Constant parameter power difference charging and discharging control method based on improved particle swarm optimization

Abstract

The invention provides a constant parameter power difference charging and discharging control method based on an improved particle swarm algorithm, which is characterized in that a daily load curve is predicted and calculated, optimization is carried out by using the particle swarm algorithm, and chaotic mapping and contraction factors are introduced, so that a local optimal solution is avoided, and the global search capability is improved. According to the method, power grid power balance, SOC constraint and physical constraint of charging and discharging power are considered, the charging and discharging process of the energy storage system can be effectively controlled, and the purpose of peak load shifting is achieved.

Description

Technical Field

[0001] The present invention relates to, in particular to, a constant parameter power difference charging and discharging control method based on an improved particle swarm algorithm. Background Art

[0002] In today's society, with the rapid development of science and technology and the significant improvement of people's living standards, the demand for electricity has shown an unprecedented growth trend. Especially in the context of accelerated urbanization, the fluctuation of power load has become increasingly severe, which has brought great challenges to the stable operation of the power system. As an important part of the distributed energy system, microgrids have shown great potential in alleviating the pressure on large power grids and improving energy efficiency with their flexibility, reliability and sustainability. However, the contradiction between supply and demand of microgrids in dealing with peak power loads has become increasingly prominent, becoming a key factor restricting their further development.

[0003] Traditionally, the problem of insufficient supply and demand in microgrids is often solved by increasing the installed capacity of generator sets. However, the expansion of generator sets not only requires huge investment, but also easily leads to idle resources when the load is low, reducing economic benefits. At the same time, frequent start and stop of units will increase equipment wear, shorten service life, and further increase maintenance costs. Battery Energy Storage System (BESS) has the characteristics of high energy density, fast charging and discharging, and rapid response. It can store excess electric energy when the power load is low and release electric energy when the load is peak, effectively realizing peak shaving and valley filling, reducing the pressure on generator sets, and improving the overall power supply reliability and economy. At the same time, through reasonable charging and discharging control strategies, BESS can also optimize the energy configuration of microgrids, reduce unnecessary energy waste, and promote the consumption of clean energy.

[0004] However, the capacity configuration and charging and discharging control strategy of BESS need to comprehensively consider multiple factors such as the load characteristics of the microgrid, the operating conditions of the generator set, the cost-effectiveness of the energy storage system, etc. Although excessive capacity configuration can significantly improve the peak shaving and valley filling effect, it will also increase the system investment cost; too small capacity may not be able to meet actual needs, resulting in poor peak shaving and valley filling effect. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a constant parameter power difference charging and discharging control method based on an improved particle swarm algorithm.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: a constant parameter power difference charging and discharging control method based on improved particle swarm algorithm, the control method is as follows: S1: predict the daily load curve and calculate its average value; Use historical load data, machine learning or time series analysis models to predict the daily load curve of electricity and calculate the average value ; Then the power load standard deviation D is used as an indicator to evaluate the peak shaving and valley filling effect; S2: Determine constraints; Only the physical model related to BESS and the constraints generated by the power balance of the power grid are considered; Including: Grid power balance constraints: ; SOC constraints: ; BESS charging and discharging power constraints: ; S3: Set basic particle swarm parameters: The basic particle swarm parameters include the number of particles, inertia weight coefficient , particle dimensions, upper and lower limits of each particle dimension, random factor value , learning factor value , maximum number of iterations, particle accuracy, initialization of chaos variables and 5 fixed in the chaos iteration equation; S4: Calculate the fitness value of the particle and update the particle's speed and position: make When , D chaotic variable factors are generated, and t is the number of iterations; by adding the contraction factor, the particle swarm algorithm can be prevented from falling into the local optimal solution and the global search capability can be enhanced; S5: Determine whether the particle is trapped in the local optimum: If it is not a local optimum, execute step S7; if it is a local optimum, reassign the particle position through chaotic mapping to avoid the dilemma of local optimal solution. S6: Determine the solutions gbest and pbest of particle multi-objective fitness; S7: Determine whether the particle meets the termination condition. If not, execute step S5; otherwise, execute step S8; S8: Whether the measured load is within the power range; If the load exceeds the maximum power range, the system will not perform charging and discharging operations. S9: Output charging and discharging power scheme; Load interval 1, BESS charging, charging power is: ; Load interval 2, BESS is at rest (BESS is neither charging nor discharging): ; In load interval 1, BESS discharges, and the discharge power is: ; S10: Compare the measured load with the set charge and discharge power; Is the measured load within the upper and lower limits? If so, proceed to the next step; if not, the energy storage system does not operate; the charging and discharging power is calculated by the formula to ensure that the charging and discharging plan meets the power requirements; S11: Determine the final peak-shaving and valley-filling charging and discharging plan.

