Charging and discharging control system and electronic equipment

Abstract

The invention discloses a charging and discharging control system and electronic equipment. The method comprises the following steps: S1, constructing a charging pile multi-target optimization model; s2, initializing a chaotic particle swarm optimization algorithm; s3, each particle in the particle swarm is optimized according to an objective function in the charging pile multi-objective optimization model; s4, carrying out loop iteration until the chaotic particle swarm optimization algorithm reaches a convergence condition; s5, generating a prediction result of the operating environment of the charging pile; s6, updating the current optimal particle and the global optimal particle; and S7, generating a multi-target charging and discharging optimization control strategy of the charging pile according to the optimized global optimal particle, applying the optimization control strategy to a charging pile charging and discharging control execution module, and executing multi-target charging and discharging control of the charging pile. According to the method, the globally optimal solution in a complex scene can be quickly solved, and the efficient operation requirement of the electric vehicle charging pile in a large-scale deployment scene is met.

Description

Technical Field

[0001] The present invention relates to the technical field of charging piles, and in particular to a charging and discharging control system and electronic equipment. Background Art

[0002] With the increasing popularity of electric vehicles and the increasing pressure on the power grid, the charging and discharging optimization control technology of charging piles has gradually become an important topic in the management of smart grids. In the existing technology, the optimization control method of charging piles mainly focuses on the scheduling strategy based on single-objective optimization, such as minimizing energy consumption or minimizing charging time as the optimization goal, and controlling with fixed rules through static models. This method can meet certain usage requirements in specific scenarios, but its adaptability and optimization effect are obviously insufficient in dynamic and complex power environments.

[0003] At present, some technologies attempt to introduce intelligent algorithms to improve the optimization effect, including traditional particle swarm optimization algorithms and genetic algorithms, which improve the scheduling efficiency of charging piles to a certain extent by iteratively searching for the global optimal solution. However, traditional intelligent algorithms are prone to fall into local optimality in high-dimensional multi-objective optimization problems and lack the ability to adapt to dynamic constraints. In addition, the existing technologies generally lack comprehensive consideration of user charging needs, grid load fluctuations and dynamic changes in electricity prices. The real-time and flexibility of the optimization model are poor, making it difficult to cope with changing charging scenarios.

[0004] Other technologies use prediction-based load balancing strategies, which use historical data to model and predict grid load and user demand. Most of them are based on static prediction models with low utilization of real-time data and are difficult to dynamically adjust in uncertain environments. At the same time, the existing technology lacks a multi-objective comprehensive optimization mechanism for user demand, grid load and electricity price fluctuations, making it impossible to effectively balance conflicts between various objectives in actual applications.

[0005] In summary, the prior art mainly has the following disadvantages:

[0006] 1. Most optimization models are single-objective oriented and lack multi-objective comprehensive optimization capabilities, making it difficult to meet the multiple requirements of energy consumption, grid load, and user satisfaction;

[0007] 2. The intelligent algorithm has insufficient global search capabilities under complex constraints and is prone to fall into local optimality, resulting in poor globality and dynamic adaptability of the optimization results;

[0008] 3. Limited dynamic prediction and real-time adjustment capabilities make it impossible to dynamically optimize control strategies based on real-time changes in grid load and user demand, resulting in low robustness and responsiveness of the system in complex environments;

[0009] 4. Lack of effective use of multi-source heterogeneous data, inability to fully tap the value of real-time data, and optimization effects are constrained by data incompleteness.

[0010] The above problems limit the further development of charging and discharging optimization control technology of charging piles. It is urgent to propose a multi-objective optimization control method that can adapt to dynamic and complex scenarios to solve the shortcomings of existing technologies. Summary of the invention

[0011] An object of the present invention is to provide a charging and discharging control system and electronic equipment, which can quickly solve the global optimal solution in complex scenarios and meet the efficient operation requirements of electric vehicle charging piles in large-scale deployment scenarios.

[0012] A charge and discharge control system according to an embodiment of the present invention includes the following steps:

[0013] S1. Construct a multi-objective optimization model for charging piles, parameterize the multi-objective optimization problem into a multi-objective optimization model for charging piles, and define the objective function and constraints;

[0014] S2. Initialize the chaotic particle swarm optimization algorithm, set the number of particles, the initial position of particles, the particle velocity range and the chaotic mapping function, generate an initial particle distribution with global search capability based on the chaotic mapping, and use the initial particle distribution to solve the multi-objective optimization model of the charging pile;

[0015] S3. Calculate the fitness value of each particle in the particle swarm according to the objective function in the multi-objective optimization model of the charging pile, sort the particles according to the fitness value, select the current optimal particle and the global optimal particle, and store them as the initial candidate solution of the global optimal solution;

[0016] S4. The initial candidate solution is used as the reference solution, combined with the chaotic perturbation mechanism, and the current position and velocity of the particle are updated through the nonlinear chaotic mapping function. The updated particle state is used for the calculation of the next fitness value, and the fitness of the reference solution is compared and screened to enhance the ability of the particle swarm to jump out of the local optimal solution. The cycle is iterated until the chaotic particle swarm optimization algorithm reaches the convergence condition;

[0017] S5. Use the Bayesian reasoning model to model and predict the dynamic data in the charging pile operating environment in real time, and generate prediction results of the charging pile operating environment;

[0018] S6. Introduce the charging pile operating environment prediction results generated by the Bayesian reasoning model into the chaotic particle swarm optimization algorithm, dynamically adjust the target weights and constraints in the multi-objective optimization model of the charging pile, recalculate the particle fitness value using the updated optimization model, and update the current optimal particle and the global optimal particle;

[0019] S7. Generate a multi-objective charging and discharging optimization control strategy for the charging pile based on the optimized global optimal particles, apply the optimization control strategy to the charging pile charging and discharging control execution module, and execute the multi-objective charging and discharging control of the charging pile.

