Energy scheduling and peak-valley arbitrage collaborative optimization method and system of photovoltaic storage and charging integrated power station

Abstract

The invention belongs to the technical field of photovoltaic power stations, and particularly relates to an energy scheduling and peak-valley arbitrage collaborative optimization method and system for a photovoltaic power station, and the method employs a two-stage optimization architecture: in a day-ahead planning stage, building a distributed robust optimization model based on prediction data, and generating a reference scheduling plan; in the real-time scheduling stage, different control instructions are generated by calculating the photovoltaic output deviation ratio and the income deviation value and combining the state of the storage battery. And when the system is in a power shortage state, switching to a power supply reliability guaranteeing mode, otherwise, executing a peak-valley arbitrage strategy through the random dynamic programming model. According to the method, the deviation between the expected income and the actual income is compared in real time, the photovoltaic output fluctuation and the battery state are combined to switch the operation mode, and the dynamic balance path breaks through the limitation that a traditional scheduling scheme is too conservative or too dangerous, so that the system can cope with uncertainty and keep the optimal economical efficiency at the same time.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic power station technology, specifically relating to a method and system for coordinated optimization of energy dispatch and peak-valley arbitrage in integrated photovoltaic-storage-charging power stations. Background Technology

[0002] As a key infrastructure for coordinating the consumption of new energy and the charging demand of electric vehicles, the energy dispatch optimization of integrated photovoltaic-storage-charging power stations has become a research focus. Existing technologies have yielded various dispatch schemes aimed at improving the system's economy and stability. For example, Chinese invention patent CN118693817A, entitled "An Energy Dispatch System for Integrated Photovoltaic-Storage-Charging Power Stations Based on Robust Control," proposes a scheme that adopts an AC bus-coupled system architecture and addresses the uncertainties of photovoltaic output and load demand based on robust control methods. This scheme establishes a robust optimization model and formulates a dispatch strategy considering constraints such as transformer capacity limitations and battery SOC thresholds, aiming to achieve coordinated operation of the "source-grid-load-storage" system and ensure the stability of the power station operation.

[0003] However, in the actual operation of integrated photovoltaic-storage-charging power stations, the system's energy state is a dynamic equilibrium process, describing the real-time state of each energy unit under a specific operating strategy. The energy generated by photovoltaics not only directly supplies charging loads but may also be stored in batteries or interact with the grid. Changes in solar intensity, electricity prices, and load demand lead to non-linear coupling characteristics among photovoltaic output, energy storage status, and load demand at different time scales, with these factors mutually constraining each other. Existing dispatch systems face significant technical challenges in dynamically linking photovoltaic output deviations, economic operation deviations, and energy storage status, making it difficult to maximize economic benefits while ensuring power supply reliability. Summary of the Invention

[0004] The purpose of this invention is to provide a collaborative optimization method and system for energy scheduling and peak-valley arbitrage of integrated photovoltaic-storage-charging power stations, in order to solve the problems that existing photovoltaic-storage-charging station scheduling methods are not economically viable due to overly conservative robust optimization, and cannot dynamically adjust optimization targets according to real-time operating status, thus making it difficult to maximize economic benefits while ensuring power supply reliability.

[0005] The present invention achieves the above objectives through the following technical solutions: Firstly, this invention proposes a collaborative optimization method for energy dispatch and peak-valley arbitrage in an integrated photovoltaic-storage-charging power station. This method is applied to an energy dispatch system, which includes a photovoltaic power generation system connected to an AC bus, a battery, a charging pile, and an energy storage converter for energy exchange with the public power grid. The method includes: Determine the forecast values ​​of photovoltaic power output and load demand for a given future time period; With the goal of minimizing overall operating costs, a distributed robust optimization model is established and solved based on the predicted values ​​to generate a baseline scheduling plan that includes the set values ​​of battery charging and discharging power for each future period. Real-time scheduling is performed based on the baseline scheduling plan, and system operation data is collected in real time, including: actual photovoltaic output, battery state of charge, and real-time grid electricity price; Based on the baseline scheduling plan and real-time system operation data, the photovoltaic power output deviation rate and revenue deviation value are determined. Based on the photovoltaic output deviation rate, revenue deviation value, and battery state of charge, a first control instruction is generated to switch the system optimization objective to minimize the charging power deficit, or a second control instruction is generated to switch the system optimization objective to maximize the peak-valley electricity price difference revenue. The energy storage converter responds to the first control command or the second control command and triggers the next round of real-time scheduling.

