Method, device and equipment for evaluating power regulation potential interval of low-voltage transformer area, storage medium and program product

Abstract

The invention discloses a low-voltage transformer area power regulation potential interval evaluation method, device and equipment, a storage medium and a program product. The method comprises the following steps: establishing regulation and control models of typical flexible resources of an electric vehicle, energy storage and an air conditioner in a low-voltage transformer area; using the prediction error rate of each historical similar day to correct the source load power prediction value of the prediction day, and using each corrected source load power prediction value as a random scene of the prediction day; a transformer area economic operation model is established and solved, the baseline interaction power of the low-voltage transformer area and the superior power grid is obtained, and the transformer area economic operation model takes the minimum sum of the interaction cost and the energy storage loss of the low-voltage transformer area and the grid in the random scene as an objective function; calculating maximum upward and downward adjustment values based on baseline interaction power in all random scenes, and selecting an interval with minimum interaction power in all random scenes as a power adjustment potential interval of a low-voltage transformer area and a superior power grid in a prediction day; according to the invention, accurate power regulation potential interval evaluation can be realized.

Description

Technical Field

[0001] The present invention relates to a power regulation potential interval evaluation technology, and in particular to a method, device, equipment, storage medium and program product for evaluating the power regulation potential interval of a low-voltage station area. Background Art

[0002] With the massive resources connected to the distribution network, the dimensional disaster, complex operation and control, and other problems it brings make it difficult for traditional centralized control methods to directly control all distributed power sources and flexible resources. Therefore, the low-voltage substations at the end of the current distribution network are equipped with intelligent fusion terminals. The fusion terminals can realize the optimal control of distributed power sources and flexible negative resources downward; and can report the operation status of the substations to the distribution network dispatching center upward. Therefore, forming an autonomous system based on low-voltage substations and rationally regulating source, load and storage resources can reduce the dispatching pressure of the upper power grid.

[0003] During the autonomy process, low-voltage substations need to report the interactive power range to the superior power grid. The interactive power range reflects the power regulation potential of low-voltage substations. At present, there is a problem that the evaluation of the power regulation potential of substations cannot be accurately quantified, which aggravates the difficulty of low-voltage substation autonomy. Therefore, it is necessary to carry out research on the power regulation potential evaluation for the autonomy needs of low-voltage substations. Summary of the invention

[0004] In view of the problems existing in the prior art, the purpose of the present invention is to provide a method, device, equipment, storage medium and program product for evaluating the power regulation potential interval of a low-voltage substation.

[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0006] A method for evaluating the power regulation potential interval of a low voltage station area, comprising:

[0007] Establish power control models for typical flexible resources such as electric vehicles, energy storage, and air conditioning in low-voltage areas;

[0008] Determine the prediction error rate of each historical similar day based on the predicted values ​​and actual values ​​of the source and load power on several historical similar days similar to the predicted day;

[0009] The forecast error rate of each historical similar day is used to correct the source load power forecast value of the forecast day, and each corrected source load power forecast value is used as a random scenario of the forecast day;

[0010] Establish and solve the economic operation model of the substation to obtain the baseline interaction power between the low-voltage substation and the upper-level power grid. The economic operation model of the substation takes the minimum sum of the interaction cost between the low-voltage substation and the grid and the energy storage loss under random scenarios as the objective function, and takes the power control model of electric vehicles, energy storage, air conditioning and power balance constraints as constraints.

[0011] The maximum upward and downward adjustment values ​​based on the baseline interactive power under all random scenarios are calculated, and the interval with the minimum interactive power in all random scenarios is selected as the power regulation potential interval between the low-voltage substation and the upper-level power grid on the forecast day.

[0012] A low voltage station area power regulation potential interval assessment device, comprising:

[0013] The power control model establishment module is used to establish the power control model of typical flexible resources such as electric vehicles, energy storage, and air conditioners in low-voltage areas;

[0014] An error rate calculation module, used to determine the prediction error rate of each historical similar day based on the source load power prediction value and actual value of several historical similar days similar to the prediction day;

[0015] The random scenario calculation module is used to use the prediction error rate of each historical similar day to correct the source load power prediction value of the prediction day, and use each corrected source load power prediction value as a random scenario of the prediction day;

[0016] The baseline interaction power calculation module is used to establish and solve the economic operation model of the substation to obtain the baseline interaction power between the low-voltage substation and the upper-level power grid. The economic operation model of the substation takes the minimum sum of the interaction cost between the low-voltage substation and the grid and the energy storage loss under random scenarios as the objective function, and takes the power control model of electric vehicles, energy storage, air conditioning and power balance constraints as constraints.

[0017] The power regulation potential interval determination module is used to calculate the maximum upward and downward regulation values ​​based on the baseline interactive power under all random scenarios, and select the interval with the minimum interactive power in all random scenarios as the power regulation potential interval between the low-voltage substation and the upper-level power grid on the forecast day.

[0018] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above method.

[0019] A computer-readable storage medium having a computer program / instruction stored thereon, wherein the computer program / instruction implements the above method when executed by a processor.

[0020] A computer program product comprises a computer program / instruction, wherein the computer program / instruction implements the above method when executed by a processor.

