Energy control method and system based on multi-objective collaborative optimization

Abstract

The invention relates to an energy control method and system based on multi-objective collaborative optimization, and the method takes the maximum consumption of photovoltaic power generation and the minimization of outsourcing electric charge as optimization objectives. Comprising the following steps: a day-ahead plan strategy: performing photovoltaic output prediction and charging load prediction in different time periods of the next day based on actual power generation data of a photovoltaic array in the photovoltaic storage and charging integrated power station, charging load data of a charging pile and historical weather data, and making a day-ahead plan; and a real-time scheduling strategy: monitoring the actual output of the photovoltaic array, the real-time charging load of the charging pile and the real-time electric quantity of the energy storage subsystem in real time, and performing real-time rolling adjustment on the basis of the day-ahead plan according to the deviation between the actual operation condition and the day-ahead plan by taking the minimum adjustment cost to ensure the power balance as a target. Compared with the prior art, the method has the advantages of realizing efficient utilization of energy, optimizing cost, improving the stability of a power grid and the like.

Description

Technical Field

[0001] The present invention relates to the field of new energy technologies, and in particular to an energy control method and system based on multi-objective collaborative optimization. Background Art

[0002] With the increasing popularity of electric vehicles, the demand for charging stations is growing. Traditional single-station charging stations rely on the power grid for power, which can put significant pressure on the grid during peak hours and result in high electricity costs. Meanwhile, photovoltaic power generation, as a clean energy source, is becoming increasingly widespread, but it is characterized by intermittent and unstable characteristics. Energy storage subsystems can store excess electricity, helping to smooth out peak demand. However, in integrated photovoltaic, storage, and charging power stations, the charge and discharge control of energy storage systems, as well as their coordinated operation with photovoltaic and charging stations, are currently not fully developed. How to effectively coordinate the energy flow between photovoltaics, energy storage, and charging stations to achieve efficient energy utilization, reduce operating costs, and improve grid stability has become a pressing issue.

[0003] Existing photovoltaic, energy storage and charging systems mostly adopt fixed priority strategies (such as photovoltaics first, energy storage second), which do not fully tap the potential of multi-energy complementarity; some control strategies rely on local historical data and lack the ability to dynamically respond to real-time market electricity prices, weather forecasts and user behavior.

[0004] The existing technical problems have the following problems:

[0005] 1) Photovoltaic power generation is greatly affected by weather, resulting in unstable power supply to charging stations;

[0006] 2) The energy storage subsystem's charging and discharging strategy is single and fails to dynamically adjust based on electricity prices and load demand;

[0007] 3) The power distribution efficiency of charging piles is low, which can easily lead to queues or waste of resources during peak hours;

[0008] 4) The lack of a multi-objective collaborative optimization mechanism makes it difficult to balance economic efficiency, environmental protection and grid interaction needs.

[0009] After searching, Chinese invention patent application publication number CN116151486A discloses a multi-time-scale stochastic optimization method for a photovoltaic charging station with an energy storage system, comprising the following steps: predicting the photovoltaic output and charging load of the next day based on the historical data of the photovoltaic output and charging load in the photovoltaic charging station system with the energy storage system; determining the typical scenarios and corresponding probabilities of multiple photovoltaic outputs and charging loads on the previous day based on the prediction errors of the photovoltaic output and charging load predictions of the next day; optimizing with the goal of minimizing the daily operating cost based on the typical scenarios and corresponding probabilities on the previous day, and obtaining the optimized initial charge state of the energy storage system and the reference power exchange value between the energy storage system and the regional distribution network in each scheduling period of the next day; within the next day, optimizing the energy storage system with the optimal initial charge state and the reference power exchange value between the energy storage system and the regional distribution network in each scheduling period of the next day; Initialize the photovoltaic charging station system by optimizing the initial state of charge and the reference power exchange value between the energy storage system and the regional distribution network in each scheduling cycle of the next day; in each scheduling cycle of the next day, update the current energy storage system state of charge, charging load and photovoltaic output measurement values, and predict the photovoltaic output and charging load forecast for the next scheduling cycle; based on the prediction error of the photovoltaic output and charging load forecast for the next scheduling cycle, determine multiple typical daily scenarios of photovoltaic output and charging load and the corresponding probabilities, optimize with the goal of minimizing daily operating costs, and obtain the charge and discharge power of the energy storage system in each scheduling cycle from the current time to the end of the day; based on the charge and discharge power of the energy storage system in each scheduling cycle from the current time to the end of the day, update the energy storage system charge and discharge strategy. This existing patent application has the problem of not considering the adjustment strategy problem that photovoltaic power generation cannot be absorbed and the surplus power is not connected to the grid.

