Distributed energy collaborative scheduling method and system of intelligent energy monitoring system

Abstract

The application belongs to the technical field of energy scheduling, and discloses a distributed energy collaborative scheduling method and system of an intelligent energy monitoring system; comprising: collecting state data of an energy storage converter and a power grid price signal, and obtaining initial data of the system; calculating an online estimation value of instantaneous total power loss and junction temperature, and generating a continuous junction temperature time sequence; calculating a global thermal cycle damage value, and updating a health state of the energy storage converter; calculating a current maximum allowable power loss; integrating and converting the maximum allowable power loss and inherent voltage and current constraints into linear inequality constraints; solving an optimal power scheduling sequence in a limited time domain; extracting a power value at a first time step in the sequence as a power setting value, and returning to enter a next cycle process after execution; the application greatly reduces the risk of accidental shutdown caused by overheating of power devices by introducing dynamic safe working area constraints, and effectively prolongs the comprehensive service life of the entire energy storage system.

Description

Technical Field

[0001] This invention relates to the field of energy dispatching technology, and more specifically, to a distributed energy collaborative dispatching method and system for intelligent energy monitoring systems. Background Technology

[0002] Current distributed energy resource scheduling methods, especially those based on model predictive control (MPC) or stochastic optimization, primarily focus on maximizing economic benefits and minimizing the degradation costs of the battery energy storage system (BESS). In these models, the energy storage converter, which serves as the critical interface between the battery and the grid, is typically greatly simplified and treated as an ideal black box with fixed efficiency and power constraints. Its health status and degradation mechanisms are ignored in scheduling decisions.

[0003] Independent component reliability studies have clearly shown that energy storage converters are a major point of failure in power electronic systems, with their lifespan primarily driven by junction temperature changes (i.e., thermal cycling) caused by power fluctuations. Therefore, an optimal strategy that involves frequent power dispatch solely for battery health or short-term economic gains may impose severe thermal stress on the energy storage converter, accelerating its aging and generating negative impacts not factored into the model cost. This disconnect between dispatch optimization and component-level reliability engineering constitutes a critical technical blind spot, preventing existing methods from achieving truly global optimization of the entire energy storage system's lifecycle cost.

[0004] In view of this, the present invention proposes a distributed energy collaborative scheduling method and system for intelligent energy monitoring system to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a distributed energy collaborative scheduling method for an intelligent energy monitoring system, comprising:

[0006] S1: Real-time acquisition of the output current, output voltage, case temperature and grid electricity price signals of the energy storage converter to form a real-time measurement vector, and to obtain the initial state of charge of the battery, the initial health state of the energy storage converter, the grid electricity price at the initial moment and the case temperature at the initial moment to form an initial state vector;

[0007] S2: Based on the collected output current and output voltage, calculate the instantaneous total power loss, combine the instantaneous total power loss with the real-time case temperature, calculate the online estimate of the output IGBT junction temperature, and generate a continuous junction temperature time series based on the obtained online estimate of the IGBT junction temperature.

[0008] S3: Decompose the continuous junction temperature time series into thermal cycles, calculate the global thermal cycle damage value, and update the health status of the energy storage converter.

[0009] S4: Based on the current health status of the energy storage converter, determine the maximum allowable junction temperature through a preset derating strategy, and calculate the current maximum allowable power loss by combining the real-time case temperature and thermal resistance formula; integrate the maximum allowable power loss with the inherent voltage and current constraints of the energy storage converter and transform it into a linear inequality constraint describing the power feasible region of the energy storage converter, and express it as a dynamic safe operating area matrix.

[0010] S5: Calculate the economic benefit sub-objective and the battery degradation sub-objective, and solve for the optimal power scheduling sequence in the finite time domain under the conditions of satisfying the battery state of charge constraint, the fixed power constraint of the energy storage converter and the dynamic safe operating area constraint.

[0011] S6: Based on the rolling time domain control principle, extract the power value of the first time step in the optimal power scheduling sequence as the current power setting value of the energy storage converter, and return to S1 after execution to enter the next loop process.

[0012] Furthermore, the instantaneous total power loss includes conduction loss and switching loss.

[0013] Furthermore, decomposing the continuous junction temperature time series into thermal cycles involves identifying temperature extremes starting from the beginning of the junction temperature time series. Temperature extremes include peak values ​​and valley values, and thermal cycles are formed by pairing adjacent peak values ​​with valley values.

