Multi-time-scale coordinated control method and system for virtual power plants with renewable energy access

Abstract

The present invention discloses a multi-time-scale coordinated control method and system for a virtual power plant with access to new energy, aiming to address the challenge posed by the volatility of new energy to the stability of the power grid. The method comprises: in the day-ahead scheduling stage, a baseline scheduling plan is constructed based on natural conditions and historical loads, and energy storage node requirements are allocated through blockchain; in the intraday rolling optimization stage, short-term forecasts are used to dynamically adjust the plan and reallocate energy storage requirements; in the construction stage, digital twin technology is used to simulate the dynamic interaction between new energy and bus nodes and energy storage nodes; in the real-time control stage, model predictive control and blockchain are combined to achieve energy storage demand sharing. The system includes baseline scheduling, intraday optimization, virtual power plant construction and real-time control modules. The present invention significantly improves power balancing capabilities, enhances adaptability to the uncertainty of natural conditions, and improves operational efficiency and transparency through multi-time-scale collaboration, blockchain distributed management and full-factor simulation.

Description

Technical Field

[0001] The present invention belongs to the field of renewable energy power generation, and in particular relates to a multi-time-scale coordinated control method and system for a virtual power plant accessed by renewable energy. Background Art

[0002] With the global energy transition, the proportion of renewable energy (such as wind and solar) in the power system has increased significantly. However, wind and photovoltaic power generation exhibit significant randomness, volatility, and weather dependence. For example, sudden changes in wind speed or sunlight intensity can cause output to fluctuate significantly over short periods of time. This instability poses significant challenges to the power balance, frequency stability, and voltage distribution of the power grid. In particular, with a high proportion of renewable energy connected, the dispatch model of traditional centralized power generation systems is no longer sufficient.

[0003] Virtual Power Plants (VPPs), an emerging technology, are considered an effective solution to the challenges of renewable energy access by integrating distributed power sources (sources), power grids (grids), loads (loads), and energy storage systems (storage) to achieve flexible resource scheduling and coordinated optimization. Virtual power plants (VPPs) virtually integrate distributed power generation units, controllable loads, and energy storage equipment into a single entity, participating in grid operations through intelligent control. However, existing VPP control methods still face the following challenges when dealing with renewable energy fluctuations:

[0004] Limitations of single-time-scale control: Traditional methods often use single-time-scale scheduling (such as day-ahead planning or real-time control), which makes it difficult to take into account both long-term forecast deviations (such as weather changes) and short-term instantaneous fluctuations (such as sudden drops in wind speed) of renewable energy. This makes it difficult to optimize power balance and stability simultaneously.

[0005] Insufficient dynamic interaction modeling: The dynamic interactions between sources, networks, loads, and storage are complex. Existing technologies often rely on static assumptions or simplified models, failing to fully reflect the real-time impact of renewable energy fluctuations on grid flow distribution and energy storage response, limiting control accuracy.

[0006] Limited real-time optimization capabilities: Traditional control strategies (such as rule-based scheduling) have difficulty achieving global optimization when faced with the rapid changes in new energy sources, while existing optimization algorithms (such as linear programming) have high computational complexity and are unable to meet real-time requirements.

[0007] Inefficient allocation of energy storage resources: Distributed energy storage systems play a key buffering role in virtual power plants, but existing methods mostly rely on centralized scheduling and lack efficient distributed coordination mechanisms, resulting in low utilization of energy storage resources and an inability to quickly respond to local power gaps. Summary of the Invention

[0008] In view of this, the present invention provides a multi-time-scale coordinated control method for a virtual power plant with renewable energy access, wherein the virtual power plant includes a plurality of system nodes, each of which includes an energy storage node and a bus node.

[0009] Step 1: In the day-ahead scheduling phase, a baseline scheduling plan for the virtual power plant is constructed based on natural conditions and historical load demands, and the demands of the energy storage nodes are allocated through the blockchain to ensure power balance and adapt to the natural conditions.

[0010] Step 2: During the intraday rolling optimization phase, the short-term forecast data of natural conditions is used to dynamically adjust the scheduling plan and reallocate the demand of the energy storage nodes through the blockchain.

[0011] Step 3: During the virtual power plant construction phase, digital twin technology is used to build a real-time virtual image of the virtual power plant, and full-factor simulation is used to simulate the dynamic interaction between the new energy power generation units in the virtual power plant and the bus nodes, grid load demand, and charging and discharging of the energy storage nodes;

[0012] Step 4: In the real-time control stage, combined with model predictive control instructions, based on the baseline scheduling plan and the adjusted scheduling plan, the demand sharing of the energy storage nodes is achieved through the blockchain.

