Collaborative allocation and storage method and device considering confidence level constraint, equipment and medium

Abstract

The application discloses a kind of collaborative configuration method, device and equipment considering confidence level constraint and medium, belong to energy storage control field, the method is: real-time acquisition municipal power-energy storage-diesel generator three-source switching system state data and carry out event identification, generate state vector based on identification result;State vector is based on the construction random variable set, and according to the edge distribution of random variable set, construct joint probability model;Based on the performance data of diesel generator, construct the opportunity constraint based on pre-set confidence level, and solve the preset objective function under the opportunity constraint, obtain collaborative configuration result;Collaborative configuration result is mapped as control signal and is issued to PCC grid-connected switch, DG grid-connected switch and energy storage system, to make municipal power-energy storage-diesel generator three-source switching system according to collaborative configuration result carries out configuration storage.Therefore, by implementing the present application, the configuration storage strategy robustness of municipal power-energy storage-diesel generator three-source switching system under confidence level constraint can be improved.

Description

Technical Field

[0001] This invention relates to the field of energy distribution and storage, and in particular to a collaborative distribution and storage method, apparatus, equipment and medium that takes into account confidence level constraints. Background Technology

[0002] Emergency power supply scenarios for major events typically feature high load levels, stringent continuity requirements, and extremely low fault tolerance. Therefore, a three-source architecture—mains power, energy storage, and diesel generators—is commonly adopted in engineering projects: mains power serves as the primary source, energy storage provides millisecond-level rapid bridging support, and diesel generators offer second-level sustainable backup power. As the scope of protection expands to large venues, command centers, and critical infrastructure, the switching process after a mains power failure or outage is no longer a simple power replacement. It involves a dynamic process simultaneously addressing power gap compensation within the switching window, suppressing voltage and frequency transient fluctuations, constraining equipment safety boundaries, and coordinating the startup and takeover timing of the backup power source. Especially under response indicator constraints, coordinated control must ensure that the energy storage response time is no greater than 8ms and the diesel generator startup delay is no greater than 8s. This allows for continuous power supply within a very short time window, with energy storage providing initial bridging support and the diesel generator subsequently integrating and taking over, achieving a smooth handover. This places higher demands on the rationality of capacity configuration and the robustness of the switching strategy.

[0003] In current engineering practice, the allocation strategy for diesel generators and energy storage often adopts the method of empirical margin or worst-case scenario superposition. That is, in the absence of systematic probabilistic analysis, conservative upper bounds are taken for the duration of power outages, the magnitude and duration of sudden load increases, and the availability of energy storage (such as SOC fluctuations, temperature rise and fall). Although this method reduces the risk of single uncertain factors to a certain extent, it usually has two major shortcomings: First, because it does not explicitly characterize the possible statistical correlation between power outage events, load disturbances, and energy storage availability, the configuration results often have to compromise between significant redundancy and the risk of failure in extreme cases, making it difficult to quantify the probability of adverse scenarios occurring at a given reliability level. Second, traditional configurations focus more on steady-state power supply capability and do not form a closed-loop coordination of diesel generator start-up delay and ramp-up characteristics, energy storage bridging output and safety boundaries, and switching timing constraints. Therefore, there may be engineering risks where the nominal capacity is met, but a power gap or transient over-limit still occurs within the switching window. Summary of the Invention

[0004] This invention provides a collaborative energy storage allocation method, apparatus, equipment, and medium that considers confidence level constraints, which can improve the robustness of the energy storage allocation strategy in a grid-energy storage-diesel generator three-source switching system under confidence level constraints.

[0005] This invention provides a collaborative energy storage allocation method considering confidence level constraints, applied to an energy management system. The energy management system is communicatively connected to a three-source switching system (mains-energy storage-diesel generator). This system includes a mains power system, a diesel generator, and an energy storage system connected to the same AC bus. The mains power system is connected to the AC bus via a PCC grid-connection switch, the diesel generator is connected to the AC bus via a DG grid-connection switch, and the energy storage system is connected in parallel to the AC bus. The collaborative storage method includes: The status data of the mains-energy storage-diesel generator three-source switching system is collected in real time, events are identified based on the status data, and a status vector is generated based on the identification results. A set of random variables is constructed based on the state vector, and a joint probability model is constructed based on the marginal distribution of the set of random variables; Based on the performance data of the diesel generator, a chance constraint based on a pre-set confidence level is constructed, and based on the joint probability model, a preset objective function is solved under the chance constraint to obtain the coordinated configuration result between the rated power of the diesel generator, the rated power of the energy storage system, and the rated energy of the energy storage system. The collaborative configuration result is mapped into a control signal and sent to the PCC grid-connected switch, the DG grid-connected switch, and the energy storage system, so that the mains-energy storage-diesel generator three-source switching system can allocate and store energy according to the collaborative configuration result.

[0006] This invention, through real-time acquisition of status data and event identification, provides status information and trigger signals for subsequent coordinated energy storage allocation. By constructing a set of random variables and a joint probability model, it provides a probabilistic basis for describing the distribution of each random variable and their interrelationships, avoiding misjudgments of risk caused by independent assumptions. By constructing opportunity constraints based on a pre-set confidence level and solving the objective function, it can obtain the coordinated configuration results of the diesel generator's rated power, energy storage's rated power, and energy storage's rated energy at a given confidence level. By mapping the coordinated configuration results into control signals and distributing them to each execution unit, a closed loop from capacity configuration to control execution can be achieved. Compared to existing technologies that struggle to quantify the probability of adverse scenarios occurring at a given reliability level, this application improves the robustness of the energy storage allocation strategy in a grid-energy storage-diesel generator three-source switching system under confidence level constraints.

[0007] Furthermore, the real-time acquisition of status data from the mains-energy storage-diesel generator three-source switching system, the performance of event identification based on the status data, and the generation of a status vector based on the identification results include: The status data of the AC bus, the energy storage system, the diesel generator, the PCC grid-connected switch, and the DG grid-connected switch are collected in real time. The effective value of the bus voltage, the bus frequency, and the active power of the load are calculated based on the state data of the AC bus, the window length, and the sampling period. The load power increment and the load power change rate are calculated based on the load active power and the sampling period. Event identification is performed based on the status data of the PCC grid-connected switch, the effective value of the bus voltage, the load power increment, and the load power change rate. When an event is detected, the effective value of the bus voltage, the bus frequency, the load active power, the status data of the energy storage system, the status data of the diesel generator, the status data of the PCC grid-connected switch, and the status data of the DG grid-connected switch are encapsulated into a status vector.

[0008] By refining the specific steps of data acquisition, calculation, and event identification, this invention can accurately obtain the effective value of bus voltage, bus frequency, load active power, load power increment, and load power change rate. Event identification is performed based on PCC switch status, effective voltage value, power increment, and change rate, providing a state vector for subsequent risk modeling and coordinated energy allocation.

[0009] Further, the step of constructing a set of random variables based on the state vector and constructing a joint probability model according to the marginal distribution of the set of random variables includes: A set of random variables is constructed based on the state vector; wherein, the set of random variables includes the duration of mains power outage, load disturbance intensity, energy storage availability, and diesel generator availability; the load disturbance intensity is constructed from the load power increment and the load power change rate, the energy storage availability is constructed from the state data of the energy storage system, and the diesel generator availability is constructed from the state data of the diesel generator. Obtain the triplet corresponding to each random variable in the set of random variables, and construct marginal distributions based on each triplet; Each of the aforementioned edge distributions is mapped to a preset interval, and a joint probability model is constructed based on the mapping results.

[0010] This invention, by clearly defining the composition of the set of random variables and the construction method of each variable, and by specifying triples for each random variable to construct a marginal distribution and mapping it to a preset interval to construct a joint probability model, can form a computable joint probability model based on the engineering boundary and online state, providing input for risk quantification.

[0011] Furthermore, the construction of opportunity constraints based on a pre-set confidence level based on the performance data of the diesel generator includes: Based on the rated power, gradeability, and start-up delay of the diesel generator, calculate the equivalent output of the diesel generator within the switching window; Based on the equivalent output and the active power of the load, calculate the energy storage bridging power gap; Based on the energy storage bridging power gap and switching window length, calculate the energy storage bridging power demand and energy storage bridging energy demand. Based on the energy storage bridging power demand, the energy storage bridging energy demand, and the state data of the energy storage system, an opportunity constraint based on a pre-set confidence level is constructed.

[0012] This invention calculates the equivalent output based on the diesel generator's rated power, ramp rate, and start-up delay; calculates the power gap based on the equivalent output and the load's active power; calculates the bridging power and bridging energy demand based on the power gap and the switching window length; and constructs opportunity constraints based on the bridging demand and energy storage status data. This allows the diesel generator's start-up delay and ramp characteristics, as well as the energy storage bridging demand and availability boundary, to be coupled into opportunity constraints, ensuring that capacity configuration and the switching dynamic process remain consistent.

