Light storage and charging multi-energy cooperative regulation and control optimization method and device based on elite quantum ant colony

Abstract

The invention relates to the technical field of new energy power dispatching, in particular to a light storage and charging multi-energy cooperative regulation and control optimization method and device based on elite quantum ant colonies, and the method comprises the steps: collecting the charging and discharging power of each device in a light storage and charging system, the net load power of an AC bus, the SOC of an energy storage battery and load data in real time; predicting the charging load of the electric vehicle based on a statistical simulation dynamic simulation method; based on data collected in real time and a predicted electric vehicle charging load, an elite quantum ant colony algorithm is adopted to generate multiple sets of power scheduling schemes, the value of each set of scheduling scheme is quantified based on a light storage and charging peak scheduling priority target, elite ant colonies are screened, pheromones are dynamically updated, and an optimal power scheduling scheme is generated through iteration. And light storage and charging multi-energy cooperative regulation and control optimization is realized. Compared with the prior art, the method has the advantages of rapid and stable regulation and control optimization of light storage and charging multi-energy coordination and the like.

Description

Technical Field

[0001] This invention relates to the field of new energy power dispatching technology, and in particular to a method and device for coordinated regulation and optimization of photovoltaic, energy storage, and charging based on elite quantum ant colonies. Background Technology

[0002] With the deepening of the new energy revolution, photovoltaic-storage-charging systems (PV power generation, energy storage systems, and electric vehicle charging facilities) are rapidly becoming widespread in communities, industrial parks, and other settings, serving as an important vehicle for achieving "source-storage-load" synergy. The intermittency of PV power generation, the randomness of electric vehicle charging, and the volatility of daily loads in communities combine to significantly increase the difficulty of balancing system power. During midday when PV output is surplus, energy waste is easily caused, while during peak evening charging demand, there is a reliance on large-scale power purchases from the grid. This not only widens the peak-valley difference but also triggers grid power fluctuations and a surge in power supply pressure, severely impacting energy utilization efficiency and power supply stability.

[0003] Traditional photovoltaic-storage-charging scheduling methods often rely on empirical rules or simple algorithms, such as the traditional ant colony algorithm. These methods suffer from limitations such as scheduling lag and poor coordination among multiple energy sources. They either fail to accurately match the timing differences between photovoltaic output and charging demand, or struggle to balance energy storage charging and discharging efficiency with grid security constraints, leading to frequent phenomena such as "curtailment of photovoltaic power" and "high peak electricity purchase costs." While existing intelligent scheduling technologies attempt to introduce optimization algorithms, they often encounter problems such as slow convergence speed and low solution quality when dealing with multi-objective coupled issues such as peak-valley smoothing, fluctuation reduction, and cost optimization, making it difficult to adapt to the dynamic scheduling needs of complex scenarios.

[0004] A search revealed Chinese invention patent application publication number CN118569550A, which discloses a vehicle-grid collaborative optimization method, device, and storage medium considering charging load transfer. The method includes: predicting electric vehicle charging demand using the Monte Carlo method based on an electric vehicle user travel probability model; establishing an uncertain output model for wind and photovoltaic generators based on the probability distributions of wind speed and sunlight; constructing a vehicle-grid collaborative scheduling model by combining the disordered nature of fast-charging loads and the adjustability of slow-charging loads with distributed power output; and solving the multi-objective optimization model using a non-dominated sorting genetic algorithm with an elitist strategy (NSGA-II) and fuzzy decision-making. This invention can reduce distribution network scheduling costs while also reducing the peak-to-valley difference and volatility of distribution network loads. This existing patent application suffers from problems with the efficiency and effectiveness of solving the optimal scheduling scheme.

[0005] How to achieve rapid, stable, and optimized regulation of photovoltaic, energy storage, and charging synergy has become a technical problem that needs to be solved. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method and device for coordinated regulation and optimization of optical storage and charging based on elite quantum ant colonies.

[0007] The objective of this invention can be achieved through the following technical solutions: According to one aspect of the present invention, a method for coordinated regulation and optimization of light storage and charging multi-energy based on elite quantum ant colonies is provided, the method comprising: Real-time acquisition of charging and discharging power of each device in the photovoltaic-storage-charging system, net load power of the AC bus, SOC of the energy storage battery, and load data; Predicting electric vehicle charging load based on statistical simulation dynamic simulation method; Based on real-time collected data and predicted electric vehicle charging load, multiple power scheduling schemes are generated using the elite quantum ant colony algorithm. The value of each scheduling scheme is quantified based on the peak scheduling priority target of photovoltaic, energy storage and charging, and elite ant colonies are selected. The pheromone is dynamically updated and the optimal power scheduling scheme is generated iteratively to achieve multi-energy coordinated regulation and optimization of photovoltaic, energy storage and charging.

