A county power grid comprehensive regulation system and method considering distributed power consumption and energy storage benefits

Abstract

The application discloses a county power grid comprehensive regulation system and method considering distributed power consumption and energy storage benefits, and relates to the technical field of power grid regulation. The system comprises a data collection unit, a control unit and an execution unit. The data collection unit is used for collecting power grid operation data, photovoltaic power generation, wind power generation and energy storage state in real time. The control unit is used for making decisions on energy storage operation strategy, distributed power generation operation strategy and county power grid branch line connection switch connection strategy according to the collected data. The execution unit is used for executing the decisions of the control unit and regulating power grid operation. Through real-time data collection, intelligent control decision and automatic execution, the energy storage operation strategy, the distributed power generation operation strategy and the county power grid branch line connection switch connection strategy are optimized, and the maximization of power grid economic benefits and the optimization of distributed power consumption are realized.

Description

TECHNICAL FIELD

[0001] The application relates to a county power grid comprehensive regulation system and method, in particular to a county power grid comprehensive regulation system and method considering distributed power consumption and energy storage benefits, and belongs to the technical field of power grid regulation. BACKGROUND

[0002] With the large-scale grid connection of new energy, the distribution network faces the trade-off problem between economy, stability and new energy consumption capacity. Single-objective optimization methods are difficult to meet these demands at the same time.

[0003] Currently, distributed power generation, especially photovoltaic and wind power generation, is continuously increasing its penetration in the power grid. However, the volatility and unpredictability of these distributed power generation resources pose challenges to power grid regulation. At the same time, as an important means of power grid regulation, the charging and discharging strategy of the energy storage system has a significant impact on the stability and economic benefits of the power grid. The application aims to optimize the operation of distributed power generation and energy storage systems through intelligent regulation, thereby improving the regulation efficiency and economy of the power grid. In contrast, the patent represented by application number CN202311499035.X does not consider economic benefits. SUMMARY

[0004] The purpose of the application is to provide a county power grid comprehensive regulation system and method that can improve the utilization rate of photovoltaic and wind power generation and consider distributed power consumption and energy storage benefits.

[0005] Technical solution: The application provides a county power grid comprehensive regulation system that considers distributed power consumption and energy storage benefits, comprising:

[0006] a data collection unit for collecting real-time power grid operation data, photovoltaic power generation capacity, wind power generation capacity and energy storage status;

[0007] a control unit for making decisions on energy storage operation strategy, distributed power generation operation strategy and county power grid branch tie-in switch connection strategy based on the collected data;

[0008] an execution unit for executing the decisions of the control unit to regulate power grid operation.

[0009] Further, the data collection unit comprises:

[0010] a photovoltaic power generation monitoring module for collecting photovoltaic power generation capacity data;

[0011] a wind power generation monitoring module for collecting wind power generation capacity data;

[0012] a power grid operation monitoring module for collecting power grid operation data;

[0013] An energy storage system monitoring module is configured to collect energy storage state data.

[0014] Further, the data collection unit further comprises a preset data collection frequency, and the collected data is preprocessed, and the preprocessed data is stored in a database, and the preprocessing comprises:

[0015] The initial collected data is filled with missing data; when the data at a certain time is missing, the missing data is filled with a weighted average of the data at the previous and next two times; when the data of a certain time period is missing, the missing data is filled with a Lagrange interpolation method;

[0016] The filled data is cleaned, and wavelet transform is adopted to remove abnormal values and noise;

[0017] Minimum-maximum normalization is adopted for standardization operation, and the cleaned data is converted into a unified dimension;

[0018] Features are extracted from the standardized data, and the features include a power selling price, a power grid load demand, a photovoltaic power output, a wind power output, a storage discharging power, a battery replacement cost, a battery design cycle life, a battery use cycle number, a time, and a capacity parameter.

[0019] Further, the control unit comprises:

[0020] An energy storage operation strategy module is configured to determine a charging and discharging plan of the energy storage system through a benefit optimization model, and optimize energy storage benefits;

[0021] A distributed power generation operation strategy module is configured to optimize photovoltaic and wind power outputs through an output optimization model, and reduce abandoned light and wind;

[0022] A county grid branch line contact switch connection strategy module is configured to adjust and optimize a model, and adjust a connection state of a branch line contact switch according to a power grid load and a power price.