[0013] Furthermore, the step of solving a preset objective function based on the joint probability model under the chance constraints to obtain the coordinated configuration result between the rated power of the diesel generator, the rated power of the energy storage system, and the rated energy of the energy storage system includes: The rated power of the diesel generator, the rated power of the energy storage system, and the rated energy of the energy storage system are used as decision variables, and multiple sets of random variable samples are generated from the joint probability model. Constraint judgments are performed on each random variable sample under different decision variable values. When the judgment result meets the preset conditions, the corresponding decision variable value is determined to satisfy the chance constraint. Among all decision variable values ​​that satisfy the opportunity constraints, the decision variable value that minimizes the total life cycle cost is determined, and this decision variable value is used as the collaborative configuration result.

[0014] This invention generates multiple sets of random variable samples from a joint probability model based on decision variables. It performs constraint judgment on samples with different values ​​of decision variables and counts the proportion of samples that meet the constraints. When the preset conditions are met, the decision variables are determined to meet the opportunity constraints. The decision variable with the lowest total life cycle cost among all feasible solutions is used as the collaborative configuration result. This can transform the opportunity constraints into a computable sample approximation form and obtain the configuration scheme with the lowest cost under the premise of meeting the opportunity constraints.

[0015] Further, the step of mapping the coordinated configuration result into control signals and sending them to the PCC grid-connected switch, the DG grid-connected switch, and the energy storage system, so that the mains-energy storage-diesel generator three-source switching system can allocate and store power according to the coordinated configuration result, includes: When a mains power failure event is detected, a first control command is sent to the PCC grid-connected switch to disconnect the mains power system. Within a preset energy storage response time window, a second control command is issued to the energy storage system to enable the energy storage system to perform bridging support; wherein, during the bridging support phase, the cumulative discharge energy is integrated and verified online, and when the cumulative discharge energy reaches a preset threshold, a power limiting or load shedding strategy is triggered; When the diesel generator meets the starting and grid connection conditions, a third control command is sent to the DG grid connection switch to enable the diesel generator to take over grid connection and complete the distribution and storage.

[0016] This invention, through issuing a disconnection command when the mains power fails, issuing a bridging support command within the energy storage response window, and performing online integration verification of the accumulated discharge energy during the bridging phase, enables energy storage to cover the power gap during the diesel generator startup delay and triggers power limiting or load shedding strategies when the energy approaches the available boundary. By issuing a grid connection command when the diesel generator meets the startup and grid connection conditions, the invention enables the diesel generator to be connected to the grid and take over, thus completing the allocation and storage.

[0017] Furthermore, after mapping the coordinated configuration result into control signals and sending them to the PCC grid-connected switch, the DG grid-connected switch, and the energy storage system, so that the mains-energy storage-diesel generator three-source switching system can allocate and store power according to the coordinated configuration result, the method further includes: Based on the real-time voltage and frequency of the AC bus, over-limit risk events are identified, and when an over-limit risk event is identified, a preset protection action is triggered. When a mains power restoration event is detected, the PCC grid-connected switch is issued a fourth control command based on the bus voltage stability condition, bus frequency stability condition, and grid connection logic, so that the mains power system is restored to grid connection.

[0018] The embodiments of the present invention identify and trigger protection actions based on bus voltage and frequency after distribution and storage, and can continuously monitor power quality after switching is completed; and can realize grid connection by issuing grid connection instructions based on voltage and frequency stability conditions and grid connection logic when the mains power is restored.

[0019] Another embodiment of the present invention provides a collaborative energy storage and distribution device considering confidence level constraints, applied to an energy management system; wherein, the energy management system is communicatively connected to a mains-energy storage-diesel generator three-source switching system, the mains-energy storage-diesel generator three-source switching system includes a mains system connected to the same AC bus, a diesel generator and an energy storage system; the mains system is connected to the AC bus via a PCC grid-connected switch, the diesel generator is connected to the AC bus via a DG grid-connected switch, and the energy storage system is connected to the AC bus in parallel; The collaborative storage device includes: an event identification module, a risk modeling module, a strategy generation module, and a strategy execution module; The event recognition module is used to collect the status data of the mains-energy storage-diesel generator three-source switching system in real time, perform event recognition based on the status data, and generate a status vector based on the recognition results. The risk modeling module is used to construct a set of random variables based on the state vector, and to construct a joint probability model based on the marginal distribution of the set of random variables. The strategy generation module is used to construct opportunity constraints based on the performance data of the diesel generator and a preset confidence level, and to solve a preset objective function under the opportunity constraints based on the joint probability model, so as to obtain the coordinated configuration result between the rated power of the diesel generator, the rated power of the energy storage system and the rated energy of the energy storage system. The strategy execution module is used to map the collaborative configuration result into a control signal and send it to the PCC grid-connected switch, the DG grid-connected switch and the energy storage system, so that the grid-energy storage-diesel generator three-source switching system can allocate and store energy according to the collaborative configuration result.

[0020] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of a collaborative storage allocation method considering confidence level constraints as described in the present invention.

[0021] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is executed, controls the device where the computer-readable storage medium is located to perform steps of a collaborative storage method considering confidence level constraints as described in the present invention. Attached Figure Description

[0022] Figure 1 A flowchart illustrating an embodiment of the collaborative storage allocation method considering confidence level constraints provided by the present invention; Figure 2A schematic diagram of a structure of an embodiment of the mains-energy storage-diesel generator three-source switching system provided by the present invention; Figure 3 A flowchart illustrating an embodiment of the energy management system software implementation provided by the present invention; Figure 4 A schematic flowchart illustrating one embodiment of the three-source switching from mains power to energy storage to diesel generator provided by the present invention; Figure 5 This is a flowchart illustrating another embodiment of the collaborative storage allocation method considering confidence level constraints provided by the present invention. Figure 6 A flowchart illustrating an embodiment of data interaction in a collaborative storage and distribution system considering confidence level constraints provided by the present invention; Figure 7 This is a schematic diagram of an embodiment of the collaborative storage device considering confidence level constraints provided by the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0025] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0026] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0027] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0028] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0029] See Figure 1 To address the problem that existing technologies struggle to quantify the probability of adverse scenarios occurring at a given reliability level, an embodiment of this invention provides a collaborative energy storage allocation method considering confidence level constraints, applied to an energy management system. The energy management system is communicatively connected to a three-source switching system (mains-energy storage-diesel generator). This system includes a mains power system, a diesel generator, and an energy storage system connected to the same AC bus. The mains power system is connected to the AC bus via a PCC grid-connected switch, the diesel generator is connected to the AC bus via a DG grid-connected switch, and the energy storage system is connected to the AC bus in parallel.

[0030] In one embodiment, the overall architecture of the mains-energy storage-diesel generator three-source switching system is as follows: Figure 2 As shown, the mains-energy storage-diesel generator three-source switching system uses the AC bus as the common coupling point. The mains system is connected to the AC bus via the PCC grid-connected switch, and the diesel generator is connected to the AC bus via the DG grid-connected switch. The energy storage system (battery + BMS + PCS, BESS) is connected in parallel on the bus side to provide rapid support for critical loads. The energy management system (EMS) obtains operating information such as bus voltage, bus frequency, load power, SOC, available power, and diesel generator status from the bus and each equipment side. It also sends data to the PCC grid-connected switch and the DG grid-connected switch respectively. and Wait for the switch command, and simultaneously send it to BESS. (Energy storage bridging command or energy storage support command) to coordinate the distribution and switching of power from the three sources under operating conditions such as normal mains power, power outage and power recovery.

[0031] In one embodiment, the software architecture and functional modules within the EMS are as follows: Figure 3 As shown, a closed-loop processing chain is formed according to the logic of configuration decision-constraint generation-execution verification: First, the configuration decision is triggered by mains power status events and load disturbance information. Uncertainties such as power outage duration, load disturbance intensity, and energy storage availability are used as unified inputs. The risk of adverse situations is quantified by combining a correlation joint probability model, and the coordinated configuration result of diesel generator rated power, energy storage system rated power, and energy storage system rated energy is solved under pre-set confidence level constraints. Second, the constraint generation stage maps the state information such as the SOC and available power boundary on the BESS side, and the available output and grid connection conditions on the diesel generator side into computable constraints, so that the configuration result and the switching The timing requirements are consistent, and the energy storage is constrained to quickly bridge its output after the mains power fails through P_ref. At the same time, the timing coordination between the mains power disconnection and the diesel generator grid connection is completed through Grid_Brk_Cmd and DG_Brk_Cmd. Thirdly, the execution and verification links continuously collect key quantities such as bus voltage, bus frequency and load power during operation to verify the power distribution effect during the switching phase online. When the constraint is detected to be approaching or the risk level deviates from the preset confidence level, the protective derating or recalculation mechanism is triggered, thus forming a closed-loop control process of estimation-decision-issuance-verification to ensure the power supply continuity and power quality stability of key loads during the three-source switching process.

