Mpc-based emergency power optimal start-stop and power distribution method and system

Abstract

The application discloses an MPC-based emergency power optimal start-stop and power distribution method and system, which comprises the following steps: collecting operation parameters of various emergency power supplies, and acquiring key load demand in a future time period; constructing an optimization control model; constructing a control objective function based on a model prediction control method; adding scheduling constraints; performing rolling prediction optimization according to the control objective function and the constraint conditions, and calculating optimal start-stop states of various emergency power supplies and key load prediction values in a future control period; and issuing the output optimal start-stop states of various emergency power supplies and the key load prediction values to field power supply equipment to realize dynamic scheduling control; the application realizes collaborative operation and intelligent scheduling among multiple power supplies during disasters through the model prediction control method, improves system power supply stability and energy use efficiency, and is suitable for emergency power supply systems in earthquake, typhoon, flood and other multi-disaster scenarios.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of emergency power supply dispatching in disasters, in particular to an MPC-based optimal start-stop and power distribution method and system for emergency power supply. BACKGROUND

[0002] Currently, in the emergency scenarios caused by natural disasters such as floods, typhoons, and freezing, the main power supply system loses power, and various forms of emergency power supply equipment are generally deployed, such as mobile energy storage vehicles, diesel generators, and portable photovoltaic power supplies. These emergency power supplies have advantages such as rapid deployment and distributed operation, but due to insufficient controlled operation and unintelligent response strategies, the actual power supply coverage and energy utilization efficiency are far from optimal.

[0003] The existing power supply dispatching strategies in the market usually use fixed priority switching or manual start-stop control, which is difficult to cope with dynamic uncertain factors such as load fluctuations and equipment failures. For example, in some areas, the rapid depletion of energy storage may occur due to sudden load surges, or fuel waste may occur due to premature operation of the generator. In addition, there is a lack of unified optimization model between different power sources, leading to frequent conflicts in multi-source grid-connected operation and lack of global coordination in resource scheduling.

[0004] The existing scheme still has the following shortcomings: lack of key load priority constraints, unable to guarantee continuous power supply for key loads such as hospitals and communication base stations; energy storage systems generally use fixed SOC upper and lower limits, lack of dynamic SOC window adjustment based on disaster level, and difficult to ensure safety redundancy in long power outage situations; lack of rapid switching and coordination mechanism between multiple power sources (diesel engine, energy storage, photovoltaic), leading to start-up delay or energy waste; the existing dispatching target is mainly economic or consumption rate, lacking resilience optimization indicators, making it difficult to evaluate and improve sustainable power supply capability in disaster environments.

[0005] With the rapid development of MPC (Model Predictive Control) in the field of power electronics and microgrid regulation, it has advantages such as predicting future load, optimizing control strategy, and handling constraint conditions, and has become an important direction of intelligent regulation. Therefore, there is an urgent need for an intelligent control method that introduces MPC into emergency power supply dispatching in disasters, to realize collaborative prediction and optimization of power supply start-stop state, output power, and load matching, and improve the operation efficiency and resilience of emergency power supply systems. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides an MPC-based optimal start-stop and power distribution method and system for emergency power supply, which aims to solve the problems in the background art.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical scheme: an MPC-based optimal start-stop and power distribution method for emergency power supply, comprising the following steps:

[0008] Step S1: The dispatch center collects the operating parameters of various emergency power supplies and obtains future... Critical load demand for specific time periods; various emergency power sources including energy storage devices, diesel generators, and photovoltaic power generation systems;

[0009] Step S2: Construct an optimized control model; based on the model predictive control method, construct the control objective function;

[0010] Step S3: Add scheduling constraints; scheduling constraints include power boundary constraints, start / stop constraints, dynamic SOC energy boundary constraints, and critical load priority constraints;

[0011] Step S4: In At any given time, rolling prediction optimization is performed based on the control objective function and constraints to calculate the optimal start-stop status of various emergency power supplies and the predicted value of critical loads for the next control cycle.

[0012] Step S5: Send the optimal start / stop status of various emergency power supplies and the predicted values ​​of critical loads to the field power equipment to achieve dynamic scheduling and control.