[0010] How to effectively coordinate the energy flow between photovoltaics, energy storage, and charging piles, control photovoltaic backflow when the surplus power is not connected to the grid, and achieve efficient energy utilization has become a technical problem that needs to be solved. Summary of the Invention

[0011] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide an energy control method and system based on multi-objective collaborative optimization.

[0012] The purpose of the present invention can be achieved by the following technical solutions:

[0013] According to one aspect of the present invention, an energy control method based on multi-objective collaborative optimization is provided. The method takes maximizing the consumption of photovoltaic power generation and minimizing the cost of purchased electricity as optimization objectives, and includes the following steps:

[0014] Day-ahead planning strategy: Based on the actual power generation data of the photovoltaic array in the integrated photovoltaic storage and charging power station, the charging load data of the charging piles, and historical weather data, the photovoltaic output and charging load forecasts for different time periods of the next day are made, and a day-ahead plan is formulated;

[0015] Real-time scheduling strategy: Real-time monitoring of the actual output of the photovoltaic array, the real-time charging load of the charging pile, and the real-time power of the energy storage subsystem, with the goal of ensuring power balance with minimal adjustment cost. Based on the deviation between the actual operating conditions and the day-ahead plan, real-time rolling adjustments are made on the basis of the day-ahead plan.

[0016] Preferably, the photovoltaic output forecast and charging load forecast for different time periods of the next day are specifically as follows: based on the collected actual power generation data of the photovoltaic array, the charging load data of the charging pile and the historical weather data, the photovoltaic power generation curves under different weather conditions in the historical data and the charging demand change trend of the corresponding date are analyzed, and combined with the weather forecast information of the next day, the photovoltaic output forecast and charging load forecast for different time periods of the next day are conducted.

[0017] Preferably, the day-ahead plan is formulated as follows: using the charge and discharge power of the energy storage battery and the power of purchased electricity as decision variables, the optimization target as the objective function, and solving the problem in combination with the constraints to obtain the charge and discharge status of the energy storage subsystem at different time periods the next day and the power arrangement for purchasing electricity from the power grid.

[0018] More preferably, the constraint condition is a safe and healthy operation constraint of the energy storage subsystem, that is, limiting the charge and discharge depth and charge and discharge rate of the energy storage battery.

[0019] Preferably, the real-time scheduling strategy includes: ensuring power balance with the minimum adjustment cost as the goal, when the actual operation situation deviates from the day-ahead plan, and the deviation is greater than the set threshold, real-time rolling adjustment is performed based on the day-ahead plan; if the deviation is less than or equal to the set threshold, it is executed according to the day-ahead plan.

[0020] More preferably, the real-time rolling adjustment specifically includes:

[0021] S2021, determine whether the actual photovoltaic output is higher than the photovoltaic output forecast. If yes, execute S2022; otherwise, execute S2024;

[0022] S2022, when the actual photovoltaic output is higher than the forecast, if the energy storage subsystem is not fully charged, increase the charging power of the energy storage subsystem to the maximum value; otherwise, execute S2023;

[0023] S2023: If the energy storage subsystem is fully charged and the charging pile load has not reached saturation, increase the power supplied to the charging pile; if the energy storage subsystem is fully charged and the charging load has reached saturation, reduce the photovoltaic output;

[0024] S2024: When the actual photovoltaic output is not higher than the photovoltaic output forecast, determine whether the charging load is greater than or equal to the charging load forecast. If so, execute S2025; otherwise, maintain power balance;

[0025] S2025, determine whether the energy storage SOC has reached the lower limit. If not, the energy storage subsystem has sufficient power, and the discharge power of the energy storage subsystem is increased to discharge to the lower limit. Otherwise, if it means that the energy storage subsystem has insufficient power, decide whether to purchase electricity from the grid and the power of the purchased electricity based on the electricity price.