[0032] Specifically, the collaborative storage allocation method considering confidence level constraints includes steps S101 to S104: Step S101: Collect the status data of the mains-energy storage-diesel generator three-source switching system in real time, identify events based on the status data, and generate a status vector based on the identification results.

[0033] It should be noted that real-time acquisition of the status data of the mains-energy storage-diesel generator three-source switching system, event identification based on the status data, and generation of a status vector based on the identification results refer to the EMS synchronously collecting key operating quantities on the bus side, load side, energy storage side, and diesel generator side at a fixed sampling period, and identifying events such as mains power outages, power restorations, and load disturbances based on threshold criteria and time confirmation mechanisms, thereby triggering the subsequent algorithm flow of correlation risk modeling, confidence level constraint configuration solution, and switching strategy issuance. This step ensures that the system can still obtain stable and calculable key input quantities through online acquisition and event confirmation without relying on historical scenario databases, providing a data foundation for the subsequent generation of coordinated power allocation and switching strategies under confidence level constraints.

[0034] Preferably, the real-time acquisition of status data from the mains-energy storage-diesel generator three-source switching system, event identification based on the status data, and generation of a status vector based on the identification results include: The status data of the AC bus, the energy storage system, the diesel generator, the PCC grid-connected switch, and the DG grid-connected switch are collected in real time. The effective value of the bus voltage, the bus frequency, and the active power of the load are calculated based on the state data of the AC bus, the window length, and the sampling period. The load power increment and the load power change rate are calculated based on the load active power and the sampling period. Event identification is performed based on the status data of the PCC grid-connected switch, the effective value of the bus voltage, the load power increment, and the load power change rate. When an event is detected, the effective value of the bus voltage, the bus frequency, the load active power, the status data of the energy storage system, the status data of the diesel generator, the status data of the PCC grid-connected switch, and the status data of the DG grid-connected switch are encapsulated into a status vector.

[0035] In one embodiment, the EMS sampling period is set to be , No. The AC bus status data collected at each sampling time includes the three-phase bus voltage. Three-phase bus current Phase with the fundamental wave of the bus The collected status data of the PCC grid-connected switch is recorded as follows: (Closed is 1, open is 0), the collected status data of the DG grid-connected switch is recorded as follows: (Closed is 1, open is 0), the collected status data of the energy storage system includes energy storage Available power boundary for energy storage and the available energy boundary for energy storage The collected diesel generator status data includes the diesel generator's readiness / operation status. And the usable output boundary of the diesel generator wait.

[0036] Furthermore, to establish unified bus and load characterization parameters, the EMS performs necessary online calculations on the acquired signals. Bus voltage RMS value. It can be calculated using the root mean square of the window (window length is...). point): ; bus frequency It can be obtained from the phase increment: ; Load active power Three-phase instantaneous power Summing and applying a moving average yields: ; ; Meanwhile, to identify sudden additions and removals of disturbances, the EMS calculates the load power increment. With load power change rate : ; ; in, Calculate the number of window points for the rate of change.

[0037] Furthermore, regarding event triggering criteria, EMS preferably employs a threshold + duration confirmation method to suppress false positives for transient spikes. (Mainstream power outage event) (1 for triggering, 0 for not triggering) Can be triggered by one of the following conditions: First, the PCC grid connection switch is in the open state and remains open. Each sampling period, i.e. .

[0038] Secondly, it is triggered when the PCC grid-connected switch remains closed but the bus voltage drops significantly (used to identify mains voltage collapse or loss), i.e. ,in This is a preset threshold.

[0039] Mains power restoration incident (Trigger is 1, no trigger is 0) This can be confirmed by the bus voltage recovering and remaining stable, and by meeting the grid-connected switch closing conditions. ,in This is a preset threshold.

[0040] Load mutation event (1 for triggering, 0 for not triggering) is used to trigger an update to the disturbance intensity variable. Its triggering condition can be defined as follows: ,in and This is a preset threshold.

[0041] Furthermore, once any event is confirmed to be triggered, EMS encapsulates the current state data into a state vector, which is then used as input for subsequent algorithms. .

[0042] Step S102: Construct a set of random variables based on the state vector, and construct a joint probability model according to the marginal distribution of the set of random variables.

[0043] It should be noted that constructing a set of random variables based on the state vector and constructing a joint probability model based on the marginal distribution of the set of random variables means that, based on the state vector formed in step 101, EMS abstracts the key uncertainties in the three-source switching process into several random variables, and further constructs a joint model of their marginal distribution and correlation, which is used to quantify the power supply risk under the superposition of adverse situations, and to provide probabilistic input for subsequent collaborative configuration solutions under pre-set confidence level constraints.

[0044] Preferably, the step of constructing a set of random variables based on the state vector and constructing a joint probability model according to the marginal distribution of the set of random variables includes: A set of random variables is constructed based on the state vector; wherein, the set of random variables includes the duration of mains power outage, load disturbance intensity, energy storage availability, and diesel generator availability; the load disturbance intensity is constructed from the load power increment and the load power change rate, the energy storage availability is constructed from the state data of the energy storage system, and the diesel generator availability is constructed from the state data of the diesel generator. Obtain the triplet corresponding to each random variable in the set of random variables, and construct marginal distributions based on each triplet; Each of the aforementioned edge distributions is mapped to a preset interval, and a joint probability model is constructed based on the mapping results.

[0045] In one embodiment, for the main risk sources during the switchover phase from mains power to energy storage to diesel generator, the EMS preferably constructs the following set of random variables. : ; in, Characterizes the duration of mains power outage; Characterizes the intensity of load disturbances (a comprehensive index composed of sudden increase amplitude, rate of change, and duration, etc.); Characterize energy storage availability (energy storage available power boundary and energy storage available energy boundary). Characterize the availability of the diesel generator (readiness state and diesel generator available output boundary). To correspond with the real-time quantity in step S101, the random variable can be expressed in the second step. The time parameterization is represented as: ; ; Load disturbance intensity The load power increment and load power change rate extracted in step 101 can be used to construct a weighted form, which is preferably defined as follows: ; in, An estimate characterizing the duration of a disturbance; , which is a weighting coefficient used to unify the dimensions and highlight the disturbance characteristics most sensitive to the handover bridging.

[0046] Furthermore, in the absence of a historical scenario library and large-scale simulation samples, EMS uses an engineering boundary + online state approach to generate probabilistic inputs, that is, it specifies a triplet of lower bound - typical value - upper bound for each random variable. And based on this, a calculable marginal distribution is constructed. Preferably, a triangular distribution can be used to describe the engineering fluctuations of uncertainties: ; Its probability density function is: .

[0047] Furthermore, the corresponding marginal distribution function can be obtained based on the probability density function. ;in, The triplet can be given by the protection level or design specifications; The triplet can be given by combining the intensity of the disturbance detected online with the engineering margin; The boundaries are determined by the available power boundary and available energy boundary of the energy storage output online by the BMS or PCS; The boundary is determined by the available output boundary of the diesel generator and the diesel generator's ready state. The above construction ensures that a computable probabilistic input can still be formed even without historical data, and allows the triangular distribution to be replaced with an empirical distribution to improve accuracy after data accumulation.

[0048] Furthermore, to characterize the potential correlation between power outage duration, load disturbance, and energy storage availability, the EMS, after obtaining each edge distribution, maps it as... Uniform variable on: ; And through the Copula function Construct a joint distribution model: ; The Copula structure can be selected from Gaussian Copulas, t-Copulas, or Archimedesian Copulas (such as Clayton or Gumbel) that possess tail correlation characteristics. Under no-sample conditions, a conservative setting strategy is adopted for the correlation parameters, that is, the dependence strength is constrained within an engineering-acceptable range. Risk assessment was conducted using the most unfavorable correlation coefficient. Kendall's rank correlation coefficient was used. When characterizing the dependence strength, the parameterized Copula can be written as: ; in, This is the mapping function from the correlation coefficient to the Copula parameter; when subsequent samples are obtained, it can be estimated from the sample rank correlation. And update .