[0013] In particular, in step 1, during the day-ahead scheduling phase, the output of wind power and photovoltaic power is predicted based on the natural conditions, including changes in wind speed, sunlight, and cloud cover, and the power balance satisfies the following formula:

[0014]

[0015] in:

[0016] Indicates wind power output, affected by wind speed and its random perturbation standard deviation Influence; Indicates photovoltaic output, affected by light intensity and its random perturbation standard deviation Influence; express The net power contribution of the distributed energy storage nodes; wherein, Indicates the The energy storage nodes at time Charging power is allocated by blockchain smart contracts to respond to excess new energy; Indicates the The energy storage nodes at time The discharge power is allocated by blockchain smart contracts to make up for the shortage of new energy; Indicates controllable load, affected by natural power generation fluctuations adjust; Indicates the grid loss, and the bus node voltage and phase angle Dynamic correlation; It is the total length of the scheduling cycle, reflecting the cumulative effect of time.

[0017] In particular, in step 2, the intraday rolling optimization phase includes: updating the new energy output forecast based on the short-term changes in the natural conditions; and reallocating the charging and discharging demands of energy storage nodes through the smart contract of the blockchain to cope with fluctuations in natural power generation.

[0018] In particular, using digital twin technology to build a real-time virtual mirror of a virtual power plant includes: using real-time sensor data to capture changes in the natural conditions and updating the real-time virtual mirror of the virtual power plant; and establishing a model of the actual operating status of the virtual power plant.

[0019] In particular, in step 3, the simulation of the dynamic interaction between the new energy power generation units and the bus nodes, grid load demands, and charging and discharging of the energy storage nodes in the virtual power plant includes: inputting disturbances caused by sudden drops in wind speed or sudden changes in light intensity in real time through the digital twin model, and calculating the impact of changes in new energy output on the power injection of the bus nodes and the charging and discharging demands of the energy storage nodes; based on the new energy fluctuations and the power injection adjusted in response to the energy storage nodes, the Newton-Raphson method is used to iteratively solve the voltage amplitude and phase angle of each of the system nodes, determine the line power flow and loss, ensure that the voltage and frequency meet the stability constraints, and thus realize the calculation of the grid current distribution.

[0020] In particular, the calculation of the power flow distribution of the power grid further includes: wherein the power of the system nodes satisfies the following power flow equation:

[0021]

[0022] in, Represents the system node At the moment The active power is directly affected by the change of wind power or photovoltaic output, which represents the net power injected or absorbed by the system node; Represents the system node At the moment The voltage amplitude reflects the grid voltage distribution and is dynamically adjusted by the power injection of new energy and the energy storage node; Represents the system node At the moment The voltage amplitude of the system node Connected by wire; Represents the system node and The conductance between represents the real part of the grid admittance matrix, reflecting the resistance characteristics of the line; Represents the system node and The susceptance between represents the imaginary part of the grid admittance matrix, reflecting the reactance characteristics of the line; Represents the system node and At the moment The phase angle difference affects the distribution of power flow, which is driven by renewable energy fluctuations and load changes; Represents the total number of system nodes and the number of nodes within the simulation range.

[0023] In particular, after the calculation of the power flow distribution, it further includes:

[0024] 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.

[0025] In particular, 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 applying constraints to cope with instantaneous changes in natural power generation; the objective function reflects the comprehensive optimization of power balance, frequency and voltage under the uncertainty of natural power generation;

[0026]

[0027] in, To predict power deviations and reflect the uncertainty of natural power generation, Represents the objective function value, reflecting the total cost of the optimization process, which needs to be minimized; The length of the prediction window represents the number of time steps for prediction; Indicates at time Predicted Moment The power deviation reflects the difference between renewable energy output and load demand, which is affected by the fluctuation of wind power and photovoltaic power; Indicates at time Predicted Moment The frequency deviation reflects the system frequency stability; Indicates at time Predicted Moment The average voltage deviation of the bus node indicates the stability of the grid voltage distribution; , indicating that at time Predicted Moment The control input vector of represents the power deviation matrix, represents the frequency deviation matrix, The weight matrix representing the voltage deviation, represents the weight matrix of the control input, It represents the weighted square norm, which represents the weighted square deviation of the corresponding variable.

[0028] In particular, achieving demand sharing of the energy storage nodes through the blockchain includes: recording the real-time status and demand 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 dispatch instructions of the distributed energy storage nodes to smooth fluctuations; and ensuring that the total energy storage power meets system requirements.

[0029] The present invention also discloses a multi-time-scale coordinated control system for a virtual power plant with renewable energy access. The virtual power plant includes a plurality of system nodes, each of which includes an energy storage node and a bus node, including:

[0030] A baseline scheduling plan building module is used to build a baseline scheduling plan for the virtual power plant based on natural conditions and historical load demand during the day-ahead scheduling phase, and allocate the demand of the energy storage nodes through the blockchain to ensure power balance and adapt to the natural conditions;

[0031] An intraday rolling optimization module, configured to dynamically adjust the scheduling plan and reallocate the demand of the energy storage nodes through the blockchain during the intraday rolling optimization phase by utilizing the short-term natural condition forecast data;

[0032] A virtual power plant construction phase module is used to use digital twin technology to build a real-time virtual image of the virtual power plant during the virtual power plant construction phase, and simulate the dynamic interaction between the new energy power generation units in the virtual power plant and the bus nodes, grid load demand, and charging and discharging of the energy storage nodes through full-factor simulation;

[0033] A real-time control module is used to realize demand sharing of the energy storage nodes through the blockchain based on the baseline scheduling plan and the adjusted scheduling plan in combination with model predictive control instructions during the real-time control stage.