[0006] Furthermore, establishing and solving the distributed robust optimization model includes: Construct the overall operating cost objective function as follows: ; Where F is the objective function for minimizing cost; λ is an adjustable weighting coefficient determined based on economic efficiency and grid support requirements; C is the total electricity purchase cost calculated based on load demand forecasts and electricity price information; I is the total electricity sales revenue calculated based on photovoltaic output forecasts and electricity price information; k is the penalty coefficient; P1 is the power exchanged with the public grid at time t; and P0 is the average power exchanged during the dispatch cycle. The objective function is solved using power balance constraints, battery SOC safety threshold constraints, transformer capacity constraints, and ancillary service capacity constraints as constraints. The ancillary service capacity constraint includes the following formula: ; Among them, P b (t) represents the planned discharge power of the battery at time t, P f (t) represents the frequency modulation capacity reserved at time t, P r (t) represents the reserve capacity reserved at time t, P bm This is the maximum discharge power of the battery.

[0007] Furthermore, the generation of the baseline scheduling plan, which includes the battery charging and discharging power setting values ​​for each future time period, includes: The optimal solution of the objective function F under the constraints is transformed into a time-series sequence of charge and discharge power setpoints {P}. x (t1), P x(t2), ..., P x (t N )}, where P x (t N ) represents the battery charging and discharging power setting value for time period N; Each element in the sequence is assigned to a scheduling period, and the value of each element is determined based on the optimal solution of the objective function F under the constraints. The values ​​of the total electricity purchase cost C and the total electricity sales revenue I in the objective function F at the optimal solution are used as expected economic indicators (C(t), I(t)). Based on the setpoint sequence of charging and discharging power and the expected economic indicators (C(t), I(t)), a baseline scheduling plan is determined, forming set A: ; Among them, P x C(t) is the setpoint for the battery charging and discharging power in time period t; C(t) is the expected electricity purchase cost in time period t; I(t) is the expected electricity sales revenue in time period t; N is the total number of dispatch periods.

[0008] Furthermore, determining the photovoltaic power output deviation rate and revenue deviation value includes: Real-time collected photovoltaic output data P pa The photovoltaic power output forecast P for the corresponding time period in the baseline scheduling plan pb By comparison, the photovoltaic power output deviation rate η(t) is calculated: ; By comparing and analyzing the real-time collected economic indicators with the expected economic indicators in the baseline scheduling plan, the revenue deviation value δ(t) is obtained, as shown in the following formula: ; Where C a (t) represents the actual electricity purchase cost, I a (t) represents the actual revenue from electricity sales.

[0009] Furthermore, the first control command is generated according to the following steps: When the photovoltaic output deviation rate η(t) exceeds the first threshold, the revenue deviation value δ(t) exceeds the second threshold, and the battery state of charge is lower than the third threshold, a first control command is generated. The first control instruction performs the following operations: Control the energy storage converter to stop drawing power from the battery; Switch to prioritizing power supply to the charging piles from the public power grid; Based on the real-time charging demand of each charging pile and the current total power supply capacity of the system, the power limit command for each charging pile is dynamically calculated and issued.

[0010] Furthermore, the second control command is generated according to the following steps: When the conditions for generating the first control command are not met, the second control command is generated based on the charging and discharging power setpoint sequence in the baseline scheduling plan and combined with the real-time electricity price of the power grid through a stochastic dynamic programming model. The second control command performs the following operations: Control battery charging during off-peak electricity pricing periods; Control battery discharge during peak electricity price periods; The charging and discharging power is dynamically adjusted according to the real-time electricity price to maximize peak-valley arbitrage profits.

[0011] Furthermore, the generation of the second control command through the stochastic dynamic programming model includes: A multi-stage decision-making model for real-time scheduling is established. The model takes the system state vector as input, which includes the battery state of charge, actual photovoltaic output, real-time grid electricity price, and revenue deviation value. Construct an objective function, which includes a peak-valley arbitrage profit term, a battery loss cost term, and an economic deviation penalty term; The optimal control strategy sequence is obtained by solving the multi-stage decision model in reverse recursion. The optimal charging / discharging command for the current time period is extracted from the optimal control strategy sequence and output as the second control command.

[0012] Furthermore, the method also includes: Real-time monitoring of transformer load rate and power grid frequency; When the transformer load rate is detected to exceed the set load threshold, or the power grid frequency exceeds the preset first range, a third control command is generated. The energy storage converter responds to the third control command by forcibly starting the electric vehicle's discharge mode to the grid and / or adjusting the output power of the energy storage converter to support grid stability.

