energy storage node; represents the frequency deviation, which is used to reflect the system frequency stability; represents the average voltage deviation of the bus nodes, which is used to reflect the grid voltage distribution; represents the total natural power output, which is used to reflect the real-time power of wind power and photovoltaic power; for example, (total power of 5 energy storage nodes), (frequency deviation), (average bus node voltage deviation), (real-time new energy output) represents the control input vector, where, represents the i total distributed energy storage discharge power of the energy storage node represents the i total distributed energy storage charging power of the energy storage node represents the controllable load adjustment amount, represents the exchange power with the main grid; denotes the perturbation vector, where denotes the random perturbation of wind power output, caused by the change of wind speed; denotes the random perturbation of photovoltaic power output, caused by the change of illumination; denotes the non-linear dynamic term, reflecting the cumulative effect of energy storage aging or grid loss; denotes the output vector, where, denotes the system frequency, denotes the average node voltage, denotes the power balance deviation, denotes the measurement noise vector, reflecting the sensor error.

[0036] Update the virtual power plant image using real-time sensor data (such as anemometers, light sensors), and simulate the perturbations of sudden drops in wind speed and sudden changes in illumination.

[0037] During the full-factor simulation process, simulate the impact of new energy output changes on the power injection of bus nodes: Input the perturbations of sudden drops in wind speed or sudden changes in illumination into the digital twin model in real time, and calculate the impact of new energy output changes on the power injection of the bus nodes and the charging and discharging requirements of the energy storage nodes; Based on the power injection after adjusting for new energy fluctuations and the response of the energy storage nodes, use the Newton-Raphson method to iteratively solve the voltage amplitude and phase angle of each system node, determine the line power flow and losses, and ensure that the voltage and frequency meet the stability constraints, thereby realizing the calculation of the power grid power flow distribution.

[0038] The power injection of bus node 1 decreases by 5 MW, and that of bus node 2 decreases by 3 MW.

[0039] Response of the energy storage node: Discharge 5 MW and inject it into the bus node, and the power flow calculation shows that the loss increases by 0.2 MW.

[0040] Use the Newton-Raphson method to iteratively solve the voltage amplitude of 10 system nodes and the phase angle : Initial value: p.u., .

[0041] Result: The voltage of bus node 1 drops to 0.98 p.u., the phase angle changes by - , and the power flow is stable.

[0042] The calculation of the power grid power flow distribution further includes: where the power of the system node satisfies the following power flow equation:

[0043] , where, denotes the system node at time The active power, which is directly affected by the changes in wind power or photovoltaic output, represents the net power injected or absorbed by the system nodes; Represents the system node At time The voltage magnitude, which reflects the grid voltage distribution and is dynamically regulated by the power injection of new energy and the energy storage nodes; Represents the system node At time The voltage magnitude, which is connected to the system node Via a line; Represents the system node And The conductance between them represents the real part of the grid admittance matrix and reflects the resistance characteristics of the line; Represents the system node And The susceptance between them represents the imaginary part of the grid admittance matrix and reflects the reactance characteristics of the line; where, Represents the system node And At time The phase angle difference, which affects the power flow distribution and is driven by new energy fluctuations and load changes; Represents the total number of system nodes and represents the number of nodes within the simulation scope.

[0044] Analyze the power flow calculation results and check whether the node voltages are within the safe range. Analyze the power flow calculation results and check whether the node voltages are within the safe range.

[0045] Step 4, in the real-time control stage, in combination with the model predictive control instructions, based on the baseline scheduling plan and the adjusted scheduling plan, realize the demand sharing of the energy storage nodes through the blockchain. In step 4, the optimization process of the model predictive control includes: defining an objective function through the following formula to minimize the power deviation caused by natural power generation fluctuations and imposing constraint conditions to cope with the instantaneous changes of natural power generation; the objective function reflects the comprehensive optimization of power balance, frequency, and voltage under the uncertainty of natural power generation;

[0046] , where, Is the predicted power deviation, which reflects the uncertainty of natural power generation, Represents the objective function value, which reflects the total cost of the optimization process and needs to be minimized; Represents the prediction window length, which represents the number of time steps of the prediction; Represents at time The predicted time The power deviation at, which reflects the difference between new energy output and load demand and is affected by wind power and photovoltaic fluctuations; represents the frequency deviation at time the predicted time , reflecting the system frequency stability; represents the average voltage deviation of the bus nodes at time the predicted time , indicating the stability of the grid voltage distribution; , represents the control input vector at time the predicted time ; represents the power deviation matrix, represents the frequency deviation matrix, represents the weight matrix of the voltage deviation, represents the weight matrix of the control input, represents the weighted squared norm, indicating the weighted squared deviation of the corresponding variable.

