Park micro-grid regulation and control system and method based on fusion terminal

Abstract

The invention discloses a park micro-grid regulation and control system and method based on a fusion terminal. The system comprises a micro-grid management and control system and the fusion terminal. The method comprises the steps that a fusion terminal collects and preprocesses multi-source data; the micro-grid management and control system receives the real-time operation state data uploaded by the fusion terminal, constructs a multi-target optimization model for optimization, and constructs a full-dimensional constraint condition; the micro-grid management and control system executes multi-target optimization scheduling to generate a regulation and control strategy; and the fusion terminal issues and executes the regulation and control instruction, and carries out closed-loop feedback adjustment at the same time. Multi-protocol data fusion is realized through the fusion terminal, a'source network load storage and charging 'island is broken, and global + real-time regulation and control are realized through cloud-edge collaboration; through multi-objective optimization scheduling, the operation cost is effectively reduced, the network loss is reduced, the new energy consumption rate is improved, the capability of coping with distributed energy fluctuation of the micro-grid is enhanced, and the operation reliability of the system is improved.

Description

Technical Field

[0001] This invention relates to the field of energy management, and in particular to a campus microgrid control system and method based on a converged terminal. Background Technology

[0002] The penetration rate of distributed resources, represented by distributed photovoltaics, energy storage, charging piles, and flexible loads, in industrial parks has increased significantly. As a crucial form of achieving efficient energy utilization and coordinated operation, the system complexity and control requirements of industrial park microgrids have increased significantly. Currently, most industrial park microgrids still adopt traditional decentralized energy management methods. Photovoltaic power generation units, energy storage systems, and various loads typically operate independently, lacking a unified coordination and control mechanism, resulting in obvious "source-grid-load-storage-charging" operational silos. Although some monitoring systems can collect and display the operating status of various devices, their control strategies mostly rely on preset fixed thresholds or human experience, lacking intelligent decision-making and dynamic optimization capabilities based on real-time data.

[0003] In actual operation, due to the intermittency of photovoltaic output, the time-varying nature of load demand, and the volatility of time-of-use pricing signals from the power grid, existing systems struggle to achieve precise matching and dynamic balance across multiple stages, including energy sources, grid, load, energy storage, and charging. Particularly when facing multi-energy coordinated dispatch, they often cannot quickly respond to changes in internal and external conditions, resulting in poor system operating economics, insufficient renewable energy absorption capacity, and limited overall energy efficiency improvement. Therefore, there is an urgent need for a microgrid control method and system based on a fusion terminal that can deeply integrate multi-dimensional data from energy sources, grid, load, and energy storage, possess intelligent optimization and analysis capabilities, and support the coordinated operation of multiple devices, in order to achieve optimized energy allocation and efficient utilization. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a campus microgrid control system and method based on a converged terminal.

[0005] Technical Solution: The microgrid control system based on a converged terminal described in this invention includes a microgrid management system and a converged terminal; the microgrid management system adopts a cloud computing architecture, deploys multi-objective optimization algorithms and big data processing modules, and communicates with the converged terminal via 4G or Ethernet; the converged terminal has a built-in edge computing module and connects to the grid interface, energy storage unit, photovoltaic unit, charging pile unit, and load unit.

[0006] The campus microgrid control method based on a converged terminal described in this invention includes the following steps:

[0007] (1) The fusion terminal collects and preprocesses multi-source data;

[0008] (2) The microgrid management and control system receives real-time operating status data uploaded by the fusion terminal, constructs a multi-objective optimization model for optimization, and constructs full-dimensional constraints;

[0009] (3) The microgrid management and control system executes multi-objective optimization scheduling to generate control strategies; the integrated terminal issues and executes control commands, and at the same time carries out closed-loop feedback adjustment.

[0010] Furthermore, step (2) of constructing a multi-objective optimization model for optimization includes:

[0011] (2.1) Minimize operating costs: including grid purchase and sale costs, energy storage charging and discharging operation and maintenance costs, adjustable load adjustment costs, and carbon emission costs;

[0012] (2.2) Maximize the renewable energy consumption rate: reduce solar curtailment and prioritize the consumption of solar power output;

[0013] (2.3) Minimize network loss: Optimize power transmission path and reduce line loss;

[0014] (2.4) Minimum carbon emissions: Combine the carbon emission factors of each energy unit to reduce the total carbon emissions of the system.

