Photovoltaic power generation and base station fused efficient load power supply configuration method based on intelligent optimization

Abstract

The invention relates to the technical field of energy storage, and discloses a photovoltaic power generation and base station fused efficient load power supply configuration method based on intelligent optimization, which comprises the following steps: acquiring photovoltaic power generation prediction data, energy storage equipment charge and discharge loss characteristics and power consumption data of a base station load; obtaining an optimized time step length based on the load demand satisfaction degree; energy scheduling and energy storage management are taken as double optimization objectives, a scheduling-energy storage double-objective optimization model is constructed based on an ant lion optimization algorithm, the scheduling-energy storage double-objective optimization model is iteratively updated to obtain an optimal solution, and base station load power supply configuration is carried out based on the optimal solution. According to the invention, the dual targets of energy scheduling and energy storage management can be optimized through the ant lion optimization algorithm, so that the maximum energy utilization efficiency and the minimum loss of energy storage equipment are realized.

Description

Technical Field

[0001] The present invention relates to the field of energy storage technology, and in particular to a high-efficiency load power supply configuration method based on intelligent optimization and fusion of photovoltaic power generation and base stations. Background Art

[0002] With the increasing global demand for sustainable energy and the rapid expansion of communication network infrastructure, how to achieve energy conservation and emission reduction while ensuring the stable operation of base stations has become an important issue. Traditional base stations mainly rely on city electricity and need to be equipped with diesel generators in remote areas, which not only increases operating costs but also puts great pressure on the environment. In addition, due to Massive MIMO technology and high-frequency band characteristics, the power consumption of 5G base stations is significantly higher than that of 4G base stations, further exacerbating the contradiction between energy supply and demand.

[0003] In recent years, the development of photovoltaic power generation technology has provided a new solution to this problem, especially in remote areas far away from the power grid, such as mountainous areas and deserts. Base stations mainly rely on photovoltaic power generation to power their base stations. During the day, the base station load directly uses photovoltaic power and stores excess power in energy storage devices. At night, the energy storage devices are used to supplement electricity to reduce dependence on municipal electricity. By integrating the photovoltaic power generation system with the base station, energy utilization efficiency can be significantly improved, carbon emissions can be reduced, and power supply reliability can be enhanced.

[0004] However, the traditional photovoltaic-base station fusion system has significant defects: energy supply and demand are mismatched, photovoltaic power generation is intermittent, and there is a time mismatch with the peak power consumption of base stations; the energy storage battery is frequently charged and discharged, and its life is greatly shortened, affecting the stability of the system. The traditional photovoltaic system adopts a fixed priority power supply (such as photovoltaic priority and city power backup), ignoring the real-time electricity price and battery status, and the energy management is extensive, resulting in frequent charging and discharging of energy storage batteries, which greatly shortens the life; and does not consider the load characteristics of the base station (such as the periodic start and stop of the air conditioner, the constant power consumption of the transmission equipment), and the energy utilization rate is low; lack of multi-source data fusion (weather forecast, grid electricity price, load forecast), can not achieve advance scheduling. Therefore, photovoltaic-base station fusion needs to be further optimized to meet the needs. Summary of the invention

[0005] The present invention aims to provide an efficient load power supply configuration method based on intelligent optimization of photovoltaic power generation and base station integration, so as to optimize the dual objectives of energy scheduling and energy storage management through the ant lion optimization algorithm, so as to maximize the energy utilization efficiency and minimize the loss of energy storage equipment.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: An efficient load power supply configuration method based on intelligent optimization of photovoltaic power generation and base station integration, comprising: S1, obtain photovoltaic power generation forecast data, energy storage equipment charging and discharging loss characteristics and base station load power consumption data; S2, obtaining the load demand satisfaction based on the photovoltaic power generation prediction data and the power consumption data of the base station load, and obtaining the optimized time step based on the load demand satisfaction; S3, based on photovoltaic power generation prediction data, charging and discharging loss characteristics of energy storage equipment and power consumption data of base station loads, takes energy scheduling and energy storage management as dual optimization goals, builds a scheduling-energy storage dual-objective optimization model based on the Ant Lion optimization algorithm, iteratively updates the scheduling-energy storage dual-objective optimization model to obtain the optimal solution, and configures the base station load power supply based on the optimal solution.

