Dual-energy-storage collaborative energy distribution method

Abstract

The invention discloses a dual-energy-storage collaborative energy distribution method, which belongs to the technical field of energy management of an energy storage system, and comprises the following steps: 1) multi-dimensional data are collected in real time, the collection frequency is not lower than 10Hz, and the data comprise: the state of an energy storage unit comprises battery SOC, SOH, temperature T and power range; a super capacitor SOC, a temperature T and a power range; the external demand comprises a load power Pload and a renewable energy output Prenew; according to the method, the operation states, load requirements and power grid signals of the battery and the super capacitor are collected in real time, a collaborative distribution model considering energy storage life, economical efficiency and power supply stability is constructed, an energy distribution strategy is dynamically generated by adopting an improved optimization algorithm, and real-time adjustment is performed through closed-loop feedback. The problems that an existing method is fixed in strategy, ignores the energy storage health state and lags in response are solved, maximum utilization of double-energy-storage characteristic complementation is achieved, the service life of the system is prolonged, and economical efficiency and reliability are improved.

Description

Technical Field

[0001] This invention belongs to the field of energy management technology for energy storage systems, and particularly relates to a method for coordinated energy distribution of dual energy storage systems. Background Technology

[0002] Dual energy storage systems are widely used due to the complementary characteristics of batteries and supercapacitors, but existing methods have the following problems:

[0003] Fixed strategy: It adopts time-sharing or fixed power threshold allocation, which cannot be dynamically adjusted according to real-time status, resulting in low utilization rate;

[0004] Ignoring: Only considering the state of charge as a constraint, without taking into account the correlation between battery life and charge / discharge, which can easily shorten the lifespan;

[0005] Response lag: Relying on historical data for modeling results in a slow response to sudden load fluctuations, failing to meet the requirements of high reliability scenarios. Summary of the Invention

[0006] The purpose of this invention is to propose a dual-energy storage collaborative energy distribution method to solve the above-mentioned problems.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a dual-energy storage collaborative energy distribution method, which includes the following steps:

[0008] 1) Real-time acquisition of multi-dimensional data at a frequency of no less than 10Hz. The data includes: energy storage unit status, including battery SOC1, SOH1, temperature T1, and power range; supercapacitor SOC2, temperature T2, and power range; external demand, including load power P_load and renewable energy output P_renew; and grid signals, including real-time electricity price and voltage / frequency stability.

[0009] 2) Construct a multi-objective collaborative allocation model. Let the battery power allocation be P1 and the supercapacitor power allocation be P2, satisfying P1+P2=P_load-P_renew. The objective function is F=α・F1+β・F2+γ・F3, where F1 is the total energy storage loss, F2 is the economic cost, and F3 is the power supply stability; α, β, and γ are dynamic weights, and the constraints include SOC constraints, power constraints, and SOH constraints.

[0010] 3) An improved optimization algorithm is used to solve the model, and the optimal P1 and P2 are output as allocation instructions. The solution time is <50ms.

[0011] 4) Closed-loop feedback and adjustment: data is resampled every 500ms, and re-optimization is performed when the deviation exceeds 5%. In the event of any energy storage failure, its power is transferred to another unit and an alarm is triggered.

[0012] As a further description of the above technical solution:

[0013] The SOC constraint is: battery SOC1∈[20%,80%], supercapacitor SOC2∈[10%,90%]; the SOH constraint is: when battery SOH1 is low, the absolute value of P1 is reduced.

[0014] As a further description of the above technical solution:

[0015] The improved optimization algorithm adopts a "dynamic convergence mechanism," initially exploring the global optimum and later converging to the local optimum.

[0016] As a further description of the above technical solution:

[0017] The faults include excessive battery temperature, abnormal supercapacitor voltage, and power transfer requiring compliance with the power range of another unit.

[0018] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0019] This invention constructs a collaborative energy allocation model that balances energy storage lifespan, economy, and power supply stability by real-time acquisition of the operating status of batteries and supercapacitors, load demand, and grid signals. An improved optimization algorithm dynamically generates energy allocation strategies, which are then adjusted in real-time through closed-loop feedback. This invention solves the problems of fixed strategies, neglect of energy storage health status, and delayed response in existing methods, achieving maximum utilization of the complementary characteristics of dual energy storage systems, extending system lifespan, and improving economy and reliability. Attached Figure Description

[0020] Figure 1 This is a flowchart of a dual-energy storage collaborative energy distribution method. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In the description of the embodiments of this invention, it should be noted that the terms "upper," "inner," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product is in use. They are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0022] Example:

[0023] System Configuration:

[0024] Battery: 100kWh capacity, 50kW maximum discharge power, and a reasonable SOC range of 20%-80%;

[0025] Supercapacitor: Capacity 5kWh, maximum discharge power 100kW, SOC safe range 10%-90%;

[0026] Load: Power fluctuation 20-120kW; New energy: Photovoltaic output 0-20kW;

[0027] Electricity prices are higher during peak hours (daytime) and lower during off-peak hours (nighttime).

[0028] Example 1:

[0029] S01: Data Acquisition: Peak Time Acquisition: Battery SOC1=60%, SOH1=88%; Supercapacitor SOC2=50%; Load 100kW, Photovoltaic 15kW (requires dual energy storage discharge 85kW).

[0030] S02: Model construction: Increase the weight of β in the objective function F (prioritize economy), and constrain P1≤50kW, P2≤100kW;

[0031] S03: Optimized solution: The algorithm solves quickly and outputs P1=40kW (battery) and P2=45kW (supercapacitor), which meets the total requirements and has the best cost.

