Micro-grid hybrid energy storage scheduling method and system

Abstract

The invention belongs to the field of micro-grids, and particularly relates to a micro-grid hybrid energy storage scheduling method, which comprises the following steps of: acquiring a historical load curve, renewable energy source prediction data and energy storage unit parameters, and determining a charging and discharging strategy of an energy storage unit by utilizing the historical load curve and the renewable energy source prediction data; an SOC safety interval is initially set, a multi-target weight coefficient is continuously optimized, and a filtering frequency band threshold value is obtained; mPC scheduling optimization is periodically carried out, a power instruction is issued in real time, and closed-loop control of an SOC safety interval, a multi-target weight coefficient and a filtering frequency band threshold value is formed; on the premise of hybrid energy storage of the micro-grid, the service life of an energy storage unit is prolonged and the optimal charging and discharging frequency is kept in the scheduling process, an optimal power instruction sequence is continuously output in a rolling optimization mode to assist scheduling, and scheduling control is carried out on sub-devices of the energy storage unit under assistance of SOH estimation. The problem that a single energy storage sub-device is overused is effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of microgrids, and particularly to a microgrid hybrid energy storage scheduling method and system. Background Art

[0002] A microgrid refers to a small power system composed of distributed power sources, loads, energy storage, power transformation and distribution, and control systems. It is an autonomous power system capable of self-control, protection, and management, with complete power generation, power distribution, and power consumption functions, and can effectively optimize the energy within the grid. A microgrid is a concept relative to a traditional large power grid. The traditional large power grid is mainly unidirectional interaction, where electric energy reaches users layer by layer from the power plant through transmission lines, while a microgrid is a bidirectional interaction mode that can generate electricity locally and balance locally to maximize the energy use efficiency. With the global energy crisis, increasing energy consumption, and the increase in new energy technologies, new energy power generation is becoming more widespread and gradually forming a new energy and power market. However, the energy density of new energy is generally low, and suitable locations are required for high-power power generation, so it belongs to an intermittent power source. The proposal of microgrid technology provides an important technical direction for the efficient utilization of these new energy powers.

[0003] The hybrid energy storage system in a microgrid usually includes different types of energy storage technologies, such as batteries, supercapacitors, flywheels, etc. Each energy storage unit has different characteristics. For example, a battery has a high energy density but limited charge and discharge cycles, while a supercapacitor has a high power density and a long cycle life. Therefore, the scheduling method needs to consider how to reasonably allocate the load to make each energy storage unit play its advantages and avoid overusing a certain type, especially the easily aged battery. Summary of the Invention

[0004] (I) Object of the Invention

[0005] To solve the technical problems in the background art, the present invention proposes a microgrid hybrid energy storage scheduling method, which has the characteristics of extending the service life of energy storage units and maintaining the optimal charge and discharge frequency.

[0006] (II) Technical Solution

[0007] To solve the above technical problems, the present invention provides a microgrid hybrid energy storage scheduling method, including the following steps:

[0008] Obtain the historical load curve, renewable energy prediction data, and energy storage unit parameters, and use the historical load curve and renewable energy prediction data to determine the charge and discharge strategy of the energy storage unit;

[0009] Initially set the SOC safety interval, continuously optimize the multi-objective weight coefficient, and obtain the filtering frequency band threshold;

[0010] Perform MPC scheduling optimization periodically, issue power commands in real time, and form a closed-loop control for the SOC safety range, multi-objective weight coefficients, and filter frequency band thresholds;

[0011] Estimate the SOH online through a Kalman filter, track the aging degree of the energy storage unit in real time, and thus optimize the energy storage scheduling strategy.

[0012] Preferably, the historical load curve relates to power demand, the renewable energy prediction data relates to power generation, and the energy storage parameters relate to capacity and lifespan. The net load calculation formula for the three associated and interacting is: P net (t) = P load (t) - P re (t);

[0013] Among them, P net (t): the net load at time t (unit: kW);

[0014] P load (t): the power of the historical load curve at time t (unit: kW);

[0015] P re (t): the predicted power of renewable energy at time t (unit: kW);

[0016] Among them, the net load represents the power that actually needs to be supplemented by the energy storage unit or the power grid;

[0017] When P net (t) > 0, the energy storage unit needs to discharge;

[0018] When P net (t) < 0, the energy storage unit can be charged.