[0008] Preferably, the process of iteratively generating the optimal power scheduling scheme includes: Power scheduling schemes are generated through path search; Based on the priority objective of peak scheduling for photovoltaic, energy storage and charging, the value of each scheme is quantified by the discharge contribution and elite ant colonies are selected. Pheromone dynamic update: Based on the selected elite ant colony, the pheromone concentration of each path is dynamically updated to guide the next round of ants to prioritize the selection of high-quality power scheduling schemes; The new power scheduling scheme is evaluated based on the collaborative optimization objective. If it is, the new power scheduling scheme is received and updated to the global optimal solution, thus obtaining the optimal power scheduling scheme. Otherwise, the iteration continues until the iteration number is reached, and the optimal power scheduling scheme is output.

[0009] More preferably, the power scheduling scheme generated through path search specifically involves: using a set of optical storage and charging power allocation schemes as each ordinary ant in the elite quantum ant colony algorithm, enhancing the diversity of path search through qubits, and selecting the power supply path based on the pheromone concentration of the path.

[0010] More preferably, the collaborative optimization objective is to reduce the peak-to-valley difference, smooth out power fluctuation rate, and reduce the maximum interactive power fluctuation; Peak-to-valley load is the difference between the maximum peak load and the minimum valley load of the power grid during a day, reflecting the magnitude of power fluctuations. Maximum interactive power fluctuation is the maximum absolute change in power interaction with the grid at adjacent times within a day, reflecting the severity of power changes. Power volatility is a relative indicator used to measure the overall dispersion of power interaction in a power grid relative to its average level, and is used to quantify the relative severity of power fluctuations.

[0011] Preferably, the process of dynamically updating pheromones needs to balance the preservation of historical high-quality paths with the exploration of new paths, including three sub-steps: pheromone evaporation, superposition of pheromone contributions from ordinary ants, and weighted reinforcement of pheromone contributions from elite ants.

[0012] Preferably, the selection of elite ant colonies specifically involves: Elite ants are selected based on the energy storage discharge contribution in each iteration, thereby guiding the path search process; The discharge contribution of the energy storage represents the scheduling stability effect of the power dispatch scheme, and is the weighted sum of the energy storage discharge during peak and normal power periods.

[0013] Preferably, the prediction of electric vehicle charging load based on statistical simulation dynamic simulation method includes: Assume that the daily mileage of electric vehicles follows a log-normal distribution, and assume that the starting charging time of electric vehicles after arriving at the charging station follows a normal distribution; The initial charge of an electric vehicle follows a uniform distribution. The state of charge of the electric vehicle at the end of charging is related to the initial charge, battery rated capacity, charging time, and charging power, specifically:

[0014] In the formula, The rated capacity of the battery for electric vehicles; Charging time for electric vehicles; The charging power for electric vehicles, Initial charge level for electric vehicles.

[0015] According to another aspect of the present invention, a photoelectric storage-charging multi-energy coordinated regulation and optimization device based on elite quantum ant colonies is provided, the device comprising: The multi-source information fusion unit performs deep fusion of multi-source data, including real-time acquisition of the charging and discharging power of each device in the photovoltaic-storage-charging system, the net load power of the AC bus, the SOC of the energy storage battery, and the residential load forecast data for a period of time in the future. The Elite Quantum Ant Colony Optimization Module predicts the charging load of electric vehicles based on statistical simulation dynamic simulation method, and generates multiple power scheduling schemes using the Elite Quantum Ant Colony Algorithm. It quantifies the value of each scheduling scheme based on the peak scheduling priority target of photovoltaic, energy storage and charging, and selects elite ant colonies to guide the next round of ants to prioritize the selection of high-quality new power scheduling schemes. The multi-objective power allocator evaluates whether a new power scheduling scheme is the global optimal solution based on the collaborative optimization objective. If it is, the new power scheduling scheme is received and updated to the global optimal solution, thus obtaining the optimal power scheduling scheme; otherwise, the pheromone is updated and the optimal power scheduling scheme is generated iteratively through the elite quantum ant colony optimization module. The converter control unit executes PWM drive based on the received optimal power scheduling scheme to achieve coordinated regulation and optimization of photovoltaic, energy storage and charging multi-energy systems.