[0023] Further, the energy storage operation strategy module adopts a linear programming method to solve the benefit optimization model, and the benefit optimization model is as follows:

[0024]

[0025] Wherein, T is a total time period number, P sell (t) is a power grid power selling price at time t, P charge (t) is a storage charging power at time t;

[0026] An energy balance constraint is set:

[0027] P load (t) = P grid (t) + P discharge(t)-P charge (t)-P pv (t)-P wind (t)

[0028] wherein P discharge (t) is the energy storage discharging power at time t, P grid (t) is the power obtained from the grid at time t, P load (t) is the total load power at time t, P pv (t) is the photovoltaic power, P wind (t) is the wind power;

[0029] Set capacity limit:

[0030] E min ≤ E(t) ≤ E max

[0031] wherein E(t) is the energy storage state at time t, E min and E max are the minimum and maximum energy of the energy storage system;

[0032] Set power constraints:

[0033]

[0034] wherein P charge_max and P discharge_max are the maximum charging power and the maximum discharging power, respectively;

[0035] Further, the output optimization model of the distributed power generation operation strategy module is as follows:

[0036] Set the objective function as follows:

[0037] P PV (t) + P Wind (t) + P Storage (t) ≈ L(t)

[0038] wherein L(t) is the grid load demand at time t, P PV (t) is the output power of photovoltaic power at time t, P Wind (t) is the output power of wind power at time t, P Storage (t) is the power of the energy storage system at time t;

[0039] Set the charging constraint condition:

[0040]

[0041] wherein SOC(t) is the charging state of the energy storage system at time t; D chargeis the charging signal of the energy storage system, when the value is 0, it represents not to execute, and when the value is 1, it represents to execute; ANFIS() is an adaptive neural network model, used to determine the priority of regulation;

[0042] The discharge constraint condition is set as:

[0043]

[0044] wherein, D discharge is the discharge signal of the energy storage system, when the value is 0, it represents not to execute, and when the value is 1, it represents to execute;

[0045] wherein, the discharge of the energy storage and the distributed power generation operation strategy module of the energy storage operation strategy module is based on the priority scheduling predicted by ANFIS:

[0046]

[0047] wherein, is the load demand of the branch i, is the total load demand; Priority represents the priority of regulation, Priority∈{1,2,3,4}, wherein level 1 represents not to regulate, 2 represents energy storage, 3 represents discharge, and 4 represents priority discharge; ANFIS learns according to the historical data of input values SOC(t), P sell (t) and , and the output value is Priority of the current time t.

[0048] Further, the fuzzy rule of the ANFIS is designed as follows:

[0049] when SOC(t) is biased unsaturated, P sell (t) is low price, is low demand, and Priority is evaluated as energy storage;

[0050] when SOC(t) is biased unsaturated, P sell (t) is low price, is high demand, and Priority is evaluated as discharge;

[0051] when SOC(t) is biased unsaturated, P sell (t) is high price, is low demand, and Priority is evaluated as not to participate in regulation;

[0052] when SOC(t) is biased unsaturated, P sell (t) is high price, is high demand, and Priority is evaluated as discharge;

[0053] when SOC(t) is biased saturated, Psell (t) is low, is low demand, Priority is rated as not to participate in regulation;

[0054] When SOC(t) is partial saturation, P sell (t) is low, is high demand, Priority is rated as discharging;

[0055] When SOC(t) is partial saturation, P sell (t) is high, is low demand, Priority is rated as not to participate in regulation;

[0056] When SOC(t) is partial saturation, P sell (t) is high, i is high demand, Priority is rated as priority discharging;

[0057] The fuzzy formula is described as follows:

[0058]

[0059] Further, the adjustment optimization model of the county area power grid branch line contact switch interconnection strategy module is as follows:

[0060] Set the objective function:

[0061]

[0062] Wherein, P sell (t) is the power grid selling price at time t, P discharge (t) is the energy storage discharging power at time t, is the operation cost of the contact switch j;

[0063] Set the constraint condition:

[0064]

[0065] Wherein, is the load demand of the branch line i, is the power transferred by the contact switch j, is the energy storage discharging power of the branch line i,

[0066] The energy storage discharging cost of the branch line i is as follows:

[0067]

[0068] Wherein, C replace is the battery maintenance cost, N cycleCycle life is designed for the battery, N used The cycle number is used for the battery.

[0069] Further, the execution unit comprises:

[0070] Energy storage system execution interface: responsible for receiving control instructions and controlling the charging and discharging operation of the energy storage device;

[0071] Distributed power generation execution interface: responsible for receiving control instructions and adjusting the output of photovoltaic and wind power generation system;

[0072] The tie switch control interface is responsible for receiving control instructions and executing the connection and disconnection of the branch tie switch.

