Optimization method, system and equipment of photovoltaic energy storage charging station and medium

Abstract

The invention discloses an optimization method, system and device of a photovoltaic energy storage charging station and a medium, and relates to the technical field of energy scheduling, and the method comprises the steps: building a double-layer planning model of the photovoltaic energy storage charging station: building an upper layer model with the maximum benefit of the photovoltaic energy storage charging station as a first objective function and investment, balance and safety as first constraint conditions; taking benefit maximization of the electric vehicle user and the air conditioner user as a second objective function, and taking the power demand constraint of the electric vehicle and the load demand constraint of the air conditioner as second constraint conditions to construct a lower-layer model; and solving the bilayer planning model. According to the invention, the operation of the photovoltaic energy storage charging station is stable and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of energy dispatching, and in particular to an optimization method, system, equipment and medium for a photovoltaic energy storage charging station. Background Art

[0002] Photovoltaic power generation, as one of the most important renewable energy sources, has become a key technology in energy transformation due to its clean and efficient characteristics. However, the intermittent and instability of photovoltaic power generation has brought significant challenges to the stable operation of the power system. In order to cope with the instability of photovoltaic power generation, energy storage technology has gradually become an important part of the power system. Energy storage technology can not only effectively alleviate the volatility of photovoltaic power generation, but also improve the utilization efficiency of photovoltaic power and reduce the phenomenon of abandoned light. With the development of photovoltaic and energy storage technology and the increase in the popularity of electric vehicles (EV), photovoltaic energy storage charging stations have been widely built and put into use. Photovoltaic energy storage charging stations are microgrids that integrate photovoltaic power generation, energy storage and smart charging station technologies. The popularity of EVs has also brought new load management requirements to the power system. Similar to the charging load of EVs, air conditioning loads also have the characteristics of concentrated loads. Affected by EVs and air conditioning loads, the power load of the power grid has grown rapidly, resulting in a widening gap between peak loads and valley loads, which has deteriorated the load characteristics of the power grid. Considering that the periodic changes in air conditioning loads and the periodic changes in photovoltaic power generation capacity have a large overlap in time, the coordinated optimization scheduling that combines air conditioning loads, charging vehicles and photovoltaic energy storage power stations has a large demand response potential.

[0003] In the existing technology, the optimization technology of the power system composed of photovoltaic energy storage charging stations mainly focuses on capacity configuration and cost-benefit analysis, and is usually planned independently of demand-side management. For example, the existing air-conditioning load demand response is not integrated with the power system, and the response capability is limited to load regulation, which can only alleviate the load fluctuation of the power system to a certain extent.

[0004] Comprehensive analysis shows that the above-mentioned technology is unable to comprehensively analyze the operation of the power system by combining the actual demand on the demand side with the energy storage allocation of the power system, which makes the overall operation of the system only consider some factors on the demand side, resulting in instability. Summary of the invention

[0005] The embodiments of the present invention provide a method, system, device and medium for optimizing a photovoltaic energy storage charging station, which can solve the problem in the prior art that the operation of the power system cannot be comprehensively analyzed in combination with the actual demand on the demand side and the energy storage allocation of the power system, resulting in unstable overall operation of the system.

[0006] The embodiment of the present invention provides an optimization method for a photovoltaic energy storage charging station, comprising the following steps: obtaining electric vehicle charging demands and user air conditioning load demands associated with the photovoltaic energy storage charging station; constructing a double-layer programming model for the photovoltaic energy storage charging station: taking the maximum benefit of the photovoltaic energy storage charging station as a first objective function, and taking investment, balance and safety as first constraints, constructing an upper-layer model in the double-layer programming model for determining the optimal capacity configuration and pricing of the photovoltaic energy storage charging station; taking the maximization of benefits of electric vehicle users and air conditioning users as a second objective function, and taking electric vehicle power demand constraints and air conditioning load demands as second constraints, constructing a lower-layer model in the double-layer programming model for achieving a dynamic balance among electricity prices, load demands and power supply capabilities; solving the double-layer programming model to obtain an optimization result that can optimize the operation of the photovoltaic energy storage charging station.

[0007] Furthermore, the maximum profit of the photovoltaic energy storage charging station is taken as the first objective function, and the specific steps include: obtaining the expected profit according to the difference between the expected profit and the investment cost, the expected profit includes the charging service profit of the electric vehicle users and the interactive transaction profit with the power grid, and the investment cost includes the charging, photovoltaic and energy storage facility construction costs, and land rental fees; taking the expected profit as the first objective function.

[0008] Furthermore, the upper model for determining the optimal capacity configuration and pricing of the photovoltaic energy storage charging station in the two-layer planning model is constructed, and the specific steps include:

[0009] The first objective function is constructed by taking the maximization of expected return as the goal;

[0010] The first objective function F CS , the formula is:

[0011] Max F CS =B Ope -C Inv

[0012] C Inv =k cf c cf P Ncf +k rdg c rdg P Nrdg +k es (c esp P Nes +c ese E Nes )

[0013]

[0014] Among them, B Ope is the expected return, C Inv is the investment cost; kcf , k rdg and k es Respectively represent the capital recovery coefficients of charging, photovoltaic and energy storage; c cf 、c rdg 、c esp and c ese represents the unit investment cost of charging, photovoltaic and energy storage facilities; P Ncf , P Nrdg , P Nes and E Nes They represent the rated installed power of charging, photovoltaic and energy storage, and the installed storage capacity of energy storage respectively; θ represents the number of days in a year; and They represent the price per kilowatt-hour of electricity sold by the photovoltaic energy storage charging station and traded with the main power grid; and They represent the charging power of the electric vehicle user at time t and the power when interacting with the main grid; Δt represents the time of each cycle; c cfm 、c rdgm and c esm denote the annual unit operating costs of charging, photovoltaic and energy storage facilities respectively;

[0015] Investment, balance and safety are taken as the first constraint conditions, which include: charging in photovoltaic energy storage charging stations, maximum installable rated power constraints of photovoltaic and energy storage, maximum installable storage capacity of energy storage, price constraints of electric vehicle charging market, the exchange power between charging stations and main power grid should not exceed the capacity of substation transformer, maximum photovoltaic dispatching power and charging capacity constraints, charging and discharging constraints of energy storage, energy change characteristics during the operation of energy storage units and their charging and discharging state SOC constraints, sustainable performance constraints of charging stations, power balance constraints of photovoltaic energy storage charging stations and constraints that power loads must always meet the charging needs of electric vehicle users;

[0016] An upper-level model for determining the optimal capacity configuration and pricing of a photovoltaic energy storage charging station is constructed according to the first objective function and the first constraint condition.