[0016] Preferably, the process by which the elite quantum ant colony optimization module iteratively generates the optimal power scheduling scheme includes: Power scheduling schemes are generated through path search; Based on the priority objective of peak scheduling for photovoltaic, energy storage and charging, the value of each scheme is quantified by the discharge contribution and elite ant colonies are selected. Pheromone dynamic update: Based on the selected elite ant colony, the pheromone concentration of each path is dynamically updated to guide the next round of ants to prioritize the selection of high-quality power scheduling schemes; The new power scheduling scheme is evaluated based on the collaborative optimization objective. If it is, the new power scheduling scheme is received and updated to the global optimal solution, thus obtaining the optimal power scheduling scheme. Otherwise, the iteration continues until the iteration number is reached, and the optimal power scheduling scheme is output.

[0017] Preferably, the power scheduling scheme generated through path search specifically involves: using a set of optical storage and charging power allocation schemes as each ordinary ant in the elite quantum ant colony algorithm, enhancing the diversity of path search through qubits, and selecting the power supply path based on the pheromone concentration of the path.

[0018] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention uses statistical simulation dynamic simulation method to dynamically simulate the charging behavior of electric vehicles. Compared with traditional experience prediction or single distribution assumption, this method can better fit the actual characteristics of community vehicles and provide reliable load input for subsequent power scheduling. Then, the elite quantum ant colony algorithm is used for ant colony selection and update mechanism. Through dynamic adjustment of pheromone evaporation, ordinary ant contribution and elite ant weighted strengthening, the problem of traditional ant colony algorithm being prone to local optima and slow convergence is solved: pheromone evaporation ensures that the algorithm does not overly rely on old paths, ordinary ant contribution maintains search diversity, and the high weight of elite ants accelerates the convergence of high-quality paths, realizing rapid regulation and optimization of photovoltaic, storage and charging multi-energy synergy.

[0019] (2) This invention constructs a multi-dimensional collaborative optimization objective to improve the stability of power grid operation. Peak-valley difference, maximum interactive power fluctuation, and volatility are used as core optimization indicators to form a multi-dimensional evaluation framework. Compared with existing technologies that only focus on a single indicator, this system can more comprehensively reflect the system's operating status: reducing the peak-valley difference directly reduces the extreme load pressure on the power grid, smoothing the maximum interactive power fluctuation reduces instantaneous power impact, and optimizing the volatility reduces the overall operational dispersion. Through the collaborative optimization of the three indicators using the elite quantum ant colony algorithm, the disturbance of the community photovoltaic-storage-charging system to the power grid is reduced, and the timing matching of photovoltaic power generation, energy storage charging and discharging, and electric vehicle charging and discharging is achieved, significantly improving the stability and reliability of power supply.

[0020] (3) This invention captures the randomness and uncertainty of electric vehicle charging behavior by fitting the daily driving mileage with a log-normal distribution, describing the initial power charge with a uniform distribution, and characterizing the starting charging time with a normal distribution, thus providing a reliable load input for subsequent power scheduling.

[0021] (4) This invention enhances the diversity of path search by quantum bits, enabling the algorithm to explore innovative strategies. Compared with traditional algorithms, the global optimal solution search efficiency is improved. It does not depend on specific scene parameters, has strong scene adaptability, can adapt to multiple lighting conditions, and flexibly adapt to different operating modes of electric vehicles. It has the advantages of fast convergence speed, strong robustness, and the ability to effectively handle multi-constraint optimization problems. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the elite quantum ant colony algorithm of the present invention; Figure 2 This is a schematic diagram of the structure of the photoelectric storage and charging multi-energy coordinated regulation and optimization device in this invention; Figure 3 This is a schematic diagram of the power flow of the optical energy storage and charging system of the present invention; Figure 4 This is a schematic diagram illustrating the electric vehicle charging load prediction in a community according to the present invention. Figure 5 This is a schematic diagram illustrating the scheduling effect of the ACO algorithm in a typical operating mode. Figure 6 This is a schematic diagram illustrating the scheduling effect of the EQACO algorithm, a typical operating mode of this invention. Figure 7 This is a schematic diagram illustrating the scheduling effect of the ACO algorithm in typical operating mode two. Figure 8 This is a schematic diagram illustrating the scheduling effect of the EQACO algorithm in the typical operating mode two of this invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] This invention aims to combine the global optimization advantages of the elite quantum ant colony algorithm with the multi-energy characteristics of photovoltaic-storage-charging systems to construct a dynamic scheduling model. By monitoring photovoltaic output, load demand, and energy storage status in real time, the model intelligently optimizes power allocation strategies, smooths power fluctuations, reduces peak-valley differences, and ultimately improves the stability and economy of system operation.

