Multi-agent-based industrial park user side energy storage control method under multiple working modes

Abstract

The invention discloses a multi-agent-based industrial park user side energy storage control method under multiple working modes, and relates to the technical field of user side energy storage control. Comprising the following steps: S1, acquiring real-time data and historical load data of a micro-grid; s2, judging whether the external power supply of the industrial park breaks down or not, if so, converting the energy storage system into a standby mode, and otherwise, entering S3; s3, judging whether the current time period is in a power utilization peak / valley or not, if yes, adjusting the power system through a peak regulation mode, and if not, entering S4; and S4, judging whether the micro-grid area control deviation is higher than a threshold value or not, and if so, adjusting the frequency through a frequency modulation mode. The method can effectively provide reference for the user side energy storage to participate in the auxiliary service market, and has good economic benefits and practical values.

Description

Technical Field

[0001] The present invention relates to the technical field of user-side energy storage control, and in particular to a multi-agent-based industrial park user-side energy storage control method in a multi-working mode. Background Art

[0002] With the transformation of the global energy structure and the rapid development of smart grids, renewable energy such as solar energy and wind energy are increasingly used in the power system due to their clean and renewable characteristics. However, the intermittent and uncertain nature of these renewable energy sources poses a huge challenge to the stable operation of the power grid. Especially in large-scale electricity consumption areas such as industrial parks, the power load fluctuates greatly, which puts higher requirements on the peak and frequency regulation capabilities of the power grid.

[0003] In order to meet the above challenges, user-side energy storage systems, as a key means to improve the flexibility and reliability of power systems, have received widespread attention and application in recent years. The energy storage system can not only store electrical energy when the grid load is low and release it at peak times, effectively alleviating the pressure on the grid, but also participate in the frequency modulation process of the grid and improve the frequency stability of the power system. In addition, the energy storage system can also serve as a backup power source to provide continuous power supply to important loads in the event of grid failure or emergency, thereby enhancing the resilience of the system. However, traditional energy storage controllers are often designed in a single way and are difficult to adapt to the complex and changeable grid operating environment and diverse user needs. Especially in the special scenario of industrial parks, due to the presence of a variety of electrical equipment and complex power load characteristics, traditional energy storage control strategies often find it difficult to achieve the optimal regulation effect. Summary of the invention

[0004] In order to solve at least one of the technical problems mentioned in the background technology, the purpose of the present invention is to provide a user-side energy storage control method for an industrial park based on multi-agent in a multi-working mode, which can effectively provide a reference for user-side energy storage to participate in the auxiliary service market and has good economic benefits and practical value.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A multi-agent-based industrial park user-side energy storage control method in a multi-working mode includes the following steps:

[0007] S1, obtain the real-time data and historical load data of the microgrid;

[0008] S2, determine whether the external power supply of the industrial park fails. If a failure occurs, the energy storage system will switch to the backup mode, otherwise enter S3;

[0009] S3, determine whether the current period is at a peak / valley point in electricity consumption. If so, adjust the power system through the peak load mode. Otherwise, enter S4;

[0010] S4, determining whether the microgrid area control deviation is higher than a threshold, and if so, adjusting the frequency through a frequency modulation mode.

[0011] In certain embodiments of the present invention, the real-time data includes: microgrid bus voltage, current, and frequency; wind turbine, photovoltaic, external power supply, and energy storage system voltage, current, and power; the delay time between AGC action and the power generation unit receiving the AGC command, and the average standard regulation rate of the AGC power generation unit within the unified frequency control area of ​​the power grid.

[0012] In certain embodiments of the present invention, the historical load data includes peak / valley power index, power generation unit regulation error, power generation unit regulation allowable error, and power generation unit measured rate.

[0013] In certain embodiments of the present invention, in the backup mode, the microgrid is powered only by renewable energy, and the energy storage system acts as a buffer module. When the renewable energy is insufficient, the energy storage system discharges, and when the load cannot absorb excess energy, the energy storage system charges.

[0014] In some embodiments of the present invention, the standby mode includes the following constraints:

[0015] P PV +P W +P B =P Load

[0016] SoC min ≤SoC≤SoC max

[0017]

[0018] Where: P PV is the photovoltaic power generation power, P W is the wind turbine power, P B is the power generated by the energy storage system, P Load For load requirements, SoC min The minimum state of charge set for the energy storage power supply, SoC max is the maximum state of charge set for the energy storage power supply, SoC is the state of charge, and W is the capacity of the energy storage system.

