A method for maximizing the benefits of a photovoltaic energy storage charging station participating in demand response of a power grid, a computer program product and the photovoltaic energy storage charging station

Abstract

The application provides a method for maximizing the income of a photovoltaic storage charging station participating in grid demand response, a computer program product and the photovoltaic storage charging station. The method comprises the following steps: calculating a load baseline of the photovoltaic storage charging station, a charging service income of the photovoltaic storage charging station and an income of the photovoltaic storage charging station participating in grid demand response; performing optimization calculation according to the charging service income of the photovoltaic storage charging station, a set of charging service transaction days, the income of the photovoltaic storage charging station participating in grid demand response, a set of demand response transaction days and a maximum sum of the charging service income and the demand response income; and obtaining a globally optimal solution after iteration convergence by solving the optimization calculation according to a particle swarm algorithm, so that the income of the photovoltaic storage charging station participating in various energy markets is improved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic-storage charging station technology, and in particular to a method for maximizing the benefits of photovoltaic-storage charging stations participating in grid demand response, a computer program product, and a photovoltaic-storage charging station. Background Technology

[0002] In recent years, building a new power system based on new energy sources has become a significant transformation in the power industry, and photovoltaic-storage-charging systems have gained industry attention in scenarios such as industrial parks and urban charging stations. Photovoltaic-storage-charging stations are a type of photovoltaic-storage-charging system, mainly composed of photovoltaic equipment, energy storage equipment, and charging equipment. Their primary profit-generating mechanism is participating in the charging service market by providing charging services. Photovoltaic-storage-charging stations can also participate in the electricity market, providing ancillary services such as grid demand response and peak shaving, and have multiple ways to profit from participating in various energy markets.

[0003] As policies tighten restrictions on the grid connection of distributed photovoltaic surplus electricity for some industrial and commercial users, the operation model of photovoltaic-storage charging stations participating in the electricity market is immature (including the operation of system strategies and business models, where system strategies mainly combine "photovoltaic, energy storage and charging" to formulate charging and discharging strategies, and business models refer to the operation strategies of charging services), and the multi-module collaborative operation in photovoltaic-storage charging stations places high demands on the intelligence of the energy management platform, the returns of photovoltaic-storage charging stations participating in various energy markets are low. Summary of the Invention

[0004] The technical problem this invention aims to solve is how to improve the profitability of photovoltaic energy storage charging stations when participating in multiple energy markets.

[0005] To address the aforementioned technical problems, this invention provides a method for maximizing the revenue of photovoltaic-storage charging stations participating in grid demand response scheduling, comprising the following steps:

[0006] S1. Input historical electricity consumption data, photovoltaic equipment parameters, energy storage equipment parameters, charging equipment parameters, charging service market data, and demand response market data for the photovoltaic-storage charging station;

[0007] S2. The power / energy constraints of the photovoltaic-storage charging station are calculated based on the parameters of the photovoltaic equipment, energy storage equipment, charging equipment, charging service market data, and demand response market data.

[0008] S3. Based on the historical electricity consumption data of the photovoltaic-storage charging station during the predetermined period, calculate the load baseline that reflects the time scale of the electricity purchased by the photovoltaic-storage charging station from the grid.

[0009] S4. Calculate the charging service revenue of the photovoltaic-storage charging station based on the charging service price of the photovoltaic-storage charging station, the amount of electricity purchased from the grid, and the electricity purchase price;

[0010] S5. Calculate the revenue of the photovoltaic-storage charging station from participating in grid demand response based on the load baseline of the photovoltaic-storage charging station, the electricity purchased from the grid, and the price at which the photovoltaic-storage charging station benefits from participating in grid demand response.

[0011] S6. Based on the charging service revenue of the photovoltaic-storage charging station, the set of charging service transaction days, and the revenue of the photovoltaic-storage charging station participating in grid demand response, and the set of demand response transaction days, optimize the calculation with the goal of maximizing the sum of charging service revenue and demand response revenue.

[0012] S7. Solve the optimization calculation using the particle swarm optimization algorithm to obtain the global optimal solution after iterative convergence;

[0013] S8. Output the global optimal solution.