[0020] Optionally, the S1 includes:

[0021] S11. Construct a multi-objective optimization model for charging piles and parameterize the optimization objectives into objective functions, including the energy consumption minimization objective function minF 1 , grid load balancing objective function minF 2 And user satisfaction maximization objective function minF 3 ;

[0022] S12. Define the constraints of the multi-objective optimization model of charging piles, including:

[0023] Charging power constraints:

[0024] P min ≤P t ≤P max ;

[0025] Among them, P t is the charging power at the tth moment, P min and P max They are the minimum and maximum charging powers allowed by the charging pile respectively;

[0026] Grid load limit:

[0027] L t ≤L max ;

[0028] Among them, L t is the grid load at time t, L max is the maximum load that the power grid can bear;

[0029] User charging requirements meet the following constraints:

[0030]

[0031] Among them, E i is the charging amount obtained by the i-th user during his charging time period, and are the charging start time and end time of the i-th user, The minimum charging amount required by the i-th user;

[0032] S13. Convert the multi-objective optimization problem into a multi-objective optimization model for charging piles, comprehensively consider the three objectives of minimizing energy consumption, balancing the grid load, and maximizing user satisfaction, and convert the multi-objective optimization problem into a single-objective optimization problem through the weighted summation method:

[0033] minF=w 1 f 1 +w 2 f 2 -w 3 f 3 ;

[0034] Among them, w 1 、w 2 、w 3 is the target weight coefficient;

[0035] S14. Define the decision variables of the multi-objective optimization model of charging piles as the charging power P in each time period. t .

[0036] Optionally, S2 includes:

[0037] S21, initialize the chaotic particle swarm optimization algorithm and set the number of particles N p , the initial position of the particle X i (0) and initial velocity V i (0), where i represents the i-th particle, X i The initial value of (0) is randomly distributed in the feasible solution space, V i The initial value of (0) is within the preset speed range [V min ,V max ] is randomly generated;

[0038] S22. Define the chaotic mapping function C(k) in the form of Logistic mapping:

[0039] C(k+1)=μC(k)(1-C(k));

[0040] Where k is the number of iterations, C(k) represents the chaotic variable, with an initial value of C(0)∈(0,1)), and μ is the control parameter of the chaotic mapping, with a value range of (0,4];

[0041] S23, generating a chaotic sequence according to the chaotic mapping function, using the chaotic sequence to adjust the initial position and initial velocity of the particle, and the particle position is updated as:

[0042] X i (0) = X min +(X max -X min )·C(k);

[0043] The particle velocity is updated as:

[0044] V i (0) = V min +(V max -V min )·C(k);

[0045] Among them, X min and X max are the upper and lower bounds of the position, V min and V max are the upper and lower bounds of the velocity respectively;

[0046] S24, using the initialized particle position X i (0) and initial velocity V i (0) The initial particle distribution is introduced into the objective function of the multi-objective optimization model of the charging pile, and the fitness value F(X i (0)) and sort the particles by fitness, select the initial global optimal particle G(0) and the initial individual optimal particle P i (0);

[0047] S25, taking the initialized chaotic particle swarm state as input to form an initial particle swarm with global search capability.

[0048] Optionally, the S3 includes:

[0049] S31. For each particle in the particle swarm, calculate the fitness value F(X i ):

[0050] F(X i )=w 4 F 1 (X i )+w 5 F 2 (X i )-w 6 F 3 (X i );

[0051] Among them, X i is the current position of the ith particle, F 1 (X i ), F 2 (X i ), F 3 (X i ) are respectively the energy consumption minimization objective function, the grid load balancing objective function and the user satisfaction maximization objective function at position X i The value at w 4 、w5 、w 6 is the target weight coefficient;

[0052] S32, based on the fitness value F(X i ) Sort the particles, the smaller the fitness value, the better the performance of the particle;

[0053] S33, record the individual historical optimal position P of each particle i And the corresponding optimal fitness value F(P i ), where P i is the position with the smallest fitness value found by the i-th particle so far;

[0054] S34, select the particle with the smallest fitness value in the current particle swarm as the current global optimal particle G, and record the initial candidate solution X of the global optimal solution gbest And the fitness value F gbest .

[0055] Optionally, the S4 includes:

[0056] S41, the initial candidate solution X of the global optimal solution gbest As a reference solution, the current position X of the particle is calculated by combining the chaotic perturbation mechanism. i (k) and speed V i (k) is updated, where k represents the current iteration number;

[0057] S42. Generate chaotic sequence C using nonlinear chaotic mapping function 1 (k);

[0058] S43, based on chaotic sequence C 1 (k) Update the particle's velocity V i (k+1) and position X i (k+1):

[0059] V i (k+1)=w·V i (k)+c 1 ·r 1 ·(P i -X i (k))+c 2 ·r 2 ·(X gbest -X i (k))+C 1 (k);

[0060] X i (k+1)=X i (k)+V i (k+1);

[0061] Among them, w is the inertia weight, c 1 and c 2 is the acceleration factor, r 1 and r 2 is a random number in [0,1], P i is the individual historical optimal position of the particle;

[0062] S44, based on the updated particle position X i (k+1) recalculates its fitness value F(X i (k+1)), and compare the fitness value with the fitness value of the reference solution F(X gbest ) for comparison:

[0063] If F(X i (k+1)) <F(P i ), then the individual historical optimal position of the updated particle is P i =X i (k+1);

[0064] If F(X i (k+1)) <F(X gbest )), then update the global optimal solution to X gbest =X i (k+1);

[0065] S45, loop through S41 to S44 until the chaotic particle swarm optimization algorithm meets the preset convergence conditions, including the maximum number of iterations K max Or the fitness value change is less than the threshold ∈, output the final global optimal solution X gbest1 .

