Optical storage and charging station optimization control method and device

Abstract

The invention relates to the technical field of smart power grids and renewable energy power generation, and particularly provides an optical storage and charging station optimization control method and device, and the method comprises the steps: constructing a target function which takes the maximization of the total revenue when an optical storage and charging station participates in auxiliary service as an optimization target, the objective function comprises earnings and weights of all subsystems of the optical storage and charging station; performing preliminary calculation on the target function to obtain a preliminary total income; carrying out multi-objective optimization calculation on the objective function on the basis of the initial total income, and obtaining a weight meeting an optimization objective; and determining the priority of each subsystem of the optical storage and charging station according to the weight meeting the optimization target, and controlling each subsystem of the optical storage and charging station based on the priority. Therefore, the weight meeting the optimization target is obtained through multi-target optimization calculation, then the priority among the subsystems is determined according to the weight, optimization control is carried out on the optical storage and charging station according to the priority, and the optimization target of maximizing the total revenue can be achieved.

Description

Technical Field

[0001] The present invention relates to the field of smart grid and renewable energy power generation control technology, and in particular to a photovoltaic storage and charging station optimization control method and a photovoltaic storage and charging station optimization control device. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy technology, photovoltaic power generation, as a clean and low-carbon energy form, is increasingly used in power systems. At the same time, the popularization of energy storage equipment also provides new possibilities for the stable operation of power systems. However, the intermittent and unstable nature of photovoltaic power generation has brought many challenges to the dispatching and management of power systems. In order to meet these challenges, the introduction of power markets and ancillary service markets has become an inevitable trend. This enables photovoltaic power generation, energy storage equipment, charging piles and other facilities to participate more flexibly in demand response and grid ancillary services, thereby improving the operating efficiency and reliability of the entire power system.

[0003] As a new type of comprehensive energy application model, the photovoltaic storage and charging integrated station (hereinafter referred to as "photovoltaic storage and charging station") can not only maximize economic benefits but also provide flexible dispatching resources for the power grid by using the energy storage system to carry out peak-valley arbitrage. This station organically combines multiple equipment such as photovoltaic power generation, energy storage and charging piles, and realizes efficient utilization and optimal configuration of energy through intelligent control systems.

[0004] At present, photovoltaic storage and charging stations face the technical challenge of maximizing revenue and being difficult. The revenue of photovoltaic storage and charging stations comes from many aspects, including the direct supply revenue of various subsystems such as photovoltaic power generation, the peak-valley arbitrage revenue of energy storage, and the response revenue of demand response. However, due to the difficulty in coordinating the priorities of various subsystems such as photovoltaic power generation and energy storage, it is difficult for the system to effectively balance the revenue of various goals, resulting in the inability to fully realize the revenue potential. Summary of the invention

[0005] In order to solve one of the above technical problems, the present invention proposes the following technical solution.

[0006] The first aspect of the present invention proposes an optimization control method for a photovoltaic storage and charging station, comprising the following steps: constructing an objective function with the maximization of the total benefit when the photovoltaic storage and charging station participates in auxiliary services as the optimization goal, wherein the objective function includes the benefits and weights of each subsystem of the photovoltaic storage and charging station; performing a preliminary calculation of the objective function to obtain a preliminary total benefit; performing a multi-objective optimization calculation on the objective function based on the preliminary total benefit, and obtaining weights that meet the optimization goals; determining the priorities of each subsystem of the photovoltaic storage and charging station according to the weights that meet the optimization goals, and controlling each subsystem of the photovoltaic storage and charging station based on the priorities.

[0007] In addition, the photovoltaic storage and charging station optimization control method according to the above embodiment of the present invention may also have the following additional technical features.

[0008] According to one embodiment of the present invention, the objective function is:

[0009] R_total=w1*R_pv+w2*R_storage+w3*R_dr

[0010] Among them, R_total represents the total revenue of the photovoltaic storage and charging station when participating in ancillary services, R_pv, R_storage and R_dr represent the revenues of photovoltaic power generation, energy storage system and demand response respectively, w1, w2 and w3 represent the weights of photovoltaic power generation, energy storage system and demand response respectively, and the sum of w1, w2 and w3 is 1.

