A distributed energy internet regulation system and method based on dynamic field balance

Abstract

This invention discloses a distributed energy internet control system and method based on dynamic field balance, belonging to the field of energy internet and smart grid technology. This system maps the power generation units, energy storage units, and electricity loads in a distributed energy network into a continuous energy operation field. The system includes: a field generation unit, used to collect the output power and power generation prediction data of each power generation node in real time, corresponding to the system's positive drive (+1); a field harmonization unit, used to collect the remaining capacity and state of charge of each energy storage node and the demand prediction data of each load node in real time, corresponding to the system's negative damping (-1); and a field balance controller (0), which has a built-in single composite calculation hardware module composed of exponential, logarithmic, and difference calculations, used to calculate the supply-demand deviation value B of the energy operation field, and dynamically generate unified control commands for each power generation node, energy storage node, and load node based on the B value. This invention realizes closed-loop control of the "source-grid-load-storage" four-element synergy, enabling the entire energy field to operate stably near a preset zero-state equilibrium point, and is applicable to distributed energy internet systems of various scales such as city-level, park-level, and regional-level.

Description

Technical Field

[0001] This invention belongs to the field of energy internet and smart grid technology, specifically involving a system and method for uniformly mapping "source-grid-load-storage" into a continuous energy operation and transformation field and performing dynamic balance control. It is applicable to distributed energy internet systems of various scales, such as city-level, park-level, and regional-level. Background Technology

[0002] With the advancement of the "dual carbon" target and the rapid development of distributed energy, the energy internet has become the core form of the new power system. The integration of numerous distributed resources, such as distributed photovoltaics, wind power, energy storage systems, and electric vehicle charging stations, into the distribution network has made the traditional rigid dispatch mode of "source follows load" unsustainable. The operation of the energy system has shifted from a unidirectional, centralized, and controllable mode to a multidirectional, distributed, and highly uncertain complex system.

[0003] Against this backdrop, there is an urgent need for a novel distributed energy internet regulation method. Drawing upon the "dynamic field balance" principle revealed in the second edition of *Runjin Guiyuan Yuandian*, this invention constructs a system that maps "source-grid-load-storage" into a continuous energy operation field and performs dynamic balance regulation based on a single composite operation. The technical concept of this invention is inspired by philosophy and combines modern power system and complex systems science. Summary of the Invention

[0004] The purpose of this invention is to provide a distributed energy internet control system and method based on dynamic field balance, to solve the problems of existing technologies where "source-grid-load-storage" operate independently, control lags, and are unable to cope with highly uncertain scenarios. Its core idea is to view the entire distributed energy network as a unified, continuous energy transformation field, and through a dedicated hardware controller, calculate the supply and demand balance of this field in real time, and generate unified control commands accordingly.

[0005] The system of this invention consists of three major functional modules, forming a closed-loop architecture of "perception-decision-execution".

[0006] Field Status Acquisition Module: This module is the system's perception layer, responsible for real-time acquisition of multi-dimensional operational data from various nodes within the energy internet. Logically, this module is divided into two sub-units.

[0007] Forward-driven acquisition unit (+1): Responsible for collecting data from all power generation nodes, including but not limited to the real-time output power of photovoltaic inverters, the real-time output power of wind power converters, and the power generation of natural gas turbines. Simultaneously, it receives power generation forecast data for the next 24 hours generated by a pre-trained time-series prediction model. This model comprehensively considers historical power generation data, weather forecast data (irradiance, wind speed, temperature, etc.), and time characteristics (time of day, weekday, holidays, etc.).

[0008] Negative Damping Acquisition Unit (-1): Responsible for collecting data from all energy storage nodes and energy load nodes. Energy storage node data includes the current state of charge (SOC), state of health (SOH), maximum charging and discharging power, and remaining capacity of each energy storage system. Load node data includes the real-time power consumption of each power-consuming unit (enterprise, building, charging pile, etc.) and the load forecast data for the next 24 hours generated by the prediction model. This forecast is also modeled based on historical load data, meteorological data, and time characteristics. The field status acquisition module aggregates the above distributed, multi-source heterogeneous data in real time through SCADA system, IoT sensors, and communication gateways, providing complete field information for the field balance controller.