[0047] Implementing the demand sharing of the energy storage nodes through the blockchain includes: recording the real-time status and demands of each energy storage node through a distributed ledger to cope with the randomness of natural power generation; using smart contracts to automatically match the supply and demand of the energy storage nodes and trigger the scheduling instructions of the distributed energy storage nodes to smooth out fluctuations; ensuring that the total power of the energy storage meets the system demand.

[0048] Parameters: prediction window (1 hour), weight . Optimization results: The discharge power of the energy storage node is adjusted to 6 MW, the controllable load is increased by 1 MW, the frequency deviation is reduced to 0.01 Hz, and the average voltage deviation of the bus nodes is reduced to 0.05 kV.

[0049] The distributed ledger records the status of the energy storage nodes (Node 1: 48 MWh, Node 2: 49 MWh), and the smart contract triggers the discharge instruction to smooth out the fluctuations of the new energy.

[0050] The present invention also provides a multi-time scale coordinated control system for a virtual power plant with new energy access. The virtual power plant includes multiple system nodes, and the system nodes include energy storage nodes and bus nodes. The virtual power plant includes multiple system nodes, and the system nodes include energy storage nodes and bus nodes. In a regional power grid with a high proportion of new energy access, the virtual power plant (VPP) integrates distributed wind power, photovoltaic power generation, energy storage systems, and controllable loads, with a total installed capacity of 100 MW, of which wind power accounts for 40 MW, photovoltaic power accounts for 30 MW, energy storage systems account for 20 MW, and controllable loads account for 10 MW. The power grid is connected to the virtual power plant through 10 main bus nodes, and the energy storage systems are distributed on 5 energy storage nodes. This embodiment shows how to achieve power balance and system stability under new energy fluctuations through a multi-time scale coordinated control system.

[0051] The system includes: a baseline scheduling plan construction module, which is used to construct a baseline scheduling plan for the virtual power plant based on natural conditions and historical load demands during the day-ahead scheduling phase, and allocate the demands of the energy storage nodes through the blockchain to ensure power balance and adapt to the natural conditions; during the day-ahead scheduling phase, predict the output of wind power and photovoltaic power according to the natural conditions, where the natural conditions include changes in wind speed, illumination, and cloud cover. Natural condition prediction: Based on meteorological data, predict the wind speed for the next 24 hours (average , standard deviation ), and illumination intensity (average , standard deviation ). - Historical load demand: Predict the fixed load is 50MW, and the adjustable range of the controllable load is

[0052] The power balance satisfies the following formula:

[0053] Where: represents the output of wind power, which is affected by the wind speed and its standard deviation of random disturbance ; represents the output of photovoltaic power, which is affected by the illumination intensity and its standard deviation of random disturbance ; represents the net power contribution of the distributed energy storage nodes; where represents the charging power of the -th energy storage node at time , which is allocated by the blockchain smart contract in response to the excess of new energy; represents the discharging power of the -th energy storage node at time , which is allocated by the blockchain smart contract to make up for the shortage of new energy; represents the controllable load, which is regulated by the natural power generation fluctuation ; represents the grid loss, which is dynamically related to the bus node voltage and phase angle ; is the total length of the scheduling period, reflecting the time cumulative effect.

[0054] Calculation results: The average output of wind power is 32 MW, the average output of photovoltaic power is 24 MW, the net power contribution of the energy storage node is -2 MW (mainly charging), and the power exchanged with the main power grid is -4 MW (output to the main power grid), and the loss is about 1 MW. Through the blockchain smart contract, the charging requirements of 5 energy storage nodes are allocated as follows: Node 1 charges 2 MW, Node 2 charges 1.5 MW, and the remaining nodes standby.