[0011] According to one embodiment of the present invention, the revenue R_pv of photovoltaic power generation is calculated by the following formula:

[0012] R_pv=P_pv*(λ_sell*u_sell+λ_self*u_self)

[0013] Among them, P_pv represents the photovoltaic power generation; λ_sell represents the electricity price when photovoltaic power is connected to the grid; u_sell represents the utilization coefficient of photovoltaic power connected to the grid, which takes a value of 1 or 0; λ_self represents the profit when photovoltaic power is used for self-use or demand response; u_self represents the utilization coefficient of photovoltaic power for self-use, which takes a value of 1 or 0.

[0014] According to one embodiment of the present invention, the revenue R_storage of the energy storage system is calculated by the following formula:

[0015] R_storage=(P_discharge*λ_peak*u_discharge-P_charge*λ_valley*

[0016] u_charge)

[0017] Among them, P_discharge represents the discharge power of the energy storage system; λ_peak represents the discharge electricity price; u_discharge represents the utilization factor of the energy storage system discharge, which takes a value of 1 or 0; P_charge represents the charging power of the energy storage system; λ_valley represents the charging electricity price; u_charge represents the utilization factor of the energy storage system charging, which takes a value of 1 or 0.

[0018] According to one embodiment of the present invention, the revenue R_dr of demand response is calculated by the following formula:

[0019] R_dr=P_dr*λ_dr*u_dr

[0020] Among them, P_dr represents the power participating in demand response; λ_dr represents the demand response subsidy; u_dr represents the utilization coefficient of demand response, which takes a value of 1 or 0.

[0021] According to one embodiment of the present invention, a multi-objective optimization calculation is performed on the objective function based on the preliminary total benefit, including: obtaining photovoltaic power generation, electricity price and grid demand signal, and dynamically adjusting the corresponding weights in the objective function according to the photovoltaic power generation, the electricity price and the grid demand signal; and performing a multi-objective optimization calculation on the adjusted objective function based on the preliminary total benefit until the optimization goal is met.

[0022] According to one embodiment of the present invention, the corresponding weights in the objective function are dynamically adjusted according to the photovoltaic power generation, the electricity price and the grid demand signal, including: when the photovoltaic power generation is greater than a preset power generation threshold, increasing the weight of the photovoltaic power generation; when the peak-valley electricity price difference is greater than a preset difference threshold, increasing the weight of the energy storage system; when the frequency of the grid demand response signal is greater than a preset frequency threshold, increasing the weight of the demand response.

[0023] According to one embodiment of the present invention, the weight of photovoltaic power generation is increased according to the electricity price when photovoltaic power is connected to the grid and the profit when photovoltaic power is used for self-use or in response to demand, the weight of the energy storage system is increased according to the discharge electricity price and the charging electricity price, or the weight of demand response is increased according to the demand response subsidy and the demand response frequency.

[0024] The second aspect of the present invention proposes an optimization control device for a photovoltaic storage and charging station, including: a construction module, used to construct an objective function with the maximization of the total benefit when the photovoltaic storage and charging station participates in auxiliary services as the optimization goal, wherein the objective function includes the benefits and weights of each subsystem of the photovoltaic storage and charging station; a preliminary calculation module, used to perform preliminary calculations on the objective function to obtain a preliminary total benefit; an optimization calculation module, used to perform multi-objective optimization calculations on the objective function based on the preliminary total benefit, and obtain weights that meet the optimization goals; a control module, used to determine the priorities of each subsystem of the photovoltaic storage and charging station according to the weights that meet the optimization goals, and control each subsystem of the photovoltaic storage and charging station based on the priorities.

[0025] The technical solution of the embodiment of the present invention obtains the weights that meet the optimization objectives through multi-objective optimization calculation, and then determines the priorities among the subsystems according to the weights, and optimizes and controls the photovoltaic storage and charging stations according to the priorities, which is conducive to achieving the optimization goal of maximizing total revenue. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1This is a schematic diagram of multi-objective optimization control of a photovoltaic storage and charging station participating in auxiliary services according to an embodiment of the present invention.