[0026] More preferably, whether to purchase electricity from the grid and the power of electricity purchased are determined according to the electricity price: if the current electricity price is a valley price or a normal price, electricity is purchased from the grid on demand; otherwise, the charging power is reduced to meet the minimum charging demand.

[0027] Preferably, the method further includes dynamically adjusting the power supply priorities of photovoltaic, energy storage, and power grid according to real-time electricity prices, energy storage SOC, photovoltaic output, and user charging needs, specifically:

[0028] P charge =α·P PV +β·P ESS +γ·P grid

[0029] Among them, the weight coefficients α, β, and γ are updated in real time by the reinforcement learning model according to the optimization goal. charge is the charging power, P PV is the photovoltaic power generation power, P ESS is the energy storage charging and discharging power (charging is negative, discharging is positive), P grid is the power supplied to the grid.

[0030] According to another aspect of the present invention, there is provided an energy control system based on multi-objective collaborative optimization, the system comprising a photovoltaic array, an energy storage subsystem and a charging pile, the system comprising an energy management system and a coordination controller;

[0031] The coordination controller is responsible for collecting data of various energy sources;

[0032] The energy management system monitors and analyzes the energy of the entire system based on the energy data collected by the coordination controller, formulates day-ahead plans and real-time scheduling strategies, and sends them to the coordination controller;

[0033] The coordination controller executes the day-ahead plan and real-time scheduling strategy issued by the energy management system to adjust the power of each energy source.

[0034] Preferably, the energy management system predicts the photovoltaic output and charging load at different times of the next day based on the collected energy data and weather data, and formulates a day-ahead plan in combination with the optimization objectives and constraints.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1) The present invention formulates a day-ahead plan based on the forecast of photovoltaic output and charging load for the next day based on historical energy data and weather data. The next day, the plan is adjusted in real time based on the deviation between the actual operation situation and the day-ahead plan, achieving the optimization goals of maximizing the consumption of photovoltaic power generation and minimizing the cost of purchased electricity, thus realizing efficient energy utilization and cost optimization.

[0037] 2) The real-time mobilization strategy of the present invention gives priority to the use of photovoltaic power generation, stores excess electricity in the energy storage system, and releases the stored energy when photovoltaic power generation is insufficient or when electricity consumption is at peak, reducing dependence on the power grid and improving the comprehensive utilization efficiency of new energy. Combined with the time-of-use electricity price policy, the energy storage system is charged during off-peak electricity price periods, and the charging piles are powered by stored energy discharge during peak electricity price periods, reducing the amount of electricity purchased from the power grid during peak periods, thereby reducing the electricity bill expenditure of the charging station, achieving the goal of minimizing the cost of purchased electricity, and effectively reducing operating costs.

[0038] 3) The present invention uses a reinforcement learning model to dynamically adjust the power supply priorities of photovoltaics, energy storage, and the power grid to achieve optimization goals and achieve a dynamic balance between economy, efficiency, and power grid support capabilities.

[0039] 4) The energy control strategy of this invention can smooth the power load of charging stations and reduce the impact on the power grid. During peak power consumption, the energy storage system discharges to power the charging piles, reducing the pressure on the power grid. During low power consumption, the energy storage system charges and absorbs excess power from the grid, thus reducing peak load and filling valleys, helping to improve the stability and reliability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a schematic diagram of the energy control system structure of the integrated photovoltaic storage and charging station of the present invention;

[0041] Figure 2 Schematic diagram of the flow of the energy control method of the present invention;

[0042] Figure 3 It is a flow chart of the real-time scheduling strategy in the energy control method of the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0044] Example 1

[0045] This embodiment relates to an energy control method based on multi-objective collaborative optimization. By dynamically coordinating the operating status of photovoltaic power generation, energy storage subsystems, and charging piles, it solves the problems of uneven energy distribution, delayed response, and insufficient economy in the integrated photovoltaic storage and charging system, thereby achieving efficient energy utilization, cost optimization, and improved grid stability.