[0014] Further, in step S2, the power / energy constraints of the photovoltaic-energy storage charging station include the output power / energy constraints of the photovoltaic equipment, the output power / energy constraints of the charging equipment, and the charging / discharging power constraints of the energy storage equipment, calculated as follows:

[0015] The formula for calculating the output power / power constraint of photovoltaic equipment is:

[0016]

[0017] In the formula, Q PV (t) represents the output power of the photovoltaic equipment; P PVM (t) represents the output power of the photovoltaic device at time t; K T Temperature coefficient (%); P SET S is the rated output power of the photovoltaic equipment. SET S(t) represents the standard irradiance of the photovoltaic equipment; S(t) represents the irradiance of the photovoltaic equipment at time t (kW / m²). 2 T(t) is the ambient temperature (°C) at time t; T SET Standard ambient temperature (°C);

[0018] The formula for calculating the output capacity / power constraint of charging equipment is:

[0019]

[0020] In the formula: Q PV (t) represents the output power of the charging device; P chr (t) represents the output power of the charging device at time t; ΣP rate P is the sum of the rated output power of all charging piles in the photovoltaic-storage charging station. EV (t) represents the electric vehicle user characteristic curve, P ESS (t) represents the dispatchability of the energy storage system;

[0021] The formula for calculating the charging and discharging power constraints of energy storage devices is as follows:

[0022]

[0023] In the formula: SOC(t) is the state of charge of the energy storage device at time t, SOC(t+Δt) is the state of charge of the energy storage device at time t+Δt, and P in (t) represents the energy storage charging power; P out (t) represents the energy storage discharge power; E n This represents the maximum energy storage capacity.

[0024] Furthermore, in step S2, the power / energy constraints of the photovoltaic-storage charging station also include grid power purchase constraints:

[0025] P grid (t)+P PV (t)≤P ESSmax ;

[0026] Σ(Q PV (t)+Q grid (t))+Q ESS0 =∑Q chr (t)+Q ESS1 ;

[0027] In the formula: P grid (t) represents the power purchased by the grid at time t, which is numerically related to the energy storage charging power P. in (t) are equal; P PV (t) represents the charging power from the photovoltaic system to the energy storage system at time t; ∑Q PV (t) represents the amount of electricity charged from the photovoltaic system to the energy storage system; ΣQ grid (t) represents the amount of electricity purchased for energy storage; ∑Q chr (t) represents the amount of electricity that the photovoltaic-storage charging station can provide for charging electric vehicles; Q ESS0 Q ESS1 These represent the initial and final charge levels of the energy storage system, respectively.

[0028] Furthermore, in step S3, the specific method for calculating the load baseline is as follows:

[0029] First, calculate the average electricity purchased by the photovoltaic-storage charging station during time period t. The formula is as follows:

[0030]

[0031] In the formula: P represents the average electricity purchased by the photovoltaic-storage charging station during period t; N represents the number of typical days selected from historical electricity consumption data when calculating the load baseline; P gridi(t) represents the electricity purchased by the photovoltaic-storage charging station during the t-th time period on the i-th day;

[0032] Then, based on the average electricity purchased by the photovoltaic and energy storage charging stations The electricity purchased by the photovoltaic-storage charging station is screened, and then the average of the screened electricity purchases is calculated to obtain the load baseline of the photovoltaic-storage charging station. The formula is as follows:

[0033]

[0034] In the formula, P Mi (t) represents the electricity consumption in time period t, obtained by filtering according to the requirements; there are a total of M values. base (t) represents the load baseline of the photovoltaic-storage charging station.

[0035] Furthermore, in step S4, the charging service revenue of the photovoltaic-storage charging station is calculated as follows:

[0036] F chr (t)=P new (t)×[C chr (t)-C grid (t)];

[0037] In the formula: F chr (t) represents the revenue of the photovoltaic-storage charging station participating in charging services; P new (t) represents the optimized electricity purchase volume, C chr (t) represents the charging service price, C grid (t) represents the electricity purchase price.

