A simulation method and system for aggregating small single loads into a large load

Abstract

The present invention relates to a simulation method and system for aggregating single small loads into a large load. The method comprises: step 1, simulating the multi-source uncertainty of the single small load based on a physical model; wherein the multi-source uncertainty includes the energy capacity, initial SOC, whether energy replenishment is required, expected duration of access, actual duration of access, and random selection of energy replenishment mode of the single small load; step 2, simulating different states of the energy conversion device, establishing a state transition simulation model, and simulating the behavior of the energy conversion device transitioning between different states over time; step 3, treating the aggregated large load as the load of all energy conversion devices, and modeling and simulating the aggregated large load based on time series simulation and random sampling. The present invention takes into account the multi-source uncertainty of the single small load, and transmits the uncertainty upward during the aggregation process, and finally describes the time series average power and uncertainty through the aggregated large load.

Description

Technical Field

[0001] The present invention belongs to the technical field of aggregated large loads, and in particular relates to a simulation method and system for aggregating individual small loads into a large load. Background Art

[0002] Aggregated large loads (referred to as "large loads"), such as those at electric vehicle charging stations and electric boiler clusters, possess three characteristics: 1. They meet energy requirements within a limited timeframe; 2. They are formed from the bottom-up aggregation of multiple small individual loads. For example, the load at an electric vehicle charging station is the aggregation of multiple charging piles, while the load at an electric boiler cluster is the aggregation of multiple electric boilers; and 3. The lowest-level individual loads exhibit high volatility, randomness, and intermittency (three characteristics). With the advancement of electric energy substitution, the growth of these large loads is increasingly impacting the safe and stable operation of the power system. On the one hand, large loads can cause significant load spikes, posing challenges to frequency and peak regulation and exacerbating grid congestion. On the other hand, large loads formed by the aggregation of multiple small individual loads offer potential flexibility, supporting frequency regulation, peak regulation, and congestion reduction. To exploit this flexibility, it is first necessary to develop methods and systems for aggregating small individual loads (such as individual charging piles) into large loads. Individual small loads also exhibit multiple sources of uncertainty. For example, in the case of electric vehicles, these sources include: uncertain travel characteristics, uncertain initial state of charge (SOC), uncertain charging patterns, and diverse and uncertain battery configurations. These uncertainties are transmitted upward during the aggregation of individual small loads into large ones, forming overall uncertainty at the aggregated large load level. Modeling individual small loads and aggregated large loads has the following implications: 1. It supports grid dispatchers and planners in assessing the severity and solutions to problems such as frequency regulation, peak regulation, distribution network congestion, and low voltage caused by large loads on the grid at both the operational and planning scales, and assesses and exploits the flexibility of large loads to mitigate these issues. 2. It supports virtual power plant operators in assessing and exploiting the flexibility of large loads to provide ancillary services to the grid. 3. It supports microgrid operators in enabling microgrids containing distributed power sources, energy storage, and large loads to minimize the cost of purchasing electricity from the grid and maximize the timing of large loads with wind and solar power generation to ensure that large loads utilize green electricity. Therefore, a bottom-up simulation technology and system for the aggregation of individual small loads into large loads based on physical properties is urgently needed. Summary of the Invention

[0003] The purpose of the present invention is to provide a simulation method and system for aggregating single small loads into a large load. The method takes into account the multi-source uncertainty of the single small loads and transmits the uncertainty upward during the aggregation process. Finally, the time-series average power and uncertainty are described by the aggregated large load. The method is suitable for scenarios where single small loads are aggregated into a large load.

[0004] The present invention provides a simulation method for aggregating a small monomer load into a large load, comprising the following steps:

[0005] Step 1: Simulate the multi-source uncertainty of a single small load based on a physical model; wherein the multi-source uncertainty includes the energy capacity of the single small load, the initial SOC, whether recharging is required, the expected duration of connection, the actual duration of connection, and the random selection of recharging modes; the recharging modes include automatic full energy replenishment mode, fixed duration recharging mode, and fixed power recharging mode;

[0006] Step 2: Simulate different states of the energy conversion device and establish a state transition simulation model to simulate the behavior of the energy conversion device transitioning between different states over time; the states include: i) connected to a single load and outputting energy, ii) occupied by a single load but not outputting energy, iii) idle state not occupied by a single load, iv) unavailable due to being occupied by a non-load individual, and v) fault; wherein, states i) and ii) can transition to each other; states ii) and iii) can transition to each other; states iii) and iv) can transition to each other; states i)-iv) can transition to state v); and state v) can transition to state iii).