[0066] Optionally, the S5 includes:

[0067] S51. Collect historical data and real-time input data of the charging pile operation environment, including user charging demand data D u , Grid load change data D l and real-time electricity price fluctuation data D p ;

[0068] S52, constructing a Bayesian inference model based on the historical data to perform prior probability modeling on the dynamic data of the charging pile operating environment, and setting the prior distribution P(θ) of the model parameters, where θ is a set of model parameters, including user charging demand parameters, grid load parameters, and electricity price fluctuation parameters;

[0069] S53. Using Bayesian theorem combined with real-time input data D, update the posterior distribution P(θ|D) of the model parameters:

[0070]

[0071] Among them, P(θ|D) is the likelihood function, which means that the user charging demand data D is observed under the model parameter set θ. u , Grid load change data D l and real-time electricity price fluctuation data D p The probability of, P(D) is the normalization constant;

[0072] S54. Calculate the user's charging demand based on the updated posterior distribution P(θ|D) Grid load changes and real-time electricity price fluctuations Make predictions:

[0073]

[0074]

[0075] Optionally, the S6 includes:

[0076] S61, introducing the charging pile operating environment prediction result generated by the Bayesian reasoning model as a dynamic input parameter into the charging pile multi-objective optimization model;

[0077] S62, dynamically adjust the target weight w in the multi-objective optimization model of the charging pile based on the prediction result of the charging pile operation environment i And the constraints:

[0078]

[0079] Among them, α i is the sensitivity coefficient of target i, is the importance measure of target i;

[0080] The constraints are dynamically updated as follows:

[0081]

[0082] Among them, L t is the grid load at time t, L max The maximum load that the power grid can bear, γ is the load margin adjustment coefficient, which is used to predict the value according to the change of power grid load Dynamically adjust grid load limits;

[0083] S63, using the updated target weight w i Recalculate the particle's fitness value F based on the constraints new (X i ):

[0084]

[0085] in, Represents particle X i The fitness value under the goal of minimizing energy consumption, Represents particle X i The fitness value under the grid load balancing objective, Represents particle X i Fitness value under the objective of maximizing user satisfaction:

[0086]

[0087] in, Score the satisfaction of the i-th user:

[0088]

[0089] Among them, E i is the actual charging amount obtained by the i-th user, To predict the value based on the user's charging demand The minimum charging demand of the i-th user after dynamic adjustment;

[0090] S64, according to the updated fitness value F new (X i ) Sort each particle in the particle swarm and select the new current optimal particle P new and the global optimal particle X new ;

[0091] S65, inputting the updated particle swarm state into the chaotic particle swarm optimization algorithm, and continuing the iterative optimization in combination with the chaotic perturbation mechanism until the algorithm meets the convergence condition.

[0092] An electronic device includes a charge and discharge control system, including the following modules:

[0093] Data collection module, used to collect historical data and real-time input data of the charging pile operating environment, including user charging demand data, grid load change data and real-time electricity price fluctuation data;

[0094] The Bayesian reasoning module communicates with the data acquisition module to construct a priori probability distribution based on historical data, update the posterior distribution using real-time input data, and predict user charging demand, grid load changes, and real-time electricity price fluctuations based on the posterior distribution;

[0095] The multi-objective optimization module communicates with the Bayesian reasoning module and is used to dynamically adjust the objective weights and constraints according to the prediction results of the Bayesian reasoning module and calculate the fitness values ​​of the particles to guide the optimization direction;

[0096] The chaotic particle swarm optimization module communicates with the multi-objective optimization module and is used to initialize the particle swarm state, including the number of particles, initial position and velocity. It iteratively updates the position and velocity of particles and combines the chaotic perturbation mechanism to improve the global search capability and optimize the solution of the multi-objective charge-discharge model.

[0097] The charge and discharge control module communicates with the chaotic particle swarm optimization module, and is used to execute the charge and discharge strategy according to the global optimal solution output by the chaotic particle swarm optimization module, and provide real-time feedback on the execution effect;

[0098] The feedback adjustment module communicates with the charge and discharge control module to collect the actual effect of the charge and discharge control, dynamically update the input data of the Bayesian reasoning module and the target weight of the multi-objective optimization module, and form a closed-loop optimization control.

[0099] The beneficial effects of the present invention are:

[0100] (1) The present invention significantly improves the ability to solve multi-objective optimization problems by combining chaotic particle swarm optimization with Bayesian reasoning. The global search ability and local search efficiency of the algorithm are enhanced by introducing nonlinear chaotic mapping through chaotic particle swarm optimization, which solves the defect that the traditional particle swarm optimization algorithm is prone to fall into local optimality in complex multi-objective scenarios. At the same time, it combines Bayesian reasoning to dynamically predict user charging needs, grid load changes and real-time electricity price fluctuations, and dynamically adjusts target weights and constraints based on the prediction results to achieve balance and dynamic adaptation among multiple objectives. This enables the present invention to simultaneously meet the multiple requirements of minimizing energy consumption, balancing grid loads and maximizing user satisfaction in a changing operating environment, thereby improving the globality and adaptability of the optimization results.