[0019] Preferably, the energy storage charge and discharge power constraint formula is:

[0020]

[0021] P storage (t): the actual energy storage charge and discharge power at time t (unit: kW);

[0022] P rated : the rated power of the energy storage unit (unit: kW, from the energy storage parameters);

[0023] The energy storage power is limited by the rated value to avoid overcharging or over-discharging. The rated power P rated is a key parameter of the energy storage unit.

[0024] Preferably, the initial range of the SOC safety range is (20% - 80%);

[0025] The optimized weight coefficients are used for multi-objective optimization problems, including factors such as grid cost, energy storage unit degradation cost, and tracking error cost;

[0026] The filtering frequency band threshold is used for signal processing to filter out high-frequency noise or low-frequency interference.

[0027] Preferably, the MPC scheduling optimization is performed periodically, with a single period of at least 15 minutes. Based on the latest data and the prediction model, the power instruction for the future time period is generated. The MPC objective function is:

[0028]

[0029] N: Prediction horizon length (for example, N = 4 corresponds to 1 hour);

[0030] C grid (k): Grid electricity price at the k-th time period (yuan / kWh);

[0031] w 1 ,w 2 ,w 3 : Weight coefficients (need to satisfy w1 + w2 + w3 = 1);

[0032] β: Battery degradation coefficient (related to SOHR);

[0033] P ref (k): Reference power instruction (such as grid scheduling demand).

[0034] Preferably, the SOC dynamic model is:

[0035]

[0036] SOC(k): State of charge of the battery at the k-th time period (range: [0, 1]);

[0037] P(k): Charge and discharge power at the k-th time period (P > 0 for discharging, P < 0 for charging);

[0038] η: Charge and discharge efficiency (ηdis for discharging, ηch for charging);

[0039] Δt: Control period (15 minutes);

[0040] C nom : Nominal battery capacity (kWh);

[0041] SOH C : Health state (capacity degradation rate, range: [0, 1]).

[0042] Preferably, the constraint formula for the SOC safety interval is: SOC min ≤SOC(k)≤SOC max,

[0043] SOC min ,SOC max : Dynamic safety range [0.2, 0.8];

[0044] Through the optimization calculation of the MPC objective function, the SOC is always within [0.2, 0.8], the power command does not exceed the rated value of the converter, and the optimal power command sequence for the next 15 minutes is output.

[0045] Preferably, the Kalman filter is a recursive algorithm applicable to state estimation in dynamic systems, which processes noise and updates the estimated value in real time. For energy storage units, SOH is usually related to capacity attenuation or internal resistance increase. The changes in capacity and internal resistance can be used as state variables, while real-time data such as voltage, current, and temperature are used as observation inputs.

[0046] Preferably, the SOH estimation is integrated into the MPC scheduling, and the charge and discharge strategies of each energy storage unit are adjusted according to the SOH of each energy storage unit. The energy storage units include batteries, supercapacitors, and flywheels.

[0047] The present invention also provides a microgrid hybrid energy storage scheduling system, including:

[0048] Data acquisition module: Obtain historical load curves, renewable energy prediction data, and energy storage unit parameters, and use the historical load curves and renewable energy prediction data to determine the charge and discharge strategies of the energy storage units;

[0049] Initialization configuration module: Initially set the SOC safety range, continuously optimize the multi-objective weight coefficients, and obtain the filtering frequency band thresholds;

[0050] Rolling optimization module: Periodically perform MPC scheduling optimization, issue power commands in real time, and form a closed-loop control of the SOC safety range, multi-objective weight coefficients, and filtering frequency band thresholds;

[0051] Health monitoring module: Online estimate the SOH through the Kalman filter, and track the aging degree of the energy storage units in real time, so as to optimize the energy storage scheduling strategy.

[0052] The above technical solutions of the present invention have the following beneficial technical effects: Under the premise of microgrid hybrid energy storage, the service life of the energy storage units is extended and the optimal charge and discharge frequency is maintained during the scheduling process. The rolling optimization continuously outputs the optimal power command sequence to assist the scheduling. With the assistance of SOH estimation, the scheduling control of each sub-device of the energy storage unit is carried out, effectively solving the problem of overuse of a single energy storage sub-device. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the method of the present invention;

[0054] Figure 2 It is a schematic flow chart of the present invention;

[0055] Figure 3 It is a schematic structural diagram of the rolling optimization flow chart of the present invention. Specific embodiments

[0056] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In addition, in the following description, the descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.