[0007] Preferably, the chaotic variable generation includes iterating through Logistic mapping, and mapping the chaotic variable to the particle velocity update process, so as to further avoid the particle falling into a local optimal solution.

[0008] Preferably, the fitness function of the particle swarm takes into account the SOC constraint of the energy storage battery, the power balance constraint of the power grid, and the physical limitation of the charge and discharge power to ensure the feasibility and efficiency of the charge and discharge control.

[0009] Preferably, in step S4, the speed update of the particle swarm algorithm includes a contraction factor, and the contraction factor is used to effectively control the flying speed of the particles so that the algorithm can achieve a balance between global search and local search.

[0010] Preferably, in step S5, by comparing the relative distances of the particle positions, it is determined whether the particle is trapped in a local optimum. When a local optimum is detected, the particle position is reassigned through chaotic variables to jump out of the local optimum and continue to search for the global optimal solution.

[0011] A control device for a constant parameter power difference charge and discharge control method based on an improved particle swarm algorithm, comprising a processing unit, a storage unit and a communication unit, wherein the processing unit is used to perform a predicted load calculation, an optimization algorithm, and calculation and decision-making of a charge and discharge control strategy; The storage unit is used to store load data, algorithm parameters and control strategies; The communication unit is used to exchange data with the energy storage battery system and the power grid and perform charging and discharging operations.

[0012] A computer readable medium stores a computer program, wherein the computer program can enable a computer to execute a constant parameter power difference charging and discharging control method based on an improved particle swarm algorithm.

[0013] Compared with the prior art, the invention has the following beneficial effects: the method of the invention optimizes the charge and discharge control of BESS (battery energy storage system) in the process of peak load shaving and valley filling by improving the particle swarm algorithm (PSO). Its main benefits are reflected in the following aspects: Accurate charge and discharge control: By predicting the load curve and calculating its average value, combined with the standard deviation as an evaluation indicator, the load fluctuation of the power grid can be effectively controlled, thereby achieving more accurate peak shaving and valley filling. The smaller the load standard deviation, the better the charge and discharge effect.

[0014] Global and local search balance: The particle swarm algorithm avoids the algorithm from falling into the local optimum by introducing shrinkage factors and chaotic variables, thereby improving the global optimization capability and enhancing the adaptability and robustness of the system.

[0015] Optimized constraint handling: The constraints of grid power balance, SOC (battery state) and charge and discharge power are considered in the design to ensure the stability and safety of BESS operation.

[0016] Dynamic adaptability: Dynamically adjust the charging and discharging scheme according to the actual load fluctuations, and be able to respond flexibly under different load conditions to ensure the efficient operation of the power system.

[0017] In summary, the method of the present invention can improve the charging and discharging efficiency of BESS, optimize the load regulation of the power grid, reduce the volatility and instability of the power system, and thus improve the overall energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a constant parameter power difference charging and discharging control method based on improved particle swarm algorithm; Figure 2 To determine the load interval reference diagram; Figure 3 is the BESS structure diagram; Figure 4 It is a schematic diagram of a series battery string; Figure 5 It is a schematic diagram of parallel battery string; Figure 6 This is the basic structure diagram of BESS. DETAILED DESCRIPTION

[0019] In order to provide a further understanding of the purpose, structure, features, and functions of the present invention, the following detailed description is given in conjunction with the embodiments.

[0020] Please refer to Figure 1 to Figure 2 The present invention provides a constant parameter power difference charging and discharging control method based on improved particle swarm algorithm.