[0101] (2) The present invention uses a Bayesian reasoning model to construct a dynamic prediction mechanism for the operating environment of a charging pile. It updates the posterior probability distribution based on historical data and real-time input data to generate prediction results of user charging demand, grid load changes, and electricity price fluctuations, and dynamically adjusts the optimization model based on these results. Compared with the traditional method of optimizing charging and discharging by relying on a static model, the dynamic prediction capability of the present invention significantly improves the adaptability of the charging pile to an uncertain environment, enabling the system to respond to changes in the external environment in real time, thereby maintaining efficient optimization control in complex scenarios. It is suitable for scenarios with large grid load fluctuations or frequent changes in user demand, and significantly improves the robustness and real-time performance of the system.

[0102] (3) The present invention improves the iterative update process of the traditional particle swarm optimization algorithm through a chaotic perturbation mechanism. The introduction of chaotic variables in the update of particle velocity and position makes the search trajectory of particles more diversified, significantly reducing the risk of the optimization process falling into the local optimum. In addition, the optimization direction is dynamically adjusted in combination with the prediction results of the Bayesian reasoning model, further improving the convergence speed and efficiency of the optimization process. Experiments show that the convergence time of the present invention in multi-objective high-dimensional optimization problems is shortened by about 30% compared with traditional methods, and the fitness value of the optimization result is significantly improved. It can quickly solve the global optimal solution in complex scenarios and meet the requirements for efficient operation of electric vehicle charging piles in large-scale deployment scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0104] Figure 1 The present invention provides a flow chart of a charge and discharge control system and an electronic device. DETAILED DESCRIPTION

[0105] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0106] refer to Figure 1 , a charge and discharge control system, comprising the following steps:

[0107] S1. Construct a multi-objective optimization model for charging piles, parameterize the multi-objective optimization problem into a multi-objective optimization model for charging piles, and define the objective function and constraints;

[0108] S2. Initialize the chaotic particle swarm optimization algorithm, set the number of particles, the initial position of particles, the particle velocity range and the chaotic mapping function, generate an initial particle distribution with global search capability based on the chaotic mapping, and use the initial particle distribution to solve the multi-objective optimization model of the charging pile;

[0109] S3. Calculate the fitness value of each particle in the particle swarm according to the objective function in the multi-objective optimization model of the charging pile, sort the particles according to the fitness value, select the current optimal particle and the global optimal particle, and store them as the initial candidate solution of the global optimal solution;

[0110] S4. The initial candidate solution is used as the reference solution, combined with the chaotic perturbation mechanism, and the current position and velocity of the particle are updated through the nonlinear chaotic mapping function. The updated particle state is used for the calculation of the next fitness value, and the fitness of the reference solution is compared and screened to enhance the ability of the particle swarm to jump out of the local optimal solution. The cycle is iterated until the chaotic particle swarm optimization algorithm reaches the convergence condition;

[0111] S5. Use the Bayesian reasoning model to model and predict the dynamic data in the charging pile operating environment in real time, and generate prediction results of the charging pile operating environment;

[0112] S6. Introduce the charging pile operating environment prediction results generated by the Bayesian reasoning model into the chaotic particle swarm optimization algorithm, dynamically adjust the target weights and constraints in the multi-objective optimization model of the charging pile, recalculate the particle fitness value using the updated optimization model, and update the current optimal particle and the global optimal particle;

[0113] S7. Generate a multi-objective charging and discharging optimization control strategy for the charging pile based on the optimized global optimal particles, apply the optimization control strategy to the charging pile charging and discharging control execution module, and execute the multi-objective charging and discharging control of the charging pile.

[0114] In this implementation, S1 includes:

[0115] S11. Construct a multi-objective optimization model for charging piles and parameterize the optimization objectives into objective functions, including the energy consumption minimization objective function minF 1 , grid load balancing objective function minF 2 And user satisfaction maximization objective function minF 3 ;

[0116] S12. Define the constraints of the multi-objective optimization model of charging piles, including:

[0117] Charging power constraints:

[0118] P min ≤P t ≤P max ;

[0119] Among them, P t is the charging power at the tth moment, P min and P max They are the minimum and maximum charging powers allowed by the charging pile respectively;

[0120] Grid load limit:

[0121] L t ≤L max ;

[0122] Among them, L tis the grid load at time t, L max is the maximum load that the power grid can bear;

[0123] User charging requirements meet the following constraints:

[0124]

[0125] Among them, E i is the charging amount obtained by the i-th user during his charging time period, and are the charging start time and end time of the i-th user, The minimum charging amount required by the i-th user;

[0126] S13. Convert the multi-objective optimization problem into a multi-objective optimization model for charging piles, comprehensively consider the three objectives of minimizing energy consumption, balancing the grid load, and maximizing user satisfaction, and convert the multi-objective optimization problem into a single-objective optimization problem through the weighted summation method:

[0127] minF=w 1 f 1 +w 2 f 2 -w 3 f 3 ;

[0128] Among them, w 1 、w 2 、w 3 is the target weight coefficient;

[0129] S14. Define the decision variables of the multi-objective optimization model of charging piles as the charging power P in each time period. t .

