Light storage and charging collaborative optimization method and system for residential distribution transformer area

Abstract

The invention discloses an optical storage and charging collaborative optimization method and system for a residential distribution area, and belongs to the technical field of electric power optimization, and the method specifically comprises the steps: collecting real-time data in the residential distribution area, carrying out the preprocessing of the collected real-time data in the residential distribution area, building a first prediction model, a second prediction model and a third prediction model of the residential distribution area, and carrying out the optimization of the optical storage and charging of the residential distribution area. The method comprises the following steps: setting an optimization target, establishing a multi-target optimization model, setting constraint conditions of the multi-target optimization model, constraining the multi-target optimization model, solving the multi-target optimization model, configuring the capacity and position of photovoltaic, energy storage and charging equipment according to the output of the optimization model, optimizing a power distribution network transformation scheme, and optimizing the power distribution network transformation scheme through optical storage and charging collaborative optimization. Voltage fluctuation caused by photovoltaic power generation can be effectively relieved, the operation risk of the power distribution network is reduced, the operation stability of the power distribution network is improved, and extra cost caused by power demand increase in the peak period is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power optimization, and more specifically, is a method and system for optimizing the synergy between photovoltaic storage and charging in residential distribution areas. Background Art

[0002] With the continuous development of the power system and the transformation of the energy structure, distributed photovoltaic power generation, energy storage systems and electric vehicle charging piles are increasingly used in distribution networks. Traditional distribution networks are mainly used for one-way power transmission. After the massive access of new energy and distributed power sources, distribution networks face many challenges, such as voltage fluctuations, load imbalance, local overload and other problems. At the same time, with the popularization of electric vehicles, the increase in charging loads has also put forward higher requirements for distribution networks.

[0003] In this context, the coordinated optimization of photovoltaic power generation, energy storage systems and charging piles has become an effective means to solve the pressure on distribution networks. By organically combining photovoltaic power generation, energy storage and charging loads, the energy utilization efficiency can be improved and the operating pressure of distribution networks can be reduced. However, there are many problems: 1) The coordination problem of distributed energy access. Due to the intermittent and volatile nature of photovoltaic power generation, a single photovoltaic power generation system can easily cause voltage fluctuations in the distribution network; 2) The impact of electric vehicle charging on the distribution network. The centralized charging of electric vehicles may cause excessive loads in local areas; 3) The problem of energy utilization efficiency. How to improve the self-consumption rate of photovoltaic power generation and reduce the abandonment rate.

[0004] For example, a Chinese patent with authorization announcement number CN117254526B discloses an energy collaborative optimization control method for a photovoltaic storage charging and inspection microgrid integrated station, which is applied to the field of photovoltaic storage charging station technology. It includes the following steps: collecting load data and photovoltaic system power generation data; establishing load models in different time periods based on the historical load data of the transformer; establishing power generation models of the photovoltaic system in different time periods based on the historical power generation data; comparing the load model of the transformer with the power generation model of the corresponding time period and the preset output power of the energy storage system; switching different working modes based on the comparison results, the collective electricity price time and the real-time power generation of the photovoltaic system, and adjusting the working status of the photovoltaic system, the energy storage system and the power grid. The invention improves the power utilization efficiency of the photovoltaic storage charging and inspection microgrid integrated station by switching different working modes.

[0005] For example, a Chinese patent with the authorization announcement number CN111900727B discloses a PSO-based coordinated optimization scheduling method and device for an integrated photovoltaic, storage and charging station. The method includes the following steps: obtaining the measurement point information of each photovoltaic power generation module, energy storage module and electric vehicle charging module in the integrated photovoltaic, storage and charging station; establishing a coordinated scheduling strategy model based on the acquired information, and the coordinated scheduling strategy model includes the objective function and constraints for minimizing the operating cost of the integrated photovoltaic, storage and charging station; solving the coordinated scheduling strategy model through a particle swarm algorithm; and formulating the grid power input and energy storage output plan of the integrated photovoltaic, storage and charging station based on the optimal solution obtained. Compared with the prior art, the invention coordinates and optimizes the output of each module in the charging station according to the coordinated control characteristics of each module during operation, effectively reducing the impact of the integrated charging station on the stability of the power grid system, while also ensuring the safe and stable operation of the integrated charging station itself.

