Industrial and commercial energy storage cabinet remote control system based on Internet of Things data acquisition

Abstract

The invention relates to the technical field of remote control, and particularly discloses an industrial and commercial energy storage cabinet remote control system based on Internet of Things data acquisition, and the system comprises an Internet of Things data acquisition module which is used for collecting the operation state data, power grid data and environment data of an industrial and commercial energy storage cabinet in real time; the remote control center is in communication connection with the Internet of Things data acquisition module and is used for receiving the data uploaded by the Internet of Things data acquisition module and generating a dynamic charging and discharging control instruction based on a preset self-adaptive charging and discharging algorithm; the energy storage cabinet local control module is in communication connection with the remote control center and is used for receiving and executing the dynamic charging and discharging control instruction and feeding back an instruction execution result to the remote control center; and the communication module is used for realizing data transmission among the Internet of Things data acquisition module, the remote control center and the energy storage cabinet local control module. The peak-valley electricity price difference of the power grid can be accurately captured, and the charging and discharging time period and power of the energy storage cabinet can be reasonably planned.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote control, in particular to a remote control system for industrial and commercial energy storage cabinets based on Internet of Things data acquisition. BACKGROUND

[0002] Industrial and commercial energy storage cabinets are integrated energy storage devices mainly used in industrial and commercial fields, which can realize the storage, management and release of electric energy, help enterprises optimize electricity costs, improve power supply reliability, etc. They are usually designed in a modular way, integrating lithium iron phosphate batteries, battery management systems (BMS), power conversion systems (PCS), energy management systems (EMS), temperature control systems, fire protection systems, power distribution systems, etc. in an outdoor cabinet. They are mainly used to charge at low valley prices and discharge at high peak prices according to local price characteristics, thereby helping enterprises save electricity costs.

[0003] With the rapid development of the new energy industry, industrial and commercial energy storage cabinets, as an important energy storage and management device, are widely used. Currently, most existing industrial and commercial energy storage cabinets use fixed charging and discharging plans, i.e. charging and discharging according to preset time and power.

[0004] However, this fixed charging and discharging plan has obvious defects: on the one hand, it cannot be adjusted according to real-time grid prices, load changes and other external factors, resulting in low energy utilization efficiency and high operating costs; on the other hand, it cannot dynamically optimize the charging and discharging strategy according to the operating state of the energy storage cabinet (such as battery SOC value, temperature, etc.), which may affect the battery life and even pose a safety hazard. SUMMARY

[0005] The purpose of the present application is to provide a remote control system for industrial and commercial energy storage cabinets based on Internet of Things data acquisition to solve the above technical problems.

[0006] The purpose of the present application can be achieved by the following technical solutions: The remote control system for industrial and commercial energy storage cabinets based on Internet of Things data acquisition comprises the following steps: An Internet of Things data acquisition module for real-time acquisition of operating state data, grid data and environmental data of the industrial and commercial energy storage cabinet; A remote control center in communication connection with the Internet of Things data acquisition module for receiving data uploaded by the Internet of Things data acquisition module and generating dynamic charging and discharging control instructions based on a preset adaptive charging and discharging algorithm; An energy storage cabinet local control module in communication connection with the remote control center for receiving and executing the dynamic charging and discharging control instructions and feeding back instruction execution results to the remote control center; The communication module is used to realize data transmission between the IoT data acquisition module, the remote control center, and the local control module of the energy storage cabinet.

[0007] As a further aspect of the present invention: the remote control center includes: The data storage and analysis unit is used to store various types of collected data and perform trend analysis and anomaly detection on the data; The adaptive charge / discharge strategy generation unit generates a dynamic charge / discharge plan based on data analysis results and preset optimization objectives. The instruction issuing unit is used to issue the generated charging and discharging control instructions to the local control module of the energy storage cabinet.

[0008] As a further aspect of the present invention: the adaptive charge / discharge strategy generation unit specifically includes: A peak-valley arbitrage algorithm based on real-time electricity prices adjusts the charging and discharging periods according to the difference between peak and valley electricity prices in the power grid. Responding to the demand of the power grid load, discharging when the power grid load is too high and charging when the load is too low; Health maintenance based on battery status involves controlling the charge and discharge rates according to the battery's SOC value and temperature to avoid overcharging and over-discharging.