[0038] Furthermore, in step S5, the revenue calculation for the photovoltaic-storage charging station participating in grid demand response is as follows:

[0039] F dem (t)=[P base (t)-P new (t)]×C dem (t);

[0040] In the formula, F dem (t) represents the revenue of the photovoltaic-storage charging station participating in grid demand response; P base (t) represents the load baseline of the photovoltaic-storage charging station, P new (t) represents the optimized electricity purchase volume, C dem (t) represents the price at which the photovoltaic-storage charging station benefits from participating in grid demand response.

[0041] Furthermore, in step S6, the formula for optimizing the calculation with the objective of maximizing the sum of charging service revenue and demand response revenue is as follows:

[0042]

[0043] In the formula: {1,...,N} is the set of charging service transaction days; {N+1} is the set of demand response transaction days; maxF is the total revenue of the photovoltaic-storage charging station; F chr (t) represents the revenue of the photovoltaic-storage charging station participating in charging services; F dem (t) represents the revenue of the photovoltaic-storage charging station participating in grid demand response; P new (t) represents the optimized electricity purchase volume, C chr (t) represents the charging service price, C grid (t) represents the electricity purchase price; P base (t) represents the load baseline of the photovoltaic-storage charging station; C dem (t) represents the price at which the photovoltaic-storage charging station benefits from participating in grid demand response.

[0044] Furthermore, in step S7, a set of random solutions is first selected as initial values ​​according to the particle swarm optimization algorithm, and then the optimal solution is obtained through iterative search using a formula, the standard form of which is as follows:

[0045] X i =(x1,x2,…,x i );

[0046] V i =(v1,v2,…,v i );

[0047] In the formula: X i V i These are the position set and velocity set of the particle swarm, respectively.

[0048] Each iteration requires comparing the current fitness value of all particles with the fitness value of the historical best position to obtain the current optimal position; comparing the individual extreme value with the group extreme value, and updating the individual extreme value solution. and group extreme solutions Specifically as follows:

[0049]

[0050] Then, iterative calculations are performed using the iterative update formula until convergence is obtained to obtain the global optimal solution, where the velocity x of particle i is... ik and position v ik The update should be performed using the following formula:

[0051]

[0052] In the formula: ω is the inertia factor; k is the iteration number in the current iteration; It is the best position in the history of an individual particle; It represents the historical optimal position of the particle swarm; c1 and c2 are learning factors, usually taken as 2; r1 and r2 are uniformly distributed random numbers in the interval [0,1].

[0053] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in the method described above.

[0054] The present invention also provides a photovoltaic-storage charging station, including a photovoltaic device, an energy storage device, a charging device, and a control module. The photovoltaic device, the energy storage device, and the charging device are electrically connected in sequence. The energy storage device also receives electricity from the power grid. The control module controls the photovoltaic device, the energy storage device, and the charging device respectively. The control module includes a memory and a processor connected to each other. The memory stores the above-mentioned computer program product.

[0055] The present invention has the following beneficial effects: The present invention optimizes the calculation based on the charging service revenue of the photovoltaic-storage charging station, the set of charging service transaction days, the revenue of the photovoltaic-storage charging station participating in grid demand response, and the set of demand response transaction days, with the goal of maximizing the sum of charging service revenue and demand response revenue. Then, the optimization calculation is solved by the particle swarm optimization algorithm to obtain the global optimal solution after iterative convergence, thereby improving the revenue of the photovoltaic-storage charging station when participating in multiple energy markets. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating the method for maximizing the benefits of photovoltaic-storage charging stations participating in grid demand response scheduling. Detailed Implementation

[0057] The present invention will be further described in detail below with reference to specific embodiments.

[0058] This embodiment provides a photovoltaic-storage-charging station, which includes photovoltaic equipment, energy storage equipment, charging equipment, and a control module. The photovoltaic equipment includes a photovoltaic unit for converting solar energy into electrical energy; the energy storage equipment includes an energy storage battery for storing electrical energy; and the charging equipment includes a charging pile for charging electric vehicles. The photovoltaic equipment, energy storage equipment, and charging equipment are electrically connected in sequence. The energy storage equipment also receives power from the power grid. The control module controls the photovoltaic equipment, energy storage equipment, and charging equipment respectively. The control module includes an interconnected memory and a processor. The memory stores a computer program product, including a computer program, which, when executed by the processor, implements... Figure 1 The method for maximizing the benefits of photovoltaic-storage charging stations participating in grid demand response, as shown, specifically includes the following steps S1, S2, S3, S4, S5, S6, S7, and S8.