[0007] Step 3: Consider the aggregated large load as the load of all energy conversion devices, and perform modeling and simulation on the aggregated large load based on time series simulation and random sampling, including:

[0008] Establish a time series simulation model to simulate the behavior of single loads, energy conversion devices, and aggregated large loads at a set time in the future using a manually set sampling period;

[0009] At each simulation time point, the state of each energy conversion device is traversed and probabilistic simulation is performed to obtain the load power result of each energy conversion device based on the probability description;

[0010] Perform random simulation on the new single load flow;

[0011] Traverse each new single load and perform probabilistic simulation, including simulation of its interaction with the energy conversion device, and update the load power results of the energy conversion device connected to the single load;

[0012] The load power results of all energy conversion devices are aggregated and accumulated to obtain the aggregated large load power result.

[0013] Furthermore, the uncertainty simulation of the energy capacity in step 1 includes:

[0014] 1) Set the minimum capacity C at 25°C min and the maximum capacity C maxTwo parameters;

[0015] 2) According to the ambient temperature, the minimum capacity C min and the maximum capacity C max Correction is made, assuming that the two capacities will not decay above 5°C, but will decay linearly below 5°C until they decay to 60% at -20°C, and remain unchanged below -20°C, thus obtaining the minimum capacity C' after temperature correction. min and the maximum capacity C′ max ;

[0016] 3) Based on [C′ min ,C′ max ] a probability density function uniformly distributed within the interval, performing a sampling operation based on the probability density function to obtain a sampling value of the single small load energy capacity;

[0017] Furthermore, the uncertainty simulation of the initial SOC in step 1 includes:

[0018] 1) Set the statistical average μ of the initial SOC Soc , standard deviation σ soc , lower bound l soc =20%, upper bound U soc =80%;

[0019] 2) Establish μ soc is the mean, σ soc is the standard deviation, l soc is the lower bound, U soc is the probability density function of the truncated normal distribution with an upper bound;

[0020] 3) performing a sampling operation based on the probability density function to obtain a sampling value of the initial SOC of the single cell under light load;

[0021] The uncertainty simulation of whether energy replenishment is needed includes:

[0022] Assume that the discrete probability distribution is a function of SOC, and the probability of needing energy replenishment P need for:

[0023]

[0024] Given a single small load and its initial SOC, sampling is performed according to a discrete probability distribution to obtain a sampling value of whether the single small load needs energy replenishment. The sampling value is 0 or 1, where 0 represents no energy replenishment and 1 represents energy replenishment.

[0025] Furthermore, the uncertainty simulation of the expected access maintenance duration in step 1 includes:

[0026] Set the expected duration of connection, the average value, standard deviation, upper and lower bounds, substitute the parameters of the duration, the average value, standard deviation, and upper and lower bounds into a probability density function of a truncated normal distribution, and perform sampling based on the probability density function to obtain a sampled value of the expected duration of connection for a single light load.

[0027] The uncertainty simulation of the actual duration of maintaining access includes:

[0028] The actual average duration of access is set as the expected average duration of access, standard deviation, upper and lower bounds, and the average duration, standard deviation, upper and lower bound parameters are substituted into the function to establish a probability density function of the truncated normal distribution. The sampling operation is performed according to the probability density function to obtain the sampling value of the actual duration of access of the single small load.

[0029] Furthermore, the uncertainty simulation of the random selection of the energy replenishment mode in step 1 includes:

[0030] 1) Set the probability of selecting the automatic full energy mode to P1, the probability of selecting the fixed duration energy mode to P2, and the probability of selecting the fixed power energy mode to 1-P1-P2;

[0031] 2) Performing random sampling based on the probability distribution of the three energy replenishment modes to obtain a sampling value for the energy replenishment mode selected by the single small load, wherein the sampling value is 1, 2, or 3, corresponding to the automatic full energy replenishment mode, the fixed duration energy replenishment mode, and the fixed power energy replenishment mode, respectively;

[0032] 3) If the sampling value is 2, set the mean, standard deviation, lower bound, and upper bound of the charging duration, and substitute these four parameters into a probability density function of a truncated normal distribution. Sampling is performed based on this probability density function to obtain the sampling value of the fixed charging duration of a single small load.

[0033] 4) If the sampling value is 3, set the mean, standard deviation, lower bound, and upper bound of the charging duration, and substitute these four parameters to establish the probability density function of the truncated normal distribution. Perform the sampling operation based on this probability density function to obtain the sampling value of the fixed charging power of the single small load.

[0034] Furthermore, the state transition simulation model in step 2 is as follows:

[0035] At any moment, if a small single load that needs to be replenished arrives and there is at least one energy conversion device in state iii), the small single load will be connected to one of the energy conversion devices in state iii) and the energy conversion device will be transferred from state iii) to state i);

[0036] At any moment, if a small single load that is being replenished has just been fully replenished, the energy conversion device connected to it will be transferred from state i) to state ii);

[0037] At any moment, if a single small load is fully charged but still connected to the energy conversion device, the state of the device is ii);

[0038] At any moment, if the single small load that occupied the energy conversion device at the previous moment leaves, the device is transferred from state i) or ii) to state iii);

[0039] At any moment, if the energy conversion device is in state iii) at the previous moment and a non-load individual occupies the position of the energy conversion device at this moment, the device is transferred from state iii) to state iv);

[0040] At any moment, if the energy conversion device suddenly fails, the state will be transferred from other states to state v).