[0009] As a further aspect of the present invention: a peak-valley arbitrage algorithm based on real-time electricity prices, which adjusts the charging and discharging periods according to the difference between peak and valley electricity prices in the power grid, specifically includes: Real-time electricity price data is acquired periodically at preset time intervals, and a time series p(t) of electricity price for the whole day is established, where t represents the number of samplings; Calculate the average electricity price for the whole day and divide the time period into peak period, flat period and off-peak period according to the preset threshold; Calculate the current available capacity C of the energy storage cabinet. available =C rated ×(SOC current -SOC min ), C rated For the rated capacity of the energy storage cabinet, SOC current The current state of charge (SOC) min Minimum protected state of charge; Calculate the maximum rechargeable capacity C of the energy storage cabinet. charge_max =C rated ×(SOC max -SOC current SOC max To maximize the state of charge for protection; Construct an optimization function based on arbitrage throughout the day to calculate the maximum profit: Profit=Σ[D(t)×P discharge(t) x P charge (t) x P where D(t) is the discharging power, C(t) is the charging power, P discharge (t) is the electricity price of the discharging time period, P charge (t) is the electricity price of the charging time period.

[0010] As a further scheme of the present application: constructing the charging and discharging power constraint condition: 0 ≤ C(t) ≤ P charge_max ; 0 ≤ D(t) ≤ P discharge_max ; where P charge_max and P discharge_max respectively represent the maximum charging and discharging power.

[0011] As a further scheme of the present application: the calculation method of the maximum charging power P charge_max is: P charge_max = C charge_max / (Δt x η charge ); Δt represents the sampling interval; the calculation method of the maximum discharging power P discharge_max is: P discharge_max = C available (Δt x η discharge ).

[0012] As a further scheme of the present application: further including the energy conservation constraint: ∑[C(t) x Δt] - ∑[D(t) x Δt] = C rated x (SOC end - SOC start ); SOC start and SOC end respectively represent the state of charge before and after sampling.

[0013] As a further scheme of the present application: further including the state of charge constraint: SOC min ≤ SOC(t+1) ≤ SOC max SOC(t+1) = SOC(t) + [C(t) x η charge - D(t) / η discharge ] / C rated x 100%; where SOC(t) and SOC(t+1) respectively represent the state of charge at time t and time t+1, η chargeand η discharge respectively represent the charging efficiency and the discharging efficiency.

[0014] The application has the advantages that: the application can accurately capture the difference between peak and valley electricity prices of the power grid and reasonably plan the charging and discharging period and power of the energy storage cabinet through the peak-valley arbitrage algorithm based on real-time electricity prices. The energy storage cabinet is charged at a low cost in the low electricity price period and is discharged at a high value in the high electricity price period, so that the peak-valley arbitrage income of industrial and commercial enterprise users is greatly improved. BRIEF DESCRIPTION OF DRAWINGS

[0015] The application will be further described below with reference to the drawings.

[0016] Figure 1 is a process schematic diagram of the industrial and commercial energy storage cabinet remote control system based on the Internet of Things data acquisition of the application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0018] Please refer to Figure 1 The application is an industrial and commercial energy storage cabinet remote control system based on Internet of Things data acquisition, which is specifically divided into hardware configuration and software installation configuration, wherein: Hardware deployment: install various sensors (such as SOC sensors, temperature sensors, power sensors, etc.), data acquisition terminals and communication modules required for the Internet of Things data acquisition module on the industrial and commercial energy storage cabinet site; deploy servers, databases, monitoring terminals and other equipment in the remote monitoring center to build the hardware environment of the remote control center; install a local control module inside the energy storage cabinet to ensure that it is effectively connected with the charging and discharging circuit of the energy storage cabinet and the battery management system (BMS).

[0019] Software installation and configuration: install data storage and analysis software, adaptive charging and discharging strategy generation software, instruction issuing and receiving software, etc. on the server of the remote control center; install corresponding embedded software in the Internet of Things data acquisition terminal and the local control module of the energy storage cabinet; initialize and configure the parameters of each module, including sampling interval (Δt), SOC min , SOC max , initial value of maximum charging power (P charge_max ), initial value of maximum discharging power (P discharge_max ), electricity price threshold (used to divide peak and valley periods), abnormal data alarm threshold, etc.

[0020] After the above hardware and software configuration is completed, the system background starts to run: The Internet of Things data acquisition module collects real-time energy storage cabinet operation state data, power grid data and environmental data according to the preset sampling interval (Δt). After the collection is completed, the data is encrypted and transmitted to the remote control center through the communication module. After the data storage and analysis unit of the remote control center receives the data, it first checks the legality of the data (such as whether the data is within a reasonable range, whether there is data missing, etc.), stores the data that passes the check, and marks the data that fails the check, and sends a re-collection instruction to the collection module.

[0021] The data storage and analysis unit of the remote control center retrieves real-time electricity price data for a period of time (such as one day) from the database, organizes the electricity price data according to the preset time interval (Δt), and establishes a full-day electricity price time sequence p(t), where t represents the sampling number (such as Δt = 1 hour, 24 hours a day, t = 1, 2, …, 24). At the same time, the current SOC value (SOC current ), rated capacity (C rated ), charging efficiency (η charge ), discharging efficiency (η discharge ) and other data of the energy storage cabinet are retrieved.