[0073] Based on the same inventive concept, the present application also provides a county power grid comprehensive regulation and control system considering the comprehensive consideration of distributed power consumption and energy storage benefit according to any one of the above, comprising:

[0074] Step 1: Real-time collection of power grid operation data, photovoltaic power generation, wind power generation and energy storage state;

[0075] Step 2: Determine the charging and discharging plan of the energy storage system through the benefit optimization model, and optimize the energy storage benefit;

[0076] Step 3: Optimize the output of photovoltaic and wind power generation through the output optimization model to reduce the abandoned light and wind;

[0077] Step 4: Adjust the connection state of the branch tie switch according to the power grid load and electricity price through the adjustment optimization model;

[0078] Step 5: Execute the energy storage operation strategy, distributed power generation operation strategy and county power grid branch tie switch connection strategy output by steps 2-4 to regulate the power grid operation.

[0079] The present application has the following beneficial effects: compared with the prior art, the present application optimizes the distributed power generation operation strategy, reduces the abandoned light and wind phenomenon, and improves the utilization rate of photovoltaic and wind power generation; the energy storage system and the tie switch connection strategy are used, combined with the peak-valley electricity price difference, the economic benefit optimization is realized, and the power grid operation cost is reduced. By real-time adjustment of the charging and discharging of the energy storage system and the connection state of the tie switch, the power grid load is effectively balanced, the stability and reliability of the power grid are improved, and the application prospect and practical value are wide. BRIEF DESCRIPTION OF DRAWINGS

[0080] Figure 1 The system schematic diagram of the embodiment of the present application;

[0081] Figure 2 The ANFIS auxiliary layer design schematic diagram of the embodiment of the present application;

[0082] Figure 3 The ANFIS auxiliary layer reasoning schematic diagram of the embodiment of the application. DETAILED DESCRIPTION

[0083] In order to make the person skilled in the art better understand the scheme of the present application, the system and method of the present application will be combined with the drawings in the embodiments of the present application to show how the system and method of the present application are applied in the actual county power grid. The following embodiments are only for illustrating the technical concept and characteristics of the present application, the purpose is to enable the person skilled in the art to understand the content of the present application and to implement it, and cannot limit the protection scope of the present application. Any equivalent or modification made according to the spirit and principle of the present application should be covered within the protection scope of the present application.

[0084] As shown in the figure, the control system of the county power grid comprehensive control system considering distributed power consumption and energy storage benefit of the embodiment includes: Figure 1 A data collection unit for collecting power grid operation data, photovoltaic power generation, wind power generation and energy storage state in real time.

[0085] A control unit for making decisions on energy storage operation strategy, distributed power generation operation strategy and county power grid branch tie switch connection strategy according to the collected data.

[0086] An execution unit for executing the decisions of the control unit to control power grid operation.

[0087] Specifically, the data collection unit includes a photovoltaic power generation monitoring module, a wind power generation monitoring module, a power grid operation monitoring module and an energy storage system monitoring module, which are mainly used for collecting power grid operation data, photovoltaic power generation, wind power generation and energy storage state in real time.

[0088] The data collection unit also includes a preset data collection frequency, which is once per minute, and the collected data is preprocessed and stored in a database, the preprocessing includes:

[0089] Data filling: to ensure that the data is of the same time scale, the initial collected data is filled.

[0090] Data cleaning: after data filling, the operation of removing outliers and noise is performed to ensure the reliability of the data.

[0091] Data standardization: after data cleaning, different dimensional data is converted to a unified dimension for subsequent processing.

[0092] Feature extraction: after data standardization, features that have important influence on power grid control are extracted.

[0093]

[0094] ​In the data filling, the application has two processing schemes for the missing data:

[0095] (1) When the data of a certain time is missing, the application fills the missing data by weighted average of the data of the two adjacent times.

[0096] (2) When the data of a certain time period is missing, the application fills the missing data by Lagrange interpolation method.

[0097] In the data cleaning, the application removes noise components by wavelet transform.

[0098] In the data standardization, the application adopts minimum-maximum normalization for standardization.

[0099] In the feature extraction, the application extracts features such as electricity selling price, power grid load demand, photovoltaic power output, wind power output, energy storage discharge power, battery replacement cost, battery design cycle life, battery use cycle number, time, capacity and other parameters.