[0015] Furthermore, the multi-objective optimization model in step (2) is expressed as:

[0016]

[0017] Where T is the total number of time periods within a scheduling cycle; , These represent the electricity purchase price and the electricity sales price for time period t, respectively. , These represent the power purchased and the power sold during time period t, respectively. This refers to the battery degradation coefficient. The energy storage charging and discharging power during time period t; The duration of each time period.

[0018] Furthermore, the constraints satisfied by the optimization process in step (2) include power balance constraints, energy storage system operation constraints, grid interaction power constraints, and adjustable load and charging pile constraints.

[0019] Furthermore, the power balance constraint satisfies:

[0020]

[0021] in, Photovoltaic power generation capacity, For fixed load power, For transferable load power, This represents the total power of the charging station. This is due to network loss.

[0022] Furthermore, the operating constraints of the energy storage system satisfy:

[0023]

[0024]

[0025]

[0026] in, The state of charge of the energy storage during time period t. , For charging and discharging efficiency, This is the rated capacity.

[0027] Furthermore, the power grid interaction constraints satisfy:

[0028]

[0029]

[0030] in, , These represent the power purchased and the power sold during time period t, respectively. ,

[0031] These represent the maximum power purchased and the maximum power sold during time period t, respectively.

[0032] Furthermore, the multi-objective optimization scheduling generation control strategy in step (3) includes daily optimization scheduling, intraday rolling optimization, and real-time power balancing.

[0033] Furthermore, the intraday rolling optimization includes:

[0034] The fusion terminal receives actual measurements in 15-minute intervals. , With SOC(t) as a reference to the day-ahead plan and a future time period as a rolling window, real-time rolling optimization is performed. Its objective function is based on the multi-objective optimization model, with the addition of a penalty term for deviation from the day-ahead plan. The optimal power command for the current time period is obtained by solving the problem and is executed immediately, dynamically adjusting the energy storage charging and discharging power and the adjustable load status.

[0035] The real-time power balance includes:

[0036] For second-level fluctuations in photovoltaic output and load, the integrated terminal does not perform complex optimization calculations, but adopts rule-based adaptive droop control. When the tie-line power is detected... When the contract limit is about to be exceeded, the following rules are triggered:

[0037]

[0038] Where K is a proportional coefficient, which maintains the power of the tie line within a safe range by rapidly adjusting the energy storage output.

[0039] Beneficial Effects: Compared with the prior art, the present invention has the following significant advantages: The present invention achieves multi-protocol data fusion through a converged terminal, breaking down the "source-grid-load-storage-charging" silos, and enabling global and real-time control through cloud-edge collaboration; through multi-objective optimized scheduling, it effectively reduces operating costs, reduces network losses, and improves the renewable energy absorption rate, while enhancing the microgrid's ability to cope with distributed energy fluctuations and improving system operational reliability; the edge computing capability of the converged terminal enables local data processing and rapid response, reducing command response time from "seconds" to "milliseconds," and reducing the transmission delay of centralized control. Attached Figure Description

[0040] Figure 1 This is a system architecture diagram of the present invention;

[0041] Figure 2 This is a flowchart of the microgrid control method of the present invention. Detailed Implementation

[0042] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0043] like Figure 1As shown, the microgrid control system based on a converged terminal described in this invention includes a microgrid management system and a converged terminal. The converged terminal connects to the power grid, energy storage, photovoltaics, charging piles, and loads, enabling data acquisition, edge computing, and real-time control of multiple energy units. The microgrid management system performs global multi-objective optimization scheduling based on the processed data uploaded by the converged terminal. The microgrid management system adopts a cloud computing architecture, deploying multi-objective optimization algorithms and big data processing modules, and is responsible for global optimization scheduling. It communicates with the converged terminal via 4G / Ethernet (transmission latency < 50ms). The converged terminal, as a core device on the edge side, has a built-in edge computing module (supporting Kalman filtering, PID control, etc.). The system incorporates control algorithms, enabling data acquisition, preprocessing, state estimation, command execution, and closed-loop feedback to achieve millisecond-level local response. The grid interface module includes smart meters, transformers, and a communication unit, facilitating power and data interaction between the converged terminal and the external power grid. The energy storage unit, composed of lithium battery packs and PCS, receives charging and discharging commands from the converged terminal, regulating the storage and release of microgrid energy. The photovoltaic unit, consisting of a photovoltaic array, string inverters, and irradiance / temperature sensors, converts solar energy into electrical energy, outputs power to the microgrid, and uploads power output data to the converged terminal. The charging pile unit includes multiple fast-charging piles and a charging scheduling module, receiving charging power control commands from the converged terminal to achieve orderly charging. The load unit is divided into critical loads (uninterruptible) and adjustable loads (such as air conditioners, lighting, and water pumps), adjusting power demand according to converged terminal commands to participate in microgrid load balancing.