[0014] Paired temperature extreme points will no longer participate in subsequent thermal cycle identification until all temperature extreme points have completed thermal cycle disassembly; for edge temperature data that cannot be completely paired, their unpaired state will be retained until they are fully supplemented in the next sampling cycle before processing.

[0015] Furthermore, the initial value of the global thermal cycle damage value is 0. The damage value of each thermal cycle is calculated and accumulated into the global thermal cycle damage value until all thermal cycles are processed.

[0016] Furthermore, by integrating the maximum allowable power loss with the inherent voltage and current constraints of the energy storage converter, a linear inequality constraint describing the power feasible region of the energy storage converter is constructed. The method for obtaining this constraint is as follows:

[0017] S231: Constructing power upper limit constraints: ,in, The current time in the MPC time domain Energy storage converter power command, For the current moment Maximum permissible power loss Rated power of the energy storage converter;

[0018] S232: Constructing a lower power constraint: ;

[0019] S233: Based on the relationship between power, voltage, and current, construct derived constraints: when hour, and ;when hour, and ;

[0020] S234: Rearrange the constraints of S231-S233 into matrix form: ,in, for Constraint coefficient matrix, where n is the total number of constraints. for Control input vector, corresponding to the scalar energy storage converter power command , for Constraint boundary vector.

[0021] Furthermore, a constraint update cycle is set and dynamically adjusted according to the system's real-time requirements; within each update cycle, S231-S234 are executed repeatedly to achieve dynamic refresh of the dynamic safe working area constraints;

[0022] When the rate of change of the health status of the energy storage converter exceeds the threshold, the constraint update process is forcibly triggered without waiting for the current cycle to end.

[0023] Furthermore, the steps for solving the optimal power scheduling sequence in the finite time domain include:

[0024] S241: Set the prediction time-domain parameters and control time-domain parameters, including the prediction time-domain length. Obtained based on the system's dynamic characteristics; control the time domain length. ,satisfy Time step It is consistent with the dynamic safety work area constraint update cycle;

[0025] Import system model parameters, including battery capacity, battery charge / discharge efficiency, and battery degradation cost coefficient, and obtain the grid peak / valley electricity price curve price(t), according to... Discretize into sequence;

[0026] S242: Calculate the economic benefit sub-objective and battery degradation sub-target The weighted coefficient method is used to transform the bi-objective optimization problem into a single-objective optimization problem;

[0027] S243: Integrate constraints, including dynamic constraints on battery state of charge, fixed power constraints on energy storage converter, and time-varying constraints on dynamic safe operating area;

[0028] S244: Transform the optimization problem into a standard quadratic programming form. After solving, output the optimal power scheduling sequence. Covering the Future One control step size.

[0029] Furthermore, if the energy storage converter supports direct power command mode, the power value of the first time step of the extracted optimal power scheduling sequence is directly used as the setpoint; if it is converted to current command, the current setpoint is generated based on the rated parameters of the energy storage converter through the power-current relationship.

[0030] A distributed energy collaborative dispatch system based on an intelligent energy monitoring system includes:

[0031] The data acquisition module collects the output current, output voltage, case temperature, and grid electricity price signals of the energy storage converter in real time to form a real-time measurement vector, and obtains the initial state of charge of the battery, the initial health state of the energy storage converter, the grid electricity price at the initial moment, and the case temperature at the initial moment to form an initial state vector.

[0032] The junction temperature estimation module calculates the instantaneous total power loss based on the collected output current and output voltage, combines the instantaneous total power loss with the real-time case temperature, calculates the online estimate of the IGBT junction temperature, and generates a continuous junction temperature time series based on the obtained online estimate of the IGBT junction temperature.

[0033] The status update module decomposes the continuous junction temperature time series into thermal cycles, calculates the global thermal cycle damage value, and updates the health status of the energy storage converter.

[0034] The constraint generation module determines the maximum allowable junction temperature based on the current health status of the energy storage converter through a preset derating strategy, and calculates the current maximum allowable power loss by combining the real-time case temperature and thermal resistance formula. It then integrates the maximum allowable power loss with the inherent voltage and current constraints of the energy storage converter, transforming it into a linear inequality constraint describing the power feasible region of the energy storage converter, and expresses it as a dynamic safe operating area matrix.

