Simulation method and system for polymerizing monomer small load into large load

Abstract

The invention relates to a simulation method and system for aggregating a single small load into a large load. The method comprises the following steps: step 1, simulating the multi-source uncertainty of the single small load based on a physical model; wherein the multi-source uncertainty comprises the energy capacity of a single small load, the initial SOC, whether energy supplementation is needed or not, the expected access keeping time length, the actual access keeping time length and random selection of an energy supplementation mode; step 2, simulating different states of the energy conversion device, establishing a state transition simulation model, and simulating a behavior of the energy conversion device transitioning along with time among the different states; and 3, regarding the aggregated large load as a load for aggregating all the energy conversion devices, and carrying out modeling simulation on the aggregated large load based on time sequence simulation and random sampling. According to the method, the multi-source uncertainty of the single small load is considered, the uncertainty is conducted upwards in the polymerization process, and finally the time sequence average power and the uncertainty are described through the polymerized 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 small monomer loads into a large load. Background Art

[0002] Aggregate large loads (referred to as "large loads"), represented by electric vehicle charging station loads and electric boiler clusters, have three characteristics: 1. They meet the required energy within a limited time period; 2. They are all aggregated from bottom to top by multiple single small loads. For example, the load of electric vehicle charging stations is aggregated by multiple charging piles, and the load of electric boiler clusters is aggregated by multiple electric boilers; 3. The single loads at the bottom are highly volatile, random, and intermittent (three characteristics). With the advancement of electric energy substitution, the growth of these large loads is increasingly affecting the safe and stable operation of the power system. On the one hand, large loads will bring significant load peaks, bring challenges to the frequency regulation and peak regulation of the power system, and aggravate grid congestion; on the other hand, large loads aggregated by multiple single small loads have potential flexibility, which can provide support for the frequency regulation, peak regulation, and congestion reduction of the power system. To tap this flexibility, it is first necessary to develop aggregation methods and systems for aggregating single small loads (such as single charging piles) into large loads. Single small loads also have multi-source uncertainty. Taking electric vehicles as an example, multi-source uncertainty includes: uncertain travel characteristics, uncertain initial state of charge (SOC), uncertain charging mode, and diverse and uncertain battery configuration. These uncertainties will be transmitted upward in the process of aggregating individual small loads into large loads, forming an overall uncertainty at the aggregated large load level. Modeling individual small loads and aggregated large loads has the following significance: 1. Supporting grid dispatchers and planners to evaluate the severity and solutions of frequency regulation, peak regulation, distribution network congestion, low voltage and other problems caused by large loads to the grid at the operation and planning scales, and to evaluate and tap the flexibility of large loads themselves to alleviate the above problems; 2. Supporting operators of virtual power plants to evaluate and tap the flexibility of large loads to provide auxiliary services to the grid; 3. Supporting operators of microgrids to enable microgrids containing distributed power sources, energy storage, and large loads to minimize the cost of purchasing electricity from the grid, and maximize the time matching degree between large loads and wind and solar power generation to ensure that large loads use green electricity. Therefore, there is an urgent need for a simulation technology and system for aggregating individual small loads into large loads from the bottom up based on physical characteristics. 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, taking into account the multi-source uncertainty of the single small loads, and transmitting the uncertainty upward during the aggregation process, and finally describing the time-series average power and uncertainty through the aggregated large load, which is suitable for the scenario 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, simulating 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 energy replenishment is required, the expected duration of connection, the actual duration of connection, and the random selection of energy replenishment mode; the energy replenishment mode includes an automatic full energy replenishment mode, a fixed duration energy replenishment mode, and a fixed power energy replenishment mode;

[0006] Step 2, simulating different states of the energy conversion device, establishing a state transfer simulation model, and simulating the behavior of the energy conversion device transferring 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 transfer to each other; states ii) and iii) can transfer to each other; states iii) and iv) can transfer to each other; states i)-iv) can transfer to state v); state v) can transfer to state iii);

[0007] 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, 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 with 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 probability description;

[0010] Perform random simulation on the incoming 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 result 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 these 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, and performing a sampling operation based on the probability density function to obtain a sampling value of a single small load energy capacity;

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

[0018] 1) Set the statistical average value 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 small load;

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

[0022] Assume that the discrete probability distribution is a function of SOC, 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, and the sampling value is 0 or 1, where 0 represents that energy replenishment is not required and 1 represents that energy replenishment is required.