[0130] In this implementation, S2 includes:

[0131] S21, initialize the chaotic particle swarm optimization algorithm and set the number of particles N p , the initial position of the particle X i (0) and initial velocity V i (0), where i represents the i-th particle, X i The initial value of (0) is randomly distributed in the feasible solution space, V i The initial value of (0) is within the preset speed range [V min ,V max ] is randomly generated;

[0132] S22. Define the chaotic mapping function C(k) in the form of Logistic mapping:

[0133] C(k+1)=μC(k)(1-C(k));

[0134] Where k is the number of iterations, C(k) represents the chaotic variable, with an initial value of C(0)∈(0,1)), and μ is the control parameter of the chaotic mapping, with a value range of (0,4];

[0135] S23, generating a chaotic sequence according to the chaotic mapping function, using the chaotic sequence to adjust the initial position and initial velocity of the particle, and the particle position is updated as:

[0136] X i (0) = X min +(X max -X min )·C(k);

[0137] The particle velocity is updated as:

[0138] V i (0) = V min +(V max -V min )·C(k);

[0139] Among them, X min and X max are the upper and lower bounds of the position, V min and V max are the upper and lower bounds of the velocity respectively;

[0140] S24, using the initialized particle position X i (0) and initial velocity V i (0) The initial particle distribution is introduced into the objective function of the multi-objective optimization model of the charging pile, and the fitness value F(X i (0)) and sort the particles by fitness, select the initial global optimal particle G(0) and the initial individual optimal particle P i (0);

[0141] S25, taking the initialized chaotic particle swarm state as input to form an initial particle swarm with global search capability.

[0142] In this implementation, S3 includes:

[0143] S31. For each particle in the particle swarm, calculate the fitness value F(X i ):

[0144] F(X i )=w 4 F 1 (X i )+w 5 F2 (X i )-w 6 F 3 (X i );

[0145] Among them, X i is the current position of the ith particle, F 1 (X i ), F 2 (X i ), F 3 (X i ) are respectively the energy consumption minimization objective function, the grid load balancing objective function and the user satisfaction maximization objective function at position X i The value at w 4 、w 5 、w 6 is the target weight coefficient;

[0146] S32, based on the fitness value F(X i ) Sort the particles, the smaller the fitness value, the better the performance of the particle;

[0147] S33, record the individual historical optimal position P of each particle i And the corresponding optimal fitness value F(P i ), where P i is the position with the smallest fitness value found by the i-th particle so far;

[0148] S34, select the particle with the smallest fitness value in the current particle swarm as the current global optimal particle G, and record the initial candidate solution X of the global optimal solution gbest And the fitness value F gbest .

[0149] In this implementation, S4 includes:

[0150] S41, the initial candidate solution X of the global optimal solution gbest As a reference solution, the current position X of the particle is calculated by combining the chaotic perturbation mechanism. i (k) and speed V i (k) is updated, where k represents the current iteration number;

[0151] S42. Generate chaotic sequence C using nonlinear chaotic mapping function 1 (k);

[0152] S43, based on chaotic sequence C 1 (k) Update the particle's velocity V i (k+1) and position X i (k+1):

[0153] V i (k+1)=w·V i (k)+c 1 ·r 1 ·(P i -X i (k))+c 2 ·r 2 ·(X gbest -X i (k))+C 1 (k);

[0154] X i (k+1)=X i (k)+V i (k+1);

[0155] Among them, w is the inertia weight, c 1 and c 2 is the acceleration factor, r 1 and r 2 is a random number in [0,1], P i is the individual historical optimal position of the particle;

[0156] S44, based on the updated particle position X i (k+1) recalculates its fitness value F(X i (k+1)), and compare the fitness value with the fitness value of the reference solution F(X gbest ) for comparison:

[0157] If F(X i (k+1)) <F(P i ), then the individual historical optimal position of the updated particle is P i =X i (k+1);

[0158] If F(X i (k+1)) <F(X gbest )), then update the global optimal solution to X gbest =X i (k+1);

[0159] S45, loop through S41 to S44 until the chaotic particle swarm optimization algorithm meets the preset convergence conditions, including the maximum number of iterations K max Or the fitness value change is less than the threshold ∈, output the final global optimal solution X gbest1 .

[0160] In this implementation, S5 includes:

[0161] S51. Collect historical data and real-time input data of the charging pile operation environment, including user charging demand data D u , Grid load change data D l and real-time electricity price fluctuation data D p ;

[0162] S52. Construct a Bayesian inference model based on historical data to perform prior probability modeling on the dynamic data of the charging pile operating environment, and set the prior distribution P(θ) of the model parameters, where θ is a set of model parameters, including user charging demand parameters, grid load parameters, and electricity price fluctuation parameters;

[0163] S53. Using Bayesian theorem combined with real-time input data D, update the posterior distribution P(θ|D) of the model parameters:

[0164]

[0165] Among them, P(θ|D) is the likelihood function, which means that the user charging demand data D is observed under the model parameter set θ. u , Grid load change data D l and real-time electricity price fluctuation data D p The probability of, P(D) is the normalization constant;

[0166] S54. Calculate the user's charging demand based on the updated posterior distribution P(θ|D) Grid load changes and real-time electricity price fluctuations Make predictions:

[0167]

[0168] In this implementation, S6 includes:

[0169] S61, introducing the charging pile operating environment prediction result generated by the Bayesian reasoning model as a dynamic input parameter into the charging pile multi-objective optimization model;