[0017] Furthermore, the construction of the lower model in the two-layer planning model for achieving a dynamic balance among electricity price, load demand and power supply capacity comprises the following specific steps:

[0018] Maximizing the benefits of electric vehicle users and air conditioner users is taken as the second objective function;

[0019] The second objective function The formula is;

[0020]

[0021] in, represents the user utility, represents the power demand of the V-type electric vehicle at time t, U v represents the utility function of electric vehicle users, Expressed as charging cost;

[0022] The electric vehicle power demand constraint and the air conditioning load demand are used as the second constraint condition. The electric vehicle power demand constraint is that the electric vehicle capacity is kept between the minimum driving range power set for the electric vehicle and the battery rated capacity. The air conditioning load demand includes: active power balance constraint and air conditioning load aggregation power constraint;

[0023] A lower model for achieving a dynamic balance among electricity price, load demand and power supply capacity in the two-layer programming model is constructed according to the second objective function and the second constraint condition.

[0024] Furthermore, the two-level programming model is solved, and the specific steps include: piecewise linearizing the lower model through a piecewise linear function; incorporating the piecewise linearized lower model into the upper model through the Karush-Kuhn-Tucker condition KKT, so that the two-level programming model used to characterize the two-level optimization problem can be converted into an equivalent single-level optimization problem; linearizing the single-level optimization problem through a linear approximation method, and obtaining the model result using a commercial solver for processing optimization problems.

[0025] Furthermore, the charging cost specifically includes: the total cost of the sum of the operating cost of the photovoltaic energy storage charging station and the compensation cost of the air-conditioning load and the basic flexible load response.

[0026] Furthermore, the piecewise linearizing the lower model by means of a piecewise linear function specifically includes: piecewise linearizing the electric vehicle user benefits in the lower model into a user utility function for measuring consumer satisfaction by means of a piecewise linear function.

[0027] Furthermore, the model results are obtained using a commercial solver for processing optimization problems, and the specific steps include: setting the initial parameters of the two-layer programming model according to user needs, photovoltaic power generation forecast data and historical load data, and adjusting the two-layer programming model structure according to various scenarios, as the problem initialization stage of the two-layer programming model, and the various scenarios include changes in the intensity of photovoltaic power generation, time differences between peak and trough electricity consumption, and various electricity price policies; linearizing the nonlinear part of the two-layer programming model structure, as the linearization stage of the two-layer programming model; solving the upper model of the two-layer programming model to determine the capacity configuration plan; inputting the upper plan results into the lower model to optimize the real-time scheduling strategy, as the two-layer solution stage of the two-layer programming model.

[0028] An embodiment of the present invention provides an optimization system for a photovoltaic energy storage charging station, comprising:

[0029] A data acquisition module is used to obtain the charging demand of electric vehicles and the air conditioning load demand of users associated with the photovoltaic energy storage charging station; a model construction module is used to construct a two-level planning model of the photovoltaic energy storage charging station: taking the maximum benefit of the photovoltaic energy storage charging station as the first objective function, and taking investment, balance and safety as the first constraint conditions, constructing an upper model in the two-level planning model for determining the optimal capacity configuration and pricing of the photovoltaic energy storage charging station; taking the maximization of the benefits of electric vehicle users and air conditioning users as the second objective function, and taking the electric vehicle power demand constraint and the air conditioning load demand as the second constraint conditions, constructing a lower model in the two-level planning model for achieving a dynamic balance between electricity price, load demand and power supply capacity; a model analysis module is used to solve the two-level planning model to obtain an optimization result that can optimize the operation of the photovoltaic energy storage charging station.

[0030] An embodiment of the present invention provides a computer device, comprising: a memory and a processor; the memory stores a computer program, and the processor implements the above-mentioned method for optimizing a photovoltaic energy storage charging station when executing the computer program.

[0031] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for optimizing a photovoltaic energy storage charging station is implemented.

[0032] The embodiments of the present invention provide a method, system, device and medium for optimizing a photovoltaic energy storage charging station. Compared with the prior art, the advantages thereof are as follows:

[0033] A two-layer planning model for photovoltaic energy storage charging stations is constructed. An upper-layer model for determining the optimal capacity configuration and pricing of photovoltaic energy storage charging stations is established from the perspective of photovoltaic energy storage charging stations, taking into account the energy storage factors of the power system composed of photovoltaic energy storage charging stations; a lower-layer model for achieving a dynamic balance among electricity prices, load demand and power supply capacity is established from the perspective of electric vehicles and air conditioners, taking into account the factors of power demand measured by demand measurement; the two-layer planning model is then solved to obtain an optimization result that can optimize the operation of photovoltaic energy storage charging stations. This result comprehensively considers the factors of energy storage and power demand measured by demand measurement, so that the overall operation of the power system composed of photovoltaic energy storage charging stations is stable. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A two-layer model framework diagram for optimizing the design and decision-making of a photovoltaic energy storage charging station provided by an embodiment of the present invention;

[0035] Figure 2A schematic diagram of a two-level programming algorithm flow chart provided by an embodiment of the present invention;

[0036] Figure 3 Energy and information flow and internal structure diagram of the photovoltaic energy storage charging station provided by the embodiment of the present invention;

[0037] Figure 4 A schematic diagram of the air conditioning load scheduling algorithm flow provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0039] See also Figure 1 The embodiment of the present invention provides a method for optimizing a photovoltaic energy storage charging station, comprising the following steps:

[0040] Step 1: Analyze and obtain that the charging demand of electric vehicles and the air conditioning load demand both come from photovoltaic energy storage charging stations.

[0041] Step 2: Construct a two-level planning model for photovoltaic energy storage charging stations: Taking the maximum benefit of photovoltaic energy storage charging stations as the first objective function, and investment, balance and safety as the first constraints, construct an upper-level model in the two-level planning model for determining the optimal capacity configuration and pricing of photovoltaic energy storage charging stations; taking the maximization of benefits of electric vehicle users and air-conditioning users as the second objective function, and taking electric vehicle power demand constraints and air-conditioning load demand as the second constraints, construct a lower-level model in the two-level planning model for achieving a dynamic balance among electricity prices, load demand and power supply capacity.

[0042] Step 3: Solve the two-level programming model: The lower-level model is piecewise linearized through piecewise linear functions, and the piecewise linearized lower-level model is incorporated into the upper-level model through the Karush-Kuhn-Tucker condition KKT. The two-level programming model used to characterize the two-level optimization problem can be converted into an equivalent single-level optimization problem; the single-level optimization problem is linearized through a linear approximation method, and the model results are obtained using a commercial solver for processing optimization problems. The results can optimize the operation of photovoltaic energy storage charging stations.

[0043] 1. Photovoltaic energy storage charging station system.