[0007] The principle and advantages of this solution are: in actual application, the photovoltaic power generation forecast data, the charging and discharging loss characteristics of the energy storage equipment and the power consumption data of the base station load are obtained, so as to accurately grasp the basic data of supply, demand and loss, and lay the foundation for subsequent optimization. The optimized time step is obtained based on the load demand satisfaction, so as to dynamically adjust the time step according to the real-time data to ensure that the system is in the best state. By accurately controlling the time step, energy scheduling and energy storage management can be controlled more accurately, and the overall efficiency, response speed and stability can be improved. Taking energy scheduling and energy storage management as dual optimization goals, it can maximize the utilization rate of photovoltaic power, minimize the cost of city power procurement, and optimize the charging and discharging strategy of energy storage equipment, extend its service life and reduce loss costs. Since the Ant Lion Optimization Algorithm has a strong global search capability, a scheduling-energy storage dual-objective optimization model is constructed based on the Ant Lion Optimization Algorithm, and the scheduling-energy storage dual-objective optimization model is iteratively updated to obtain the optimal solution, which can find the global optimal solution in a complex multi-dimensional space. By dynamically adjusting parameters, fine search can be performed in a local range to improve the quality of the solution. The Ant Lion Optimization Algorithm can also flexibly handle different constraints and objective functions to improve the overall performance and adaptability of the solution. Finally, the base station load power supply configuration is performed based on the optimal solution to effectively improve the efficiency and reliability of the base station load power supply configuration, while reducing operating costs and maximizing the utilization of green energy. For base stations in remote areas, it can reduce the problem of insufficient grid coverage and reduce the frequency of diesel generator use; for base stations in high-energy-consuming urban areas, it can reduce dependence on city power; for base stations in special environments, such as high-temperature and high-humidity areas and dusty areas in the northwest, dynamic adjustment can facilitate the improvement of weather resistance.

[0008] Preferably, as an improvement, the S2 further includes: S21, construct the load demand satisfaction model as follows:

[0009]

[0010] in, is the predicted photovoltaic power generation within time step t, is the predicted discharge of energy storage device i in time step t, is the predicted base station load demand within time step t, is the loss value within time step t; S22, obtaining the peak value of the load demand satisfaction, and taking the t value of the most recent bottom peak as the time step.

[0011] Technical effect: It is easy to reduce the number of optimizations, reduce resource waste, and meet the needs at the same time.

[0012] Preferably, as an improvement, the base station load demand is a dynamic demand determined according to the load priority and the load demand satisfaction, and the determination model is:

[0013] in, To meet the needs of all base station loads, ~ To meet the needs of the current priority load.

[0014] Technical effect: The load demand varies in different time periods. By dynamically adjusting the load demand, resources can be optimized more accurately.

[0015] Preferably, as an improvement, the S3 further includes: S31, initialize the population and randomly generate a set of initial energy scheduling and energy storage management plans; S32, defining a comprehensive fitness function and constraints, wherein the comprehensive fitness function includes:

[0016] in, represents the comprehensive fitness function, and is the weight coefficient, and are the energy scheduling fitness function and energy storage management fitness function respectively, is the penalty function; S33, the quality of each solution is calculated based on the comprehensive fitness function; S34, construct a trap according to the quality of the current solution, and let the ants move randomly in the trap, gradually moving closer to the center of the trap; S35, outputting the optimal solution after iterative updating for a preset number of times.

[0017] Technical effect: It is convenient to perform dual-objective optimization by simulating the hunting process of ant lions in nature through the ant lion optimization algorithm.

[0018] Preferably, as an improvement, the energy scheduling fitness function includes:

[0019] in, =[ ], Represents the photovoltaic utilization ratio, Indicates the proportion of energy storage equipment used, , are weight coefficients used to balance the utilization rate of photovoltaic power generation and the cost of city electricity, is the photovoltaic power generation, is the base station load demand, is the capacity of the device storing energy i, It is the city electricity price.

[0020] Technical effect: Maximize the utilization rate of photovoltaic power generation, use the electricity generated by photovoltaics as much as possible, and reduce dependence on municipal electricity.