[0013] Secondly, this invention proposes an energy dispatching and peak-valley arbitrage collaborative optimization system for an integrated photovoltaic-storage-charging power station, which is used to realize the energy dispatching and peak-valley arbitrage collaborative optimization method of the integrated photovoltaic-storage-charging power station as described above. The system includes: a photovoltaic power generation system, a battery, a charging pile, and an energy storage converter for interacting with the public power grid. Also includes: The data acquisition unit is used to collect system operation data in real time, including actual photovoltaic output, battery state of charge, charging pile power demand, and real-time grid electricity price. Optimize the decision-making unit for: The predicted values ​​of photovoltaic power output and load demand are determined based on historical data; Establish and solve a distributed robust optimization model to generate a baseline scheduling plan that includes the battery charging and discharging power setpoints for each future time period. Based on the aforementioned baseline scheduling plan and real-time system operation data, the photovoltaic power output deviation rate and revenue deviation value are determined. Based on the photovoltaic power output deviation rate, revenue deviation value and battery state of charge, generate a first control command or a second control command; The control execution unit is used to control the energy storage converter to respond to the first control command or the second control command, complete the real-time scheduling of the current round, and trigger the next round of scheduling.

[0014] The beneficial effects of this invention are as follows: This invention constructs a two-stage optimization architecture of "day-ahead planning - real-time scheduling." In the day-ahead stage, distributed robust optimization is used to generate a baseline scheduling plan that fully considers uncertainties. In the real-time stage, by monitoring photovoltaic output deviations, revenue deviations, and battery status, the operating mode is intelligently switched: prioritizing charging services during periods of power shortage, and maximizing peak-valley arbitrage profits through stochastic dynamic programming during normal operation. This dynamic decision-making overcomes the dilemma of traditional scheduling schemes between conservatism and risk-taking, enabling the system to effectively cope with photovoltaic output fluctuations and electricity price changes while ensuring the reliability of charging services. Simultaneously, the system possesses proactive grid support capabilities, providing rapid power support during grid anomalies, thus enhancing the stability of the regional power grid. Attached Figure Description

[0015] Figure 1 A flowchart illustrating a collaborative optimization method for energy dispatch and peak-valley arbitrage in an integrated photovoltaic-storage-charging power station provided in an embodiment of this application; Figure 2 Another flowchart illustrating the energy dispatching and peak-valley arbitrage collaborative optimization method for the integrated photovoltaic-storage-charging power station provided in this application embodiment; Figure 3 A flowchart for generating the first control instruction in this application; Figure 4 This is a flowchart illustrating the generation of the second control instruction in this application; Figure 5 This is a system block diagram of an energy dispatching and peak-valley arbitrage collaborative optimization system for an integrated photovoltaic, energy storage, and charging power station provided in the embodiments of this application. Detailed Implementation

[0016] The present application will now be described in further detail with reference to the accompanying drawings. It should be noted that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the scope of protection of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application based on the above application content.

[0017] Example 1 The energy scheduling and peak-valley arbitrage collaborative optimization method of the integrated photovoltaic-storage-charging power station according to an embodiment of the present invention is described below with reference to the accompanying drawings.

[0018] Figures 1-2 This is a flowchart of the energy scheduling and peak-valley arbitrage collaborative optimization method for an integrated photovoltaic-storage-charging power station according to an embodiment of the present invention.

[0019] like Figure 1-2 As shown, a collaborative optimization method for energy dispatch and peak-valley arbitrage in an integrated photovoltaic-storage-charging power station is applied to an energy dispatch system. The system includes a photovoltaic power generation system connected to the AC bus, batteries, charging piles, and an energy storage converter for energy exchange with the public power grid. The method includes: S1. Determine the predicted values ​​of photovoltaic power output and load demand for a given future time period.

[0020] In one implementation, during the day-ahead planning phase, the system forecasts photovoltaic output and load demand for a future set period (e.g., 20 hours) based on historical operating data and external environmental information.

[0021] Specifically, the photovoltaic power output forecast adopts a time-series forecasting method that takes weather factors into account: based on historical photovoltaic power output data and weather forecast information (including meteorological parameters such as irradiance, cloud cover, and temperature), a long short-term memory network (LSTM) model is used to generate a sequence of photovoltaic power output forecast values ​​for each future time period (such as 15 minutes or 1 hour as a time period).