[0032] S04: Closed-loop adjustment: During operation, the load suddenly increases to 110kW, triggering re-optimization. New instructions P1=45kW, P2=53kW, to stabilize power supply;

[0033] Example 2:

[0034] S01: Data Acquisition: Off-peak hour data acquisition: Battery SOC1=30% (low), SOH1=85%; Supercapacitor SOC2=20%; Load 30kW, Photovoltaic output 0kW (no sunlight at night); Grid electricity price is the lowest off-peak price. At this time, renewable energy output is 0, the 30kW load needs grid power, and the dual energy storage can be charged from the grid (total charging power = grid power supply - load power; assuming the grid can provide 100kW, the available charging power is 70kW).

[0035] S02: Model Construction: The weight of β in the objective function F is significantly increased (lower charging cost during off-peak hours, prioritizing maximizing energy storage capacity), while the weight of α is decreased (allowing slightly higher losses for more charging). Constraints: Battery charging power P1 ≤ 50kW (maximum charging power is consistent with discharge power), supercapacitor charging power P2 ≤ 100kW; Battery SOC1 needs to be charged to within 80% (currently 30%, can charge 50kWh), and supercapacitor SOC2 needs to be charged to within 90% (currently 20%, can charge 4kWh).

[0036] S03: Optimization Solution: Algorithm output: P1=50kW (battery fully charged, utilizing off-peak hours for low prices and energy storage), P2=20kW (supercapacitor charged to 70%, avoiding overcharging), total charging power 70kW, meeting grid power requirements and minimizing cost;

[0037] S04: Closed-loop adjustment: After 1 hour, the load suddenly drops to 10kW (the grid's available charging power increases to 90kW), triggering re-optimization. New instructions: P1=50kW (maintain full charge), P2=40kW (supercapacitor continues charging to 90%). After 2 hours, the battery SOC1 rises to 80%, automatically stopping charging, with only the supercapacitor maintaining 20kW charging (stopping after SOC2=90%).

[0038] Example 3:

[0039] S01: Data Acquisition: Data collected during off-peak hours: Battery SOC1=50%, SOH1=65% (low), temperature T1=55℃ (exceeds the safety threshold of 50℃, judged as a fault); Supercapacitor SOC2=60%; Load 90kW, photovoltaic output 10kW (requires dual energy storage discharge of 80kW).

[0040] S02: Model Construction: Due to battery failure, SOH (State of Health) is triggered. Constraints and Fault Mechanism: Battery P1 is limited to ≤20kW (40% of the original 50kW, forced derating protection). The weight of γ in the objective function F is increased (prioritizing power supply stability), and the weight of α is increased (to avoid overload of the faulty battery). Constraints: P1≤20kW, P2≤100kW (the supercapacitor needs to bear the main power).

[0041] S03: Optimized solution: Algorithm output: P1=15kW (battery discharges at low power to reduce heat generation), P2=65kW (supercapacitors bear the main load, SOC2=60% can support this power), total discharge of 80kW meets the requirements, and the battery temperature does not continue to rise;

[0042] S04: Closed-loop adjustment: Feedback after 3 minutes: Battery temperature is still 55℃ (not dropped), triggering the fault redundancy mechanism. New instructions P1=0kW (disconnect the battery), P2=80kW (supercapacitor discharges at full power, SOC2=60%, which can be maintained for 3 minutes, enough to support until the backup power starts), and at the same time issue a battery high temperature alarm to ensure that the load power supply is not interrupted.

[0043] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A dual-energy storage collaborative energy distribution method, characterized in that: Includes the following steps: 1) Real-time acquisition of multi-dimensional data at a frequency of no less than 10Hz. The data includes: energy storage unit status, including battery SOC1, SOH1, temperature T1, and power range; supercapacitor SOC2, temperature T2, and power range; external demand, including load power P_load and renewable energy output P_renew; and grid signals, including real-time electricity price and voltage / frequency stability. 2) Construct a multi-objective collaborative allocation model. Let the battery power allocation be P1 and the supercapacitor power allocation be P2, satisfying P1+P2=P_load-P_renew. The objective function is F=α・F1+β・F2+γ・F3, where F1 is the total energy storage loss, F2 is the economic cost, and F3 is the power supply stability; α, β, and γ are dynamic weights, and the constraints include SOC constraints, power constraints, and SOH constraints. 3) The model is solved using an improved optimization algorithm, and the optimal P1 and P2 are output as allocation instructions. The solution time is <50ms. 4) Closed-loop feedback and adjustment: data is resampled every 500ms, and re-optimization is performed when the deviation exceeds 5%. In the event of any energy storage failure, its power is transferred to another unit and an alarm is triggered.

2. The dual-energy storage collaborative energy distribution method according to claim 1, characterized in that, In step 2), the SOC constraint is: battery SOC1∈[20%,80%], supercapacitor SOC2∈[10%,90%]; the SOH constraint is: when battery SOH1 is low, the absolute value of P1 is reduced.

3. The dual-energy storage collaborative energy distribution method according to claim 1, characterized in that, In step 3), the improved optimization algorithm adopts a "dynamic convergence mechanism", which initially explores the global optimum and then converges to the local optimum in the later stage.

4. The dual-energy storage collaborative energy distribution method according to claim 1, characterized in that, In step 4), the faults include excessive battery temperature, abnormal supercapacitor voltage, and power transfer needs to meet the power range of another unit.

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