A rapid calculation and analysis method and system for the energy management response characteristics of a micro energy grid

Abstract

The embodiments of this specification provide a method and system for quickly calculating and analyzing the energy management response characteristics of a micro energy network. Among them, the method includes obtaining statistical data on the regional energy micro network architecture; constructing an aggregated analysis model for distributed energy storage systems within the micro energy network, an aggregated response model for various types of electric vehicle users, and an aggregated analysis model for distributed photovoltaic systems; taking the maximization of the overall benefit of the regional micro energy network as the optimization goal, based on the classification aggregation results of the distributed energy storage power station system, electric vehicle users, and distributed photovoltaic system, and taking the operation constraint conditions after the classification aggregation of the distributed energy storage power station system, electric vehicle users, and distributed photovoltaic system, and the gateway power constraint conditions of the regional micro energy network as the premise, establishing an internal energy management response model for the micro energy network; solving the internal energy management response model of the micro energy network to obtain the response characteristics of the regional micro energy network. The present invention solves the problem of quickly analyzing and calculating the overall energy management of the micro energy network.

Description

Technical Field

[0001] This document relates to the technical field of distribution network analysis, and particularly to a method and system for quickly calculating and analyzing the energy management response characteristics of a micro energy network. Background Art

[0002] Since a large number of energy conversion devices, storage devices, and renewable energy devices are coupled in the micro energy network, and the number and types of these devices are large, the difficulty of optimizing the energy management research of the micro energy network has increased. Under the management of the overall operator of the existing micro energy network, generally the entire micro energy network is regarded as an aggregated operation entity. However, under the premise of pursuing the maximum overall benefit in different external distribution network operation environments, if each device is regarded as a calculation unit, it will greatly increase the difficulty of analysis and calculation.

[0003] In view of this, there is an urgent need to provide a method for quickly and effectively obtaining the power response characteristics of various types of devices inside the micro energy network and the overall external power characteristics of the micro energy network for a micro energy network containing a large number of distributed energy storage power stations, electric vehicles, and distributed photovoltaic systems, so as to solve the problem of rapid analysis and calculation of the overall energy management of the micro energy network. Summary of the Invention

[0004] One or more embodiments of this specification provide a method for quickly calculating and analyzing the energy management response characteristics of a micro energy network, including the steps of:

[0005] Obtaining the regional energy micro network architecture data; constructing an aggregated analysis model of the distributed energy storage system inside the micro energy network, an aggregated response model of various types of electric vehicle users, and an aggregated analysis model of the distributed photovoltaic system; taking the maximization of the overall benefit of the regional micro energy network as the optimization goal, based on the classification aggregation results of the distributed energy storage power station system, electric vehicle users, and distributed photovoltaic system, and taking the operation constraint conditions after classification aggregation of the distributed energy storage power station system, electric vehicle users, and distributed photovoltaic system, and the power constraint conditions at the gateway of the regional micro energy network as the premise, establishing an internal energy management response model of the micro energy network; solving the internal energy management response model of the micro energy network to obtain the response characteristics of the regional micro energy network.

[0006] One or more embodiments of this specification provide a system for quickly calculating and analyzing the energy management response characteristics of a micro energy network, including:

[0007] Data acquisition unit: Obtaining the regional energy micro network architecture data;

[0008] Energy storage system classification aggregation model construction unit: Used to construct an aggregated analysis model of the distributed energy storage power station inside the micro energy network according to the obtained regional energy micro network architecture data;

[0009] Electric vehicle classification aggregation model construction unit: used to construct an aggregation response model for electric vehicle users of various types within the micro energy network according to the obtained regional energy micro network architecture data;

[0010] Distributed photovoltaic system classification aggregation model construction unit: used to construct an aggregation analysis model for the distributed photovoltaic system within the micro energy network according to the obtained regional energy micro network architecture data;

[0011] Micro energy network energy management response model construction unit: with the maximization of the overall benefit of the regional micro energy network as the optimization goal, based on the classification aggregation results of the distributed energy storage power station system, electric vehicle users, and distributed photovoltaic system, and taking the operation constraint conditions after the classification aggregation of the distributed energy storage power station system, electric vehicle users, and distributed photovoltaic system, and the power constraint conditions at the gateway of the regional micro energy network as conditions, establish an internal energy management response model for the micro energy network;

[0012] Model solving unit: used to solve the internal energy management response model of the micro energy network and obtain the response characteristics of the regional micro energy network.

[0013] One or more embodiments of this specification provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the fast calculation and analysis method for the energy management response characteristics of the micro energy network as described above.

[0014] One or more embodiments of this specification provide a storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the fast calculation and analysis method for the energy management response characteristics of the micro energy network as described above.

[0015] The present invention regards the entire interior of the micro energy network as an aggregated operation entity. Under the premise of pursuing the maximum overall benefit in different external distribution network operation environments, it classifies and aggregates a large number of distributed energy storage power stations, electric vehicles, and distributed photovoltaic systems in the micro energy network, constructs an energy management response model for the micro energy network based on the aggregation model, obtains the power response characteristics of various types of equipment within the micro energy network, and can also obtain the overall external power characteristics of the micro energy network, solving the problem of fast analysis and calculation of the overall energy management of the micro energy network, and providing an efficient technical means for the internal operation of the micro energy network and the prediction and guidance of the external characteristics of the micro energy network. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 A flowchart of a method for quickly calculating and analyzing the energy management response characteristics of a micro energy grid provided for one or more embodiments of this specification;

[0018] Figure 2 A schematic block diagram of a system for quickly calculating and analyzing the energy management response characteristics of a micro energy grid provided for one or more embodiments of this specification;

[0019] Figure 3 A schematic structural diagram of a computer device provided for one or more embodiments of this specification. Detailed implementation manners

[0020] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will clearly and completely describe the technical solutions in one or more embodiments of this specification in conjunction with the accompanying drawings in one or more embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this document.