[0013] Furthermore, based on the model predictive control method, a control objective function is constructed, expressed as:

[0014] ;

[0015] In the formula, Represents the control objective function; express Critical load requirements at any given time; Indicates the first One power supply Output power at any given moment; Indicates the first One power supply The start / stop status at any given moment; Indicates the first One power supply The unit cost of operation at any given moment; Indicates the first Weighting coefficients for each power supply start / stop state; Indicates the first Weighting coefficient for the operating cost per unit of power supply; Indicates that the diesel generator is in Fuel consumption indicators at any given time; The weighting coefficient representing the fuel consumption cost of diesel generators; Indicates that energy storage devices are in Reference state of charge at any given time; Indicates that energy storage devices are in The actual state of charge at any given moment; The weighting coefficient representing the state-of-charge deviation penalty for energy storage devices; This represents a resilience indicator that comprehensively considers critical load fulfillment rate, energy storage surplus ratio, and power switching success rate. This represents the weighting coefficient for the resilience index penalty.

[0016] Furthermore, the power boundary constraint is as follows: ; Indicates the first The minimum output power of a power supply Indicates the first The maximum output power of each power supply;

[0017] The start / stop constraints are: ; Indicates the first One power supply The start / stop status at any given moment;

[0018] The dynamic SOC energy boundary constraint is: ; Indicates based on the level of disaster and estimated repair time Dynamically set lower limit for energy storage devices; Indicates the upper limit of energy storage devices;

[0019] The critical load priority constraint is as follows: , Indicates the first One power supply Output power at any given moment; express Power demand of critical loads at all times.

[0020] Furthermore, the operating parameters of various emergency power supplies include the state of charge of energy storage devices. Maximum / minimum power limits for each power source, and operating cost factor.

[0021] An MPC-based emergency power supply optimal start-up and power allocation system is used to execute an MPC-based emergency power supply optimal start-up and power allocation method, including:

[0022] The data acquisition module is used by the dispatch center to collect operating parameters of various emergency power supplies and obtain future data. Key load demand for a specific time period;

[0023] The model building module is used to build the optimal control model; and to build the control objective function based on the model predictive control method.

[0024] The scheduling constraint module is used to add scheduling constraints; scheduling constraints include power boundary constraints, start-stop constraints, dynamic SOC energy boundary constraints, and critical load priority constraints.

[0025] The prediction module is used to... At any given time, rolling prediction optimization is performed based on the control objective function and constraints to calculate the optimal start-stop status of various emergency power supplies and the predicted value of critical loads for the next control cycle.

[0026] The dispatch control module is used to send the optimal start / stop status of various emergency power supplies and the critical load prediction values ​​to the field power equipment to realize dynamic dispatch control.

[0027] The multi-power fast switching module is used to achieve three-layer connection between photovoltaic power generation system, energy storage equipment and diesel generator.

[0028] Furthermore, in the model building module, the control objective function is expressed as:

[0029] ;

[0030] In the formula, Represents the control objective function; express Critical load requirements at any given time; Indicates the first One power supply Output power at any given moment; Indicates the first One power supply The start / stop status at any given moment; Indicates the first One power supply The unit cost of operation at any given moment; Indicates the first Weighting coefficients for each power supply start / stop state; Indicates the first Weighting coefficient for the operating cost per unit of power supply; Indicates that the diesel generator is in Fuel consumption indicators at any given time; The weighting coefficient representing the fuel consumption cost of diesel generators; Indicates that energy storage devices are in Reference state of charge at any given time; Indicates that energy storage devices are in The actual state of charge at any given moment; The weighting coefficient representing the state-of-charge deviation penalty for energy storage devices; This represents a resilience indicator that comprehensively considers critical load fulfillment rate, energy storage surplus ratio, and power switching success rate. This represents the weighting coefficient for the resilience index penalty.

[0031] Furthermore, in the scheduling constraint module, the power boundary constraint is: ; Indicates the first The minimum output power of a power supply Indicates the first The maximum output power of each power supply;

[0032] The start / stop constraints are: ; Indicates the first One power supply The start / stop status at any given moment;

[0033] The dynamic SOC energy boundary constraint is: ; Indicates based on the level of disaster and estimated repair time Dynamically set lower limit for energy storage devices; Indicates the upper limit of energy storage devices;

[0034] The critical load priority constraint is as follows: , Indicates the first One power supply Output power at any given moment; express Power demand of critical loads at all times.