[0035] The optimization solution module calculates the economic benefit sub-objective and the battery degradation sub-objective, and solves the optimal power scheduling sequence in the finite time domain under the conditions of satisfying the battery state of charge constraint, the fixed power constraint of the energy storage converter and the dynamic safe operating area constraint.

[0036] The instruction execution module, based on the rolling time domain control principle, extracts the power value of the first time step in the optimal power scheduling sequence as the current power setting value of the energy storage converter. After execution, it returns to the data acquisition module to enter the next loop process.

[0037] The technical effects and advantages of the distributed energy collaborative scheduling method and system of the intelligent energy monitoring system of this invention are as follows:

[0038] First, by introducing a dynamic safe operating area constraint, this invention effectively extends the overall service life of the entire energy storage system and reduces the total life cycle operation and maintenance cost. This constraint actively limits the thermal stress of the energy storage converter, especially in the later stages of its aging process, and can effectively slow down the degradation rate of key power devices.

[0039] Secondly, this scheme significantly enhances the system's operational safety. Unlike traditional "soft constraints" that weigh degradation as a cost, the dynamic safe operating area is a "hard constraint" based on a physical failure model. It enforces physical safety boundaries based on real-time health status, avoiding scheduling decisions that prioritize short-term economic benefits over excessive thermal stress in the energy storage converter, thereby significantly reducing the risk of unexpected shutdowns due to power device overheating.

[0040] Finally, this approach enhances the transparency and interpretability of the control strategy. Compared to adjusting abstract and difficult-to-tune cost weights in the objective function, the contraction or relaxation of the dynamic safe working area is directly related to measurable physical quantities (such as temperature) and understandable health indicators (SOH). This gives the conservative or aggressive nature of the control strategy clear physical meaning and explicit engineering basis, making it easier for operations personnel to understand and trust the system's autonomous decision-making. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the distributed energy collaborative scheduling method of the intelligent energy monitoring system in Embodiment 1 of the present invention;

[0042] Figure 2 This is a schematic diagram of the optimal power scheduling sequence acquisition method in Embodiment 1 of the present invention;

[0043] Figure 3 This is a schematic diagram of the distributed energy collaborative scheduling system of the intelligent energy monitoring system in Embodiment 2 of the present invention. Detailed Implementation

[0044] 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, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Example 1:

[0046] See Figure 1As shown, this embodiment provides a distributed energy collaborative scheduling method for an intelligent energy monitoring system, including:

[0047] The system collects the output current of the energy storage converter in real time. Output voltage and shell temperature Combined with the time-of-use electricity price signal from the power grid obtained through the communication interface, a real-time measurement vector is formed; the system acquires the initial state of charge (SOC0) of the battery (range 20%-80%) and the initial state of health (SOH) of the energy storage converter. pcs,0 The initial grid electricity price (price0, in yuan / kWh) and the initial energy storage converter casing temperature are compared with the initial grid electricity price (price0, in yuan / kWh). (Unit: °C) constitutes the initial state vector x0 of the system.

[0048] Based on the collected current and voltage data, the instantaneous total power loss is obtained by calculating the conduction loss and switching loss using the IGBT loss model. This loss is then used as the input heat source, and combined with the real-time case temperature, an estimated value of the output IGBT junction temperature is calculated. Based on the obtained IGBT junction temperature estimate, a continuous junction temperature time series is generated. The specific steps are as follows:

[0049] S11: Calculate the conduction loss based on the collected real-time output voltage and output current. and switching losses Adding these two losses together gives the instantaneous total power loss that serves as the input source for the thermal model. .

[0050] First, based on the fundamental definition of electrical power, the instantaneous power of a device... For voltage With current The product of, i.e.:

[0051]

[0052] Regarding conduction losses, when the IGBT is in the on state, its current is approximately constant. The voltage across the terminals is the saturation voltage. , It is not a constant and is usually fitted to a linear model through experiments.

[0053] Conduction loss The calculation formula is:

[0054] ;

[0055]

[0056] in, For conduction loss, This refers to the collector-emitter saturation voltage of the real-time IGBT. Output current of the energy storage converter for high-frequency sampling Zero current saturation voltage, The values ​​are equivalent on-resistances, all from the IGBT datasheet.