[0021] The following will make a detailed description of the present invention in conjunction with the detailed implementation manners and the accompanying drawings of the specification.

[0022] Method embodiments

[0023] According to an embodiment of the present invention, a method for quickly calculating and analyzing the energy management response characteristics of a micro energy grid is provided. As Figure 1 shown, it is the method for quickly calculating and analyzing the energy management response characteristics of a micro energy grid provided by the present invention. The method for quickly calculating and analyzing the energy management response characteristics of a micro energy grid according to an embodiment of the present invention includes the steps:

[0024] Step 1: Obtain the statistical data of the regional energy micro grid architecture.

[0025] Step 2: Construct an aggregated analysis model for distributed energy storage systems within the micro energy grid.

[0026] Step 3: Construct an aggregated response model for various types of electric vehicle users within the micro energy grid.

[0027] Step 4: Construct an aggregated analysis model for distributed photovoltaic systems within the micro energy grid.

[0028] Step 5: Taking the maximization of the overall benefit of the regional micro energy grid as the optimization goal, based on the classification and aggregation results of distributed energy storage power stations, electric vehicles, and distributed photovoltaic systems, and taking the operating constraints after the classification and aggregation of the distributed energy storage power station system, electric vehicle users, and distributed photovoltaic systems, and the gateway power constraints of the regional micro energy grid as the premise, establish an internal energy management response model for the micro energy grid.

[0029] Step 6: Solve the internal energy management response model of the micro energy grid to obtain the response characteristics of the regional micro energy grid.

[0030] The method of this embodiment provides a fast calculation and analysis method for the energy management response characteristics of a micro energy grid. It is aimed at the energy management characteristics of the micro energy grid containing a large number of distributed energy storage power stations, electric vehicles, and distributed photovoltaic systems. By classifying and aggregating different systems, an energy management response model of the micro energy grid based on the aggregation model is constructed, reducing the calculation dimension in the analysis of energy management characteristics, and can quickly calculate the energy management response characteristics of the micro energy grid under the external distribution network operation environment, providing an efficient technical means for the internal operation of the micro energy grid and the prediction and guidance of the external characteristics of the micro energy grid. The above technical solutions of the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0031] In the method of this embodiment, the micro energy grid generally includes multiple distributed energy storage power stations, and the capacity and charge-discharge power of each energy storage power station are different. Therefore, when quickly analyzing and calculating the power characteristics of multiple energy storage power stations, they can be classified and aggregated according to the response parameters of the energy storage power stations. Therefore, first classify the energy storage power stations according to their own capacity and charge-discharge power, and then aggregate and model the sets of various types of energy storage power stations; specifically, it includes the steps:

[0032] S21: Classify the energy storage power stations based on the shortest charge-discharge time for the maximum cycle charge.

[0033] In the method of this embodiment, the distributed energy storage power stations are classified according to the shortest charge-discharge time for the maximum cycle charge in each charge-discharge cycle of each energy storage power station. The shortest charge-discharge time of each energy storage power station is:

[0034]

[0035] where t i is the shortest single charge or discharge time of the i-th energy storage power station, C i is the charge-discharge rate of the i-th energy storage power station, SOC max,i and SOC min,iThey are the maximum ratio and the minimum ratio of the stored electricity when maintaining the normal operation of the energy storage device respectively.

[0036] After calculation, the set of the shortest charge-discharge times of all distributed energy storage power stations is obtained:

[0037] t c ={t c1 ,K,t ci ,K,t cm} (2)

[0038] In the formula, t c is the set of the shortest charge-discharge times, and m is the number of distributed energy storage power stations.

[0039] Classify the distributed energy storage power stations according to the shortest charge-discharge times:

[0040] (1) Set the set of the shortest charge-discharge time categories of the distributed energy storage power stations:

[0041] T={T1,K,T j ,K,T n} (3)

[0042]

[0043] T j =T j-1 +1 (6)

[0044] In the formula, T is the set of the shortest charge-discharge time categories of the distributed energy storage power stations, and T j is the category of the jth energy storage power station.

[0045] The classification feature of each energy storage power station is:

[0046]

[0047] In the formula, t si is the classification feature of the shortest charge-discharge time of the ith energy storage power station; when formula (8) holds, the ith energy storage power station is classified into the jth energy storage power station category.

[0048] S22. Aggregate and model the energy storage power stations of each category based on the classification results;

[0049] Aggregate the parameters of the energy storage power stations of the jth category:

[0050]

[0051] In the formula, N j is the number of energy storage power stations in the jth category, E j is the overall rated electricity of the aggregated energy storage power stations of the jth category, and P max,jis the maximum charge and discharge power after aggregation of the j-th category of energy storage power stations, E max,j is the maximum ratio of stored electricity after aggregation of the j-th category of energy storage power stations, E min,j is the minimum ratio of stored electricity after aggregation of the j-th category of energy storage power stations; e k is the rated electricity of the k-th energy storage power station in the j-th category, p max,k is the maximum charge and discharge power of the k-th energy storage power station in the j-th category, SOC max,k is the maximum ratio of stored electricity of the k-th energy storage power station in the j-th category, SOC min,k is the minimum ratio of stored electricity of the k-th energy storage power station in the j-th category.