[0025] Example 1 This embodiment relates to an optimization method for coordinated control of light storage and charging across multiple energy sources based on elite quantum ant colonies, such as... Figure 1 The method includes the following steps: Step 1: Multi-source information acquisition and preprocessing, collecting charging and discharging power of each device, net load power of the AC bus, SOC of the energy storage battery and load data; Step 2: Predict electric vehicle charging load based on statistical simulation dynamic simulation method; Step 3: Based on the data collected in Step 1 and the electric vehicle charging load predicted in Step 2, set the core parameters of the Elite Quantum Ant Colony Algorithm to prepare for iterative optimization. Step 4, Ant Path Search and Power Scheduling Scheme Generation: Each ordinary ant corresponds to one set of photovoltaic-storage-charging power allocation scheme, and the search diversity is enhanced by qubits; the power supply path is selected according to the pheromone concentration of the path, and the higher the pheromone concentration, the greater the probability that the corresponding path is selected. Step 5: Calculate discharge contribution and select elite ant colonies. Based on the peak scheduling priority target of photovoltaic storage and charging, quantify the value of each scheme and select elite ant colonies. Step 6, Multi-objective collaborative optimization evaluation: Based on the three core indicators (peak-valley difference, maximum interactive power fluctuation, and fluctuation rate), evaluate whether the current new solution is the global optimal solution. If it is, then receive the current new solution and update it as the global optimal solution, that is, obtain the optimal power scheduling scheme; otherwise, return to step 4. Step 7: Pheromone dynamic update and iteration judgment. Based on the elite ant colony in Step 5, update the pheromone concentration of each path to guide the ants in the next round to prioritize the selection of a high-quality scheduling scheme; determine whether the iteration number has been reached. If yes, output the globally optimal power scheduling scheme; otherwise, return to Step 4 to continue iterating until the iteration number is reached, and output the globally optimal power scheduling scheme.

[0026] Step 8, Multi-objective power allocation and coordinated control execution, converts the optimal power scheduling scheme output in step 7 into actual converter control signals to realize the coordinated operation and scheduling of the photovoltaic-storage-charging system.

[0027] In step 2, such as Figure 4 As shown, the electric vehicle charging load is predicted based on the statistical simulation dynamic simulation method. The statistical simulation dynamic simulation method is a stochastic simulation method based on probability and statistical principles, used to establish probabilistic models for complex problems. It correlates the solution to the complex problem with certain characteristics of random variables in the model to achieve the goal of solving the problem. By generating random samples and using statistical techniques to analyze community sample data, the solution to the problem can be approximately estimated or the uncertainty can be assessed.

[0028] Because the data on the starting charging time, charging duration, daily mileage, and energy consumption per unit distance for each electric vehicle in the community are disordered and uncertain, and vary depending on the driving characteristics of different vehicle types, a statistical simulation dynamic simulation method is used to simulate the dynamic behavior of electric vehicles' driving and charging choices in the community. Through extensive random sampling of relevant electric vehicle data, and by estimating statistical characteristic parameters based on the sampling results, a probabilistic model is established to simulate the charging load demand of electric vehicles, specifically including: Step 21: Assume the daily mileage of community vehicles. k It follows a log-normal distribution, and its probability density function is... As shown in formula (1): (1) In the formula, , They are respectively The mean and standard deviation.

[0029] Step 22: Assume the initial charge level of electric vehicles in the community. It follows a uniform distribution, and its probability density function is shown in Equation (2): (2) In the formula, , They are respectively The maximum and minimum values.

[0030] State of charge of an electric vehicle at the end of charging As shown in formula (3): (3) In the formula, The rated capacity of the battery for electric vehicles; Charging time for electric vehicles; The charging power of electric vehicles varies depending on the brand and model, as do their battery rated capacities.

[0031] Step 23: Assume the initial charging time after the electric vehicle arrives at the charging station. It follows a normal distribution, and its probability density function is shown in formula (4): (4) in, The starting charging time for an electric vehicle upon arrival at a charging station.

[0032] In step 5, the selection and updating design of the elite ant colony: The core role of the elite ant colony is to enhance the algorithm's optimization capability for the peak discharge strategy of the photovoltaic-storage-charging system through differentiated pheromone updates and targeted guidance of high-quality individuals. Its mechanism design is closely integrated with the objectives of the multi-objective collaborative optimization model, specifically including two major stages: dynamic pheromone updates and elite ant selection.