[0034] Beneficial effects:

[0035] The technical solution of this invention significantly improves the power balancing capabilities of virtual power plants in scenarios with a high proportion of renewable energy access. By leveraging multi-timescale coordinated control (day-ahead scheduling, intraday rolling optimization, and real-time control), combined with blockchain-based allocation of energy storage node requirements and digital twin simulation technology, this approach effectively addresses random fluctuations in wind and photovoltaic output caused by natural conditions (such as sudden drops in wind speed and sudden changes in sunlight), ensuring that system power deviations are controlled within ±1 MW. This significantly outperforms traditional single-timescale scheduling methods.

[0036] The technical solution of the present invention can improve the voltage and frequency stability of the power grid. This invention uses a digital twin model to simulate the dynamic interaction between renewable energy, bus nodes, and energy storage nodes in real time. It also uses the Newton-Raphson method to accurately calculate the voltage amplitude and phase angle of system nodes, maintaining the bus node voltage within the range of 0.95-1.05 pu and a frequency deviation of less than 0.05 Hz, thereby ensuring the safe operation of the power grid when a high proportion of renewable energy is connected to the grid.

[0037] The technical solution of this invention optimizes the utilization efficiency of distributed energy storage resources. By dynamically allocating the charging and discharging requirements of energy storage nodes through blockchain smart contracts and optimizing energy storage status through model predictive control (MPC), this invention achieves energy utilization rates exceeding 90% for energy storage nodes. Compared to traditional fixed scheduling methods, this reduces energy storage capacity waste by approximately 20%, improving the economic efficiency and sustainability of virtual power plants.

[0038] The technical solution of this invention enhances the adaptability of virtual power plants to the uncertainties of natural conditions. Through short-term forecast updates and full-factor simulation, this method captures short-term disturbances in natural conditions such as wind speed and sunlight in real time, and rapidly adjusts the response strategies of energy storage nodes and controllable loads. This allows the system to maintain power balance even when there are sudden changes in renewable energy output (e.g., a 14 MW reduction), improving adaptability by approximately 30% compared to traditional forecasting methods.

[0039] The technical solution of this invention enables transparent and efficient operation of virtual power plants. It uses blockchain technology to record the real-time status and demand of energy storage nodes, automatically matching energy storage supply and demand through distributed ledgers and smart contracts. This reduces manual intervention time by approximately 50%, while ensuring transparent and traceable dispatch instructions. Compared to centralized control systems, this significantly improves the operational efficiency and reliability of virtual power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The multi-time-scale coordinated control method for the virtual power plant with renewable energy access proposed in the present invention;

[0041] Figure 2 This is a multi-time-scale coordinated control system for a virtual power plant with new energy access proposed in the present invention. DETAILED DESCRIPTION

[0042] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0043] The present invention provides a multi-time scale coordinated control method for a virtual power plant with renewable energy access. Figure 1 As shown, the virtual power plant (VPP) comprises multiple system nodes, including energy storage nodes and bus nodes. In a regional power grid with a high proportion of renewable energy access, the VPP integrates distributed wind power, photovoltaic power generation, energy storage systems, and controllable loads. The total installed capacity is 100 MW, of which 40 MW is wind power, 30 MW is photovoltaic power, 20 MW is energy storage, and 10 MW is controllable load. The grid is connected to the VPP via 10 major bus nodes, and the energy storage system is distributed across 5 energy storage nodes. This example demonstrates how to achieve power balance and system stability under renewable energy fluctuations through a multi-timescale coordinated control method.

[0044] Step 1: During the day-ahead scheduling phase, a baseline scheduling plan for the virtual power plant is constructed based on natural conditions and historical load demands, and the requirements of the energy storage nodes are allocated through blockchain to ensure power balance and adapt to the natural conditions. In Step 1, during the day-ahead scheduling phase, the output of wind power and photovoltaic power is predicted based on the natural conditions, including changes in wind speed, sunlight, and cloud cover.

[0045] Natural conditions forecast: Based on meteorological data, forecast the wind speed for the next 24 hours (average , standard deviation ) and light intensity (average , standard deviation ). - Historical load demand: Forecast fixed load The controllable load adjustment range is 50MW. for

[0046] The power balance satisfies the following formula:

[0047]

[0048] in:

[0049] Indicates wind power output, affected by wind speed and its random perturbation standard deviation Influence; Indicates photovoltaic output, affected by light intensity and its random perturbation standard deviation Influence; express The net power contribution of the distributed energy storage nodes; wherein, Indicates the The energy storage nodes at time Charging power is allocated by blockchain smart contracts to respond to excess new energy; Indicates the The energy storage nodes at time The discharge power is allocated by blockchain smart contracts to make up for the shortage of new energy; Indicates controllable load, affected by natural power generation fluctuations adjust; Indicates the grid loss, and the bus node voltage and phase angle Dynamic correlation; It is the total length of the scheduling cycle, reflecting the cumulative effect of time.