[0021] Compared with the prior art, the beneficial effect of the present invention is that the power regulation capability of the flexible resources of the substation is mapped to the interaction power range between the substation as a whole and the upper-level power grid. The optimized interaction power range not only reflects the maximum power regulation potential of the substation, but also takes into account the randomness of the internal source and load of the substation, thereby reducing the power over-limit situation of the interaction with the upper-level power grid caused by exaggerating the power regulation capability of the substation, thereby reducing the difficulty of substation autonomy. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flow chart of a method for evaluating a low voltage area power regulation potential interval provided by an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram of the station area structure;

[0024] Figure 3 This is a schematic diagram of electric vehicle model parameters;

[0025] Figure 4 It is the baseline interactive power diagram between the substation and the upper power grid;

[0026] Figure 5 It is the interactive power interval diagram between the substation and the upper power grid;

[0027] Figure 6 This is the optimization result diagram of the adjustable device corresponding to the interactive power lower limit;

[0028] Figure 7 This is the optimization result diagram of the adjustable device corresponding to the interaction power upper limit;

[0029] Figure 8 This is a comparison chart of the interaction power corresponding to the predicted scenario and the reported interaction power. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0031] Embodiment 1

[0032] The embodiment of the present invention provides a method for evaluating the power regulation potential interval of a low voltage station area, such as Figure 1 As shown, including:

[0033] S1. Establish power control models for typical flexible resources such as electric vehicles, energy storage, and air conditioning in low-voltage areas.

[0034] Among them, the parameters of the electric vehicle power control model are predicted by the historical data recorded by the electric vehicle charging pile, and the power control model parameters of the air conditioner and energy storage are known.

[0035] The power control model of the electric vehicle is specifically as follows:

[0036]

[0037] In the formula, They represent the power of the electric vehicle cluster at time t and t-1 respectively, is the change in the electric vehicle cluster power caused by the arrival or departure of electric vehicles in period t, η ch , η dis are the charging and discharging efficiency of electric vehicle clusters, Δt is the control time interval, and P t EV,ch , P t EV,dis They represent the charging and discharging power of the electric vehicle cluster in period t, and are the maximum charging and discharging power of the electric vehicle cluster in period t respectively; and are the minimum and maximum power of the electric vehicle cluster in period t respectively.

[0038] The process of establishing the power regulation model is as follows:

[0039] The coupling relationship between the charging and discharging power and battery capacity of a single electric vehicle can be modeled as a generalized energy storage model as shown below:

[0040]

[0041] In the formula, and They represent the charging power and discharging power of electric vehicle n in period t respectively; and They represent the upper limit of charging power and the upper limit of discharging power of electric vehicle n respectively; and They represent the battery power of electric vehicle n in period t and its previous period respectively; and They respectively represent the battery power safety boundaries of electric vehicle n.

[0042] The electric vehicle charging station will record the charging data of the electric vehicles it serves every day, including the arrival time, departure time, battery power at arrival, battery power at departure, the maximum charging and discharging power of different models of electric vehicles and their battery power boundaries.

[0043]

[0044] In the formula, and The time when electric vehicle n arrives at the charging pile and leaves the charging pile; s min,n and max,nThey are the upper and lower boundaries of the battery power of the electric vehicle n, respectively; and are the power of electric vehicle n when it arrives at and leaves the charging station; and are the maximum charging and discharging powers of electric vehicle n respectively.

[0045] In addition, the binary parameter X is introduced n,t Indicates the charging status of electric vehicle n:

[0046]

[0047] Where, X n,t 0 means the electric car is not on the charging station, X n,t A value of 1 indicates that the electric vehicle is on the charging station.

[0048] Using the charging data recorded by electric vehicle charging piles, the generalized energy storage model of a single electric vehicle can be extended to the generalized energy storage model of an electric vehicle cluster:

[0049]

[0050] Considering that the arrival and departure of electric vehicles in each period will cause changes in the power of the cluster, we introduce Indicates the change in the amount of electricity of the electric vehicle cluster during period t:

[0051]

[0052] Where N EV For electric vehicles. It means that during the period t, the battery power of the electric vehicle arriving at the charging pile is greater than the battery power of the electric vehicle leaving the charging pile.

[0053] Introduction Finally, the generalized energy storage model of electric vehicle cluster is further expressed as:

[0054]

[0055] The historical charging and discharging data recorded by the charging piles are organized into a generalized energy storage model for electric vehicle clusters and obtained The historical data of model parameters such as , and the model parameters of the electric vehicle control model for the next day are predicted based on the historical data. Among them, the power control model of the air conditioner is specifically:

[0056]

[0057] Where, T t in , are the indoor temperatures at time periods t and t-1 respectively, is the outdoor temperature during the period t-1, η is the air conditioning efficiency, P t , P t-1 They represent the cooling power of the air conditioner during the period t-1, R and C are the equivalent heat capacity and equivalent thermal resistance of the room where the air conditioner is located, which are known data, Δt is the control time interval, and are the minimum and maximum indoor temperatures that meet user comfort, P min , P max They are the minimum and maximum cooling power of the air conditioner, respectively, and can be found on the air conditioner nameplate.

[0058] The power regulation model of the energy storage is specifically:

[0059]

[0060] Where P t b P is the charging / discharging power of the energy storage in period t after considering the charging / discharging efficiency. t ch , P t dis are the charging and discharging power of the energy storage in period t, η ch , η dis are the charging and discharging efficiency of energy storage, E t 、E t-1 are the amount of energy stored in time periods t and t-1, Δt is the regulation time interval, β b It is a 0-1 variable, with a value of 1 during charging and a value of 0 during discharging. is the maximum charge and discharge power of energy storage, E min and E max are the minimum and maximum energy storage boundaries respectively. The model parameter η in this model ch , η dis , β b , E min and E max All are known data.