[0046] The method includes:

[0047] 1) Day-ahead planning operation strategy, such as Figure 2 As shown, the following steps are included:

[0048] S101, Data Collection and Forecasting: Before the end of each day, the EMS collects the actual power generation data of the photovoltaic array, the charging load data of the charging piles, and historical weather data. It then uses machine learning algorithms to predict the photovoltaic output and charging load for the next day. For example, by analyzing the photovoltaic power generation curves under different weather conditions in historical data and the charging demand trends of the corresponding days, combined with the next day's weather forecast information, it can predict the photovoltaic output (i.e., photovoltaic power generation) and charging load (i.e., charging load) for different time periods of the next day.

[0049] S102, Optimization Target Setting: Two primary optimization goals are set: maximizing the consumption of photovoltaic power generation and minimizing the cost of purchased electricity. To maximize the consumption of photovoltaic power generation, photovoltaic power is prioritized for powering charging stations, with the remaining power used to charge the energy storage subsystem. The energy storage subsystem must reserve capacity based on the predicted photovoltaic surplus power. To minimize the cost of purchased electricity, purchases from the grid during peak electricity price periods are avoided as much as possible. During off-peak electricity price periods, the energy storage subsystem is charged more frequently to reserve sufficient power for the net load during peak periods.

[0050] S103, Constraint Consideration: Consider the safe and healthy operation constraints of the energy storage subsystem, such as limiting the depth of charge and discharge, and charge and discharge rate of the energy storage battery. For example, the depth of discharge of the energy storage battery cannot be less than 10%, and the charge rate cannot exceed 80% of its rated charging power to extend the service life of the energy storage battery.

[0051] S104, Plan Formulation: Based on the aforementioned optimization objectives and constraints, an optimization algorithm is used to determine the energy storage battery charge and discharge plan and the power purchase plan for the next day. For example, a linear programming algorithm is employed, using the energy storage battery charge and discharge power and the power purchase plan as decision variables, and maximizing photovoltaic power consumption and minimizing the cost of purchased electricity as the objective functions. The constraints are then combined to determine the energy storage subsystem charge and discharge status at different times of the next day, as well as the power purchase plan from the grid.

[0052] 2) Real-time scheduling strategies, such as Figure 3 , including the following steps:

[0053] S201, Real-time Data Monitoring: The EMS monitors the actual output of the PV array, the real-time charging load of the charging pile, and the real-time power of the energy storage subsystem. For example, sensors collect the output voltage and current of the PV array in real time to calculate the actual power generation; the control system of the charging pile obtains the real-time charging current and voltage to calculate the charging load; and the battery management system (BMS) obtains the real-time power, voltage, current, and other information of the energy storage battery.

[0054] S202, adjustment strategy: The goal is to ensure power balance with the minimum power purchase cost. When the actual operation situation deviates from the day-ahead plan and the deviation is greater than a set threshold (for example, 20%), real-time rolling adjustments are made based on the day-ahead plan. If the deviation is less than or equal to the set threshold, the day-ahead plan is executed.

[0055] Specifically include:

[0056] S2021, determine whether the actual photovoltaic output is higher than the photovoltaic output forecast. If yes, execute S2022; otherwise, execute S2024;

[0057] S2022: If the energy storage subsystem is not fully charged, increase the charging power of the energy storage subsystem to a maximum value; otherwise, execute S2023;

[0058] S2023: If the energy storage subsystem is fully charged and the charging load has not reached saturation, increase the power supplied to the charging pile; if the energy storage subsystem is fully charged and the charging load has reached saturation and the remaining power is not connected to the grid, reduce the photovoltaic output;

[0059] S2024: When the actual photovoltaic output is not higher than the photovoltaic output forecast, determine whether the charging load is greater than or equal to the charging load forecast. If so, execute S2025; otherwise, maintain power balance.