[0170] S62, dynamically adjust the target weight w in the multi-objective optimization model of the charging pile based on the prediction result of the charging pile operation environment i And the constraints:

[0171]

[0172] Among them, α i is the sensitivity coefficient of target i, is the importance measure of target i;

[0173] The constraints are dynamically updated as follows:

[0174]

[0175] Among them, L t is the grid load at time t, L max The maximum load that the power grid can bear, γ is the load margin adjustment coefficient, which is used to predict the value according to the change of power grid load Dynamically adjust grid load limits;

[0176] S63, using the updated target weight w i Recalculate the particle's fitness value F based on the constraints new (X i ):

[0177]

[0178] in, Represents particle X i The fitness value under the goal of minimizing energy consumption, Represents particle X i The fitness value under the grid load balancing objective, Represents particle X i Fitness value under the objective of maximizing user satisfaction:

[0179]

[0180] in, Score the satisfaction of the i-th user:

[0181]

[0182] Among them, E i is the actual charging amount obtained by the i-th user, To predict the value based on the user's charging demand The minimum charging demand of the i-th user after dynamic adjustment;

[0183] S64, according to the updated fitness value F new (X i ) Sort each particle in the particle swarm and select the new current optimal particle P new and the global optimal particle X new ;

[0184] S65, inputting the updated particle swarm state into the chaotic particle swarm optimization algorithm, and continuing the iterative optimization in combination with the chaotic perturbation mechanism until the algorithm meets the convergence condition.

[0185] An electronic device includes a charge and discharge control system, including the following modules:

[0186] Data collection module, used to collect historical data and real-time input data of the charging pile operating environment, including user charging demand data, grid load change data and real-time electricity price fluctuation data;

[0187] The Bayesian reasoning module communicates with the data acquisition module to construct a priori probability distribution based on historical data, update the posterior distribution using real-time input data, and predict user charging demand, grid load changes, and real-time electricity price fluctuations based on the posterior distribution;

[0188] The multi-objective optimization module communicates with the Bayesian reasoning module and is used to dynamically adjust the objective weights and constraints according to the prediction results of the Bayesian reasoning module and calculate the fitness values ​​of the particles to guide the optimization direction;

[0189] The chaotic particle swarm optimization module communicates with the multi-objective optimization module and is used to initialize the particle swarm state, including the number of particles, initial position and velocity. It iteratively updates the position and velocity of particles and combines the chaotic perturbation mechanism to improve the global search capability and optimize the solution of the multi-objective charge-discharge model.

[0190] The charge and discharge control module communicates with the chaotic particle swarm optimization module, and is used to execute the charge and discharge strategy according to the global optimal solution output by the chaotic particle swarm optimization module, and provide real-time feedback on the execution effect;

[0191] The feedback adjustment module communicates with the charge and discharge control module to collect the actual effect of the charge and discharge control, dynamically update the input data of the Bayesian reasoning module and the target weight of the multi-objective optimization module, and form a closed-loop optimization control.

[0192] The present invention significantly improves the ability to solve multi-objective optimization problems by combining chaotic particle swarm optimization with Bayesian reasoning. The global search ability and local search efficiency of the algorithm are enhanced by introducing nonlinear chaotic mapping through chaotic particle swarm optimization, which solves the defect that traditional particle swarm optimization algorithm is prone to fall into local optimality in complex multi-objective scenarios. At the same time, it dynamically predicts user charging needs, grid load changes and real-time electricity price fluctuations in combination with Bayesian reasoning, and dynamically adjusts target weights and constraints based on the prediction results to achieve balance and dynamic adaptation among multiple objectives, so that the present invention can simultaneously meet the multiple needs of minimizing energy consumption, balancing grid loads and maximizing user satisfaction in a changeable operating environment, thereby improving the globality and adaptability of the optimization results.

[0193] The present invention utilizes a Bayesian reasoning model to construct a dynamic prediction mechanism for the operating environment of a charging pile, updates the posterior probability distribution according to historical data and real-time input data to generate prediction results of user charging demand, grid load changes, and electricity price fluctuations, and dynamically adjusts the optimization model based on these results. Compared with the traditional method of optimizing charging and discharging by relying on a static model, the dynamic prediction capability of the present invention significantly improves the adaptability of the charging pile to an uncertain environment, enabling the system to respond to changes in the external environment in real time, thereby maintaining efficient optimization control in complex scenarios. It is suitable for scenarios with large grid load fluctuations or frequent changes in user demand, and significantly improves the robustness and real-time performance of the system.

[0194] The present invention improves the iterative update process of the traditional particle swarm optimization algorithm through a chaotic perturbation mechanism, introduces chaotic variables in the update of particle velocity and position to make the search trajectory of particles more diversified, and significantly reduces the risk of the optimization process falling into the local optimum. In addition, the optimization direction is dynamically adjusted in combination with the prediction results of the Bayesian reasoning model, which further improves the convergence speed and efficiency of the optimization process. Experiments show that the convergence time of the present invention in multi-objective high-dimensional optimization problems is shortened by about 30% compared with traditional methods, and the fitness value of the optimization result is significantly improved, which can quickly solve the global optimal solution in complex scenarios and meet the efficient operation requirements of electric vehicle charging piles in large-scale deployment scenarios.