[0009] Field Balance Controller (0): This module is the core decision-making layer of the system and the "central hub" of the entire energy circulation field. It is implemented by a dedicated, physically existing hardware computing device, rather than a software module on a general-purpose server, to ensure real-time performance and reliability. The controller incorporates a single composite computing hardware module consisting of exponential, logarithmic, and difference operation circuits. Its core functions are: It receives real-time data such as total power generation P_gen, total load power P_load, and network loss P_loss from the field status acquisition module.

[0010] Perform the preset single composite operation B = e^(|P_gen - P_load - P_loss| / P_base) - ln(Z_energy) to calculate the "zero deviation" B value of the current energy field from steady state.

[0011] Determine the state of B value and generate control commands based on preset control logic.

[0012] Field Execution Module: This module is the system's execution layer. It receives control commands from the field balance controller and parses and distributes them to specific execution devices. Command types include: power regulation commands for photovoltaic / wind power inverters (such as power-limited operation, maximum power point tracking, etc.), charging and discharging mode and power setting commands for energy storage converters, and flexible scheduling commands for adjustable loads (such as air conditioning systems, some industrial production lines, and electric vehicle charging stations).

[0013] The core of this invention lies in the single composite operation function in the field balance controller and the control logic based on its output value B, the principle of which originates from the simulation of the dynamic balance law of natural systems.

[0014] The single composite operation function is defined as: B = e^(|P_gen - P_load - P_loss| / P_base) -ln(Z_energy) The engineering significance of each parameter is as follows: P_gen (Total Power Driven Forward): The algebraic sum of the output power of all generating nodes at the current moment.

[0015] P_load (Total Energy Demand): The sum of the rigid power of all non-adjustable loads and the current operating power of adjustable loads at the current moment.

[0016] P_loss (total transmission loss power): The total power loss of network lines, transformers, and other equipment at the current moment.

[0017] P_base (baseline power constant): A constant preset according to the system size, used to make the power difference dimensionless. Its value is usually set to a certain proportion (such as 10%) of the total installed capacity of the system or the historical maximum load, so that the input value of the function is in a suitable range for calculation.

[0018] Z_energy (steady-state baseline value in the field): A preset constant greater than 1, defining the ideal state of the system when supply and demand are in equilibrium. Its physical meaning is equivalent to the lowest point of a "potential well", and the natural tendency of the system is to make the B value return to zero.

[0019] The engineering advantages of this function are: Nonlinear amplification: Through the exponential term e^x, when supply and demand are out of balance (B value deviates from zero), the function output will amplify the deviation signal at an exponential rate, making the system highly sensitive to small disturbances and have a fast response capability.

[0020] Dynamic damping: Through the natural logarithm term ln(Z_energy), an intrinsic "returning force" is provided to the system. No matter how large the exponential term is, the logarithmic term always pulls it back to zero. This simulates the relationship between damping and potential energy in a physical system, avoiding system oscillations caused by over-tuning.

[0021] The objective is clear: the ultimate driving force of the entire operation is to make B ≡ 0. When B equals 0, e^(|ΔP| / P_base) = ln(Z_energy), which means that the explicit positive driving force and the intrinsic regressive force reach a dynamic balance, and the energy field is in an optimal steady state. This steady state is not a rigid absolute balance, but a vibrant and robust dynamic balance.

[0022] Control logic: After the field balance controller calculates the B value, it generates instructions according to the following rules: If B ≈ 0: the system is determined to be in a steady state of supply and demand balance, and the field balance controller maintains the current operating strategy and does not issue new adjustment commands.

[0023] If B>0: It is determined that the forward drive is too strong and the power generation is greater than the sum of the load and network consumption. At this time, the controller generates a "converge forward drive" instruction. The specific measures are ordered in order of priority as follows: (1) Increase the energy storage charging power to convert the excess electrical energy into chemical energy, potential energy and other forms of storage; (2) If the energy storage is full or the charging power is insufficient to absorb all the excess power, the power limiting operation instruction is issued to some dispatchable photovoltaic and wind power inverters to actively reduce the power generation output.