[0022] Load demand forecasting targets the charging power demand of charging piles: based on historical charging data, date type (weekdays, weekends, holidays), real-time vehicle queuing information, and user charging behavior characteristics, a series of predicted load demand values ​​for each future period are generated using an autoregressive integral moving average (ARIMA) model or a similar time series forecasting model.

[0023] S2. With the goal of minimizing overall operating costs, a distributed robust optimization model is established and solved based on the predicted values ​​to generate a baseline scheduling plan that includes the set values ​​of battery charging and discharging power for each future period.

[0024] S2.1 Construct the comprehensive operating cost objective function, as follows: ; Where F is the objective function for minimizing cost; λ is an adjustable weighting coefficient, determined based on economic efficiency and grid support requirements, and its specific value can be set through the system configuration interface. For example, when the grid has strong peak-shaving demand, λ can be appropriately increased to improve the stability of grid interaction power; C is the total electricity purchase cost calculated based on load demand forecast and electricity price information; I is the total electricity sales revenue calculated based on photovoltaic output forecast and electricity price information; k is the penalty coefficient; P1 is the power exchanged with the public grid at time t; and P0 is the average power exchanged during the dispatch cycle.

[0025] S2.2 Setting Constraints (1) Power balance constraint: At any time t, the power generation, power consumption and power exchange with the grid of the system must be kept in balance.

[0026] (2) Battery SOC safety threshold constraint: The battery's state of charge (SOC) must always be maintained within the preset safety range.

[0027] (3) Transformer capacity constraint: The exchange power at the connection point between the power station and the power grid shall not exceed the rated capacity of the transformer.

[0028] (4) Ancillary service capacity constraint: Provide a certain ancillary service capacity (such as frequency regulation, reserve) to the power grid. This constraint ensures that the battery discharge capacity can meet the planned demand and reserve some capacity for ancillary services. ; Among them, P b (t) represents the planned discharge power of the battery at time t, P f (t) represents the frequency modulation capacity reserved at time t, P r (t) represents the reserve capacity reserved at time t, P bm This is the maximum discharge power of the battery.

[0029] S2.3, Generate a baseline scheduling plan Solving the above distributed robust optimization model yields the optimal solution for the objective function F under all constraints. This optimal solution is then converted into a sequence of battery charging and discharging power setpoints {P} for future time periods. x (t1), P x (t2), ..., P x (t N )}, where P x (t N ) represents the battery charging and discharging power setting value for time period N. Wherein, P x (t N ) > 0 indicates discharge, P x (t N <0 indicates charging.

[0030] At the same time, the values ​​of total electricity purchase cost C and total electricity sales revenue I in the objective function F are recorded at the optimal solution, and they are decomposed by time period to form expected economic indicators (C(t), I(t)).

[0031] Ultimately, the baseline scheduling plan A is composed of a sequence of charge and discharge power setpoints and expected economic indicators, forming a set A containing detailed operating strategies for each future time period: ; Among them, P x C(t) is the setpoint for the battery charging and discharging power in time period t; C(t) is the expected electricity purchase cost in time period t; I(t) is the expected electricity sales revenue in time period t; N is the total number of dispatch periods.

[0032] Plan A will serve as the benchmark for the subsequent real-time scheduling phase.

[0033] S3. Execute real-time scheduling based on the baseline scheduling plan, and collect system operation data in real time, including: actual photovoltaic output, battery state of charge, and real-time grid electricity price.

[0034] After the baseline scheduling plan is generated, the real-time scheduling phase begins. This phase is executed cyclically at a high frequency (e.g., every 5 minutes) to ensure that the power plant can quickly respond to deviations between actual operating conditions and predictions.

[0035] Specifically, the actual output of photovoltaic (PV) power is obtained through the communication interface of the PV inverter or smart meters, which acquires the real-time AC output power of the PV power generation system. The state of charge (SOC) of the battery is read through the communication protocol of the battery management system (BMS). The real-time electricity price is obtained by receiving real-time price signals from the power trading center or grid dispatching agency, providing current and short-term (within the next hour) electricity price information; this information is a key input for implementing peak-valley arbitrage strategies. The power demand of charging piles is obtained through communication with the controllers of each charging pile, acquiring its current actual operating power and the power demand of the vehicles waiting to be charged.

[0036] S4. Based on the baseline scheduling plan and real-time system operation data, determine the photovoltaic power output deviation rate and revenue deviation value.