[0059] S1. Input historical electricity consumption data, photovoltaic equipment parameters, energy storage equipment parameters, charging equipment parameters, charging service market data, and demand response market data for the photovoltaic-storage charging station.

[0060] When implementing the revenue-maximizing scheduling method for photovoltaic-storage charging stations participating in grid demand response, the control module is first input with historical electricity consumption data, photovoltaic equipment parameters, energy storage equipment parameters, charging equipment parameters, charging service market data, and demand response market data. Historical electricity consumption data includes the amount of electricity purchased by the charging station from the grid, the purchase price, and the purchase date. Photovoltaic equipment parameters include the rated output power of the photovoltaic equipment, the standard irradiance, and the standard ambient temperature. Energy storage equipment parameters include charging power, discharging power, and maximum storage capacity. Charging equipment parameters include the type and number of charging piles, the actual output power of each charging pile, and the electric vehicle user characteristic curve. Charging service market data includes charging service prices and electricity consumption, while demand response market data includes demand response market prices and electricity consumption.

[0061] S2. The power / energy constraint of the photovoltaic-storage charging station is calculated based on the parameters of the photovoltaic equipment, energy storage equipment, charging equipment, charging service market data, and demand response market data.

[0062] The power and energy consumption of a photovoltaic-storage charging station are constrained by the output / consumption power of its various devices. For example, they are constrained by the output power / energy consumption of the photovoltaic equipment, the output power / energy consumption of the charging equipment, and the charging and discharging power of the energy storage equipment. These power / energy constraints can be calculated based on the parameters of the photovoltaic equipment, the energy storage equipment, and the charging equipment, as detailed below.

[0063] The output power of photovoltaic equipment depends on its power generation capacity, which is affected by light intensity and temperature. The specific calculation formula is as follows:

[0064]

[0065] In the formula, Q PV (t) represents the output power of the photovoltaic equipment; P PVM (t) represents the output power of the photovoltaic device at time t; K T Temperature coefficient (%); P SET S is the rated output power of the photovoltaic equipment. SET S(t) represents the standard irradiance of the photovoltaic equipment; S(t) represents the irradiance of the photovoltaic equipment at time t (kW / m²). 2 T(t) is the ambient temperature (°C) at time t; T SET The standard ambient temperature is (°C).

[0066] The charging capacity of an electric vehicle is the output capacity of the charging equipment, which depends on the output power of the charging equipment. The output power of the charging equipment is limited by the characteristics of the electric vehicle users on the load side and the power limit of the charging equipment. The calculation formula is as follows:

[0067]

[0068] In the formula: Q PV (t) represents the output power of the charging device; P chr (t) represents the output power of the charging device at time t; ∑P rate P is the sum of the rated output power of all charging piles in the photovoltaic-storage charging station. EV (t) represents the electric vehicle user characteristic curve, P ESS (t) represents the dispatchability of the energy storage system.

[0069] Energy storage devices can be classified into two states based on their State of Charge (SOC): charging and discharging. The formula for calculating charging and discharging power constraints can be expressed as follows:

[0070]

[0071] In the formula: SOC(t) is the state of charge of the energy storage device at time t, SOC(t+Δt) is the state of charge of the energy storage device at time t+Δt, and P in (t) represents the energy storage charging power; P out (t) represents the energy storage discharge power; E n This represents the maximum energy storage capacity.

[0072] Due to the influence of internal chemical reactions in energy storage devices, their charging power is limited by the charging rate C, specifically as follows:

[0073] P ESSmax =E n C;

[0074] In the formula: P ESSmax The upper limit of the charging and discharging power of the energy storage device; for a given energy storage resource, its charging rate C is a known quantity.

[0075] For a photovoltaic-storage charging station, its input power is the electricity purchased from the grid, and its output power is the electricity provided for electric vehicle charging services. When establishing the energy / power model of the photovoltaic-storage charging station, each resource should meet the grid purchase power constraints in addition to satisfying the output power constraints of photovoltaic equipment, the output power constraints of charging equipment, and the charging and discharging power constraints of energy storage equipment.