[0052] Finally, after the aggregation of energy storage power station categories, when the aggregated operating power of the j-th category of energy storage power stations is P ESS,j (t), the operating power distribution of the k-th energy storage power station in this category is:

[0053]

[0054] In the formula, P ESS,j (t) is the operating power of the aggregated power station of the j-th category of energy storage power stations at time t. When P ESS,j (t) is positive, the power station is in the charging state. When P ESS,j (t) is negative, the power station is in the discharging state. p ESS,k (t) is the operating power of the k-th distributed energy storage power station in the aggregated power station of the j-th category of energy storage power stations at time t.

[0055] Based on the above steps for the classification of distributed energy storage power stations in the micro energy network and the aggregated modeling of each type of energy storage power station, the variable dimension in the energy management response model of multiple distributed energy storage power stations in the micro energy network is reduced, and the calculation speed of the energy management response model of the micro energy network is improved.

[0056] In this embodiment, for step three to construct the aggregated response model of each type of electric vehicle user in the micro energy network, it is specifically implemented through the following steps.

[0057] For multiple types of electric vehicle users, a method of classifying electric vehicle users based on the charging type and performing aggregated modeling based on the classification results is proposed, including the steps:

[0058] S31. Classify electric vehicle users according to the charging type, including private car evening charging, private car daytime charging, official car daytime charging, etc. Set the set of electric vehicle user types as:

[0059] EV = {EV1,K,EV l ,K,EV u} (14)

[0060] Wherein, EV is the set of electric vehicle user categories, and EV l is the l-th electric vehicle user category, and u is the number of electric vehicle user categories.

[0061] S32. Determine the EV user response willingness probability of each electric vehicle user type in each time period according to the statistical data, and determine each response rate vector as:

[0062]

[0063] Wherein, R l is the response willingness probability vector of the l-th type of electric vehicle user, and r l (t) is the response willingness probability of the l-th electric vehicle user at time t, and N T is the number of time periods of each micro energy grid response cycle, are the time periods within the cycle.

[0064] S33. Calculate the unordered charging load without considering the optimized response of electric vehicle users, including the steps:

[0065] S331. Obtain the statistical models of various types of electric vehicle users.

[0066] soc arrive,l = f1(t) (16)

[0067] soc leave,l = f2(t) (17)

[0068] t arrive,l = f3(t) (18)

[0069] t leave,l = f4(t) (19)

[0070] Wherein, soc arrive,l and soc leave,l are the state of charge when the l-th type of electric vehicle user arrives and departs respectively, and t arrive,l and t leave,l are the arrival and departure times of the l-th type of electric vehicle user respectively. The corresponding f1(t), f2(t), f3(t), and f4(t) are the probability density functions of the state of charge and time of arrival / departure respectively. The specific expressions of these functions can be various forms such as normal distribution function and uniform distribution function.

[0071] S332. Calculate the unordered charging loads of various types without considering the optimized response of electric vehicle users.

[0072] Use the Monte Carlo sampling method to obtain the sampling vectors of various types of electric vehicles:

[0073]

[0074] Wherein, SOC arrive,l and SOC leave,l are respectively the sampled vectors of the state of charge when the users of the l-th type of electric vehicle arrive and leave, Τ arrive,l and Τ leave,l are respectively the sampled vectors of the time when the users of the l-th type of electric vehicle arrive and leave.

[0075] Calculate the charging duration vector of the users of the l-th type of electric vehicle:

[0076]

[0077] Wherein, T charge,l is the charging duration vector of the users of the l-th type of electric vehicle, and are respectively the average rated power of the battery and the charging power of the l-th type of electric vehicle.

[0078] Then the actual charging end time vector is:

[0079]

[0080] According to the start and end times of charging and discharging of each cell of the users of the l-th type of electric vehicle, convert them into a charging time status vector, that is, when the charging start time of the single vehicle is t 1,l , and the charging end time is t 1,l , its charging time status vector is:

[0081]

[0082] Wherein, S 1,l is the charging time status vector of the single cell of the first electric vehicle, s 1,l (t1), K, are the respective elements of the corresponding charging time status vector.

[0083] Then the charging time status matrix of the users of the l-th type of electric vehicle is:

[0084]

[0085] The charging load vector of each time period of the users of the l-th type of electric vehicle, that is, the aggregation response model of users of each type of electric vehicle is:

[0086]

[0087] Through the above steps, the classification of various types of electric vehicle users within the micro energy network is achieved, as well as the aggregated modeling of the unordered charging loads of each type of electric vehicle, reducing the variable dimension in the energy management response model of large-scale and multi-type electric vehicle users within the micro energy network and improving the calculation speed of the energy management response model of the micro energy network.

[0088] In this embodiment, the construction of the aggregated analysis model of the distributed photovoltaic system within the micro energy network in Step 4 is specifically achieved through the following steps.

[0089] For the distributed photovoltaic system, a classification and aggregation method based on the similarity of historical lighting conditions is used, including the steps:

[0090] S41. Classify the distributed photovoltaic systems according to the typical lighting areas. Since the power generation of the photovoltaic system is closely related to the irradiance, according to the statistical data, the distributed photovoltaic systems are classified according to the irradiance similarity. Assume that the irradiance data matrix of each distributed photovoltaic system within a certain time period is:

[0091]

[0092] In the formula, I is the irradiance data matrix of all distributed photovoltaic systems within a certain time period, is the irradiance of the z-th distributed photovoltaic system at time period, and N T2 is the number of typical time periods used for the classification of the distributed photovoltaic system.

[0093] Calculate the irradiance similarity coefficient between every two distributed photovoltaic systems:

[0094]

[0095] In the formula, d max,vw is the maximum deviation degree, and d vw is the comprehensive deviation degree.