[0057] As Figures 1 - 3 shown, a microgrid hybrid energy storage scheduling method proposed by the present invention includes the following steps:

[0058] Step S100: Obtain the historical load curve, renewable energy prediction data, and energy storage unit parameters, and use the historical load curve and renewable energy prediction data to determine the charge and discharge strategy of the energy storage unit;

[0059] Step S200: Initially set the SOC safety interval, continuously optimize the multi-objective weight coefficient, and obtain the filtering frequency band threshold;

[0060] Step S300: Periodically perform MPC scheduling optimization, and issue power commands in real time to form a closed-loop control of the SOC safety interval, multi-objective weight coefficient, and filtering frequency band threshold;

[0061] Step S400: Online estimate the SOH through a Kalman filter, and track the aging degree of the energy storage unit in real time, so as to optimize the energy storage scheduling strategy.

[0062] The historical load curve involves power demand, the renewable energy prediction data involves power generation, and the energy storage parameters involve capacity and life. The net load calculation formula for the three associated interactions is: P net (t) = P load (t) - P re (t);

[0063] Among them, P net (t): the net load at time t (unit: kW);

[0064] P load (t): the power of the historical load curve at time t (unit: kW);

[0065] P re (t): the predicted power of renewable energy at time t (unit: kW);

[0066] Among them, the net load represents the power that actually needs to be supplemented by the energy storage unit or the power grid;

[0067] When P net (t) > 0, the energy storage unit needs to discharge;

[0068] When P net (t) < 0, the energy storage unit can be charged.

[0069] The energy storage charge and discharge power constraint formula is:

[0070]

[0071] P storage (t): The actual charge and discharge power of the energy storage at time t (unit: kW);

[0072] P rated : The rated power of the energy storage unit (unit: kW, from the energy storage parameters);

[0073] The energy storage power is limited by the rated value to avoid overcharging or over-discharging. The rated power P rated is a key parameter of the energy storage unit.

[0074] The purpose of initially setting the SOC safety interval, the optimization weight coefficient, and the filtering frequency band threshold is to improve the system performance, safety, and reliability;

[0075] Among them, the initial range of the SOC safety interval is (20% - 80%). On the one hand, it prevents the energy storage unit from overcharging / over-discharging, avoids the energy storage unit from working in too high or too low charge states, and prevents damage to the energy storage unit or potential safety hazards;

[0076] On the other hand, it can slow down the aging speed of the energy storage unit and extend its life.

[0077] The optimization weight coefficient is used for multi-objective optimization problems. For example, in factors such as grid cost, energy storage unit attenuation cost, and tracking error cost, it is necessary to adjust the weight coefficients of each objective to achieve the best overall performance. It can be understood that the sum of the weight coefficients of each item is one.

[0078] Finally, the filtering frequency band threshold is used for signal processing to filter out high-frequency noise or low-frequency interference, ensure the accuracy of the signal, retain useful signals in data processing, and remove noise.

[0079] For example, in the energy storage system:

[0080] The SOC interval limits the charge and discharge depth and protects the battery life;

[0081] When optimizing the weight coefficient, priority is given to smoothing the grid power fluctuation (high-frequency filtering), and at the same time, the SOC change rate is considered (weight allocation);

[0082] The filtering threshold filters out measurement noise to ensure the accuracy of SOC estimation.

[0083] By reasonably setting these parameters, the system can achieve an optimal balance among safety, efficiency, and stability.

[0084] Perform MPC scheduling optimization periodically, issue power commands in real time, and form a closed-loop control for the SOC safety interval, multi-objective weight coefficients, and filtering frequency band thresholds; in the scenario where MPC (Model Predictive Control) scheduling optimization is executed once every 15 minutes and power commands are issued in real time, combined with the design of the SOC safety interval, optimized weight coefficients, and filtering frequency band thresholds, the control accuracy, safety, and economy of the system can be significantly improved.

[0085] Control period: Trigger MPC optimization once every 15 minutes, and generate power commands for future time periods based on the latest data and prediction model.

[0086] Real-time issuance: Issue the optimized power commands (such as charge and discharge power, grid interaction power, etc.) to the execution equipment (such as the energy storage unit) in real time.