[0044] Photovoltaic energy storage charging stations usually consist of multiple electric vehicle (EV) charging facilities, energy storage units and photovoltaic units, such as Figure 3 As shown. The charging station is connected to the external distribution network through a power electronic converter. In the case of insufficient power generation, it can obtain energy from the main grid. The energy storage unit is installed in the system to make full use of renewable energy. When the photovoltaic output power is higher than the charging demand of the connected electric vehicle EV and the air conditioning load demand, the energy storage unit starts power charging and stores excess energy. When the photovoltaic PV (Photovoltaic) output is insufficient, the energy storage unit can discharge and supply power. Therefore, by coordinating the control of the energy storage unit and the photovoltaic PV output, the energy consumption of the energy storage charging station can be minimized.

[0045] The present invention takes into account the charging station operator CSO (Charging Station Operator) of the photovoltaic energy storage charging station, and the EV and air conditioning users in the area. CSO is responsible for the construction and operation of the charging station, and considers the selection strategy of EV users and the willingness of air conditioning users in the charging storage planning. Under the premise of meeting internal balance and safety constraints, CSO comprehensively considers construction costs, operation and maintenance costs, and electricity sales revenue to determine capacity allocation and electricity prices. EV and air conditioning users can choose from the electricity price plans provided by the power grid to obtain better economic benefits.

[0046] The present invention assumes that the investment and operation of the PV energy storage charging station are undertaken by the CSO. The PV storage charging station earns income by providing energy from the PV device or the external grid to EV users. In addition, the CSO can also profit from the transaction of PV power generation with the grid. The operating performance of the charging station varies with the floating EV charging demand and the changes in the air conditioning load in the area. The CSO must fully consider the potential reactions of EV users and air conditioning users, participate in the electricity price design, and coordinate to determine the optimal configuration and control strategy to maximize profits.

[0047] On the other hand, EV users need to decide their charging load at the charging station every time they charge (assuming that the minimum charging demand is sufficient to meet the next driving distance), and air conditioning users need to decide their air conditioning load, and their electricity demand is affected by electricity prices. Therefore, the optimal decision of EV users and air conditioning users depends not only on their own preferences, but also on the electricity price of the CSO, which may conflict with the user's goals.

[0048] The optimal planning and decision-making problem of CSO can be expressed as a two-level programming model, such as Figure 1As shown. The upper problem represents the CSO's decision on photovoltaic, energy storage, charging station configuration and electricity price to maximize the expected benefits. The lower problem considers the CSO's pricing scheme and predicts the response of EV users and air conditioning users by calculating the optimal charging benefits for each user. The price implemented by the CSO is real-time and can be adjusted to influence the behavior of users, thereby encouraging them to actively respond to the photovoltaic output curve. The establishment of this model will help to obtain the optimal capacity configuration and price scheme.

[0049] 2. Objective function and constraints of the upper-level model for planning photovoltaic energy storage charging stations.

[0050] The upper optimization problem involves the optimal configuration and pricing of photovoltaic energy storage charging stations. The upper model objective function is shown in formula (1):

[0051] Max F CS =B Ope -C Inv (1)

[0052] C Inv =k cf c cf P Ncf +k rdg c rdg P Nrdg +k es (c esp P Nes +c ese E Nes ) (2)

[0053]

[0054] k=ζ(1+ζ) d / [(1+ζ) d -1] (4)

[0055] In the two-layer model, the objective function of the upper model is the maximum return of CSO, and the expected return is the expected return B Ope and investment cost C Inv In formula (2), C Inv represents the investment cost, which is related to the construction cost of charging, photovoltaic and energy storage facilities, land leasing and other related expenses. Ncf , P Nrdg , P nes and E nes Represent the rated installed power of charging, photovoltaic and energy storage, and the storage installed capacity of energy storage. cf 、c rdg 、c esp and c ese Represents the unit investment cost of charging, photovoltaic and energy storage facilities.cf , k rdg and k es They represent the capital recovery coefficients of charging, photovoltaic and energy storage, respectively, and can be calculated by formula (4). In formula (3), B Ope represents the expected revenue, including the charging service revenue of electric vehicle users and the interactive transaction revenue with the power grid. θ represents the number of days in a year. and They represent the price per kilowatt-hour of electricity sold by the CSO and traded with the main grid. and They represent the charging power of the electric vehicle user at time t and the power when interacting with the main power grid. Δt represents the time of each cycle, which is defined as 0.5h in the present invention. When is positive, electricity is purchased from the main grid. When it is negative, the electricity is sold to the main grid. cfm 、c rdgm and c esm They represent the annual unit operating costs of charging, photovoltaic and energy storage facilities respectively. In formula (4), k represents the capital recovery coefficient, d and ζ represent the facility life and discount rate respectively.

[0056] The constraints that CSO needs to consider during facility construction and operation include investment constraints, balance constraints, and safety constraints. The specific constraints are shown in equations (5) to (19).

[0057] Formulas (5) to (9) are investment constraints and price constraints:

[0058]

[0059] Formulas (5) to (7) represent the maximum installable rated power constraints for charging, photovoltaics, and energy storage in the charging station. Due to funding or space constraints, their investment capacity has an upper limit. Formula (8) represents the maximum installable storage capacity of energy storage. Formula (9) represents the price constraints imposed by CSO on the electric vehicle charging market, which helps to ensure the stability of the electric vehicle charging market. Formulas (10) to (19) are operational constraints:

[0060]

[0061]

[0062] Formula (10) stipulates that the exchange power between the charging station and the main grid should not exceed the capacity of the substation transformer. Formula (11) and Formula (12) represent the maximum photovoltaic dispatch power and charging capacity constraints, where represents the photovoltaic output factor. Equations (13) and (14) represent the charging and discharging constraints of energy storage, respectively, where and Respectively represent the charging and discharging power at time t.

[0063]

[0064] Equations (15) and (16) give the energy change characteristics and charge / discharge state (SOC) constraints of the energy storage unit during operation, where and Represent the minimum and maximum charge and discharge states, η esc and η esd represents the charging and discharging efficiency. Formula (17) specifies the available capacity of energy storage at the end of the dispatch operation: Must be the same as the starting capacity To ensure the sustainable performance of the charging station, the power load provided by the CSO must always meet the charging needs of electric vehicle users. represents the power demand of the V-type electric vehicle at time t, f v,t represents the number of V-type electric vehicles in use. Formula (19) is a constraint connecting the upper and lower layers, including the upper variable Pch t and the lower decision variable, where Δt represents the time scale, which is set to 0.5h. For simplicity, it is assumed that each electric vehicle user only charges at the charging station within one time period t, without considering the charging behavior in multiple time periods.

[0065] 3. Objective function and constraints of the lower-level model for photovoltaic energy storage charging station planning.