[0021] S1: predict the daily load curve and calculate its average value; Use historical load data, machine learning or time series analysis models to predict the daily load curve of electricity and calculate the average value ; Then the standard deviation D of power load is used as an indicator to evaluate the effect of peak shaving and valley filling; the smaller D is, the better the charging and discharging effect is.

[0022] ;

[0023] In the formula, is the load power at time t before charging and discharging, The charging and discharging power of BESS; When , BESS discharges; When BESS is charged; When the temperature is 0.0400 °C, the BESS is left to stand.

[0024] S2: Determine constraints; Only the physical model related to BESS and the constraints generated by the power balance of the power grid are considered; Including: Grid power balance constraints: ; In the formula, Output power to the grid; is the user load power; Output power for energy storage battery.

[0025] SOC constraints: ; BESS charging and discharging power constraints: ; In the formula, Charging power for BESS, is the BESS discharge power.

[0026] S3: Set basic particle swarm parameters: The basic particle swarm parameters include the number of particles, inertia weight coefficient , particle dimensions, upper and lower limits of each particle dimension, random factor value , learning factor value , maximum number of iterations, particle accuracy, initialization of chaotic variables, and 5 fixed values ​​(0, 0.25, 0.5, 0.75, 1) in the chaotic iteration equation, etc.

[0027] S4: Calculate the fitness value of the particle and update the particle's speed and position: make When , D chaotic variable factors are generated, and t is the number of iterations. By adding the contraction factor, the particle swarm algorithm can avoid falling into the local optimal solution and enhance the global search ability.

[0028] ; Since particle swarms are prone to fall into local optimality, adding a shrinkage factor to the speed update formula can more effectively control the flight speed of particles, allowing the algorithm to achieve a balance between global detection and local development.

[0029] ; in .

[0030] Secondly, the inertia weight coefficient changes according to the evolutionary state, enhancing the global search capability.

[0031] ; The improved particle velocity update formula can be expressed as: ; S5: Determine whether the particle is trapped in the local optimum: If it is not a local optimum, execute step S7; if it is a local optimum, reassign the particle position through chaotic mapping to avoid the dilemma of local optimal solution.

[0032] Use chaotic variables to assign chaotic values ​​to particle positions, substitute them into the Logistic image, and iterate and pay attention to distinguishing the chaotic variable interval [0,1] and mapping the corresponding variable value interval.

[0033] ; By comparing the relative distances of particle positions, it is determined whether the particles are trapped in the local optimum. When the local optimum is detected, the particle positions are reassigned through chaotic variables to jump out of the local optimum and continue to search for the global optimal solution.

[0034] S6: Determine the solutions gbest and pbest of particle multi-objective fitness; S7: Determine whether the particle meets the termination condition. If not, execute step S5; otherwise, execute step S8; S8: Whether the measured load is within the power range; If the load exceeds the maximum power range, the system will not perform charging and discharging operations; S9: Output charging and discharging power scheme; Load interval 1, BESS charging, charging power is: ; Load interval 2, BESS is at rest (BESS is neither charging nor discharging): ; In load interval 1, BESS discharges, and the discharge power is: ; S10: Compare the measured load with the set charge and discharge power; Is the measured load within the upper and lower limits? If so, proceed to the next step; if not, the energy storage system does not operate; the charging and discharging power is calculated by the formula to ensure that the charging and discharging plan meets the power requirements; S11: Determine the final peak-shaving and valley-filling charging and discharging plan.

[0035] Better, refer to Figure 3 ,The composition and simplified model of BESS are as follows.

[0036] BESS is formed by connecting multiple battery strings in series or parallel, and each battery string is formed by connecting multiple single cells in series or parallel.

[0037] The BESS is directly connected to a DC / DC (converting a fixed or variable DC voltage into another fixed or variable DC voltage) or DC / AC (converting DC (direct current voltage) into AC (alternating current voltage)) converter through a switch H, achieving flexible and efficient bidirectional energy flow between the BESS and the microgrid.