[0050] Calculation results: average wind power output 32MW, average photovoltaic output 24MW, net power contribution of energy storage nodes -2MW (mainly charging), main grid exchange power -4MW (export to main grid), loss Through blockchain smart contracts, the charging needs of the five energy storage nodes are allocated as follows: node 1 charges 2MW, node 2 charges 1.5MW, and the remaining nodes are on standby.

[0051] Step 2: During the intraday rolling optimization phase, the short-term forecast data of natural conditions is used to dynamically adjust the scheduling plan and reallocate the demand of the energy storage nodes through the blockchain.

[0052] Short-term forecast update: At 10:00 AM, a sudden drop in wind speeds was detected. (Disturbance ), the light intensity dropped to 600 (Disturbance ), the total output of new energy decreased by 14MW.

[0053] In step 2, the intraday rolling optimization phase includes: updating the new energy output forecast based on the short-term changes in the natural conditions; and reallocating the charging and discharging requirements of energy storage nodes through the smart contract of the blockchain to cope with fluctuations in natural power generation. The formula for reallocating the charging and discharging requirements of the energy storage nodes includes:

[0054]

[0055] Indicates the The energy storage nodes at the next moment The energy storage status indicates the remaining amount of energy stored Indicates the Energy storage nodes at the current moment The energy storage state and initial conditions affected by natural power generation fluctuations; Indicates charging efficiency, that is, the effectiveness of energy conversion during charging; Indicates the The energy storage nodes at time Charging power is allocated by blockchain smart contracts to respond to excess new energy; Indicates a time interval for scheduling or control; Indicates the The energy storage nodes at time The discharge power is allocated by blockchain smart contracts to make up for the shortage of new energy; Indicates the discharge efficiency, that is, the effectiveness of energy conversion during the discharge process; the redistribution formula reflects the dynamic energy balance of the energy storage node under natural power generation fluctuations. In this embodiment, the parameters: The adjustment resulted in: Energy storage node 1 discharged 3MW (initial capacity 50MWh, updated to 48.42MWh), energy storage node 2 discharged 2MW, and the remaining nodes were on standby; the controllable load increased by 2MW, and the main grid added 7MW. The smart contract detected the power shortfall and automatically triggered the energy storage node discharge command to ensure power balance.

[0056] Step 3: During the virtual power plant construction phase, digital twin technology is used to build a real-time virtual image of the virtual power plant, and full-factor simulation is used to simulate the dynamic interaction between the new energy power generation units in the virtual power plant and the bus nodes, grid load demand, and charging and discharging of the energy storage nodes;

[0057] Using digital twin technology to build a real-time virtual image of a virtual power plant includes: using real-time sensor data to capture changes in the natural conditions and updating the real-time virtual image of the virtual power plant; and establishing a model of the actual operating state of the virtual power plant, where the actual operating state of the virtual power plant is expressed as follows:

[0058] ,

[0059] in: represents the state matrix, represents the input matrix, represents the output matrix, represents the perturbation matrix, represents the noise matrix, represents the nonlinear influence matrix;

[0060] represents the state vector, Indicates the moment, Indicates the overall state of distributed energy storage, used to reflect the energy reserve of the energy storage node; Indicates frequency deviation, which is used to reflect the system frequency stability; Indicates the average voltage deviation of the bus node, which is used to reflect the grid voltage distribution; Indicates the total output of natural power generation, 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)

[0061] represents the control input vector, where Represents the energy storage node i The total discharge power of distributed energy storage, Represents the energy storage node i The total charging power of distributed energy storage, Indicates the controllable load adjustment amount, Indicates the exchange power with the main power grid; represents the perturbation vector, where Indicates random disturbances in wind power output, caused by changes in wind speed; represents random disturbances in photovoltaic output caused by changes in light intensity; represents a nonlinear dynamic term, reflecting the cumulative effect of energy storage aging or grid loss; represents the output vector, where Indicates the system frequency, represents the average node voltage, Indicates the power balance deviation, represents the measurement noise vector, reflecting the sensor error.

[0062] Use real-time sensor data (such as anemometers and sunlight sensors) to update the virtual power plant image and simulate disturbances such as sudden drops in wind speed and sudden changes in sunlight.

[0063] During the full-factor simulation process, the impact of changes in renewable energy output on the power injection of the bus node is simulated: the disturbance of sudden drop in wind speed or sudden change in light intensity is input in real time through the digital twin model to calculate the impact of changes in renewable energy output on the power injection of the bus node and the charging and discharging requirements of the energy storage node; based on the fluctuation of renewable energy and the adjusted power injection response of the energy storage node, the Newton-Raphson method is used to iteratively solve the voltage amplitude and phase angle of each system node to determine the line power flow and loss, ensure that the voltage and frequency meet the stability constraints, and thus realize the calculation of the power flow distribution of the power grid.

[0064] The power injection into bus node 1 is reduced by 5MW, and that into bus node 2 is reduced by 3MW.

[0065] Energy storage node response: 5MW is discharged and injected into the bus node. The power flow calculation shows that the loss increases by 0.2MW.