[0066] First, the air conditioner approximate aggregation model is given. The power consumption of a single air conditioner is P i It can be easily obtained through its physical model. However, for load integrators and dispatch centers, the total power after a large number of air conditioners are gathered is their biggest concern. The total power of N air conditioners at time t can be expressed as:

[0067]

[0068] Where P agg is the total power of the air conditioner at time t, which is only related to the air conditioner that is turned on at time t, m i (t) represents the on or off state of air conditioner i at time t. When the value is 0 or 1, it means "stop" and "start", respectively.

[0069] In steady state, the average power consumption of a single air conditioner is related to the duty cycle of its power-on cycle in the entire operation cycle, so the probability that air conditioner i is in the power-on state is P on,i , which can be expressed by its duty cycle as:

[0070]

[0071] Considering that there are enough N independently operated air conditioners and the external temperature is constant, the total power of the N air conditioners can be approximately expressed as:

[0072]

[0073] According to the air conditioning physical model formula (21):

[0074]

[0075] According to the inequality transformation in higher mathematics, equation (23) can be further transformed into:

[0076]

[0077] Among them, x on,i A dimensionless parameter that indicates the change in indoor temperature when the air conditioning load is turned on. It reflects the difference between the indoor temperature relative to the set temperature upper limit and the outside temperature. This parameter is used to describe the temperature change when the air conditioner is turned on. off,i A dimensionless parameter that represents the change in indoor temperature when the air conditioning load is turned off. It describes the difference between the indoor temperature relative to the lower limit of the set temperature and the outside temperature when the air conditioning is turned off.

[0078] According to the air conditioning physical model formula (24):

[0079]

[0080] Among them, η is the air conditioning energy efficiency ratio; P is the air conditioning power; R is the equivalent thermal resistance of the room; δ is the air conditioning temperature dead zone width.

[0081] In the same distribution network area, air conditioners installed by different residents and commercial users are divided into homogeneous air conditioners and heterogeneous air conditioners according to the similarities and differences of their parameters. For heterogeneous air conditioners, assuming that the independent parameters are distributed according to a given probability density function, the upper and lower bounds of the total power of N air conditioners are approximately expressed by equations (22) and (25) (26) and (27). For homogeneous air conditioners, equations (26) and (27) can also be applied. Since the parameters are the same, the calculation process is simpler. Homogeneous air conditioners lack a variety of states and often show power oscillations when adjusting the temperature setting. To address this problem, scholars have proposed various control strategies to "heterogeneize" homogeneous air conditioners, compensate for the lack of diversity in air conditioner states, and enhance the stability of aggregated power. In summary, the aggregated power of both homogeneous and heterogeneous air conditioners can be expressed by equations (26) and (27).

[0082]

[0083]

[0084] In the formula, E(X) and E(Y) represent the mathematical expectations of random variables X and Y respectively, and the calculation formula is as follows:

[0085]

[0086] Furthermore, the total power estimation of air conditioning load can be expressed as,

[0087]

[0088] From (26)(27)(30), we can see that when determining the air conditioning parameters, the dispatch center only needs to use the current outdoor temperature θ a and air conditioning temperature setting value θ set The total power of the cluster air conditioning load at the current moment can be obtained, which greatly reduces the calculation workload of the dispatch center.

[0089] Next, an air conditioning cluster response potential evaluation model considering multiple factors is established. The purpose is to quantitatively analyze the maximum demand response potential that can be achieved by a regional fixed-frequency air conditioning cluster under direct load control mode during a demand response event. The model first considers the impact of user thermal comfort on the response potential, and takes the user thermal comfort temperature range as the initial adjustable temperature margin. A user willingness model is introduced to analyze the impact of factors such as electricity price information, family income, education level, and age distribution on the user's willingness to participate in demand response. If users show a high willingness to participate in demand response, it can be assumed that they are willing to sacrifice some thermal comfort to obtain incentives. Therefore, these users will have a larger adjustable temperature margin. Finally, when calculating the response potential, it is necessary to consider the proportion of users who install terminal control devices, that is, the impact of user controllability.

[0090] (1) User thermal comfort.

[0091] In order to quantify the impact of temperature on human comfort, the Franker thermal comfort equation was used to establish an approximate estimate of the predicted mean vote PMV index for evaluating thermal comfort. The relationship between the PMV value and human sensation shows that the smaller the PMV value, the higher the user's comfort.

[0092] When other factors are at a comfortable level, the relationship between the predicted average voting PMV value (denoted as IPMV) and the temperature x is expressed by the function:

[0093]

[0094] From (31), we can see that when the temperature is 26℃, the PMV value is the smallest and the comfort level is the highest. ISO7730 gives the PMV value when the user is in the best comfort state between -0.5 and 0.5, and the corresponding indoor temperature is 24.8-27.3℃, which is the initial temperature adjustable margin.

[0095] (2) User's意愿

[0096] First, define the user's expected electricity price as the benchmark price P base , if the current electricity price P real is higher than the benchmark price P base , it indicates that the electricity cost is higher than the user's psychological expectation. The user is more inclined to participate in demand response to obtain subsidies, reduce electricity costs, and lower the requirements for thermal comfort. The temperature adjustable margin increases. Conversely, when the user is more concerned about the thermal comfort level, their willingness to participate in demand response is lower, and the temperature adjustable margin decreases relatively.

[0097] In addition, the user's sensitivity to price is closely related to factors such as family income, education level, and user age. For users from high-income families, their response to compensation is relatively small, and they are less sensitive to changes in electricity prices. On the contrary, users with a higher education level and younger users are more receptive to new things, and they tend to be more actively involved in the response. The impacts of the above factors are represented by the user education level influence coefficient Family income influence coefficient Average age influence coefficient respectively. These three factors jointly determine the subjective response coefficient which reflects the user's response willingness and sensitivity to electricity price.

[0098]

[0099]

[0100] In the formula: 0 ≤ a1 < a2 < a3 ≤ 1, 0 < a4 ≤ 1; ω1, ω2, ω3 are the user education levels from high to low; ω4 represents the threshold value of the user's willingness affected by family income; E is the user's educational level; I is the family income; A is the average age of the family.

[0101] In formulas (32) to (36), the user willingness factor μ i,t is the comprehensive willingness degree of the i-th air-conditioning terminal user to participate in demand response at time t. When μ i,t is a positive value, it indicates that the user has a positive response willingness; when μ i,t is a negative value, the user has a negative response willingness, and its value reflects the strength of this willingness.

[0102] The user's willingness to participate in demand response will change dynamically with the change of external conditions, that is, the indoor temperature adjustable range set by the air-conditioning terminal will change dynamically according to the user's thermal comfort temperature adjustability. In the cooling mode, the control parameters are defined as:

[0103]

[0104] In the formula, and are the lower limit and upper limit of the adjustable margin of the initial thermal comfort temperature of the i-th air-conditioning user at time t; and are the lower and upper limits of the adjustable temperature margin considering the user's wishes; Δθ i,t is the temperature margin adjustment amount of the i-th air-conditioning terminal at time t according to the user's wishes; Δθ i,t and user willingness influencing factor μ i,t is proportional to the value of .