[0096] Classify according to the irradiance similarity coefficient. Two conditions for two distributed photovoltaic systems v and w to be in the same class:

[0097] (1) When the d max,vw and d vw of the v-th and w-th distributed photovoltaic systems are both less than the corresponding thresholds;

[0098] (2) When all the distributed photovoltaic systems in the class where the v-th distributed photovoltaic system and the w-th distributed photovoltaic system are located satisfy the above condition (1);

[0099] After completing the classification of the distributed photovoltaic systems, the classification result is:

[0100] PV = {PV1, K, PVv , K, PV z} (33)

[0101] Wherein, PV is the set of distributed photovoltaic system categories, and PV v is the v-th electric vehicle user category, and z is the number of distributed photovoltaic system categories.

[0102] S42. Calculate the aggregated power generation of the distributed photovoltaic system according to the classification results;

[0103] Assume that the rated power capacity vector of the distributed photovoltaic systems in the v-th distributed photovoltaic system type is:

[0104]

[0105] Wherein, N v is the number of distributed photovoltaic systems in the v-th distributed photovoltaic system type.

[0106] Then, the power generation of each time period of the v-th distributed photovoltaic system type, that is, the aggregated analysis model of the distributed photovoltaic system is:

[0107]

[0108] Wherein, S r,v is the irradiance of each time period, is the overall average system efficiency, is the dust or rain shielding coefficient, is the component series mismatch coefficient, is the inverter loss coefficient, is the cable loss coefficient, is the transformer loss coefficient, is the tracking system accuracy coefficient.

[0109] Through the above steps of classifying multiple distributed photovoltaic systems inside the micro energy network, the variable dimension in the energy management response model of the large-scale distributed photovoltaic system inside the micro energy network is reduced, and the calculation speed of the energy management response model of the micro energy network is improved.

[0110] In this embodiment, for step five, with the goal of maximizing the overall benefit of the regional micro energy network, based on the operation constraint conditions after classification and aggregation of the distributed energy storage power station, electric vehicles, and distributed photovoltaic systems, and the gateway power constraint conditions of the regional micro energy network, an internal energy management response model of the micro energy network is established, which is specifically implemented through the following steps.

[0111] Based on the aggregated analysis model of the energy storage system, the aggregated response model of electric vehicle users, and the aggregated analysis model of the photovoltaic system constructed respectively in steps two to four, an internal energy management response model of the micro energy network is established, including the steps:

[0112] The internal energy management response model of this embodiment is a fast analysis model that calculates the power response characteristics presented by the micro energy grid as a whole under the external distribution network operation environment, considering the impact of the operation environment on each equipment entity within the micro energy grid.

[0113] S51. With the goal of maximizing the overall benefit of the regional micro energy grid, construct the internal energy management response model of the micro energy grid.

[0114] The internal energy management of the micro energy grid is mainly reflected in the pursuit of maximizing the operation benefit by the micro energy grid as a whole. Within the micro energy grid, this embodiment mainly considers the operation characteristics of each energy entity such as distributed energy storage power stations, electric vehicles, and distributed photovoltaic systems when the overall benefit of the micro energy grid is maximized, so as to guide the energy management of the micro energy grid and provide a response analysis tool for the distribution network operator to better guide the operation characteristics of the micro energy grid.

[0115] min F = F1 + F2 + F3 (37)

[0116]

[0117]

[0118] In the formula, F1 is the operation cost of the distributed energy storage power station, F2 is the charging cost of electric vehicle users, F3 is the loss of abandoned light of the distributed photovoltaic system, C(t) is the electricity price per unit of electricity at time t, and P PV,v (t) is the abandoned light power of the v-th type of distributed photovoltaic system at time t.

[0119] S52. Determine the constraint conditions of the internal energy management response model of the micro energy grid.

[0120] The internal energy management of the micro energy grid should consider the self-demand constraints and operation constraints of each type of aggregated entity.

[0121] (1) Construct the constraint conditions of the distributed energy storage power station

[0122] For each type of distributed energy storage power station, the following constraints are established, mainly including the constraints on the overall charge-discharge power and stored energy of each type of aggregated energy storage power station:

[0123] -P max,j ≤P ESS,j (t)≤P max,j (41)

[0124] E min,j ≤E ESS,j (t)≤E max,j (42)

[0125] E ESS,j E(t) = ESS,j E(t - 1)+P ESS,j (t)Δt (43)

[0126] Wherein, E ESS,j (t) is the reserved power of the j-th energy storage power station at time t.

[0127] (2) Construct the constraints of electric vehicles

[0128] The following constraints are established for each type of electric vehicle;

[0129] ① The probability constraint that the active response power of the electric vehicle conforms to the user's response willingness is:

[0130] [1 - r l (t)]×p EV,l (t) ≤ P EV,l (t) ≤ [1 + r l (t)]×p EV,l (t) (44);

[0131] ② Under the time-of-use electricity price, the charging load of the electric vehicle has a certain time shift, and the charging power cannot ensure full charge, but it can still meet a certain driving demand. Therefore, the charging power constraint during its charging cycle is:

[0132]

[0133] t start = min{T arrive,l} (46)

[0134] t end = max{T leave,l} (47)

[0135] Wherein, α is the minimum charging demand ratio of electric vehicle users.

[0136] ③ In the actual charging process, the number of charging facilities in the micro energy network also restricts the charging load response characteristics of each time period. Therefore, the constraint of the charging facilities on the charging power of each time period is:

[0137]

[0138] Wherein, N ch is the number of charging piles.

[0139] (3) Construct the constraints of the distributed photovoltaic system

[0140] The following constraints are established for each type of distributed photovoltaic system:

[0141] P PV,v P(t)=βp PV,v P(t) (49)

[0142] 0 ≤ β ≤ 1 (50)

[0143] In the formula, β is the curtailment ratio of wind power.