[0060] S2025, determine whether the energy storage SOC has reached the lower limit. If not, the energy storage subsystem has sufficient power, and the discharge power of the energy storage subsystem is increased to discharge to the lower limit. Otherwise, it means that the energy storage subsystem is insufficient. Then, decide whether to purchase electricity from the grid and the power of the purchased electricity based on the electricity price. Specifically: if the current electricity price is the valley price or the normal price, purchase electricity from the grid; otherwise, reduce the charging power to meet the minimum charging demand.

[0061] After executing the adjustment strategy, return to S201 to monitor data in real time and make real-time rolling adjustments based on the day-ahead plan.

[0062] The method also includes a dynamic priority adjustment algorithm, which dynamically adjusts the power supply priorities of photovoltaics, energy storage, and power grids based on real-time electricity prices, energy storage SOC, photovoltaic output, and user charging needs, and is used in day-ahead planning and real-time scheduling strategies.

[0063] P charge =α·P PV +β·P ESS +γ·P grid

[0064] Among them, the weight coefficients α, β, and γ are updated in real time by the reinforcement learning model. charge is the charging power, P PV is the photovoltaic power generation power, P ESS is the energy storage charging and discharging power (charging is negative, discharging is positive), P grid The objective function is to maximize the consumption of photovoltaic power and minimize the cost of purchased electricity, so as to achieve a dynamic balance among economy, efficiency and grid support capacity.

[0065] Example 2

[0066] This embodiment also relates to a multi-objective collaborative optimization energy control method, which is applicable to daily operation under various working conditions. Three typical daily operation conditions are used as examples for illustration.

[0067] For typical sunny day operation, energy control methods include:

[0068] 201) Day-Ahead Plan: Based on historical data and weather forecasts, the energy management system predicts sunny skies and high photovoltaic output for the next day. To maximize the absorption of photovoltaic power generation, calculations indicate a significant amount of surplus photovoltaic power for the next day. Therefore, the day-ahead plan reserves a large capacity for the energy storage system. To minimize the cost of purchased electricity, priority is given to using the grid to charge the energy storage system during off-peak periods, with the charging capacity required to meet the majority of net load demands during peak periods. An optimization algorithm is used to determine the energy storage system's charging power and duration during off-peak periods, as well as its discharge power and duration during peak periods.

[0069] 202) Real-time scheduling: During actual operation, the energy management system monitors the actual output of the photovoltaic array, the real-time charging load of the charging pile, and the real-time power of the energy storage system in real time. During the morning period of high photovoltaic power generation, the actual photovoltaic output is higher than the forecast. The energy management system increases the charging power to the energy storage system to make the energy storage system fully charged faster. During the noon period, the charging load increases slightly. Since the coordinated power supply of photovoltaics and energy storage can meet the demand, there is no need to purchase electricity from the grid. During the evening peak period, the energy storage system discharges according to plan to power the charging pile. When the energy storage power is close to the minimum power, the energy management system purchases electricity from the grid appropriately according to the real-time electricity price and charging load to ensure the normal operation of the charging pile.

[0070] For typical daytime operation on cloudy days, energy control methods include:

[0071] 211) Day-ahead planning: The next day is forecast to be cloudy, with PV output fluctuating and generally lower than on sunny days. In the day-ahead plan, given the limited surplus PV power, the energy storage system is charged as fully as possible during off-peak periods to meet electricity demand in subsequent periods. At the same time, based on the predicted charging load, a plan is developed to continue charging from the grid if the energy storage system is not fully charged during parity periods, ensuring sufficient power is available during peak periods.

[0072] 212) Real-time Scheduling: During actual operation on cloudy days, the photovoltaic output fluctuates greatly. When the photovoltaic output suddenly drops, the energy management system promptly adjusts the discharge power of the energy storage system to maintain normal power supply to the charging piles. During the parity period, if the energy storage system is not fully charged, the energy management system reasonably controls the charging power from the grid based on the grid capacity and electricity price, so that the energy storage system is fully charged as soon as possible. During peak price periods, the energy storage system and photovoltaics jointly power the charging piles. When the photovoltaic output is zero, only the energy storage system discharges. The energy management system dynamically adjusts the discharge power of the energy storage system based on the real-time charging load to ensure the stability of the charging service.