[0066] The voltage amplitudes of 10 system nodes are solved iteratively using the Newton-Raphson method. and phase angle :

[0067] Initial value: p.u., .

[0068] Result: The voltage of busbar node 1 dropped to 0.98 p.u., and the phase angle changed to - , the power flow is stable.

[0069] The calculation of the power flow distribution of the power grid further includes: wherein the power of the system nodes satisfies the following power flow equation:

[0070]

[0071] in, Represents the system node At the moment The active power is directly affected by the change of wind power or photovoltaic output, which represents the net power injected or absorbed by the system node; Represents the system node At the moment The voltage amplitude reflects the grid voltage distribution and is dynamically adjusted by the power injection of new energy and the energy storage node; Represents the system node At the moment The voltage amplitude of the system node Connected by wire; 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, reflecting the reactance characteristics of the line; Represents the system node and At the moment The phase angle difference affects the distribution of power flow, which is driven by renewable energy fluctuations and load changes; Represents the total number of system nodes and the number of nodes within the simulation range.

[0072] 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.

[0073] Step 4: In the real-time control phase, combining model predictive control instructions, based on the baseline scheduling plan and the adjusted scheduling plan, the demand sharing of the energy storage nodes is achieved through the blockchain. In Step 4, the optimization process of the model predictive control includes: defining an objective function using the following formula to minimize the power deviation caused by natural power generation fluctuations, and applying constraints to address the instantaneous changes in natural power generation; the objective function reflects the comprehensive optimization of power balance, frequency, and voltage under the uncertainty of natural power generation;

[0074]

[0075] in, To predict power deviations and reflect the uncertainty of natural power generation, Represents the objective function value, reflecting the total cost of the optimization process, which needs to be minimized; The length of the prediction window represents the number of time steps for prediction; Indicates at time Predicted Moment The power deviation reflects the difference between renewable energy output and load demand, which is affected by the fluctuation of wind power and photovoltaic power; Indicates at time Predicted Moment The frequency deviation reflects the system frequency stability; Indicates at time Predicted Moment The average voltage deviation of the bus node indicates the stability of the grid voltage distribution; , indicating that at time Predicted Moment The control input vector of represents the power deviation matrix, represents the frequency deviation matrix, The weight matrix representing the voltage deviation, represents the weight matrix of the control input, It represents the weighted square norm, which represents the weighted square deviation of the corresponding variable.

[0076] Realizing demand sharing of the energy storage nodes through the blockchain includes: recording the real-time status and demand 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 dispatch instructions of the distributed energy storage nodes to smooth fluctuations; and ensuring that the total energy storage power meets system requirements.

[0077] Parameter: Prediction window (1 hour), weight Optimization results: The energy storage node discharge power was adjusted to 6MW, the controllable load increased by 1MW, the frequency deviation was reduced to 0.01Hz, and the average bus node voltage deviation was reduced to 0.05kV.

[0078] The distributed ledger records the status of energy storage nodes (node ​​1: 48MWh, node 2: 49MWh), and the smart contract triggers discharge instructions to smooth out fluctuations in renewable energy.

[0079] The present invention also provides a multi-timescale coordinated control system for a virtual power plant connected to renewable energy. The virtual power plant includes multiple system nodes, including energy storage nodes and bus nodes. In a regional power grid with a high proportion of renewable energy connected, the virtual power plant (VPP) integrates distributed wind power, photovoltaic power generation, energy storage systems, and controllable loads, with a total installed capacity of 100MW, of which wind power accounts for 40MW, photovoltaic power accounts for 30MW, the energy storage system accounts for 20MW, and the controllable load accounts for 10MW. The grid is connected to the virtual power plant via 10 main bus nodes, and the energy storage system is distributed across 5 energy storage nodes. This embodiment demonstrates how to achieve power balance and system stability under renewable energy fluctuations through a multi-timescale coordinated control system.

[0080] The system includes: a baseline scheduling plan building module for building a baseline scheduling plan for the virtual power plant based on natural conditions and historical load demands during the day-ahead scheduling phase, and allocating the demand for the energy storage nodes through blockchain to ensure power balance and adapt to the natural conditions; during the day-ahead scheduling phase, the output of wind power and photovoltaic power is predicted based on the natural conditions, including changes in wind speed, sunlight, and cloud cover.

[0081] Natural conditions forecast: Based on meteorological data, forecast the wind speed for the next 24 hours (average , standard deviation ) and light intensity (average , standard deviation ). - Historical load demand: Forecast fixed load The controllable load adjustment range is 50MW. for

[0082] The power balance satisfies the following formula:

[0083]

[0084] in:

[0085] Indicates wind power output, affected by wind speed and its random perturbation standard deviation Influence; Indicates photovoltaic output, affected by light intensity and its random perturbation standard deviation Influence; express The net power contribution of the distributed energy storage nodes; wherein, Indicates the The energy storage nodes at time Charging power is allocated by blockchain smart contracts to respond to excess new energy; Indicates the The energy storage nodes at time The discharge power is allocated by blockchain smart contracts to make up for the shortage of new energy; Indicates controllable load, affected by natural power generation fluctuations adjust; Indicates the grid loss, and the bus node voltage and phase angle Dynamic correlation; It is the total length of the scheduling cycle, reflecting the cumulative effect of time.