[0041] Furthermore, the step 3 specifically includes:

[0042] Construct a three-layer loop, the outermost layer is a timing large loop, recorded as the first layer loop, set the simulation start time and end time and the simulation step size Δt; at each simulation time t, execute the loop traversal for each energy conversion device, recorded as the second layer loop. For each energy conversion device, if it has a single small load connected and needs to be replenished, perform the energy replenishment operation for Δt, accumulate the load to the load of the aggregated large load at time t, and update the status of the energy conversion device to connected single load and outputting energy; if the single small load leaves at this time, the status of the energy conversion device is updated to unconnected single load;

[0043] After traversing all energy conversion devices, according to the preset traffic flow parameters, the normal distribution probability density function of the number of new single small loads in the time period [t, t+Δt] is established, and the function is sampled to obtain the number m of new single small loads in the time period Δt; the single small load capacity established in step 1 is obtained in [C′ min ,C′ max], sample the probability density function of uniform distribution in the interval, and obtain the capacity of all m single small loads; obtain the probability density function of the truncated normal distribution of the single small load SOC established in step 1, and sample the function to obtain the SOC value of all m single small loads; obtain the probability density function of the expected maintenance connection time of the single small load established in step 2, and sample the function to obtain the expected maintenance connection time of all m single small loads; obtain the probability density function of the actual maintenance connection time of the single small load established in step 2, and sample the function to obtain the actual maintenance connection time of all m single small loads; obtain the energy replenishment mode of each single small load and the parameters under the mode according to step 2; according to step 1, sample and obtain the switch value of whether all m single small loads need energy replenishment;

[0044] After that, the second-layer loop is entered, traversing the loops of m single-unit small loads; for the i-th single-unit small load, if it does not need energy replenishment, it will proceed to the next single-unit small load; if the single-unit small load needs energy replenishment, it will enter the third-layer loop and traverse all energy conversion devices; for the j-th energy conversion device, if it is not in an idle state not occupied by a single-unit load, it will proceed to the next energy conversion device; if it is in an idle state not occupied by a single-unit load, the i-th single-unit small load will be connected to the j-th energy conversion device, and the latter will replenish energy to the former for Δt time, and the load will be accumulated to the load of the aggregated large load at time t;

[0045] Finally, after three cycles, the aggregated large load values ​​at all times t are output, achieving a single simulation from bottom to top, from individual small loads to energy conversion devices, and then to aggregated large loads.

[0046] This process is repeated multiple times, and each time different aggregated large load timing results are obtained due to randomness. Based on the different results obtained multiple times, a probability density function is fitted for each moment t to obtain the timing probability results of the aggregated large load, realizing a bottom-up probability simulation from single small load to energy conversion device, and then to aggregated large load.

[0047] The present invention also provides a simulation system for aggregating single small loads into a large load, comprising a calculation module, wherein the calculation module executes the simulation method for aggregating single small loads into a large load.

[0048] The present invention also provides a non-transitory computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the simulation method of aggregating a single small load into a large load is implemented.

[0049] The present invention also provides an electronic device, comprising:

[0050] A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the simulation method of aggregating a single small load into a large load by executing the computer instructions.

[0051] The above scheme provides a simulation method and system for aggregating individual small loads into a large load, which has the following technical effects:

[0052] 1) It can describe the multi-source uncertainty of single small load.

[0053] 2) Various charging modes such as “free charging”, “fixed-time charging”, and “fixed-power charging” can be modeled.

[0054] 3) Various states of energy conversion devices can be described.

[0055] 4) Aggregation based on timing simulation can obtain the aggregated large load of the timing.

[0056] 5) The large load quantile estimates after the aggregation of monomer small loads can be obtained, including quartile estimates and decile estimates.

[0057] 6) It is suitable for the polymerization of any number of monomers with small loads, can meet a wide range of practical needs, and supports the selection of an appropriate range for polymerization based on actual needs.

[0058] 7) It has a wide range of application scenarios and is suitable for photovoltaic storage and charging microgrid systems, power systems, virtual power plant systems, integrated energy systems, as well as the optimized operation, optimization planning, and optimization scheduling scenarios of each system.