[0006] The defects of the above patents are: 1) Failure to fully consider the optimization of the energy storage system may affect the effect of overall energy collaborative optimization; 2) Simple time set switching strategies may not be able to fully cope with the situation, resulting in failure to achieve optimal energy scheduling and cost control. Summary of the invention

[0007] In view of the shortcomings of the existing technology, the present invention proposes a method and system for the coordinated optimization of photovoltaic storage and charging in residential distribution areas. Through the coordinated optimization of photovoltaic storage and charging, the voltage fluctuations caused by photovoltaic power generation can be effectively alleviated, the operating risks of the distribution network can be reduced, and the operating stability of the distribution network can be improved.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] The synergistic optimization method of photovoltaic storage and charging in Jupei Substation includes:

[0010] Collect real-time data in the Jupei area;

[0011] Preprocessing the collected real-time data in the Jupei area, and establishing a first prediction model, a second prediction model and a third prediction model for the Jupei area;

[0012] Set optimization goals and establish multi-objective optimization models;

[0013] Set constraints of the multi-objective optimization model, constrain the multi-objective optimization model, and solve the multi-objective optimization model;

[0014] According to the output of the optimization model, the capacity and location of photovoltaic, energy storage and charging equipment are configured to optimize the distribution network transformation plan.

[0015] Specifically, the establishment of the first prediction model, the second prediction model and the third prediction model of the Jupei District includes:

[0016] The first prediction model is established to predict the future power demand of the Jupei substation. The specific formula is:

[0017] ,

[0018] Among them, P load (t) represents the load forecasting model of the Ibei area, P load (t-1) represents the historical load of Jupei District at time t-1, P load (t-24) represents the load data of the Jupei area at time t on the previous day, T(t) represents the ambient temperature of the Jupei area at time t, H(t) represents the relative humidity of the Jupei area at time t, and D(t) represents the sunshine intensity of the Jupei area at time t. , , , and represents the regression coefficient of the model, represents the random error term;

[0019] The second prediction model is established to predict the photovoltaic power generation at time t. The specific formula is:

[0020] ,

[0021] Among them, P pv (t) represents the predicted value of photovoltaic power generation at time t, G(t) represents the solar irradiance at time t, A represents the effective area of ​​the photovoltaic module, represents the initial efficiency of the PV module, Represents the temperature coefficient, T cell (t) represents the actual temperature of the PV module at time t, T ref represents the reference temperature, represents the incident angle of sunlight at time t;

[0022] The third prediction model is established to predict the electric vehicle charging power at time t. The specific formula is:

[0023] ,

[0024] Among them, P char (t) represents the predicted value of the train charging power at time t, N represents the number of trains in the Ibei area, represents the charging efficiency of the i-th electric car in the Ibei area, C i represents the battery capacity of the ith tram in the Ibei area, t arr,i represents the estimated arrival time of the i-th tram, It represents the charging time distribution width parameter of the i-th tram in the Ibei area.

[0025] Specifically, the establishment of a multi-objective optimization model includes:

[0026] The specific formula of the multi-objective optimization model is:

[0027] ,

[0028] Among them, F(x) represents the multi-objective optimization model, that is, the objective optimization function, , , and Represents the current weight factor, C total represents the total operating cost, R pv represents the consumption rate of photovoltaic power generation, Xt sta Represents the standard deviation of photovoltaic power generation stability, C tr Represents the transformation cost function of photovoltaic power generation.

[0029] Specifically, the constraints of the multi-objective optimization model include:

[0030] Voltage constraints, power balance constraints, energy storage device charging and discharging constraints, and tram charging constraints. The voltage constraint is greater than or equal to the minimum voltage of the energy storage device and less than or equal to the maximum voltage of the energy storage device. The energy storage device charging and discharging constraints are greater than or equal to zero and less than or equal to the maximum capacity of the energy storage device. The tram charging constraint is greater than or equal to zero and less than or equal to the maximum charging power of the tram.

[0031] Specifically, solving the multi-objective optimization model includes:

[0032] Generate a population of initial solutions for a multi-objective optimization model;

[0033] Calculate the fitness of each initial solution of the multi-objective optimization model. The calculation formula is:

[0034] ,

[0035] Among them, G(x) represents the fitness of the xth initial solution of the multi-objective optimization model, C total (x) represents the total operating cost corresponding to the xth initial solution of the multi-objective optimization model, represents the minimum operating cost in the population of the initial solution of the multi-objective optimization model, R pv (x) represents the photovoltaic power generation consumption rate corresponding to the xth initial solution of the multi-objective optimization model, The maximum value of the photovoltaic power generation consumption rate in the population representing the initial solution of the multi-objective optimization model, Xt sta(x) represents the standard deviation of the stability of the energy storage device corresponding to the xth initial solution of the multi-objective optimization model, The minimum value of the standard deviation of the stability of energy storage devices in the population representing the initial solution of the multi-objective optimization model, C tr (x) represents the photovoltaic power generation transformation cost corresponding to the xth initial solution of the multi-objective optimization model, represents the minimum value of the photovoltaic power generation transformation cost in the population of the initial solution of the multi-objective optimization model, , , and Represents the weighting coefficient.