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

[0026] The expected time to maintain access is set to have a mean value, standard deviation, upper and lower bounds, and the mean value, standard deviation, upper and lower bounds are substituted into the parameters to establish a probability density function of a truncated normal distribution, and a sampling operation is performed according to the probability density function to obtain a sampling value of the expected time to maintain access for a single small load;

[0027] The uncertain 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 to establish a probability density function of a truncated normal distribution. The sampling operation is performed based on this probability density function to obtain the sampling value of the actual duration of access for a 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, the mean value, standard deviation, lower bound and upper bound of the energy replenishment duration are set, and the probability density function of the truncated normal distribution is established by substituting these four parameters. The sampling operation is performed according to this probability density function to obtain the sampling value of the fixed energy replenishment duration of the single small load;

[0033] 4) If the sampling value is 3, the mean value, standard deviation, lower bound and upper bound of the energy replenishment duration are set, and these four parameters are substituted into the probability density function of the truncated normal distribution. The sampling operation is performed according to this probability density function to obtain the sampling value of the fixed energy replenishment power of the single small load.

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

[0035] At any time, if a single small load that needs to be supplemented arrives and there is at least one energy conversion device in state iii), the single small 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 time, if a single small load that is being replenished has just fully replenished its energy, the energy conversion device connected to it will transfer from state i) to state ii);

[0037] At any time, if a single small load is fully replenished but still remains 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 time, if the energy conversion device fails suddenly, it will transfer 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 length 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, then perform the energy replenishment operation for Δt, accumulate the load to the load of the aggregated large load at time t, and update the state of the energy conversion device to be connected to the single load and outputting energy; if the single small load leaves the scene at this time, then update the state of the energy conversion device to be unconnected to the 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 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 access time of the single small load established in step 2, sample the function to obtain the expected access time of all m single small loads; obtain the probability density function of the actual access time of the single small load established in step 2, sample the function to obtain the actual access 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 the switch value of whether all m single small loads need energy replenishment;

[0044] After that, enter the second layer loop and traverse the loops of m single small loads; for the i-th single small load, if it does not need energy replenishment, it will progress to the next single small load; if the single small load needs energy replenishment, 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 load, it will progress 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;

[0045] Finally, after three layers of loops, the aggregated large load values ​​at all times t are output, realizing a single simulation from bottom to top from single small loads to energy conversion devices and then to aggregated large loads;

[0046] This process is repeated many times, and different aggregated large load timing results are obtained each time due to randomness. Based on the different results obtained multiple times, the probability density function is fitted for each time t to obtain the timing probability results of the aggregated large load, thereby realizing the probability simulation from bottom to top 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 connected to communicate with 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] By means of the above scheme, the simulation method and system for aggregating small single loads into a large load have the following technical effects:

[0052] 1) The multi-source uncertainty of single small load can be described.

[0053] 2) Various charging methods such as "free charging", "fixed time charging", and "fixed power charging" can be modeled.

[0054] 3) Various states of the energy conversion device 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 a suitable range for polymerization according to 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 in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of the simulation method of polymerizing a monomer with a small load into 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 specific implementation of the present invention is further described in detail below in conjunction with 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 suitable for scenarios where single small loads are aggregated into a large load, and takes into account the following uncertainties of single small loads: the single small load flow that changes with time and its uncertainty (taking electric vehicles as an example, the change in its flow rate and its uncertainty), the uncertainty of the initial state of charge (SOC), the uncertainty of 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 length of time the single small load is connected (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 and whether it is not currently 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, simulating 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 energy replenishment (charging) 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 the energy replenishment mode; the energy replenishment mode includes an automatic full energy replenishment mode, a fixed duration energy replenishment mode, and a fixed power energy replenishment mode;

[0065] Step S2, simulate the different states of the energy conversion device, establish a state transfer 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 load of all energy conversion devices, and modeling and simulating 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 future time (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 probability description;

[0069] Perform random simulation on the incoming 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 result 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 these 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 in 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 the battery capacity of electric vehicles 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 according to the ambient temperature. The rule is that at -10°C, the lower the temperature, the lower the maximum power required, which reflects the phenomenon that the battery capacity decreases due to severe cold.