[0059] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a flow chart of a simulation method for polymerizing a monomer from a small load to a large load according to the present invention;

[0061] Figure 2 It is a schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0062] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0063] Ginseng Figure 1As shown, this embodiment provides a simulation method for aggregating single small loads into a large load, which is applicable to the scenario where single small loads are aggregated into a large load, and takes into account the following uncertainties of single small loads: the time-varying single small load flow rate and its uncertainty (taking electric vehicles as an example, the change in its inbound and outbound flow rate and its uncertainty), the uncertainty of the initial state of charge (SOC), the uncertainty of the capacity (the total amount of electricity that can be absorbed), the uncertainty of whether the single small load is connected, and the uncertainty of the duration of the single small load connection (if connected). The simulation method first models the single small load, and then models the energy conversion device (such as a charging pile), considering the uncertainty of whether the energy conversion device is idle or not in a fault maintenance state, and finally aggregates the energy conversion device to form an aggregated large load. The specific steps are as follows:

[0064] Step S1: Simulate the multi-source uncertainty of a single small load based on a physical model; wherein the multi-source uncertainty includes the energy capacity of the single small load (taking an electric vehicle as an example, that is, its battery capacity), the initial SOC (state of charge), whether recharging is required, the expected duration of connection (for example, the expected duration of the electric vehicle staying and remaining connected to the charging pile), the actual duration of connection, and the random selection of recharging modes; the recharging modes include automatic full energy mode, fixed duration recharging mode, and fixed power recharging mode;

[0065] Step S2, simulate the different states of the energy conversion device, establish a state transition simulation model, and simulate the behavior of the energy conversion device transferring between different states over time. Taking electric vehicles as an example, it is the modeling and simulation of charging piles. This embodiment has 5 states: i) connected to a single load and outputting energy, ii) occupied by a single load but not outputting energy, iii) idle state not occupied by a single load, iv) unavailable due to being occupied by a non-load individual (for example, an electric vehicle charging position is occupied by a fuel vehicle), and v) fault; among them, states i) and ii) can be transferred to each other; states ii) and iii) can be transferred to each other; states iii) and iv) can be transferred to each other; states i)-iv) can be transferred to state v); state v) can be transferred to state iii);

[0066] Step S3, treating the aggregated large load as the aggregated load of all energy conversion devices, and performing modeling and simulation on the aggregated large load based on time series simulation and random sampling, including:

[0067] Establish a time series simulation model to simulate the behavior of single loads, energy conversion devices, and aggregated large loads at a set time in the future (such as the next 24 hours) with a manually set sampling period (such as 5 minutes);

[0068] At each simulation time point, the state of each energy conversion device is traversed and probabilistic simulation is performed to obtain the load power result of each energy conversion device based on the probability description;

[0069] Perform random simulation on the new single load flow;

[0070] Traverse each new single load and perform probabilistic simulation, including simulation of its interaction with the energy conversion device, and update the load power results of the energy conversion device connected to the single load;

[0071] The load power results of all energy conversion devices are aggregated and accumulated to obtain the aggregated large load power result.

[0072] In this embodiment, the uncertainty simulation of energy capacity in step S1 includes:

[0073] 1) Set the minimum capacity (kWh) at 25°C min and maximum capacity (kWh) C max Two parameters;

[0074] 2) According to the ambient temperature (determined by the climate) the minimum capacity C min and the maximum capacity C max Correction is made, assuming that the two capacities will not decay above 5°C, but will decay linearly below 5°C until they decay to 60% at -20°C, and remain unchanged below -20°C, thus obtaining the minimum capacity C' after temperature correction. min and the maximum capacity c′ max ;

[0075] 3) Based on [C′ min ,C′ max ] is a probability density function uniformly distributed within the interval, and a sampling operation is performed based on the probability density function to obtain a sampling value of the single-cell small load energy capacity.

[0076] Formula (1) describes the probability density function followed by the single small load energy capacity:

[0077]

[0078] Where: μ c ,σ c ,α c ,β c are the mean, standard deviation, lower limit and upper limit of electric vehicle battery capacity respectively; x c is the nominal battery capacity; φ and Φ are the probability density function and cumulative distribution function of the general Gaussian distribution (unbounded), respectively.

[0079] Formula (2) describes the correction factor of the single small load energy capacity affected by temperature:

[0080]

[0081] Where: K c is the battery capacity factor, and T is the ambient temperature. The actual battery capacity is the product of the nominal battery capacity and the capacity factor.

[0082] In the uncertainty simulation of energy capacity, the maximum power required for a single load can be corrected based on the ambient temperature. The rule is that at -10°C, the lower the temperature, the lower the maximum power required, reflecting the phenomenon that severe cold causes the battery capacity to decrease.

[0083] The uncertainty simulation of the initial SOC includes:

[0084] 1) Set the statistical average μ of the initial SOC soc , standard deviation σ soc , lower bound l soc =20%, upper bound U soc =80%;

[0085] 2) Establish μ soc is the mean, σ soc is the standard deviation, l soc is the lower bound, U soc The probability density function of the truncated normal distribution with :

[0086]

[0087] Where: μ SOC ,σ SOC ,α SOC ,β SOC are the mean, standard deviation, lower limit and upper limit of the initial SOC of the electric vehicle battery; x SOC is the initial SOC; the definitions of other variables are the same as those in formula (1).