[0036] Specifically, solving the multi-objective optimization model also includes:

[0037] Calculate the average fitness G of the initial solution population of the multi-objective optimization model avg and the maximum fitness G max , perform adaptive crossover and mutation operations on the initial solution of the multi-objective optimization model, and iteratively update the initial solution population of the multi-objective optimization model;

[0038] If the fitness value meets the set fitness threshold during the iteration process, the iteration is stopped and the optimal solution is output.

[0039] Specifically, the real-time data in the Jupei substation area includes: historical load data, photovoltaic power generation data, charging data and meteorological data.

[0040] The photovoltaic storage and charging collaborative optimization system of the Jupei substation is used to implement the photovoltaic storage and charging collaborative optimization method of the Jupei substation, including: a data acquisition module, a model building module, a target optimization module, a constraint solving module and an optimization transformation module;

[0041] The data collection module is used to collect real-time data in the housing distribution area;

[0042] The model building module is used to pre-process the collected real-time data in the Jupei area and establish the first prediction model, the second prediction model and the third prediction model of the Jupei area;

[0043] The target optimization module is used to set optimization targets and establish a multi-objective optimization model;

[0044] The constraint solving module is used to set the constraint conditions of the multi-objective optimization model, constrain the multi-objective optimization model, and solve the multi-objective optimization model;

[0045] The optimization and transformation module is used to configure the capacity and location of photovoltaic, energy storage and charging equipment according to the output of the optimization model, and optimize the distribution network transformation plan.

[0046] Specifically, the constraint solving module includes: a constraint unit and a solving unit;

[0047] The constraint unit is used to constrain the multi-objective optimization model and set constraint conditions;

[0048] The solution unit is used to find the optimal solution for the multi-objective optimization model.

[0049] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a method for optimizing the synergy of photovoltaic storage and charging in a residential area are implemented.

[0050] A computer-readable storage medium stores computer instructions, which, when executed, execute the steps of a method for co-optimizing the PV-storage-charging in a distribution area.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. The present invention proposes a photovoltaic-storage-charging coordinated optimization method for residential distribution areas. Through the photovoltaic-storage-charging coordinated optimization, the voltage fluctuation caused by photovoltaic power generation can be effectively alleviated, the operation risk of the distribution network can be reduced, and the operation stability of the distribution network can be improved.

[0053] 2. The present invention proposes a photovoltaic storage and charging coordinated optimization method for the distribution area. By optimizing the charging and discharging optimization of the energy storage system and the charging optimization of electric vehicles, the peak load of the distribution network can be reduced and the additional cost caused by the increase in peak power demand can be reduced.

[0054] 3. The present invention proposes a photovoltaic storage and charging coordinated optimization method for the Jupei substation area. Through the coordinated optimization scheduling of photovoltaic power generation and energy storage systems, the self-consumption rate of photovoltaic power generation is improved, power losses are reduced, and efficient utilization of clean energy is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A flow chart of the photovoltaic storage and charging coordinated optimization method for the Jupei area provided by the present invention;

[0056] Figure 2 A constraint solving flow chart provided by the present invention;

[0057] Figure 3 This is a diagram of the architecture of the photovoltaic storage and charging coordinated optimization system in the Jupei area provided by the present invention;

[0058] Figure 4 A schematic diagram of an electronic device provided by the present invention;

[0059] Figure 5 A schematic diagram of the computer-readable storage medium structure provided by the present invention. DETAILED DESCRIPTION

[0060] Example 1

[0061] See also Figure 1 , an embodiment of the present invention provides: a method for optimizing the synergy between photovoltaic storage and charging in a residential area, comprising the following specific steps:

[0062] Step S1: Collect real-time data in the distribution area through smart meters and sensors, including historical load data, photovoltaic power generation data, charging data, and meteorological data;