[0057] In engineering applications, device manufacturers provide single-cycle turn-on and turn-off losses through experiments, and calculate switching losses in combination with the switching frequency.

[0058] Switching losses The calculation formula is:

[0059]

[0060] in, For switching losses, The switching frequency is obtained based on the configuration parameters of the energy storage converter controller. To account for activation losses, The turn-off losses are all from the IGBT datasheet.

[0061] Instantaneous total power loss The calculation formula is:

[0062] .

[0063] S12: The calculated instantaneous total power loss As the input heat source, the shell temperature is measured in real time. As a condition, the calculation output affects the IGBT junction temperature. Based on the analogy between thermal and electrical systems, the online estimate of the thermal resistance model can be expressed as:

[0064] ;

[0065] in, For temperature difference, For heat source power, This represents the corresponding thermal resistance. In this embodiment, the IGBT junction temperature... The specific formula is:

[0066]

[0067] in, The junction-to-case thermal resistance is obtained from the IGBT datasheet.

[0068] S13: Based on the obtained IGBT junction temperature Online estimates generate continuous junction temperature ,..., Where M is the number of sampling points, and the sampling frequency and duration are configured according to actual needs. The junction temperature of the IGBT during the Mth sampling. Online estimates;

[0069] The continuous junction temperature time series was processed using an online rainflow counting method, decomposing it into several independent thermal cycles. Let the thermal cycle number be... Each thermal cycle corresponds to a set of characteristic parameters, including: the amplitude of the g-th thermal cycle. ,in, The peak temperature of the g-th thermal cycle is... The valley temperature of the g-th thermal cycle and the average temperature of the g-th thermal cycle are given. Output thermal cycle characteristic parameter list , where a is the total number of thermal cycles obtained from the disassembly;

[0070] Temperature extremes, including peaks and valleys, are identified starting from the beginning of the continuous junction temperature time series. Adjacent peaks and valleys are paired to form thermal cycles. Paired temperature extremes do not participate in subsequent cycle identification until all temperature extremes have completed thermal cycle decomposition. For edge temperature data that cannot be completely paired, their unpaired state is retained until the next sampling period is completed before further processing.

[0071] Based on a simplified Coffin-Manson lifetime model, the degree of damage caused by each thermal cycle is calculated. The Coffin-Manson lifetime model is used for predicting the low-cycle fatigue lifetime of metals. Its fundamental relationship describes the functional relationship between material strain amplitude and the number of fatigue cycles. Based on this principle, in power electronic devices, the fatigue of solder joints, solder, and packaging materials caused by temperature cycling can be considered as corresponding strain cycles, thus deriving a simplified formula for device thermal cycling damage. Furthermore, to consider the impact of average junction temperature on accelerated device damage, an average temperature correction function is introduced, with the following formula:

[0072]

[0073] in, Let be the loss value for the g-th thermal cycle, A be a constant related to the physical characteristics of the power devices (such as IGBT modules) in the energy storage converter, and m be the device fatigue index, reflecting the degree of influence of temperature amplitude on damage. The larger m is, the smaller the impact of "small temperature fluctuations" on damage. The value is determined based on the device material and structure. This is the average temperature correction function, used to compensate for the effect of average temperature on damage acceleration;

[0074] It should be noted that, The specific formula is as follows: Where e is the natural constant, and B is a constant related to the thermal properties of the device. B reflects the sensitivity of the average temperature to the damage acceleration effect; the larger the value of B, the more significant the accelerating effect of the average temperature on device damage. The reference temperature is generally taken as the midpoint of the device's rated operating temperature.

[0075] The global thermal cycling damage value is initialized based on the Miner linear cumulative damage criterion. The damage value of each thermal cycle is then calculated sequentially. The damage is added to the global thermal cycle damage value until all thermal cycles have been processed.

[0076] With the energy storage converter in its new state of health Based on this, calculate the current health status of the energy storage converter. The formula is:

[0077]

[0078] in, The range of values ​​is 1 indicates that the device is in a brand new state, and 0 indicates that the device is completely failed.