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

[0084] 1) Set the statistical average value 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 the upper bound is:

[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 with a small load.

[0089] Regarding the uncertainty of whether a single small load needs to be recharged (charged), the probability depends on the initial SOC value of the single small load. Taking electric vehicles as an example, the lower the initial SOC value, the more the user needs to charge.

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

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

[0092]

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

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

[0095] Set the expected duration of connection, standard deviation, upper and lower bounds, substitute the duration, standard deviation, upper and lower bounds into the probability density function of the truncated normal distribution, and perform sampling according to the probability density function to obtain the sampling value of the expected duration of connection of the single small load.

[0096] The expected duration of a single small 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 respectively; x es is the expected residence time; the definitions of other variables are the same as those in formula (1).

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

[0100] The actual average duration of access is set as the expected average duration, standard deviation, upper and lower bounds, and the probability density function of the truncated normal distribution is established by substituting the average duration, standard deviation, upper and lower bounds into the parameters, and the sampling operation is performed according to the probability density function to obtain the actual access duration sampling value of the single small load. Where:

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

[0102]

[0103] Where: 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 energy replenishment mode), the mean value, standard deviation, lower bound, and upper bound of the energy replenishment duration are set, and these four parameters are substituted into a probability density function of a truncated normal distribution. The sampling operation is performed according to this probability density function to obtain the sampling value of the fixed energy replenishment duration of a single small load;

[0108] 4) If the sampling value is 3 (fixed power replenishment mode), the mean value, standard deviation, lower limit and upper limit of the replenishment time are set, and these four parameters are substituted into the probability density function of the truncated normal distribution. The sampling operation is performed according to this probability density function to obtain the sampling value of the fixed power replenishment 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 time, if a single small load that needs to be supplemented arrives and there is at least one energy conversion device in state iii), the single small 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 time, if a single small load that is being replenished has just fully replenished its energy, the energy conversion device connected to it will transfer from state i) to state ii);

[0113] At any time, if a single small load is fully replenished but still remains 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 time, if the energy conversion device fails suddenly, it will transfer 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 to be replenished, then perform the energy replenishment operation for Δt, accumulate the load to the load of the aggregated large load at time t, and update the state of the energy conversion device to the connected single load and output energy; if the single small load leaves the scene at this moment, then update the state of the energy conversion device to the unconnected 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 ], 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; according to step 2, obtain the energy replenishment mode of each single small load and the parameters under the mode; according to step 1, sample to obtain the switch value of whether all m single small loads need energy replenishment.

[0120] Afterwards, enter the second layer loop and traverse the loop of m single small loads; for the i-th single small load, if it does not need energy replenishment, it will proceed to the next single small load; if the single small load needs energy replenishment, enter the third layer (innermost 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 load, it will proceed 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 allowed 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 layers of loops, 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; the process is repeated many times, and different aggregated large load timing results are obtained each time 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 the 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 including the charging station 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 power 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 including the electric boiler cluster to guide the optimized operation of the virtual power plant; can be output to the energy management platform of the comprehensive energy system including the electric boiler cluster to guide the optimized operation of the system; can be output to the power grid dispatching platform to guide the power grid dispatching to perform dispatching operations to maintain the safe and stable operation of the power grid. The above are typical application scenarios for the aggregation of large load results, and other application scenarios that are not listed can also be included.

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

[0124] 1) The multi-source uncertainty of single small load can be described.