[0022] Calculate the average electricity price for the whole day, compare it with the preset electricity price threshold, and divide the whole day time period into peak period, flat period and valley period. For example, set the period when the electricity price is higher than 120% of the average electricity price as the peak period, the period when the electricity price is between 80%-120% of the average electricity price as the flat period, and the period when the electricity price is lower than 80% of the average electricity price as the valley period.

[0023] Calculate the current available capacity C available of the energy storage cabinet: C available =C rated ×(SOC current -SOC min ), which represents the maximum energy that can be released by the energy storage cabinet at present. Calculate the maximum chargeable capacity C charge_max of the energy storage cabinet: C charge_max =C rated ×(SOC max -SOC current ), which represents the maximum energy that can be charged into the energy storage cabinet at present.

[0024] Charge and discharge power constraint: according to the hardware performance and safety requirements of the energy storage cabinet, set 0≤C(t)≤P charge_max , 0≤D(t)≤P discharge_maxwhere C(t) is the charging power at time t, D(t) is the discharging power at time t, P charge_max is the maximum charging power, P discharge_max is the maximum discharging power.

[0025] State of Charge constraint: To ensure the safety of the battery, the SOC min ≤ SOC(t+1) ≤ SOC max , where SOC(t+1) is the state of charge at time t+1, and SOC(t+1) = SOC(t) + [C(t) × η charge -D(t) / η discharge ] / C rated × 100%, SOC(t) is the state of charge at time t.

[0026] Maximum charging power P charge_max is calculated as: P charge_max =C charge_max / (Δt × η charge ), which takes into account the charging efficiency and sampling interval on the maximum charging power, ensuring that the charging amount within the sampling interval does not exceed the maximum chargeable amount.

[0027] Maximum discharging power P discharge_max is calculated as: P discharge_max =C available / (Δt × η discharge ), which takes into account the discharging efficiency and sampling interval on the maximum discharging power, ensuring that the discharging amount within the sampling interval does not exceed the current available capacity.

[0028] To ensure energy balance during charging and discharging, the energy conservation constraint condition is set as: Σ[C(t) × Δt] - Σ[D(t) × Δt] = C rated × (SOC end -SOC start ), where SOC start is the state of charge at the beginning of sampling, and SOC end is the state of charge at the end of sampling, which ensures that the input and output of energy during charging and discharging comply with the change rule of the state of charge of the energy storage cabinet.

[0029] To maximize the peak-valley arbitrage profit throughout the day, the optimization function is constructed as: Profit = Σ[D(t) × P discharge (t) - C(t) × P charge (t)], where P discharge (t) is the electricity price at time t (discharging period), and P charge(t) is the electricity price at time t (charging period). Under the premise of meeting the charging and discharging power constraints, state of charge constraints, and energy conservation constraints, the optimization function is solved by using optimization algorithms such as linear programming and genetic algorithm to obtain the optimal charging power C(t) and optimal discharging power D(t) at each time, thereby determining the charging and discharging strategy of the energy storage cabinet at different time periods throughout the day.

[0030] The instruction issuing unit of the remote control center converts the solved charging and discharging strategy into specific charging and discharging control instructions, which are issued to the local control module of the energy storage cabinet through the communication module. After receiving the instructions, the local control module of the energy storage cabinet controls the energy storage cabinet to charge according to the optimal charging power C(t) during the valley period and to discharge according to the optimal discharging power D(t) during the peak period. During the execution of the instructions, the local control module collects the actual operating data of the energy storage cabinet in real time and feeds it back to the remote control center as the execution result. The remote control center adjusts the charging and discharging strategy in real time according to the feedback result (such as re-solving the optimization function and updating the charging and discharging instructions when the actual electricity price deviates from the predicted electricity price), ensuring the maximization of peak-valley arbitrage income.

[0031] When the data storage and analysis unit of the remote control center detects abnormal data (such as battery temperature exceeding the preset threshold, SOC value being lower than SOC min or higher than SOC max , abnormal fluctuation of grid load, etc.), it immediately sends an abnormal signal to the adaptive charging and discharging strategy generation unit. After receiving the abnormal signal, the adaptive charging and discharging strategy generation unit suspends the current charging and discharging strategy and generates an emergency protection strategy (such as stopping charging, stopping discharging, reducing charging and discharging power, etc.) in priority. The instruction issuing unit issues the emergency protection instructions to the local control module of the energy storage cabinet, and the local control module immediately executes the instructions to ensure the safety of the energy storage cabinet and the grid. At the same time, the remote control center sends abnormal alarm information (such as SMS, APP push, sound and light alarm, etc.) to the management personnel, and the management personnel receives the alarm information and promptly investigates and handles the abnormal situation. After the abnormal situation is solved, the system resumes the normal charging and discharging strategy generation and execution process.