[0144] (4) Construct the power constraint condition of the micro - energy grid connection point

[0145] Considering that the power of the connection point between the micro - energy grid and the external distribution network is limited by the equipment capacity, the energy management response characteristic model of the micro - energy grid needs to consider the power constraint of the connection point.

[0146]

[0147] In the formula, P load P(t) is the power of the conventional non - adjustable load inside the micro - energy grid at time t, and P N P(t) is the rated power of the connection point between the micro - energy grid and the external distribution network.

[0148] For the internal energy management response model of the micro - energy grid established in step four, this model takes P ESS,j P(t), P EV,l P(t) and P PV,v P(t) as the solution variables and is a linear programming model. By classifying and aggregating a large number of distributed energy storage power stations, electric vehicles and distributed photovoltaic systems, the model solution dimension is reduced. In this embodiment, through the linear programming method, the internal energy management response characteristics of the micro - energy grid under the external distribution network operation environment can be quickly calculated, and the overall external power characteristics of the micro - energy grid can also be obtained.

[0149] A fast calculation and analysis method for the energy management response characteristics of a micro - energy network proposed in this embodiment. For a distributed energy storage power station, a classification and aggregation method based on the shortest charge - discharge time with the maximum cycle power is proposed; for multiple types of electric vehicle users, based on the classification of charging types, a calculation method for the disordered charging load of each type of electric vehicle is proposed, which is based on the state of charge and time sampling when the electric vehicle users arrive and leave. For a distributed photovoltaic system, a classification method based on the similarity of historical lighting conditions is proposed. On this basis, an internal energy management response model of the micro - energy network is constructed. Its optimization goal is the pursuit of maximizing the operating benefits of the micro - energy network as a whole, and its constraint conditions include the operating constraints after the classification and aggregation of the distributed energy storage power station, electric vehicles, and distributed photovoltaic system, as well as the power constraints at the gateway of the micro - energy network. The constraints of the distributed energy storage power station include the constraints on the overall charge - discharge power and stored energy of each type of aggregated energy storage power station. This method regards the entire interior of the micro - energy network as an aggregated operation entity. Under the premise of pursuing the maximum overall benefit in different external distribution network operation environments, a large number of distributed energy storage power stations, electric vehicles, and distributed photovoltaic systems in the micro - energy network are classified and aggregated, and an energy management response model of the micro - energy network is constructed based on the aggregation model to obtain the power response characteristics of various types of equipment inside the micro - energy network, and the overall external power characteristics of the micro - energy network can also be obtained, solving the problem of rapid analysis and calculation of the overall energy management of the micro - energy network, and providing an efficient technical means for the internal operation of the micro - energy network and the prediction and guidance of the external characteristics of the micro - energy network.

[0150] System embodiment

[0151] According to an embodiment of the present invention, a system for fast calculation and analysis of the energy management response characteristics of a micro - energy network is provided. As Figure 2 shown, it is the system for fast calculation and analysis of the energy management response characteristics of the micro - energy network provided by the present invention. The system for fast calculation and analysis of the energy management response characteristics of the micro - energy network according to an embodiment of the present invention includes:

[0152] Data acquisition unit: Acquire the statistical data of the regional energy micro - network architecture.

[0153] Energy storage system classification and aggregation model construction unit: Used to construct an aggregation analysis model of distributed energy storage power stations inside the micro - energy network according to the acquired regional energy micro - network architecture data.

[0154] Electric vehicle classification and aggregation model construction unit: Used to construct an aggregation response model of various types of electric vehicle users inside the micro - energy network according to the acquired regional energy micro - network architecture data.

[0155] Distributed photovoltaic system classification and aggregation model construction unit: Used to construct an aggregation analysis model of distributed photovoltaic systems inside the micro - energy network according to the acquired regional energy micro - network architecture data.

[0156] Micro - energy grid energy management response model construction unit: Taking the maximization of the overall benefit of the regional micro - energy grid as the optimization goal, based on the classification and aggregation results of the distributed energy storage power station system, electric vehicle system, and distributed photovoltaic system, and taking the operation constraints after the classification and aggregation of the distributed energy storage power station system, electric vehicle users, and distributed photovoltaic system, and the power constraint conditions at the gateway of the regional micro - energy grid as conditions, an internal energy management response model of the micro - energy grid is established.

[0157] Model solving unit: Used to solve the internal energy management response model of the micro - energy grid and obtain the response characteristics of the regional micro - energy grid.

[0158] In this embodiment, preferably, the energy storage system classification and aggregation model construction unit first classifies the energy storage power stations according to the self - capacity and charge - discharge power of the energy storage power stations, and then conducts aggregation modeling on the sets of various types of energy storage power stations; specifically including:

[0159] Energy storage power station classification module: Classify the energy storage power stations based on the shortest charge - discharge time of the maximum cycle charge.

[0160] This module classifies the distributed energy storage power stations according to the shortest charge - discharge time of the maximum cycle charge in each charge - discharge cycle process of each energy storage power station. The shortest charge - discharge time of each energy storage power station is:

[0161]

[0162] In the formula, t i is the shortest single - charge or single - discharge time of the i - th energy storage power station, C i is the charge - discharge rate of the i - th energy storage power station, SOC max,i and SOC min,i are respectively the maximum ratio and minimum ratio of the stored electricity when maintaining the normal operation of the energy storage device.

[0163] After calculation, the set of the shortest charge - discharge times of all distributed energy storage power stations is obtained:

[0164] t c ={t c1 ,K,t ci ,K,t cm} (53)

[0165] In the formula, t c is the set of the shortest charge - discharge times, and m is the number of distributed energy storage power stations.