[0019] In certain embodiments of the present invention, the peak load shaving mode provides a load forecasting model and a peak / valley model for electricity consumption;

[0020] The load forecasting model is as follows:

[0021] P(t)=β 1 P(1)+β 2 P(2)+...+β n P(t-1)+u

[0022] Where: P(1)~P(t-1) are the data from the 1st to the t-1th time in the historical load data, P(t) is the predicted load of the energy storage power source, β 1 ~β n is the fitting coefficient, u is the interference term;

[0023] The peak / valley model of electricity consumption is as follows:

[0024] Solve the load demand fitting curve y:

[0025] y=P(t)

[0026] Set P h and P l They are the peak load demand threshold and the valley load demand threshold respectively. Based on these two thresholds, the peak period of the day before is obtained. and low periods

[0027] Based on the peak and off-peak periods determined a few days ago, the energy storage power source is set to discharge during the peak period and to charge during the off-peak period.

[0028] In some embodiments of the present invention, the frequency modulation mode works as follows:

[0029] When the mechanical power is unbalanced with the load, it is converted into the angular acceleration of the rotating generator mass, thereby changing the electrical frequency. The generator model is as follows:

[0030]

[0031] Where: D is the damping torque, P s is the synchronization coefficient, δ is the electric power angle, ω s Electrical angular velocity, H is the unit inertia constant;

[0032] The transient and dynamic characteristics of the grid frequency are shown below:

[0033]

[0034] Where: ΔP L is the load change, ΔΩ is the output frequency change, τ g and τ T are the governor and turbine time constants, R is the droop or speed regulation percentage;

[0035]

[0036] Where: Δω ss is the deviation from the steady-state frequency.

[0037] In certain embodiments of the present invention, the historical load data is sampled at one point every 5 minutes and is provided for more than half a year.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] To this end, the present invention proposes a multi-agent-based industrial park user-side energy storage controller in multiple working modes, taking into account three auxiliary service models: frequency regulation, peak regulation, and energy storage backup, and demonstrating the energy storage system's ability to regulate microgrids in three modes. The proposed method can effectively provide a reference for user-side energy storage to participate in the auxiliary service market, and has good economic benefits and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of energy storage control at the user side of an industrial park based on multi-agent in the multi-working mode of the present invention.

[0041] Figure 2 It is a comparison diagram of peak and valley values ​​before and after energy storage control according to an embodiment of the present invention.

[0042] Figure 3 It is a frequency comparison diagram before and after energy storage control according to an embodiment of the present invention.

[0043] Figure 4 This is a comparison diagram of the standby startup time before and after energy storage control according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] See also Figure 1 This embodiment provides a multi-agent-based industrial park user-side energy storage control method in a multi-working mode, comprising the following steps:

[0046] S1, obtain the real-time data and historical load data of the microgrid and determine the control indicators in advance.

[0047] Specifically, the real-time data include: microgrid bus voltage, current, and frequency; wind turbine, photovoltaic, external power supply, and energy storage system voltage, current, and power; the delay time between AGC action and the power generation unit receiving the AGC command, and the average standard regulation rate of the AGC power generation unit within the unified frequency control area of ​​the power grid.

[0048] The historical load data includes peak / valley power index, power generation unit regulation error, power generation unit regulation allowable error, and power generation unit measured rate. The historical load data is sampled at one point every 5 minutes and is provided for more than half a year.

[0049] S2, determine whether the external power supply of the industrial park fails. If a failure occurs, the energy storage system will switch to the backup mode, otherwise enter S3;

[0050] When the energy storage controller detects an external power failure signal from the microgrid, the energy storage system enters the backup mode. In the backup mode, the microgrid is powered only by new energy sources. The amount of wind energy depends on the size of the wind, and the amount of solar energy depends on the intensity of light. In this mode, the energy storage system acts as a buffer module. When the energy of new energy is insufficient, the energy storage system discharges. When the load cannot absorb the excess energy, the energy storage system charges to ensure the stable operation of the power system.

[0051] The backup mode includes the following constraints:

[0052] P PV +P W +P B =P Load

[0053] SoC min ≤SoC≤SoC max

[0054]

[0055] Where: P PV is the photovoltaic power generation power, P W is the wind turbine power, P B is the power generated by the energy storage system, P Load For load requirements, SoC min The minimum state of charge set for the energy storage power supply, SoC max is the maximum state of charge set for the energy storage power supply, SoC is the state of charge, and W is the capacity of the energy storage system.