[0087] Closed-loop feedback: Collect the system status (such as SOC, power) in real time through sensors, filter it, and input it into the MPC model to form a closed-loop control.

[0088] The SOC dynamic model is:

[0089]

[0090] SOC(k): State of charge of the battery at the k-th time period (range: [0,1]);

[0091] P(k): Charge and discharge power at the k-th time period (P>0 for discharging, P<0 for charging);

[0092] η: Charge and discharge efficiency (ηdis for discharging, ηch for charging);

[0093] Δt: Control period (15 minutes);

[0094] C nom : Nominal battery capacity (kWh);

[0095] SOH C : Health state (capacity attenuation rate, range: [0,1]).

[0096] The MPC objective function is:

[0097]

[0098] N: Prediction horizon length (for example, N = 4 corresponds to 1 hour);

[0099] C grid (k): Grid electricity price in the k-th period (yuan / kWh);

[0100] w 1 , w 2 , w 3 : Weight coefficient (needs to satisfy w1 + w2 + w3 = 1);

[0101] β: Battery attenuation coefficient (related to SOHR);

[0102] P ref (k): Reference power command (such as grid dispatching demand).

[0103] Among them, the constraint formula for the SOC safety interval is: SOC min ≤ SOC(k) ≤ SOC max ,

[0104] SOC min , SOC max : Dynamic safety interval (for example, [0.2, 0.8]);

[0105] The actual value is adjusted according to SOH C (such as when SOH C < 0.9, SOC max = 0.75);

[0106] It should be noted that: Through the optimization calculation of the MPC objective function, SOC is always in [0.2, 0.8], the power command does not exceed the rated value of the converter, and the optimal power command sequence for the next 15 minutes is output.

[0107] The first power command (i.e., the current moment command) is sent to the execution device in real time, and the remaining command sequence is used as a reference and dynamically corrected according to the feedback in the next cycle to achieve closed-loop feedback.

[0108] It can be understood that the actual SOC change can be monitored during the optimization calculation of the MPC objective function. If the deviation from the predicted value exceeds the threshold (such as ±5%), the MPC objective function is triggered to be re-optimized to ensure that SOC is always within the dynamic safety interval.

[0109] In one embodiment, peak-valley arbitrage is achieved through 15-minute MPC scheduling, and at the same time, the life of the energy storage unit is extended.

[0110] Among them, the settings of each parameter are as follows:

[0111] SOC interval: 25% - 75% (reserving 25% of the capacity to cope with prediction errors);

[0112] Weight coefficients: grid cost: energy storage unit degradation cost: tracking error is 0.7:0.2:0.1;

[0113] Filtering threshold: Perform 10Hz low-pass filtering on the power command to prevent the converter from switching frequently;

[0114] Inputting each parameter into the MPC objective function gives the results: the daily average revenue increases by 12%, and the cycle life of the energy storage unit is extended by 15%.

[0115] In an alternative embodiment, the priorities of different optimization objectives in the MPC objective function are defined to achieve multi-objective dynamic trade-off.

[0116] The weight coefficients include: economy (such as maximizing the electricity price difference revenue), energy storage unit life (such as reducing the charge-discharge cycle depth), and grid demand (such as smoothing load fluctuations);

[0117] Assign weights to different objectives in the objective function (such as economy weight 0.6, energy storage unit life weight 0.3, grid demand weight 0.1).

[0118] Dynamically adjust the weights: Update the weights in real time according to external conditions (such as peak electricity prices, emergency frequency regulation requirements).

[0119] As another example: during low-price periods in the electricity market (such as early morning), increase the economy weight and give priority to charging; when the frequency regulation demand is urgent, increase the grid weight and give priority to responding to the power command.

[0120]

[0121] Online estimate the SOH (State of Health) of the energy storage unit through a Kalman filter, and track the aging degree of the energy storage unit in real time, so as to optimize the energy storage management strategy (such as dynamically adjusting the SOC safety range, charge-discharge power limit). The following is the specific implementation logic and its collaborative application with MPC control:

[0122] Among them, the Kalman filter is a recursive algorithm suitable for state estimation in dynamic systems, which can handle noise and update the estimated value in real time. For the energy storage unit, the SOH is usually related to capacity degradation or internal resistance increase. The changes in capacity and internal resistance can be used as state variables, and real-time data such as voltage, current, and temperature are used as observation inputs.