[0033] Step 51: Pheromone Dynamic Update Mechanism. Pheromone serves as a guiding signal for ant colony exploration. Its update needs to balance the preservation of historical high-quality paths with the exploration of new paths, ensuring that the algorithm neither gets trapped in local optima nor fails to converge quickly to a globally optimal solution. The update process consists of three sub-steps: pheromone evaporation, superposition of pheromone contributions from ordinary ants, and weighted reinforcement of pheromone contributions from elite ants. The specific formula is as follows: (5) in, For the first t Path in the next iteration ij The corresponding pheromone concentration, corresponding to the first t In the next iteration, the higher the pheromone concentration of the energy source i to load j path scheduling value, the more preferentially the next round of scheduling will select this path for power supply. For the first t In +1 iterations, the path ij Updated pheromone concentration; The evaporation rate of pheromones; N This represents the total number of ordinary ants; For the first k Only ordinary ants on the path ij The amount of pheromone contribution; The weighting coefficients for elite ants; The number of elite ants; For the first e Only elite ants follow the path ij The amount of pheromone contribution.

[0034] Step 52: Dynamic selection mechanism for elite ants. Elite ants are the best-performing group of ants in each iteration. They are selected based on their energy storage discharge contribution in each iteration, thus guiding the search process. Elite ants are selected using formula (6): (6) in, For the first k The contribution of each ant's discharge, corresponding to the first ant. k The scheduling stability effect of the photovoltaic-storage-charging power scheduling scheme; This refers to the energy stored and discharged during peak electricity demand periods; The energy stored and discharged in the level segment; These are the weighting coefficients corresponding to peak and normal electricity periods, respectively. Much larger ; This is a collection of elite ant colonies, corresponding to a set of high-quality photovoltaic-storage-charging scheduling schemes. By comparing the discharge contribution of each ant, a group of ants with the largest discharge contribution are selected as elite ants, and the optimal photovoltaic-storage-charging scheduling scheme in each iteration is output.

[0035] In step 6, the pheromone update mechanism balances search diversity and the enhancement of high-quality paths through dynamic adjustments of evaporation, superposition, and weighting; the elite ant selection mechanism anchors the core objective of peak discharge, ensuring that the direction of algorithm iteration is consistent with the stability requirements of the photovoltaic storage and charging system. The two work together to improve the optimization efficiency and quality of the scheduling strategy.

[0036] In step 7, multi-objective collaborative optimization is achieved through quantum computing and iterative optimization. Quantum computing is used to enhance the ant's path-making ability by converting pheromone values ​​into the rotation angle of qubits. The rotation of the qubits enhances the diversity of ant path choices, thereby expanding the diversity of the search space. (Qubit rotation angle) As shown in formula (7): (7) in, The value in the pheromone matrix determines the rotation angle of the qubit.

[0037] In the main loop, each ant updates its pheromone based on its performance. Elite ants contribute more, resulting in their paths being reinforced and their pheromones being updated more frequently. Ultimately, the pheromone matrix update combines the contributions of elite and ordinary ants to guide the search path of the next generation of ants, as shown in formula (8): (8) in, For the first t Path in the next iteration ij Current pheromone concentration; For the first t In +1 iterations, the path ij The current pheromone concentration; in each iteration, first based on the evaporation rate The existing pheromones are attenuated, and then ordinary ants add the corresponding pheromones based on the path they find. Elite ants add more pheromones along the paths they find. These contributions are typically greater than those of ordinary ants. The set of ants (ordinary ants) and the set of elite ants (elite ants) are updated to focus the search process on paths found by elite ants, thus improving the quality of the solution.

[0038] The collaborative optimization objective is to reduce peak-to-valley differences, smooth power fluctuations, and decrease maximum interactive power fluctuations, thereby enhancing the optimization effect on grid operation stability and iteratively generating the optimal energy storage charging and discharging strategy. Each iteration generates multiple discharge strategies, selecting the solution that best meets the collaborative optimization objective as the optimal solution. This strategy is strengthened through pheromone updates, guiding subsequent iterations of the ants to prioritize the better strategy, including: The peak-valley difference of the power purchased by the power grid refers to the difference between the maximum peak load and the minimum valley load of the power grid within a 24-hour period of a day, reflecting the amplitude of power fluctuation, as shown in formula (9): (9) in, The peak-to-valley difference in power. This represents the maximum daily grid load power. This represents the minimum power load of the power grid during the day.

[0039] Maximum Interaction Power Fluctuation refers to the maximum absolute change in power interaction with the power grid at adjacent times within a 24-hour period, reflecting the severity of power changes, as shown in formula (10): (10) in, For the first i Power at any moment The power at the previous moment. For the first i Interaction power at time ( =2,3,…,24), This represents the maximum interactive power fluctuation.

[0040] Power volatility is a relative indicator used to measure the overall dispersion of power interaction in the power grid relative to its average level. It is used to quantify the relative severity of power fluctuations, as shown in formula (11): (11) in, For the first i The power purchased by the power grid at any given time; n The number of hours within a day; The average power purchased by the power grid; The standard deviation of the power purchased by the power grid is used to measure the degree of deviation of the power from the mean at each time point; The power fluctuation rate of the photovoltaic energy storage and charging system.