[0076] P grid (t)+P PV (t)≤P ESSmax ;

[0077] ∑(Q PV (t)+Q grid (t))+Q ESS0 =∑Q chr (t)+Q ESS1 ;

[0078] In the formula: P grid (t) represents the power purchased by the grid at time t, which is numerically related to the energy storage charging power P. in (t) are equal; P PV (t) represents the charging power from the photovoltaic system to the energy storage system at time t; ∑Q PV (t) represents the amount of electricity charged from the photovoltaic system to the energy storage system; ∑Q grid (t) represents the amount of electricity purchased for energy storage; ∑Q chr (t) represents the amount of electricity that the photovoltaic-storage charging station can provide for charging electric vehicles; Q ESS0 Q ESS1 These represent the initial and final charge levels of the energy storage system, respectively.

[0079] S3. Based on the historical electricity consumption data of the photovoltaic-storage charging station during the predetermined period, calculate the load baseline that reflects the time scale of the electricity purchased by the photovoltaic-storage charging station from the grid.

[0080] While providing charging services, photovoltaic and energy storage charging stations also participate in grid demand response. The charging service transactions follow the operating model of charging piles: photovoltaic and energy storage are given priority in power supply, and electricity is purchased from the grid when insufficient; the demand response option is day-ahead invitation type, requiring the calculation of the load baseline as follows.

[0081] The specific process for a photovoltaic-storage charging station to provide charging services is as follows: During the charging service process, the electricity purchased from the external power grid is adjustable, and the purchased electricity volume for each time period can be calculated based on the aforementioned power grid purchase constraints. Considering that the electricity market trading mechanism stipulates that a load baseline must be provided when participating in the transaction, the photovoltaic-storage charging station calculates the load baseline based on historical electricity consumption data when simultaneously participating in grid demand response. This load baseline can reflect the time scale of the electricity purchased by the photovoltaic-storage charging station from the grid. Therefore, this embodiment uses the load baseline as the load characteristic curve of the photovoltaic-storage charging station. The specific calculation method for the load baseline is as follows:

[0082] First, calculate the average electricity purchased by the photovoltaic-storage charging station during time period t. The formula is as follows:

[0083]

[0084] In the formula: P represents the average electricity purchased by the photovoltaic-storage charging station during period t; N represents the number of typical days selected from historical electricity consumption data when calculating the load baseline; P gridi (t) represents the electricity purchased by the photovoltaic-storage charging station during the t-th time period on the i-th day.

[0085] Then, based on the average electricity purchased by the photovoltaic and energy storage charging stations The electricity purchased by the photovoltaic-storage charging station is screened, and then the average of the screened electricity purchases is calculated to obtain the load baseline of the photovoltaic-storage charging station. The formula is as follows:

[0086]

[0087]

[0088] In the formula, P Mi (t) represents the electricity consumption in time period t, obtained by filtering according to the requirements; there are a total of M values. base (t) represents the load baseline of the photovoltaic-storage charging station.

[0089] S4. Calculate the charging service revenue of the photovoltaic-storage charging station based on the charging service price of the photovoltaic-storage charging station, the amount of electricity purchased from the grid, and the electricity purchase price.

[0090] The electricity purchase volume of the photovoltaic-storage charging station before optimization was P. old (t), the optimized electricity purchase volume is P new (t), the charging service revenue F of the photovoltaic-storage charging station chr The calculation is as follows:

[0091] F chr (t)=P new (t)×[C chr (t)-C grid (t)];

[0092] In the formula: F chr (t) represents the revenue of the photovoltaic-storage charging station participating in charging services; P new (t) represents the optimized electricity purchase volume, C chr (t) represents the charging service price, C grid (t) represents the electricity purchase price.

[0093] S5. Calculate the revenue of the photovoltaic-storage charging station from participating in grid demand response based on the load baseline of the photovoltaic-storage charging station, the electricity purchased from the grid, and the price at which the photovoltaic-storage charging station benefits from participating in grid demand response.