[0037] S4.1, The real-time photovoltaic output data P pa The photovoltaic power output forecast P for the corresponding time period in the baseline scheduling plan pb By comparison, the photovoltaic power output deviation rate η(t) is calculated: ; This deviation rate reflects the degree to which the uncertainty of photovoltaic power generation affects the original plan. When the lighting conditions change drastically (such as cloud cover), this value will increase significantly.

[0038] S4.2 Calculate the profit deviation value By comparing and analyzing the real-time collected economic indicators with the expected economic indicators in the baseline scheduling plan, the revenue deviation value δ(t) is obtained, as shown in the following formula: ; Where C a (t) represents the actual electricity purchase cost, I a (t) represents the actual electricity sales revenue; C(t) and I(t) represent the expected electricity purchase cost and expected electricity sales revenue for this period in the baseline dispatch plan.

[0039] This deviation value comprehensively reflects the deviation of the overall economic performance of the system from expectations due to power fluctuations (photovoltaic output and load demand) and electricity price fluctuations.

[0040] S5. Based on the photovoltaic output deviation rate, revenue deviation value, and battery state of charge, generate a first control command to switch the system optimization objective to minimize the charging power deficit, or generate a second control command to switch the system optimization objective to maximize the peak-valley electricity price difference revenue.

[0041] S5.1, Generation and execution of the first control command (power supply reliability assurance mode), can be combined with Figure 3 , Generation conditions: When the following three conditions are met simultaneously, the system determines that it is in a power deficit risk state and generates the first control command: If the photovoltaic power output deviation rate η(t) exceeds the first threshold (e.g., 30%), it indicates that the actual photovoltaic power generation is far lower than expected.

[0042] If the revenue deviation δ(t) exceeds the second threshold (e.g., 25%), it indicates that the system's economics are deviating significantly from expectations, usually due to an unexpected increase in electricity purchase costs.

[0043] If the real-time state of charge (SOC) of the battery is below the third threshold (e.g., 25%), it indicates that the backup support capacity of the energy storage system is insufficient.

[0044] The specific values ​​of the above thresholds can be set and adjusted according to the reliability requirements of the power station, battery characteristics, and operating experience.

[0045] The first control instruction performs the following operations: Control the energy storage converter to stop drawing power from the battery: immediately stop the battery's discharge behavior and preserve its remaining power for unforeseen needs.

[0046] Switching to prioritize power supply to charging stations from the public power grid: Adjusting power flow to ensure that charging load is prioritized to the public power grid, maximizing the uninterrupted charging service for electric vehicles.

[0047] Dynamically calculate and issue power limit instructions for each charging pile: When the total power supply capacity of the system (mainly from the power grid) is limited, the available power is dynamically allocated proportionally or according to priority based on the real-time charging demand priority of each charging pile (which can be set based on contract, fee or queuing order), and the maximum allowed charging power instruction is issued to each charging pile controller to achieve orderly charging.

[0048] S5.2, Generation and execution of the second control command (peak-valley arbitrage optimization mode), can be combined with Figure 4 .

[0049] Generation conditions: If the generation conditions of the first control instruction are not met, the current operating state is considered to be stable and there is room to execute the economic optimization strategy, and then the second control instruction is generated.

[0050] The second control command is generated according to the following steps: Model Input: Establish a multi-stage decision-making model for real-time scheduling. Its input is a system state vector, including: battery state of charge (SOC), actual photovoltaic output, real-time grid electricity price, and revenue deviation value.

[0051] Objective Function: The objective function comprises three core terms: Peak-Valley Arbitrage Profit Term: Maximizing the profit from the electricity price difference between charging during low-price periods and discharging during high-price periods. Battery Loss Cost Term: Incorporating the cost of battery cycle aging into the optimization process to avoid overcharging and discharging. Economic Deviation Penalty Term: Penalizing significant deviations between actual and baseline planned profits to ensure that real-time optimization does not deviate severely from the long-term economic objective.

[0052] Solution and Output: The multi-stage decision model is solved using dynamic programming algorithms such as inverse recursion to obtain a sequence of optimal control strategies for several future time periods. The optimal charging / discharging power command for the current time period is extracted from this sequence and used as the core output of the second control command.

[0053] Execute peak-valley arbitrage operations based on instructions below the second control command: During off-peak or normal electricity periods, the battery is controlled to charge and store low-priced electrical energy.

[0054] During peak electricity prices, the system controls the battery to discharge to charging stations or sell electricity to the grid, releasing stored electrical energy and generating revenue from the price difference.

[0055] Based on real-time electricity price fluctuations, the charging and discharging power is dynamically adjusted to maximize arbitrage profits while meeting system constraints.