[0041] The elite quantum ant colony algorithm of this invention integrates the parallelism of quantum computing with the global search capability of ant colony optimization. It boasts advantages such as fast convergence speed, strong robustness, and the ability to effectively handle multi-constraint optimization problems. When applied to a photovoltaic-storage-charging system, it can precisely coordinate the timing of photovoltaic power generation, energy storage charging and discharging, and electric vehicle charging and discharging, responding to power fluctuations in real time. Therefore, this invention's multi-energy coordinated regulation and optimization based on elite quantum ant colonies enables efficient multi-energy coordinated operation, possessing significant research value and practical application value for reducing grid pressure and improving energy utilization efficiency.

[0042] To verify the effectiveness of the collaborative regulation and optimization method for photovoltaic-storage-charging systems based on the Elite Quantum Ant Colony Algorithm (EQACO), two typical operating modes of a community-based photovoltaic-storage-charging system were selected for case testing. The scheduling effects of the traditional Ant Colony Algorithm (ACO) and EQACO were compared. The core parameters and optimization results are as follows: Typical operating mode 1: On sunny days with strong sunlight and ample photovoltaic output, electric vehicles only charge and do not participate in V2G discharge. The system relies on photovoltaic power, energy storage batteries, and grid power purchases to collaboratively meet residential electricity load, energy storage charging, and electric vehicle charging. For example... Figure 5 Under the ACO strategy, the energy storage discharge timing is too early, resulting in low energy storage discharge during the evening peak. This premature depletion of power leads to a large peak-to-valley difference in grid power purchases, reaching 930.62 kW, with a maximum interactive power fluctuation of 409.92 kW, a standard deviation of 276.64, and a volatility as high as 56.5%. Figure 6EQACO expands the search space by rotating qubits and explores a better energy storage discharge strategy. Combined with the selection of elite ants for discharge contribution, it significantly increases the discharge capacity of the energy storage battery during peak hours. The peak-to-valley power purchase from the grid is reduced to 808.51kW, a 13.1% reduction compared to ACO. The maximum interactive power fluctuation is reduced to 309.92kW, a 24.5% reduction compared to ACO. The standard deviation is 234.59, and the volatility is reduced to 47.76%. This verifies the ability of the elite quantum ant colony algorithm to optimize the scheduling of energy storage resources in the system when electric vehicles do not participate in discharge.

[0043] Typical operating mode two: On a sunny day, with sufficient photovoltaic output, electric vehicles simultaneously participate in V2G discharge, forming a collaborative and optimized scheduling of photovoltaics, energy storage batteries, and electric vehicles. For example... Figure 7 The ACO strategy misaligns the discharge timing of energy storage batteries and EVs, but due to algorithmic scheduling, the grid's power purchase during the middle of the evening peak still reaches 600kW, limiting the optimization of peak-valley difference. The peak-valley difference of grid power purchase reaches 792.72kW, the maximum interactive power fluctuation is 242.84kW, the standard deviation is 261.66, and the volatility reaches 53.98%. Figure 8 EQACO guides energy storage batteries and electric vehicles to discharge synchronously at night through an elite ant selection mechanism: the combined output of energy storage battery discharge and electric vehicle discharge covers most of the load, reducing the peak power purchase of the grid to 450kW; the quantum bit rotation operation enhances the exploration of the flexibility of energy storage battery discharge, reducing the peak power purchase of the grid to 701.54kW, a reduction of 11.5% compared to ACO, and the maximum interactive power fluctuation to 199.54kW, a reduction of 17.8% compared to ACO, with a standard deviation of 211.68 and a volatility of 46.38%, demonstrating the optimized scheduling advantages of elite quantum ant colonies under multi-source collaboration.

[0044] Example 2 This embodiment relates to a photoelectric storage-charging multi-energy collaborative regulation and optimization device based on elite quantum ant colonies, such as... Figure 2 The device includes: The photovoltaic-storage-charging bus unit is the energy source and core hub of the photovoltaic-storage-charging system, such as... Figure 3 As shown, the photovoltaic-storage-charging bus unit integrates photovoltaic power generation, energy storage batteries, and charging facilities. It combines the electrical energy generated by the photovoltaic array, the electrical energy stored in the energy storage battery, and the charging load demand onto the same bus (summarizing the net power of photovoltaic, energy storage battery, charging pile, and residential load), realizing the coordinated and flexible allocation of multiple energy sources such as photovoltaic, energy storage, and charging, and providing a physical basis for subsequent data collection and optimization control.