[0021] Preferably, as an improvement, the energy storage management fitness function includes:

[0022] in, =[ ], Indicates the charging power ratio, Indicates the discharge power ratio, is the common sense coefficient used to balance the loss cost of energy storage equipment, is the charging power of energy storage device i, is the discharge power of energy storage device i.

[0023] Technical effect: Based on the above objective function, it is easy to minimize operating costs, reduce electricity procurement costs and losses of energy storage equipment.

[0024] Preferably, as an improvement, the constraint condition is:

[0025]

[0026]

[0027]

[0028] in, is the photovoltaic power generation, is the discharge capacity of the i energy storage device, is the base station load demand, and are the minimum and maximum power of energy storage device i, () indicates the priority of energy storage equipment, is the charging power of energy storage device i, is the discharge power of energy storage device i, is the energy storage loss of device i in time step t.

[0029] Technical effect: Through constraints, the load demand of the base station is always met and the power of the energy storage device is within the threshold range, thereby ensuring the stable operation of the base station, ensuring that the load demand is always met and avoiding power outages.

[0030] Preferably, as an improvement, the penalty function is:

[0031] in, represents the penalty function, m is the number of constraints, is the penalty coefficient, is the degree of violation of the mth constraint.

[0032] Technical effect: It is convenient to increase the corresponding penalty value when the solution does not meet the constraints.

[0033] Preferably, as an improvement, the and For dynamic weights, the dynamic adjustment model includes:

[0034]

[0035] in, is the weight adjustment function.

[0036] Technical effect: It makes it easier to prioritize different goals at different stages.

[0037] Preferably, as an improvement, the dynamic adjustment parameters of the ant lion optimization algorithm include:

[0038]

[0039] Among them, a and b are dynamically adjusted parameters. and is the initial value, is the current iteration number, and N is the maximum iteration number.

[0040] Technical effect: Balance the ability of global search and local search, and gradually reduce the search scope by two dynamically adjusted parameters with the number of iterations. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic structural diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following is further described in detail through specific implementation methods: The embodiment is basically as shown in the attached Figure 1 As shown, a high-efficiency load power supply configuration method based on intelligent optimization of photovoltaic power generation and base station integration includes: S1, obtain photovoltaic power generation forecast data, energy storage equipment charging and discharging loss characteristics and base station load power consumption data; For photovoltaic power generation prediction data, parameters such as light intensity and temperature are usually obtained through meteorological stations, satellite images, historical data, etc., and predictions are made using time series analysis methods (such as ARIMA and LSTM); the charging and discharging loss characteristics of energy storage equipment include the technical specifications provided by the energy storage equipment manufacturer, including maximum charging power, maximum discharging power, charging and discharging efficiency, etc. Based on the technical parameters and actual operating data of the energy storage equipment, a charging and discharging loss model is established. For example, an empirical formula is used to fit the charging and discharging loss function; the power consumption data of the base station load includes the historical power consumption data recorded by the base station management system (BMS). By performing statistical analysis on the historical data, the load change pattern is identified, and future loads are predicted in combination with business needs.

[0043] S2, based on the load demand satisfaction, obtains the optimized time step, that is, analyzes the load demand satisfaction under different time steps, selects the optimal time step, ensures that the system is always in the best state, thereby more accurately controlling energy scheduling and energy storage management, and improving the overall efficiency of the system. S2 also includes: S21, construct the load demand satisfaction model as follows:

[0044]

[0045] in, is the predicted photovoltaic power generation within time step t, is the predicted discharge of energy storage device i in time step t, is the predicted base station load demand within time step t, is the loss value within time step t.

[0046] The base station load demand is a dynamic demand determined according to the load priority and load demand satisfaction, and the determination model is:

[0047] in, To meet the needs of all base station loads, ~ To meet the needs of the current priority load.

[0048] Since the photovoltaic power generation data and load demand are both forecast data, the longer the time, the lower the accuracy.

[0049] Adjust the error. for:

[0050] in, is a constant.

[0051] S22, obtaining the peak value of the load demand satisfaction, and taking the t value of the most recent bottom peak as the time step. That is, when the load demand satisfaction is at the local minimum, the t value is taken as the time step.