[0094] The benefits F of photovoltaic-storage charging stations participating in grid demand response dem The calculation is as follows:

[0095] F dem (t)=[P base (t)-P new (t)]×C dem (t)

[0096] In the formula: F dem(t) represents the revenue of the photovoltaic-storage charging station participating in grid demand response; P base (t) represents the load baseline of the photovoltaic-storage charging station, P new (t) represents the optimized electricity purchase volume, C dem (t) represents the price at which the photovoltaic-storage charging station benefits from participating in grid demand response.

[0097] For photovoltaic-storage charging stations to participate in grid demand response, the following constraints must be met:

[0098] P chr (t)≥β·P EV (t); This formula represents the minimum charging power constraint for the photovoltaic-storage charging station, where β is a value selected manually as needed (usually 0≤β≤100%).

[0099] P grid ≥0; This formula represents the constraint that the energy storage system cannot discharge in reverse in a photovoltaic-energy storage charging station;

[0100] This formula represents the minimum constraint for photovoltaic curtailment. In the formula, To participate in the grid demand response market, the photovoltaic output portion of the power, This refers to the curtailment of solar power.

[0101] S6. Based on the charging service revenue of the photovoltaic-storage charging station, the set of charging service transaction days, and the revenue of the photovoltaic-storage charging station participating in grid demand response, and the set of demand response transaction days, optimize the calculation with the goal of maximizing the sum of charging service revenue and demand response revenue.

[0102] In this embodiment, the photovoltaic-storage charging station simultaneously provides charging services and participates in grid demand response. According to the day-ahead demand response trading rules, the number of typical days selected by the photovoltaic-storage charging station when calculating the load baseline is N. Therefore, the photovoltaic-storage charging station participates in charging service market transactions for the first N days and in grid demand response market transactions on the (N+1)th day. Thus, the set of charging service transaction days is {1,...,N}, and the set of demand response transaction days is {N+1}. Then, the control module performs optimization calculations based on the charging service revenue of the photovoltaic-storage charging station, the set of charging service transaction days {1,...,N}, and the revenue of the photovoltaic-storage charging station from participating in grid demand response, and the set of demand response transaction days {N+1}, with the objective of maximizing the sum of charging service revenue and demand response revenue.

[0103] The calculation formula is as follows:

[0104]

[0105] In the formula: {1,...,N} is the set of charging service transaction days; {N+1} is the set of demand response transaction days; maxF is the total revenue of the photovoltaic-storage charging station; Fchr (t) represents the revenue of the photovoltaic-storage charging station participating in charging services; F dem (t) represents the revenue of the photovoltaic-storage charging station participating in grid demand response; P new (t) represents the optimized electricity purchase volume, C chr (t) represents the charging service price, C grid (t) represents the electricity purchase price; P base (t) represents the load baseline of the photovoltaic-storage charging station; C dem (t) represents the price at which the photovoltaic-storage charging station benefits from participating in grid demand response.

[0106] S7. Solve the optimization calculation using the particle swarm optimization algorithm to obtain the global optimal solution after iterative convergence.

[0107] This embodiment uses the particle swarm optimization algorithm to find the optimal solution for the above optimization calculation. The algorithm first selects a set of random solutions as initial values, and then obtains the optimal solution through iterative search using a formula. Its standard form is as follows:

[0108] X i =(x1,x2,…,x i );

[0109] V i =(v1,v2,…,v i );

[0110] In the formula: X i V i These are the position set and velocity set of the particle swarm, respectively.

[0111] In this embodiment, the decision variable is the optimized electricity purchase volume P. new (t), therefore the information of the particle swarm is in P i This is represented as follows: The fitness value of each particle is calculated, and at the k-th iteration, the position information and fitness value are stored in the individual extreme value. In the process, the objective function is simultaneously input, and the particle with the optimal fitness value is selected from the individual extreme values. The position information and fitness value of this particle are stored in the population extreme values. In each iteration, the current fitness value of all particles is compared with the fitness value of the historical best position to obtain the current optimal position; the individual extreme value and the group extreme value are compared to update the individual extreme value solution. and group extreme solutions Specifically as follows:

[0112]

[0113] Then, iterative calculations are performed using the updated formula until convergence to obtain the global optimal solution. The velocity x of particle i... ik and position v ik The update should be performed using the following formula:

[0114]

[0115] In the formula: ω is the inertia factor; k is the iteration number in the current iteration; It is the best position in the history of an individual particle; It represents the historical optimal position of the particle swarm; c1 and c2 are learning factors, usually taken as 2; r1 and r2 are uniformly distributed random numbers in the interval [0,1].