[0073] For typical daily operation on rainy days, energy control methods include:

[0074] 221) Daily Planning: Because the next day is forecast to be cloudy and rainy, with minimal PV output, the primary focus is on the energy storage system's charging and discharging to reduce electricity costs. In the day-ahead plan, the energy storage system is scheduled to be fully charged from the grid during off-peak and some parity periods to meet charging demands during the morning and evening peak hours.

[0075] 222) Real-time Scheduling: During rainy days, photovoltaic power generation has almost no output. During off-peak periods, the energy management system controls the energy storage system to charge from the grid at the maximum allowable charging power, fully charging the energy storage battery as quickly as possible. During the morning peak period, the energy storage system discharges as planned to power the charging piles until the power level approaches the minimum. During the parity period, if the energy storage system power level is low, it will be charged again from the grid to a full charge. During the evening peak period, the energy storage system continues to discharge to power the charging piles. When the power level is too low, the energy management system decides whether to purchase electricity from the grid based on the actual situation to ensure basic charging services.

[0076] From the above examples, it can be seen that the energy control strategy of the integrated photovoltaic, energy storage, and charging pile charging station of the present invention can reasonably coordinate the energy flow between photovoltaic, energy storage, and charging piles according to different weather conditions and electricity demand, thereby achieving the goals of efficient energy utilization, reducing operating costs, and improving grid stability.

[0077] Example 3

[0078] This embodiment also relates to a multi-objective collaborative optimization energy control system, such as Figure 1The energy control system of an integrated photovoltaic, storage, and charging station consists of a photovoltaic array, an energy storage subsystem, charging piles, an energy management system (EMS), and a coordination controller. The photovoltaic array converts solar energy into electricity; the energy storage subsystem consists of multiple battery packs that store excess energy and release it when needed; the charging piles provide charging services for electric vehicles; and the coordination controller, an embedded system, is responsible for collecting energy data, converting protocols, and executing local policies issued by the energy management system. Based on the data collected by the coordination controller, the energy management system monitors and analyzes the energy of the entire system and issues optimization strategies to the coordination controller.

[0079] The energy management system integrates real-time data collection (photovoltaic output, energy storage SOC, charging pile load, grid electricity price), prediction module (weather and load forecast) and optimization coordination controller strategy.

[0080] Based on the different working conditions of the integrated photovoltaic storage and charging station, there are multiple operating modes, including photovoltaic high-power mode, valley price no-light mode, parity preparation mode, peak price discharge mode and peak-to-parity no-storage mode.

[0081] PV High-Power Mode: When PV output exceeds the charging load, the principle of maximum generation and utilization is adhered to, regardless of the electricity price period. PV power is preferentially supplied to charging stations, with the remainder supplied to energy storage. Energy storage charging power follows PV output. Based on the predicted PV output, the EMS reserves sufficient capacity for the energy storage subsystem during the day-ahead planning phase to ensure it can fully absorb excess PV power.

[0082] Valley Price, No-Sun Mode: During valley price periods and when PV is not contributing, the energy storage system maximizes storage capacity while meeting external constraints. The energy storage subsystem charges from the grid, reserving capacity for potential PV surplus and ensuring the energy storage battery is fully charged. Charging stations also receive power from the grid if needed. The EMS controls the charging power of the energy storage subsystem, ensuring the battery is fully charged as quickly as possible while staying within grid capacity limits and ensuring safe charging conditions for the energy storage battery.

[0083] Parity Preparation Mode: When electricity prices are at parity and the next peak period is approaching, sufficient energy storage capacity is prepared to reduce external power purchases during the next peak period. If the energy storage subsystem is not fully charged, it is charged from the grid; if it is fully charged, it remains idle, reserving power for the next peak period. Charging stations prioritize photovoltaic power supply, with any shortfalls from the grid. The EMS adjusts the energy storage subsystem's charging strategy based on the system's current capacity and the predicted net load demand during the next peak period, ensuring sufficient power is available during the peak period.