[0052] S3, taking energy scheduling and energy storage management as dual optimization goals, constructing a scheduling-energy storage dual-objective optimization model based on the ant lion optimization algorithm, iteratively updating the scheduling-energy storage dual-objective optimization model to obtain the optimal solution, and performing base station load power supply configuration based on the optimal solution. S3 also includes: S31, initialize the population and randomly generate a set of initial energy scheduling and energy storage management plans.

[0053] S32, defining a comprehensive fitness function and constraints, wherein the comprehensive fitness function includes:

[0054] in, represents the comprehensive fitness function, and is the weight coefficient, and are the energy scheduling fitness function and energy storage management fitness function respectively, is the penalty function; The energy scheduling fitness function includes:

[0055] in, =[ ], Represents the photovoltaic utilization ratio, Indicates the proportion of energy storage equipment used, , are weight coefficients used to balance the utilization rate of photovoltaic power generation and the cost of city electricity, is the photovoltaic power generation, is the base station load demand, is the capacity of the device storing energy i, It is the city electricity price.

[0056] The energy storage management fitness function includes:

[0057] in, =[ ], Indicates the charging power ratio, Indicates the discharge power ratio, is the common sense coefficient used to balance the loss cost of energy storage equipment, is the charging power of energy storage device i, is the discharge power of energy storage device i.

[0058] The constraints are:

[0059]

[0060]

[0061]

[0062] in, is the photovoltaic power generation, is the discharge capacity of the i energy storage device, is the base station load demand, and are the minimum and maximum power of energy storage device i, () indicates the priority of energy storage equipment, is the charging power of energy storage device i, is the discharge power of energy storage device i, is the energy storage loss of device i in time step t.

[0063] By adding a penalty term, it is convenient to increase the corresponding penalty value when the solution does not meet the constraint conditions. The penalty function is:

[0064] in, represents the penalty function, m is the number of constraints, is the penalty coefficient, is the degree of violation of the mth constraint.

[0065] Said and For dynamic weights, the dynamic adjustment model includes:

[0066]

[0067] in, is the weight adjustment function.

[0068] S33, calculate the quality of each solution based on the comprehensive fitness function. The comprehensive fitness function is an indicator used to quantify the quality of each solution. In the dual-objective optimization problem of energy scheduling and energy storage management, this process determines which solutions are closer to the optimal solution. For example, consider three different solutions , , ,in, of =0.8, =0.9, =0.1; of =0.7, =0.85, no constraint violation, that is =0; of =0.6, =0.95, =0.05; and are 0.6 and 0.4 respectively, then the solution is The score is 0.6×0.8+0.4×0.9-0.1=0.74, solution The score is 0.6×0.7+0.4×0.85-0=0.76. Solution The score is 0.6×0.6+0.4×0.95-0.05=0.71, so in this example, the solution is It is considered to be the solution with the best quality in this iteration due to its high comprehensive fitness score.

[0069] S34, build a trap based on the quality of the current solution, and let the ants move randomly in the trap and gradually move closer to the center of the trap. Specifically include: Constructing traps: Each trap corresponds to an antlion (high-quality solution). The position of the antlion determines the center position of the trap. The center position of the trap is:

[0070] in, is the weight of the cth solution, which is inversely proportional to its fitness, is the trap center location of the vth decision variable.

[0071] Hunting: The ants move randomly in the trap, gradually approaching the center of the trap, and the position of the ants is updated after each movement:

[0072] Among them, r is a random number, a and b are dynamically adjusted parameters, specifically:

[0073]

[0074] in, and is the initial value, is the current iteration number, and N is the maximum iteration number.

[0075] S35, after iterating and updating for a preset number of times, the optimal solution is output. The antlion optimization algorithm is used to simulate the hunting process of antlions in nature, to build traps to catch ants, and to gradually find the optimal solution. The base station load power supply is configured based on the optimal solution to achieve a reasonable distribution of the proportion of photovoltaic, energy storage and city power, maximize the utilization of photovoltaics and ensure the stable operation of the base station, and optimize the charging and discharging strategy of the energy storage equipment to extend its service life and reduce losses.