[0061] S2. Determine the prediction error rate of each historical similar day based on the predicted values ​​and actual values ​​of the source-load power of several historical similar days similar to the predicted day.

[0062] This step specifically includes: searching for N historical similar days that are most similar to the predicted day from historical days, for example, the k-means algorithm can be used; according to the photovoltaic output power and load power of each historical similar day, the prediction error rate of each period of each historical similar day is calculated according to the following formula:

[0063]

[0064] In the formula, They represent the prediction error rates of PV output power and load power in period t on the s-th historical similar day, respectively; They represent the actual values ​​of PV output power and load power during period t on the s-th historical similar day; They respectively represent the predicted values ​​of PV output power and load power in period t on the s-th historical similar day.

[0065] S3. Use the prediction error rate of each similar historical day to correct the source-load power prediction value of the prediction day, and use each corrected source-load power prediction value as a random scenario of the prediction day.

[0066] This step specifically includes: taking the prediction error rate of each period of each historical similar day as the prediction error rate of each corresponding period of the prediction day under the random scenario, and generating the photovoltaic output power and load power of each period of the prediction day under the random scenario according to the following formula:

[0067]

[0068] In the formula, They are the corrected predicted values ​​of photovoltaic output power and load power during period t on the predicted day under random scenario s, respectively; is the predicted value of photovoltaic output power and load power, They respectively represent the prediction error rates of PV output power and load power in period t on the s-th historical similar day, and N is the number of random scenarios.

[0069] S4. Establish and solve the economic operation model of the substation to obtain the baseline interaction power between the low-voltage substation and the upper-level power grid.

[0070] The economic operation model of the substation takes the minimum sum of the interaction cost between the low-voltage substation and the grid and the energy storage loss under random scenarios as the objective function, and takes the power regulation model of electric vehicles, energy storage, air conditioning and power balance constraints as constraints, specifically:

[0071]

[0072] st

[0073] Where N is the number of random scenes, ρ s is the probability of occurrence of random scenario s, C grid is the interaction cost between the station area and the grid, is the energy storage charging and discharging loss under random scenario s, P t buy,grid is the power purchased from the grid during period t, P is the time-of-use electricity price,t sell,grid is the power sold from the substation to the grid during period t, is the electricity price, Δt is the regulation time interval, t 0 is the initial value of the time period, T is the final value of the time period, n is the number of energy storage, pr i b is the unit charge and discharge loss of energy storage i, is the charging efficiency of energy storage i, is the discharge efficiency of energy storage i, are the charging and discharging power of energy storage i in period t under random scenario s, is the photovoltaic output power and load power in period t under random scenario s, is the power of the jth air conditioner in period t under random scenario s, m is the number of air conditioners involved in the regulation, are the charging and discharging power of the electric vehicle cluster in period t under random scenario s; the baseline interaction power P t grid = {P t buy,grid ,P t sell,grid}.

[0074] S5. Calculate the maximum upward and downward adjustment values ​​based on the baseline interaction power under all random scenarios, and select the interval with the minimum interaction power in all random scenarios as the power regulation potential interval between the low-voltage substation and the upper-level power grid on the predicted day.

[0075] Assume that the upward and downward adjustment amount of the area based on the baseline interaction power during period t is The upper and lower limits of the interactive power interval in period t can be expressed as follows:

[0076]

[0077] and It is for a single period, while generating interactive power intervals requires obtaining upper and lower limits for multiple periods throughout the day. Assume that the upward / downward adjustment amount for each period is and is a dimension of the polyhedron, then solving the upper and lower limits of interactive prediction power for multiple time periods throughout the day is equivalent to finding the volume of the polyhedron. Therefore, the objective function can be expressed as:

[0078]

[0079] Taking the solution of the maximum upward adjustment potential as an example, in the actual solution process, since the objective function contains product terms, it is not easy to solve, so the objective function is approximated as:

[0080]

[0081] Constraints:

[0082]

[0083] The above optimization model is a deterministic model. However, when evaluating the power regulation potential of the substation, it is also necessary to consider the uncertainty of the source and load, that is, randomness. Therefore, N random scenarios are substituted into the above optimization model, and a total of N optimization results can be obtained. When the substation reports the interactive power interval to the superior power grid, it should ensure that even if there is a source and load fluctuation during the substation's autonomy the next day, the power regulation of the substation can still reach the reported interactive power interval boundary. Therefore, the interactive power interval reported by the substation should be the most robust interval to deal with source and load fluctuations.

[0084] The above objective function can be further expressed as:

[0085]

[0086] st

[0087] Similarly, the optimization model of the maximum downward adjustment value can be obtained:

[0088]

[0089] st

[0090] Combining the above two models, the power regulation range evaluation model can be obtained as follows:

[0091]

[0092] st

[0093]

[0094] In the formula, are the upward and downward adjustment amounts of the station area based on the baseline interaction power in the random scenario s and time period t, respectively. t grid is the baseline interaction power, N is the number of random scenes, t 0 is the initial value of the time period, T is the final value of the time period, are the optimal values ​​of the upward and downward adjustment of the base station based on the baseline interaction power in all random scenarios during period t, is the photovoltaic output power and load power in period t under random scenario s, are the charging and discharging power of energy storage i in period t under random scenario s, is the power of the jth air conditioner in period t under random scenario s, m is the number of air conditioners involved in the regulation, n is the number of energy storage units, are the charging and discharging powers of the electric vehicle cluster in period t under random scenario s.