[0056] S6. The energy storage converter responds to the first control command or the second control command and triggers the next round of real-time scheduling.

[0057] Understandably, the energy storage converter, as the core execution unit, receives and parses the first or second control command. Based on the command requirements, the converter controls the power conversion between its AC and DC sides, achieving rapid and precise adjustment of the battery charging and discharging power, as well as the power exchanged with the grid.

[0058] After the current round of instructions is executed, the system will not stop, but will automatically trigger the next round of real-time scheduling cycle. It will collect the latest real-time operating data again and repeat steps S3 to S6 to form a continuously optimized control process, ensuring that the power plant always maintains the optimal or suboptimal operating state in a dynamically changing environment.

[0059] In one optional implementation, to further enhance grid-friendliness, the system may add active grid support functionality, and the method further includes: The system monitors the transformer load rate and grid frequency in real time. When the transformer load rate exceeds the set load threshold or the grid frequency exceeds the preset first range (e.g., 50±0.2 Hz), the system prioritizes economic objectives and generates a third control command. The energy storage converter responds to the third control command by forcibly starting the electric vehicle to discharge to the grid and / or adjusting the output power of the energy storage converter to support grid stability.

[0060] Understandably, under the action of the third control command, if the charging pile and electric vehicle support V2G function, the command is sent to the eligible vehicle to control its power battery to discharge to the grid; and / or to control the local battery to quickly increase or decrease the power exchange with the grid through the energy storage converter to provide active power support, such as increasing the discharge power or decreasing the charging power when the grid frequency drops.

[0061] Based on the above embodiments, this invention provides a collaborative optimization method for energy dispatch and peak-valley arbitrage in integrated photovoltaic-storage-charging power plants. Its core lies in constructing a dynamic implementation process in two stages: "day-ahead planning" and "real-time dispatch." In the day-ahead planning stage, based on photovoltaic output and load demand forecast data, a distributed robust optimization model is established with the goal of minimizing overall operating costs. This generates a benchmark dispatch plan that considers multiple constraints such as power balance, battery safety, transformer capacity, and ancillary service capacity, providing a benchmark strategy that combines economic efficiency and robustness for power plant operation.

[0062] During the real-time scheduling phase, operational data such as actual photovoltaic output, battery state of charge, and real-time grid electricity price are collected at high frequencies to dynamically calculate the photovoltaic output deviation rate and revenue deviation value, quantifying the degree of deviation between actual operation and the day-ahead plan. Based on a comprehensive judgment of these deviation indicators and battery status, the operating mode is intelligently switched: when the deviation exceeds the threshold and energy storage is insufficient, the mode is switched to one that prioritizes power supply reliability, with priority given to grid power supply and orderly charging; otherwise, an optimization strategy aimed at maximizing peak-valley arbitrage revenue is executed through a stochastic dynamic programming model to dynamically adjust the battery charging and discharging behavior.

[0063] This method, through real-time feedback and dynamic decision-making, overcomes the limitations of traditional scheduling schemes that are either too conservative or too risky when dealing with uncertainties, and achieves the synergistic optimization goal of effectively improving the operational economy of power plants while ensuring the reliability of power supply.

[0064] Example 2 Combination Figure 5 A specific embodiment of the present invention proposes an energy scheduling and peak-valley arbitrage collaborative optimization system for an integrated photovoltaic-storage-charging power station, which is used to implement the steps of the energy scheduling and peak-valley arbitrage collaborative optimization method for an integrated photovoltaic-storage-charging power station as described in Embodiment 1.

[0065] The system adopts an AC bus coupling architecture, and its core components include: a photovoltaic power generation system, a battery, a charging pile cluster, and an energy storage converter for power exchange with the public power grid.

[0066] Above the basic electrical architecture, the system also includes the following functional units, which together achieve energy management and optimized scheduling: The data acquisition unit is used to collect system operation data in real time, including actual photovoltaic output, battery state of charge, charging pile power demand, and real-time grid electricity price. Optimize the decision-making unit for: The predicted values ​​of photovoltaic power output and load demand are determined based on historical data; Establish and solve a distributed robust optimization model to generate a baseline scheduling plan that includes the battery charging and discharging power setpoints for each future time period. Based on the aforementioned baseline scheduling plan and real-time system operation data, the photovoltaic power output deviation rate and revenue deviation value are determined. Based on the photovoltaic power output deviation rate, revenue deviation value and battery state of charge, generate a first control command or a second control command; The control execution unit is used to control the energy storage converter to respond to the first control command or the second control command, complete the real-time scheduling of the current round, and trigger the next round of scheduling.