[0076] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A high-efficiency load power supply configuration method based on intelligent optimization of photovoltaic power generation and base station integration, characterized in that: include: S1, obtain photovoltaic power generation forecast data, energy storage equipment charging and discharging loss characteristics and base station load power consumption data; S2, obtaining the load demand satisfaction based on the photovoltaic power generation prediction data and the power consumption data of the base station load, and obtaining the optimized time step based on the load demand satisfaction; S3, based on photovoltaic power generation prediction data, charging and discharging loss characteristics of energy storage equipment and power consumption data of base station loads, takes energy scheduling and energy storage management as dual optimization goals, builds a scheduling-energy storage dual-objective optimization model based on the Ant Lion optimization algorithm, iteratively updates the scheduling-energy storage dual-objective optimization model to obtain the optimal solution, and configures the base station load power supply based on the optimal solution.

2. According to claim 1, a high-efficiency load power supply configuration method based on intelligent optimization of photovoltaic power generation and base station integration is characterized in that: The S2 further includes: S21, construct the load demand satisfaction model as follows: in, is the predicted photovoltaic power generation within time step t, is the predicted discharge of energy storage device i in time step t, is the predicted base station load demand within time step t, is the loss value within time step t; S22, obtaining the peak value of the load demand satisfaction, and taking the t value of the most recent bottom peak as the time step.

3. According to claim 2, a high-efficiency load power supply configuration method based on intelligent optimization of photovoltaic power generation and base station integration is characterized in that: The base station load demand is a dynamic demand determined according to the load priority and load demand satisfaction, and the determination model is: in, To meet the needs of all base station loads, ~ To meet the needs of the current priority load.

4. According to claim 1, a high-efficiency load power supply configuration method based on intelligent optimization of photovoltaic power generation and base station integration is characterized in that: The S3 further includes: S31, initialize the population and randomly generate a set of initial energy scheduling and energy storage management plans; S32, defining a comprehensive fitness function and constraints, wherein the comprehensive fitness function includes: in, represents the comprehensive fitness function, and is the weight coefficient, and are the energy scheduling fitness function and energy storage management fitness function respectively, is the penalty function; S33, the quality of each solution is calculated based on the comprehensive fitness function; S34, construct a trap according to the quality of the current solution, and let the ants move randomly in the trap, gradually moving closer to the center of the trap; S35, outputting the optimal solution after iterative updating for a preset number of times.

5. According to claim 4, a high-efficiency load power supply configuration method based on intelligent optimization of photovoltaic power generation and base station integration is characterized in that: The energy scheduling fitness function includes: in, =[ ], Represents the photovoltaic utilization ratio, Indicates the proportion of energy storage equipment used, , are the weight coefficients used to balance the utilization rate of photovoltaic power generation and the cost of city electricity, is the photovoltaic power generation, is the base station load demand, is the capacity of the device storing energy i, It is the city electricity price.

6. According to claim 4, a high-efficiency load power supply configuration method based on intelligent optimization of photovoltaic power generation and base station integration is characterized in that: The energy storage management fitness function includes: in, =[ ], Indicates the charging power ratio, Indicates the discharge power ratio, is the common sense coefficient used to balance the loss cost of energy storage equipment, is the charging power of energy storage device i, is the discharge power of energy storage device i.

7. According to claim 4, a high-efficiency load power supply configuration method based on intelligent optimization of photovoltaic power generation and base station integration is characterized in that: The constraints are: in, is the photovoltaic power generation, is the discharge capacity of i energy storage devices, is the base station load demand, and are the minimum and maximum power of energy storage device i, () indicates the priority of energy storage equipment, is the charging power of energy storage device i, is the discharge power of energy storage device i, is the energy storage loss of device i in time step t.

8. The method for configuring efficient load power supply based on intelligent optimization of photovoltaic power generation and base station integration according to claim 4 is characterized in that: The penalty function is: in, represents the penalty function, m is the number of constraints, is the penalty coefficient, is the degree of violation of the mth constraint.

9. The method for configuring efficient load power supply based on intelligent optimization of photovoltaic power generation and base station integration according to claim 4, characterized in that: Said and For dynamic weights, the dynamic adjustment model includes: in, is the weight adjustment function.

10. The method for configuring efficient load power supply based on intelligent optimization of photovoltaic power generation and base station integration according to claim 1, characterized in that: The dynamic adjustment parameters of the Ant Lion Optimization Algorithm include: Among them, a and b represent the dynamically adjusted parameters. and is the initial value, is the current iteration number, and N is the maximum iteration number.

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