[0055] The intra-day rolling optimization module is used to dynamically adjust the scheduling plan during the intra-day rolling optimization stage by using the short-term prediction data of the natural conditions, and re-allocate the requirements of the energy storage nodes through the blockchain; Short-term prediction update: At 10:00 am, it is detected that the wind speed suddenly drops to (perturbation ), and the light intensity drops to 600 (perturbation ), and the total output of new energy decreases by 14 MW.

[0056] In the intra-day rolling optimization module, the intra-day rolling optimization stage includes: updating the prediction of the output of new energy according to the short-term changes of the natural conditions; re-allocating the charging and discharging requirements of the energy storage nodes through the smart contract of the blockchain to cope with the fluctuations of natural power generation; among them, the re-allocation formula for the charging and discharging requirements of the energy storage nodes includes:

[0057] represents the th energy storage state of the energy storage node at the next moment, indicating the remaining power of the energy storage represents the th energy storage state of the energy storage node at the current moment , which is the initial condition affected by the fluctuations of natural power generation; represents the charging efficiency, that is, the effectiveness of energy conversion during the charging process; represents the th charging power of the energy storage node at time , which is allocated by the blockchain smart contract in response to the excess of new energy; represents the th discharging power of the energy storage node at time , which is allocated by the blockchain smart contract to make up for the shortage of new energy; Minutes. Adjustment result: Energy storage node 1 discharges 3 MW (initial power 50 MWh, updated to 48.42 MWh), energy storage node 2 discharges 2 MW, and the remaining nodes standby; the controllable load increases by 2 MW, and the main grid supplements 7 MW. The smart contract detects a power gap and automatically triggers the discharge instruction of the energy storage node to ensure power balance.

[0058] The virtual power plant construction phase module is used to construct a real-time virtual mirror of the virtual power plant using digital twin technology during the virtual power plant construction phase, and simulate the dynamic interaction between the new energy generation unit, the bus node, the grid load demand, and the charge and discharge of the energy storage node in the virtual power plant through full-element simulation; constructing a real-time virtual mirror of the virtual power plant using digital twin technology includes: capturing the changes in the natural conditions using real-time sensor data and updating the real-time virtual mirror of the virtual power plant; establishing a model of the actual operating state of the virtual power plant, and the actual operating state of the virtual power plant is realized through the following expression: , Where: Represents the state matrix, Represents the input matrix, Represents the output matrix, Represents the disturbance matrix, Represents the noise matrix, Represents the non-linear influence matrix; Represents the state vector, Represents the time, Represents the total state of distributed energy storage, which is used to reflect the energy reserve of the energy storage node; Represents the frequency deviation, which is used to reflect the system frequency stability; Represents the average voltage deviation of the bus node, which is used to reflect the grid voltage distribution; Represents the total natural power generation output, which is used to reflect the real-time power of wind power and photovoltaic power; for example, (Total power of 5 energy storage nodes), (Frequency deviation), (Average bus node voltage deviation), (Real-time new energy output) Represents the control input vector, where Represents the energy storage node i The total distributed energy storage discharge power of, Represents the energy storage node i The total distributed energy storage charging power of, Represents the controllable load adjustment amount, Represents the exchange power with the main grid; Represents the disturbance vector, where represents the random disturbance of wind power output, caused by wind speed changes; represents the random disturbance of photovoltaic power output, caused by light changes; represents the non-linear dynamic term, reflecting the cumulative effect of energy storage aging or grid losses; represents the output vector, where, represents the system frequency, represents the average node voltage, represents the power balance deviation, represents the measurement noise vector, reflecting sensor errors.

[0059] Use real-time sensor data (such as anemometers, light sensors) to update the virtual power plant mirror and simulate the disturbances of sudden drops in wind speed and sudden changes in light.