[0035] An electronic device includes a processor, a memory, and a bus, wherein the processor and the memory are connected via the bus, wherein the memory is used to store a set of program code, and the processor is used to call the program code stored in the memory to execute an MPC-based emergency power supply optimal start-stop and power allocation method.

[0036] A non-volatile computer storage medium stores computer-executable instructions that execute an MPC-based optimal start-stop and power allocation method for emergency power supplies.

[0037] Compared with existing technologies, the present invention has the following advantages:

[0038] (1) By introducing the MPC rolling prediction optimization mechanism, the present invention makes dynamic prediction and optimization decisions for future periods based on real-time load data and equipment status parameters in each control cycle, realizing accurate dynamic matching between emergency power output and key load demand. It also realizes continuous connection between photovoltaic, energy storage and diesel engine through the multi-power rapid switching mechanism, effectively solving the problem of power outage caused by load changes, photovoltaic fluctuations or equipment status fluctuations in disaster scenarios under the traditional static scheduling strategy. It significantly improves the operational stability and power supply resilience of the emergency power supply system under multiple disaster scenarios such as earthquakes, typhoons and floods.

[0039] (2) This invention achieves coordinated optimization of power supply reliability, equipment protection and cost economy by integrating load tracking term, equipment start-stop penalty term and operating cost weight term through the design of multi-objective optimization objective function: the load tracking term ensures that the power supply is neither excessive nor insufficient by minimizing power deviation, the start-stop penalty term reduces mechanical wear of equipment such as diesel generators by limiting the state switching frequency, and the cost weight term prioritizes scheduling low-cost clean energy such as photovoltaic, which can reduce operating costs and extend the service life of equipment.

[0040] (3) This invention constructs a full-dimensional constraint system including power boundary, start-stop logic, dynamic SOC energy boundary and critical load constraint, and defines safe operation thresholds for various types of emergency power supplies: power constraint avoids the risk of equipment burnout due to overpower operation, start-stop constraint prevents the surge in energy consumption caused by repeated switching in a short period of time, dynamic SOC constraint matches the charge state limit of energy storage equipment with the disaster level and the expected repair time, ensuring that there is still power supply redundancy capability under long-term power outage, and critical load constraint ensures that the power supply at any time is not lower than the demand of critical loads such as hospitals and communication base stations, effectively solving the hidden dangers of equipment overload, overcharging and over-discharging of energy storage and power supply interruption of critical loads in traditional scheduling, greatly reducing the failure rate of core equipment, and significantly improving the stability and safety of emergency power supply system in disasters. Attached Figure Description

[0041] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0042] like Figure 1 As shown, the present invention provides a technical solution: an optimal start-up and power allocation method for emergency power supplies based on MPC, comprising the following steps:

[0043] Step S1: The dispatch center collects operating parameters of various emergency power sources (energy storage devices, diesel generators, photovoltaic power generation systems, etc.) and obtains future... Key load demand for specific time periods; operating parameters of various emergency power supplies, including energy storage devices. State of charge at time t Maximum / minimum power limits for each power source, operating cost coefficient, etc.

[0044] Step S2: Construct an optimized control model; based on the model predictive control method, construct the control objective function:

[0045] ;

[0046] In the formula, Represents the control objective function; express Critical load requirements at any given time; Indicates the first One power supply Output power at any given moment; Indicates the first One power supply The start / stop status at any given moment; Indicates the first One power supply The unit cost of operation at any given moment; Indicates the first Weighting coefficients for each power supply start / stop state; Indicates the first Weighting coefficient for the operating cost per unit of power supply; Indicates that the diesel generator is in Fuel consumption indicators at any given time; The weighting coefficient representing the fuel consumption cost of diesel generators; Indicates that energy storage devices are in Reference state of charge at any given time; Indicates that energy storage devices are in The actual state of charge at any given moment; The weighting coefficient representing the state-of-charge deviation penalty for energy storage devices; This represents a resilience indicator that comprehensively considers critical load fulfillment rate, energy storage surplus ratio, and power switching success rate. This represents the weighting coefficient for the resilience index penalty.