[0105] Since the subsequent control strategy has a low frequency of controlling the device, the impact of the duration of the device's start-stop state on user willingness is not considered for the time being.

[0106] (3) User controllability

[0107] The higher the proportion of users who install terminal control devices, the stronger the controllability of users. A large number of demand response pilot projects have been carried out abroad, providing rich practical experience. Although the implementation of demand response projects in China is relatively limited and the proportion of users who install terminal control devices is relatively low, considering the overall progress of smart grid construction in the past decade, substantial progress has been made. It is reasonable to assume that the current user controllability is 60%.

[0108] At this point, combining equations (39), (40), and (43), we can get that at time t, the initial temperature setting point of the air conditioner is When α is 0.5, the total power of the air conditioner The adjustable range is:

[0109]

[0110] In the formula, are the lower and upper limits of the adjustable range of the total power of the air conditioner respectively; The initial temperature set point is The initial total power of the air conditioner; ξ represents user controllability.

[0111] At this time, the maximum load reduction capacity of the cluster air conditioner is:

[0112]

[0113] The lower optimization problem is the behavior selection problem of electric vehicle users and air conditioner users based on real-time pricing. The charging demand in each time period is determined. The objective function of the lower problem is shown in formula (45):

[0114]

[0115] The objective function of the lower level problem is to maximize the benefits of electric vehicle users and air conditioner users, and the benefits are expressed as user utility The difference between the charging cost and the v represents the utility function of electric vehicle users, and its specific form will be introduced in detail in the next section.

[0116] In the lower model, the goal is to minimize the sum of the operating cost of the photovoltaic energy storage power station and the compensation cost of the air conditioning load and the basic flexible load response during the period, which is recorded as:

[0117]

[0118] Where T is the total number of time intervals in the dispatch cycle; N is the number of dispatchable generating units; Ct Gi is the power generation cost of unit i in time interval t; is the output of unit i in time interval t; F AC The dispatch cost of air conditioning load; It is the unit power dispatching fee of air conditioning load; is the power of the air conditioning load in the time interval t before the response; is the power of the air conditioning load within the response time interval t; F sh 、F cut 、F tr are the dispatching costs of movable, contractable and transferable loads respectively.

[0119] Formula (48) is the electric vehicle power demand constraint, which is used to ensure that the user's charging demand meets the minimum charging load and the maximum charging limit, that is, the electric vehicle capacity remains between the minimum driving range power and the battery rated capacity.

[0120]

[0121] The air conditioning load constraints are as follows:

[0122] (1) Active power balance constraints

[0123]

[0124] in, They are the powers of base load, air conditioning load, movable, reducible and portable load at time t respectively.

[0125] (2) Air conditioning load aggregate power constraint

[0126]

[0127] The essence of low-level optimization is that electric vehicle users and air-conditioning users make choices based on electricity prices. That is, users only need to consider their own benefits and costs, and do not need to consider the constraints of charging stations and power grids, because the capacity configuration in the upper-level model and the optimal charging load in each period based on electricity prices are determined by CSO.

[0128] 4. Double-layer photovoltaic charging station planning algorithm.

[0129] The EV user benefits are explained using piecewise linear functions, where the EV user benefits in the lower-level problem are partially linearized, and on this basis, the KKT condition is used to transform the double-level optimization problem into a single-level optimization problem. Then, linear approximation is used to transform the single-level problem into a mixed integer linear programming problem, and finally the classical solution is used to solve it.

[0130] The two-level optimization problem is transformed into a single-level mixed integer linear programming problem through the following three steps: First, the lower-level function is piecewise linearized to represent the user utility. Second, the KKT conditions used to replace the lower-level problem are incorporated into the upper-level problem to obtain an equivalent single-level problem. Finally, the nonlinear terms in the obtained single-level problem are linearized to obtain a single-level mixed integer linear programming.

[0131] The utility function is used to measure consumer satisfaction as a function of the consumption of a given commodity combination. The consumers of the present invention are electric vehicle users, and the commodity is a charging power supply. According to the utility function, the utility increases as the number of purchased commodities increases, while the marginal utility per unit of commodity decreases.

[0132] The present invention assumes that the utility of electric vehicle users is a piecewise linear function of charging capacity, and the piecewise function is a typical function that characterizes the relationship between welfare utility and energy consumption. The proposed two-level optimization model and solution method can linearize the utility of electric vehicle users. Without loss of generality, the present invention assumes that the partition function is divided into 5 segments, each segment corresponding to a predetermined marginal utility value. Representing the charging demand of each segment of electric vehicle users, the total utility of CSO can be expressed by piecewise linearization using equations (51) and (52):

[0133]

[0134] In the lower layer of the original two-layer model, the EV user model can be expressed as:

[0135]

[0136] Equations (54) to (58) are the charging constraints for electric vehicle users. Equation (25) is a logical constraint that transforms the decision variables With its segmented form Assume that the total capacity of electric vehicle users meets the daily driving distance requirement without exceeding the battery capacity.

[0137]

[0138]

[0139] In the formula, and ε ev Indicates the battery capacity of electric vehicles and the capacity consumption per kilometer, represents the expected daily driving distance of the V-type electric vehicle, and represents the minimum and maximum SOC of the electric vehicle battery and the initial SOC of the charging station. From equation (57), it can be seen that the charging capacity of electric vehicles in each section is less than the upper limit of the section. From equation (58), it can be seen that the charge capacity of each section is positive. It should be noted that the successor variable of each equation is its dual variable. On this basis, the linear expression of the two-level problem can be obtained.

[0140] In the two-layer problem proposed by the present invention, according to its structure and form transformation, the upper-layer variables can be regarded as parameters of the lower-layer problem. For a given variable δt, each lower-layer problem is a structurally continuous and convex linear optimization problem. Therefore, the KKT condition can be used to replace the lower-layer problem in equations (53) to (58) with the corresponding KKT optimality condition and incorporate it into the upper-layer problem. By doing so, the original two-layer model is transformed into an equivalent single-layer problem.

[0141] The equation for the KKT condition is as follows:

[0142] Formula (61) represents the Lagrangian function of the next level problem, λ v，t , represents the Lagrange multiplier of the corresponding next-level constraint. Equations (64) to (67) are relaxed complementary constraints, where 0≤μ⊥d≥0 means 0≤μ, d≥0, μd=0. It should be noted that Equations (64) to (67) obtain bilinear terms by multiplying the dual variable with the primal variable.

[0143]

[0144]

[0145] Substituting equations (53) to (58) into equations (62) to (67), we can obtain a single-stage model that is equivalent to a two-stage optimization.

