Demand control method of energy storage system

Abstract

The invention discloses a demand control method of an energy storage system, which is characterized in that a setting module acquires data once every five minutes, the real-time performance of enterprise demand control is not met, an edge computing gateway needs to be erected in an enterprise, and the edge computing technology moves computing and data storage to the edge of a network, namely equipment or a terminal. Therefore, the state change of the energy storage equipment can be quickly responded, and real-time control is realized; therefore, the energy storage system can meet various requirements of the power system more flexibly, the charging and discharging strategy of the energy storage system can be adjusted according to the real-time requirements of the power system, and stable and safe operation of the power system is ensured. The calculation method provided by the invention is fine in granularity, has real-time performance, can cope with an electric charge calculation mode under two conditions, effectively prevents excess cost caused by overdemand, optimizes the electric charge cost, improves the stability and safety of a power grid, promotes the utilization of renewable energy sources, and improves the economical efficiency of an energy storage system.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage, and in particular relates to a demand control method for an energy storage system. Background Art

[0002] When managing large users, power companies often use maximum demand. The so-called maximum demand, as stipulated in my country's electricity price policy, refers to the average maximum load (kW) of large users every 15 minutes when using electricity. The power metering device (multi-function electronic energy meter) can automatically record the average maximum load each time it occurs. For those who calculate according to the maximum demand, the application capacity cannot be less than 40% of the capacity (kVA) of the receiving transformer. If it is less than 40%, it will be charged at 40%. If it exceeds 15% during use, the excess will be charged double.

[0003] Demand control is a key technology to ensure that the energy storage system does not incur additional electricity charges. In the case of electricity charges paid according to demand, energy storage systems without demand control functions still have to complete charging tasks even when the load is high, resulting in an increase in demand charges. In general, the control logic is: monitor the real-time power of the transformer. When the power on the low-voltage side of the transformer exceeds the demand, the energy storage system starts to discharge / increase the discharge power, reduce the transformer output, and ensure that the transformer power does not exceed the limit. The battery and PCS conditions need to be considered when the energy storage system is discharging.

[0004] Disadvantages of existing technology:

[0005] In order to save costs and maintain the function of demand control, many manufacturers in the industry use fixed-value demand control, setting the demand in the previous unit time as the boundary value of the energy storage system, without considering the situation where the demand decreases due to subsequent load changes. This extensive mode of demand control cannot avoid the increase of maximum demand due to the charging of the energy storage system, leading to disputes when settling accounts with users. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and propose a demand control method for an energy storage system with fine granularity and real-time performance, which can cope with electricity fee calculation methods in two situations, effectively prevent excess charges caused by over-demand, optimize electricity costs, improve grid stability and security, promote the use of renewable energy, and improve the economy of energy storage systems.

[0007] The technical solution of the present invention to solve the above technical problems is as follows:

[0008] A demand control method for an energy storage system. Because the module is set to collect data every five minutes, it cannot meet the real-time demand control requirements of the enterprise. An edge computing gateway needs to be set up in the enterprise. Since edge computing technology moves computing and data storage to the edge of the network, that is, the device or terminal, it can quickly respond to state changes of the energy storage device and achieve real-time control. This enables the energy storage system to respond to various demands of the power system more flexibly, and can also adjust the charging and discharging strategy of the energy storage system according to the real-time demands of the power system to ensure the stable and safe operation of the power system.

[0009] Preferably, the edge computing gateway processes data at the edge of the device or network to reduce the time it takes to transmit data to a central data center, thereby reducing latency and improving response speed and user experience.

[0010] Preferably, the edge computing of the edge computing gateway is used to reduce the transmission of large amounts of data in the network, reduce network congestion and bandwidth costs, while improving the overall efficiency of the network and reducing the network burden.

[0011] Preferably, the edge computing of the edge computing gateway is used to process and store data near the data source, reducing the transmission of data on the Internet, reducing the risk of data leakage, and improving data security and privacy protection.

[0012] Preferably, the edge computing of the edge computing gateway is used to realize real-time data processing and analysis, and provide real-time feedback and intelligent decision support for IoT devices and applications.

[0013] Preferably, the edge computing of the edge computing gateway is used to reduce the processing burden of the data center, reduce energy consumption and operating costs, and at the same time, by performing data processing on edge devices, it can reduce the demand for high-performance processors and further reduce costs.

[0014] Preferably, the edge computing gateway communicates using MODBUS-TCP to ensure communication security.