[0067] In practical implementation, the system proposed in this invention can be applied to various integrated energy sites equipped with photovoltaic, energy storage, and charging facilities. Based on a two-stage optimization architecture, the system generates a baseline scheduling plan that balances economy and robustness through distributed robust optimization in the day-ahead phase. In the real-time operation phase, it dynamically switches operating modes by continuously monitoring photovoltaic output deviation, revenue deviation, and battery status: when the system faces power supply pressure, it prioritizes ensuring the reliability of charging services; under normal operating conditions, it maximizes peak-valley arbitrage revenue through stochastic dynamic programming. This dynamic balancing mechanism enables the system to effectively cope with the uncertainty of photovoltaic output and electricity price fluctuations, improving operational economy while ensuring power supply reliability. The system also has grid support capabilities; when transformer overload or grid frequency exceeding limits is detected, it can automatically adjust its operating strategy to participate in grid regulation, enhancing regional grid stability.

[0068] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated.

[0069] The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0070] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0071] In addition, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0072] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A collaborative optimization method for energy dispatch and peak-valley arbitrage in an integrated photovoltaic-storage-charging power station, applied to an energy dispatch system. The system includes a photovoltaic power generation system connected to an AC bus, batteries, charging piles, and an energy storage converter for energy exchange with the public power grid; characterized in that... The method includes: Determine the forecast values ​​of photovoltaic power output and load demand for a given future time period; With the goal of minimizing overall operating costs, a distributed robust optimization model is established and solved based on the predicted values ​​to generate a baseline scheduling plan that includes the set values ​​of battery charging and discharging power for each future period. Real-time scheduling is performed based on the baseline scheduling plan, and system operation data is collected in real time, including: actual photovoltaic output, battery state of charge, and real-time grid electricity price; Based on the baseline scheduling plan and real-time system operation data, the photovoltaic power output deviation rate and revenue deviation value are determined. Based on the photovoltaic output deviation rate, revenue deviation value, and battery state of charge, a first control instruction is generated to switch the system optimization objective to minimize the charging power deficit, or a second control instruction is generated to switch the system optimization objective to maximize the peak-valley electricity price difference revenue. The energy storage converter responds to the first control command or the second control command and triggers the next round of real-time scheduling.

2. The energy dispatching and peak-valley arbitrage collaborative optimization method for integrated photovoltaic-storage-charging power stations according to claim 1, characterized in that, The establishment and solution of the distributed robust optimization model includes: Construct the overall operating cost objective function as follows: ; Where F is the objective function for minimizing cost; λ is an adjustable weighting coefficient determined based on economic efficiency and grid support requirements; C is the total electricity purchase cost calculated based on load demand forecasts and electricity price information; I is the total electricity sales revenue calculated based on photovoltaic output forecasts and electricity price information; k is the penalty coefficient; P1 is the power exchanged with the public grid at time t; and P0 is the average power exchanged during the dispatch cycle. The objective function is solved using power balance constraints, battery SOC safety threshold constraints, transformer capacity constraints, and ancillary service capacity constraints as constraints. The ancillary service capacity constraint includes the following formula: ; Among them, P b (t) represents the planned discharge power of the battery at time t, P f (t) represents the frequency modulation capacity reserved at time t, P r (t) represents the reserve capacity reserved at time t, P bm This is the maximum discharge power of the battery.

3. The energy dispatching and peak-valley arbitrage collaborative optimization method for integrated photovoltaic-storage-charging power stations according to claim 2, characterized in that, The generation of the baseline scheduling plan, which includes the set values ​​of battery charging and discharging power for future time periods, includes: The optimal solution of the objective function F under the constraints is transformed into a time-series sequence of charge and discharge power setpoints {P}. x (t1), P x (t2), ..., P x (t N )}, where P x (t N ) represents the battery charging and discharging power setting value for time period N; Each element in the sequence is assigned to a scheduling period, and the value of each element is determined based on the optimal solution of the objective function F under the constraints. The values ​​of the total electricity purchase cost C and the total electricity sales revenue I in the objective function F at the optimal solution are used as expected economic indicators (C(t), I(t)). Based on the setpoint sequence of charging and discharging power and the expected economic indicators (C(t), I(t)), a baseline scheduling plan is determined, forming set A: ; Among them, P x C(t) is the setpoint for the battery charging and discharging power in time period t; C(t) is the expected electricity purchase cost in time period t; I(t) is the expected electricity sales revenue in time period t; N is the total number of dispatch periods.