[0049] It is worth noting that although the Copula function is preferred for coupling the marginal distribution into a joint distribution to characterize the correlation between power outage duration, load disturbance, and energy storage availability, this correlation modeling method is not the only implementation. Alternative methods include: First, empirical joint distributions or empirical Copulas can be used to replace parameterized Copulas, directly constructing the dependency structure based on online operating data or the minimum representative set of operating conditions; Second, when samples are insufficient or correlations are difficult to reliably calibrate, the approach can degenerate into the independent marginal hypothesis, and coverage of extreme superimposed risks can be achieved by introducing conservative correlation bounds or increasing the risk margin coefficient; Third, rank correlation constraints, interval correlation (the range of correlation coefficient values), or correlation control methods based on scenario generators can also be used to replace Copulas, so that subsequent opportunity constraints can still be solved at a given confidence level, thereby achieving compatibility with different data conditions and engineering experience systems.

[0050] Specifically, based on the joint distribution model The EMS forms the risk measurement interface required for subsequent optimization, and optimizes the probabilistic expression of power supply gap risk and availability lower bound risk. For example, it uses the equivalent power gap random variable within the switching bridging window. The result representing unfavorable superposition can be defined as: ; And its probability of exceeding the limit is used as a risk indicator: ; This risk indicator will be transformed into an opportunity constraint under a pre-set confidence level in the next step, which will be used to collaboratively determine the rated power of the diesel generator, the rated power of the energy storage system, the rated energy of the energy storage system, and the power allocation strategy during the switching phase.

[0051] Step S103: Based on the performance data of the diesel generator, construct a chance constraint based on a preset confidence level, and based on the joint probability model, solve the preset objective function under the chance constraint to obtain the coordinated configuration result between the rated power of the diesel generator, the rated power of the energy storage system and the rated energy of the energy storage system.

[0052] It should be noted that, based on the performance data of the diesel generator, constructing a chance constraint based on a pre-set confidence level, and solving a pre-set objective function under the chance constraint based on the joint probability model, to obtain the coordinated configuration result between the rated power of the diesel generator, the rated power of the energy storage system, and the rated energy of the energy storage system, refers to: the joint probability model formed by the EMS in step S102. Based on this, the power supply reliability and transient stability requirements during the three-source switching process are written into the opportunity constraints in the form of confidence levels. The coordinated configuration results of the rated power of the diesel generator, the rated power of the energy storage system, and the rated energy of the energy storage system are solved with the goal of full life cycle economy. At the same time, the switching power allocation strategy and control command sequence are generated.

[0053] Preferably, the step of constructing opportunity constraints based on a pre-set confidence level based on the performance data of the diesel generator includes: Based on the rated power, gradeability, and start-up delay of the diesel generator, calculate the equivalent output of the diesel generator within the switching window; Based on the equivalent output and the active power of the load, calculate the energy storage bridging power gap; Based on the energy storage bridging power gap and switching window length, calculate the energy storage bridging power demand and energy storage bridging energy demand. Based on the energy storage bridging power demand, the energy storage bridging energy demand, and the state data of the energy storage system, an opportunity constraint based on a pre-set confidence level is constructed.

[0054] Preferably, the step of solving a preset objective function under the chance constraints based on the joint probability model to obtain the coordinated configuration result between the rated power of the diesel generator, the rated power of the energy storage system, and the rated energy of the energy storage system includes: The rated power of the diesel generator, the rated power of the energy storage system, and the rated energy of the energy storage system are used as decision variables, and multiple sets of random variable samples are generated from the joint probability model. Constraint judgments are performed on each random variable sample under different decision variable values. When the judgment result meets the preset conditions, the corresponding decision variable value is determined to satisfy the chance constraint. Among all decision variable values ​​that satisfy the opportunity constraints, the decision variable value that minimizes the total life cycle cost is determined, and this decision variable value is used as the collaborative configuration result.

[0055] In one embodiment, decision variables Defined as: ; in, This refers to the rated power of the diesel generator. This refers to the rated power of the energy storage system. Let be the rated energy of the energy storage system. Considering the switching timing constraints after a mains power outage, let the trigger time of the mains power outage be . The delay in starting the diesel generator and enabling it to connect to the grid is... ,satisfy Energy storage response time meets In configuration calculations, this can be equivalent to... It is immediately available. To illustrate the process of taking over the diesel generator during ramp-up, a ramp-up rate is introduced. (Unit: kW / s) indicates the equivalent output that the diesel generator can provide within the switching window. It can be represented as: ; Let the length of the switching window be 1. (In engineering practice, the process typically covers the period from power outage to stable diesel generator takeover, which can be taken as...) (Or set according to the protection level), then at any time The energy storage bridging power gap that the energy storage system needs to cover for: ; The energy storage bridging power requirement can be obtained based on the energy storage bridging power gap. Bridging energy demand with energy storage : ; The corresponding configuration feasibility constraints are written as two types of hard constraints: power and energy. ; Furthermore, an energy storage availability boundary (given by BMS or PCS) is superimposed to form a realized output constraint. If the random variables of the energy storage available power boundary and the energy storage available energy boundary are respectively denoted as... and During the switching phase, the following conditions must be met: ; because , and All of these are random variables, and the above constraints are applied in engineering at confidence levels. (For example, 0.95), transformed into a chance constraint, is preferably written as: .

[0056] Specifically, reliability opportunity constraints can be added as needed to ensure zero power shortage during critical load uninterrupted operation or switching phases, for example: ; in, The actual output of the energy storage system according to the strategy, to meet And corresponding amplitude limiting constraints and slope constraints.

[0057] Specifically, regarding transient stability requirements, if we consider bus voltage over-limit events and bus voltage frequency over-limit events as... and Then, these can be uniformly incorporated into the risk opportunity constraint (for subsequent simulation verification or as a penalty item): .

[0058] It is worth noting that chance constraints expressed in the form of joint probabilities can be replaced by equivalent deterministic constraints or sample approximation constraints. Firstly, Monte Carlo sampling, Latin hypercube sampling, or orthogonal trial methods can be used to generate the sample set, ensuring that the probability of satisfying the condition is not less than a certain threshold. Transform into satisfying a sample proportion of not less than First, it provides scenario constraints, thus avoiding strict assumptions about the distribution form; second, it can construct equivalent risk constraints based on quantiles (VaR) or conditional quantiles (CVaR), using bridging power and bridging energy under the quantiles as configuration design values ​​to achieve equivalent control over the confidence level; third, when rapid engineering calculations are required or easy embedded implementation is needed, upper bound estimation and conservative linearization methods can be used to transform the stochastic constraints of bridging power and bridging energy requirements into deterministic inequality constraints that can be directly solved, while still ensuring that the confidence level target holds in a conservative sense.

[0059] In one embodiment, the EMS constructs an objective function with the goal of minimizing the total lifecycle cost, preferably in the form of capacity cost + operating cost or depreciation cost + risk penalty: ; in, , and These are the equivalent cost coefficients for the rated power of the diesel generator, the rated power of the energy storage system, and the rated energy of the energy storage system, respectively. For the joint probability model The actual probability of satisfaction obtained from the assessment; Risk penalty weights. EMS can be derived from the joint probability model. Sample generation sample set Transform the chance constraint into a sample approximation form, for example, requiring at least the following conditions to be met. Power opportunity constraints and energy opportunity constraints for each sample: ; This yields a directly solvable collaborative configuration result. .

[0060] It is worth noting that the expression aiming at minimizing the total lifecycle cost can be replaced by a multi-objective or hierarchical objective. Firstly, a hierarchical optimization strategy prioritizing reliability and then cost can be adopted. This involves first finding the feasible region under the constraints of confidence level and transient stability, and then minimizing the cost within that region. Secondly, cost items such as energy storage lifespan reduction, diesel fuel costs, and supply failure penalties can be replaced with equivalent weights or interval coefficients to adapt to the cost scope and acceptance requirements of different projects. Thirdly, regarding the solution algorithm, mixed integer programming, sequential quadratic programming, heuristic intelligent optimization, or rule-based fast search can replace the general nonlinear optimization solver, as long as the output satisfies the confidence level constraints. The corresponding switching strategy is an equivalent implementation of the idea of ​​this invention.

[0061] Specifically, after obtaining the coordinated configuration results, the EMS formulates a power allocation strategy for the switching phase and maps the corresponding control signals. For example, after a mains power failure event is triggered, the EMS issues a control signal. (i.e., the first control command) disconnects the PCC grid-connected switch and calculates the energy storage reference power command based on the energy storage bridging gap power. (i.e., the second control command): ; in, The limiting function ensures that the energy storage output does not exceed the rated power; when the diesel generator meets the readiness and grid connection requirements, the EMS issues a command. Close the circuit and connect to the grid, and update according to the ramp constraint. and This allows the energy storage system to smoothly transition from bridging the main power supply to auxiliary regulation, thereby meeting the requirements. Under the premise of meeting the requirements of rapid energy storage response, the power supply continuity and risk controllability of the entire switching process are achieved.