[0032] The above describes one embodiment of the present application in detail, but the content described is only the preferred embodiment of the present application and cannot be considered as limiting the scope of the implementation of the present application. Any equivalent changes and improvements made within the scope of the present application should still be included in the scope of the present application.

Claims

1. Industrial and commercial energy storage cabinet remote control system based on Internet of Things data acquisition, characterized in that, The method comprises the following steps: The Internet of Things data acquisition module is used to collect the operation state data, power grid data and environmental data of the industrial and commercial energy storage cabinet in real time. The remote control center is in communication connection with the Internet of Things data acquisition module, and is used to receive the data uploaded by the Internet of Things data acquisition module, and generate dynamic charging and discharging control instructions based on a preset adaptive charging and discharging algorithm. The energy storage cabinet local control module is in communication connection with the remote control center, and is used to receive and execute the dynamic charging and discharging control instructions, and feed back the instruction execution results to the remote control center. The communication module is used to realize the data transmission among the Internet of Things data acquisition module, the remote control center and the energy storage cabinet local control module.

2. The remote control system for industrial and commercial energy storage cabinets based on Internet of Things data collection according to claim 1, characterized in that, The remote control center comprises: The data storage and analysis unit is used to store the collected various data, and perform trend analysis and anomaly detection on the data. The adaptive charging and discharging strategy generation unit generates a dynamic charging and discharging plan based on the data analysis result and in combination with a preset optimization target. The instruction issuing unit is used to issue the generated charging and discharging control instructions to the energy storage cabinet local control module.

3. The remote control system for industrial and commercial energy storage cabinets based on Internet of Things data collection according to claim 1, characterized in that, The adaptive charging and discharging strategy generation unit specifically comprises: The peak-valley arbitrage algorithm based on real-time electricity price is used to adjust the charging and discharging period according to the difference between the peak and valley electricity prices of the power grid. The demand response based on the power grid load is used to discharge when the power grid load is too high, and charge when the power grid load is too low. The health maintenance based on the battery state is used to control the charging and discharging rate according to the battery SOC value and temperature, so as to avoid overcharging and overdischarging.

4. The remote control system for industrial and commercial energy storage cabinets based on Internet of Things data collection according to claim 3, characterized in that, The peak-valley arbitrage algorithm based on real-time electricity price specifically comprises: Real-time electricity price data is periodically acquired according to a preset time interval, and a whole-day electricity price time sequence p(t) is established, wherein t represents the sampling number. The whole-day average electricity price is calculated, and the whole-day time period is divided into a peak period, a flat period and a valley period according to a preset threshold. Calculate the current available capacity C of the energy storage cabinet available = C rated × (SOC current - SOC min ), C rated is the rated capacity of the energy storage cabinet, SOC current is the current state of charge, SOC min is the minimum protection state of charge; Calculating the maximum chargeable capacity C of the energy storage cabinet charge_max = C rated × (SOC max - SOC current ), SOC max is the maximum protection state of charge An optimization function is constructed based on the arbitrage in the whole day to calculate the maximum profit Profit: Profit =∑[D(t) x P discharge (t) - C(t) x P charge (t)]; where D(t) is the discharging power, C(t) is the charging power, P discharge (t) is the electricity price of the discharging time period, P charge (t) is the electricity price of the charging time period.

5. The remote control system for industrial and commercial energy storage cabinets based on Internet of Things data collection according to claim 4, characterized in that, A charging and discharging power constraint condition is constructed: 0 < C(t) < P charge_max ; 0 < D(t) < P discharge_max ; where P charge_max and P discharge_max represent the maximum charging and discharging power, respectively.

6. The remote control system for industrial and commercial energy storage cabinets based on Internet of Things data collection according to claim 5, characterized in that, Δt represents the sampling interval. SOC min ≤ SOC(t+1) ≤ SOC max SOC(t+1) = SOC(t) + [C(t) x η charge - D(t) / η discharge ] / C rated x 100%; where SOC(t) and SOC(t+1) represent the state of charge at time t and t+1, respectively, η charge and η discharge represent the charging efficiency and discharging efficiency, respectively.

7. The remote control system for industrial and commercial energy storage cabinets based on Internet of Things data collection according to claim 6, characterized in that, The maximum charging power P charge_max The calculation method is: P charge_max =C charge_max / (Δt×η charge ); An energy conservation constraint is also included: Maximum discharge power P discharge_max The calculation method is: P discharge_max =C available (Δt×η discharge )。 8. The remote control system for industrial and commercial energy storage cabinets based on Internet of Things data collection according to claim 6, characterized in that, ​ ∑ [C(t) x Δt] - ∑ [D(t) x Δt] = C rated x (SOC end - SOC start ); SOC start and SOC end respectively represent the state of charge before and after sampling.

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