[0095] Then, by solving the power regulation range evaluation model, we can get According to The power regulation potential range of the low-voltage substation and the upper-level power grid on the predicted day is calculated.

[0096] In order to enable those skilled in the art to better understand the present invention, the applicant further elaborates on the present invention in conjunction with specific embodiments.

[0097] The structure of the station area is shown as follows Figure 2 As shown in Figure 1, there are three lines in this area, which are connected to distributed photovoltaic, energy storage, air conditioning load, electric vehicle charging pile and basic load. The flexible loads that can participate in regulation are mainly air conditioning and electric vehicles. The area is equipped with intelligent fusion terminals, which can complete information collection and control signal transmission. Electric vehicle model parameters are shown in Figure 1. Figure 3 As shown. There are 20 air conditioners in this area that can participate in load regulation. The human comfort temperature is set to 24.7℃~27.5℃ based on GB / T 18049. The initial room temperature is set to the upper limit of the most comfortable temperature range. The air conditioning parameters are shown in Table 1. This area is equipped with two types of energy storage. The energy storage parameters are shown in Table 2.

[0098] Table 1 Air conditioning parameters

[0099]

[0100] Table 2 Energy storage parameters

[0101]

[0102] In the day-ahead stage, with 1 hour as the time period scale, based on the day-ahead PV output and basic load forecast values, and combined with historical forecast error data, 1000 PV output and basic load scenarios are constructed by searching for historical days similar to the forecast day. The random optimization method is used to optimize the adjustable equipment control strategy under each scenario, and the baseline interaction power is obtained, such as Figure 4 After the baseline interaction power is obtained, the upward / downward adjustment value of the substation based on the baseline interaction power is obtained by solving the substation regulation potential interval model proposed in the present invention, as shown in FIG. Figure 5 In addition, the optimization results of each adjustable device corresponding to the interactive prediction power lower limit are shown in Figure 6 The optimization results of each adjustable device corresponding to the interactive prediction power upper limit are shown in Figure 7 shown.

[0103] Depend on Figure 4 , 5, 6, and 7 show that the substation has a large potential for power reduction during the 0:00-7:00 period, a certain potential for power reduction during the 8:00-10:00 period and the 16:00-18:00 period, and no obvious potential for power reduction during the other periods. In addition, the substation has a large potential for power increase during the 11:00-15:00 period, the 19:00-21:00 period, and the 23:00-0:00 period, and the potential for power increase during the other periods is small. By analyzing the optimization results of the adjustable equipment corresponding to the interactive power upper / lower limit, it can be seen that when the substation has a large potential for power reduction, the electric vehicle charging power corresponding to the interactive prediction power lower limit is significantly smaller than the electric vehicle charging power corresponding to the interactive prediction power upper limit, while the electric vehicle discharge situation is just the opposite. In addition, the air conditioning load will also reduce the operating power on the premise of meeting the user's comfort. When the substation has a large potential for power increase, the electric vehicle will appropriately increase the charging power while reducing the discharge power on the premise of meeting the user's travel needs. The air conditioning load will increase the operating power on the premise of meeting the user's comfort.

[0104] In order to avoid being punished by the upper power grid due to exaggerating its own power regulation potential and exceeding the reported interactive power interval in the actual autonomous process, the reported interactive power interval is the most conservative interval among 1000 random scenarios. The present invention does not consider the uncertainty of source and load, and directly compares the adjustable power interval of the substation obtained by the source and load prediction scenario with the reported interactive power interval. The results are as follows Figure 8 As shown in the figure, when the uncertainty of source and load is not considered, the interactive power range reported by the substation to the superior power grid will exaggerate the power regulation potential of the substation, resulting in the interactive power exceeding the limit in the process of autonomy.

[0105] Embodiment 2

[0106] The embodiment of the present invention provides a low voltage station area power regulation potential interval assessment device, comprising:

[0107] The power control model establishment module is used to establish the power control model of typical flexible resources such as electric vehicles, energy storage, and air conditioners in low-voltage areas;

[0108] An error rate calculation module, used to determine the prediction error rate of each historical similar day based on the source load power prediction value and actual value of several historical similar days similar to the prediction day;

[0109] The random scenario calculation module is used to use the prediction error rate of each historical similar day to correct the source load power prediction value of the prediction day, and use each corrected source load power prediction value as a random scenario of the prediction day;

[0110] The baseline interaction power calculation module is used to establish and solve the economic operation model of the substation to obtain the baseline interaction power between the low-voltage substation and the upper-level power grid. The economic operation model of the substation takes the minimum sum of the interaction cost between the low-voltage substation and the grid and the energy storage loss under random scenarios as the objective function, and takes the power control model of electric vehicles, energy storage, air conditioning and power balance constraints as constraints.

[0111] The power regulation potential interval determination module is used to calculate the maximum upward and downward regulation values ​​based on the baseline interactive power under all random scenarios, and select the interval with the minimum interactive power in all random scenarios as the power regulation potential interval between the low-voltage substation and the upper-level power grid on the forecast day.