[0044] like Figure 2 As shown, the campus microgrid control method based on a converged terminal according to the present invention includes the following steps:

[0045] (I) Data Acquisition and Preprocessing

[0046] The integrated terminal collects operational data from the power grid (voltage, current, power), energy storage (SOC, charging and discharging power), photovoltaics (output, irradiance), charging piles (charging power, charging gun status), and loads (power demand, operating status) through sensors, smart meters, and communication interfaces; and preprocesses the data.

[0047] ① Outlier removal: Outliers are identified and removed using the 3σ criterion;

[0048] ② Missing value imputation: Filling in missing data using linear interpolation;

[0049] ③ Time alignment: Unify multi-source heterogeneous data to a 5-minute time granularity;

[0050] ④ Protocol Conversion: Convert heterogeneous protocol data such as Modbus and IEC 61850 into the JSON standard format.

[0051] (II) Establishment of the objective function

[0052] The microgrid management and control system receives real-time operating status data uploaded by the fusion terminal and constructs a multi-objective optimization model. The optimization objectives include:

[0053] (1) Minimize operating costs: including grid purchase and sale costs, energy storage charging and discharging operation and maintenance costs, adjustable load adjustment costs, and carbon emission costs;

[0054] (2) Maximize the renewable energy consumption rate: reduce solar curtailment and prioritize the consumption of solar power output;

[0055] (3) Minimize network loss: Optimize power transmission path and reduce line loss;

[0056] (4) Minimum carbon emissions: Combine the carbon emission factors of each energy unit (such as carbon emissions from grid purchases and zero carbon emissions from photovoltaics) to reduce the total carbon emissions of the system.

[0057] In light of the cost of user relationships, we select (1) a mathematical model with the objective of minimizing the total operating cost on a daily basis: (1)

[0058] Where T is the total number of time periods within a scheduling cycle; , These represent the electricity purchase price and the electricity sales price for time period t, respectively. , These represent the power purchased and the power sold during time period t, respectively. This refers to the battery degradation coefficient. The energy storage charging and discharging power during time period t; The duration of each time period.

[0059] (III) Construction of full-dimensional constraints

[0060] The optimization process must meet the following key constraints:

[0061] Attack rate balance constraint: (2)

[0062] in, Photovoltaic power generation capacity, For fixed load power, For transferable load power, This represents the total power of the charging station. This is due to network loss.

[0063] Energy storage system operating constraints:

[0064]

[0065]

[0066] (3)

[0067] in, The state of charge of the energy storage during time period t. , For charging and discharging efficiency, This is the rated capacity.

[0068] Power grid interaction constraints:

[0069]

[0070] (4)

[0071] (1) Adjustable load and charging pile constraints:

[0072] The main constraints are upper and lower power limits and total energy limits (such as the total daily charging amount must meet the demand).

[0073] (iv) Strategy Execution Step 1: Daily Optimization Scheduling (Executed on Cloud Platform)

[0074] Before the start of each day, the cloud platform uses accurate photovoltaic power generation forecast curves. and load forecast cancellation and known time-of-use electricity pricing information;

[0075] The optimization solver is invoked, and the baseline plan for 96 time points on the next day is solved with the objective function (1) as the core, including: , wait;

[0076] The scheduling plan was recently issued to the integrated terminals of each park.

[0077] (V) Strategy Execution Step Two: Intraday Rolling Optimization (Led by Converged Terminals)

[0078] The fusion terminal receives actual measurements in 15-minute intervals. , and SOC(t);

[0079] Using the current day's plan as a reference, and with a rolling window of 4-8 future periods, real-time rolling optimization is performed. The objective function is based on equation (1), with an added penalty term for deviations from the current day's plan:

[0080] (5)

[0081] Where N is the scrolling optimization window, This is the penalty coefficient.

[0082] The optimal power command for the current time period is obtained and executed immediately, dynamically adjusting the energy storage charging and discharging power, adjustable load status, etc.

[0083] (vi) Strategy Execution Step 3: Real-time Power Balancing (Fast Response from Converged Terminals)

[0084] For the second-level fluctuations in photovoltaic output and load, the integrated terminal does not perform complex optimization calculations, but instead adopts rule-based adaptive droop control.