[0123] SOH characterization index:

[0124] Capacity degradation rate: SOH_Capacity = current maximum capacity / initial rated capacity × 100% (for example, below 80% is regarded as the end of life).

[0125] Internal resistance growth rate: SOH_Resistance = current internal resistance / initial internal resistance × 100% (if it is above 150%, it is regarded as failed).

[0126] Through the estimation of SOH characterization indicators, the charge and discharge strategies can be dynamically adjusted to avoid overstress and accelerate aging; predict the remaining battery life and optimize the maintenance plan.

[0127] The aging model of the energy storage unit needs to describe the change law of SOH over time, and usually an empirical model is adopted:

[0128] Capacity attenuation model (empirical formula): SOH_Capacity(k + 1) = SOH_Capacity(k) - α·I·Δt / Q_initial

[0129] Among them, α is the aging coefficient (which needs to be calibrated online);

[0130] I is the charge and discharge current;

[0131] Q_initial is the initial capacity.

[0132] Dynamically feedback the SOH estimation result to the MPC scheduling to achieve the management of the energy storage unit with aging self - adaptation.

[0133] In one embodiment, the SOH estimation is integrated into the MPC scheduling. According to the SOH of each energy storage unit, their charge and discharge strategies are adjusted. The energy storage unit includes batteries, supercapacitors and flywheels. In the power grid frequency modulation, the supercapacitor deals with high - frequency fluctuations, the battery deals with medium - and long - term demands, and the flywheel provides instantaneous support. When it is necessary to limit the charge and discharge power of the aging battery, the supercapacitor is preferentially used for high - frequency operations, and the response threshold of the flywheel is adjusted according to its wear condition. The real - time estimation of SOH can dynamically adjust the task allocation of each device and extend the overall system life.

[0134] Among them, the core formula of energy storage scheduling is

[0135]

[0136] P sc : Power allocated to the supercapacitor (kW)

[0137] P bat t: Power allocated to the battery (kW)

[0138] P grid : Power of interaction with the power grid (kW)

[0139] Δt: Duration of power fluctuation

[0140] Application scenario: Combine the real-time estimation of SOH and the real-time power distribution decision-making to perform the charge and discharge task allocation of the energy storage unit. When if Δt < 10s, use the supercapacitor for instantaneous discharge;

[0141] When if 10s ≤ Δt ≤ 1h, use the battery for continuous discharge.

[0142] The present invention also provides a microgrid hybrid energy storage scheduling system, including:

[0143] Data acquisition module: Obtain the historical load curve, renewable energy prediction data, and energy storage unit parameters, and use the historical load curve and renewable energy prediction data to determine the charge and discharge strategy of the energy storage unit;

[0144] Initialization configuration module: Initially set the SOC safety interval, continuously optimize the multi-objective weight coefficient, and obtain the filtering frequency band threshold;

[0145] Rolling optimization module: Periodically perform MPC scheduling optimization, issue power commands in real time, and form a closed-loop control of the SOC safety interval, multi-objective weight coefficient, and filtering frequency band threshold;

[0146] Health monitoring module: Online estimate the SOH through the Kalman filter, and real-time track the aging degree of the energy storage unit, so as to optimize the energy storage scheduling strategy.

[0147] It should be understood that the above specific embodiments of the present invention are only used for exemplary illustration or explanation of the principle of the present invention, and do not constitute a limitation to the present invention. Therefore, any modifications, equivalent replacements, improvements, etc. made without departing from the spirit and scope of the present invention shall be included within the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modification examples falling within the scope and boundary of the appended claims, or equivalent forms of such scope and boundary.

Claims

1. A microgrid hybrid energy storage scheduling method, characterized in that: The following steps are involved: Obtain historical load curves, renewable energy forecast data, and energy storage unit parameters, and use historical load curves and renewable energy forecast data to determine the charging and discharging strategy of the energy storage unit; Initially set the SOC safety range, continuously optimize the multi-objective weight coefficients, and obtain the filter frequency band threshold; Periodically perform MPC scheduling optimization, issue power commands in real time, and form a closed-loop control of SOC safety range, multi-objective weight coefficients, and filter frequency band thresholds; The SOH is estimated online through the Kalman filter, and the aging degree of the energy storage unit is tracked in real time, thereby optimizing the energy storage scheduling strategy.