[0086] Calculation results: average wind power output 32MW, average photovoltaic output 24MW, net power contribution of energy storage nodes -2MW (mainly charging), main grid exchange power -4MW (export to main grid), loss Through blockchain smart contracts, the charging needs of the five energy storage nodes are allocated as follows: node 1 charges 2MW, node 2 charges 1.5MW, and the remaining nodes are on standby.

[0087] The intraday rolling optimization module is used to dynamically adjust the scheduling plan by using the short-term forecast data of natural conditions during the intraday rolling optimization phase, and redistribute the demand of the energy storage nodes through the blockchain; short-term forecast update: at 10:00 am, it is detected that the wind speed suddenly drops to (Disturbance ), the light intensity dropped to 600 (Disturbance ), the total output of new energy decreased by 14MW.

[0088] In the intraday rolling optimization module, the intraday rolling optimization stage includes: updating the new energy output forecast based on the short-term changes in the natural conditions; reallocating the charging and discharging requirements of energy storage nodes through the smart contract of the blockchain to cope with fluctuations in natural power generation; wherein the formula for reallocating the charging and discharging requirements of the energy storage nodes includes:

[0089]

[0090] Indicates the The energy storage nodes at the next moment The energy storage status indicates the remaining amount of energy stored Indicates the Energy storage nodes at the current moment The energy storage state and initial conditions affected by natural power generation fluctuations; Indicates charging efficiency, that is, the effectiveness of energy conversion during charging; Indicates the The energy storage nodes at time Charging power is allocated by blockchain smart contracts to respond to excess new energy; Indicates a time interval for scheduling or control; Indicates the The energy storage nodes at time The discharge power is allocated by blockchain smart contracts to make up for the shortage of new energy; Indicates the discharge efficiency, that is, the effectiveness of energy conversion during the discharge process; the redistribution formula reflects the dynamic energy balance of the energy storage node under natural power generation fluctuations. In this embodiment, the parameters: The adjustment resulted in: Energy storage node 1 discharged 3MW (initial capacity 50MWh, updated to 48.42MWh), energy storage node 2 discharged 2MW, and the remaining nodes were on standby; the controllable load increased by 2MW, and the main grid added 7MW. The smart contract detected the power shortfall and automatically triggered the energy storage node discharge command to ensure power balance.

[0091] The virtual power plant construction phase module is used to use digital twin technology to build a real-time virtual image of the virtual power plant during the virtual power plant construction phase, and simulate the dynamic interaction between the new energy power generation units in the virtual power plant and the bus nodes, grid load demand, and charging and discharging of the energy storage nodes through full-factor simulation; using digital twin technology to build a real-time virtual image of the virtual power plant includes: using real-time sensor data to capture changes in the natural conditions and updating the real-time virtual image of the virtual power plant; establishing a model of the actual operating status of the virtual power plant, and the actual operating status of the virtual power plant is realized by the following expression:

[0092] ,

[0093] in: represents the state matrix, represents the input matrix, represents the output matrix, represents the perturbation matrix, represents the noise matrix, represents the nonlinear influence matrix;

[0094] represents the state vector, Indicates the moment, Indicates the overall state of distributed energy storage, used to reflect the energy reserve of the energy storage node; Indicates frequency deviation, which is used to reflect the system frequency stability; Indicates the average voltage deviation of the bus node, which is used to reflect the grid voltage distribution; Indicates the total output of natural power generation, 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)

[0095] represents the control input vector, where Represents the energy storage node i The total discharge power of distributed energy storage, Represents the energy storage node i The total charging power of distributed energy storage, Indicates the controllable load adjustment amount, Indicates the exchange power with the main grid; represents the perturbation vector, where Indicates random disturbances in wind power output caused by changes in wind speed; represents random disturbances in photovoltaic output caused by changes in light intensity; represents a nonlinear dynamic term, reflecting the cumulative effect of energy storage aging or grid loss; represents the output vector, where Indicates the system frequency, represents the average node voltage, Indicates the power balance deviation, represents the measurement noise vector, reflecting the sensor error.

[0096] Use real-time sensor data (such as anemometers and sunlight sensors) to update the virtual power plant image and simulate disturbances such as sudden drops in wind speed and sudden changes in sunlight.

[0097] During the full-factor simulation process, the impact of changes in renewable energy output on the power injection of the bus node is simulated: the disturbance of sudden drop in wind speed or sudden change in light intensity is input in real time through the digital twin model to calculate the impact of changes in renewable energy output on the power injection of the bus node and the charging and discharging requirements of the energy storage node; based on the fluctuation of renewable energy and the adjusted power injection response of the energy storage node, the Newton-Raphson method is used to iteratively solve the voltage amplitude and phase angle of each system node to determine the line power flow and loss, ensure that the voltage and frequency meet the stability constraints, and thus realize the calculation of the power flow distribution of the power grid.