[0063] Step S2: pre-processing the collected real-time data in the Jupei area, including data cleaning and data smoothing, and establishing a load forecasting model, a photovoltaic power generation forecasting model, and a tram charging demand forecasting model for the Jupei area;

[0064] The specific steps of step S2 are:

[0065] Step S201: predict the future power demand of the Jupei substation area and establish a load prediction model for the Jupei substation area. The specific formula is:

[0066] ,

[0067] Among them, P load (t) represents the load forecasting model of Jupei District, that is, the load forecast value of Jupei District at time t, P load (t-1) represents the historical load of Jupei District at time t-1, P load (t-24) represents the load data of the Jupei area at time t on the previous day, reflecting the daily periodic characteristics. T(t) represents the ambient temperature of the Jupei area at time t. H(t) represents the relative humidity of the Jupei area at time t. D(t) represents the sunshine intensity of the Jupei area at time t. , , , and Represents the regression coefficient of the model, that is, the weight coefficient, which is determined by training through machine learning algorithms (such as random forest or gradient boosting decision tree). represents the random error term, which is used to capture unexplained fluctuations;

[0068] In this embodiment, the load forecasting model of the Jupei substation combines historical load data (short-term and long-term) and weather-related factors (temperature, humidity, sunshine, etc.) to forecast the load. The historical data reflects the time series characteristics of the load, while the weather factors take into account the impact of environmental conditions on the load, consider a variety of external factors, and introduce daily periodic characteristics, which enhances the model's adaptability to long-term load changes;

[0069] Step S202: Establish a photovoltaic power generation prediction model to predict the photovoltaic power generation at time t. The specific formula is:

[0070] ,

[0071] Among them, P pv (t) represents the predicted value of photovoltaic power generation at time t, G(t) represents the solar irradiance at time t, A represents the effective area of ​​the photovoltaic module, represents the initial efficiency of the PV module, It represents the temperature coefficient, reflecting the effect of temperature on photovoltaic efficiency. cell (t) represents the actual temperature of the PV module at time t, T ref Indicates the reference temperature, generally 25°C. It represents the incident angle of sunlight at time t, taking into account factors such as geographical location, time and component inclination;

[0072] In this embodiment, the photovoltaic power generation is affected by factors such as solar irradiance, photovoltaic module temperature and light incident angle. This model accurately predicts the photovoltaic power generation by combining these physical factors, while taking into account the effect of temperature on module efficiency, making the prediction more accurate.

[0073] The introduction of temperature and incident angle factors can better reflect the actual working conditions compared to the linear model of existing technologies, and can make dynamic adjustments based on real-time solar irradiance and temperature changes to improve the prediction effect;

[0074] Step S203: Establish a vehicle charging demand prediction model to predict the vehicle charging power at time t. The specific formula is:

[0075] ,

[0076] Among them, P char (t) represents the predicted value of the train charging power at time t, N represents the number of trains in the Ibei area, represents the charging efficiency of the i-th electric car in the Ibei area, C i represents the battery capacity of the ith tram in the Ibei area, t arr,i represents the estimated arrival time of the i-th tram, It represents the charging time distribution width parameter of the i-th tram in the Ibei area, reflecting the randomness of user behavior.

[0077] In this embodiment, the electric vehicle charging demand prediction model describes the distribution of electric vehicle charging demand over time, taking into account the superposition effect of multiple electric vehicles and the randomness of user behavior. By counting the arrival time and charging time distribution of electric vehicles, the charging demand of electric vehicles in each time period can be accurately predicted.

[0078] Step S3: setting optimization goals, including maximizing economic benefits, maximizing photovoltaic consumption rate, optimizing system stability and minimizing operating costs, and establishing a multi-objective optimization model;

[0079] In step S3, a multi-objective optimization model is established, including:

[0080] The specific formula of the multi-objective optimization model is:

[0081] ,

[0082] Among them, F(x) represents the multi-objective optimization model, that is, the objective optimization function, , , and Represents the current weight factor, which is used to adjust the trade-off between various objectives. total represents the total operating cost, R pv represents the consumption rate of photovoltaic power generation, Xt sta Represents the standard deviation of photovoltaic power generation stability, C tr represents the transformation cost function of photovoltaic power generation;

[0083] , , and Obtained by technicians in this field through a large number of experimental calculations;

[0084] Total operating cost C total The calculation formula is:

[0085] ,

[0086] Among them, C gen (t) represents the sum of the power generation costs of photovoltaic power generation, energy storage equipment and power grid purchase at time t, C stor (t) represents the charging and discharging cost of photovoltaic power generation and energy storage equipment at time t, including charging loss and battery aging cost, C char (t) represents the electric vehicle charging cost at time t, C pena (t) represents the penalty cost at time t, such as the penalty for failing to meet load demand or excessive discharge of energy storage equipment. T represents the total operation time of photovoltaic power generation in Jupei District. , P pv (t) represents the predicted value of photovoltaic power generation at time t, c pv Represents the unit cost of photovoltaic power generation, P grid (t) represents the power purchased from the grid at time t, c grid represents the unit cost of purchasing electricity from the grid, E stor(t) represents the remaining energy in the energy storage device at time t, E max represents the maximum capacity of the energy storage device, c stor represents the unit cost of the energy storage system, , , Respectively represent the charging and discharging efficiency of the energy storage device, P dischar (t) represents the discharge power at time t, c char represents the unit cost of charging the energy storage device, c dischar Represents the unit cost of discharging the energy storage device, C aging (t) represents the aging cost of the battery in the energy storage device, , c ev (t) represents the unit cost of charging the electric vehicle, which changes with time t;

[0087] Total operating cost C total The principle is to evaluate the economic feasibility of the photovoltaic storage and charging system by comprehensively calculating various costs in multiple time periods (usually one day, one week or one month). The formula comprehensively reflects the economic benefits and operating efficiency of the system during the entire operation cycle by adding the power generation cost, energy storage cost, electric vehicle charging cost and penalty cost caused by unmet demand. The power generation cost in the formula reflects the power generation cost at time t, including the cost of photovoltaic power generation and the cost of purchasing electricity from the power grid. It also considers the cost of purchasing electricity from the power grid when photovoltaic power generation is insufficient. The penalty cost is used to measure the economic loss incurred by the system when it fails to meet the load demand. It is obtained by calculating the difference between the load demand and the actual supply, and then multiplying it by the penalty coefficient. This part of the cost ensures that the system pays more attention to the balance of supply and demand during planning and operation, avoids high fines, and improves the overall economic benefits.

[0088] Photovoltaic power generation consumption rate R pv The calculation formula is: , where P pv_used (t) represents the photovoltaic power generation actually used for load demand and energy storage charging at time t, P pv_used (t)=min(P pv (t),P load (t)+P char (t)), P load (t) represents the load power at time t, min represents the minimum value function, represents the total amount of photovoltaic power generation within the operation period T;

[0089] The principle of this formula: The consumption rate of photovoltaic power generation R pvThe formula is used to evaluate the proportion of photovoltaic power generation that is actually used (i.e. absorbed) within a certain period (such as a day or a week). The formula measures the utilization efficiency of photovoltaic power generation by comparing the total power generation and the power generation actually used by the load and energy storage system. The purpose of the formula is to maximize the absorption of photovoltaic power generation, thereby reducing waste and improving the economic and environmental benefits of the system;

[0090] Standard deviation of photovoltaic power generation stability Xt sta The calculation formula is: , It represents the average value of the power purchased from the power grid during the operation period T;

[0091] The standard deviation of photovoltaic power generation stability is used to measure the volatility of the power purchased by the power grid. By calculating the difference between the purchased power and its average value and performing the square root operation of the sum of squares, this formula can reflect the stability of the purchased power. Compared with the existing technology that simply measures system stability by voltage or frequency fluctuations, this formula is more suitable for evaluating the stability of power purchase demand and the economy of the system.

[0092] Photovoltaic power generation transformation cost function C tr The calculation formula is: , where Ns represents the total number of photovoltaic power generation nodes in the Jupei area that need to be transformed, C upgrad (j) represents the photovoltaic power generation upgrade cost of the jth node in Jupei District, C install (j) represents the installation cost of the energy storage equipment at the jth node;

[0093] The photovoltaic power transformation cost function is used to evaluate the total cost of upgrading or transforming the existing distribution network. The basic principle of this function is to calculate all the costs required to upgrade, transform or install new equipment in the distribution area, and add up these costs to evaluate the economic feasibility of different transformation plans in the optimization process.