[0045] The analog signal acquisition module is directly connected to the photovoltaic-storage-charging bus device and is used to acquire continuous analog signals such as voltage, current and power in the photovoltaic-storage-charging system in real time, including the power generation of the photovoltaic array (affected by sunlight and weather), the charging and discharging power of the energy storage battery, and the real-time charging power of the electric vehicle charging pile.

[0046] The analog-to-digital converter module converts the analog signals sent from the analog acquisition module into discrete digital signals that can be recognized and processed by a computer.

[0047] The load forecasting module combines real-time operating data of the photovoltaic-storage-charging system with possible environmental factors (such as weather forecasts). By analyzing the trends and patterns of power load changes in historical residential electricity consumption data, it predicts the power consumption of the load in the future (such as the next few hours or day) and forecasts the charging load of electric vehicles based on statistical simulation dynamic simulation method.

[0048] The energy storage status detection module is used to detect the current status of the energy storage battery in real time, including the state of charge (SOC, a positive value indicates discharge, and a negative value may indicate charging), state of health (SOH), temperature, etc.

[0049] The multi-source information fusion unit connects to the analog quantity acquisition module, load forecasting module, and energy storage status monitoring module. It receives real-time operating data after analog-to-digital conversion, load forecasting data, and current status data of the energy storage batteries, and performs comprehensive analysis and deep fusion of this multi-source, multi-dimensional information. Its function is to generate a comprehensive and accurate panoramic view of the system status, providing precise data and decision support for the ultimate realization of safe, stable, and economical multi-energy coordinated regulation and optimization of photovoltaic, energy storage, and charging systems.

[0050] The Elite Quantum Ant Colony Optimization Module generates priority sequences for energy storage charging and discharging and electric vehicle (EV) charging and discharging based on statistical simulation dynamic load prediction and pheromone dynamic updates. The multi-objective power divider is equipped with monitoring devices for peak-to-valley difference, maximum interactive power fluctuation, and fluctuation rate. Based on the monitoring results, it evaluates the control effect in real time and outputs the converter control signal (i.e. the optimal power dispatch scheme) to the converter control unit. The inverter control unit executes PWM drive based on the received inverter control signal to achieve coordinated control of photovoltaic power consumption, energy storage stabilization, and orderly charging and discharging of electric vehicles.

[0051] The photoelectric storage and charging multi-energy collaborative regulation and optimization device based on elite quantum ant colonies in this invention is not limited to the collaborative scheduling of photoelectric storage and charging systems in communities, but is also applicable to scenarios such as industrial parks and commercial areas.

[0052] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for coordinated regulation and optimization of light storage and charging multi-energy based on elite quantum ant colonies, characterized in that, The method includes: Real-time acquisition of charging and discharging power of each device in the photovoltaic-storage-charging system, net load power of the AC bus, SOC of the energy storage battery, and load data; Predicting electric vehicle charging load based on statistical simulation dynamic simulation method; Based on real-time collected data and predicted electric vehicle charging load, multiple power scheduling schemes are generated using the elite quantum ant colony algorithm. The value of each scheduling scheme is quantified based on the peak scheduling priority target of photovoltaic, energy storage and charging, and elite ant colonies are selected. The pheromone is dynamically updated and the optimal power scheduling scheme is generated iteratively to achieve multi-energy coordinated regulation and optimization of photovoltaic, energy storage and charging.

2. The method for coordinated regulation and optimization of light storage and charging based on elite quantum ant colonies according to claim 1, characterized in that, The process of iteratively generating the optimal power scheduling scheme includes: Power scheduling schemes are generated through path search; Based on the priority objective of peak scheduling for photovoltaic, energy storage and charging, the value of each scheme is quantified by the discharge contribution and elite ant colonies are selected. Pheromone dynamic update: Based on the selected elite ant colony, the pheromone concentration of each path is dynamically updated to guide the next round of ants to prioritize the selection of high-quality power scheduling schemes; The new power scheduling scheme is evaluated based on the collaborative optimization objective. If it is, the new power scheduling scheme is received and updated to the global optimal solution, thus obtaining the optimal power scheduling scheme. Otherwise, the iteration continues until the iteration number is reached, and the optimal power scheduling scheme is output.

3. The method for coordinated regulation and optimization of light storage and charging based on elite quantum ant colonies according to claim 2, characterized in that, The aforementioned power scheduling scheme generated through path search is specifically as follows: a set of optical storage and charging power allocation schemes is used as each ordinary ant in the elite quantum ant colony algorithm. The diversity of path search is enhanced by qubits, and the power supply path is selected according to the pheromone concentration of the path.