[0084] Peak-price discharge mode: When electricity prices are at their peak, the synergy between photovoltaics and energy storage is fully utilized. The energy storage subsystem prepares sufficient electricity in advance based on the net load demand during high-price periods, and minimizes the use of purchased electricity during this period. If photovoltaic power is output, photovoltaic power and energy storage simultaneously supply power to the charging pile. If photovoltaic power is not output, only energy storage discharges to the charging pile, and the energy storage discharge power is determined by the net load. The EMS monitors the charging load and the power of the energy storage subsystem in real time, dynamically adjusting the discharge power of the energy storage subsystem to meet the power demand of the charging pile and ensure that the power of the energy storage subsystem is within a safe range.

[0085] Peak-Off-Peak No-Storage Mode: When the energy storage is at its lowest level and cannot be discharged, there is no photovoltaic power generation, and electricity prices are at peak times, or at normal prices with the next off-peak period approaching, the charging pile is charged with purchased utility power, minimizing the load on the charging pile. The charging pile is powered by the grid, and the energy storage subsystem is inactive, maintaining a minimum charge until the next off-peak period. The EMS rationally controls the output power of the charging pile based on current energy supply and electricity prices, ensuring basic charging needs while minimizing electricity costs.

[0086] The real-time scheduling strategy in Example 1 is applicable to various operating modes of the integrated photovoltaic storage and charging station. Through a reasonable energy control strategy, photovoltaic power generation is prioritized, excess electricity is stored in the energy storage system, and the stored energy is released when photovoltaic power generation is insufficient or when electricity consumption is at peak, thereby reducing dependence on the power grid and improving the comprehensive utilization efficiency of new energy. Combined with the time-of-use electricity price policy, the energy storage system is charged during the off-peak electricity price period, and the energy storage is used to discharge to power the charging pile during the peak electricity price period, reducing the amount of electricity purchased from the power grid during peak periods, thereby reducing the electricity bill of the charging station. The energy control strategy formulated by the optimization algorithm can achieve the goal of minimizing the cost of purchased electricity and effectively reduce operating costs. The energy control strategy can smooth the power load of the charging station and reduce the impact on the power grid. During peak electricity consumption, the energy storage system discharges to power the charging pile, reducing the power supply pressure of the power grid; during low electricity consumption, the energy storage system charges and absorbs excess electricity from the power grid, playing the role of peak shaving and valley filling, which helps to improve the stability and reliability of the power grid.

[0087] Example 4

[0088] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0089] Many components in a device are connected to the I / O interface, including: input units, such as a keyboard and mouse; output units, such as various types of displays and speakers; storage units, such as magnetic disks and optical disks; and communication units, such as network cards, modems, and wireless communication transceivers. The communication unit allows the device to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks.

[0090] The processing unit performs the various methods and processes described above. For example, in some embodiments, the method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the method described above can be performed. Alternatively, in other embodiments, the CPU can be configured to execute the method in any other appropriate manner (e.g., by means of firmware).

[0091] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0092] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0093] In the context of the present invention, machine-readable medium can be a tangible medium that can contain or store a program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0094] 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 such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. An energy control method based on multi-objective collaborative optimization, characterized in that: The method takes maximizing the consumption of photovoltaic power generation and minimizing the cost of purchased electricity as the optimization goal, and includes the following steps: Day-ahead planning strategy: Based on the actual power generation data of the photovoltaic array in the integrated photovoltaic storage and charging power station, the charging load data of the charging piles, and historical weather data, the photovoltaic output and charging load forecasts for different time periods of the next day are made, and a day-ahead plan is formulated; Real-time scheduling strategy: Real-time monitoring of the actual output of the photovoltaic array, the real-time charging load of the charging pile, and the real-time power of the energy storage subsystem, with the goal of ensuring power balance with minimal adjustment cost. Based on the deviation between the actual operating conditions and the day-ahead plan, real-time rolling adjustments are made on the basis of the day-ahead plan.

2. The energy control method based on multi-objective collaborative optimization according to claim 1 is characterized in that: The photovoltaic output forecast and charging load forecast for different periods of the next day are specifically as follows: based on the collected actual power generation data of the photovoltaic array, the charging load data of the charging pile and the historical weather data, the photovoltaic power generation curves under different weather conditions in the historical data and the charging demand change trend of the corresponding date are analyzed, and combined with the weather forecast information of the next day, the photovoltaic output forecast and charging load forecast for different periods of the next day are made.