[0100] The preprocessed data is stored in a database for historical data analysis and long-term trend prediction.

[0101] In this system, ANFIS (Adaptive Neuro-Fuzzy Inference System) is integrated into the control unit for extracting long-term trends from preprocessed historical data.

[0102] The control unit is used to make decisions on energy storage operation strategy, distributed power generation operation strategy and county grid branch tie-in switch connection strategy according to the collected data and preset algorithm.

[0103] The energy storage operation strategy module is used to determine the charge and discharge plan of the energy storage system, and to realize the benefit optimization according to the parameters such as electricity selling price, capacity limit of energy storage battery, etc.

[0104] Before formulating the energy storage operation strategy, the characteristics of the energy storage system are analyzed in depth, including energy storage capacity, cycle life, battery replacement and maintenance cost and other key parameters. These parameters determine the potential role and limitations of the energy storage system in grid regulation.

[0105] The operation goals of the energy storage system in the grid mainly include:

[0106] Maximize economic benefits: use the difference between peak and valley electricity prices to reasonably arrange the charge and discharge plan of the energy storage system, and reduce the operation cost of the grid.

[0107] Support grid stability: discharge at the peak of grid load or renewable energy generation, and charge at the trough of grid load or renewable energy generation to balance the supply and demand of the grid.

[0108] A mathematical model of the energy storage operation strategy is constructed, including an objective function and constraint conditions. The objective function is the economic benefit of the energy storage system operation, and the constraint conditions include the charge and discharge limits of the energy storage system, grid demand, etc. The mathematical model is as follows:

[0109] 1. Objective function

[0110]

[0111] 2. Energy balance constraint

[0112] P load (t) = P grid (t) + P discharge (t) - P charge (t) - P pv (t) - P wind (t)

[0113] 3. Capacity limit

[0114] E min ≤ E(t) ≤ E max

[0115] 4. Power constraint

[0116]

[0117] where T is the total number of time periods, P sell (t) is the grid electricity selling price at time t, P discharge (t) is the energy storage discharge power at time t. P grid (t) is the power obtained from the grid at time t. P charge (t) is the energy storage charging power at time t, P charge_max and P discharge_max are the maximum charging and discharging powers, respectively. P load (t) is the total load power at time t, P pv (t) is the photovoltaic power generation, P wind (t) is the wind power generation, E(t) is the energy storage state (electricity) at time t, E min and E max are the minimum and maximum electricity of the energy storage system, respectively.

[0118] The objective function is to maximize the benefits of energy storage when the electricity price is the lowest, and to maximize the economic benefits. The constraint conditions are to ensure that the charging and discharging powers meet the grid load range, the battery capacity is within the capacity range, and the energy of the grid system is conserved.

[0119] The distributed power generation operation strategy module is used to optimize the output of photovoltaic and wind power generation and reduce the abandoned light and wind.

[0120] The operation objectives of the distributed power generation system mainly include:

[0121] Maximize the absorption of photovoltaic and wind power by the grid, reducing the phenomenon of abandoned light and wind.

[0122] Optimize economic benefits: maximize the economic benefits of distributed power generation under the premise of meeting the demand of the grid.

[0123] Grid support: adjust the output of distributed power generation to provide necessary support for the grid during peak or trough of grid load.

[0124] A mathematical model for constructing the operation strategy of distributed power generation is established, including the objective function and constraint conditions. The objective function is the economic benefit of distributed power generation, and the constraint conditions include the operation limit of power generation equipment, grid demand, etc. The mathematical model is as follows: (1) Objective function

[0125] P PV (t)+P Wind (t)+P Storage (t)≈L(t)

[0126] Where L(t) is the grid load demand at time t. P PV (t) is the output power of photovoltaic power generation at time t. P Wind (t) is the output power of wind power generation at time t;

[0127] (2) Charging constraint condition

[0128]

[0129] Where SOC(t) is the state of charge of the energy storage system at time t; D charge is the charging signal of the energy storage system, with a value of 0 representing not to execute and a value of 1 representing to execute; ANFIS() is an adaptive neural network model for determining the control priority.

[0130] (3) Discharge constraint condition

[0131]

[0132] Where D discharge is the discharge signal of the energy storage system, with a value of 0 representing not to execute and a value of 1 representing to execute.