[0146] Next, the two-layer model is transformed into a single-layer problem, but the single-layer problem is bilinear and has nonlinear terms, and cannot be accurately solved using classical algorithms. The nonlinear terms in the single-layer problem should be linearized. The nonlinear terms in the single-level constraints are generated when the KKT conditions are introduced to realize the single-level transformation. The relaxed complementary conditions introduced in the constraints have nonlinear terms by multiplying two variables, and these nonlinear terms can be transformed into mixed integer linear programming. When 0≤μ⊥d≥0, it is:

[0147] μ≥0,d≥0

[0148] μ≤(1-w)M

[0149] d≤wM

[0150] w∈{0,1}(68)

[0151] Where M is a sufficiently large constant and w is a variable between 0 and 1. It should be pointed out that since the value of M affects the accuracy of the problem and the efficiency of calculation, decision makers need to make appropriate choices.

[0152] Using the above method, the nonlinear complementarity constraints in equations (64) to (67) can be rewritten as:

[0153]

[0154] In addition to the upper objective function, the above process also uses an equivalent mixed integer linear programming model to linearize the nonlinear constraints. The present invention uses a classical linear approximation method to linearize the nonlinear terms in the upper objective function. Since this method is simple and easy to solve, it has been fully applied in some studies. The specific method is as follows.

[0155] According to the linearized model, assuming that z = a × b is a bilinear term, a∈[a min ,a max ],b∈[b min ,b max ], then the bilinear term z = a × b can be linearized as:

[0156] z≥a min b+b min aa min b min

[0157] z≥a max b+b max aa max b max

[0158] z≤a min b+b max aa min bmax

[0159] z≤a max b+b min aa max b min (78)

[0160] Applying this method to the upper objective function, the converted objective function can be expressed as:

[0161]

[0162] The constraints are added accordingly:

[0163]

[0164]

[0165] The above method is used to effectively linearize the nonlinear terms in the upper objective function. Therefore, the nonlinear two-level optimization programming model is transformed into a single-level mixed integer linear programming, which can be effectively solved by commercial solvers.

[0166] The present invention has significant technical effects, which are specifically embodied in the following aspects:

[0167] (1) Improved the utilization rate of photovoltaic power generation.

[0168] The intermittent and instability of traditional photovoltaic power generation often leads to an imbalance in electricity supply and demand, thus affecting the stability of the power grid. The present invention, through the introduction of a photovoltaic energy storage system, combines photovoltaic power generation with an energy storage system, EV charging load, and air conditioning load, thereby achieving full utilization of photovoltaic power generation. When there is excess photovoltaic power generation, the energy storage system stores excess electricity, while price incentive strategies are used to promote the use of EV charging and air conditioning loads, significantly reducing the phenomenon of abandoned light. This optimized configuration not only improves the utilization efficiency of photovoltaic power, but also effectively alleviates the supply and demand pressure of the power grid.

[0169] (2) Optimize grid load management.

[0170] With the popularity of electric vehicles and the growth of air conditioning load, the imbalance of power grid load is becoming increasingly serious. The present invention significantly smoothes the load curve of the power grid by combining the scheduling optimization of EV charging load and air conditioning load with photovoltaic energy storage power station. During the peak period of power demand, the load peak is reduced by dynamically adjusting the scheduling of EV charging plan and air conditioning load, avoiding the occurrence of overload of the power grid. Figure 4 In addition, the introduction of PV-storage direct-flexible technology makes grid dispatch more flexible and effectively improves the stability and reliability of the power system.

[0171] (3) Enhance the ability to respond to user needs.

[0172] The present invention fully considers the user's demand response potential during the optimization process, and guides EV users and air-conditioning users to reasonably arrange their electricity usage time through real-time electricity price strategies. This flexible demand response mechanism not only guarantees the economic benefits of users, but also takes into account the user's comfort, and encourages users to actively participate in the optimization management of the power system. Users can charge their EVs or turn on air conditioners during periods of low electricity prices, thereby reducing electricity costs and obtaining additional benefits by participating in demand response. This model increases users' enthusiasm for participating in demand response and promotes the effective implementation of demand-side management.

[0173] (4) Improve the economic benefits of the power system.

[0174] Through the two-layer optimization framework, the present invention not only optimizes the capacity configuration of the photovoltaic energy storage system, but also effectively improves the economic benefits of the charging station operators. In the upper-layer optimization, the operator maximizes the benefits by rationally configuring photovoltaic, energy storage and charging facilities and designing the optimal electricity price strategy. The lower-layer optimization focuses on real-time control, guiding user behavior through electricity prices, thereby further optimizing the configuration and utilization of power resources. During the overall optimization process, charging station operators can effectively reduce investment and operating costs, while obtaining revenue through transactions with the power grid and providing charging services, thereby improving the economic benefits of the entire system.

[0175] (5) Improve the intelligence level of system operation.

[0176] The intelligent scheduling algorithm based on the double-layer optimization model of the present invention not only takes into account the various complex factors of the photovoltaic energy storage system, EV charging load and air conditioning load, but also realizes dynamic optimization configuration through real-time electricity price regulation. The system can automatically adjust the charging and discharging strategy of the energy storage system and the user's electricity consumption behavior according to the photovoltaic power generation situation, EV charging demand and air conditioning load changes in different time periods. This intelligent scheduling mechanism not only improves the operating efficiency of the system, but also significantly improves the system's ability to cope with complex working conditions, ensuring the safe and stable operation of the entire power system.

[0177] (6) Provide an extensible optimization framework.

[0178] The two-layer optimization method proposed in the present invention has good scalability, and the optimization model can be expanded and adjusted according to actual needs. For example, more flexible load types can be introduced, or the impact of more external factors on the system can be considered. In addition, the optimization method can also be combined with smart grid, Internet of Things and big data technology to further improve the intelligence and automation level of the system. This scalable optimization framework provides broad space and flexible solutions for the further development of photovoltaic energy storage systems in the future.

[0179] (7) Promote the market-oriented development of energy management.

[0180] With the gradual opening up and market-oriented development of the energy market, the technical solution of the present invention incorporates photovoltaic energy storage systems, EV users and air conditioning load users into a unified optimization scheduling framework, and promotes the active participation of market players by rationally designing the electricity price mechanism and revenue distribution plan. This market-oriented energy management model not only optimizes resource allocation, but also encourages more users and enterprises to participate in the utilization and management of renewable energy, promoting the diversification and marketization of the energy management market.

[0181] In summary, the present invention has significant technical effects in improving the utilization rate of photovoltaic power generation, optimizing grid load management, enhancing user demand response capabilities, improving system economic benefits and intelligence levels, and promoting sustainable development. Through the implementation of the double-layer optimization method, the present invention provides important support for the technological innovation and development of photovoltaic energy storage power stations and related fields, and lays a solid foundation for achieving a more efficient, more stable, and more environmentally friendly energy system.