[0085] When the power of the tie line is monitored When the contract limit is about to be exceeded, the following rules are triggered:

[0086] (6)

[0087] Where K is a proportionality coefficient. By rapidly adjusting the energy storage output, the power of the tie line is maintained within a safe range.

[0088] This method achieves comprehensive control from long-term economic planning to second-level real-time stability, effectively improving the operational economy, stability, and renewable energy absorption capacity of the park's microgrid.

Claims

1. A campus microgrid control system based on a converged terminal, characterized in that, It includes a microgrid management and control system and a converged terminal; the microgrid management and control system adopts a cloud computing architecture, deploys multi-objective optimization algorithms and big data processing modules, and communicates with the converged terminal via 4G or Ethernet; the converged terminal has a built-in edge computing module and connects to the grid interface, energy storage unit, photovoltaic unit, charging pile unit and load unit.

2. A method for controlling a campus microgrid based on a converged terminal, characterized in that, Includes the following steps: (1) The fusion terminal collects and preprocesses multi-source data; (2) The microgrid management and control system receives real-time operating status data uploaded by the fusion terminal, constructs a multi-objective optimization model for optimization, and constructs full-dimensional constraints; (3) The microgrid management and control system executes multi-objective optimization scheduling to generate control strategies; The integrated terminal issues and executes control commands, while simultaneously conducting closed-loop feedback adjustments.

3. The method for controlling a campus microgrid based on a fusion terminal according to claim 2, characterized in that, Step (2) involves constructing a multi-objective optimization model for optimization, including: (2.1) Minimize operating costs: including grid purchase and sale costs, energy storage charging and discharging operation and maintenance costs, adjustable load adjustment costs, and carbon emission costs; (2.2) Maximize the renewable energy consumption rate: reduce solar curtailment and prioritize the consumption of solar power output; (2.3) Minimize network loss: Optimize power transmission path and reduce line loss; (2.4) Minimum carbon emissions: Combine the carbon emission factors of each energy unit to reduce the total carbon emissions of the system.

4. The method for controlling a campus microgrid based on a fusion terminal according to claim 2, characterized in that, The multi-objective optimization model in step (2) is expressed as follows: Where T is the total number of time periods within a scheduling cycle; , These represent the electricity purchase price and the electricity sales price for time period t, respectively. , These represent the power purchased and the power sold during time period t, respectively. This refers to the battery degradation coefficient. The energy storage charging and discharging power during time period t; The duration of each time period.

5. The method for controlling a campus microgrid based on a fusion terminal according to claim 2, characterized in that, The constraints satisfied by the optimization process in step (2) include power balance constraints, energy storage system operation constraints, grid interaction power constraints, and adjustable load and charging pile constraints.

6. The method for controlling a campus microgrid based on a fusion terminal according to claim 5, characterized in that, The power balance constraint satisfies: in, Photovoltaic power generation capacity, For fixed load power, For transferable load power, This represents the total power of the charging station. This is due to network loss.

7. The method for controlling a campus microgrid based on a converged terminal according to claim 5, characterized in that, The energy storage system operates under the following constraints: in, The state of charge of the energy storage during time period t. , For charging and discharging efficiency, This is the rated capacity.

8. The method for controlling a campus microgrid based on a converged terminal according to claim 5, characterized in that, The power grid interaction constraints satisfy: in, , These represent the power purchased and the power sold during time period t, respectively. , These represent the maximum power purchased and the maximum power sold during time period t, respectively.

9. The method for controlling a campus microgrid based on a converged terminal according to claim 1, characterized in that, The multi-objective optimization scheduling generation control strategy in step (3) includes daily optimization scheduling, intraday rolling optimization, and real-time power balancing.

10. The method for controlling a campus microgrid based on a fusion terminal according to claim 9, characterized in that, The intraday rolling optimization includes: The fusion terminal receives actual measurements in 15-minute intervals. , With SOC(t) as a reference to the day-ahead plan and a future time period as a rolling window, real-time rolling optimization is performed. Its objective function is based on the multi-objective optimization model, with the addition of a penalty term for deviation from the day-ahead plan. The optimal power command for the current time period is obtained by solving the problem and is executed immediately, dynamically adjusting the energy storage charging and discharging power and the adjustable load status. The real-time power balance includes: For second-level fluctuations in photovoltaic output and load, the integrated terminal does not perform complex optimization calculations, but adopts rule-based adaptive droop control. When the tie-line power is detected... When the contract limit is about to be exceeded, the following rules are triggered: Where K is a proportional coefficient, which maintains the power of the tie line within a safe range by rapidly adjusting the energy storage output.

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