[0166] Classify the distributed energy storage power stations according to the shortest charge - discharge time:

[0167] (1) Set the set of the shortest charge - discharge time categories of the distributed energy storage power stations:

[0168] T={T1,K,Tj , K, T n} (54)

[0169]

[0170]

[0171] T j = T j-1 +1 (57)

[0172] Wherein, T is the set of the shortest charge-discharge time categories of the distributed energy storage power station, and T j is the j-th energy storage power station category.

[0173] The classification characteristics of each energy storage power station are as follows:

[0174]

[0175] Wherein, t si is the shortest charge-discharge time classification characteristic of the i-th energy storage power station; when Equation (59) holds, the i-th energy storage power station is classified into the j-th energy storage power station category.

[0176] Energy storage power station aggregation modeling module: Aggregate and model various types of energy storage power stations based on the classification results of the energy storage power station classification module. Specifically:

[0177] Aggregate the parameters of the energy storage power stations in the j-th category:

[0178]

[0179] Wherein, N j is the number of energy storage power stations in the j-th category, E j is the overall rated power of the aggregated energy storage power stations in the j-th category, P max,j is the maximum charge-discharge power of the aggregated energy storage power stations in the j-th category, E max,j is the maximum ratio of the stored power of the aggregated energy storage power stations in the j-th category, E min,j is the minimum ratio of the stored power of the aggregated energy storage power stations in the j-th category; e k is the rated power of the k-th energy storage power station in the j-th category, p max,k is the maximum charge-discharge power of the k-th energy storage power station in the j-th category, SOC max,k is the maximum ratio of the stored power of the k-th energy storage power station in the j-th category, SOC min,k is the minimum ratio of the stored power of the k-th energy storage power station in the j-th category.

[0180] Finally, after the aggregation of the energy storage power station categories, when the aggregated operating power of the j-th category of energy storage power stations is P ESS,jWhen (t), the operating power distribution of the k-th energy storage power station in this category is:

[0181]

[0182] In the formula, P ESS,j (t) is the operating power of the aggregated power station of the j-th type of energy storage power station at time t. When P ESS,j (t) is positive, the power station is in the charging state. When P ESS,j (t) is negative, the power station is in the discharging state. p ESS,k (t) is the operating power of the k-th distributed energy storage power station in the aggregated power station of the j-th type of energy storage power station at time t.

[0183] In this embodiment, the electric vehicle classification aggregation model construction unit classifies classified electric vehicle users based on the charging type and performs aggregation modeling based on the classification results, specifically including:

[0184] Electric vehicle user classification module: Classify electric vehicle users according to the charging type, including private car evening charging, private car daytime charging, official car daytime charging, etc. Set the electric vehicle user type set as:

[0185] EV = {EV1,K,EV l ,K,EV u} (65)

[0186] In the formula, EV is the electric vehicle user category set, EV l is the l-th electric vehicle user category, and u is the number of electric vehicle user categories.

[0187] Determination module for response rate vectors of each type: Determine the EV user response willingness probability of each type of electric vehicle user in each time period according to statistical data, and determine the response rate vector of each type of electric vehicle user as:

[0188]

[0189] In the formula, R l is the response willingness probability vector of the l-th type of electric vehicle user, r l (t) is the response willingness probability of the l-th electric vehicle user at time t, N T is the number of time periods in each micro energy network response cycle, is each time period within the cycle.

[0190] Unordered charging load calculation module: Calculate the unordered charging load without considering the optimized response of electric vehicle users according to statistical data, specifically including the following steps:

[0191] A1. Obtain the statistical models of various types of electric vehicle users:

[0192] soc arrive,l = f1(t) (67)

[0193] soc leave,l = f2(t) (68)

[0194] t arrive,l = f3(t) (69)

[0195] t leave,l = f4(t) (70)

[0196] wherein, soc arrive,l and soc leave,l are the state of charge when the l-th type of electric vehicle user arrives and departs respectively, t arrive,l and t leave,l are the arrival and departure times of the l-th type of electric vehicle user respectively, and the corresponding f1(t), f2(t), f3(t), and f4(t) are the probability density functions of the state of charge and time of arrival / departure, and the specific expressions of these functions can be various forms such as normal distribution function, uniform distribution function, etc.

[0197] A2. Calculate the unordered charging loads of various types without considering the optimized response of electric vehicle users.

[0198] Use the Monte Carlo sampling method to obtain the sampling vectors of various types of electric vehicles:

[0199]

[0200] wherein, SOC arrive,l and SOC leave,l are the sampling vectors of the state of charge when the l-th type of electric vehicle user arrives and departs respectively, Τ arrive,l and Τ leave,l are the sampling vectors of the arrival and departure times of the l-th type of electric vehicle user respectively.

[0201] Calculate the charging duration vector of the l-th type of electric vehicle user:

[0202]

[0203] wherein, T charge,l is the charging duration vector of the l-th type of electric vehicle user, and are the average rated battery capacity and charging power of the l-th type of electric vehicle respectively.

[0204] Then the actual charging end time vector is:

[0205]

[0206] Convert the charging start and end times of each cell of the Class-l electric vehicle users into a charging time status vector. That is, when the charging start time of the single vehicle is t 1,l , and the charging end time is t 1,l , its charging time status vector is:

[0207]

[0208] In the formula, S 1,l is the charging time status vector of the single cell of the first electric vehicle, and s 1,l (t1), K, are the elements of the corresponding charging time status vector.