[0056] S3, determine whether the current period is at a peak / valley point in electricity consumption. If so, adjust the power system through the peak load mode. Otherwise, enter S4;

[0057] The peak load regulation mode provides a load forecasting model and a peak / valley model for electricity consumption;

[0058] The load forecasting model is as follows:

[0059] P(t)=β 1 P(1)+β 2 P(2)+...+β n P(t-1)+u

[0060] Where: P(1)~P(t-1) are the data from the 1st to the t-1th time in the historical load data, P(t) is the predicted load of the energy storage power source, β 1 ~β n is the fitting coefficient, u is the interference term;

[0061] The peak / valley model of electricity consumption is as follows:

[0062] Solve the load demand fitting curve y:

[0063] y=P(t)

[0064] Set P h and P l They are the peak load demand threshold and the valley load demand threshold respectively. Based on these two thresholds, the peak period of the day before is obtained. and low periods

[0065] Peak shaving and valley filling: Based on the peak and valley periods determined a few days ago, the energy storage power source is set to discharge during the peak period to provide power for the load; during the valley period, the energy storage power source is charged to absorb the excess power in the system to ensure the stability of the power system.

[0066] S4, determining whether the microgrid area control deviation is higher than a threshold, and if so, adjusting the frequency through a frequency modulation mode.

[0067] The frequency modulation mode works as follows:

[0068] During an emergency or fault (such as loss of a generator), the grid frequency drops. When considering generators, the frequency changes when the mechanical power is not balanced with the load.

[0069] In this mode, when the mechanical power is unbalanced with the load, any imbalance is converted into angular acceleration of the rotating generator mass, thereby changing the electrical frequency. The generator model is as follows:

[0070]

[0071] Where: D is the damping torque, which can be determined by design data or test data, P sis the synchronization coefficient, δ is the electric power angle, ω s Electrical angular velocity, H is the unit inertia constant (depending on the size and type of synchronous motor);

[0072] The transient and dynamic characteristics of the grid frequency are shown below:

[0073]

[0074] Where: ΔP L is the load change, ΔΩ is the output frequency change, τ g and τ T are the governor and turbine time constants, R is the droop or speed regulation percentage;

[0075]

[0076] Where: Δω ss is the deviation from the steady-state frequency.

[0077] The following is a further introduction in conjunction with a specific implementation plan:

[0078] Data collection and real-time monitoring, based on a Ningbo industrial park, the main power demand in the park comes from production workshops, office areas and living areas. Wind turbines and solar photovoltaic panels are installed in the park, and user-side energy storage systems are also equipped to achieve energy optimization management and auxiliary services.

[0079] Furthermore, sensors and data acquisition systems are deployed to monitor the voltage, current, frequency and other power parameters of the microgrid in real time, as well as the output power of wind turbines and solar photovoltaic panels; historical load data is collected, including the operation plan of the production workshop, the power consumption pattern of the office area and the living area, etc., to support the construction of the load forecasting model. The specific data is shown in Table 1 below:

[0080] Table 1

[0081]

[0082] The data comparison test compares the data before and after the park uses the three modes of frequency regulation, peak regulation and energy storage backup in the industrial park user-side energy storage controller in summer, and analyzes the impact of the user-side energy storage controller on the industrial park power system in peak regulation, frequency regulation and energy storage backup modes, so as to verify its effectiveness and benefits.

[0083] Furthermore, if Figure 2 , Figure 3 , Figure 4As shown in the figure, by collecting and comparing data before and after implementation, the impact of frequency regulation, peak regulation and energy storage standby on the power system of the industrial park was deeply analyzed. In the implementation of the peak regulation mode, during the peak electricity consumption period in summer, the energy storage system effectively reduced the load pressure of the power grid by discharging, and the peak load also dropped from 150MW to 110MW. During the low-peak period at night, the energy storage system uses excess electricity for charging, which increases the energy utilization rate of the power grid and reduces energy waste. This peak-shaving and valley-filling strategy not only optimizes energy distribution, but also reduces dependence on the external power grid and improves self-sufficiency.

[0084] In frequency regulation mode, the fluctuation range of grid frequency was significantly reduced from ±0.05Hz before implementation to ±0.02Hz through the real-time response of the intelligent agent system. This shows that the energy storage system is able to quickly supplement or absorb power to counteract frequency deviations caused by the volatility of renewable energy, thereby improving the stability of the grid and the quality of power supply.

[0085] In energy storage backup mode, when the external power supply fails, the energy storage system can seamlessly switch to backup power and immediately provide the required power, avoiding production interruptions caused by power switching delays. This rapid response capability significantly improves the reliability of power supply in the industrial park and ensures the continuity of key production activities.