2. A microgrid hybrid energy storage scheduling method according to claim 1, characterized in that: The historical load curve involves power demand, the renewable energy forecast data involves power generation, and the energy storage parameters involve capacity and life. The net load calculation formula for the interaction of the three is: P net (t) = P load (t)-P re (t); Among them, P net (t): net load at time t (unit: kW); P load (t): historical load curve power at time t (unit: kW); P re (t): predicted renewable energy power at time t (unit: kW); Among them, net load represents the power that actually needs to be supplemented by the energy storage unit or the grid; When P net When (t)>0, the energy storage unit needs to discharge; When P net When (t)<0, the energy storage unit can be charged.

3. A microgrid hybrid energy storage scheduling method according to claim 2, characterized in that: The energy storage charging and discharging power constraint formula is: P storage (t): actual charging and discharging power of energy storage at time t (unit: kW); P rated : Rated power of the energy storage unit (unit: kW, from energy storage parameters); The energy storage power is limited by the rated value to avoid overcharging or over-discharging. The rated power P rated It is the key parameter of the energy storage unit.

4. A microgrid hybrid energy storage scheduling method according to claim 1, characterized in that: The initial range of SOC safety interval is (20%~80%); The optimization weight coefficients are used for multi-objective optimization problems, including factors such as grid cost, energy storage unit decay cost, and tracking error cost; The filter frequency band threshold is used for signal processing to filter out high-frequency noise or low-frequency interference.

5. A microgrid hybrid energy storage scheduling method according to claim 1, characterized in that: MPC scheduling optimization is performed periodically, with a single cycle of at least 15 minutes. Power instructions for future periods are generated based on the latest data and prediction models. The MPC objective function is: N: prediction time domain length (e.g. N=4 corresponds to 1 hour); C grid (k): the grid electricity price in the kth period (yuan / kWh); w1, w2, w3: weight coefficients (must satisfy w1+w2+w3=1); β: battery attenuation coefficient (related to SOHR); P ref (k): Reference power instruction (such as grid dispatching requirements).

6. A microgrid hybrid energy storage scheduling method according to claim 1, characterized in that: The SOC dynamic model is: SOC(k): battery state of charge at the kth period (range: [0,1]); P(k): charging and discharging power in the kth period (P>0 for discharge, P<0 for charging); η: charge and discharge efficiency (ηdis discharge, ηch charge); Δt: control period (15 minutes); C nom : Battery nominal capacity (kWh); SOH C : Health status (capacity decay rate, range: [0,1]).

7. A microgrid hybrid energy storage scheduling method according to claim 1, characterized in that: The constraint formula of SOC safety interval is: SOC min ,SOC max : Dynamic safety interval [0.2,0.8]; Through the MPC objective function optimization calculation, the SOC is always in [0.2, 0.8], the power command does not exceed the converter rated value, and the optimal power command sequence for the next 15 minutes is output.

8. A microgrid hybrid energy storage scheduling method according to claim 1, characterized in that: The Kalman filter is a recursive algorithm suitable for state estimation in dynamic systems. It processes noise and updates the estimated value in real time. For energy storage units, SOH is usually related to capacity decay or internal resistance increase. The changes in capacity and internal resistance can be used as state variables, while real-time data such as voltage, current and temperature are used as observation inputs.

9. A microgrid hybrid energy storage scheduling method according to claim 1, characterized in that: The SOH estimation is integrated into the MPC dispatch, and the charging and discharging strategies of each energy storage unit are adjusted according to the SOH of each energy storage unit, including batteries, supercapacitors and flywheels.

10. A microgrid hybrid energy storage dispatching system according to any one of claims 1 to 9, characterized in that: include: Data acquisition module: obtain historical load curves, renewable energy forecast data, and energy storage unit parameters, and use historical load curves and renewable energy forecast data to determine the charging and discharging strategy of the energy storage unit; Initialization configuration module: initially set the SOC safety range, continuously optimize the multi-objective weight coefficient, and obtain the filter frequency band threshold; Rolling optimization module: Periodically performs MPC scheduling optimization, issues power commands in real time, and forms a closed-loop control of SOC safety interval, multi-objective weight coefficients, and filter frequency band thresholds; Health monitoring module: The Kalman filter is used to estimate SOH online and track the aging degree of energy storage units in real time, thereby optimizing the energy storage scheduling strategy.

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