[0079] The system is based on the current health status of the energy storage converter. The maximum allowable junction temperature under the current healthy state is calculated using a preset derating strategy function (e.g., linear derating). Combined with real-time measured shell temperature Using the thermal resistance formula Calculate the current time Maximum allowable power loss This power limit, along with other voltage and current limits, is transformed into a set of linear inequality constraints describing the power feasible region of the energy storage converter, and is expressed as the Dynamic Safe Operating Area (D-SOA). The specific steps are as follows:

[0080] S21: Based on the current health status of the energy storage converter The maximum allowable junction temperature of the energy storage converter is dynamically adjusted using a preset derating strategy function. A completely new energy storage converter is designed, namely... At that time, the device's rated maximum junction temperature is This is determined by the device datasheet;

[0081] Taking a linear derate reduction strategy as an example, the derate reduction function is defined as:

[0082]

[0083] in, The derating factor is determined based on the accelerated aging test of the device, and its value ranges from [1,2].

[0084] S22: Using the thermal resistance formula, the junction temperature constraint is transformed into a power loss limitation. Based on the principle of thermal equilibrium, the IGBT junction temperature... With shell temperature Instantaneous total power loss The relationship is: ;

[0085] To ensure that the junction temperature does not exceed The maximum allowable power loss at the current time k is obtained by deformation:

[0086]

[0087] S23: Will Integrating with the inherent voltage and current constraints of the energy storage converter, a linear inequality constraint describing the power feasible region of the energy storage converter is constructed. The specific steps are as follows:

[0088] S231: Constructing power upper limit constraints: ,in, The current time in the MPC time domain Energy storage converter power command, For the current moment Maximum permissible power loss Rated power of the energy storage converter;

[0089] S232: Constructing a lower power constraint: ;

[0090] S233: Based on the relationship between power, voltage, and current, construct derived constraints; when hour, and ;when hour, and ;

[0091] It should be noted that, This is the maximum allowable voltage value for the energy storage converter. This represents the maximum allowable current value for the energy storage converter. This is the minimum allowable voltage value for the energy storage converter. This is the minimum allowable current value for the energy storage converter.

[0092] S234: Rearrange the constraints of S231-S233 into matrix form: ,in, for Constraint coefficient matrix, where n is the total number of constraints. for Control input vector, corresponding to the scalar energy storage converter power command , for Constraint boundary vector.

[0093] It should be noted that setting the constraint update cycle The value range is [0.1s, 1s], and it is dynamically adjusted according to the real-time requirements of the system. In each update cycle, steps S231-S234 are repeated to realize the dynamic refresh of the dynamic safe working area D-SOA constraints. When the change rate of the energy storage converter health status is detected to exceed the threshold (i.e., the change rate of the energy storage converter health status is 1% in each update cycle), the constraint update process is forcibly triggered without waiting for the end of the current cycle.

[0094] S24: The controller, based on the system's initial state vector, aims to maximize economic benefits and minimize battery degradation, while satisfying the battery SOC constraints, the energy storage converter's fixed power constraints, and the Dynamic Safe Operating Area (D-SOA) constraints generated in the previous step. and Under the given conditions, solve an optimization problem in a finite time domain, the solution of which is an optimal power scheduling sequence covering the future prediction time domain.

[0095] See Figure 2 As shown, the specific steps are as follows:

[0096] S241: Set the prediction time-domain parameters and control time-domain parameters, including the prediction time-domain length. The value ranges from 5 to 20 minutes, and is obtained based on the dynamic characteristics of the system; the control time domain length is... ,satisfy Time step With the dynamic safe working area D-SOA constraint update cycle Maintain consistency.

[0097] Import system model parameters, including battery capacity. The unit is kWh, determined by the parameters on the battery nameplate; battery charge / discharge efficiency. The battery degradation cost factor is calibrated through charge-discharge tests. The peak-valley electricity price curve (price(t)) was obtained through cyclic aging tests; it was obtained 24 hours in advance and calculated according to... Discretize into sequence.

[0098] It should be noted that t is a continuous time variable of the peak-valley electricity price curve of the power grid. Let be the electricity price corresponding to the i-th time step.

[0099] S242: Calculate the economic benefit sub-objective The formula is:

[0100]

[0101] in, This is the power command for the energy storage converter in step i; This indicates the sale of electricity to the power grid. This indicates that electricity is purchased from the power grid; This indicates the power output. This indicates the amount of electricity purchased; The units are converted to hours to ensure unit consistency.