[0086] Through the implementation of this case, the microgrid in the industrial park has achieved efficient management and optimization of energy with the support of three auxiliary service models: frequency regulation, peak regulation, and energy storage backup. In the case of a sudden drop in wind speed, the rapid response of the frequency regulation mode ensures the stability of the power grid; during peak hours, the peak regulation mode effectively alleviates the load pressure of the power grid through the discharge of the energy storage system; at night, the energy storage backup mode ensures uninterrupted power supply to key facilities. In addition, the intelligent management of the system reduces manual intervention and improves the convenience and safety of operation.

[0087] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be considered exemplary and non-restrictive in all respects, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present invention.

Claims

1. A multi-agent-based industrial park user-side energy storage control method in a multi-working mode, characterized in that: The following steps are involved: S1, obtain the real-time data and historical load data of the microgrid; S2, determine whether the external power supply of the industrial park fails. If a failure occurs, the energy storage system will switch to the backup mode, otherwise enter S3; S3, determine whether the current period is at a peak / valley point in electricity consumption. If so, adjust the power system through the peak load mode. Otherwise, enter S4; S4, determining whether the microgrid area control deviation is higher than a threshold, and if so, adjusting the frequency through a frequency modulation mode.

2. The method for controlling energy storage at the user side of an industrial park based on multi-agent in a multi-working mode according to claim 1 is characterized in that: The real-time data include: microgrid bus voltage, current, frequency; wind turbine, photovoltaic, external power supply, energy storage system voltage, current, power; AGC action and power generation unit receiving AGC command delay time, the average standard regulation rate of AGC power generation unit within the unified frequency control area of ​​the power grid.

3. The method for controlling energy storage at the user side of an industrial park based on multi-agent in a multi-working mode according to claim 1 is characterized in that: The historical load data includes peak / valley power index, power generation unit regulation error, power generation unit regulation allowable error, and power generation unit measured rate.

4. The method for controlling energy storage at the user side of an industrial park based on multi-agent in a multi-working mode according to claim 1 is characterized in that: In the backup mode, the microgrid is powered only by renewable energy, and the energy storage system acts as a buffer module. When the renewable energy is insufficient, the energy storage system discharges, and when the load cannot absorb the excess energy, the energy storage system charges.

5. The method for controlling energy storage at the user side of an industrial park based on multi-agent in a multi-working mode according to claim 4 is characterized in that: The backup mode includes the following constraints: P PV +P W +P B =P Load SoC min ≤SoC≤SoC max Where: P PV is the photovoltaic power generation power, P W is the wind turbine power, P B is the power generated by the energy storage system, P Load For load requirements, SoC min The minimum state of charge set for the energy storage power supply, SoC max is the maximum state of charge set for the energy storage power supply, SoC is the state of charge, and W is the capacity of the energy storage system.

6. The method for controlling energy storage at the user side of an industrial park based on multi-agent in a multi-working mode according to claim 1 is characterized in that: The peak load regulation mode provides a load forecasting model and a peak / valley model for electricity consumption; The load forecasting model is as follows: P(t)=β1P(1)+β2P(2)+…+β n P(t-1)+u Where: P(1)~P(t-1) are the data from the 1st to the t-1th time in the historical load data, P(t) is the predicted load of the energy storage power source, β1~β n is the fitting coefficient, u is the interference term; The peak / valley model of electricity consumption is as follows: Solve the load demand fitting curve y: y=P(t) Set P h and P l They are the peak load demand threshold and the valley load demand threshold respectively. Based on these two thresholds, the peak period of the day before is obtained. and low periods Based on the peak and off-peak periods determined a few days ago, the energy storage power source is set to discharge during the peak period and to charge during the off-peak period.

7. The method for controlling energy storage at the user side of an industrial park based on multi-agent in a multi-working mode according to claim 1 is characterized in that: The frequency modulation mode works as follows: When the mechanical power is unbalanced with the load, it is converted into the angular acceleration of the rotating generator mass, thereby changing the electrical frequency. The generator model is as follows: Where: D is the damping torque, P s is the synchronization coefficient, δ is the electric power angle, ω s Electrical angular velocity, H is the unit inertia constant; The transient and dynamic characteristics of the grid frequency are shown below: Where: ΔP L is the load change, ΔΩ is the output frequency change, τ g and τ T are the governor and turbine time constants, R is the droop or speed regulation percentage; Where: Δω ss is the deviation from the steady-state frequency.

8. The method for controlling energy storage at the user side of an industrial park based on multi-agent in a multi-working mode according to claim 1 or 3, characterized in that: The historical load data is sampled at one point every 5 minutes and is provided for more than half a year.

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