[0094] Step S4: setting constraints of the multi-objective optimization model, constraining the multi-objective optimization model, and solving the multi-objective optimization model;

[0095] The constraints of the multi-objective optimization model in step S4 include: voltage constraint, power balance constraint, energy storage device charging and discharging constraint and electric vehicle charging constraint. The voltage constraint is greater than or equal to the minimum voltage of the energy storage device and less than or equal to the maximum voltage of the energy storage device. The energy storage device charging and discharging constraint is greater than or equal to zero and less than or equal to the maximum capacity of the energy storage device. The electric vehicle charging constraint is greater than or equal to zero and less than or equal to the maximum charging power of the electric vehicle. The formula of the power balance constraint is: P load (t) = P pv (t)+P stor (t)+P grid (t)-Pchar (t).

[0096] Solving the multi-objective optimization model in step S4 includes:

[0097] Step S401: generating a population of initial solutions for a multi-objective optimization model;

[0098] Step S402: Calculate the fitness of each initial solution of the multi-objective optimization model, and the calculation formula is:

[0099] ,

[0100] Among them, G(x) represents the fitness of the xth initial solution of the multi-objective optimization model, C total (x) represents the total operating cost corresponding to the xth initial solution of the multi-objective optimization model, represents the minimum operating cost in the population of the initial solution of the multi-objective optimization model, R pv (x) represents the photovoltaic power generation consumption rate corresponding to the xth initial solution of the multi-objective optimization model, The maximum value of the photovoltaic power generation consumption rate in the population representing the initial solution of the multi-objective optimization model, Xt sta (x) represents the standard deviation of the stability of the energy storage device corresponding to the xth initial solution of the multi-objective optimization model, The minimum value of the standard deviation of the stability of energy storage devices in the population representing the initial solution of the multi-objective optimization model, C tr (x) represents the photovoltaic power generation transformation cost corresponding to the xth initial solution of the multi-objective optimization model, represents the minimum value of the photovoltaic power generation transformation cost in the population of the initial solution of the multi-objective optimization model, , , and Represents the weighted coefficient, which can be adjusted according to actual needs and optimization goals. It can flexibly balance the total cost, absorption rate and stability, and focus on optimizing one or several indicators according to the actual application scenario;

[0101] Step S403: Calculate the average fitness G of the initial solution population of the multi-objective optimization model avg and the maximum fitness G max , perform adaptive crossover and mutation operations on the initial solution of the multi-objective optimization model, and iteratively update the initial solution population of the multi-objective optimization model;

[0102] The specific formula of adaptive crossover is: , where P c (x) represents the adaptive crossover probability, , Respectively represent the maximum and minimum values ​​of the crossover probability. The specific formula for adaptive mutation is: , where P c (x) represents the adaptive mutation probability, , Respectively represent the maximum and minimum values ​​of the mutation probability;

[0103] Step S404: If the fitness value meets the set fitness threshold during the iteration process, the iteration is stopped and the optimal solution is output.

[0104] In this embodiment, compared with the traditional optimization algorithm with fixed crossover rate, mutation rate or inertia weight, the optimization algorithm of the present invention can dynamically change the search strategy according to the actual situation, thereby improving the search ability and convergence speed of the global optimal solution. This method has better robustness and adaptability when facing complex, nonlinear, and multi-peak optimization problems, and can effectively avoid falling into the local optimum, and finally find a better solution;

[0105] Step S5: Based on the output of the optimization model, configure the capacity and location of photovoltaic, energy storage and charging equipment, optimize the distribution network transformation plan, and monitor and adjust the equipment operation strategy in real time during the operation phase to ensure the efficiency and safety of the system and optimize the power quality and user experience.

[0106] Example 2

[0107] See also Figure 3 , another embodiment provided by the present invention: a photovoltaic storage and charging coordinated optimization system for a Jupei substation area, comprising: a data acquisition module, a model building module, a target optimization module, a constraint solving module and an optimization transformation module;

[0108] The data collection module is used to collect real-time data in the housing distribution area;

[0109] The model building module is used to pre-process the collected real-time data in the Jupei area and establish the first prediction model, the second prediction model and the third prediction model of the Jupei area;

[0110] The target optimization module is used to set optimization targets and establish a multi-objective optimization model;

[0111] The constraint solving module is used to set the constraint conditions of the multi-objective optimization model, constrain the multi-objective optimization model, and solve the multi-objective optimization model;

[0112] The optimization and transformation module is used to configure the capacity and location of photovoltaic, energy storage and charging equipment according to the output of the optimization model, and optimize the distribution network transformation plan.

[0113] The constraint solving module includes: a constraint unit and a solving unit;

[0114] The constraint unit is used to constrain the multi-objective optimization model and set constraint conditions;

[0115] The solution unit is used to find the optimal solution for the multi-objective optimization model.