[0062] Step S104: Map the collaborative configuration result into a control signal and send it to the PCC grid-connected switch, the DG grid-connected switch and the energy storage system, so that the mains-energy storage-diesel generator three-source switching system can allocate and store energy according to the collaborative configuration result.

[0063] It should be noted that mapping the collaborative configuration result into control signals and sending them to the PCC grid-connected switch, the DG grid-connected switch, and the energy storage system, so that the grid-storage-diesel three-source switching system can allocate power according to the collaborative configuration result, means that after obtaining the collaborative configuration result and power allocation strategy in step S103, the EMS maps it into corresponding control signals and sends them to each execution unit to complete the grid disconnection, energy storage bridging support, and diesel grid connection takeover. Simultaneously, during the execution process, the bus power quality and capacity boundaries are checked online, and when the risk approaches the threshold, strategy resetting or recalculation is triggered, thus forming a closed-loop operation mechanism. The overall event triggering and state switching process is as follows: Figure 4 As shown.

[0064] Preferably, the step of mapping the coordinated configuration result into control signals and sending them to the PCC grid-connected switch, the DG grid-connected switch, and the energy storage system, so that the mains-energy storage-diesel three-source switching system can allocate and store power according to the coordinated configuration result, includes: When a mains power failure event is detected, a first control command is sent to the PCC grid-connected switch to disconnect the mains power system. Within a preset energy storage response time window, a second control command is issued to the energy storage system to enable the energy storage system to perform bridging support; wherein, during the bridging support phase, the cumulative discharge energy is integrated and verified online, and when the cumulative discharge energy reaches a preset threshold, a power limiting or load shedding strategy is triggered; When the diesel generator meets the starting and grid connection conditions, a third control command is sent to the DG grid connection switch to enable the diesel generator to take over grid connection and complete the distribution and storage.

[0065] Preferably, after mapping the coordinated configuration result into control signals and sending them to the PCC grid-connected switch, the DG grid-connected switch, and the energy storage system, so that the mains-energy storage-diesel generator three-source switching system can allocate and store power according to the coordinated configuration result, the method further includes: Based on the real-time voltage and frequency of the AC bus, over-limit risk events are identified, and when an over-limit risk event is identified, a preset protection action is triggered. When a mains power restoration event is detected, the PCC grid-connected switch is issued a fourth control command based on the bus voltage stability condition, bus frequency stability condition, and grid connection logic, so that the mains power system is restored to grid connection.

[0066] In one embodiment, when a mains power failure event is detected... EMS was the first to issue the order. The PCC grid-connected switch is disconnected; subsequently, within the energy storage response time window (engineering constraints are...) Issue energy storage reference power command To cover the power gap during the switching process; when the diesel generator meets the requirements for starting and grid connection, an order will be issued. (i.e., the third control command) closes the DG grid connection switch to connect to the bus, and dynamically reduces the energy storage output during the diesel generator ramp-up takeover process to achieve smooth power handover.

[0067] Furthermore, to ensure that the energy constraint can be met, the EMS measures the cumulative discharge energy during the bridging phase. Conduct online points verification: ; Among them, when Approaching or In such cases, the EMS triggers a power limiting or load shedding strategy (as an alternative implementation) to prevent over-discharge of energy storage from causing protection actions.

[0068] Furthermore, the EMS uses the over-limit of bus voltage and bus frequency as the core criteria for online verification. If the effective value of bus voltage and the effective value of bus frequency satisfy: ; If this is determined to be an out-of-limit risk event, the EMS will preferentially trigger one of the following actions: increase energy storage support (within the power and energy limits), accelerate diesel generator takeover (increase ramp-up targets or grid connection priority), or enter protective degradation mode (e.g., restrict non-critical loads). In the event of grid power restoration... Subsequently, the EMS issues a closing command based on the bus voltage stability condition, bus frequency stability condition, and grid connection logic to restore the mains power grid connection and switch the system back to the mains power supply mode.

[0069] It is worth noting that the formation is driven by the power gap. Furthermore, there are alternative paths to the strategy of smooth handover after the diesel generator is connected to the grid. Firstly, the energy storage control method can be replaced from power command tracking to bus voltage / frequency support control (such as droop control or grid-type control), with the EMS only providing limiting and energy constraint boundaries. Secondly, the diesel generator takeover process can be replaced from a fixed ramping strategy to an adaptive ramping strategy based on bus frequency or power deviation to improve transient stability. Thirdly, a tiered load management strategy can be introduced on the load side as a supplementary strategy, ensuring the continuity of power supply to critical loads by cutting off non-critical loads when energy storage is insufficient or the diesel generator is unavailable. All of these alternative methods do not change the technical essence of collaboratively determining the diesel generator and energy storage capacity and generating the power allocation strategy during the switching phase under a pre-set information level; therefore, they can be considered alternative implementations of the technical solution of this invention.

[0070] Specifically, when the EMS detects a decrease in the available power boundary or available energy boundary of energy storage (such as reduced SOC, temperature rise / fall, etc.), unavailability of diesel generators or insufficient takeover capability, or persistent risk of exceeding limits, the EMS will trigger a strategy resetting or return to steps S102-S103 to recalculate the collaborative configuration results and switching strategy, thereby ensuring that the system's reliability and transient stability requirements are continuously met under different operating conditions.

[0071] This invention, through real-time acquisition of status data and event identification, provides status information and trigger signals for subsequent coordinated energy storage allocation. By constructing a set of random variables and a joint probability model, it provides a probabilistic basis for describing the distribution of each random variable and their interrelationships, avoiding misjudgments of risk caused by independent assumptions. By constructing opportunity constraints based on a pre-set confidence level and solving the objective function, it can obtain the coordinated configuration results of the diesel generator's rated power, energy storage's rated power, and energy storage's rated energy at a given confidence level. By mapping the coordinated configuration results into control signals and distributing them to each execution unit, a closed loop from capacity configuration to control execution can be achieved. Compared to existing technologies that struggle to quantify the probability of adverse scenarios occurring at a given reliability level, this application improves the robustness of the energy storage allocation strategy in a grid-energy storage-diesel generator three-source switching system under confidence level constraints.

[0072] Optionally, in this embodiment of the invention, the real-time acquisition of status data of the mains-energy storage-diesel generator three-source switching system, the performance of event identification based on the status data, and the generation of a status vector based on the identification results include: The status data of the AC bus, the energy storage system, the diesel generator, the PCC grid-connected switch, and the DG grid-connected switch are collected in real time. The effective value of the bus voltage, the bus frequency, and the active power of the load are calculated based on the state data of the AC bus, the window length, and the sampling period. The load power increment and the load power change rate are calculated based on the load active power and the sampling period. Event identification is performed based on the status data of the PCC grid-connected switch, the effective value of the bus voltage, the load power increment, and the load power change rate. When an event is detected, the effective value of the bus voltage, the bus frequency, the load active power, the status data of the energy storage system, the status data of the diesel generator, the status data of the PCC grid-connected switch, and the status data of the DG grid-connected switch are encapsulated into a status vector.

[0073] By refining the specific steps of data acquisition, calculation, and event identification, this invention can accurately obtain the effective value of bus voltage, bus frequency, load active power, load power increment, and load power change rate. Event identification is performed based on PCC switch status, effective voltage value, power increment, and change rate, providing a state vector for subsequent risk modeling and coordinated energy allocation.

[0074] Optionally, in this embodiment of the invention, the step of constructing a set of random variables based on the state vector and constructing a joint probability model according to the marginal distribution of the set of random variables includes: A set of random variables is constructed based on the state vector; wherein, the set of random variables includes the duration of mains power outage, load disturbance intensity, energy storage availability, and diesel generator availability; the load disturbance intensity is constructed from the load power increment and the load power change rate, the energy storage availability is constructed from the state data of the energy storage system, and the diesel generator availability is constructed from the state data of the diesel generator. Obtain the triplet corresponding to each random variable in the set of random variables, and construct marginal distributions based on each triplet; Each of the aforementioned edge distributions is mapped to a preset interval, and a joint probability model is constructed based on the mapping results.

[0075] This invention, by clearly defining the composition of the set of random variables and the construction method of each variable, and by specifying triples for each random variable to construct a marginal distribution and mapping it to a preset interval to construct a joint probability model, can form a computable joint probability model based on the engineering boundary and online state, providing input for risk quantification.