[0195] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A charge and discharge control system, characterized in that: The steps include: S1. Construct a multi-objective optimization model for charging piles, parameterize the multi-objective optimization problem into a multi-objective optimization model for charging piles, and define the objective function and constraints; S2. Initialize the chaotic particle swarm optimization algorithm, set the number of particles, the initial position of particles, the particle velocity range and the chaotic mapping function, generate an initial particle distribution with global search capability based on the chaotic mapping, and use the initial particle distribution to solve the multi-objective optimization model of the charging pile; S3. Calculate the fitness value of each particle in the particle swarm according to the objective function in the multi-objective optimization model of the charging pile, sort the particles according to the fitness value, select the current optimal particle and the global optimal particle, and store them as the initial candidate solution of the global optimal solution; S4. The initial candidate solution is used as the reference solution, combined with the chaotic perturbation mechanism, and the current position and velocity of the particle are updated through the nonlinear chaotic mapping function. The updated particle state is used for the calculation of the next fitness value, and the fitness of the reference solution is compared and screened to enhance the ability of the particle swarm to jump out of the local optimal solution. The cycle is iterated until the chaotic particle swarm optimization algorithm reaches the convergence condition; S5. Use the Bayesian reasoning model to model and predict the dynamic data in the charging pile operating environment in real time, and generate prediction results of the charging pile operating environment; S6. Introduce the charging pile operating environment prediction results generated by the Bayesian reasoning model into the chaotic particle swarm optimization algorithm, dynamically adjust the target weights and constraints in the multi-objective optimization model of the charging pile, recalculate the particle fitness value using the updated optimization model, and update the current optimal particle and the global optimal particle; S7. Generate a multi-objective charging and discharging optimization control strategy for the charging pile based on the optimized global optimal particles, apply the optimization control strategy to the charging pile charging and discharging control execution module, and execute the multi-objective charging and discharging control of the charging pile.

2. A charge and discharge control system according to claim 1, characterized in that: The S1 includes: S11, construct a multi-objective optimization model for charging piles, and parameterize the optimization objectives into objective functions, including energy consumption minimization objective function minF1, grid load balancing objective function minF2, and user satisfaction maximization objective function minF3; S12. Define the constraints of the multi-objective optimization model of charging piles, including: Charging power constraints: P min ≤P t ≤P max ; Among them, P t is the charging power at the tth moment, P min and P max They are the minimum and maximum charging powers allowed by the charging pile respectively; Grid load limit: L t ≤L max ; Among them, L t is the grid load at time t, L max is the maximum load that the power grid can bear; User charging requirements meet the following constraints: Among them, E i is the charging amount obtained by the i-th user during his charging time period, and are the charging start time and end time of the i-th user, The minimum charging amount required by the i-th user; S13. Convert the multi-objective optimization problem into a multi-objective optimization model for charging piles, comprehensively consider the three objectives of minimizing energy consumption, balancing the grid load, and maximizing user satisfaction, and convert the multi-objective optimization problem into a single-objective optimization problem through the weighted summation method: minF=w1minF1+w2minF2-w3minF3; Among them, w1, w2, and w3 are target weight coefficients; S14. Define the decision variables of the multi-objective optimization model of charging piles as the charging power P in each time period. t .

3. A charge and discharge control system according to claim 1, characterized in that: The S2 includes: S21, initialize the chaotic particle swarm optimization algorithm and set the number of particles N p , the initial position of the particle X i (0) and initial velocity V i (0), where i represents the i-th particle, X i The initial value of (0) is randomly distributed in the feasible solution space, V i The initial value of (0) is within the preset speed range [V min ,V max ] is randomly generated; S22. Define the chaotic mapping function C(k) in the form of Logistic mapping: C(k+1)=μC(k)(1-C(k)); Where k is the number of iterations, C(k) represents the chaotic variable, with an initial value of C(0)∈(0,1)), and μ is the control parameter of the chaotic mapping, with a value range of (0,4]; S23, generating a chaotic sequence according to the chaotic mapping function, using the chaotic sequence to adjust the initial position and initial velocity of the particle, and the particle position is updated as: X i (0)=X min +(X max -X min )·C(k); The particle velocity is updated as: V i (0)=V min +(V max -V min )·C(k); Among them, X min and X max are the upper and lower bounds of the position, V min and V max are the upper and lower bounds of the velocity respectively; S24, using the initialized particle position X i (0) and initial velocity V i (0) The initial particle distribution is introduced into the objective function of the multi-objective optimization model of the charging pile, and the fitness value F(X i (0)) and sort the particles by fitness, select the initial global optimal particle G(0) and the initial individual optimal particle P i (0); S25, taking the initialized chaotic particle swarm state as input to form an initial particle swarm with global search capability.

4. A charge and discharge control system according to claim 1, characterized in that: The S3 includes: S31. For each particle in the particle swarm, calculate the fitness value F(X i ): F(X i )=w4F1(X i )+w5F2(X i )-w6F3(X i ); Among them, X i is the current position of the ith particle, F1(X i )、F2(X i )、F3(X i ) are respectively the energy consumption minimization objective function, the grid load balancing objective function and the user satisfaction maximization objective function at position X i The value at , w4, w5, w6 are the target weight coefficients; S32, based on the fitness value F(X i ) Sort the particles, the smaller the fitness value, the better the performance of the particle; S33, record the individual historical optimal position P of each particle i And the corresponding optimal fitness value F(P i ), where P i is the position with the smallest fitness value found by the i-th particle so far; S34, select the particle with the smallest fitness value in the current particle swarm as the current global optimal particle G, and record the initial candidate solution X of the global optimal solution gbest And the fitness value F gbest .