[0038] By increasing the number of battery cells or battery strings in the series direction, the output voltage of the BESS can be effectively increased; and by increasing their number in the parallel direction, the current carrying capacity of the BESS can be enhanced.

[0039] Series connection increases the output voltage of the battery string. In a microgrid or power system, when a higher voltage output is required to adapt to the needs of the grid, the total voltage can be increased by increasing the number of battery strings. This is especially important for applications that require higher voltages, such as when connected to a DC / AC converter or a high-voltage grid.

[0040] Parallel connection increases the current carrying capacity of the battery pack and improves the capacity of the system. In the parallel configuration, multiple batteries connected in series and parallel can increase the discharge current and charging current of the entire battery pack, enhance the total power output of the battery pack, and improve the continuous power supply capacity of the system. The number of parallel connections determines the current carrying capacity of the system, ensuring that the system can cope with higher power output requirements.

[0041] By flexibly adjusting the series and parallel configuration of battery cells and battery strings, not only can the total capacity of BESS be significantly increased, but also the efficiency and ability of energy exchange between it and external systems can be enhanced. When power demand is low, the battery system can store excess power; and when power demand is high, the battery system can feed the stored power back to the grid to adjust the load and stabilize the grid.

[0042] BESS can not only optimize the voltage, current and capacity requirements through flexible battery string and battery cell configuration, but also realize efficient two-way energy flow between the power grid and renewable energy. Through the reasonable configuration of series and parallel connection, the output voltage and current carrying capacity of the system can be effectively improved, while the energy exchange efficiency with microgrids and power grids can be enhanced. These advantages make BESS have broad application prospects and technical advantages in the future smart grid, renewable energy integration and power storage fields.

[0043] like Figure 4-5 As shown, S represents the battery series model, P represents the battery parallel model, and y represents the battery single body model, then the series battery string is as follows; ; Where: Indicates the parameters of series battery string; Refers to the battery's state of charge, which is used to describe the ratio of the battery's current charge to its full charge; Represents the single cell current, That is, it indicates the state of charge of single cell a. Indicates the current of single cell a.

[0044] The parallel type battery string is shown below; ; Indicates the parameters of series-connected battery strings.

[0045] Better, such as Figure 6 As shown in the figure, BESS mainly consists of five parts: energy storage battery, inverter, transformer, control unit and battery management system. After the energy storage battery is connected in series and parallel, it is seamlessly connected to the microgrid system through efficient inverters and transformers. The current balancing control strategy for power compensation through unbalanced current controls the inverter to adjust the switching of DC voltage and AC voltage, so that the energy storage battery can store and release electric energy, realizing load peak shaving and valley filling.

[0046] Energy storage batteries can store electricity when electricity demand is low and release electricity when electricity demand is high, thereby effectively reducing load peaks and balancing grid loads. When the power supply of renewable energy (such as wind power and solar energy) is unstable, BESS can respond quickly to fill the gap and ensure the stability of grid power supply. When the grid fails or loses power, BESS can immediately switch to backup mode to ensure uninterrupted power supply and improve system reliability. Through efficient management of the energy storage system, BESS can reduce energy waste caused by excess electricity and improve power utilization.

[0047] BESS optimizes battery energy storage and discharge strategies and uses inverters and control units to efficiently dispatch electrical energy. While providing reliable power support, it improves energy efficiency and significantly improves the flexibility and stability of the power system.

[0048] Preferably, a control device for a constant parameter power difference charge and discharge control method based on an improved particle swarm algorithm comprises a processing unit, a storage unit and a communication unit, wherein the processing unit is used to perform a predicted load calculation, an optimization algorithm, and a calculation and decision of a charge and discharge control strategy; The storage unit is used to store load data, algorithm parameters and control strategies; The communication unit is used to exchange data with the energy storage battery system and the power grid and perform charging and discharging operations.

[0049] Preferably, a computer readable medium stores a computer program, wherein the computer program can enable a computer to execute a constant parameter power difference charge and discharge control method based on an improved particle swarm algorithm.

[0050] The present invention has been described by the above-mentioned relevant embodiments, however, the above-mentioned embodiments are only examples for implementing the present invention. It must be pointed out that the disclosed embodiments do not limit the scope of the present invention. On the contrary, changes and modifications made without departing from the spirit and scope of the present invention are all within the scope of patent protection of the present invention.