[0027] Figure 2 Flow chart of the photovoltaic storage and charging station optimization control method according to an embodiment of the present invention.

[0028] Figure 3 The flowchart of a multi-objective optimization algorithm of a specific example of the present invention.

[0029] Figure 4 Schematic diagram of a block diagram of a photovoltaic storage and charging station optimization control device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] At present, solar-storage-charging stations face the following technical challenges:

[0032] Load fluctuations are difficult to balance: The instability of photovoltaic power generation and the limitations of energy storage equipment make the supply and demand of electricity fluctuate greatly, especially during load peaks and troughs, when the working status of equipment needs to be frequently adjusted, but traditional control strategies often cannot achieve real-time dynamic balance.

[0033] Difficulty in maximizing revenue: The revenue of photovoltaic storage and charging stations comes from many aspects, including direct supply revenue of photovoltaic power generation, peak-valley arbitrage revenue of energy storage, and response revenue of demand response. However, due to the difficulty in coordinating the priorities of photovoltaic power generation and energy storage, it is difficult for the system to effectively balance the revenue of various goals, resulting in the inability to fully realize the revenue potential.

[0034] Insufficient demand response capability: The existing system has limited dynamic control capabilities for demand response and is unable to adjust the output of photovoltaic power generation and energy storage according to fluctuations in real-time electricity demand, affecting the response speed of ancillary services and the overall benefits of the system.

[0035] To this end, the embodiment of the present invention proposes a photovoltaic storage and charging station optimization control method, which aims to use a multi-objective optimization algorithm to achieve optimization control, achieve a dynamic balance between photovoltaic power generation, energy storage scheduling and demand response, and optimize the overall revenue and auxiliary service efficiency of the photovoltaic storage and charging station. Through the multi-objective optimization algorithm, the present invention realizes the priority coordination of various power demands, so that the photovoltaic storage and charging station can respond flexibly in a high-load fluctuation environment, maximizing cost-effectiveness and system stability.

[0036] Figure 1This is a schematic diagram of multi-objective optimization control of a photovoltaic storage and charging station participating in auxiliary services according to an embodiment of the present invention.

[0037] like Figure 1 As shown, the multi-objective optimization control algorithm platform is used to execute the optimization control method of the photovoltaic storage and charging station of the embodiment of the present invention, which includes a multi-objective optimization algorithm module for optimizing the calculation of each subsystem of the photovoltaic storage and charging station. The subsystems of the photovoltaic storage and charging station include photovoltaic, charging piles, energy storage system and EMS (Energy Management System) and other systems. A two-way connection is established between the power grid organization and the multi-objective optimization control algorithm platform to realize the issuance of demand response plans and data reporting. The edge switch and the multi-objective optimization control algorithm platform are connected via TCP (Transmission Control Protocol) to realize data collection and control issuance.

[0038] Figure 2 Flow chart of the photovoltaic storage and charging station optimization control method according to an embodiment of the present invention.

[0039] like Figure 2 As shown, the photovoltaic storage and charging station optimization control method includes the following steps S1 to S4.

[0040] S1, construct an objective function with the optimization goal of maximizing the total revenue of the PV-storage-charging station when it participates in ancillary services, where the objective function includes the revenue and weight of each subsystem of the PV-storage-charging station.

[0041] Among them, the objective function is the sum of the products of the benefits of each subsystem and the weight when the photovoltaic storage and charging station participates in auxiliary services, that is, the calculation function of the total benefit.

[0042] In one example, the objective function is:

[0043] R_total=w1*R_pv+w2*R_storage+w3*R_dr

[0044] Among them, R_total represents the total revenue of the photovoltaic storage and charging station when it participates in ancillary services, R_pv, R_storage and R_dr represent the revenues of photovoltaic power generation, energy storage system and demand response respectively, w1, w2 and w3 are the weights of photovoltaic power generation, energy storage system and demand response respectively, and the sum of w1, w2 and w3 is 1.