[0116] Example 3

[0117] Figure 4 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Figure 4 As shown, according to another aspect of the present application, an electronic device 500 is also provided. The electronic device 500 may include one or more processors and one or more memories. The memories store computer readable codes, and when the computer readable codes are executed by one or more processors, a method for optimizing the synergy of photovoltaic storage and charging in a residential area may be executed.

[0118] The method or system according to the embodiment of the present application can also be used by Figure 4 The electronic device architecture shown in FIG. Figure 4 As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, ROM 503, RAM 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, and the like. The storage device in the electronic device 500, such as ROM 503 or hard disk 507, may store the photovoltaic storage and charging collaborative optimization method for the Jupei substation provided in the present application. The photovoltaic storage and charging collaborative optimization method for the Jupei substation may, for example, include: collecting real-time data within the Jupei substation, preprocessing the collected real-time data within the Jupei substation, establishing a first prediction model, a second prediction model, and a third prediction model for the Jupei substation, setting optimization goals, establishing a multi-objective optimization model, setting constraints for the multi-objective optimization model, constraining the multi-objective optimization model, and solving the multi-objective optimization model, and configuring the capacity and location of photovoltaic, energy storage, and charging equipment according to the output of the optimization model, and optimizing the distribution network transformation plan. Furthermore, the electronic device 500 may also include a user interface 508. Of course, Figure 4 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 4 One or more components of an electronic device are shown.

[0119] Example 4

[0120] Figure 5 Schematic diagram of a computer-readable storage medium structure provided by an embodiment of the present application. Figure 5As shown, a storage medium 600 according to an embodiment of the present application is shown. Computer-readable instructions are stored on the storage medium 600. When the computer-readable instructions are executed by the processor, the optical storage and charging coordinated optimization method for the residential distribution area according to the embodiment of the present application described with reference to the above figures can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache). Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0121] It should be understood that the methods, apparatuses, and devices of the present application can be implemented in many ways. For example, the methods, apparatuses, and devices of the present application can be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application can also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present application. Therefore, the present application also covers a recording medium storing a program for executing the method according to the present application.

[0122] In addition, the parts of the above-mentioned technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0123] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A photovoltaic storage and charging coordinated optimization method for Jupei substation, characterized in that: include: Collect real-time data in the Jupei area; Preprocessing the collected real-time data in the Jupei area, and establishing a first prediction model, a second prediction model and a third prediction model for the Jupei area; Set optimization goals and establish multi-objective optimization models; Set constraints of the multi-objective optimization model, constrain the multi-objective optimization model, and solve the multi-objective optimization model; According to the output of the optimization model, the capacity and location of photovoltaic, energy storage and charging equipment are configured to optimize the distribution network transformation plan.

2. The photovoltaic storage and charging coordinated optimization method for the residential area according to claim 1 is characterized in that: The first prediction model, the second prediction model and the third prediction model of the Jupei District are established, including: The first prediction model is established to predict the future power demand of the Jupei substation. The specific formula is: ， Among them, P load (t) represents the load forecasting model of the Ibei area, P load (t-1) represents the historical load of Jupei District at time t-1, P load (t-24) represents the load data of the Jupei area at time t on the previous day, T(t) represents the ambient temperature of the Jupei area at time t, H(t) represents the relative humidity of the Jupei area at time t, and D(t) represents the sunshine intensity of the Jupei area at time t. , , , and represents the regression coefficient of the model, represents the random error term; The second prediction model is established to predict the photovoltaic power generation at time t. The specific formula is: ， Among them, P pv (t) represents the predicted value of photovoltaic power generation at time t, G(t) represents the solar irradiance at time t, A represents the effective area of ​​the photovoltaic module, represents the initial efficiency of the PV module, Represents the temperature coefficient, T cell (t) represents the actual temperature of the PV module at time t, T ref represents the reference temperature, represents the incident angle of sunlight at time t; The third prediction model is established to predict the electric vehicle charging power at time t. The specific formula is: ， Among them, P char (t) represents the predicted value of the train charging power at time t, N represents the number of trains in the Ibei area, represents the charging efficiency of the i-th electric car in the Ibei area, C i represents the battery capacity of the ith tram in the Ibei area, t arr,i represents the estimated arrival time of the i-th tram, It represents the charging time distribution width parameter of the i-th tram in the Ibei area.