[0088] 3) Performing a sampling operation based on the probability density function to obtain a sampling value of the initial SOC of the single cell under light load.

[0089] The uncertainty of whether a small load needs to be recharged (recharged) depends on the initial SOC value of the small load. For example, the lower the initial SOC value of an electric vehicle, the more likely the user needs to charge.

[0090] The uncertainty simulation of whether energy replenishment (charging) is needed includes:

[0091] Assume that the discrete probability distribution is a function of SOC, and the probability of needing energy replenishment P need for:

[0092]

[0093] It should be noted that the above function can be modified based on actual conditions; the maximum SOC value is 80%. Given a small load cell and its initial SOC, sampling is performed according to a discrete probability distribution to obtain a sampled value indicating whether the small load cell requires energy replenishment. The sampled value is 0 or 1, where 0 indicates no energy replenishment is required and 1 indicates energy replenishment is required.

[0094] The uncertain simulation of the expected duration of maintaining access includes:

[0095] Set the expected duration of connection, the average value, standard deviation, upper and lower bounds, substitute the parameters of the duration, the standard deviation, and the upper and lower bounds to establish a probability density function of the truncated normal distribution, and perform sampling operations based on this probability density function to obtain the sampling value of the expected duration of connection of a single light load.

[0096] The expected duration of a single light load connection follows the following probability density function:

[0097]

[0098] Where: μ es ,σ es ,α es ,β es are the mean, standard deviation, lower limit and upper limit of the expected stay time of electric vehicles; x es is the expected residence time; the definitions of other variables are the same as those in formula (1).

[0099] The uncertainty simulation of the actual duration of maintaining access includes:

[0100] Set the actual maintenance time average as the expected maintenance time average, standard deviation, upper and lower bounds, substitute the duration average, standard deviation, upper and lower bounds into the probability density function of the truncated normal distribution, and perform sampling based on this probability density function to obtain the actual maintenance time sampling value of the single small load. Where:

[0101] The actual duration of a single light load connection follows the following probability density function:

[0102]

[0103] Where: σ es is the standard deviation of the actual residence time of electric vehicles; x as is the actual residence time, and the definitions of other variables are the same as those in formula (5).

[0104] The uncertainty simulation of random selection of the energy replenishment mode includes:

[0105] 1) Set the probability of selecting the automatic full energy mode to P1, the probability of selecting the fixed duration energy mode to P2, and the probability of selecting the fixed power energy mode to 1-P1-P2;

[0106] 2) Performing random sampling based on the probability distribution of the three energy replenishment modes to obtain a sampling value for the energy replenishment mode selected by the single small load, wherein the sampling value is 1, 2, or 3, corresponding to the automatic full energy replenishment mode, the fixed duration energy replenishment mode, and the fixed power energy replenishment mode, respectively;

[0107] 3) If the sampling value is 2 (fixed duration charging mode), set the mean, standard deviation, lower bound, and upper bound of the charging duration, and substitute these four parameters into a probability density function of a truncated normal distribution. Sampling is performed based on this probability density function to obtain the sampling value of the fixed charging duration of the single small load.

[0108] 4) If the sampling value is 3 (fixed power charging mode), then set the mean, standard deviation, lower bound, and upper bound of the charging duration, and substitute these four parameters to establish the probability density function of the truncated normal distribution. Perform the sampling operation based on this probability density function to obtain the sampling value of the fixed power charging of the single small load.

[0109] In this embodiment, the state transition simulation model in step S2 is as follows:

[0110] At any one time, an energy conversion device can be in only one state:

[0111] At any moment, if a small single load that needs to be replenished arrives and there is at least one energy conversion device in state iii), the small single load will be connected to one of the energy conversion devices in state iii) and the energy conversion device will be transferred from state iii) to state i);

[0112] At any moment, if a small single load that is being replenished has just been fully replenished, the energy conversion device connected to it will be transferred from state i) to state ii);

[0113] At any moment, if a single small load is fully charged but still connected to the energy conversion device, the state of the device is ii);

[0114] At any moment, if the single small load that occupied the energy conversion device at the previous moment leaves, the device is transferred from state i) or ii) to state iii);

[0115] At any moment, if the energy conversion device is in state iii) at the previous moment and a non-load individual occupies the position of the energy conversion device at this moment, the device is transferred from state iii) to state iv);

[0116] At any moment, if the energy conversion device suddenly fails, the state will be transferred from other states to state v).