[0112] The power control model of the electric vehicle is specifically as follows:

[0113]

[0114] In the formula, They represent the power of the electric vehicle cluster at time t and t-1 respectively, is the change in the electric vehicle cluster power caused by the arrival or departure of electric vehicles in period t, η ch , η dis are the charging and discharging efficiency of electric vehicle clusters, Δt is the control time interval, and P t EV,ch , P t EV,dis They represent the charging and discharging power of the electric vehicle cluster in period t, and are the maximum charging and discharging power of the electric vehicle cluster in period t respectively; and are the minimum and maximum power of the electric vehicle cluster in period t, respectively. As model parameters, they are predicted from the historical data recorded by the electric vehicle charging piles.

[0115] The power control model of the air conditioner is specifically:

[0116]

[0117] Where, T t in , are the indoor temperatures at time periods t and t-1 respectively, is the outdoor temperature during the period t-1, η is the air conditioning efficiency, P t , P t-1 They represent the cooling power of the air conditioner during the period t-1, R and C are the equivalent heat capacity and equivalent thermal resistance of the room where the air conditioner is located, Δt is the control time interval, and are the minimum and maximum indoor temperatures that meet user comfort, P min , P max They are the minimum and maximum cooling power of the air conditioner respectively.

[0118] The power regulation model of the energy storage is specifically:

[0119]

[0120] Where P t b P is the charging / discharging power of the energy storage in period t after considering the charging / discharging efficiency. t ch , P t dis are the charging and discharging power of the energy storage in period t, η ch , η dis are the charging and discharging efficiency of energy storage, E t 、E t-1 are the amount of energy stored in time periods t and t-1, Δt is the regulation time interval, β b It is a 0-1 variable, with a value of 1 during charging and a value of 0 during discharging. is the maximum charge and discharge power of energy storage, E min and E max They are the minimum and maximum power boundaries of energy storage respectively.

[0121] Wherein, the error rate calculation module specifically includes:

[0122] Similar day search unit, used to search for N historical similar days that are most similar to the predicted day from historical days;

[0123] The error rate calculation unit is used to calculate the prediction error rate of each period of each historical similar day according to the following formula based on the photovoltaic output power and load power of each historical similar day:

[0124]

[0125] In the formula, They represent the prediction error rates of PV output power and load power in period t on the s-th historical similar day, respectively; They represent the actual values ​​of PV output power and load power during period t on the s-th historical similar day; They respectively represent the predicted values ​​of PV output power and load power in period t on the s-th historical similar day.

[0126] The random scene calculation module specifically includes:

[0127] The prediction day error rate determination unit is used to use the prediction error rate of each period of each historical similar day as the prediction error rate of each corresponding period of the prediction day,

[0128] The source-load power prediction unit is used to generate the photovoltaic output power and load power for each period of the forecast day according to the following formula:

[0129]

[0130] In the formula, They are the corrected predicted values ​​of photovoltaic output power and load power during period t on the predicted day under random scenario s, respectively; is the predicted value of photovoltaic output power and load power, They respectively represent the prediction error rates of PV output power and load power in period t on the s-th historical similar day, and N is the number of random scenarios.

[0131] The economic operation model of the substation area is specifically as follows:

[0132]

[0133] st

[0134] Where N is the number of random scenes, ρ s is the probability of occurrence of random scenario s, C grid is the interaction cost between the station area and the grid, is the energy storage charging and discharging loss under random scenario s, P t buy,grid is the power purchased from the grid during period t, P is the time-of-use electricity price, t sell,grid is the power sold from the substation to the grid during period t, is the electricity price, Δt is the regulation time interval, t 0 is the initial value of the time period, T is the final value of the time period, n is the number of energy storage, pr i b is the unit charge and discharge loss of energy storage i, is the charging efficiency of energy storage i, is the discharge efficiency of energy storage i, are the charging and discharging power of energy storage i in period t under random scenario s, is the photovoltaic output power and load power in period t under random scenario s, is the power of the jth air conditioner in period t under random scenario s, m is the number of air conditioners involved in the regulation, are the charging and discharging power of the electric vehicle cluster in period t under random scenario s; the baseline interaction power P t grid = {Pt buy,grid ,P t sell,grid}.

[0135] The power regulation potential interval determination module specifically includes:

[0136] The model acquisition unit is used to acquire the power regulation range evaluation model:

[0137]

[0138] st

[0139]

[0140] In the formula, are the upward and downward adjustment amounts of the station area based on the baseline interaction power in the random scenario s and time period t, respectively. t grid is the baseline interaction power, N is the number of random scenes, t 0 is the initial value of the time period, T is the final value of the time period, are the optimal values ​​of the upward and downward adjustment of the base station based on the baseline interaction power in all random scenarios during period t, is the photovoltaic output power and load power in period t under random scenario s, are the charging and discharging power of energy storage i in period t under random scenario s, is the power of the jth air conditioner in period t under random scenario s, m is the number of air conditioners involved in the regulation, n is the number of energy storage units, are the charging and discharging power of the electric vehicle cluster in period t under random scenario s;

[0141] The model solving unit is used to solve the power adjustment range evaluation model according to the baseline interaction power to obtain

[0142] The interval determination unit is used to determine the The power regulation potential interval between the low-voltage substation area and the upper-level power grid on the forecast day is calculated using the following formula:

[0143]

[0144] In the formula, It is the upper and lower limits of the power regulation potential range between the low-voltage substation and the upper-level power grid during the predicted day t period.