[0102] Battery life is typically measured by the number of cycles. The equivalent number of cycles per charge-discharge cycle is proportional to the change in State of Charge (SOC). To accommodate batteries of different capacities, the degradation rate is normalized, and a battery degradation cost coefficient is introduced. Calculate the battery degradation sub-objective The formula is:

[0103]

[0104] in, , where is the change in SOC (state of charge) at step i; The baseline capacity is set at 1 kWh for normalization. This indicates the equivalent number of loops; one full charge and discharge cycle is counted as one loop.

[0105] The bi-objective optimization problem is transformed into a single-objective optimization problem using the weighted coefficient method:

[0106]

[0107] in, , For the weighting coefficients, satisfying The value range is [0,1], and can be dynamically adjusted according to the actual operating mode; the negative sign indicates... To maximize the objective, we transform it into minimizing the problem-solving process.

[0108] S243: Integrating constraints, including dynamic constraints on battery SOC, fixed power constraints on energy storage converter, and time-varying constraints on dynamic safe operating area (D-SOA).

[0109] Battery SOC is defined as the ratio of remaining usable charge to the battery's rated capacity. During the operation of the energy storage converter, the change in battery charge after a time step is:

[0110] ;

[0111] in, This represents the change in battery charge. This is the battery terminal voltage.

[0112] In practical applications, energy loss occurs during battery charging and discharging, so battery charging and discharging efficiency needs to be considered. Corrections are made for charge changes by converting the actual input / output electrical energy into effective charge changes, thereby establishing dynamic constraints on battery SOC:

[0113]

[0114] Establish SOC boundary constraints: , ∈ in The minimum permissible state of charge of the battery is set at 20%. The maximum permissible state of charge of the battery is set to 80%.

[0115] The fixed power constraint of the energy storage converter is , ∈ ,in The rated power of the energy storage converter, in kW, is determined by the equipment parameters. (Index) Indicates the first in the control sequence Each control action.

[0116] S244: Transform the optimization problem into the standard quadratic programming (QP) form:

[0117]

[0118]

[0119] in, To optimize vector variables; Q is The coefficient matrix of the quadratic terms, with diagonal elements as R is The coefficient vector of the first-order terms, with elements of A is the constraint coefficient matrix, and B is the constraint boundary vector, which integrates all previous constraints.

[0120] The solution is obtained using a fast QP solver, with the following parameters set: maximum number of iterations: 1000; convergence accuracy: [value missing]. Feasibility tolerance level is .

[0121] After the solution is completed, the optimal power scheduling sequence is output. This sequence covers the future One control step size.

[0122] in, It is the final instruction in the optimal power scheduling sequence.

[0123] This sequence is based on the predicted time domain length. The predicted future state of the internal system is obtained through MPC optimization. This is for the optimal power scheduling sequence. Extract the power value of the first time step as the execution instruction for the current control cycle, denoted as... ;

[0124] Extract Convert the power setpoint to a value that conforms to the interface specification of the underlying controller of the energy storage converter:

[0125] If the energy storage converter supports direct power command mode, it can directly use... (Unit: kW, including positive and negative signs to distinguish charging and discharging directions) is used as the set value; if it needs to be converted to a current command, it is based on the rated parameters of the energy storage converter, through the power-current relationship. Generating current set value ,in Rated voltage, It is a symbolic function, where x is a variable, and its specific definition is as follows:

[0126]

[0127] The power setpoint is sent to the underlying controller of the energy storage converter via the system communication bus. The underlying controller uses closed-loop feedback control to precisely adjust the power module output.

[0128] After issuing the current cycle command, the system immediately starts the next control cycle: collecting and updating the system state vector. Includes battery state of charge update Update the health status of the energy storage converter. Real-time grid electricity price Energy storage converter casing temperature Wait; jump to data acquisition, repeat the process, and achieve rolling optimization control.

[0129] Example 2:

[0130] See Figure 3 As shown, this embodiment provides a distributed energy collaborative scheduling system based on an intelligent energy monitoring system. The method for implementing the distributed energy collaborative scheduling based on the intelligent energy monitoring system includes:

[0131] The data acquisition module collects the output current, output voltage, case temperature, and grid electricity price signals of the energy storage converter in real time to form a real-time measurement vector, and obtains the initial state of charge of the battery, the initial health state of the energy storage converter, the grid electricity price at the initial moment, and the case temperature at the initial moment to form an initial state vector.