[0182] An embodiment of the present invention provides an optimization system for a photovoltaic energy storage charging station, comprising:

[0183] The data acquisition module is used to obtain the charging demand of electric vehicles and the air conditioning load demand of users associated with the photovoltaic energy storage charging station. The model construction module is used to construct a two-level planning model for the photovoltaic energy storage charging station: taking the maximum benefit of the photovoltaic energy storage charging station as the first objective function, and taking investment, balance and safety as the first constraint conditions, the upper model used to determine the optimal capacity configuration and pricing of the photovoltaic energy storage charging station in the two-level planning model is constructed; taking the maximization of the benefits of electric vehicle users and air conditioning users as the second objective function, and taking the electric vehicle power demand constraint and air conditioning load demand as the second constraint conditions, the lower model used to achieve a dynamic balance between electricity price, load demand and power supply capacity in the two-level planning model is constructed. The model analysis module is used to solve the two-level planning model to obtain optimization results that can optimize the operation of the photovoltaic energy storage charging station.

[0184] An embodiment of the present invention provides a computer device, including: a memory and a processor; the memory stores a computer program, and when the processor executes the computer program, the steps of an optimization method for a photovoltaic energy storage charging station are implemented.

[0185] An embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of a method for optimizing a photovoltaic energy storage charging station are implemented.

[0186] A specific embodiment is as follows:

[0187] The specific implementation of the present invention describes in detail an intelligent optimization management method that combines a photovoltaic energy storage system, an electric vehicle (EV) charging station, and an air conditioning load. The method achieves efficient utilization of distributed energy and loads by introducing a two-layer optimization model. The ultimate goal is to improve the overall system efficiency and ensure the reliability of power supply. The system architecture of the present invention consists of four parts: a photovoltaic power generation subsystem, an energy storage subsystem, an EV charging subsystem, and an air conditioning load subsystem. These four subsystems work together to maximize energy utilization efficiency and dynamically balance grid loads under different load conditions through flexible scheduling and reasonable allocation of energy.

[0188] As the energy core of the system, the photovoltaic power generation subsystem is responsible for converting solar energy into electrical energy. This electrical energy can be directly supplied to the user load, or it can be stored in the energy storage system when there is an oversupply of electricity. When the photovoltaic power generation exceeds the load demand, the system stores the excess electrical energy in the battery pack through intelligent control for use when there is insufficient sunlight or peak load on the power grid. The energy storage system plays a key role in balancing the power supply in the present invention. Through the intelligent scheduling algorithm, the system can dynamically adjust the charging and discharging state of the energy storage system according to the current electricity price, photovoltaic power generation and user electricity demand. When the electricity price is low or photovoltaic power generation is sufficient, the system gives priority to charging the energy storage system; when the electricity price is high or photovoltaic power generation is insufficient, the energy storage system releases electricity to the power grid or load, thereby balancing the load on the power grid and reducing electricity consumption during peak electricity prices.

[0189] The present invention takes into account charging station operators (CSOs), electric vehicles, and air conditioning users. CSOs are responsible for the construction and operation of charging stations, and consider the selection strategy of electric vehicle users in charging storage planning. CSOs comprehensively consider construction costs, operation and maintenance costs, and electricity sales revenues to determine capacity allocation and electricity prices under the premise of meeting internal balance and safety constraints. In the EV charging subsystem, the present invention proposes an economical and efficient charging management strategy. Through intelligent scheduling control, the system gives priority to charging operations during low electricity price periods or when photovoltaic power generation is sufficient, so as to reduce the charging cost of users and alleviate the peak load pressure of the power grid. The charging station can not only dynamically adjust the charging strategy according to the real-time electricity price, but also guide users to charge during periods with lower electricity prices through a flexible electricity price mechanism, thereby maximizing the operating income of the charging station. The coordinated work of the EV charging subsystem and the energy storage subsystem can also ensure the normal operation of the charging station through the power supply of the energy storage system during periods of insufficient photovoltaic power generation, thereby further improving the overall benefits of the system. The air conditioning load subsystem management focuses on the dynamic scheduling of loads, especially in the peak power consumption period in summer and during the day, when the air conditioning load accounts for a large proportion of the total system load. The present invention effectively reduces peak loads by precisely regulating the user's air conditioning usage behavior, using a dynamic electricity price mechanism and an intelligent load management strategy. The system recommends that users use air conditioning during off-peak hours when electricity prices are lower, and use the energy storage system to supply power during peak hours, thereby reducing the load pressure on the power grid. In the specific implementation process, the system predicts the demand for air conditioning based on real-time power grid load conditions and meteorological data, and reasonably arranges the discharge time of the energy storage system to achieve the purpose of balancing the power grid load and reducing operating costs.

[0190] In order to achieve the optimal operation of the system, the present invention constructs a two-layer optimization model, which is divided into two parts: the upper capacity planning and the lower real-time scheduling. The main goal of the upper optimization model is to determine the optimal capacity configuration and pricing of the photovoltaic energy storage system, EV charging station and air conditioning load. In the two-layer model, the objective function of the upper model is the maximum benefit of the CSO, covering three aspects: investment cost, operating income and system efficiency. The CSO must fully consider the potential response of electric vehicle and air conditioning users, participate in the design of electricity prices, and coordinate to determine the optimal configuration and control strategy to maximize profits. In terms of investment cost, the model considers photovoltaic power generation equipment, energy storage devices, charging facilities and infrastructure costs; in terms of operating income, the income obtained through photovoltaic power generation, energy storage power release and EV charging services is calculated; and the system efficiency is to maximize energy utilization and reduce power loss by optimizing the collaborative work between various systems. In order to ensure the reliability and economy of the system, the present invention fully considers multiple realistic constraints in the construction of the optimization model. These constraints include grid security constraints, equipment capacity constraints, power balance constraints and electricity price constraints. Grid safety constraints require that the system cannot exceed the safe operating range of the grid at any time to avoid grid failures due to overload or underload. Equipment capacity constraints ensure that the total installed capacity of photovoltaic power generation, energy storage systems and EV charging facilities is within a reasonable range to ensure the normal operation of the system. Power balance constraints require that at any time, the system's power generation must match the load demand to avoid an imbalance in power supply and demand. Electricity price constraints guide users to use electricity resources during reasonable periods of time by setting a reasonable electricity price adjustment range, which not only ensures market stability but also improves users' economic benefits.