4. The method for coordinated regulation and optimization of light storage and charging based on elite quantum ant colonies according to claim 2, characterized in that, The collaborative optimization objectives are to reduce peak-to-valley differences, smooth out power fluctuations, and reduce maximum interactive power fluctuations. Peak-to-valley load is the difference between the maximum peak load and the minimum valley load of the power grid during a day, reflecting the magnitude of power fluctuations. Maximum interactive power fluctuation is the maximum absolute change in power interaction with the grid at adjacent times within a day, reflecting the severity of power changes. Power volatility is a relative indicator used to measure the overall dispersion of power interaction in a power grid relative to its average level, and is used to quantify the relative severity of power fluctuations.

5. The method for coordinated regulation and optimization of light storage and charging based on elite quantum ant colonies according to claim 1, characterized in that, The process of dynamically updating pheromones needs to balance the preservation of historical high-quality paths with the exploration of new paths, including three sub-steps: pheromone evaporation, superposition of pheromone contributions from ordinary ants, and weighted reinforcement of pheromone contributions from elite ants.

6. The method for coordinated regulation and optimization of light storage and charging based on elite quantum ant colonies according to claim 1, characterized in that, The selection of elite ant colonies specifically involves: Elite ants are selected based on the energy storage discharge contribution in each iteration, thereby guiding the path search process; The discharge contribution of the energy storage represents the scheduling stability effect of the power dispatch scheme, and is the weighted sum of the energy storage discharge during peak and normal power periods.

7. The method for coordinated regulation and optimization of light storage and charging based on elite quantum ant colonies according to claim 1, characterized in that, The prediction of electric vehicle charging load based on statistical simulation dynamic simulation method includes: Assume that the daily mileage of electric vehicles follows a log-normal distribution, and assume that the starting charging time of electric vehicles after arriving at the charging station follows a normal distribution; The initial charge of an electric vehicle follows a uniform distribution. The state of charge of the electric vehicle at the end of charging is related to the initial charge, battery rated capacity, charging time, and charging power, specifically: ， In the formula, The rated capacity of the battery for electric vehicles; Charging time for electric vehicles; The charging power for electric vehicles, Initial charge level for electric vehicles.

8. An apparatus for implementing the optical-storage-charging multi-energy coordinated regulation and optimization method based on any one of claims 1 to 7, characterized in that, The device includes: The multi-source information fusion unit performs deep fusion of multi-source data, including real-time acquisition of charging and discharging power of each device in the photovoltaic-storage-charging system, net load power of the AC bus, SOC of the energy storage battery, and load data. The Elite Quantum Ant Colony Optimization Module predicts the charging load of electric vehicles based on statistical simulation dynamic simulation method, and generates multiple power scheduling schemes using the Elite Quantum Ant Colony Algorithm. It quantifies the value of each scheduling scheme based on the peak scheduling priority target of photovoltaic, energy storage and charging, and selects elite ant colonies to guide the next round of ants to prioritize the selection of high-quality new power scheduling schemes. The multi-objective power allocator evaluates whether a new power scheduling scheme is the global optimal solution based on the collaborative optimization objective. If it is, the new power scheduling scheme is received and updated to the global optimal solution, thus obtaining the optimal power scheduling scheme; otherwise, the pheromone is updated and the optimal power scheduling scheme is generated iteratively through the elite quantum ant colony optimization module. The converter control unit executes PWM drive based on the received optimal power scheduling scheme to achieve coordinated regulation and optimization of photovoltaic, energy storage and charging multi-energy systems.

9. The apparatus according to claim 8, characterized in that, The process by which the elite quantum ant colony optimization module iteratively generates the optimal power scheduling scheme includes: Power scheduling schemes are generated through path search; Based on the priority objective of peak scheduling for photovoltaic, energy storage and charging, the value of each scheme is quantified by the discharge contribution and elite ant colonies are selected. Pheromone dynamic update: Based on the selected elite ant colony, the pheromone concentration of each path is dynamically updated to guide the next round of ants to prioritize the selection of high-quality power scheduling schemes; The new power scheduling scheme is evaluated based on the collaborative optimization objective. If it is, the new power scheduling scheme is received and updated to the global optimal solution, thus obtaining the optimal power scheduling scheme. Otherwise, the iteration continues until the iteration number is reached, and the optimal power scheduling scheme is output.

10. The apparatus according to claim 9, characterized in that, The aforementioned power scheduling scheme generated through path search is specifically as follows: a set of optical storage and charging power allocation schemes is used as each ordinary ant in the elite quantum ant colony algorithm. The diversity of path search is enhanced by qubits, and the power supply path is selected according to the pheromone concentration of the path.

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