3. The energy control method based on multi-objective collaborative optimization according to claim 1 is characterized in that: The specific steps of formulating the day-ahead plan are as follows: using the charge and discharge power of the energy storage battery and the power of purchased electricity as decision variables, taking the optimization goal as the objective function, and solving the problem in combination with the constraints to obtain the charge and discharge status of the energy storage subsystem at different time periods the next day and the power arrangement for purchasing electricity from the power grid.

4. The energy control method based on multi-objective collaborative optimization according to claim 3 is characterized in that: The constraints are for the safe and healthy operation of the energy storage subsystem, i.e., they limit the depth of charge and discharge and the charge and discharge rate of the energy storage battery.

5. The energy control method based on multi-objective collaborative optimization according to claim 1 is characterized in that: The real-time scheduling strategy includes: ensuring power balance with minimal adjustment cost as the goal, and performing real-time rolling adjustments based on the day-ahead plan when the actual operation deviates from the day-ahead plan and the deviation is greater than a set threshold; if the deviation is less than or equal to the set threshold, the day-ahead plan is executed.

6. The energy control method based on multi-objective collaborative optimization according to claim 5, characterized in that: The real-time rolling adjustment specifically includes: S2021, determine whether the actual photovoltaic output is higher than the photovoltaic output forecast. If yes, execute S2022; otherwise, execute S2024; S2022, when the actual photovoltaic output is higher than the forecast, if the energy storage subsystem is not fully charged, increase the charging power of the energy storage subsystem to the maximum value; otherwise, execute S2023; S2023: If the energy storage subsystem is fully charged and the charging pile load has not reached saturation, increase the power supplied to the charging pile; if the energy storage subsystem is fully charged and the charging load has reached saturation, reduce the photovoltaic output; S2024: When the actual photovoltaic output is not higher than the photovoltaic output forecast, determine whether the charging load is greater than or equal to the charging load forecast. If so, execute S2025; otherwise, maintain power balance; S2025, determine whether the energy storage SOC has reached the lower limit. If not, the energy storage subsystem has sufficient power, and the discharge power of the energy storage subsystem is increased to discharge to the lower limit. Otherwise, if it means that the energy storage subsystem has insufficient power, decide whether to purchase electricity from the grid and the power of the purchased electricity based on the electricity price.

7. The energy control method based on multi-objective collaborative optimization according to claim 6, characterized in that: Whether to purchase electricity from the grid and the power to be purchased are determined based on the electricity price: if the current electricity price is valley price or normal price, electricity is purchased from the grid on demand; otherwise, the charging power is reduced to meet the minimum charging demand.

8. The energy control method based on multi-objective collaborative optimization according to claim 1, characterized in that: The method also includes dynamically adjusting the power supply priorities of photovoltaic, energy storage, and the grid based on real-time electricity prices, energy storage SOC, photovoltaic output, and user charging needs. Specifically: P charge =α·P PV +β·P ESS +γ·P grid Among them, the weight coefficients α, β, and γ are updated in real time by the reinforcement learning model according to the optimization goal. charge is the charging power, P PV is the photovoltaic power generation power, P ESS is the energy storage charging and discharging power (charging is negative, discharging is positive), P grid is the power connected to the grid.

9. A system using the energy control method based on multi-objective collaborative optimization according to any one of claims 1 to 8, the system comprising a photovoltaic array, an energy storage subsystem and a charging pile, characterized in that: The system includes an energy management system and a coordination controller; The coordination controller is responsible for collecting data of various energy sources; The energy management system monitors and analyzes the energy of the entire system based on the energy data collected by the coordination controller, formulates day-ahead plans and real-time scheduling strategies, and sends them to the coordination controller; The coordination controller executes the day-ahead plan and real-time scheduling strategy issued by the energy management system to adjust the power of each energy source.

10. The energy control method based on multi-objective collaborative optimization according to claim 9, characterized in that: The energy management system predicts the photovoltaic output and charging load at different times of the next day based on the collected energy data and weather data, and formulates a day-ahead plan based on the optimization objectives and constraints.

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