[0047] Regarding the optimization control objective function in the above-mentioned MPC-based optimal start-up and power allocation method for emergency power supply during disasters, it should also be noted that this function is constructed around the core requirements of emergency power supply during disasters, and its expression can be simplified to the total cost equal to the sum of load tracking error cost, power start-up and shutdown penalty cost, operating cost penalty cost, fuel consumption cost, energy storage dynamic SOC deviation cost, and resilience index penalty cost. Among them, the load tracking error cost is quantified by the squared error between the total power output and the critical load demand. Its purpose is to ensure that the total power output can accurately match the critical load demand, avoiding the interruption of critical equipment due to insufficient power supply or the energy waste caused by excess power. The power start-up and shutdown penalty cost effectively limits the frequent start-up and shutdown of equipment by setting constraints on start-up and shutdown status variables (1 for start-up, 0 for shutdown), reducing mechanical wear and start-up energy consumption, especially protecting equipment such as diesel generators that are susceptible to frequent start-up and shutdown. The operating cost penalty cost is calculated by weighting the operating cost coefficient and the power allocation amount, prioritizing the scheduling of power sources with low operating costs such as photovoltaics, reducing the unnecessary operation of high-cost equipment such as diesel generators, thereby controlling the energy consumption cost during disasters. The fuel consumption cost directly reflects the fuel consumption of diesel engines at different times. The energy storage dynamic SOC deviation cost is used to ensure that energy storage retains necessary redundancy under the constraints of disaster level and expected repair time. The resilience index penalty cost ensures that the system has higher stability and sustainability in disaster scenarios through a comprehensive evaluation of critical load satisfaction rate, energy storage remaining ratio and power switching success rate. Compared to traditional static strategies that rely on fixed priority switching or manual start-stop control, this objective function, through multi-objective collaborative design and combined with the rolling optimization mechanism of MPC, can dynamically adjust the strategy based on real-time load forecasts and power status in each scheduling cycle. This solves the problem that traditional methods cannot cope with load fluctuations and equipment status changes, avoids equipment damage from frequent start-stops and energy waste caused by the lack of cost constraints, and achieves unified coordination and intelligent scheduling of heterogeneous emergency power sources such as energy storage, generators, and photovoltaics. This significantly improves the stability, resilience, and energy utilization efficiency of the power supply system during disasters.

[0048] Step S3: Add scheduling constraints; scheduling constraints include power boundary constraints, start / stop constraints, dynamic SOC energy boundary constraints, and critical load priority constraints.

[0049] The power boundary constraints are as follows: ; Indicates the first The minimum output power of a power supply Indicates the first The maximum output power of each power supply.

[0050] Start-stop constraints are: ; Indicates the first One power supply The start / stop status at any given moment.

[0051] The dynamic SOC energy boundary constraint is: ; Indicates based on the level of disaster and estimated repair time Dynamically set lower limit for energy storage devices; This indicates the upper limit of energy storage devices.

[0052] The priority constraint for critical loads is: , Indicates the first One power supply Output power at any given moment; express Power demand of critical loads at all times.

[0053] Regarding the scheduling constraints in the above-mentioned MPC-based optimal start-up and power allocation method for emergency power supply during disasters, it should be noted that these constraints are key limitations set to ensure the safe, stable, and efficient operation of the emergency power supply system. They mainly include power boundary constraints, start-up and shutdown constraints, energy storage system state of charge (SOC) constraints, and critical load constraints. Among them, the power boundary constraints set maximum and minimum power limits for each type of emergency power source (such as energy storage power sources, diesel generators, photovoltaic power generation systems, etc.) to ensure that the output power of each power source is within the safe operating capacity of its equipment, avoiding equipment failure or inefficiency due to overload or underload; the start-stop constraints limit the frequency of power source state switching within adjacent scheduling cycles, i.e., preventing equipment from frequently switching from on to off or from off to on in a short period of time, thereby reducing mechanical wear and increased start-up energy consumption caused by frequent start-stop, extending equipment life and reducing operating losses; the dynamic SOC energy boundary constraints for energy storage power sources clarify the upper and lower limits of the state of charge of the energy storage system, ensuring that energy storage operates within a reasonable energy range, avoiding battery damage caused by overcharging or sudden drop in energy storage capacity and shortened lifespan caused by over-discharging, and ensuring the continuous and reliable power supply capability of the energy storage system during disasters; the critical load constraints require that the total power supply of the emergency power source must not be less than the power demand of the critical load at any time, thereby ensuring priority power supply and continuous operation of critical users such as hospitals and communication base stations in disaster scenarios, and avoiding core load interruption problems caused by improper resource allocation. Compared to the potential for missing or single constraints in traditional scheduling, the synergistic effect of these multi-dimensional constraints provides comprehensive protection for the safe operation of emergency power supplies. In addition, with the optimization of the objective function, optimal power allocation and start-stop control are achieved within the constraints, solving problems such as high equipment operation risk and unreasonable energy utilization caused by the lack of clear constraints in traditional methods, and improving the stability and reliability of emergency power supply systems during disasters.