[0116] S8. Output the global optimal solution.

[0117] After obtaining the globally optimal solution, the control module outputs the globally optimal solution, which is the optimized scheduling method, including: total revenue, charging service revenue and demand response revenue, charging service electricity volume in each time period, electricity volume participating in grid demand response in each time period, and optimized power consumption curves (including energy storage SOC curves) of each device in the photovoltaic-storage charging station.

[0118] The above description is merely an embodiment of the present invention and does not limit the scope of patent protection. Any non-substantial changes or substitutions made by those skilled in the art based on the present invention will still fall within the scope of patent protection.

Claims

1. A method for maximizing the revenue of photovoltaic-storage charging stations participating in grid demand response, characterized in that, Includes the following steps: S1. Input historical electricity consumption data, photovoltaic equipment parameters, energy storage equipment parameters, charging equipment parameters, charging service market data, and demand response market data for the photovoltaic-storage charging station; S2. The power / energy constraints of the photovoltaic-storage charging station are calculated based on the parameters of the photovoltaic equipment, energy storage equipment, charging equipment, charging service market data, and demand response market data. S3. Based on the historical electricity consumption data of the photovoltaic and energy storage charging station during the predetermined period, calculate the load baseline that reflects the time scale of the electricity purchased by the photovoltaic and energy storage charging station from the grid; S4. Calculate the charging service revenue of the photovoltaic-storage charging station based on the charging service price of the photovoltaic-storage charging station, the amount of electricity purchased from the grid, and the electricity purchase price; S5. Calculate the revenue of the photovoltaic-storage charging station from participating in grid demand response based on the load baseline of the photovoltaic-storage charging station, the electricity purchased from the grid, and the price at which the photovoltaic-storage charging station benefits from participating in grid demand response. S6. Based on the charging service revenue of the photovoltaic-storage charging station, the set of charging service transaction days, and the revenue of the photovoltaic-storage charging station participating in grid demand response, and the set of demand response transaction days, optimize the calculation with the goal of maximizing the sum of charging service revenue and demand response revenue. S7. Solve the optimization calculation using the particle swarm optimization algorithm to obtain the global optimal solution after iterative convergence; S8. Output the global optimal solution.

2. The method for maximizing the revenue of photovoltaic-storage charging stations participating in grid demand response as described in claim 1, characterized in that, In step S2, the power / energy constraints of the photovoltaic-energy storage charging station include the output power / energy constraints of the photovoltaic equipment, the output power / energy constraints of the charging equipment, and the charging / discharging power constraints of the energy storage equipment, calculated as follows: The formula for calculating the output power / power constraint of photovoltaic equipment is: ； In the formula, The output power of photovoltaic equipment; For photovoltaic equipment Output power at any given moment; Temperature coefficient (% / °C); This refers to the rated output power of the photovoltaic equipment. The standard light intensity for photovoltaic equipment; For photovoltaic equipment Illumination intensity at any given time (kW / m²) 2 ); The ambient temperature (°C) at that moment. Standard ambient temperature (°C); The formula for calculating the output capacity / power constraint of charging equipment is: ； In the formula: The output power of the charging device; For charging devices Output power at any given moment; This is the sum of the rated output power of all charging piles in the photovoltaic-storage charging station. For electric vehicle user characteristic curves, For the dispatchability of energy storage systems; The formula for calculating the charging and discharging power constraints of energy storage devices is as follows: ； In the formula: For energy storage devices State of charge at time t, For energy storage devices State of charge at time t, Power for energy storage charging; This refers to the energy storage discharge power; This represents the maximum energy storage capacity.

3. The method for maximizing the revenue of photovoltaic-storage charging stations participating in grid demand response as described in claim 2, characterized in that, In step S2, the power / energy constraints of the photovoltaic-storage charging station also include grid power purchase constraints: ； ； In the formula: for The power purchased by the power grid at any given time is numerically related to the power storage charging capacity. equal; for The charging power of photovoltaic cells to energy storage at any given time; The amount of electricity used to charge energy storage from photovoltaic power plants; Purchase electricity for energy storage; The amount of electricity that a photovoltaic-storage charging station can provide for charging electric vehicles; , These represent the initial and final charge levels of the energy storage system, respectively.