[0209] Then the charging time status matrix of the Class-l electric vehicle users is:

[0210]

[0211] The charging load vectors of each time period of the Class-l electric vehicle users are:

[0212]

[0213] In this embodiment, the distributed photovoltaic system classification and aggregation model construction unit uses classification and aggregation based on historical light condition similarity for the distributed photovoltaic system, specifically including:

[0214] Distributed photovoltaic system classification module: Classify the distributed photovoltaic system according to the typical light area. Since the power generation power of the photovoltaic system is closely related to the irradiance, according to the statistical data, classify the distributed photovoltaic system according to the irradiance similarity. Assume that the irradiance data matrix of each distributed photovoltaic system in a certain time period is:

[0215]

[0216] In the formula, I is the irradiance data matrix of all distributed photovoltaic systems in a certain time period, is the irradiance of the z-th distributed photovoltaic system in time period, and N T2 is the number of typical time periods used in the distributed photovoltaic system classification.

[0217] Calculate the irradiance similarity coefficient of every two distributed photovoltaic systems:

[0218]

[0219] In the formula, d max,vw is the maximum deviation degree, and d vw is the comprehensive deviation degree.

[0220] Classify according to the irradiance similarity coefficient. Two conditions for two distributed photovoltaic systems v and w to be in the same class:

[0221] (1) When the d of the v-th and w-th distributed photovoltaic systems max,vw and d vw are both less than the corresponding thresholds;

[0222] (2) When all the distributed photovoltaic systems in the class where the v-th distributed photovoltaic system is located and the w-th distributed photovoltaic system satisfy the above condition (1);

[0223] After completing the classification of the distributed photovoltaic systems, the classification result is:

[0224] PV = {PV1,K,PV v ,K,PV z} (84)

[0225] In the formula, PV is the set of distributed photovoltaic system categories, PV v is the v-th electric vehicle user category, and z is the number of distributed photovoltaic system categories.

[0226] Distributed photovoltaic system aggregated power calculation module: Based on the classification result of the distributed photovoltaic system classification module, calculate the aggregated power of the distributed photovoltaic system;

[0227] Assume that the rated power capacity vector of the distributed photovoltaic systems in the v-th distributed photovoltaic system type is:

[0228]

[0229] In the formula, N v is the number of distributed photovoltaic systems in the v-th distributed photovoltaic system type.

[0230] Then the power generation of each time period of the v-th distributed photovoltaic system type is:

[0231]

[0232] In the formula, S r,v is the irradiance of each time period, is the overall average system efficiency, is the dust or rain shielding coefficient, is the component series mismatch coefficient, is the inverter loss coefficient, is the cable loss coefficient, is the transformer loss coefficient, is the tracking system accuracy coefficient.

[0233] In this embodiment, the energy management response model construction unit of the micro energy grid constructs an energy storage system aggregation analysis model, an aggregation response model of electric vehicle users, and a photovoltaic system aggregation analysis model respectively constructed by the energy storage system classification aggregation model construction unit, the electric vehicle classification aggregation model construction unit, and the distributed photovoltaic system classification aggregation model construction unit, so as to establish an internal energy management response model of the micro energy grid, specifically including:

[0234] Energy management response model construction module: aiming at maximizing the overall benefit of the regional micro energy grid, construct an internal energy management response model of the micro energy grid.

[0235] The internal energy management of the micro energy grid is mainly reflected in the pursuit of maximizing the operation benefit of the micro energy grid as a whole. Inside the micro energy grid, this embodiment mainly considers the operation characteristics of various energy subjects such as distributed energy storage power stations, electric vehicles, and distributed photovoltaic systems when the overall benefit of the micro energy grid is maximized, so as to guide the energy management of the micro energy grid and provide a micro energy grid response analysis tool for the distribution network operator to better guide the operation characteristics of the micro energy grid.

[0236] min F = F1 + F2 + F3 (88)

[0237]

[0238] Wherein, F1 is the operation cost of the distributed energy storage power station, F2 is the charging cost of electric vehicle users, F3 is the light abandonment loss of the distributed photovoltaic system, C(t) is the electricity price per unit of electricity at time t, and P PV,v (t) is the light abandonment power of the vth type of distributed photovoltaic system at time t.

[0239] Constraint condition determination module: determine the constraint conditions of the internal energy management response model of the micro energy grid.

[0240] The internal energy management of the micro energy grid should consider the self-demand constraints and operation constraints of various types of aggregated entities.

[0241] (1) Construct the constraint conditions of the distributed energy storage power station

[0242] For each type of distributed energy storage power station, the following constraints are established, mainly including the constraints on the overall charge-discharge power and stored energy of each type of energy storage power station after aggregation:

[0243] -P max,j ≤P ESS,j (t)≤P max,j (92)

[0244] E min,j ≤E ESS,j (t)≤E max,j (93)

[0245] E ESS,j (t) = E ESS,j (t-1)+P ESS,j (t)Δt (94)

[0246] In the formula, E ESS,j (t) is the power reserve of the j-th energy storage power station category at time t.

[0247] (2) Constructing electric vehicle constraints

[0248] The following constraints are established for each type of electric vehicle;

[0249] ① The probability constraint that the active response power of electric vehicles meets the user's response willingness is:

[0250] [1-r l (t)]×p EV,l (t)≤P EV,l (t)≤[1+r l (t)]×p EV,l (t) (95);

[0251] ② Under the influence of time-of-use electricity prices, the charging load of electric vehicles has a certain time shift, which results in the charging power not being guaranteed to be full, but it can still meet certain driving needs. Therefore, the charging power constraint within the charging cycle is:

[0252]

[0253] t start =min{T arrive,l} (97)

[0254] t end =max{T leave,l} (98)

[0255] Where α is the minimum charging demand ratio of electric vehicle users.

[0256] ③ In the actual charging process, the number of charging facilities in the micro energy network also restricts the charging load response characteristics of each period. Therefore, the constraints of charging facilities on the charging power of each period are:

[0257]

[0258] Where N ch is the number of charging piles.