[0076] Optionally, in this embodiment of the invention, constructing opportunity constraints based on a preset confidence level based on the performance data of the diesel generator includes: Based on the rated power, gradeability, and start-up delay of the diesel generator, calculate the equivalent output of the diesel generator within the switching window; Based on the equivalent output and the active power of the load, calculate the energy storage bridging power gap; Based on the energy storage bridging power gap and switching window length, calculate the energy storage bridging power demand and energy storage bridging energy demand. Based on the energy storage bridging power demand, the energy storage bridging energy demand, and the state data of the energy storage system, an opportunity constraint based on a pre-set confidence level is constructed.

[0077] This invention calculates the equivalent output based on the diesel generator's rated power, ramp rate, and start-up delay; calculates the power gap based on the equivalent output and the load's active power; calculates the bridging power and bridging energy demand based on the power gap and the switching window length; and constructs opportunity constraints based on the bridging demand and energy storage status data. This allows the diesel generator's start-up delay and ramp characteristics, as well as the energy storage bridging demand and availability boundary, to be coupled into opportunity constraints, ensuring that capacity configuration and the switching dynamic process remain consistent.

[0078] Optionally, in this embodiment of the invention, the step of solving a preset objective function based on the joint probability model under the chance constraints to obtain the coordinated configuration result between the rated power of the diesel generator, the rated power of the energy storage system, and the rated energy of the energy storage system includes: The rated power of the diesel generator, the rated power of the energy storage system, and the rated energy of the energy storage system are used as decision variables, and multiple sets of random variable samples are generated from the joint probability model. Constraint judgments are performed on each random variable sample under different decision variable values. When the judgment result meets the preset conditions, the corresponding decision variable value is determined to satisfy the chance constraint. Among all decision variable values ​​that satisfy the opportunity constraints, the decision variable value that minimizes the total life cycle cost is determined, and this decision variable value is used as the collaborative configuration result.

[0079] This invention generates multiple sets of random variable samples from a joint probability model based on decision variables. It performs constraint judgment on samples with different values ​​of decision variables and counts the proportion of samples that meet the constraints. When the preset conditions are met, the decision variables are determined to meet the opportunity constraints. The decision variable with the lowest total life cycle cost among all feasible solutions is used as the collaborative configuration result. This can transform the opportunity constraints into a computable sample approximation form and obtain the configuration scheme with the lowest cost under the premise of meeting the opportunity constraints.

[0080] Optionally, in this embodiment of the invention, mapping the coordinated configuration result into a control signal and sending it to the PCC grid-connected switch, the DG grid-connected switch, and the energy storage system, so that the mains-energy storage-diesel generator three-source switching system performs power allocation and storage according to the coordinated configuration result, includes: When a mains power failure event is detected, a first control command is sent to the PCC grid-connected switch to disconnect the mains power system. Within a preset energy storage response time window, a second control command is issued to the energy storage system to enable the energy storage system to perform bridging support; wherein, during the bridging support phase, the cumulative discharge energy is integrated and verified online, and when the cumulative discharge energy reaches a preset threshold, a power limiting or load shedding strategy is triggered; When the diesel generator meets the starting and grid connection conditions, a third control command is sent to the DG grid connection switch to enable the diesel generator to take over grid connection and complete the distribution and storage.

[0081] This invention, through issuing a disconnection command when the mains power fails, issuing a bridging support command within the energy storage response window, and performing online integration verification of the accumulated discharge energy during the bridging phase, enables energy storage to cover the power gap during the diesel generator startup delay and triggers power limiting or load shedding strategies when the energy approaches the available boundary. By issuing a grid connection command when the diesel generator meets the startup and grid connection conditions, the invention enables the diesel generator to be connected to the grid and take over, thus completing the allocation and storage.

[0082] Optionally, in this embodiment of the invention, after mapping the coordinated configuration result into a control signal and sending it to the PCC grid-connected switch, the DG grid-connected switch, and the energy storage system, so that the mains-energy storage-diesel generator three-source switching system performs power allocation and storage according to the coordinated configuration result, the method further includes: Based on the real-time voltage and frequency of the AC bus, over-limit risk events are identified, and when an over-limit risk event is identified, a preset protection action is triggered. When a mains power restoration event is detected, the PCC grid-connected switch is issued a fourth control command based on the bus voltage stability condition, bus frequency stability condition, and grid connection logic, so that the mains power system is restored to grid connection.

[0083] The embodiments of the present invention identify and trigger protection actions based on bus voltage and frequency after distribution and storage, and can continuously monitor power quality after switching is completed; and can realize grid connection by issuing grid connection instructions based on voltage and frequency stability conditions and grid connection logic when the mains power is restored.

[0084] like Figure 5 As shown, based on the above-described method embodiments, another embodiment of the collaborative storage method considering confidence level constraints is provided, including steps 1 to 4.

[0085] Step 1, data acquisition and event triggering; where executing Step 1 is equivalent to executing the action of Step S101.

[0086] Step 2, uncertainty construction and correlation risk modeling; where, performing step 2 is equivalent to performing the action of step S102.

[0087] Step 3, confidence level constraint collaborative configuration and strategy generation; wherein, executing step 3 is equivalent to executing the action of step S103.

[0088] Step 4: Issuance, execution, and online verification and update of instructions; where executing step 4 is equivalent to executing the action of step S104.

[0089] Furthermore, based on the above-described method embodiments, an embodiment of a collaborative storage allocation system considering confidence level constraints is also provided, including a data acquisition layer, a decision calculation layer, and an execution control layer. The data flow and control flow of the collaborative storage allocation system are as follows: Figure 6 As shown.

[0090] Data Acquisition Layer: The EMS collects operational data of the mains-energy storage-diesel generator three-source switching system via the communication bus at fixed intervals, and forms a state vector for coordinated configuration and switching control. The real-time data includes at least: power quality indicators such as bus voltage and frequency, and load active power information, used to characterize the power demand and disturbance characteristics of critical loads; diesel generator set operating status information, including at least grid connection status, available output capacity, and operating parameters related to the takeover process, used to characterize the diesel generator's power supply capacity during start-up and takeover phases; energy storage system status information, including at least SOC and available power and energy boundaries given by the BMS / PCS, used to constrain the realized output of energy storage during the bridging phase; and mains system status information, including at least PCC grid connection switch status and mains voltage / frequency information, used to identify mains normal, power outage, and recovery events and trigger the switching process. The data acquisition layer can be used to execute the actions in step S101.

[0091] The decision calculation layer employs an EMS internal operational correlation risk quantification and confidence level constraint-based energy storage allocation algorithm to uniformly characterize uncertainties such as mains power outage duration, load disturbance intensity, and energy storage availability, and constructs a joint probability model reflecting the superimposed risks of adverse scenarios. Under pre-set confidence level constraints, it solves for the coordinated configuration results of diesel generator rated power, energy storage rated power, and energy storage rated energy, while simultaneously generating a power allocation strategy for the switching phase. This power allocation strategy includes at least: energy storage bridging support commands after mains power disconnection and takeover ramping commands after diesel generator grid connection, enabling the system to meet the timing requirements of rapid energy storage response and diesel generator start-up takeover while reducing the risk of bus voltage / frequency exceeding limits. The decision calculation layer can be used to execute steps S102 and S103.

[0092] Execution Control Layer: Each lower-level execution unit completes equipment actions and power tracking according to control commands issued by the EMS. Among them, the PCC grid-connected switch... To achieve grid connection or disconnection; the DG grid connection switch is based on... To enable diesel generator sets to connect to the bus and participate in load power supply; BESS according to The system executes energy storage output control, providing bridging support during mains power anomalies or outages, and achieving smooth power handover during diesel generator takeover. Simultaneously, the EMS continuously receives bus voltage / frequency, load power, and status variables from each power source, performing online verification of the switching process and power allocation effectiveness. When it detects a decrease in the energy storage availability boundary, limited diesel generator takeover, or bus power quality approaching a threshold, it triggers power limiting, strategy resetting, or recalculation mechanisms, thus forming a closed-loop data and control flow system consisting of acquisition, calculation, distribution, and verification. The execution control layer can be used to execute the actions in step S104.

[0093] This invention focuses on generating a diesel-power-energy storage collaborative configuration and switching strategy under confidence level constraints. By incorporating uncertainties such as the duration of mains power outages, the intensity of load disturbances, and the availability of energy storage into a unified probabilistic description, and solving for the rated power of the diesel generator, the rated power of the energy storage, and the rated energy of the energy storage under a preset confidence level, the power allocation strategy for the switching phase is output. This has produced quantifiable, verifiable, and implementable technical effects in emergency power supply scenarios for major events.