[0117] In this embodiment, step S3 specifically includes:

[0118] Construct a three-layer loop, the outermost layer is a large timing loop - recorded as the first layer loop, set the simulation start time and end time and the simulation step Δt (the default setting is 5 minutes); at each simulation time t, execute a loop traversal for each energy conversion device - recorded as the second layer loop, for each energy conversion device, if it has a single small load connected and needs energy replenishment, then perform the energy replenishment operation for Δt time, accumulate the load to the load of the aggregated large load at time t, and update the status of the energy conversion device to connected single load and outputting energy; if the single small load leaves at this moment, then update the status of the energy conversion device to not connected single load.

[0119] After traversing all energy conversion devices, according to the preset traffic flow parameters, the normal distribution probability density function of the number of new single small loads in the time period [t, t+Δt] is established, and the function is sampled to obtain the number m of new single small loads in the time period Δt; the single small load capacity established in step 1 is obtained in [C′ min ,C′ max ] interval, sample the function to obtain the capacity of all m single small loads; obtain the probability density function of the truncated normal distribution of the single small load SOC established in step 1, sample the function to obtain the SOC value of all m single small loads; obtain the probability density function of the expected connection time of the single small load established in step 2, sample the function to obtain the expected connection time of all m single small loads; obtain the probability density function of the actual connection time of the single small load established in step 2, sample the function to obtain the actual connection time of all m single small loads; obtain the energy replenishment mode of each single small load and the parameters under the mode according to step 2; according to step 1, sample to obtain the switch value of whether all m single small loads need energy replenishment.

[0120] Afterwards, the second layer loop is entered, and the loops of m single small loads are traversed; for the i-th single small load, if it does not need energy replenishment, it proceeds to the next single small load; if the single small load needs energy replenishment, it enters the third layer (innermost layer) loop, and traverses all energy conversion devices; for the j-th energy conversion device, if it is not in an idle state not occupied by a single load, it proceeds to the next energy conversion device; if it is in an idle state not occupied by a single load, the i-th single small load is connected to the j-th energy conversion device, and the latter is made to replenish energy to the former for Δt time, and the load is accumulated to the load of the aggregated large load at time t.

[0121] Finally, after three cycles, the aggregated large load values ​​at all times t are output, realizing a single simulation from bottom to top from single small load - energy conversion device - aggregated large load; this process is repeated many times, and each time a different aggregated large load timing result is obtained due to randomness. Based on the different results obtained many times, the probability density function is fitted for each time t to obtain the timing probability result of the aggregated large load, realizing a probability simulation from bottom to top from single small load - energy conversion device - aggregated large load.

[0122] The aggregated large load can be used in the following application scenarios: The aggregated electric vehicle charging station load results can be output to the energy management platform of the photovoltaic + energy storage + charging station system to guide the optimized operation of the photovoltaic + energy storage + charging station system; can be output to the management and control platform of the virtual power plant system that includes charging stations to guide the optimized operation of the virtual power plant; can be output to the planning system of the photovoltaic storage charging station to guide the planning of the photovoltaic storage charging station; can be output to the power grid dispatching platform to guide the grid dispatching to perform dispatching operations to maintain the safe and stable operation of the power grid. The aggregated electric boiler cluster load results can be output to the management and control platform of the virtual power plant system that includes the electric boiler cluster to guide the optimized operation of the virtual power plant; can be output to the energy management platform of the integrated energy system that includes the electric boiler cluster to guide the optimized operation of the system; can be output to the power grid dispatching platform to guide the grid dispatching to perform dispatching operations to maintain the safe and stable operation of the power grid. The above are typical application scenarios for aggregated large load results, and other application scenarios that are not listed here can also be included.

[0123] This simulation method of aggregating small monomer loads into a large load has the following technical effects:

[0124] 1) It can describe the multi-source uncertainty of single small load.

[0125] 2) Various charging modes such as “free charging”, “fixed-time charging”, and “fixed-power charging” can be modeled.

[0126] 3) Various states of energy conversion devices can be described.

[0127] 4) Aggregation based on timing simulation can obtain the aggregated large load of the timing.

[0128] 5) The large load quantile estimates after the aggregation of monomer small loads can be obtained, including quartile estimates and decile estimates.

[0129] 6) It is suitable for the polymerization of any number of monomers with small loads, can meet a wide range of practical needs, and supports the selection of an appropriate range for polymerization based on actual needs.

[0130] 7) It has a wide range of application scenarios and is suitable for photovoltaic storage and charging microgrid systems, power systems, virtual power plant systems, integrated energy systems, as well as the optimized operation, optimization planning, and optimization scheduling scenarios of each system.

[0131] This embodiment further provides a simulation system for aggregating individual small loads into a large load, comprising a calculation module, wherein the calculation module executes the simulation method for aggregating individual small loads into a large load.

[0132] This embodiment also provides a non-transitory computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the simulation method of aggregating a single small load into a large load is implemented.

[0133] Ginseng Figure 2 As shown, this embodiment further provides an electronic device, including:

[0134] The memory 201 and the processor 202 are communicatively connected to each other. The memory 201 stores computer instructions. The processor 202 executes the computer instructions to perform the simulation method of aggregating a single small load into a large load.