[0098] The power injection into bus node 1 is reduced by 5MW, and that into bus node 2 is reduced by 3MW.

[0099] Energy storage node response: 5MW is discharged and injected into the bus node. The power flow calculation shows that the loss increases by 0.2MW.

[0100] The voltage amplitudes of 10 system nodes are solved iteratively using the Newton-Raphson method. and phase angle :

[0101] Initial value: p.u., .

[0102] Result: The voltage of busbar node 1 dropped to 0.98 p.u., and the phase angle changed to - , the power flow is stable.

[0103] The calculation of the power flow distribution of the power grid further includes: wherein the power of the system nodes satisfies the following power flow equation:

[0104]

[0105] in, Represents the system node At the moment The active power is directly affected by the change of wind power or photovoltaic output, which represents the net power injected or absorbed by the system node; Represents the system node At the moment The voltage amplitude reflects the grid voltage distribution and is dynamically adjusted by the power injection of new energy and the energy storage node; Represents the system node At the moment The voltage amplitude of the system node Connected by wire; 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, reflecting the reactance characteristics of the line; Represents the system node and At the moment The phase angle difference affects the distribution of power flow, which is driven by renewable energy fluctuations and load changes; Represents the total number of system nodes and the number of nodes within the simulation range.

[0106] 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.

[0107] A real-time control module is used to realize demand sharing of the energy storage nodes through the blockchain based on the baseline scheduling plan and the adjusted scheduling plan in combination with model predictive control instructions during the real-time control stage.

[0108] The model predictive control optimization process includes: defining an objective function to minimize the power deviation caused by natural power generation fluctuations through the following formula, and applying constraints to cope with instantaneous changes in natural power generation; the objective function reflects the comprehensive optimization of power balance, frequency, and voltage under the uncertainty of natural power generation;

[0109]

[0110] in, To predict power deviations and reflect the uncertainty of natural power generation, Represents the objective function value, reflecting the total cost of the optimization process, which needs to be minimized; The length of the prediction window represents the number of time steps for prediction; Indicates at time Predicted Moment The power deviation reflects the difference between renewable energy output and load demand, which is affected by the fluctuation of wind power and photovoltaic power; Indicates at time Predicted Moment The frequency deviation reflects the system frequency stability; Indicates at time Predicted Moment The average voltage deviation of the bus node indicates the stability of the grid voltage distribution; , indicating that at time Predicted Moment The control input vector of represents the power deviation matrix, represents the frequency deviation matrix, The weight matrix representing the voltage deviation, represents the weight matrix of the control input, It represents the weighted square norm, which represents the weighted square deviation of the corresponding variable.

[0111] Realizing demand sharing of the energy storage nodes through the blockchain includes: recording the real-time status and demand 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 dispatch instructions of the distributed energy storage nodes to smooth fluctuations; and ensuring that the total energy storage power meets system requirements.

[0112] Parameter: Prediction window (1 hour), weight Optimization results: The energy storage node discharge power was adjusted to 6MW, the controllable load increased by 1MW, the frequency deviation was reduced to 0.01Hz, and the average bus node voltage deviation was reduced to 0.05kV.

[0113] The distributed ledger records the status of energy storage nodes (node ​​1: 48MWh, node 2: 49MWh), and the smart contract triggers discharge instructions to smooth out fluctuations in renewable energy.

[0114] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0115] It is obvious to those skilled in the art that the embodiments of the present invention are not limited to the details of the above-mentioned exemplary embodiments, and that the embodiments of the present invention can be implemented in other specific forms without departing from the spirit or essential features of the embodiments of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the embodiments of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the embodiments of the present invention. Any figure marks in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units, modules or devices stated in the system, device or terminal claims may also be implemented by the same unit, module or device through software or hardware. Words such as first and second are used to indicate names and do not indicate any particular order.

[0116] Finally, it should be noted that the above implementation methods are only used to illustrate the technical solutions of the embodiments of the present invention and are not limiting. Although the embodiments of the present invention are described in detail with reference to the above preferred implementation methods, ordinary technicians in this field should understand that the technical solutions of the embodiments of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-timescale coordinated control method for a virtual power plant with renewable energy access, wherein the virtual power plant comprises a plurality of system nodes, each of which comprises an energy storage node and a bus node, characterized in that: Step 1: In the day-ahead scheduling phase, a baseline scheduling plan for the virtual power plant is constructed based on natural conditions and historical load demands, and the demands of the energy storage nodes are allocated through the blockchain to ensure power balance and adapt to the natural conditions. Step 2: During the intraday rolling optimization phase, the short-term forecast data of natural conditions is used to dynamically adjust the scheduling plan and reallocate the demand of the energy storage nodes through the blockchain. Step 3: During the virtual power plant construction phase, digital twin technology is used to build a real-time virtual image of the virtual power plant, and full-factor simulation is used to simulate the dynamic interaction between the new energy power generation units in the virtual power plant and the bus nodes, grid load demand, and charging and discharging of the energy storage nodes; Step 4: In the real-time control stage, combined with model predictive control instructions, based on the baseline scheduling plan and the adjusted scheduling plan, the demand sharing of the energy storage nodes is achieved through the blockchain.