[0060] During the full-factor simulation process, simulate the impact of new energy output changes on the power injection of bus nodes: input the disturbances of sudden drops in wind speed or sudden changes in light into the digital twin model in real time, calculate the impact of new energy output changes on the power injection of the bus nodes and the charging and discharging requirements of the energy storage nodes; based on the power injection after adjusting for new energy fluctuations and the response of the energy storage nodes, use the Newton-Raphson method to iteratively solve the voltage magnitudes and phase angles of each system node, determine the line power flow and losses, and ensure that the voltage and frequency meet the stability constraints, thereby realizing the calculation of the power grid power flow distribution.

[0061] The power injection of bus node 1 decreases by 5 MW, and that of bus node 2 decreases by 3 MW.

[0062] Response of the energy storage node: Discharge 5 MW and inject it into the bus node, and the power flow calculation shows that the loss increases by 0.2 MW.

[0063] Use the Newton-Raphson method to iteratively solve the voltage magnitudes and phase angles of 10 system nodes: Initial value: p.u., .

[0064] Result: The voltage of bus node 1 drops to 0.98 p.u., the phase angle changes by - , and the power flow is stable.

[0065] The calculation of the power grid power flow distribution further includes: The power of the system nodes satisfies the following power flow equation:

[0066] , where, represents the system node at time The active power, which is directly affected by the changes in wind power or photovoltaic output, represents the net power injected or absorbed by the system nodes; represents the system node At time The voltage amplitude, which reflects the grid voltage distribution and is dynamically regulated by the power injection of new energy and the energy storage nodes; represents the system node At time The voltage amplitude, which is connected to the system node through a line; represents the system node and The conductance between represents the real part of the grid admittance matrix and reflects the resistance characteristics of the line; represents the system node and The susceptance between represents the imaginary part of the grid admittance matrix and reflects the reactance characteristics of the line; where represents the system node and At time The phase angle difference, which affects the distribution of power flow and is driven by the fluctuations of new energy and load changes; represents the total number of system nodes and represents the number of nodes within the simulation scope.

[0067] Analyze the power flow calculation results and check whether the node voltages are within the safe range. Analyze the power flow calculation results and check whether the node voltages are within the safe range.

[0068] The real-time control module is used in the real-time control stage to combine the model predictive control instructions, based on the baseline scheduling plan and the adjusted scheduling plan, to achieve the demand sharing of the energy storage nodes through the blockchain.

[0069] The optimization process of the model predictive control includes: defining an objective function through the following formula to minimize the power deviation caused by natural power generation fluctuations and imposing constraint conditions to cope with the instantaneous changes of natural power generation; the objective function reflects the comprehensive optimization of power balance, frequency, and voltage under the uncertainty of natural power generation;

[0070] , where is the predicted power deviation, which reflects the uncertainty of natural power generation, represents the objective function value, which reflects the total cost of the optimization process and needs to be minimized; represents the prediction window length, which represents the number of time steps of the prediction; represents at time The predicted time The power deviation reflects the difference between new energy output and load demand and is affected by the fluctuations of wind power and photovoltaic power. Indicates at time The predicted time The frequency deviation reflects the system frequency stability. Indicates at time The predicted time The average voltage deviation of the bus nodes represents the stability of the grid voltage distribution. , Indicates at time The predicted time The control input vector; Represents the power deviation matrix, Represents the frequency deviation matrix, Represents the weight matrix of the voltage deviation, Represents the weight matrix of the control input, Represents the weighted squared norm, indicating the weighted squared deviation of the corresponding variable.

[0071] Implementing the demand sharing of the energy storage nodes through the blockchain includes: recording the real-time status and demands of each energy storage node through a distributed ledger to cope with the randomness of natural power generation; using smart contracts to automatically match the supply and demand of the energy storage nodes and trigger the scheduling instructions of the distributed energy storage nodes to smooth out the fluctuations; ensuring that the total energy storage power meets the system demand.

[0072] Parameter: Prediction window (1 hour), weight . Optimization result: The discharge power of the energy storage node is adjusted to 6 MW, the controllable load is increased by 1 MW, the frequency deviation is reduced to 0.01 Hz, and the average bus node voltage deviation is reduced to 0.05 kV.

[0073] The distributed ledger records the status of the energy storage nodes (Node 1: 48 MWh, Node 2: 49 MWh), and the smart contract triggers the discharge instruction to smooth out the fluctuations of new energy.