[0135] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A simulation method for polymerizing a small monomer load into a large load, characterized in that: The steps include: Step 1: Simulate the multi-source uncertainty of a single small load based on a physical model; wherein the multi-source uncertainty includes the energy capacity of the single small load, the initial SOC, whether recharging is required, the expected duration of connection, the actual duration of connection, and the random selection of recharging modes; the recharging modes include automatic full energy replenishment mode, fixed duration recharging mode, and fixed power recharging mode; Step 2: Simulate different states of the energy conversion device and establish a state transition simulation model to simulate the behavior of the energy conversion device transitioning between different states over time; the states include: i) connected to a single load and outputting energy, ii) occupied by a single load but not outputting energy, iii) idle state not occupied by a single load, iv) unavailable due to being occupied by a non-load individual, and v) fault; wherein, states i) and ii) can transition to each other; states ii) and iii) can transition to each other; states iii) and iv) can transition to each other; states i)-iv) can transition to state v); and state v) can transition to state iii). Step 3: Consider the aggregated large load as the load of all energy conversion devices, and perform modeling and simulation on the aggregated large load based on time series simulation and random sampling, including: Establish a time series simulation model to simulate the behavior of single loads, energy conversion devices, and aggregated large loads at a set time in the future using a manually set sampling period; At each simulation time point, the state of each energy conversion device is traversed and probabilistic simulation is performed to obtain the load power result of each energy conversion device based on the probability description; Perform random simulation on the new single load flow; Traverse each new single load and perform probabilistic simulation, including simulation of its interaction with the energy conversion device, and update the load power results of the energy conversion device connected to the single load; The load power results of all energy conversion devices are aggregated and accumulated to obtain the aggregated large load power result.

2. The simulation method for polymerizing a small monomer load into a large load according to claim 1, characterized in that: The uncertainty simulation of energy capacity described in step 1 includes: 1) Set the minimum capacity C at 25°C min and the maximum capacity C max Two parameters; 2) According to the ambient temperature, the minimum capacity C min and the maximum capacity C max Correction is made, assuming that the two capacities will not decay above 5°C, but will decay linearly below 5°C until they decay to 60% at -20°C, and remain unchanged below -20°C, thus obtaining the minimum capacity C after temperature correction. ′ min and the maximum capacity C ′ max ; 3) Based on [C ′ min ,C ′ max ] is a probability density function uniformly distributed within the interval, and a sampling operation is performed based on the probability density function to obtain a sampling value of the single-cell small load energy capacity.

3. The simulation method for polymerizing a small monomer load into a large load according to claim 2, characterized in that: The uncertainty simulation of the initial SOC in step 1 includes: 1) Set the statistical average μ of the initial SOC soc , standard deviation σ soc , lower bound l soc =20%, upper bound U soc =80%; 2) Establish μ soc is the mean, σ soc is the standard deviation, l soc is the lower bound, U soc is the probability density function of the truncated normal distribution with an upper bound; 3) performing a sampling operation based on the probability density function to obtain a sampling value of the initial SOC of the single cell under light load; The uncertainty simulation of whether energy replenishment is needed includes: Assume that the discrete probability distribution is a function of SOC, and the probability of needing energy replenishment P need for: Given a single small load and its initial SOC, sampling is performed according to a discrete probability distribution to obtain a sampling value of whether the single small load needs energy replenishment. The sampling value is 0 or 1, where 0 represents no energy replenishment and 1 represents energy replenishment.

4. The simulation method for polymerizing a small monomer load into a large load according to claim 3, characterized in that: The uncertainty simulation of the expected access maintenance duration in step 1 includes: Set the expected duration of connection, the average value, standard deviation, upper and lower bounds, substitute the parameters of the duration, the average value, standard deviation, and upper and lower bounds into a probability density function of a truncated normal distribution, and perform sampling based on the probability density function to obtain a sampled value of the expected duration of connection for a single light load. The uncertainty simulation of the actual duration of maintaining access includes: The actual average duration of access is set as the expected average duration of access, standard deviation, upper and lower bounds, and the average duration, standard deviation, upper and lower bound parameters are substituted into the function to establish a probability density function of the truncated normal distribution. The sampling operation is performed according to the probability density function to obtain the sampling value of the actual duration of access of the single small load.