[0133] In the embodiment, the main goal of the design of the distributed power generation operation strategy is to achieve dynamic balance of the load in the power grid system, i.e., the target function. The charging constraint condition refers to the case that when the output power of the wind power generation and the output power of the photovoltaic power generation are greater than the load minus the energy storage amount, and the battery is in a state of partial unfullness, the energy storage operation can be performed; the discharging constraint condition refers to the case that the battery has reached a state of partial saturation, and the discharging operation can be performed.

[0134] The energy storage strategy for constructing the energy storage operation strategy module and the power generation strategy for constructing the distributed power generation operation strategy module are both based on the priority scheduling strategy of the ANFIS prediction

[0135]

[0136] wherein, is the load demand of the branch i, is the total load demand; Priority represents the priority of regulation, Priority∈{1,2,3,4}, wherein level 1 represents no regulation, level 2 represents energy storage, level 3 represents discharging, and level 4 represents priority discharging; the ANFIS learns according to the historical data of the input values SOC(t), P sell (t) and and the output value is the Priority of the current time t;

[0137] In the embodiment, the strategy implementation of the priority scheduling of the ANFIS prediction all needs to meet the corresponding constraint condition to be implemented. The prediction result of charging needs to meet the constraint condition of the energy storage operation strategy module, and the prediction result of discharging needs to meet the constraint condition of the distributed power generation operation strategy module.

[0138] In the embodiment, the ANFIS adaptive neural network is used as the priority of decision regulation, and the three of SOC(t), P sell (t) and are used as the input to evaluate the regulation priority, and the levels are as follows:

[0139] (1) not participating in regulation.

[0140] (2) energy storage.

[0141] (3) discharging.

[0142] (4) priority discharging.

[0143] In the embodiment, the priority of (1) is represented by ‘1’, the priority of (2) is represented by ‘2’, the priority of (3) is represented by ‘3’, and the priority of (4) is represented by ‘4’.

[0144] The not participating in regulation of (1) refers to that no matter charging or discharging, the resulting economic benefit is not high, or the resulting result does not meet the load demand. The priority discharging of case (4) refers to the case of optimal economic benefit and most meeting the load demand.

[0145] In the embodiment, the fuzzy rules of the ANFIS are designed as follows:

[0146] 1. When SOC(t) is partial unsaturation, P sell (t) is low price, is low demand, and the Priority is rated as energy storage;

[0147] 2. When SOC(t) is partial unsaturation, P sell (t) is low price, is high demand, and the Priority is rated as discharging;

[0148] 3. When SOC(t) is partial unsaturation, P sell (t) is high price, is low demand, and the Priority is rated as not participating in regulation;

[0149] 4. When SOC(t) is partial unsaturation, P sell (t) is high price, is high demand, and the Priority is rated as discharging;

[0150] 5. When SOC(t) is partial saturation, P sell (t) is low price, is low demand, and the Priority is rated as not participating in regulation;

[0151] 6. When SOC(t) is partial saturation, P sell (t) is low price, is high demand, and the Priority is rated as discharging;

[0152] 7. When SOC(t) is partial saturation, P sell (t) is high price, is low demand, and the Priority is rated as not participating in regulation;

[0153] 8. When SOC(t) is partial saturation, P sell (t) is high price, is high demand, and the Priority is rated as priority discharging;

[0154] As shown in the table, the fuzzy formula is described as follows: Figure 3

[0155] ​

[0156] The design diagram is as shown in Figure 2 .

[0157] The county grid branch line contact switch connection strategy module is used to adjust the connection state of the branch line contact switch according to the grid load and peak-valley electricity price.

[0158] The contact switch plays a key role in the grid, and its main goal is to optimize the power flow between branches according to the real-time load and price difference of the grid, in order to minimize the cost and maximize the stability of the grid.

[0159] A mathematical model for constructing the contact switch connection strategy is established, including the objective function and the constraint conditions. The objective function is the total cost of the grid operation, and the constraint conditions include branch capacity limit, grid stability requirement, etc. The mathematical model is as follows:

[0160] 1. Objective function

[0161]

[0162] Where P sell (t) is the grid electricity selling price at time t, P discharge (t) is the energy storage discharge power at time t, is the operating cost of contact switch j;

[0163] 2. Constraint conditions

[0164]

[0165] Where, is the load demand of branch i, is the power transferred by contact switch j, is the energy storage discharge power of branch i,

[0166] 3. Energy storage discharge cost calculation method of branch i

[0167]

[0168] Where C replace is the battery maintenance cost, N cycle is the designed cycle life of the battery, and N used is the number of cycles used by the battery.