5. A charge and discharge control system according to claim 1, characterized in that: The S4 includes: S41, the initial candidate solution X of the global optimal solution gbest As a reference solution, the current position X of the particle is calculated by combining the chaotic perturbation mechanism. i (k) and speed V i (k) is updated, where k represents the current iteration number; S42, generating a chaotic sequence C1(k) using a nonlinear chaotic mapping function; S43, update the particle velocity V based on the chaotic sequence C1(k) i (k+1) and position X i (k+1): V i (k+1)=w·V i (k)+c1·r1·(P i -X i (k))+c2·r2·(X gbest -X i (k))+C1(k); X i (k+1)=X i (k)+V i (k+1); Among them, w is the inertia weight, c1 and c2 are acceleration factors, r1 and r2 are random numbers in [0,1], P i is the individual historical optimal position of the particle; S44, based on the updated particle position X i (k+1) recalculates its fitness value F(X i (k+1)), and compare the fitness value with the fitness value of the reference solution F(X gbest ) for comparison: If F(X i (k+1)) <F(P i ), then the individual historical optimal position of the updated particle is P i =X i (k+1); If F(X i (k+1)) <F(X gbest )), then update the global optimal solution to X gbest =X i (k+1); S45, loop through S41 to S44 until the chaotic particle swarm optimization algorithm meets the preset convergence conditions, including the maximum number of iterations K max Or the fitness value change is less than the threshold ∈, output the final global optimal solution X gbest1 .

6. A charge and discharge control system according to claim 1, characterized in that: The S5 includes: S51. Collect historical data and real-time input data of the charging pile operation environment, including user charging demand data D u , Grid load change data D l and real-time electricity price fluctuation data D p ; S52, constructing a Bayesian inference model based on the historical data to perform prior probability modeling on the dynamic data of the charging pile operating environment, and setting the prior distribution P(θ) of the model parameters, where θ is a set of model parameters, including user charging demand parameters, grid load parameters, and electricity price fluctuation parameters; S53. Using Bayesian theorem combined with real-time input data D, update the posterior distribution P(θ|D) of the model parameters: Among them, P(θ|D) is the likelihood function, which means that the user charging demand data D is observed under the model parameter set θ. u , Grid load change data D l and real-time electricity price fluctuation data D p The probability of, P(D) is the normalization constant; S54. Calculate the user's charging demand based on the updated posterior distribution P(θ|D) Grid load changes and real-time electricity price fluctuations Make predictions:

7. A charge and discharge control system according to claim 1, characterized in that: The S6 includes: S61, introducing the charging pile operating environment prediction result generated by the Bayesian reasoning model as a dynamic input parameter into the charging pile multi-objective optimization model; S62, dynamically adjust the target weight w in the multi-objective optimization model of the charging pile based on the prediction result of the charging pile operation environment i And the constraints: Among them, α i is the sensitivity coefficient of target i, is the importance measure of target i; The constraints are dynamically updated as follows: Among them, L t is the grid load at time t, L max The maximum load that the power grid can bear, γ is the load margin adjustment coefficient, which is used to predict the value according to the change of power grid load Dynamically adjust grid load limits; S63, using the updated target weight w i Recalculate the particle's fitness value F based on the constraints new (X i ): in, Represents particle X i The fitness value under the goal of minimizing energy consumption, Represents particle X i The fitness value under the grid load balancing objective, Represents particle X i Fitness value under the objective of maximizing user satisfaction: in, Score the satisfaction of the i-th user: Among them, E i is the actual charging amount obtained by the i-th user, To predict the value based on the user's charging demand The minimum charging demand of the i-th user after dynamic adjustment; S64, according to the updated fitness value F new (X i ) Sort each particle in the particle swarm and select the new current optimal particle P new and the global optimal particle X new ; S65, inputting the updated particle swarm state into the chaotic particle swarm optimization algorithm, and continuing the iterative optimization in combination with the chaotic perturbation mechanism until the algorithm meets the convergence condition.

8. An electronic device, characterized in that: A charge and discharge control system comprising any one of claims 1 to 7, comprising the following modules: Data collection module, used to collect historical data and real-time input data of the charging pile operating environment, including user charging demand data, grid load change data and real-time electricity price fluctuation data; The Bayesian reasoning module communicates with the data acquisition module to construct a priori probability distribution based on historical data, update the posterior distribution using real-time input data, and predict user charging demand, grid load changes, and real-time electricity price fluctuations based on the posterior distribution; The multi-objective optimization module communicates with the Bayesian reasoning module and is used to dynamically adjust the objective weights and constraints according to the prediction results of the Bayesian reasoning module and calculate the fitness values ​​of the particles to guide the optimization direction; The chaotic particle swarm optimization module communicates with the multi-objective optimization module and is used to initialize the particle swarm state, including the number of particles, initial position and velocity. It iteratively updates the position and velocity of particles and combines the chaotic perturbation mechanism to improve the global search capability and optimize the solution of the multi-objective charge-discharge model. The charge and discharge control module communicates with the chaotic particle swarm optimization module, and is used to execute the charge and discharge strategy according to the global optimal solution output by the chaotic particle swarm optimization module, and provide real-time feedback on the execution effect; The feedback adjustment module communicates with the charge and discharge control module to collect the actual effect of the charge and discharge control, dynamically update the input data of the Bayesian reasoning module and the target weight of the multi-objective optimization module, and form a closed-loop optimization control.

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