[0045] Among them, the revenue function of photovoltaic power generation takes into account the revenue of different uses such as self-use of electricity and sale to the grid. The revenue R_pv of photovoltaic power generation is calculated by the following formula:

[0046] R_pv=P_pv*(λ_sell*u_sell+λ_self*u_self)

[0047] Among them, P_pv represents the photovoltaic power generation; λ_sell represents the electricity price when photovoltaic power is connected to the grid; u_sell represents the utilization coefficient of photovoltaic power connected to the grid, which takes a value of 1 or 0; λ_self represents the profit when photovoltaic power is used for self-use or demand response; u_self represents the utilization coefficient of photovoltaic power for self-use, which takes a value of 1 or 0.

[0048] The revenue of the energy storage system is achieved through peak-valley arbitrage, that is, charging at low valleys and discharging at high peaks. The revenue R_storage of the energy storage system is calculated by the following formula:

[0049] R_storage=(P_discharge*λ_peak*u_discharge-P_charge*λ_valley*

[0050] u_charge)

[0051] Among them, P_discharge represents the discharge power of the energy storage system; λ_peak represents the discharge electricity price (peak electricity price); u_discharge represents the utilization factor of the energy storage system discharge, which takes a value of 1 or 0; P_charge represents the charging power of the energy storage system; λ_valley represents the charging electricity price (valley electricity price); u_charge represents the utilization factor of the energy storage system charging, which takes a value of 1 or 0.

[0052] The revenue function of demand response depends on the grid demand response signal and the subsidy amount. The revenue R_dr of demand response is calculated by the following formula:

[0053] R_dr=P_dr*λ_dr*u_dr

[0054] Among them, P_dr represents the power participating in demand response; λ_dr represents the demand response subsidy; u_dr represents the utilization coefficient of demand response, which takes a value of 1 or 0.

[0055] Specifically, after constructing the objective function, when the charging station is started, the weights are initialized and the parameters are set first to facilitate the subsequent optimization algorithm execution:

[0056] Weight initialization: Set the initial weight values ​​w1, w2 and w3, which correspond to the priorities of photovoltaic power generation, energy storage system and demand response respectively. The initial weight values ​​can be set based on expected benefits, equipment status or historical data.

[0057] Optimization target definition: Define various optimization targets, namely, the self-use and grid-connected revenue of photovoltaic power generation (R_pv), the peak-valley arbitrage revenue of the energy storage system (R_storage), and the subsidy revenue of demand response (R_dr).

[0058] System status initialization: Record the current status of each device, including photovoltaic power generation, energy storage capacity, charging pile load and grid demand signal to ensure that the algorithm has the latest device data.

[0059] S2, perform preliminary calculation of the objective function to obtain the preliminary total benefit.

[0060] Specifically, the data acquisition module is used to monitor the status of each system in the photovoltaic storage and charging station in real time to ensure that the multi-objective optimization algorithm has the latest input data:

[0061] Photovoltaic power generation: monitor the current power generation P_pv of the photovoltaic system.

[0062] Energy storage status: records the charging power P_charge, discharging power P_discharge, remaining power and current electricity price of the energy storage system.

[0063] Charging pile load: Collect the real-time load demand of the charging pile and determine the need for load regulation.

[0064] Grid demand response signal: Receive grid demand response instructions and subsidy information for real-time control of demand response.

[0065] Market electricity price: record the current market electricity price as the basis for peak-valley arbitrage strategy.

[0066] Then, the data acquisition module transmits the above data to the multi-objective optimization algorithm module for use in subsequent steps.

[0067] Specifically, the multi-objective optimization algorithm module first calculates the preliminary benefits of each objective. That is, based on real-time data, the benefits of photovoltaic power generation, energy storage and demand response are calculated through the above formula. Then, each benefit and the initial value of the weight are substituted into the objective function to calculate the preliminary total benefit.

[0068] S3, based on the preliminary total benefit, multi-objective optimization calculation is performed on the objective function, and the weights that meet the optimization objectives are obtained.