[0169] Where the contact switch connection strategy is formulated to maximize the economic benefits of electricity sales, i.e. the maximum value of the selling price plus the minimum cost of adjusting the connection switch, which is the maximum economic benefit of electricity sales. The constraint condition is set to avoid overload of the connected branch load, so the total demand of the load is required to be greater than the energy storage and transferred power of the connected branch.

[0170] Wherein, wherein the contact switch interconnection strategy is judged by the decision priority in the distributed power generation operation strategy, the branch line with high priority is first decided to contact switch interconnection strategy, the contact switch satisfying the constraint condition is first judged, and the final result is locked by the target function, and the minimum value of the contact switch satisfying the condition is the result of the decision, and the corresponding contact switch is closed to achieve the effect of branch line interconnection, and the power system is reduced.

[0171] In this embodiment, the interconnection state of the contact switch is dynamically adjusted according to the real-time changes of the grid load and the fluctuations of the electricity price. The automation of the interconnection state adjustment is realized. Before actual application, different grid load and electricity price scenarios are simulated to verify the effectiveness of the contact switch interconnection strategy. The performance of the strategy in reducing the operation cost of the grid and improving the stability of the grid is evaluated. The optimized contact switch interconnection strategy is applied to the actual grid. The interconnection state and power flow of the contact switch are monitored to ensure that it operates according to the established strategy and adjusts in time to respond to changes in the grid state. Based on the actual operation data and monitoring feedback, the contact switch interconnection strategy is continuously optimized and iterated. The ANFIS adaptive neural network is used to assist decision-making, enabling the system to adapt to changes in grid operation and improving the accuracy and efficiency of decision-making.

[0172] The execution unit is used to execute the decision of the control unit and regulate the operation of the grid.

[0173] The system execution unit is composed of multiple subsystems, mainly including:

[0174] The energy storage system execution interface is responsible for receiving control instructions and controlling the charge and discharge operations of the energy storage device.

[0175] The distributed power generation execution interface is responsible for receiving control instructions and adjusting the output of photovoltaic and wind power generation systems.

[0176] The contact switch control interface is responsible for receiving control instructions and executing the interconnection and disconnection of branch contact switches.

[0177] The execution unit has the following steps:

[0178] S301: Control instruction analysis, the execution unit first analyzes the instructions from the control unit, and clearly defines the operation requirements of each device, including charge and discharge power, power generation output adjustment and contact switch state, etc.

[0179] S302: Device control logic, develop detailed control logic for each device to ensure that the device can accurately perform operations according to control instructions. The control logic includes device startup, operation monitoring, exception handling and device stop steps.

[0180] S303: Perform process synchronization to coordinate the operation processes of different devices, ensuring that the control instructions of the energy storage system, distributed power generation system, and tie switch can be executed synchronously to achieve optimal regulation of the power grid.

[0181] S304: Real-time data feedback. During execution, real-time operation data of the devices are collected, including actual charging and discharging power, actual power generation, tie switch status, etc., and these data are fed back to the control unit.

[0182] S305: Abnormality detection and processing. The execution unit has abnormality detection capability, and when the device operation state is abnormal or inconsistent with the control instruction, it can timely identify and take measures.

[0183] S306: User interface interaction. A user interface is provided to enable the operator to monitor the running state of the execution unit, manually input instructions or intervene in the automatic control process. The user interface should have functions such as data visualization, operation log recording, and alarm prompt.

[0184] S307: Safety and reliability design. Ensure the safety and reliability of the execution unit, including device redundancy design, automatic fault switching, and system self-checking mechanisms to improve the fault tolerance and stability of the system.

[0185] S308: Execution effect evaluation. Regularly evaluate the operation effect of the execution unit, including device response time, control precision, and power grid regulation effect, to ensure that the system execution meets the expected target.

[0186] S309: Execution record and audit. Record all operation history of the execution unit, including control instructions, device response, and operation data, to facilitate operation audit and problem tracking.

[0187] Establish a comprehensive feedback system that can monitor and record key indicators of power grid operation in real time, including the charging and discharging status of the energy storage system, the actual output of distributed power generation, the real-time status of the tie switch, and the changes in power grid load. The collected data are analyzed and processed in depth to extract useful information.