4. The energy dispatching and peak-valley arbitrage collaborative optimization method for integrated photovoltaic-storage-charging power stations according to claim 3, characterized in that, The determination of the photovoltaic power output deviation rate and revenue deviation value includes: Real-time collected photovoltaic output data P pa The photovoltaic power output forecast P for the corresponding time period in the baseline scheduling plan pb By comparison, the photovoltaic power output deviation rate η(t) is calculated: ; By comparing and analyzing the real-time collected economic indicators with the expected economic indicators in the baseline scheduling plan, the revenue deviation value δ(t) is obtained, as shown in the following formula: ; Where C a (t) represents the actual electricity purchase cost, I a (t) represents the actual revenue from electricity sales.

5. The energy dispatching and peak-valley arbitrage collaborative optimization method for integrated photovoltaic-storage-charging power stations according to claim 4, characterized in that, The first control command is generated according to the following steps: When the photovoltaic output deviation rate η(t) exceeds the first threshold, the revenue deviation value δ(t) exceeds the second threshold, and the battery state of charge is lower than the third threshold, a first control command is generated. The first control instruction performs the following operations: Control the energy storage converter to stop drawing power from the battery; Switch to prioritizing power supply to the charging piles from the public power grid; Based on the real-time charging demand of each charging pile and the current total power supply capacity of the system, the power limit command for each charging pile is dynamically calculated and issued.

6. The energy dispatching and peak-valley arbitrage collaborative optimization method for integrated photovoltaic-storage-charging power stations according to claim 4, characterized in that, The second control command is generated according to the following steps: When the conditions for generating the first control command are not met, the second control command is generated based on the charging and discharging power setpoint sequence in the baseline scheduling plan and combined with the real-time electricity price of the power grid through a stochastic dynamic programming model. The second control command performs the following operations: Control battery charging during off-peak electricity pricing periods; Control battery discharge during peak electricity price periods; The charging and discharging power is dynamically adjusted according to the real-time electricity price to maximize peak-valley arbitrage profits.

7. The energy dispatching and peak-valley arbitrage collaborative optimization method for integrated photovoltaic-storage-charging power stations according to claim 6, characterized in that, The generation of the second control command through a stochastic dynamic programming model includes: A multi-stage decision-making model for real-time scheduling is established. The model takes the system state vector as input, which includes the battery state of charge, actual photovoltaic output, real-time grid electricity price, and revenue deviation value. Construct an objective function, which includes a peak-valley arbitrage profit term, a battery loss cost term, and an economic deviation penalty term; The optimal control strategy sequence is obtained by solving the multi-stage decision model in reverse recursion. The optimal charging / discharging command for the current time period is extracted from the optimal control strategy sequence and output as the second control command.

8. The energy dispatching and peak-valley arbitrage collaborative optimization method for integrated photovoltaic-storage-charging power stations according to claim 1, characterized in that, The method further includes: Real-time monitoring of transformer load rate and power grid frequency; When the transformer load rate is detected to exceed the set load threshold, or the power grid frequency exceeds the preset first range, a third control command is generated. The energy storage converter responds to the third control command by forcibly starting the electric vehicle's discharge mode to the grid and / or adjusting the output power of the energy storage converter to support grid stability.

9. A collaborative optimization system for energy dispatch and peak-valley arbitrage in an integrated photovoltaic-storage-charging power station, used to implement the collaborative optimization method for energy dispatch and peak-valley arbitrage in an integrated photovoltaic-storage-charging power station as described in any one of claims 1-8, characterized in that, The system includes: a photovoltaic power generation system, a battery, a charging pile, and an energy storage converter for exchanging electrical energy with the public power grid; Also includes: The data acquisition unit is used to collect system operation data in real time, including actual photovoltaic output, battery state of charge, charging pile power demand, and real-time grid electricity price. Optimize the decision-making unit for: The predicted values ​​of photovoltaic power output and load demand are determined based on historical data; Establish and solve a distributed robust optimization model to generate a baseline scheduling plan that includes the battery charging and discharging power setpoints for each future time period. Based on the aforementioned baseline scheduling plan and real-time system operation data, the photovoltaic power output deviation rate and revenue deviation value are determined. Based on the photovoltaic power output deviation rate, revenue deviation value and battery state of charge, generate a first control command or a second control command; The control execution unit is used to control the energy storage converter to respond to the first control command or the second control command, complete the real-time scheduling of the current round, and trigger the next round of scheduling.

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