[0132] The junction temperature estimation module calculates the instantaneous total power loss based on the collected output current and output voltage, combines the instantaneous total power loss with the real-time case temperature, calculates the online estimate of the IGBT junction temperature, and generates a continuous junction temperature time series based on the obtained online estimate of the IGBT junction temperature.

[0133] The status update module decomposes the continuous junction temperature time series into thermal cycles, calculates the global thermal cycle damage value, and updates the health status of the energy storage converter.

[0134] The constraint generation module determines the maximum allowable junction temperature based on the current health status of the energy storage converter through a preset derating strategy, and calculates the current maximum allowable power loss by combining the real-time case temperature and thermal resistance formula; it integrates the maximum allowable power loss with the inherent voltage and current constraints of the energy storage converter and transforms it into a linear inequality constraint describing the power feasible region of the energy storage converter, and expresses it in the form of a dynamic safe operating area matrix.

[0135] The optimization solution module calculates the economic benefit sub-objective and the battery degradation sub-objective, and solves the optimal power scheduling sequence in the finite time domain under the conditions of satisfying the battery state of charge constraint, the fixed power constraint of the energy storage converter and the dynamic safe operating area constraint.

[0136] The instruction execution module, based on the rolling time domain control principle, extracts the power value of the first time step in the optimal power scheduling sequence as the current power setting value of the energy storage converter. After execution, it returns to the data acquisition module to enter the next loop process.

[0137] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0138] In conclusion, the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A distributed energy collaborative scheduling method for an intelligent energy monitoring system, characterized in that, include: S1: Real-time acquisition of the output current, output voltage, case temperature and grid electricity price signals of the energy storage converter to form a real-time measurement vector, and to obtain the initial state of charge of the battery, the initial health state of the energy storage converter, the grid electricity price at the initial moment and the case temperature at the initial moment to form an initial state vector; S2: Based on the collected output current and output voltage, calculate the instantaneous total power loss, combine the instantaneous total power loss with the real-time case temperature, calculate the online estimate of the output IGBT junction temperature, and generate a continuous junction temperature time series based on the obtained online estimate of the IGBT junction temperature. S3: Decompose the continuous junction temperature time series into thermal cycles, calculate the global thermal cycle damage value, and update the health status of the energy storage converter. S4: Based on the current health status of the energy storage converter, determine the maximum allowable junction temperature through a preset derating strategy, and calculate the current maximum allowable power loss by combining the real-time case temperature and thermal resistance formula; integrate the maximum allowable power loss with the inherent voltage and current constraints of the energy storage converter and transform it into a linear inequality constraint describing the power feasible region of the energy storage converter, and express it as a dynamic safe operating area matrix. S5: Calculate the economic benefit sub-objective and the battery degradation sub-objective, and solve for the optimal power scheduling sequence in the finite time domain under the conditions of satisfying the battery state of charge constraint, the fixed power constraint of the energy storage converter and the dynamic safe operating area constraint. S6: Based on the rolling time domain control principle, extract the power value of the first time step in the optimal power scheduling sequence as the current power setting value of the energy storage converter, and return to S1 after execution to enter the next loop process.

2. The distributed energy collaborative scheduling method of the intelligent energy monitoring system according to claim 1, characterized in that, The instantaneous total power loss includes conduction loss and switching loss.

3. The distributed energy collaborative scheduling method of the intelligent energy monitoring system according to claim 1, characterized in that, Decomposing a continuous junction temperature time series into thermal cycles involves identifying temperature extremes starting from the beginning of the junction temperature time series. Temperature extremes include peak values ​​and valley values, and thermal cycles are formed by pairing adjacent peak values ​​with valley values. Paired temperature extreme points will no longer participate in subsequent thermal cycle identification until all temperature extreme points have completed thermal cycle disassembly; for edge temperature data that cannot be completely paired, their unpaired state will be retained until they are fully supplemented in the next sampling cycle before processing.

4. The distributed energy collaborative scheduling method of the intelligent energy monitoring system according to claim 1, characterized in that, The initial value of the global thermal cycle damage value is 0. The damage value of each thermal cycle is calculated and accumulated into the global thermal cycle damage value until all thermal cycles are processed.