[0188] Establish a real-time feedback loop to ensure that the system can quickly respond to changes in the state of the power grid. When the power grid operation data deviates from the expected value, the system automatically adjusts the control strategy to maintain the optimal operation state of the power grid. Regularly update the optimization strategy to adapt to changes in power grid operation and external environment. Use historical data and prediction models to predict long-term trends in the power grid.

[0189] Based on the same inventive concept, the embodiment also provides a regulation method for a county power grid comprehensive regulation system that comprehensively considers distributed power generation consumption and energy storage benefits according to any one of the above, comprising:

[0190] Step 1: Collect real-time power grid operation data, photovoltaic power generation, wind power generation and energy storage status;

[0191] Step 2: Determine the charging and discharging plan of the energy storage system through the benefit optimization model, and optimize the energy storage benefit;

[0192] Step 3: Optimize the output of photovoltaic and wind power generation through the output optimization model to reduce the abandoned light and wind;

[0193] Step 4: Adjust the connection state of the branch line tie-in switch according to the power grid load and electricity price through the adjustment optimization model;

[0194] Step 5: Execute the energy storage operation strategy, distributed power generation operation strategy and county power grid branch line tie-in switch connection strategy output by steps 2-4 to regulate the power grid operation.

[0195] The above embodiments are only for illustrating the technical concept and characteristics of the present application, and the purpose is to enable those skilled in the art to understand the content of the present application and implement it, and cannot limit the protection scope of the present application. Any equivalent transformation or modification made according to the spirit and essence of the present application shall be covered within the protection scope of the present application.