Claims

1. A constant parameter power difference charging and discharging control method based on improved particle swarm algorithm, Features: The control method is specifically as follows: S1: predict the daily load curve and calculate its average value; Using historical load data and the LSTM algorithm model, the daily load curve of electricity is predicted and the average value is calculated. ; Then the power load standard deviation D is used as an indicator to evaluate the peak shaving and valley filling effect; S2: Determine constraints; Only the physical model related to BESS and the constraints generated by the power balance of the power grid are considered; Including: Grid power balance constraints: ; SOC constraints: ; BESS charging and discharging power constraints: ; S3: Set basic particle swarm parameters: The basic particle swarm parameters include the number of particles, inertia weight coefficient , particle dimensions, upper and lower limits of each particle dimension, random factor value , learning factor value , maximum number of iterations, particle accuracy, initialization of chaotic variables and 5 fixed variables in the chaotic iteration equation; S4: Calculate the fitness value of the particle and update the particle's speed and position: make When , D chaotic variable factors are generated, and t is the number of iterations; by adding the contraction factor, the particle swarm algorithm can be prevented from falling into the local optimal solution and the global search capability can be enhanced; S5: Determine whether the particle is trapped in the local optimum: If it is not a local optimum, execute step S7; if it is a local optimum, reassign the particle position through chaotic mapping to avoid the dilemma of local optimal solution. S6: Determine the solutions gbest and pbest of particle multi-objective fitness; S7: Determine whether the particle meets the termination condition. If not, execute step S5; otherwise, execute step S8; S8: Whether the measured load is within the power range; If the load exceeds the maximum power range, the system will not perform charging and discharging operations. S9: Output charging and discharging power scheme; Load interval 1, BESS charging, charging power is: ; Load interval 2, BESS is at rest (BESS is neither charging nor discharging): ; In load interval 1, BESS discharges, and the discharge power is: ; S10: Compare the measured load with the set charge and discharge power; Is the measured load within the upper and lower limits? If so, proceed to the next step; if not, the energy storage system does not operate; the charging and discharging power is calculated by the formula to ensure that the charging and discharging plan meets the power requirements; S11: Determine the final peak-shaving and valley-filling charging and discharging plan.

2. A constant parameter power difference charge and discharge control method based on improved particle swarm algorithm as claimed in claim 1, Features: The chaotic variable generation includes iterating through Logistic mapping, and mapping the chaotic variable to the particle speed update process, so as to further avoid the particle falling into the local optimal solution.

3. A constant parameter power difference charge and discharge control method based on improved particle swarm algorithm as claimed in claim 1, Features: The fitness function of the particle swarm takes into account the SOC constraint of the energy storage battery, the power balance constraint of the power grid, and the physical limitation of the charging and discharging power to ensure the feasibility and efficiency of the charging and discharging control.

4. A constant parameter power difference charge and discharge control method based on improved particle swarm algorithm as claimed in claim 1, Features: In step S4, the speed update of the particle swarm algorithm includes a contraction factor, which is used to effectively control the flying speed of the particles so that the algorithm can achieve a balance between global search and local search.

5. A constant parameter power difference charge and discharge control method based on improved particle swarm algorithm as claimed in claim 1, Features: In step S5, by comparing the relative distances of the particle positions, it is determined whether the particle is trapped in a local optimum. When a local optimum is detected, the particle position is reassigned through chaotic variables, thereby jumping out of the local optimum and continuing to search for the global optimal solution.

6. A control device for implementing the method according to any one of claims 1 to 5, comprising a processing unit, a storage unit and a communication unit, Features: The processing unit is used to perform the calculation and decision of the predicted load, optimization algorithm, and charge and discharge control strategy; The storage unit is used to store load data, algorithm parameters and control strategies; The communication unit is used to exchange data with the energy storage battery system and the power grid and perform charging and discharging operations.

7. A computer readable medium storing a computer program, Features: The computer program can enable a computer to execute the method of any one of claims 1 to 6.

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