5. The simulation method for polymerizing a small monomer load into a large load according to claim 4, characterized in that: The uncertainty simulation of random selection of the energy replenishment mode in step 1 includes: 1) Set the probability of selecting the automatic full energy mode to P1, the probability of selecting the fixed duration energy mode to P2, and the probability of selecting the fixed power energy mode to 1-P1-P2; 2) Performing random sampling based on the probability distribution of the three energy replenishment modes to obtain a sampling value for the energy replenishment mode selected by the single small load, wherein the sampling value is 1, 2, or 3, corresponding to the automatic full energy replenishment mode, the fixed duration energy replenishment mode, and the fixed power energy replenishment mode, respectively; 3) If the sampling value is 2, set the mean, standard deviation, lower bound, and upper bound of the charging duration, and substitute these four parameters into a probability density function of a truncated normal distribution. Sampling is performed based on this probability density function to obtain the sampling value of the fixed charging duration of a single small load. 4) If the sampling value is 3, set the mean, standard deviation, lower bound, and upper bound of the charging duration, and substitute these four parameters to establish the probability density function of the truncated normal distribution. Perform the sampling operation based on this probability density function to obtain the sampling value of the fixed charging power of the single small load.

6. The simulation method for polymerizing a small monomer load into a large load according to claim 5, characterized in that: The state transition simulation model described in step 2 is as follows: At any moment, if a small single load that needs to be replenished arrives and there is at least one energy conversion device in state iii), the small single load will be connected to one of the energy conversion devices in state iii) and the energy conversion device will be transferred from state iii) to state i); At any moment, if a small single load that is being replenished has just been fully replenished, the energy conversion device connected to it will be transferred from state i) to state ii); At any moment, if a single small load is fully charged but still connected to the energy conversion device, the state of the device is ii); At any moment, if the single small load that occupied the energy conversion device at the previous moment leaves, the device is transferred from state i) or ii) to state iii); At any moment, if the energy conversion device is in state iii) at the previous moment and a non-load individual occupies the position of the energy conversion device at this moment, the device is transferred from state iii) to state iv); At any moment, if the energy conversion device suddenly fails, the state will be transferred from other states to state v).

7. The simulation method for polymerizing a small monomer load into a large load according to claim 6, characterized in that: The step 3 specifically includes: Construct a three-layer loop, the outermost layer is a timing large loop, recorded as the first layer loop, set the simulation start time and end time and the simulation step size Δt; at each simulation time t, execute the loop traversal for each energy conversion device, recorded as the second layer loop. For each energy conversion device, if it has a single small load connected and needs to be replenished, perform the energy replenishment operation for Δt, accumulate the load to the load of the aggregated large load at time t, and update the status of the energy conversion device to connected single load and outputting energy; if the single small load leaves at this time, the status of the energy conversion device is updated to unconnected single load; After traversing all energy conversion devices, according to the preset traffic flow parameters, the normal distribution probability density function of the number of new single small loads in the time period [t, t+Δt] is established, and the function is sampled to obtain the number m of new single small loads in the time period Δt; the single small load capacity established in step 1 is obtained in [C ′ min ,C ′ max ], sample the probability density function of uniform distribution in the interval, and obtain the capacity of all m single small loads; obtain the probability density function of the truncated normal distribution of the single small load SOC established in step 1, and sample the function to obtain the SOC value of all m single small loads; obtain the probability density function of the expected maintenance connection time of the single small load established in step 2, and sample the function to obtain the expected maintenance connection time of all m single small loads; obtain the probability density function of the actual maintenance connection time of the single small load established in step 2, and sample the function to obtain the actual maintenance connection time of all m single small loads; obtain the energy replenishment mode of each single small load and the parameters under the mode according to step 2; according to step 1, sample and obtain the switch value of whether all m single small loads need energy replenishment; After that, the second-layer loop is entered, traversing the loops of m single-unit small loads; for the i-th single-unit small load, if it does not need energy replenishment, it will proceed to the next single-unit small load; if the single-unit small load needs energy replenishment, it will enter the third-layer loop and traverse all energy conversion devices; for the j-th energy conversion device, if it is not in an idle state not occupied by a single-unit load, it will proceed to the next energy conversion device; if it is in an idle state not occupied by a single-unit load, the i-th single-unit small load will be connected to the j-th energy conversion device, and the latter will replenish energy to the former for Δt time, and the load will be accumulated to the load of the aggregated large load at time t; Finally, after three cycles, the aggregated large load values ​​at all times t are output, achieving a single simulation from bottom to top, from individual small loads to energy conversion devices, and then to aggregated large loads. This process is repeated multiple times, and each time different aggregated large load timing results are obtained due to randomness. Based on the different results obtained multiple times, a probability density function is fitted for each moment t to obtain the timing probability results of the aggregated large load, realizing a bottom-up probability simulation from single small load to energy conversion device, and then to aggregated large load.

8. A simulation system for aggregating small individual loads into a large load, characterized in that: The method comprises a calculation module, wherein the calculation module executes the simulation method of aggregating a small single load into a large load as described in any one of claims 1 to 7.

9. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, which, when executed by a processor, implement a simulation method for aggregating a single small load into a large load as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the simulation method of aggregating a single small load into a large load as described in any one of claims 1 to 7 by executing the computer instructions.

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