[0074] In summary, the above is only the preferred embodiment of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0075] For those skilled in the art, it is obvious that the embodiments of the present invention are not limited to the details of the above-described exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the embodiments of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the embodiments of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed within the embodiments of the present invention. Any reference signs in the claims should not be construed as limiting the claimed elements. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units, modules or devices described in the system, apparatus or terminal claims can also be implemented by the same unit, module or device through software or hardware. The words such as "first" and "second" are used to denote names and do not represent any particular order.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention and not to limit them. Although the technical solutions of the embodiments of the present invention have been described in detail with reference to the above preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the embodiments of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-time scale coordinated control method for a virtual power plant with new energy access. The virtual power plant includes multiple system nodes, and the system nodes include energy storage nodes and bus nodes. It is characterized in that, Step 1, in the day-ahead scheduling stage, based on natural conditions and historical load demands, construct a baseline scheduling plan for the virtual power plant, and allocate the demands of the energy storage nodes through the blockchain to ensure power balance and adapt to the natural conditions; Step 2, in the intra-day rolling optimization stage, utilize the short-term prediction data of the natural conditions to dynamically adjust the scheduling plan, and re-allocate the demands of the energy storage nodes through the blockchain; Step 3, in the virtual power plant construction stage, adopt digital twin technology to construct a real-time virtual mirror of the virtual power plant, and simulate the dynamic interaction between the new energy generation units in the virtual power plant and the bus nodes, grid load demands, and charge and discharge of the energy storage nodes through full-element simulation; Step 4, in the real-time control stage, in combination with model predictive control instructions, based on the baseline scheduling plan and the adjusted scheduling plan, realize the demand sharing of the energy storage nodes through the blockchain.

2. The multi-time scale coordinated control method of the virtual power plant for new energy access according to claim 1, wherein In Step 1, in the day-ahead scheduling stage, predict the output of wind power and photovoltaic power according to the natural conditions. The natural conditions include changes in wind speed, sunlight, and cloud cover. The power balance satisfies the following formula: Where: represents the wind power output, affected by the wind speed and the standard deviation of its random disturbance ; represents the photovoltaic power output, affected by the light intensity and the standard deviation of its random disturbance ; represents the net power contribution of the distributed energy storage nodes; where represents the charging power of the th energy storage node at time allocated by the blockchain smart contract in response to the excess of new energy; represents the discharging power of the th energy storage node at time allocated by the blockchain smart contract to make up for the shortage of new energy; represents the controllable load, affected by the natural power generation fluctuation regulation; represents the grid loss, dynamically related to the bus node voltage and the phase angle ; is the total length of the scheduling period, reflecting the time accumulation effect.

3. The multi-time scale coordinated control method for a virtual power plant with new energy access according to claim 2, characterized in that, In Step 2, the intra-day rolling optimization stage includes: updating the prediction of new energy output according to the short-term changes of the natural conditions; re-allocate the charge and discharge demands of the energy storage nodes through the smart contract of the blockchain to cope with the fluctuations of natural power generation.

4. The multi-time scale coordinated control method of the virtual power plant for new energy access according to claim 3, characterized in that, Adopting digital twin technology to construct a real-time virtual mirror of the virtual power plant includes: capturing the changes of the natural conditions by using real-time sensor data and updating the real-time virtual mirror of the virtual power plant; establishing a model of the actual operating state of the virtual power plant.

5. The multi-time-scale coordinated control method of the virtual power plant for new energy access according to claim 4, wherein In Step 3, simulating the dynamic interaction between the new energy generation units in the virtual power plant and the bus nodes, grid load demands, and charge and discharge of the energy storage nodes includes: inputting the disturbances of sudden drop in wind speed or sudden change in sunlight through the digital twin model in real time, and calculating the impact of the change in new energy output on the power injection of the bus nodes and the charge and discharge demands of the energy storage nodes; based on the new energy fluctuations and the power injection after the response adjustment of the energy storage nodes, use the Newton-Raphson method to iteratively solve the voltage amplitude and phase angle of each system node, determine the line power flow and losses, and ensure that the voltage and frequency meet the stability constraints, so as to realize the calculation of the power grid power flow distribution.