[0024] If B < 0: it is determined that the positive drive is insufficient and the power generation cannot meet the needs of the load and network consumption. At this time, the controller generates a "compensate for insufficient positive drive" instruction. The specific measures are ordered by priority as follows: (1) Release the energy in the energy storage and prioritize the discharge of the energy storage system with the lowest cost and fastest response; (2) If the energy storage discharge still cannot meet the gap, start the backup gas power generation and other power sources that can be started quickly; (3) If the above measures are still insufficient, start flexible load dispatch and temporarily reduce some non-critical loads (such as air conditioning temperature setting adjustment, electric vehicle charging power temporary reduction, etc.) through the agreement signed with the user.

[0025] The control method of the present invention is a continuous closed-loop iterative process.

[0026] S1 (Field Construction): During system initialization, all power generation, energy storage, and load nodes within the management domain are digitally modeled and mapped to a unified energy operation field coordinate system.

[0027] S2 (Status Acquisition): The field status acquisition module collects the status data of the entire field in real time at a preset period (e.g., every second) and inputs it into the field balance controller.

[0028] S3 (deviation calculation): The single composite computing hardware module of the field balance controller completes the calculation of the B value within microseconds.

[0029] S4 (Command Generation and Execution): The controller generates the control command for this cycle based on the B value and sends it to the field execution module, which then schedules the underlying devices to complete the power adjustment.

[0030] S5 (Closed-Loop Iteration): In the next acquisition cycle, the system executes S2 again to sense the new field state after regulation, recalculates the B value, and generates new instructions. This cycle repeats continuously, forming a seamless closed loop of "real-time sensing - rapid decision-making - precise regulation - re-sensing," enabling the entire energy transportation field to be dynamically and robustly maintained near the "zero-state equilibrium" where the B value approaches zero. Detailed Implementation

[0031] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] Example: The target system for the energy internet application in an industrial park is a high-tech industrial park covering approximately 5 square kilometers. The park has a total installed capacity of 15 MWp (peak power) of rooftop distributed photovoltaic power stations, a total installed capacity of 8 MW of distributed wind turbine generators, and a total capacity of 20 MWh of lithium battery energy storage power stations. It also has the electricity load of approximately 50 enterprises and public buildings. The field status acquisition module collects real-time operating data from each node through a SCADA system and IoT sensors. The field balance controller calculates the supply-demand deviation value B in real-time based on a single composite operation and dynamically generates control commands based on the B value to schedule power generation, energy storage, and adjustable load resources. When a sudden drop in solar irradiance causes a decrease in photovoltaic output, making the B value less than zero, the controller generates and issues a command within 50 microseconds to switch the energy storage power station from standby to discharge mode. Simultaneously, it flexibly adjusts the set temperature of some air conditioning loads, allowing the system to recover to a near-steady-state of supply-demand balance within seconds, demonstrating excellent robustness and response speed. Beneficial effects

[0033] For the first time, the "dynamic field balance" principle was explicitly applied to the regulation of the distributed energy internet, realizing closed-loop regulation of the "source-grid-load-storage" four-element synergy.

[0034] The hardware response cycle of the field balance controller does not exceed 50 microseconds, which meets the response speed requirements of real-time control of the power system.

[0035] A single composite operation function B = e^(|ΔP| / P_base) - ln(Z_energy) is used as the core criterion for regulation. Its nonlinear characteristics enable the system to sensitively capture small imbalances, while the intrinsic logarithmic regression term can effectively prevent over-regulation and oscillation. The function has a simple structure, clear physical meaning, and is easy to implement in hardware and verify in the system.

[0036] The system architecture has good scalability and is suitable for distributed energy systems of various scales, from park level to city level. Attached Figure Description

[0037] Figure 1 Overall architecture diagram of the distributed energy internet control system.

[0038] Figure 2 Flowchart for supply and demand balance regulation in the energy transportation and chemical field.