[0069] Among them, the multi-objective optimization calculation includes the dynamic adjustment of the weights in the objective function. Specifically, a multi-objective optimization algorithm is used to achieve comprehensive optimization of each objective by calculating the benefits of each objective and dynamically adjusting the weights. During the multi-objective optimization calculation process, the weights are dynamically adjusted according to the real-time situation, the benefits of each subsystem are comprehensively optimized, and the optimal allocation of resources is achieved until the total benefit is maximized. The calculation is terminated and the weight at this time is obtained, which is the weight that meets the optimization objective.

[0070] S4, determining the priority of each subsystem of the photovoltaic storage and charging station according to the weight that meets the optimization target, and controlling each subsystem of the photovoltaic storage and charging station based on the priority.

[0071] Specifically, the priority of each subsystem is determined according to the weight of each subsystem of the photovoltaic storage and charging station. The larger the weight, the higher the priority. The scheduling instructions are generated according to the priority, and each subsystem is controlled according to the scheduling instructions.

[0072] Specifically, when the weight of photovoltaic power generation is the largest, photovoltaic power generation is prioritized and used for internal site needs, for self-sufficiency or demand response services, and the remaining electricity is sold online; when the weight of the energy storage system is the largest, energy storage charging and discharging is prioritized, with charging at low valleys and discharging at peak times to achieve peak-valley arbitrage; when the weight of demand response is the largest, demand response control is prioritized, and participation in demand response during grid peak hours is used to obtain subsidy benefits.

[0073] At the same time, real-time feedback and adjustments are carried out. The equipment execution feedback is sent back to the multi-objective optimization algorithm, and the scheduling strategy is further adjusted according to the latest equipment status and market information to form a closed-loop control to ensure that the system is always in the optimal state of profit.

[0074] Compared with the related art, the embodiments of the present invention have the following advantages:

[0075] Maximize revenue; be able to flexibly balance the priorities between photovoltaic power generation, energy storage scheduling and demand response, so that the site can maximize revenue under different market conditions;

[0076] Grid stability support: Through multi-objective optimization control, stations can efficiently participate in demand response during peak load periods, reduce grid load, and enhance the stability of the power system.

[0077] Therefore, the photovoltaic storage and charging station optimization control method of the embodiment of the present invention obtains the weights that meet the optimization goals through multi-objective optimization calculation, and then determines the priorities between the subsystems according to the weights, and optimizes and controls the photovoltaic storage and charging station according to the priorities, which is conducive to achieving the optimization goal of maximizing total revenue.

[0078] In one embodiment of the present invention, the multi-objective optimization calculation of the objective function based on the preliminary total benefit in step S3 may include: obtaining photovoltaic power generation, electricity price and grid demand signal, and dynamically adjusting the corresponding weights in the objective function according to the photovoltaic power generation, electricity price and grid demand signal; performing multi-objective optimization calculation on the adjusted objective function based on the preliminary total benefit until the optimization goal is met.

[0079] Furthermore, the corresponding weights in the objective function are dynamically adjusted according to the photovoltaic power generation, electricity price and grid demand signal, including: when the photovoltaic power generation is greater than the preset power generation threshold, the weight of photovoltaic power generation is increased; when the peak-valley electricity price difference is greater than the preset difference threshold, the weight of the energy storage system is increased; when the frequency of the grid demand response signal is greater than the preset frequency threshold, the weight of the demand response is increased.

[0080] Among them, the preset power generation threshold, the preset difference threshold and the preset frequency threshold are set in advance according to specific actual needs.

[0081] Specifically, when performing multi-objective optimization calculations, the charging station data is collected and the objective function is calculated, and the weights w1, w2 and / or w3 are dynamically adjusted, w1+w2+w3=1. Among them, when photovoltaic power generation is high, photovoltaic power generation is given priority, and the weight w1 is increased. Specifically, the weight w1 of photovoltaic power generation is increased according to the electricity price λ_sell when photovoltaic power is connected to the grid and the income λ_self when photovoltaic power is used for self-use or demand response, that is, w1=f(λ_sell,λ_self); when the electricity price fluctuates greatly (the gap between peak and valley electricity prices), the energy storage system is given priority, and the energy storage weight w2 is increased. Specifically, the weight w2 of the energy storage system is increased according to the discharge electricity price λ_peak and the charging electricity price λ_valley, that is, w2=f(λ_peak,λ_valley); when the demand response signal is frequent, the demand response is given priority, and the weight w3 is increased. The weight of the demand response is increased according to the demand response subsidy λ_dr and the demand response frequency f_dr, w3=f(λ_dr,f_dr). This dynamic adjustment mechanism ensures that the priority allocation of photovoltaic power generation, energy storage and demand response is optimal under different market conditions.