6. The multi-time scale coordinated control method for a virtual power plant with new energy access according to claim 5, characterized in that, The calculation of the power grid power flow distribution further includes: where the power of the system nodes satisfies the following power flow equation: ， Among them, represents the active power of the system node at time , which is directly affected by the changes in wind power or photovoltaic output, and represents the net power injected or absorbed by the system node; represents the voltage magnitude of the system node at time , which reflects the grid voltage distribution and is dynamically regulated by the power injection of new energy and the energy storage node; represents the voltage magnitude of the system node at time , which is connected to the system node through a line; represents the conductance between the system node and , which represents the real part of the grid admittance matrix and reflects the resistance characteristics of the line; represents the susceptance between the system node and , which represents the imaginary part of the grid admittance matrix and reflects the reactance characteristics of the line; Among them, represents the phase angle difference between the system node and at time , which affects the distribution of power flow and is driven by the fluctuations of new energy and load changes; represents the total number of system nodes and represents the number of nodes within the simulation range.

7. The multi-time scale coordinated control method of the virtual power plant for new energy access according to claim 6, characterized in that After the calculation of the power grid power flow distribution, it further includes: Analyze the power flow calculation results and check whether the node voltage is within the safe range. Analyze the power flow calculation results and check whether the node voltage is within the safe range.

8. The multi-time-scale coordinated control method of the virtual power plant for new energy access according to claim 7, characterized in that, In step 4, the optimization process of the model predictive control includes: defining an objective function by the following formula to minimize the power deviation caused by natural power generation fluctuations, and imposing constraint conditions to cope with the instantaneous changes of natural power generation; the objective function reflects the comprehensive optimization of power balance, frequency and voltage under the uncertainty of natural power generation; ， Among them, is the predicted power deviation, reflecting the uncertainty of natural power generation. represents the objective function value, reflecting the total cost of the optimization process, which needs to be minimized. represents the prediction window length, indicating the number of time steps for prediction. represents at time the predicted time of the power deviation, reflecting the difference between new energy output and load demand, affected by the fluctuations of wind power and photovoltaic power. represents at time the predicted time of the frequency deviation, reflecting the system frequency stability. represents at time the predicted time of the average voltage deviation of the bus nodes, indicating the stability of the grid voltage distribution. , represents at time the predicted time of the control input vector. represents the power deviation matrix. represents the frequency deviation matrix. represents the weight matrix of the voltage deviation. represents the weight matrix of the control input. represents the weighted squared norm, indicating the weighted squared deviation of the corresponding variable.

9. The multi-time scale coordinated control method for a virtual power plant with new energy access according to claim 8, wherein The realization of demand sharing of the energy storage nodes through the blockchain includes: recording the real-time status and demands of each energy storage node through a distributed ledger to cope with the randomness of natural power generation; using smart contracts to automatically match the supply and demand of the energy storage nodes, triggering the scheduling instructions of the distributed energy storage nodes to smooth the fluctuations; ensuring that the total energy storage power meets the system demands.

10. A multi-time-scale coordinated control system for a virtual power plant with new energy access, the virtual power plant includes a plurality of system nodes, the system nodes include energy storage nodes and bus nodes, characterized in that, It includes: A baseline scheduling plan construction module, configured to construct a baseline scheduling plan for the virtual power plant based on natural conditions and historical load demands during the day-ahead scheduling stage, and allocate the demands of the energy storage nodes through the blockchain to ensure power balance and adapt to the natural conditions; An intraday rolling optimization module, configured to dynamically adjust the scheduling plan by using the short-term prediction data of the natural conditions during the intraday rolling optimization stage, and re-allocate the demands of the energy storage nodes through the blockchain; A virtual power plant construction stage module, configured to adopt digital twin technology to construct a real-time virtual mirror of the virtual power plant during the virtual power plant construction stage, and simulate the dynamic interaction between the new energy generation units and the bus nodes, grid load demands and charge and discharge of the energy storage nodes in the virtual power plant through full-element simulation; A real-time control module, configured to realize the demand sharing of the energy storage nodes through the blockchain during the real-time control stage by combining the model predictive control instructions based on the baseline scheduling plan and the adjusted scheduling plan.

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