5. The distributed energy collaborative scheduling method of the intelligent energy monitoring system according to claim 1, characterized in that, By integrating the maximum allowable power loss with the inherent voltage and current constraints of the energy storage converter, a linear inequality constraint describing the power feasible region of the energy storage converter is constructed. The method for obtaining this constraint is as follows: S231: Constructing power upper limit constraints: ,in, The current time in the MPC time domain Energy storage converter power command, For the current moment Maximum permissible power loss Rated power of the energy storage converter; S232: Constructing a lower power constraint: ; S233: Based on the relationship between power, voltage, and current, construct derived constraints: when hour, and ;when hour, and ; S234: Rearrange the constraints of S231-S233 into matrix form: ,in, for Constraint coefficient matrix, where n is the total number of constraints. for Control input vector, corresponding to the scalar energy storage converter power command , for Constraint boundary vector.

6. The distributed energy collaborative scheduling method of the intelligent energy monitoring system according to claim 5, characterized in that, Set a constraint update cycle. Within each update cycle, repeat S231-S234 to achieve dynamic refresh of the dynamic safe work area constraints. When the rate of change of the health status of the energy storage converter exceeds the threshold, the constraint update process is forcibly triggered.

7. The distributed energy collaborative scheduling method of the intelligent energy monitoring system according to claim 1, characterized in that, The steps to solve for the optimal power scheduling sequence in the finite time domain include: S241: Set the prediction time-domain parameters and control time-domain parameters, including the prediction time-domain length. Obtained based on the system's dynamic characteristics; control the time domain length. ,satisfy Time step It is consistent with the dynamic safety work area constraint update cycle; Import system model parameters, including battery capacity, battery charge / discharge efficiency, and battery degradation cost coefficient, and obtain the grid peak / valley electricity price curve price(t), according to... Discretize into sequence; S242: Calculate the economic benefit sub-objective and battery degradation sub-target The weighted coefficient method is used to transform the bi-objective optimization problem into a single-objective optimization problem; S243: Integrate constraints, including dynamic constraints on battery state of charge, fixed power constraints on energy storage converter, and time-varying constraints on dynamic safe operating area; S244: Transform the optimization problem into a standard quadratic programming form. After solving, output the optimal power scheduling sequence. Covering the future One control step size.

8. The distributed energy collaborative scheduling method of the intelligent energy monitoring system according to claim 1, characterized in that, If the energy storage converter supports direct power command mode, the power value of the first time step of the extracted optimal power scheduling sequence is directly used as the setpoint; if converted to current command, the current setpoint is generated based on the rated parameters of the energy storage converter through the power-current relationship.

9. A distributed energy collaborative scheduling system based on an intelligent energy monitoring system, implementing the distributed energy collaborative scheduling method of the intelligent energy monitoring system according to any one of claims 1-8, characterized in that, include: The data acquisition module collects the output current, output voltage, case temperature, and grid electricity price signals of the energy storage converter in real time to form a real-time measurement vector, and obtains the initial state of charge of the battery, the initial health state of the energy storage converter, the grid electricity price at the initial moment, and the case temperature at the initial moment to form an initial state vector. The junction temperature estimation module calculates the instantaneous total power loss based on the collected output current and output voltage, combines the instantaneous total power loss with the real-time case temperature, calculates the online estimate of the IGBT junction temperature, and generates a continuous junction temperature time series based on the obtained online estimate of the IGBT junction temperature. The status update module decomposes the continuous junction temperature time series into thermal cycles, calculates the global thermal cycle damage value, and updates the health status of the energy storage converter. The constraint generation module determines the maximum allowable junction temperature based on the current health status of the energy storage converter through a preset derating strategy, and calculates the current maximum allowable power loss by combining the real-time case temperature and thermal resistance formula; it integrates the maximum allowable power loss with the inherent voltage and current constraints of the energy storage converter and transforms it into a linear inequality constraint describing the power feasible region of the energy storage converter, and expresses it in the form of a dynamic safe operating area matrix. The optimization solution module calculates the economic benefit sub-objective and the battery degradation sub-objective, and solves the optimal power scheduling sequence in the finite time domain under the conditions of satisfying the battery state of charge constraint, the fixed power constraint of the energy storage converter and the dynamic safe operating area constraint. The instruction execution module, based on the rolling time domain control principle, extracts the power value of the first time step in the optimal power scheduling sequence as the current power setting value of the energy storage converter. After execution, it returns to the data acquisition module to enter the next loop process.

Images