[0145] The device provided in the embodiment of the present invention can be used to execute the method provided in the first embodiment of the present invention, and has the corresponding functions and beneficial effects of executing the method.

[0146] It is worth noting that in the embodiment of the above-mentioned determination device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0147] The embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, or of course, it can be implemented only by hardware, as long as the function or effect can be achieved.

[0148] Embodiment 3

[0149] The embodiment of the present invention further provides a computer device, which provides services for implementing the method of the first embodiment of the present invention. The device may include: a memory storing a computer executable program; a processor coupled to the memory; the processor calls the computer executable program stored in the memory to execute the steps in the method described in the first embodiment.

[0150] The memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the memory may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as "hard drives"). A program / utility having a set (at least one) of program modules may be stored in, for example, the memory, such program modules including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The computer executable program of the program module typically performs the functions and / or methods in the embodiments described in the present invention.

[0151] The processor executes various functional applications and data processing by running the program stored in the memory, such as implementing the method provided in the first embodiment of the present invention.

[0152] The code of the computer executable program can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages.

[0153] Embodiment 4

[0154] An embodiment of the present invention provides a storage medium including a computer executable program. When the computer executable program is executed by a computer processor, it is used to execute the method of the first embodiment.

[0155] The storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0156] Of course, the storage medium containing a computer executable program provided by an embodiment of the present invention, the computer executable program of which is not limited to the above method operations, can also execute related operations in the method provided by any embodiment of the present invention.

[0157] Embodiment 5

[0158] The embodiment of the present invention also provides a computer product, such as an app on a mobile phone, a tablet, an installation program on a computer, etc., which includes a computer program / instruction, and the computer program / instruction is executed by a processor to implement the method described in Example 1. The code of the computer executable program for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, and the programming language includes an object-oriented programming language, such as Java, Smalltalk, C++, and also includes a conventional procedural programming language, such as "C" language or similar programming language. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0159] It should be understood that the above embodiments and descriptions only describe the principles, main features and advantages of the present invention. Without departing from the spirit and scope of the present invention, the present invention may be subject to various changes and improvements, and these changes and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for evaluating the power regulation potential interval of a low voltage station area, characterized in that: include: Establish power control models for typical flexible resources such as electric vehicles, energy storage, and air conditioning in low-voltage areas; Determine the prediction error rate of each historical similar day based on the predicted values ​​and actual values ​​of the source and load power on several historical similar days similar to the predicted day; The forecast error rate of each historical similar day is used to correct the source load power forecast value of the forecast day, and each corrected source load power forecast value is used as a random scenario of the forecast day; Establish and solve the economic operation model of the substation to obtain the baseline interaction power between the low-voltage substation and the upper-level power grid. The economic operation model of the substation takes the minimum sum of the interaction cost between the low-voltage substation and the grid and the energy storage loss under random scenarios as the objective function, and takes the power control model of electric vehicles, energy storage, air conditioning and power balance constraints as constraints. The maximum upward and downward adjustment values ​​based on the baseline interactive power under all random scenarios are calculated, and the interval with the minimum interactive power in all random scenarios is selected as the power regulation potential interval between the low-voltage substation and the upper-level power grid on the forecast day.

2. The low-voltage station area regulation potential interval evaluation method according to claim 1 is characterized in that: The power control model of the electric vehicle is specifically as follows: In the formula, They represent the power of the electric vehicle cluster at time t and t-1 respectively, is the change in the electric vehicle cluster power caused by the arrival or departure of electric vehicles in period t, η ch , η dis are the charging and discharging efficiency of electric vehicle clusters, Δt is the control time interval, and P t EV,ch , P t EV,dis They represent the charging and discharging power of the electric vehicle cluster in period t, and are the maximum charging and discharging power of the electric vehicle cluster in period t respectively; and are the minimum and maximum power of the electric vehicle cluster in period t respectively; As model parameters, they are predicted from the historical data recorded by the electric vehicle charging piles.

3. The method for evaluating the power regulation potential interval of a low voltage substation according to claim 1 is characterized in that: The power control model of the air conditioner is specifically: Where, T t in , are the indoor temperatures at time periods t and t-1 respectively, is the outdoor temperature in period t-1, η is the air conditioning efficiency, P t , P t-1 They represent the cooling power of the air conditioner during the period t-1, R and C are the equivalent heat capacity and equivalent thermal resistance of the room where the air conditioner is located, Δt is the control time interval, and are the minimum and maximum indoor temperatures that meet user comfort, P min , P max They are the minimum and maximum cooling power of the air conditioner respectively.

4. The method for evaluating the power regulation potential interval of a low voltage substation according to claim 1 is characterized in that: The power regulation model of the energy storage is specifically: Where P t b P is the charging / discharging power of the energy storage in period t after considering the charging / discharging efficiency. t ch , P t dis are the charging and discharging power of the energy storage in period t, η ch , η dis are the charging and discharging efficiency of energy storage, E t 、E t-1 are the amount of energy stored in time periods t and t-1, Δt is the regulation time interval, β b It is a 0-1 variable, with a value of 1 during charging and a value of 0 during discharging. is the maximum charging and discharging power of energy storage, E min and E max They are the minimum and maximum power boundaries of energy storage respectively.