Claims

1. A distributed energy internet regulation method based on dynamic field balance, characterized in that, Includes the following steps: S1: Energy field construction steps - The power generation units, energy storage units and electricity loads in the distributed energy network are uniformly mapped into a continuous energy operation field. Each node in the energy operation field contains its corresponding power generation capacity parameters, energy storage status parameters or load demand parameters. The power generation unit is defined as positive drive (+1), and the energy storage unit is defined as negative damping (-1). S2: Field Status Acquisition Steps — Real-time acquisition of the current output power and future preset generation forecast data of each power generation node, the remaining capacity and state of charge data of each energy storage node, and the current power consumption and future preset load forecast data of each load node. S3: Supply and demand deviation calculation step - Input the field state data into the field balance controller. The field balance controller calculates the supply and demand deviation value B of the energy transportation field through a single composite operation consisting of exponential operation, logarithmic operation and difference operation. When B approaches zero, the energy transportation field is in the optimal steady state of supply and demand balance. S4: Field control instruction generation step - When the supply-demand deviation value B deviates from the preset steady-state range, the field balance controller generates control instructions, which include output adjustment instructions for power generation nodes, charging and discharging switching instructions for energy storage nodes, and flexible scheduling instructions for adjustable load nodes. S5: Closed-loop iteration step - Repeat S2 to S4 until the supply-demand deviation value B returns to the preset steady-state range.

2. The method of claim 1, wherein, The single compound operation described in step S3 uses the following function: B = e^(|P_gen - P_load - P_loss| / P_base) - ln(Z_energy) where P_gen is the total output power of all current power generation nodes (corresponding to forward drive), P_load is the total power consumption of all current load nodes, P_loss is the current network transmission loss power, P_base is the preset base power constant, and Z_energy is the preset steady-state base value of the energy field (corresponding to the zero-state equilibrium point); when B approaches zero, the energy field is in the optimal steady state of supply and demand balance.

3. The method according to claim 2, characterized in that, The generation logic of the control command in step S4 is as follows: when B > 0, it indicates that the power generation (forward drive) is greater than the load demand. The field balance controller generates a command to increase the energy storage charging power or reduce the output of some power generation nodes to converge the forward drive. When B < 0, it indicates that the power generation (forward drive) is less than the load demand. The field balance controller generates a command to release energy storage or start the standby power generation capacity to compensate for the insufficient forward drive.

4. The method of claim 1, wherein, The power generation forecast data and load forecast data mentioned in step S2 are generated by a pre-trained time series forecast model. The time series forecast model is trained based on historical power generation data, weather forecast data, historical load data and time characteristics, and the forecast duration is not less than the next 24 hours.

5. The method of claim 1, wherein, The power generation units in the distributed energy network include at least one of photovoltaic power generation systems, wind power generation systems, natural gas distributed power generation systems, and biomass power generation systems; the energy storage units include at least one of lithium battery energy storage systems, all-solid-state battery energy storage systems, flow battery energy storage systems, and compressed air energy storage systems.

6. A distributed energy internet regulation system based on dynamic field balance, configured to perform the method of any one of claims 1 to 5, characterized in that, include: The field status acquisition module is used to collect the operating status data of each power generation node, energy storage node and load node in real time. The part that senses the power generation status is the positive drive acquisition unit (+1), and the part that senses the energy storage and load status is the negative damping acquisition unit (-1). The field balance controller (0) is connected to the field status acquisition module and has a built-in single composite computing hardware module consisting of an exponential operation circuit, a logarithmic operation circuit and a difference operation circuit. It is used to calculate the supply and demand deviation value B of the energy operation field and generate control instructions. The field execution module is connected to the field balance controller and is used to uniformly schedule each power generation node, energy storage node and adjustable load node according to the control instructions.

7. The system of claim 6, wherein, In the single composite computing hardware module of the field balance controller, the exponential computing circuit is implemented in hardware using the coordinate rotation digital calculation method (CORDIC), the logarithmic computing circuit is implemented in hardware using a lookup table (LUT), and the difference computing circuit is a 32-bit fixed-point computing circuit; the response period of the entire hardware module does not exceed 50 microseconds.

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