[0082] It should be noted that w1+w2+w3=1, so when a certain weight is increased, the other two weights or one weight can be reduced at the same time depending on the actual situation to ensure that the sum of the three weights is 1.

[0083] After the optimization calculation, that is, after calculating the maximum total benefit, the weights that meet the optimization goal are obtained, and the priorities of photovoltaic power generation, energy storage and demand response are determined according to the weights that meet the optimization goal. The algorithm generates a scheduling plan based on the priority, controls photovoltaic power generation for self-sufficiency or demand response, instructs the energy storage system to charge and discharge, and adjusts the charging pile load. Real-time feedback and adjustment are carried out during the control process to ensure the maximization of real-time total benefits.

[0084] Figure 3 The multi-objective optimization algorithm flow of the embodiment of the present invention is shown as follows: Figure 3 As shown, firstly, data collection is carried out, and real-time data such as photovoltaic power generation, energy storage status, grid demand response signals, and market electricity prices are collected. The photovoltaic power generation, energy storage, and electricity price status are evaluated, and multi-objective optimization weights are set to judge the grid load demand. If the grid load demand is at a peak period, the demand response is given priority. If the grid load demand is not at a peak period, energy storage charging or photovoltaic power generation is given priority. Further, each weight is dynamically adjusted so that tasks are scheduled according to the weight size, and the scheduling control of photovoltaic, energy storage, and demand response is carried out. The benefits are monitored and calculated in real time and feedback is provided until the maximum total benefit is reached, and the algorithm ends.

[0085] Examples of actual application scenarios:

[0086] Scenario 1: Electricity prices fluctuate significantly. When electricity prices are extremely low during the valley period and significantly rise during the peak period, the algorithm automatically increases the weight w2 of energy storage, giving priority to charging during valley periods and discharging during peak periods, to obtain peak-valley arbitrage benefits.

[0087] Scenario 2: Demand response is frequently triggered. When the grid frequently sends demand response signals and provides subsidies, the algorithm increases the weight w3 of demand response, prioritizes energy storage and photovoltaic power generation for demand response, supports grid load regulation and obtains subsidy benefits.

[0088] Scenario 3: Sufficient photovoltaic power generation. When photovoltaic power generation is high and market electricity prices are low, the algorithm increases the weight w1 of photovoltaic power generation for self-use, giving priority to internal needs of the site to reduce the cost of purchasing electricity; after meeting self-use needs, the remaining electricity is connected to the grid.

[0089] Through the above specific implementation methods, the optimization control method of the embodiment of the present invention can be dynamically adjusted under different market and power conditions to maximize the revenue of the photovoltaic storage and charging station and support system stability.

[0090] In summary, the multi-objective optimization control method of the embodiment of the present invention is applied to the photovoltaic storage and charging station, and realizes the benefit balance and system stability between photovoltaic power generation, energy storage and demand response through real-time data collection, optimization algorithm execution, dynamic scheduling and feedback adjustment. It has the following advantages:

[0091] Maximizing revenue: The multi-objective optimization algorithm can flexibly balance the priorities between photovoltaic power generation, energy storage scheduling and demand response, so that the site can maximize revenue under different market conditions;

[0092] Dynamic adaptability: The algorithm makes scheduling adjustments based on real-time electricity price fluctuations and grid load changes, ensuring that the PV-storage-charging station has flexible control capabilities in the event of load fluctuations and market price changes;

[0093] Grid stability support: Through multi-objective optimization control, stations can efficiently participate in demand response during peak load periods, reduce grid load, and enhance the stability of the power system.