5. The method for evaluating the power regulation potential interval of a low voltage substation according to claim 1 is characterized in that: Determining the prediction error rate of each historical similar day based on the source load power prediction values ​​and actual values ​​of several historical similar days similar to the prediction day specifically includes: Search the N historical similar days that are most similar to the predicted day from the historical days; According to the PV output power and load power of each historically similar day, the prediction error rate of each period of each historically similar day is calculated according to the following formula: In the formula, They represent the prediction error rates of PV output power and load power in period t on the s-th historical similar day, respectively; They represent the actual values ​​of PV output power and load power during period t on the s-th historical similar day; They respectively represent the predicted values ​​of PV output power and load power in period t on the s-th historical similar day.

6. The method for evaluating the power regulation potential interval of a low voltage substation according to claim 1 is characterized in that: The prediction error rate of each historical similar day is used to correct the source load power prediction value of the prediction day, and each corrected source load power prediction value is used as a random scenario of the prediction day, specifically including: The prediction error rate of each period of each historical similar day is used as the prediction error rate of each corresponding period of the prediction day, and the photovoltaic output power and load power of each period of the prediction day are generated according to the following formula: In the formula, They are the corrected predicted values ​​of photovoltaic output power and load power during period t on the predicted day under random scenario s, respectively; is the predicted value of photovoltaic output power and load power, They respectively represent the prediction error rates of PV output power and load power in period t on the s-th historical similar day, and N is the number of random scenarios.

7. The method for evaluating the power regulation potential interval of a low voltage substation according to claim 1, characterized in that: The economic operation optimization model of the substation area is specifically as follows: Where N is the number of random scenes, ρ s is the probability of occurrence of random scene s, C grid is the interaction cost between the station area and the grid, is the energy storage charging and discharging loss under random scenario s, P t buy,grid is the power purchased from the grid during period t, P is the time-of-use electricity price, t sell,grid is the power sold from the substation to the grid during period t, is the electricity price, Δt is the regulation time interval, t0 is the initial value of the time period, T is the final value of the time period, n is the number of energy storages, pr i b is the unit charge and discharge loss of energy storage i, is the charging efficiency of energy storage i, is the discharge efficiency of energy storage i, are the charging and discharging power of energy storage i in period t under random scenario s, is the photovoltaic output power and load power in period t under random scenario s, is the power of the jth air conditioner in period t under random scenario s, m is the number of air conditioners involved in the regulation, are respectively the charging and discharging power of the electric vehicle cluster in period t under random scenario s, and the baseline interaction power P t grid = {P t buy,grid ,P t sell,grid }.

8. The method for evaluating the power regulation potential interval of a low voltage substation according to claim 1 is characterized in that: Calculate the maximum upward and downward adjustment values ​​based on the baseline interactive power under all random scenarios, and select the interval with the minimum interactive power in all random scenarios as the power regulation potential interval between the low-voltage substation and the upper-level power grid on the forecast day, including: Get the power regulation range evaluation model: st In the formula, are the upward and downward adjustment amounts of the station area based on the baseline interaction power in the random scenario s and time period t, respectively. t grid is the baseline interaction power, N is the number of random scenes, t0 is the initial value of the time period, T is the final value of the time period, They are the upward and downward adjustment amounts of the area based on the baseline interactive power in period t, is the photovoltaic output power and load power in period t under random scenario s, are the charging and discharging power of energy storage i in period t under random scenario s, is the power of the jth air conditioner in period t under random scenario s, m is the number of air conditioners involved in the regulation, n is the number of energy storage units, are the charging and discharging power of the electric vehicle cluster in period t under random scenario s; Solve the power regulation range evaluation model and get according to The power regulation potential range between the low-voltage substation area and the upper-level power grid on the forecast day is calculated using the following formula: In the formula, It is the upper and lower limits of the power regulation potential range between the low-voltage substation and the upper-level power grid during the predicted day t period.

9. A low voltage area power regulation potential interval assessment device, characterized in that: include: The power control model establishment module is used to establish the power control model of typical flexible resources such as electric vehicles, energy storage, and air conditioners in low-voltage areas; An error rate calculation module, used to determine the prediction error rate of each historical similar day based on the source load power prediction value and actual value of several historical similar days similar to the prediction day; The random scenario calculation module is used to use the prediction error rate of each historical similar day to correct the source load power prediction value of the prediction day, and use each corrected source load power prediction value as a random scenario of the prediction day; The baseline interaction power calculation module is used to establish and solve the economic operation model of the substation to obtain the baseline interaction power between the low-voltage substation and the upper-level power grid. The economic operation model of the substation takes the minimum sum of the interaction cost between the low-voltage substation and the grid and the energy storage loss under random scenarios as the objective function, and takes the power control model of electric vehicles, energy storage, air conditioning and power balance constraints as constraints. The power regulation potential interval determination module is used to calculate the maximum upward and downward regulation values ​​based on the baseline interactive power under all random scenarios, and select the interval with the minimum interactive power in all random scenarios as the power regulation potential interval between the low-voltage substation and the upper-level power grid on the forecast day.

10. The low-voltage station area regulation potential interval evaluation device according to claim 9, characterized in that: The power control model of the electric vehicle is specifically as follows: In the formula, They represent the power of the electric vehicle cluster at time t and t-1 respectively, is the change in the electric vehicle cluster power caused by the arrival or departure of electric vehicles in period t, η ch , η dis are the charging and discharging efficiency of electric vehicle clusters, Δt is the control time interval, and P t EV,ch , P t EV,dis They represent the charging and discharging power of the electric vehicle cluster in period t, and are the maximum charging and discharging power of the electric vehicle cluster in period t respectively; and are the minimum and maximum power of the electric vehicle cluster in period t, respectively. As model parameters, they are predicted from the historical data recorded by the electric vehicle charging piles.