[0259] (3) Constructing distributed photovoltaic system constraints

[0260] The following constraints are established for each type of distributed photovoltaic system:

[0261] P PV,v P(t) = βp PV,v (t)(100)

[0262] 0 ≤ β ≤ 1 (101)

[0263] Wherein, β is the curtailment ratio of wind power.

[0264] (4) Construct the power constraint condition of the micro - energy network gateway

[0265] Considering that the gateway power at the connection point between the micro - energy network and the external distribution network is restricted by the equipment capacity, the energy management response characteristic model of the micro - energy network needs to consider the gateway power constraint.

[0266]

[0267] Wherein, P load P(t) is the power of the conventional non - adjustable load inside the micro - energy network at time t, and P N (t) is the rated power of the gateway between the micro - energy network and the external distribution network.

[0268] As Figure 3 shown, the present invention also provides a computer - readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the fast calculation and analysis method for the energy management response characteristics of the micro - energy network in the above - mentioned embodiment, or when the computer program is executed by a processor, it implements the fast calculation and analysis method for the energy management response characteristics of the micro - energy network in the above - mentioned embodiment.

[0269] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0270] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the device or system embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, refer to the description of the method embodiments. The device and system embodiments described above are only illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative work.

[0271] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A rapid calculation and analysis method for the energy management response characteristics of a micro energy grid, characterized in that Including the steps: Obtain the statistical data of the regional energy microgrid architecture; construct an aggregated analysis model for the distributed energy storage system within the micro energy grid, construct an aggregated response model for various types of electric vehicle users, and construct an aggregated analysis model for the distributed photovoltaic system; with the maximization of the overall benefit of the regional micro energy grid as the optimization goal, based on the classification aggregation results of the distributed energy storage power station system, electric vehicle users, and distributed photovoltaic system, and taking the operating constraint conditions after the classification aggregation of the distributed energy storage power station system, electric vehicle users, and distributed photovoltaic system, and the gateway power constraint conditions of the regional micro energy grid as the premise, establish an internal energy management response model for the micro energy grid; solve the internal energy management response model of the micro energy grid to obtain the response characteristics of the regional micro energy grid; The construction of the aggregated analysis model for the distributed energy storage system within the micro energy grid includes the steps: Classify the energy storage power stations based on the shortest charge-discharge time of the maximum cycle power, and perform aggregated modeling on various types of energy storage power stations based on the classification results; The construction of the aggregated response model for various types of electric vehicle users includes the steps: Classify the electric vehicle users according to the charging type, and set the set of electric vehicle user types; Determine the probability of the response willingness of each electric vehicle user type at each time period according to the statistical data, and determine each type of response rate vector; Calculate the unordered charging load without considering the optimized response of electric vehicle users according to the statistical data; The construction of the aggregated analysis model for the distributed photovoltaic system includes the steps: Classify the distributed photovoltaic system according to the irradiance similarity according to the statistical data; calculate the aggregated power generation of the distributed photovoltaic system according to the classification results.

2. A rapid calculation and analysis system for the energy management response characteristics of a micro energy network, which executes a rapid calculation and analysis method for the energy management response characteristics of a micro energy network as described in claim 1, characterized in that, Including a data acquisition unit: to obtain the statistical data of the regional energy microgrid architecture; An energy storage system classification aggregation model construction unit: used to construct an aggregated analysis model for the distributed energy storage power stations within the micro energy grid according to the obtained regional energy microgrid architecture data; An electric vehicle classification aggregation model construction unit: used to construct an aggregated response model for various types of electric vehicle users within the micro energy grid according to the obtained regional energy microgrid architecture data; A distributed photovoltaic system classification aggregation model construction unit: used to construct an aggregated analysis model for the distributed photovoltaic system within the micro energy grid according to the obtained regional energy microgrid architecture data; A micro energy grid energy management response model construction unit: with the maximization of the overall benefit of the regional micro energy grid as the optimization goal, based on the classification aggregation results of the distributed energy storage power station system, electric vehicle users, and distributed photovoltaic system, and taking the operating constraint conditions after the classification aggregation of the distributed energy storage power station system, electric vehicle users, and distributed photovoltaic system, and the gateway power constraint conditions of the regional micro energy grid as the conditions, establish an internal energy management response model for the micro energy grid; A model solving unit: used to solve the internal energy management response model of the micro energy grid to obtain the response characteristics of the regional micro energy grid; The energy storage system classification aggregation model construction unit includes: An energy storage power station classification module: classify the energy storage power stations based on the shortest charge-discharge time of the maximum cycle power; An energy storage power station aggregation modeling module: perform aggregated modeling on various types of energy storage power stations based on the classification results of the energy storage power station classification module; The electric vehicle classification aggregation model construction unit includes: Electric vehicle user classification module: Classify electric vehicle users according to the charging type and set the set of electric vehicle user types; Determination module for response rate vectors of each type: Determine the probability of response willingness of electric vehicle users of each type at each time period based on statistical data and determine the response rate vector of electric vehicle users of each type; Unordered charging load calculation module: Calculate the unordered charging load without considering the optimized response of electric vehicle users based on statistical data; The distributed photovoltaic system classification and aggregation model construction unit includes: Distributed photovoltaic system classification module: Classify distributed photovoltaic systems according to irradiance similarity based on statistical data; Distributed photovoltaic system aggregated power generation calculation module: Calculate the aggregated power generation of distributed photovoltaic systems based on the classification results of the distributed photovoltaic system classification module.

3. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the fast calculation and analysis method for the energy management response characteristics of the micro energy network as described in claim 1.

4. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the fast calculation and analysis method for the energy management response characteristics of the micro energy network as described in claim 1.

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