[0094] Corresponding to the photovoltaic storage and charging station optimization control method of the above embodiment, the present invention also proposes a photovoltaic storage and charging station optimization control device.

[0095] Figure 4 Schematic diagram of a block diagram of a photovoltaic storage and charging station optimization control device according to an embodiment of the present invention.

[0096] like Figure 4As shown, the photovoltaic storage and charging station optimization control device includes a construction module 10, a preliminary calculation module 20, an optimization calculation module 30 and a control module 40.

[0097] Among them, the construction module 10 is used to construct an objective function with the maximization of the total profit when the photovoltaic storage and charging station participates in auxiliary services as the optimization goal, wherein the objective function includes the profit and weight of each subsystem of the photovoltaic storage and charging station; the preliminary calculation module 20 is used to perform a preliminary calculation of the objective function to obtain a preliminary total profit; the optimization calculation module 30 is used to perform multi-objective optimization calculation on the objective function based on the preliminary total profit, and obtain the weight that meets the optimization goal; the control module 40 is used to determine the priority of each subsystem of the photovoltaic storage and charging station according to the weight that meets the optimization goal, and control each subsystem of the photovoltaic storage and charging station based on the priority.

[0098] In one embodiment, the optimization calculation module 30 is specifically used to: obtain photovoltaic power generation, electricity price and grid demand signal, and dynamically adjust the corresponding weights in the objective function according to the photovoltaic power generation, the electricity price and the grid demand signal; perform multi-objective optimization calculation on the adjusted objective function based on the preliminary total benefit until the optimization target is met.

[0099] In one embodiment, when the optimization calculation module 30 dynamically adjusts the corresponding weights in the objective function according to the photovoltaic power generation, the electricity price and the grid demand signal, it is specifically used to: when the photovoltaic power generation is greater than a preset power generation threshold, increase the weight of photovoltaic power generation; when the peak-valley electricity price difference is greater than a preset difference threshold, increase the weight of the energy storage system; when the frequency of the grid demand response signal is greater than a preset frequency threshold, increase the weight of the demand response.

[0100] Among them, the weight of photovoltaic power generation is increased according to the electricity price when photovoltaic power is connected to the grid and the income when photovoltaic power is used for self-use or in response to demand, the weight of the energy storage system is increased according to the discharge electricity price and charging electricity price, or the weight of demand response is increased according to the demand response subsidy and demand response frequency.

[0101] It should be noted that the specific implementation of the photovoltaic storage and charging station optimization control device can refer to the specific implementation of the photovoltaic storage and charging station optimization control method mentioned above. To avoid redundancy, it will not be described in detail here.

[0102] The photovoltaic storage and charging station optimization control device of the embodiment of the present invention obtains the weights that meet the optimization objectives through multi-objective optimization calculation, and then determines the priorities between the subsystems according to the weights, and optimizes and controls the photovoltaic storage and charging station according to the priorities, which is conducive to achieving the optimization goal of maximizing total revenue.

[0103] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be construed as limitations on the present invention, and those of ordinary skill in the art can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for optimizing and controlling a photovoltaic storage and charging station, characterized in that: The following steps are involved: Constructing an objective function with the maximization of the total revenue when the PV-storage-charging station participates in ancillary services as the optimization goal, wherein the objective function includes the revenue and weight of each subsystem of the PV-storage-charging station; Performing a preliminary calculation on the objective function to obtain a preliminary total benefit; Based on the preliminary total benefit, a multi-objective optimization calculation is performed on the objective function, and a weight that satisfies the optimization objective is obtained; The priority of each subsystem of the solar-storage-charging station is determined according to the weight that meets the optimization target, and each subsystem of the solar-storage-charging station is controlled based on the priority.

2. The method for optimizing and controlling a photovoltaic storage and charging station according to claim 1, characterized in that: The objective function is: R_total=w1*R_pv+w2*R_storage+w3*R_dr Among them, R_total represents the total revenue of the photovoltaic storage and charging station when participating in ancillary services, R_pv, R_storage and R_dr represent the revenues of photovoltaic power generation, energy storage system and demand response respectively, w1, w2 and w3 represent the weights of photovoltaic power generation, energy storage system and demand response respectively, and the sum of w1, w2 and w3 is 1.