4. The method for maximizing the revenue of photovoltaic-storage charging stations participating in grid demand response as described in claim 1, characterized in that, In step S3, the specific method for calculating the load baseline is as follows: First calculate the first photovoltaic-storage charging station Average purchase volume during the period The formula is as follows: ； In the formula: For the first photovoltaic-storage charging station Average electricity purchases during the time period; The number of typical days selected from historical electricity consumption data when calculating the load baseline; For the first Heavenly Electricity purchased by photovoltaic and energy storage charging stations during specific time periods; Then, based on the average electricity purchased by the photovoltaic and energy storage charging stations The electricity purchased by the photovoltaic-storage charging station is screened, and then the average of the screened electricity purchases is calculated to obtain the load baseline of the photovoltaic-storage charging station. The formula is as follows: ； ； In the formula, The first one obtained by filtering according to the requirements Electricity consumption during the period, total indivual; This serves as the load baseline for the photovoltaic-storage charging station.

5. The method for maximizing the revenue of photovoltaic-storage charging stations participating in grid demand response as described in claim 1, characterized in that, In step S4, the charging service revenue of the photovoltaic-storage charging station is calculated as follows: ； In the formula: Revenue from participating in charging services at photovoltaic and energy storage charging stations; To optimize the electricity purchase volume, For charging service pricing, This refers to the electricity purchase price.

6. The method for maximizing the revenue of photovoltaic-storage charging stations participating in grid demand response as described in claim 1, characterized in that, In step S5, the revenue calculation for the photovoltaic-storage charging station participating in grid demand response is as follows: ； In the formula, Benefits of photovoltaic-storage charging stations participating in grid demand response; This serves as the load baseline for the photovoltaic-storage charging station. To optimize the electricity purchase volume, The price at which photovoltaic-storage charging stations benefit from participating in grid demand response.

7. The method for maximizing the revenue of photovoltaic-storage charging stations participating in grid demand response as described in claim 1, characterized in that, In step S6, the formula for optimizing the calculation with the objective of maximizing the sum of charging service revenue and demand response revenue is as follows: ； In the formula: A collection of charging service transaction days; For the set of demand response transaction days; The total revenue of the photovoltaic-storage charging station; Revenue from participating in charging services at photovoltaic and energy storage charging stations; Benefits of photovoltaic-storage charging stations participating in grid demand response; To optimize the electricity purchase volume, For charging service pricing, The electricity purchase price; This serves as the load baseline for the photovoltaic-storage charging station; The price at which photovoltaic-storage charging stations benefit from participating in grid demand response.

8. The method for maximizing the revenue of photovoltaic-storage charging stations participating in grid demand response as described in claim 1, characterized in that, In step S7, a set of random solutions is first selected as initial values ​​according to the particle swarm optimization algorithm, and then the optimal solution is obtained through iterative search using a formula, the standard form of which is as follows: ； ； In the formula: , These are the position set and velocity set of the particle swarm, respectively. Each iteration requires comparing the current fitness value of all particles with the fitness value of the historical best position to obtain the current optimal position; comparing the individual extreme value with the group extreme value, and updating the individual extreme value solution. and group extreme solutions The details are as follows: ； ； Then, iterative calculations are performed by updating the formula until convergence is obtained to obtain the global optimal solution. speed and location The update should be performed using the following formula: ； ； In the formula: It is the inertia factor; This refers to the current iteration round; It is the best position in the history of an individual particle; It is the best position in the history of the particle swarm; and The learning factor is set to 2. and It is in the range Uniformly distributed random numbers within.

9. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

10. A photovoltaic-storage charging station, characterized in that, The device includes a photovoltaic device, an energy storage device, a charging device, and a control module. The photovoltaic device, the energy storage device, and the charging device are electrically connected in sequence. The energy storage device also receives electricity from the power grid. The control module controls the photovoltaic device, the energy storage device, and the charging device respectively. The control module includes a memory and a processor that are interconnected. The memory stores the computer program product as described in claim 9.

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