[0054] Step S4: In At any given time, rolling prediction optimization is performed based on the control objective function and constraints to calculate the optimal start / stop status of various emergency power supplies and the predicted value of critical loads for the next control cycle.

[0055] Step S5: Send the optimal start / stop status of various emergency power supplies and the predicted values ​​of critical loads to the field power equipment to achieve dynamic scheduling and control.

[0056] An MPC-based emergency power supply optimal start-up and power allocation system is used to execute an MPC-based emergency power supply optimal start-up and power allocation method, including:

[0057] The data acquisition module is used by the dispatch center to collect operating parameters of various emergency power supplies and obtain future data. Key load demand for a specific time period;

[0058] The model building module is used to build the optimal control model; and to build the control objective function based on the model predictive control method.

[0059] The scheduling constraint module is used to add scheduling constraints; scheduling constraints include power boundary constraints, start-stop constraints, dynamic SOC energy boundary constraints, and critical load priority constraints.

[0060] The prediction module is used to... At any given time, rolling prediction optimization is performed based on the control objective function and constraints to calculate the optimal start-stop status of various emergency power supplies and the predicted value of critical loads for the next control cycle.

[0061] The dispatch control module is used to send the optimal start / stop status of various emergency power supplies and the critical load prediction values ​​to the field power equipment to realize dynamic dispatch control.

[0062] The multi-power fast switching module is used to achieve three-layer connection between photovoltaic power generation system, energy storage device and diesel generator, so as to ensure that when the power of photovoltaic power generation system is insufficient, energy storage device can quickly compensate and when the power of energy storage device is insufficient, diesel generator can smoothly take over, thereby improving the continuity and stability of emergency power supply.

[0063] Furthermore, in the model building module, the control objective function is expressed as:

[0064] ;

[0065] In the formula, Represents the control objective function; express Critical load requirements at any given time; Indicates the first One power supply Output power at any given moment; Indicates the first One power supply The start / stop status at any given moment; Indicates the first One power supply The unit cost of operation at any given moment; Indicates the first Weighting coefficients for each power supply start / stop state; Indicates the first Weighting coefficient for the operating cost per unit of power supply; Indicates that the diesel generator is in Fuel consumption indicators at any given time; The weighting coefficient representing the fuel consumption cost of diesel generators; Indicates that energy storage devices are in Reference state of charge at any given time; Indicates that energy storage devices are in The actual state of charge at any given moment; The weighting coefficient representing the state-of-charge deviation penalty for energy storage devices; This represents a resilience indicator that comprehensively considers critical load fulfillment rate, energy storage surplus ratio, and power switching success rate. This represents the weighting coefficient for the resilience index penalty.

[0066] Furthermore, in the scheduling constraint module, the power boundary constraint is: ; Indicates the first The minimum output power of a power supply Indicates the first The maximum output power of each power supply;

[0067] The start / stop constraints are: ; Indicates the first One power supply The start / stop status at any given moment;

[0068] The dynamic SOC energy boundary constraint is: ; Indicates based on the level of disaster and estimated repair time Dynamically set lower limit for energy storage devices; Indicates the upper limit of energy storage devices;

[0069] The critical load priority constraint is as follows: , Indicates the first One power supply Output power at any given moment; express Power demand of critical loads at all times.

[0070] An electronic device includes a processor, a memory, and a bus, wherein the processor and the memory are connected via the bus, wherein the memory is used to store a set of program code, and the processor is used to call the program code stored in the memory to execute an MPC-based emergency power supply optimal start-stop and power allocation method.