3. The optimization control method of the photovoltaic storage and charging station according to claim 2 is characterized in that: in, The revenue R_pv of photovoltaic power generation is calculated by the following formula: R_pv=P_pv*(λ_sell*u_sell+λ_self*u_self) Among them, P_pv represents the photovoltaic power generation; λ_sell represents the electricity price when photovoltaic power is connected to the grid; u_sell represents the utilization coefficient of photovoltaic power connected to the grid, which takes a value of 1 or 0; λ_self represents the profit when photovoltaic power is used for self-use or demand response; u_self represents the utilization coefficient of photovoltaic power for self-use, which takes a value of 1 or 0.

4. The method for optimizing and controlling the photovoltaic storage and charging station according to claim 2, characterized in that: in, The revenue R_storage of the energy storage system is calculated by the following formula: R_storage=(P_discharge*λ_peak*u_discharge-P_charge*λ_valley* u_charge) Among them, P_discharge represents the discharge power of the energy storage system; λ_peak represents the discharge electricity price; u_discharge represents the utilization factor of the energy storage system discharge, which takes a value of 1 or 0; P_charge represents the charging power of the energy storage system; λ_valley represents the charging electricity price; u_charge represents the utilization factor of the energy storage system charging, which takes a value of 1 or 0.

5. The method for optimizing and controlling the photovoltaic storage and charging station according to claim 2, characterized in that: in, The benefit R_dr of demand response is calculated by the following formula: R_dr=P_dr*λ_dr*u_dr Among them, P_dr represents the power participating in demand response; λ_dr represents the demand response subsidy; u_dr represents the utilization coefficient of demand response, which takes a value of 1 or 0.

6. The photovoltaic storage and charging station optimization control method according to claim 1 is characterized in that: Based on the preliminary total benefit, a multi-objective optimization calculation is performed on the objective function, including: Acquire photovoltaic power generation, electricity price and grid demand signal, and dynamically adjust corresponding weights in the objective function according to the photovoltaic power generation, the electricity price and the grid demand signal; Based on the preliminary total benefit, a multi-objective optimization calculation is performed on the adjusted objective function until the optimization objective is met.

7. The method for optimizing and controlling the photovoltaic storage and charging station according to claim 6, characterized in that: Dynamically adjusting corresponding weights in the objective function according to the photovoltaic power generation, the electricity price and the grid demand signal includes: When the photovoltaic power generation is greater than a preset power generation threshold, increasing the weight of photovoltaic power generation; When the difference between peak and valley electricity prices is greater than the preset difference threshold, the weight of the energy storage system is increased; When the frequency of the grid demand response signal is greater than a preset frequency threshold, the weight of the demand response is increased.

8. The method for optimizing and controlling the photovoltaic storage and charging station according to claim 7, characterized in that: Increase the weight of photovoltaic power generation according to the electricity price when photovoltaic power is connected to the grid and the income when photovoltaic power is used for self-use or in response to demand, increase the weight of energy storage system according to the discharge electricity price and charging electricity price, or increase the weight of demand response according to demand response subsidies and demand response frequency.

9. An optimization control device for a photovoltaic storage and charging station, characterized in that: include: A construction module is used to construct an objective function with the maximization of the total revenue when the PV-storage-charging station participates in ancillary services as the optimization goal, wherein the objective function includes the revenue and weight of each subsystem of the PV-storage-charging station; A preliminary calculation module, used for performing preliminary calculation on the objective function to obtain a preliminary total benefit; An optimization calculation module, used for performing multi-objective optimization calculation on the objective function based on the preliminary total benefit, and obtaining weights that meet the optimization objectives; A control module is used to determine the priority of each subsystem of the photovoltaic storage and charging station according to the weight that meets the optimization target, and control each subsystem of the photovoltaic storage and charging station based on the priority.

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