11. The low voltage area power regulation potential interval evaluation device according to claim 9, characterized in that: The power control model of the air conditioner is specifically: Where, T t in , are the indoor temperatures at time periods t and t-1 respectively, is the outdoor temperature in period t-1, η is the air conditioning efficiency, P t , P t-1 They represent the cooling power of the air conditioner during the period t-1, R and C are the equivalent heat capacity and equivalent thermal resistance of the room where the air conditioner is located, Δt is the control time interval, and are the minimum and maximum indoor temperatures that meet user comfort, P min , P max They are the minimum and maximum cooling power of the air conditioner respectively.

12. The low voltage substation power regulation potential interval evaluation device according to claim 9, characterized in that: The power regulation model of the energy storage is specifically: Where P t b P is the charging / discharging power of the energy storage in period t after considering the charging / discharging efficiency. t ch , P t dis are the charging and discharging power of the energy storage in period t, η ch , η dis are the charging and discharging efficiency of energy storage, E t 、E t-1 are the amount of energy stored in time periods t and t-1, Δt is the regulation time interval, β b It is a 0-1 variable, with a value of 1 during charging and a value of 0 during discharging. is the maximum charge and discharge power of energy storage, E min and E max They are the minimum and maximum power boundaries of energy storage respectively.

13. The low voltage substation power regulation potential interval evaluation device according to claim 9, characterized in that: The error rate calculation module specifically includes: Similar day search unit, used to search for N historical similar days that are most similar to the predicted day from historical days; The error rate calculation unit is used to calculate the prediction error rate of each period of each historical similar day according to the following formula based on the photovoltaic output power and load power of each historical similar day: In the formula, They represent the prediction error rates of PV output power and load power in period t on the s-th historical similar day, respectively; They represent the actual values ​​of PV output power and load power during period t on the s-th historical similar day; They respectively represent the predicted values ​​of PV output power and load power in period t on the s-th historical similar day.

14. The low voltage substation power regulation potential interval evaluation device according to claim 9, characterized in that: The random scene calculation module specifically includes: A prediction day error rate determination unit, used to use the prediction error rate of each period of each historical similar day as the prediction error rate of each corresponding period of a random scenario on the prediction day; The source-load power prediction unit is used to generate the photovoltaic output power and load power for each period under the random scenario of the forecast day according to the following formula: In the formula, They are the corrected predicted values ​​of photovoltaic output power and load power during period t on the predicted day under random scenario s, respectively; is the predicted value of photovoltaic output power and load power, They respectively represent the prediction error rates of PV output power and load power in period t on the s-th historical similar day, and N is the number of random scenarios.

15. The low voltage area power regulation potential interval evaluation device according to claim 9, characterized in that: The economic operation model of the substation area is specifically as follows: Where N is the number of random scenes, ρ s is the probability of occurrence of random scene s, C grid is the interaction cost between the station area and the grid, is the energy storage charging and discharging loss under random scenario s, P t buy,grid is the power purchased from the grid during period t, P is the time-of-use electricity price, t sell,grid is the power sold from the substation to the grid during period t, is the electricity price, Δt is the regulation time interval, t0 is the initial value of the time period, T is the final value of the time period, n is the number of energy storages, pr i b is the unit charge and discharge loss of energy storage i, is the charging efficiency of energy storage i, is the discharge efficiency of energy storage i, are the charging and discharging power of energy storage i in period t under random scenario s, is the photovoltaic output power and load power in period t under random scenario s, is the power of the jth air conditioner in period t under random scenario s, m is the number of air conditioners involved in the regulation, are respectively the charging and discharging power of the electric vehicle cluster in period t under random scenario s, and the baseline interaction power P t grid = {P t buy,grid ,P t sell,grid }.

16. The low voltage area power regulation potential interval evaluation device according to claim 9, characterized in that: The power regulation potential interval determination module specifically includes: The model acquisition unit is used to acquire the power regulation range evaluation model: st In the formula, are the upward and downward adjustment amounts of the station area based on the baseline interaction power in the random scenario s and time period t, respectively. t grid is the baseline interaction power, N is the number of random scenes, t0 is the initial value of the time period, T is the final value of the time period, are the optimal values ​​of the upward and downward adjustment of the base station based on the baseline interaction power in all random scenarios during period t, is the photovoltaic output power and load power in period t under random scenario s, are the charging and discharging power of energy storage i in period t under random scenario s, is the power of the jth air conditioner in period t under random scenario s, m is the number of air conditioners involved in the regulation, n is the number of energy storage units, are the charging and discharging power of the electric vehicle cluster in period t under random scenario s; The model solving unit is used to solve the power adjustment range evaluation model according to the baseline interaction power to obtain The interval determination unit is used to determine the The power regulation potential range between the low-voltage substation area and the upper-level power grid on the forecast day is calculated using the following formula: In the formula, It is the upper and lower limits of the power regulation potential range between the low-voltage substation and the upper-level power grid during the predicted day t period.

17. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: The processor executes the computer program to implement the method according to any one of claims 1 to 8.

18. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: The computer program / instructions, when executed by a processor, implement the method of any one of claims 1-8.

19. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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