[0071] A non-volatile computer storage medium stores computer-executable instructions that execute an MPC-based optimal start-stop and power allocation method for emergency power supplies.

[0072] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimal start-stop and power distribution of emergency power supply based on MPC, characterized in that, The method comprises the following steps: Step S1: The dispatching center collects the operation parameters of various emergency power supplies and obtains the key load demand in the future time period; the various emergency power supplies include energy storage devices, diesel generators, and photovoltaic power generation systems; Step S2: constructing an optimal control model; based on a model predictive control method, a control objective function is constructed; Step S3: adding scheduling constraints; the scheduling constraints comprise power boundary constraints, start-stop constraints, dynamic SOC energy boundary constraints and key load priority constraints; Step S4: at the moment, according to the control target function and the constraint condition, the optimal start-stop state of each type of emergency power supply and the predicted value of the key load in the future control period are calculated through rolling prediction optimization. Step S5: issuing the output optimal start-stop state of each type of emergency power supply and the key load prediction value to the field power supply equipment to realize dynamic scheduling control; Based on a model predictive control method, a control objective function is constructed, which is represented as: ； In the formula, represents a control target function; represents a critical load demand at a moment t; represents an output power of an i-th power supply at a moment t; represents an on-off state of an i-th power supply at a moment t; represents a unit operation cost of an i-th power supply at a moment t; represents a weight coefficient of an i-th power supply on-off state; represents a weight coefficient of an i-th power supply unit operation cost; represents a fuel consumption related index of a diesel generator at a moment t; represents a weight coefficient of a diesel generator fuel consumption cost; represents a reference state of charge of an energy storage device at a moment t; represents an actual state of charge of an energy storage device at a moment t; represents a weight coefficient of an energy storage device state of charge deviation penalty; represents a resilience index considering the critical load satisfaction rate, the energy storage residual proportion and the power supply switching success rate; represents a weight coefficient of a resilience index penalty;​​​​​​​​​​​ The power boundary constraint is: ; denotes the minimum output power of the th power supply, denotes the maximum output power of the th power supply; The start-stop constraint is: ; represents the start-stop state of the th power source at the th moment. The dynamic SOC energy boundary constraint is: ; represents the lower limit of the energy storage device dynamically set according to the disaster level and the expected repair time ; represents the upper limit of the energy storage device; The critical load priority constraint is as follows: , Indicates the first One power supply Output power at any given moment; express Power demand of critical loads at all times.

2. The MPC-based emergency power optimal start-stop and power distribution method of claim 1, wherein: The operating parameters of the various emergency power sources include the state of charge of the energy storage devices , maximum / minimum power limits of the power sources, operating cost coefficients.

3. The MPC-based optimal start-stop and power distribution system for emergency power supply, configured to perform the MPC-based optimal start-stop and power distribution method according to any one of claims 1-2, characterized in that, It comprises: The data acquisition module is used by the dispatch center to collect operating parameters of various emergency power supplies and obtain future data. Key load demand for a specific time period; A model construction module, which is used for constructing an optimal control model; based on a model predictive control method, a control objective function is constructed; A scheduling constraint module, which is used for adding scheduling constraints; the scheduling constraints comprise power boundary constraints, start-stop constraints, dynamic SOC energy boundary constraints and key load priority constraints; The prediction module is used to... At any given time, rolling prediction optimization is performed based on the control objective function and constraints to calculate the optimal start-stop status of various emergency power supplies and the predicted value of critical loads for the next control cycle. A scheduling control module, which is used for issuing the output optimal start-stop state of each type of emergency power supply and the key load prediction value to the field power supply equipment to realize dynamic scheduling control; A multi-power fast switching module, which is used for realizing the three-layer connection of a photovoltaic power generation system, energy storage equipment and a diesel generator.

4. An electronic device, comprising: The system comprises a processor, a memory and a bus, wherein the processor and the memory are connected through the bus, the memory is used for storing a group of program codes, and the processor is used for calling the program codes stored in the memory to execute the MPC-based emergency power supply optimal start-stop and power distribution method according to any one of claims 1-2.

5. A non-transitory computer storage medium storing computer-executable instructions, the computer-executable instructions comprising instructions for: The computer executable instructions execute the MPC-based emergency power supply optimal start-stop and power distribution method according to any one of claims 1-2.

Images