[0015] Preferably, the control logic of the demand control method is as follows: All of the following conditions must be met: 1) A maximum demand is set, when the data processing unit calculates the high-voltage active power according to the collected current data, and subtracts it from the rate error power; if the high-voltage active power is greater than or equal to the maximum demand minus the rate error power, it is determined that the high-voltage active power is too high and may exceed the demand; 2) When the PCS is in a charging state, when all conditions are met, the energy storage charging power is reduced to 0; when the high-voltage active power is less than the maximum demand minus the rate error power, the power rises slowly to 100kwh;

[0016] After the platform issues the above steps for the first time, there is no need to issue them again. They are all executed by the edge computing gateway and the results are fed back.

[0017] Preferably, the rate error power = the enterprise's high voltage rate a*one scale 0.01*4 hours.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1. Optimize electricity costs:

[0020] Avoid extra electricity charges: When paying electricity charges based on demand, energy storage systems without demand control may continue to charge when the load is high, resulting in higher demand charges. For example, in Zhejiang, if a single 100kW energy storage cabinet is installed and demand control is not implemented, the basic electricity charge may increase by as much as 4,800 yuan (100kW×48 yuan / kW);

[0021] Peak shaving and valley filling strategy: Through demand control, the energy storage system can charge when the grid load is low and discharge when it is peak, thereby effectively reducing the user's maximum demand and reducing demand electricity charges. This strategy helps users save electricity costs and improve economic benefits;

[0022] 2. Improve grid stability and security:

[0023] Balancing the load on the power grid: The demand control of the energy storage system helps to balance the load fluctuations of the power grid and reduce the peak load pressure of the power grid. During the peak period of power demand, the energy storage system can release electricity to reduce the burden on the power grid; during the off-peak period, it can charge and store electricity to prepare for the peak period;

[0024] Improve power quality: Energy storage systems can quickly respond to changes in the power grid and stabilize grid voltage and frequency to improve power quality by adjusting charging and discharging power.

[0025] 3. Promoting the use of renewable energy:

[0026] Smoothing out fluctuations in renewable energy output: The output of renewable energy sources such as wind and solar energy is intermittent and volatile. Energy storage systems can smooth out these fluctuations through demand control, thereby improving the utilization rate and reliability of renewable energy.

[0027] Enhance grid flexibility: As the proportion of renewable energy increases, the grid's demand for flexibility is also increasing. As a "flexible load" of the grid, energy storage systems can enhance the flexibility and resilience of the grid through demand control to cope with the uncertainty of renewable energy output.

[0028] 4. Improve the economic efficiency of energy storage system:

[0029] Extend battery life: Through reasonable demand control strategies, the problem of shortened battery life caused by frequent charging and discharging of energy storage batteries can be avoided. For example, charging during low grid load and discharging during peak load can reduce the number and depth of battery charging and discharging, thereby extending the battery life;

[0030] Improve the efficiency of energy storage systems: Demand control can also optimize the charging and discharging strategies of energy storage systems and improve the overall efficiency of the system; for example, charging when electricity prices are low and discharging when electricity prices are high can achieve peak-valley arbitrage and improve the economic benefits of energy storage systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is an application architecture diagram of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical effect and technical solution of the embodiment of the present invention clearer, the technical solution in the embodiment of the present invention is clearly and completely described below in conjunction with the drawings in the embodiment of the present invention; it is obvious that the described embodiment is a part of the embodiment of the present invention. Based on the embodiment disclosed in the present invention, other embodiments obtained by ordinary technicians in this field without making creative work should all fall within the scope of protection of the present invention.

[0033] Example 1

[0034] like Figure 1 As shown, because the module is set to collect data every five minutes, it does not meet the real-time nature of the enterprise's demand control, and an edge management gateway needs to be set up in the enterprise. Since edge computing technology moves computing and data storage to the edge of the network, that is, the device or terminal, it can quickly respond to changes in the state of the energy storage device and achieve real-time control. This enables the energy storage system to respond more flexibly to various needs of the power system, such as instantaneous loads, power outages, etc. The charging and discharging strategies of the energy storage system can also be adjusted according to the real-time needs of the power system to ensure the stable and safe operation of the power system. This flexibility enables the energy storage system to better adapt to changing power demands and market environments.

[0035] The edge computing gateway has the following features:

[0036] Low latency: By performing data processing at the device or network edge, the time it takes for data to be transmitted to the central data center can be reduced, thereby reducing latency and improving response speed and user experience.

[0037] Reduce network burden: Edge computing reduces the transmission of large amounts of data in the network, reduces network congestion and bandwidth costs, and improves the overall efficiency of the network.