Claims

1. A county power grid comprehensive regulation system considering distributed power consumption and energy storage benefits in an integrated manner, characterized in that, The method comprises the following steps: A data collection unit is used to collect power grid operation data, photovoltaic power generation, wind power generation and energy storage state in real time; A control unit is used to make decisions on energy storage operation strategy, distributed power generation operation strategy and county power grid branch tie-in switch connection strategy according to the collected data; The control unit comprises: An energy storage operation strategy module is used to determine the charging and discharging plan of the energy storage system through a benefit optimization model to optimize the energy storage benefit; A distributed power generation operation strategy module is used to optimize the output of photovoltaic and wind power generation through an output optimization model to reduce abandoned light and wind; A county power grid branch tie-in switch connection strategy module is used to adjust the connection state of the branch tie-in switch according to the power grid load and electricity price through an adjustment optimization model; The decisions of the energy storage operation strategy module and the distributed power generation operation strategy module are both based on the priority scheduling of ANFIS prediction: wherein SOC(t) is the state of charge of the energy storage system at time t, P sell (t) is the grid selling price at time t, is the load demand of branch i, is the total load demand; Priority represents the priority of regulation, Priority ∈ {1, 2, 3, 4}, wherein level 1 represents no regulation, 2 represents energy storage, 3 represents discharging, and 4 represents priority discharging; ANFIS learns according to historical data of input values SOC(t), P sell (t) and , and outputs the value of Priority at the current time t. The fuzzy rule of ANFIS is designed as follows: When SOC(t) is under-saturated, P sell (t) is low, is low demand, Priority is rated for energy storage; When SOC(t) is under-saturated, P sell (t) is low, is high demand, Priority is rated as discharge; When SOC(t) is partial unsaturation, P sell (t) is high, is low demand, Priority rating does not participate in regulation; When SOC(t) is under-saturated, P sell (t) is high, is high demand, Priority is rated as discharge; When SOC(t) is partial saturation, P sell (t) is low, is low demand, Priority is rated as not participating in regulation; When SOC(t) is partial saturation, P sell (t) is low, is high demand, Priority is rated as discharge; When SOC(t) is partial saturation, P sell (t) is high, is low demand, Priority rating is not involved in regulation; When SOC(t) is partial saturation, P sell (t) is high price, is high demand, Priority is rated as priority discharge; The fuzzy formula is described as follows: An execution unit is used to execute the decisions of the control unit to regulate the power grid operation. 2.The county power grid integrated control system considering distributed power consumption and energy storage benefits according to claim 1, wherein, The data collection unit comprises: A photovoltaic power generation monitoring module is used to collect photovoltaic power generation data; A wind power generation monitoring module is used to collect wind power generation data; A power grid operation monitoring module is used to collect power grid operation data; An energy storage system monitoring module is used to collect energy storage state data. 3.The county power grid integrated control system considering distributed power consumption and energy storage benefits according to claim 1, wherein, The data collection unit further comprises a preset data acquisition frequency, and the collected data is preprocessed, and the preprocessed data is stored in a database, the preprocessing comprises: Missing data filling is performed on the initially collected data; when data is missing at a certain time, the missing data is filled by weighted average of data at two adjacent times; when data is missing in a certain time period, the missing data is filled by Lagrange interpolation method; The filled data is cleaned, and wavelet transform is adopted to remove abnormal values and noises; Minimum-maximum normalization is adopted for standardization operation, and the cleaned data is converted into a unified dimension; Features are extracted from the standardized data, the features comprising electricity selling price, power grid load demand, photovoltaic power output, wind power output, energy storage discharging power, battery replacement cost, battery design cycle life, battery use cycle number, time and capacity parameters. 4.The county power grid integrated control system considering distributed power consumption and energy storage benefits according to claim 1, wherein, The energy storage operation strategy module solves the benefit optimization model by using a linear programming method, and the benefit optimization model is as follows: Wherein, T is the total time period, P sell (t) is the grid selling price at time t, P charge (t) is the energy storage charging power at time t; An energy balance constraint is set: P load (t) = P grid (t) + P discharge (t) - P charge (t) - P pv (t) - P wind (t) P discharge (t) is the energy storage discharge power at time t, P grid (t) is the power obtained from the grid at time t, P load (t) is the total load power at time t, P pv (t) is the photovoltaic power, P wind (t) is the wind power A capacity limit is set: E min ≤ E(t) ≤ E max Wherein, E(t) is the energy storage state at time t, E min and E max are the minimum and maximum energy of the energy storage system; A power constraint is set: where P charge_max and P discharge_max are the maximum charge and discharge power, respectively. 5.The county power grid integrated control system considering distributed power consumption and energy storage benefits according to claim 1, wherein, The output optimization model of the distributed power generation operation strategy module is as follows: A target function is set as follows: P PV (t)+P Wind (t)+P Storage (t)≈L(t) Wherein, L(t) is the power grid load demand at time t, P PV (t) is the output power of photovoltaic power generation at time t, P Wind (t) is the output power of wind power generation at time t, P Storage (t) is the power of the energy storage system at time t; A charging constraint condition is set: Wherein, SOC(t) is the state of charge of the energy storage system at time t; D charge is the charging signal of the energy storage system, and when the value is 0, it represents not to execute, and when the value is 1, it represents to execute; ANFIS() is an adaptive neural network model, which is used to determine the priority of regulation. A discharging constraint condition is set: where D discharge is a discharge signal of the energy storage system, and a value of 0 represents non-execution, and a value of 1 represents execution. 6.The county power grid integrated control system considering distributed power consumption and energy storage benefits according to claim 1, wherein, The adjustment optimization model of the county power grid branch tie-in switch connection strategy module is as follows: A target function is set: where T is the total number of time periods, P sell (t) is the grid selling price at time t, P discharge (t) is the energy storage discharging power at time t, is the operating cost of tie switch j; A constraint condition is set: wherein, is the load demand for branch i, is the power transferred by tie switch j, is the energy storage discharge power for branch i, Energy storage discharge cost of spur i As follows: where C replace is the battery maintenance cost, N cycle is the battery design cycle life, N used is the number of battery usage cycles. 7.The county power grid integrated control system considering distributed power consumption and energy storage benefits according to claim 1, characterized in that, The execution unit comprises: An energy storage system execution interface is responsible for receiving control instructions and controlling the charging and discharging operation of the energy storage device; A distributed power generation execution interface is responsible for receiving control instructions and adjusting the output of the photovoltaic and wind power generation system; A tie-in switch control interface is responsible for receiving control instructions and executing the connection and disconnection of the branch tie-in switch.

8. A control method for a county-level power grid integrated control system that comprehensively considers distributed generation absorption and energy storage benefits as described in any one of claims 1-7, characterized in that, The method comprises the following steps: Step 1: Collect real-time power grid operation data, photovoltaic power generation, wind power generation, and energy storage status; Step 2: Determine the charging and discharging plan of the energy storage system through the benefit optimization model to optimize the benefit of the energy storage system; Step 3: Optimize the output of photovoltaic and wind power generation through the output optimization model to reduce the abandonment of light and wind; Step 4: Adjust the connection state of the branch line tie-in switch according to the power grid load and electricity price through the adjustment optimization model; Step 5: Execute the energy storage operation strategy, distributed power generation operation strategy, and county power grid branch line tie-in switch connection strategy output by steps 2-4 to regulate power grid operation.

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