[0094] like Figure 7 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; One embodiment of the present invention provides a collaborative energy storage and distribution device considering confidence level constraints, applied to an energy management system; wherein, the energy management system is communicatively connected to a three-source switching system of mains power-energy storage-diesel generator, the three-source switching system of mains power-energy storage-diesel generator includes a mains power system, a diesel generator and an energy storage system connected to the same AC bus; the mains power system is connected to the AC bus through a PCC grid-connection switch, the diesel generator is connected to the AC bus through a DG grid-connection switch, and the energy storage system is connected to the AC bus in parallel; The collaborative storage device includes: an event identification module 701, a risk modeling module 702, a strategy generation module 703, and a strategy execution module 704; The event recognition module 701 is used to collect the status data of the mains-energy storage-diesel generator three-source switching system in real time, perform event recognition based on the status data, and generate a status vector based on the recognition results; The risk modeling module 702 is used to construct a set of random variables based on the state vector, and to construct a joint probability model based on the marginal distribution of the set of random variables. The strategy generation module 703 is used to construct a chance constraint based on a preset confidence level based on the performance data of the diesel generator, and solve a preset objective function under the chance constraint based on the joint probability model to obtain the coordinated configuration result between the rated power of the diesel generator, the rated power of the energy storage system and the rated energy of the energy storage system. The strategy execution module 704 is used to map the collaborative configuration result into a control signal and send it to the PCC grid-connected switch, the DG grid-connected switch and the energy storage system, so that the grid-energy storage-diesel three-source switching system can allocate and store energy according to the collaborative configuration result.

[0095] Optionally, in this embodiment of the invention, the event recognition module 701 includes: a data acquisition submodule, a load power calculation submodule, and an event recognition submodule; The data acquisition submodule is used to collect the status data of the AC bus, the energy storage system, the diesel generator, the PCC grid-connected switch, and the DG grid-connected switch in real time. The load power calculation submodule is used to calculate the effective value of the bus voltage, the bus frequency and the active power of the load based on the state data of the AC bus, the window length and the sampling period, and to calculate the load power increment and the load power change rate based on the load active power and the sampling period. The event recognition submodule is used to recognize events based on the status data of the PCC grid-connected switch, the effective value of the bus voltage, the load power increment, and the load power change rate. When an event is detected, the effective value of the bus voltage, the bus frequency, the load active power, the status data of the energy storage system, the status data of the diesel generator, the status data of the PCC grid-connected switch, and the status data of the DG grid-connected switch are encapsulated into a status vector.

[0096] By refining the specific steps of data acquisition, calculation, and event identification, this invention can accurately obtain the effective value of bus voltage, bus frequency, load active power, load power increment, and load power change rate. Event identification is performed based on PCC switch status, effective voltage value, power increment, and change rate, providing a state vector for subsequent risk modeling and coordinated energy allocation.

[0097] Optionally, in this embodiment of the invention, the risk modeling module 702 includes: a random variable set construction submodule, a marginal distribution construction submodule, and a joint probability model construction submodule; The random variable set construction submodule is used to construct a random variable set based on the state vector; wherein, the random variable set includes mains power outage duration, load disturbance intensity, energy storage availability, and diesel generator availability; the load disturbance intensity is constructed from the load power increment and the load power change rate, the energy storage availability is constructed from the state data of the energy storage system, and the diesel generator availability is constructed from the state data of the diesel generator; The marginal distribution construction submodule is used to obtain the triplet corresponding to each random variable in the set of random variables, and construct marginal distributions according to each triplet. The joint probability model construction submodule is used to map each of the marginal distributions to a preset interval and construct a joint probability model based on the mapping results.

[0098] This invention, by clearly defining the composition of the set of random variables and the construction method of each variable, and by specifying triples for each random variable to construct a marginal distribution and mapping it to a preset interval to construct a joint probability model, can form a computable joint probability model based on the engineering boundary and online state, providing input for risk quantification.

[0099] Optionally, in this embodiment of the invention, the strategy generation module 703 includes: an equivalent output calculation submodule, an energy storage bridging power gap calculation submodule, an energy storage bridging demand calculation submodule, and an opportunity constraint construction submodule; The equivalent output calculation submodule is used to calculate the equivalent output of the diesel generator within the switching window based on the rated power, gradeability, and start-up delay of the diesel generator. The energy storage bridging power gap calculation submodule is used to calculate the energy storage bridging power gap based on the equivalent output and the load active power. The energy storage bridging demand calculation submodule is used to calculate the energy storage bridging power demand and energy storage bridging energy demand based on the energy storage bridging power gap and the switching window length. The opportunity constraint construction submodule is used to construct opportunity constraints based on a preset confidence level, based on the energy storage bridging power demand, the energy storage bridging energy demand, and the state data of the energy storage system.

[0100] This invention calculates the equivalent output based on the diesel generator's rated power, ramp rate, and start-up delay; calculates the power gap based on the equivalent output and the load's active power; calculates the bridging power and bridging energy demand based on the power gap and the switching window length; and constructs opportunity constraints based on the bridging demand and energy storage status data. This allows the diesel generator's start-up delay and ramp characteristics, as well as the energy storage bridging demand and availability boundary, to be coupled into opportunity constraints, ensuring that capacity configuration and the switching dynamic process remain consistent.

[0101] Optionally, in this embodiment of the invention, the strategy generation module 703 further includes: a random variable sample sampling submodule, a constraint judgment submodule, and an objective function solving submodule; The random variable sample sampling submodule is used to generate multiple sets of random variable samples from the joint probability model, using the rated power of the diesel generator, the rated power of the energy storage system, and the rated energy of the energy storage system as decision variables. The constraint judgment submodule is used to perform constraint judgment on each random variable sample under different decision variable values. When the judgment result meets the preset conditions, the corresponding decision variable value is determined to satisfy the chance constraint. The objective function solving submodule is used to find the decision variable value that minimizes the total life cycle cost among all decision variable values ​​that satisfy the opportunity constraints, and to use the decision variable value as the collaborative configuration result.

[0102] This invention generates multiple sets of random variable samples from a joint probability model based on decision variables. It performs constraint judgment on samples with different values ​​of decision variables and counts the proportion of samples that meet the constraints. When the preset conditions are met, the decision variables are determined to meet the opportunity constraints. The decision variable with the lowest total life cycle cost among all feasible solutions is used as the collaborative configuration result. This can transform the opportunity constraints into a computable sample approximation form and obtain the configuration scheme with the lowest cost under the premise of meeting the opportunity constraints.

[0103] Optionally, in this embodiment of the invention, the strategy execution module 704 includes: a first control submodule, a second control submodule, and a third control submodule; The first control submodule is used to send a first control command to the PCC grid-connected switch when a mains power failure event is detected, so as to disconnect the mains power system; The second control submodule is used to issue a second control command to the energy storage system within a preset energy storage response time window, so that the energy storage system can perform bridging support; wherein, during the bridging support stage, the cumulative discharge energy is integrated and verified online, and when the cumulative discharge energy reaches a preset threshold, a power limiting or load shedding strategy is triggered. The third control submodule is used to send a third control command to the DG grid connection switch when the diesel generator meets the starting conditions and grid connection conditions, so that the diesel generator can be connected to the grid and complete the distribution and storage.

[0104] This invention, through issuing a disconnection command when the mains power fails, issuing a bridging support command within the energy storage response window, and performing online integration verification of the accumulated discharge energy during the bridging phase, enables energy storage to cover the power gap during the diesel generator startup delay and triggers power limiting or load shedding strategies when the energy approaches the available boundary. By issuing a grid connection command when the diesel generator meets the startup and grid connection conditions, the invention enables the diesel generator to be connected to the grid and take over, thus completing the allocation and storage.

[0105] Optionally, in this embodiment of the invention, after the policy execution module 704, there are further: an over-limit risk event identification submodule and a network connection recovery submodule; The over-limit risk event identification submodule is used to identify over-limit risk events based on the real-time voltage and real-time frequency of the AC bus. When an over-limit risk event is identified, a preset protection action is triggered. The grid connection recovery submodule is used to issue a fourth control command to the PCC grid connection switch when a mains power recovery event is detected, based on the bus voltage stability condition, bus frequency stability condition and grid connection logic, so as to restore the mains power system to grid connection.

[0106] The embodiments of the present invention identify and trigger protection actions based on bus voltage and frequency after distribution and storage, and can continuously monitor power quality after switching is completed; and can realize grid connection by issuing grid connection instructions based on voltage and frequency stability conditions and grid connection logic when the mains power is restored.

[0107] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement a collaborative storage method considering confidence level constraints provided by any of the above-described method embodiments of the present invention.