3. The photovoltaic storage and charging coordinated optimization method for the residential area as claimed in claim 2 is characterized in that: The multi-objective optimization model is established, comprising: The specific formula of the multi-objective optimization model is: ， Among them, F(x) represents the multi-objective optimization model, that is, the objective optimization function, , , and Represents the current weight factor, C total represents the total operating cost, R pv represents the consumption rate of photovoltaic power generation, Xt sta Represents the standard deviation of photovoltaic power generation stability, C tr Represents the transformation cost function of photovoltaic power generation.

4. The photovoltaic storage and charging coordinated optimization method for the residential area as claimed in claim 3 is characterized in that: The constraints of the multi-objective optimization model include: Voltage constraints, power balance constraints, energy storage device charging and discharging constraints, and tram charging constraints. The voltage constraint is greater than or equal to the minimum voltage of the energy storage device and less than or equal to the maximum voltage of the energy storage device. The energy storage device charging and discharging constraints are greater than or equal to zero and less than or equal to the maximum capacity of the energy storage device. The tram charging constraint is greater than or equal to zero and less than or equal to the maximum charging power of the tram.

5. The photovoltaic storage and charging coordinated optimization method for the residential area according to claim 4 is characterized in that: The solving of the multi-objective optimization model comprises: Generate a population of initial solutions for a multi-objective optimization model; Calculate the fitness of each initial solution of the multi-objective optimization model. The calculation formula is: ， Among them, G(x) represents the fitness of the xth initial solution of the multi-objective optimization model, C total (x) represents the total operating cost corresponding to the xth initial solution of the multi-objective optimization model, represents the minimum operating cost in the population of the initial solution of the multi-objective optimization model, R pv (x) represents the photovoltaic power generation consumption rate corresponding to the xth initial solution of the multi-objective optimization model, The maximum value of the photovoltaic power generation consumption rate in the population representing the initial solution of the multi-objective optimization model, Xt sta (x) represents the standard deviation of the stability of the energy storage device corresponding to the xth initial solution of the multi-objective optimization model, The minimum value of the standard deviation of the stability of energy storage devices in the population representing the initial solution of the multi-objective optimization model, C tr (x) represents the photovoltaic power generation transformation cost corresponding to the xth initial solution of the multi-objective optimization model, represents the minimum value of the photovoltaic power generation transformation cost in the population of the initial solution of the multi-objective optimization model, , , and Represents the weighting coefficient.

6. The photovoltaic storage and charging coordinated optimization method for the residential area as claimed in claim 5, characterized in that: The solving of the multi-objective optimization model also includes: Calculate the average fitness G of the initial solution population of the multi-objective optimization model avg and the maximum fitness G max , perform adaptive crossover and mutation operations on the initial solution of the multi-objective optimization model, and iteratively update the initial solution population of the multi-objective optimization model; If the fitness value meets the set fitness threshold during the iteration process, the iteration is stopped and the optimal solution is output.

7. The photovoltaic storage and charging coordinated optimization method for the residential area according to claim 6 is characterized in that: The real-time data in the Jupei substation area includes: historical load data, photovoltaic power generation data, charging data and meteorological data.

8. A photovoltaic storage and charging coordinated optimization system for Jupei substations, used to implement the photovoltaic storage and charging coordinated optimization method for Jupei substations according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, model building module, target optimization module, constraint solving module and optimization transformation module; The data collection module is used to collect real-time data in the housing distribution area; The model building module is used to pre-process the collected real-time data in the Jupei area and establish the first prediction model, the second prediction model and the third prediction model of the Jupei area; The target optimization module is used to set optimization targets and establish a multi-objective optimization model; The constraint solving module is used to set the constraint conditions of the multi-objective optimization model, constrain the multi-objective optimization model, and solve the multi-objective optimization model; The optimization and transformation module is used to configure the capacity and location of photovoltaic, energy storage and charging equipment according to the output of the optimization model, and optimize the distribution network transformation plan.

9. The photovoltaic storage and charging coordinated optimization system for the residential area as claimed in claim 8, characterized in that: The constraint solving module includes: a constraint unit and a solving unit; The constraint unit is used to constrain the multi-objective optimization model and set constraint conditions; The solution unit is used to find the optimal solution for the multi-objective optimization model.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the PV-storage-charging coordinated optimization method for the Jupei area are implemented according to any one of claims 1 to 7.

11. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, the steps of the photovoltaic storage and charging coordinated optimization method for the residential area described in any one of claims 1-7 are executed.

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