[0038] Data security and privacy protection: Edge computing can process and store data near the data source, reducing the transmission of data on the Internet, reducing the risk of data leakage, and improving data security and privacy protection.

[0039] Real-time analysis and intelligent decision-making: Edge computing enables real-time data processing and analysis, providing real-time feedback and intelligent decision-making support for IoT devices and applications.

[0040] Energy saving and cost-effectiveness: Edge computing can reduce the processing burden of data centers, reduce energy consumption and operating costs. At the same time, by processing data on edge devices, the demand for high-performance processors can be reduced, further reducing costs.

[0041] Edge computing is widely used in the fields of Internet of Things, industrial automation, intelligent transportation, retail, healthcare, and urban management. In these fields, edge computing brings more efficient and intelligent services to various industries by improving data processing efficiency, reducing latency, and protecting data security.

[0042] Use MODBUS-TCP communication to ensure communication security.

[0043] Example 2

[0044] The control logic is as follows: All of the following conditions must be met: 1. Set a maximum demand. When the data processing unit calculates the high-voltage active power based on the collected current data, it subtracts the high-voltage multiple a* scale 0.01*4 hours from the multiple error power. If the high-voltage active power is greater than or equal to the maximum demand minus the multiple error power, it is determined that the high-voltage active power is too high and may exceed the demand. 2. When the PCS is in charging state.

[0045] When all conditions are met, the energy storage charging power is reduced to 0;

[0046] When the high voltage active power is less than the maximum demand minus the multiplier error power, the power gradually rises to 100kwh;

[0047] After the platform issues the above steps for the first time, there is no need to issue them again. They are all executed by the edge computing gateway and the results are fed back.

Claims

1. A demand control method for an energy storage system, characterized in that: Because the module is set to collect data every five minutes, it cannot meet the real-time requirements of the enterprise's demand control. Therefore, an edge computing gateway needs to be set up in the enterprise. Edge computing technology moves computing and data storage to the edge of the network, that is, the device or terminal. It can quickly respond to changes in the state of the energy storage device and achieve real-time control. This enables the energy storage system to respond more flexibly to various needs of the power system, and can also adjust the charging and discharging strategies of the energy storage system according to the real-time needs of the power system, ensuring the stable and safe operation of the power system.

2. A demand control method for an energy storage system according to claim 1, characterized in that: The edge computing gateway processes data at the edge of a device or network to reduce the time it takes to transmit data to a central data center, thereby reducing latency and improving response speed and user experience.

3. The demand control method of an energy storage system according to claim 1, characterized in that: The edge computing of the edge computing gateway is used to reduce the transmission of large amounts of data in the network, reduce network congestion and bandwidth costs, while improving the overall efficiency of the network and reducing the network burden.

4. The demand control method of an energy storage system according to claim 1, characterized in that: The edge computing of the edge computing gateway is used to process and store data near the data source, reduce the transmission of data on the Internet, reduce the risk of data leakage, and improve data security and privacy protection.

5. The demand control method of an energy storage system according to claim 1, characterized in that: The edge computing of the edge computing gateway is used to realize real-time data processing and analysis, and provide real-time feedback and intelligent decision support for IoT devices and applications.

6. A demand control method for an energy storage system according to claim 1, characterized in that: The edge computing of the edge computing gateway is used to reduce the processing burden of the data center, reduce energy consumption and operating costs. At the same time, by processing data on edge devices, the demand for high-performance processors can be reduced, further reducing costs.

7. A demand control method for an energy storage system according to claim 1, characterized in that: The edge computing gateway uses MODBUS-TCP to communicate to ensure communication security.

8. The demand control method for an energy storage system according to claim 1, characterized in that: The control logic of the demand control method is as follows: All the following conditions must be met: 1) A maximum demand is set, when the data processing unit calculates the high-voltage active power based on the collected current data and subtracts it from the rate error power; if the high-voltage active power is greater than or equal to the maximum demand minus the rate error power, it is determined that the high-voltage active power is too high and may exceed the demand; 2) When the PCS is in the charging state, when all conditions are met, the energy storage charging power is reduced to 0; when the high-voltage active power is less than the maximum demand minus the rate error power, the power slowly rises to 100kwh; After the platform issues the above steps for the first time, there is no need to issue them again. They are all executed by the edge computing gateway and the results are fed back.

9. A demand control method for an energy storage system according to claim 8, characterized in that: The rate error power = the company's high voltage rate a*one scale 0.01*4 hours.

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