[0108] This invention employs an event identification module 701 to collect status data in real time and identify events, providing status information and trigger signals for subsequent coordinated energy storage allocation. A risk modeling module 702 constructs a set of random variables and a joint probability model, providing a probabilistic basis for describing the distribution of each random variable and their interrelationships, avoiding misjudgments due to independent assumptions. A strategy generation module 703 constructs opportunity constraints based on a pre-set confidence level and solves the objective function, enabling the coordinated configuration of the diesel generator's rated power, energy storage's rated power, and energy storage's rated energy at a given confidence level. A strategy execution module 704 maps the coordinated configuration results into control signals and distributes them to each execution unit, achieving a closed loop from capacity configuration to control execution. Compared to existing technologies that struggle to quantify the probability of adverse scenarios occurring at a given reliability level, this application improves the robustness of the energy storage allocation strategy in a grid-energy storage-diesel generator three-source switching system under confidence level constraints.

[0109] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0110] Based on the above embodiment of a collaborative storage allocation method considering confidence level constraints, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a collaborative storage allocation method considering confidence level constraints according to any embodiment of the present invention.

[0111] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0112] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0113] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0114] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute a collaborative storage allocation method considering confidence level constraints as described in any of the above-described method embodiments of the present invention.

[0115] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0116] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A collaborative storage allocation method considering confidence level constraints, characterized in that, This system is applied to an energy management system. The energy management system is communicatively connected to a three-source switching system (mains-storage-diesel generator). The three-source switching system includes a mains power system, a diesel generator, and an energy storage system connected to the same AC bus. The mains power system is connected to the AC bus via a PCC grid-connected switch, the diesel generator is connected to the AC bus via a DG grid-connected switch, and the energy storage system is connected in parallel to the AC bus. The collaborative storage method includes: The status data of the mains-energy storage-diesel generator three-source switching system is collected in real time, events are identified based on the status data, and a status vector is generated based on the identification results. A set of random variables is constructed based on the state vector, and a joint probability model is constructed based on the marginal distribution of the set of random variables; Based on the performance data of the diesel generator, a chance constraint based on a pre-set confidence level is constructed, and based on the joint probability model, a preset objective function is solved under the chance constraint to obtain the coordinated configuration result between the rated power of the diesel generator, the rated power of the energy storage system, and the rated energy of the energy storage system. The collaborative configuration result is mapped into a control signal and sent to the PCC grid-connected switch, the DG grid-connected switch, and the energy storage system, so that the mains-energy storage-diesel generator three-source switching system can allocate and store energy according to the collaborative configuration result.

2. The collaborative storage allocation method considering confidence level constraints as described in claim 1, characterized in that, The system collects real-time status data from the mains-energy storage-diesel generator three-source switching system, identifies events based on the status data, and generates a status vector based on the identification results, including: The status data of the AC bus, the energy storage system, the diesel generator, the PCC grid-connected switch, and the DG grid-connected switch are collected in real time. The effective value of the bus voltage, the bus frequency, and the active power of the load are calculated based on the state data of the AC bus, the window length, and the sampling period. The load power increment and the load power change rate are calculated based on the load active power and the sampling period. Event identification is performed based on the status data of the PCC grid-connected switch, the effective value of the bus voltage, the load power increment, and the load power change rate. When an event is detected, the effective value of the bus voltage, the bus frequency, the load active power, the status data of the energy storage system, the status data of the diesel generator, the status data of the PCC grid-connected switch, and the status data of the DG grid-connected switch are encapsulated into a status vector.

3. The collaborative storage allocation method considering confidence level constraints as described in claim 2, characterized in that, The step of constructing a set of random variables based on the state vector and constructing a joint probability model based on the marginal distribution of the set of random variables includes: A set of random variables is constructed based on the state vector; wherein, the set of random variables includes the duration of mains power outage, load disturbance intensity, energy storage availability, and diesel generator availability; the load disturbance intensity is constructed from the load power increment and the load power change rate, the energy storage availability is constructed from the state data of the energy storage system, and the diesel generator availability is constructed from the state data of the diesel generator. Obtain the triplet corresponding to each random variable in the set of random variables, and construct marginal distributions based on each triplet; Each of the aforementioned edge distributions is mapped to a preset interval, and a joint probability model is constructed based on the mapping results.

4. The collaborative storage allocation method considering confidence level constraints as described in claim 2, characterized in that, The process of constructing opportunity constraints based on a pre-set confidence level using the performance data of the diesel generator includes: Based on the rated power, gradeability, and start-up delay of the diesel generator, calculate the equivalent output of the diesel generator within the switching window; Based on the equivalent output and the active power of the load, calculate the energy storage bridging power gap; Based on the energy storage bridging power gap and switching window length, calculate the energy storage bridging power demand and energy storage bridging energy demand. Based on the energy storage bridging power demand, the energy storage bridging energy demand, and the state data of the energy storage system, an opportunity constraint based on a pre-set confidence level is constructed.

5. A collaborative storage allocation method considering confidence level constraints as described in claim 1, characterized in that, The step of solving a preset objective function based on the joint probability model under the chance constraints to obtain the coordinated configuration result between the rated power of the diesel generator, the rated power of the energy storage system, and the rated energy of the energy storage system includes: The rated power of the diesel generator, the rated power of the energy storage system, and the rated energy of the energy storage system are used as decision variables, and multiple sets of random variable samples are generated from the joint probability model. Constraint judgments are performed on each random variable sample under different decision variable values. When the judgment result meets the preset conditions, the corresponding decision variable value is determined to satisfy the chance constraint. Among all decision variable values ​​that satisfy the opportunity constraints, the decision variable value that minimizes the total life cycle cost is determined, and this decision variable value is used as the collaborative configuration result.

6. A collaborative storage allocation method considering confidence level constraints as described in claim 2, characterized in that, The step of mapping the coordinated configuration result into control signals and sending them to the PCC grid-connected switch, the DG grid-connected switch, and the energy storage system, so that the mains-energy storage-diesel three-source switching system can allocate and store power according to the coordinated configuration result, includes: When a mains power failure event is detected, a first control command is sent to the PCC grid-connected switch to disconnect the mains power system. Within a preset energy storage response time window, a second control command is issued to the energy storage system to enable the energy storage system to perform bridging support; wherein, during the bridging support phase, the cumulative discharge energy is integrated and verified online, and when the cumulative discharge energy reaches a preset threshold, a power limiting or load shedding strategy is triggered; When the diesel generator meets the starting and grid connection conditions, a third control command is sent to the DG grid connection switch to enable the diesel generator to take over grid connection and complete the distribution and storage.

7. A collaborative storage allocation method considering confidence level constraints as described in claim 6, characterized in that, After mapping the coordinated configuration result into control signals and sending them to the PCC grid-connected switch, the DG grid-connected switch, and the energy storage system, so that the mains-energy storage-diesel three-source switching system can allocate and store power according to the coordinated configuration result, the system further includes: Based on the real-time voltage and frequency of the AC bus, over-limit risk events are identified, and when an over-limit risk event is identified, a preset protection action is triggered. When a mains power restoration event is detected, the PCC grid-connected switch is issued a fourth control command based on the bus voltage stability condition, bus frequency stability condition, and grid connection logic, so that the mains power system is restored to grid connection.

8. A cooperative storage device considering confidence level constraints, characterized in that, This system is applied to an energy management system. The energy management system is communicatively connected to a three-source switching system (mains-storage-diesel generator). The three-source switching system includes a mains power system, a diesel generator, and an energy storage system connected to the same AC bus. The mains power system is connected to the AC bus via a PCC grid-connected switch, the diesel generator is connected to the AC bus via a DG grid-connected switch, and the energy storage system is connected in parallel to the AC bus. The collaborative storage device includes: an event identification module, a risk modeling module, a strategy generation module, and a strategy execution module; The event recognition module is used to collect the status data of the mains-energy storage-diesel generator three-source switching system in real time, perform event recognition based on the status data, and generate a status vector based on the recognition results. The risk modeling module is used to construct a set of random variables based on the state vector, and to construct a joint probability model based on the marginal distribution of the set of random variables. The strategy generation module is used to construct opportunity constraints based on the performance data of the diesel generator and a preset confidence level, and to solve a preset objective function under the opportunity constraints based on the joint probability model, so as to obtain the coordinated configuration result between the rated power of the diesel generator, the rated power of the energy storage system and the rated energy of the energy storage system. The strategy execution module is used to map the collaborative configuration result into a control signal and send it to the PCC grid-connected switch, the DG grid-connected switch and the energy storage system, so that the grid-energy storage-diesel generator three-source switching system can allocate and store energy according to the collaborative configuration result.

9. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a collaborative storage allocation method considering confidence level constraints as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a collaborative storage allocation method considering confidence level constraints as described in any one of claims 1-7.

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