[0125] 2) Various charging methods 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 a suitable range for polymerization according to 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 also provides a simulation system for aggregating single small loads into a large load, including a calculation module, and the calculation module executes the simulation method for aggregating single 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 also provides an electronic device, including:

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

[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 a person skilled in the art can make several improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be regarded as within 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, simulating 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 energy replenishment is required, the expected duration of connection, the actual duration of connection, and the random selection of energy replenishment mode; the energy replenishment mode includes an automatic full energy replenishment mode, a fixed duration energy replenishment mode, and a fixed power energy replenishment mode; Step 2, simulating different states of the energy conversion device, establishing a state transfer simulation model, and simulating the behavior of the energy conversion device transferring 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 transfer to each other; states ii) and iii) can transfer to each other; states iii) and iv) can transfer to each other; states i)-iv) can transfer to state v); state v) can transfer to state iii); 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, 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 with 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 probability description; Perform random simulation on the incoming 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 result 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 in 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 value 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 small load; The uncertain simulation of whether energy replenishment is needed includes: Assume that the discrete probability distribution is a function of SOC, 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, and the sampling value is 0 or 1, where 0 represents that energy replenishment is not required and 1 represents that energy replenishment is required.

4. The simulation method for polymerizing a small monomer load into a large load according to claim 3, characterized in that: The uncertain simulation of the expected access maintenance time in step 1 includes: The expected time to maintain access is set to have a mean value, standard deviation, upper and lower bounds, and the mean value, standard deviation, upper and lower bounds are substituted into the parameters to establish a probability density function of a truncated normal distribution, and a sampling operation is performed according to the probability density function to obtain a sampling value of the expected time to maintain access for a single small load; The uncertain 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 to establish a probability density function of a truncated normal distribution. The sampling operation is performed based on this probability density function to obtain the sampling value of the actual duration of access for a 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, the mean value, standard deviation, lower bound and upper bound of the energy replenishment duration are set, and the probability density function of the truncated normal distribution is established by substituting these four parameters. The sampling operation is performed according to this probability density function to obtain the sampling value of the fixed energy replenishment duration of the single small load; 4) If the sampling value is 3, the mean value, standard deviation, lower bound and upper bound of the energy replenishment duration are set, and these four parameters are substituted into the probability density function of the truncated normal distribution. The sampling operation is performed according to this probability density function to obtain the sampling value of the fixed energy replenishment 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 time, if a single small load that needs to be supplemented arrives and there is at least one energy conversion device in state iii), the single small 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 time, if a single small load that is being replenished has just fully replenished its energy, the energy conversion device connected to it will transfer from state i) to state ii); At any time, if a single small load is fully replenished but still remains 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 time, if the energy conversion device fails suddenly, it will transfer 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 length 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, then perform the energy replenishment operation for Δt, accumulate the load to the load of the aggregated large load at time t, and update the state of the energy conversion device to be connected to the single load and outputting energy; if the single small load leaves the scene at this time, then update the state of the energy conversion device to be unconnected to the 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 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 access time of the single small load established in step 2, sample the function to obtain the expected access time of all m single small loads; obtain the probability density function of the actual access time of the single small load established in step 2, sample the function to obtain the actual access 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 the switch value of whether all m single small loads need energy replenishment; After that, enter the second layer loop and traverse the loops of m single small loads; for the i-th single small load, if it does not need energy replenishment, it will progress to the next single small load; if the single small load needs energy replenishment, 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 load, it will progress 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; Finally, after three layers of loops, the aggregated large load values ​​at all times t are output, realizing a single simulation from bottom to top from single small loads to energy conversion devices and then to aggregated large loads; This process is repeated many times, and different aggregated large load timing results are obtained each time due to randomness. Based on the different results obtained multiple times, the probability density function is fitted for each time t to obtain the timing probability results of the aggregated large load, thereby realizing the probability simulation from bottom to top from single small load to energy conversion device, and then to aggregated large load.

8. A simulation system for aggregating small loads into a large load, characterized in that: It comprises a calculation module, which executes the simulation method of aggregating a small monomer load into a large load as described in any one of claims 1-7.

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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