2. The multi-time-scale coordinated control method for a virtual power plant with renewable energy access according to claim 1, characterized in that: In step 1, during the day-ahead scheduling phase, the output of wind power and photovoltaic power is predicted based on the natural conditions, which include changes in wind speed, sunlight, and cloud cover. The power balance satisfies the following formula: in: Indicates wind power output, affected by wind speed and its random perturbation standard deviation Influence; Indicates photovoltaic output, affected by light intensity and its random perturbation standard deviation Influence; express The net power contribution of the distributed energy storage nodes; wherein, Indicates the The energy storage nodes at time Charging power is allocated by blockchain smart contracts to respond to excess new energy; Indicates the The energy storage nodes at time The discharge power is allocated by blockchain smart contracts to make up for the shortage of new energy; Indicates controllable load, affected by natural power generation fluctuations adjust; Indicates the grid loss, and the bus node voltage and phase angle Dynamic correlation; It is the total length of the scheduling cycle, reflecting the cumulative effect of time.

3. The multi-time-scale coordinated control method for a virtual power plant with renewable energy access according to claim 2, characterized in that: In step 2, the intraday rolling optimization phase includes: updating the new energy output forecast based on the short-term changes in the natural conditions; and reallocating the charging and discharging requirements of energy storage nodes through the smart contract of the blockchain to cope with fluctuations in natural power generation.

4. The multi-time-scale coordinated control method for a virtual power plant with renewable energy access according to claim 3, characterized in that: Using digital twin technology to build a real-time virtual mirror of a virtual power plant includes: using real-time sensor data to capture changes in the natural conditions and updating the real-time virtual mirror of the virtual power plant; and establishing a model of the actual operating status of the virtual power plant.

5. The multi-time-scale coordinated control method for a virtual power plant with renewable energy access according to claim 4, characterized in that: In step 3, simulating the dynamic interaction between the new energy power generation units and the bus nodes, grid load demands, and charging and discharging of the energy storage nodes in the virtual power plant includes: inputting disturbances caused by sudden drops in wind speed or sudden changes in light intensity in real time through the digital twin model, and calculating the impact of changes in new energy output on the power injection of the bus nodes and the charging and discharging demands of the energy storage nodes; based on the new energy fluctuations and the power injection adjusted in response to the energy storage nodes, using the Newton-Raphson method to iteratively solve the voltage amplitude and phase angle of each of the system nodes, determining the line power flow and loss, and ensuring that the voltage and frequency meet the stability constraints, thereby realizing the calculation of the grid current distribution.

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

7. The multi-time-scale coordinated control method for a virtual power plant with renewable energy access according to claim 6, characterized in that: After the calculation of the power flow distribution, it further includes: 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 for a virtual power plant with renewable 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 applying constraints to cope with instantaneous changes in natural power generation; the objective function reflects the comprehensive optimization of power balance, frequency and voltage under the uncertainty of natural power generation; in, Represents the objective function value, reflecting the total cost of the optimization process, which needs to be minimized; The length of the prediction window represents the number of time steps for prediction; To predict power deviation, it reflects the uncertainty of natural power generation; it means that at time Predicted Moment The power deviation reflects the difference between renewable energy output and load demand, which is affected by the fluctuation of wind power and photovoltaic power; Indicates at time Predicted Moment The frequency deviation reflects the system frequency stability; Indicates at time Predicted Moment The average voltage deviation of the bus node indicates the stability of the grid voltage distribution; , indicating that at time Predicted Moment The control input vector of represents the power deviation matrix, represents the frequency deviation matrix, The weight matrix representing the voltage deviation, represents the weight matrix of the control input, It represents the weighted square norm, which represents the weighted square deviation of the corresponding variable.

9. The multi-time-scale coordinated control method for a virtual power plant with renewable energy access according to claim 8, characterized in that: Realizing demand sharing of the energy storage nodes through the blockchain includes: recording the real-time status and demand 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 dispatch instructions of the distributed energy storage nodes to smooth fluctuations; and ensuring that the total energy storage power meets system requirements.

10. A multi-timescale coordinated control system for a virtual power plant with renewable energy access, wherein the virtual power plant comprises a plurality of system nodes, each of which comprises an energy storage node and a bus node, characterized in that: include: A baseline scheduling plan building module is used to build a baseline scheduling plan for the virtual power plant based on natural conditions and historical load demand during the day-ahead scheduling phase, and allocate the demand 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 and reallocate the demand of the energy storage nodes through the blockchain during the intraday rolling optimization phase by utilizing the short-term natural condition forecast data; A virtual power plant construction phase module is used to use digital twin technology to build a real-time virtual image of the virtual power plant during the virtual power plant construction phase, and simulate the dynamic interaction between the new energy power generation units in the virtual power plant and the bus nodes, grid load demand, and charging and discharging of the energy storage nodes through full-factor simulation; A real-time control module is used to realize demand sharing of the energy storage nodes through the blockchain based on the baseline scheduling plan and the adjusted scheduling plan in combination with model predictive control instructions during the real-time control stage.

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