[0191] The lower-level scheduling model is responsible for the real-time operation optimization of the system, mainly by adjusting the operating status of each system to achieve a dynamic balance between electricity price, load demand and power supply capacity. During the scheduling process, the system first adjusts the distribution of electricity in real time by monitoring key parameters such as photovoltaic power generation, energy storage status, user load demand and power grid status. The scheduling algorithm comprehensively considers electricity price fluctuations, the randomness of photovoltaic power generation and user electricity usage habits to minimize the operating cost of the system. For example, during periods of high electricity prices, the system will give priority to using the electricity in the energy storage device to supply user loads, thereby reducing the user's electricity bill expenditure; when photovoltaic power generation is sufficient, the system will give priority to charging the energy storage device in case of emergency. The present invention first approximates the air conditioner. An approximate aggregation model of the air conditioner load is established by using the law of large numbers and inequalities, and the aggregated power of the air conditioner load is obtained. On the basis of the aggregation model, the air conditioner aggregation response potential is evaluated by comprehensively considering multiple factors such as user thermal comfort, willingness and controllability, and the air conditioner aggregation power and response potential are provided as basic data to the distribution network dispatching center. This data is combined with the controllability of the basic flexible load in the jurisdiction, the daily load forecast curve and the output characteristics of the photovoltaic energy storage charging station to establish a dispatching model. The model combines the air conditioning load and the basic flexible load to minimize the operating cost of the system the next day.

[0192] In order to solve the above optimization problem, the present invention uses piecewise linear functions to explain the interests of EV and air conditioner users, in which the EV user interest part in the lower layer problem is linearized, and on this basis, the KKT condition is used to transform the double-level optimization problem into a single-level optimization problem. Then, the single-level problem is transformed into a mixed integer linear programming (MILP) problem using linear approximation, and finally solved using the classical solution, such as Figure 2 As shown. The solution process includes multiple steps such as problem initialization, model linearization, double-layer solution, and result verification and adjustment. In the problem initialization stage, the system sets the initial parameters of the model according to the user's needs, the forecast data of photovoltaic power generation and the historical load data, and adjusts the model structure according to various scenarios. Various scenarios include but are not limited to factors such as changes in the intensity of photovoltaic power generation, time differences between peak and trough electricity consumption, and different electricity price policies. In the model linearization stage, the nonlinear part is linearized so that the model can be efficiently calculated by the MILP solver. In the double-layer solution stage, the upper-level planning model is first solved to determine the optimal capacity configuration plan; then the upper-level decision results are input into the lower-level scheduling model to further optimize the real-time scheduling strategy to achieve the overall optimal goal of the system.

[0193] The present invention achieves a significant improvement in the utilization rate of photovoltaic power generation, optimization of grid load management, and enhancement of user demand response capabilities through intelligent scheduling algorithms and load optimization management technologies. While improving the economic benefits of the system, it also meets the needs of sustainable development of modern power systems. This technical solution is not only applicable to large-scale power systems, but also has broad application prospects in distributed energy management and household energy management. By improving energy utilization efficiency and reducing system operating costs, the present invention provides strong technical support for the development of future smart grids and demonstrates good social and economic benefits.

[0194] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for optimizing a photovoltaic energy storage charging station, characterized in that: The following steps are involved: Obtain the electric vehicle charging demand and user air conditioning load demand associated with the photovoltaic energy storage charging station; Construct a two-level planning model for photovoltaic energy storage charging stations: take the maximum benefit of photovoltaic energy storage charging stations as the first objective function, take investment, balance and safety as the first constraint conditions, and construct an upper-level model in the two-level planning model for determining the optimal capacity configuration and pricing of photovoltaic energy storage charging stations; take the maximization of benefits of electric vehicle users and air-conditioning users as the second objective function, take the electric vehicle power demand constraint and air-conditioning load demand as the second constraint conditions, and construct a lower-level model in the two-level planning model for achieving a dynamic balance among electricity prices, load demand and power supply capacity; The two-level programming model is solved to obtain the optimization results that can optimize the operation of the photovoltaic energy storage charging station.

2. A photovoltaic energy storage charging station optimization method as claimed in claim 1, characterized in that: The maximum benefit of the photovoltaic energy storage charging station is taken as the first objective function, and the specific steps include: The expected income is obtained based on the difference between the expected income and the investment cost. The expected income includes the charging service income of electric vehicle users and the income from interactive transactions with the power grid. The investment cost includes the construction cost of charging, photovoltaic and energy storage facilities, and land leasing fees. Take expected return as the first objective function.

3. The optimization method of a photovoltaic energy storage charging station according to claim 1, characterized in that: The upper model for determining the optimal capacity configuration and pricing of the photovoltaic energy storage charging station in the two-layer planning model is constructed, and the specific steps include: The first objective function is constructed by taking the maximization of expected return as the goal; The first objective function F CS , the formula is: Max FCS=BOpe-C C I nv=kc f cc f P N c f +kr d gcr d gP N r d g+kes(cespP N es+ceseE N es) Among them, B Ope is the expected return, C Inv is the investment cost; k cf , k rdg and k es Respectively represent the capital recovery coefficients of charging, photovoltaic and energy storage; c cf 、c rdg 、c esp and c ese represents the unit investment cost of charging, photovoltaic and energy storage facilities; P Ncf , P Nrdg , P Nes and E Nes They represent the rated installed power of charging, photovoltaic and energy storage, and the installed storage capacity of energy storage respectively; θ represents the number of days in a year; and They represent the price per kilowatt-hour of electricity sold by the photovoltaic energy storage charging station and traded with the main power grid; and They represent the charging power of the electric vehicle user at time t and the power when interacting with the main grid; Δt represents the time of each cycle; c cfm 、c rdgm and c esm denote the annual unit operating costs of charging, photovoltaic and energy storage facilities respectively; Investment, balance and safety are taken as the first constraint conditions, which include: charging in photovoltaic energy storage charging stations, maximum installable rated power constraints of photovoltaic and energy storage, maximum installable storage capacity of energy storage, price constraints of electric vehicle charging market, the exchange power between charging stations and main power grid should not exceed the capacity of substation transformer, maximum photovoltaic dispatching power and charging capacity constraints, charging and discharging constraints of energy storage, energy change characteristics during the operation of energy storage units and their charging and discharging state SOC constraints, sustainable performance constraints of charging stations, power balance constraints of photovoltaic energy storage charging stations and constraints that power loads must always meet the charging needs of electric vehicle users; An upper-level model for determining the optimal capacity configuration and pricing of a photovoltaic energy storage charging station is constructed according to the first objective function and the first constraint condition.

4. The optimization method of a photovoltaic energy storage charging station according to claim 1, characterized in that: The step of constructing the lower model in the two-layer planning model for realizing the dynamic balance among electricity price, load demand and power supply capacity comprises: Maximizing the benefits of electric vehicle users and air conditioner users is taken as the second objective function; The second objective function The formula is; in, represents the user utility, represents the power demand of the V-type electric vehicle at time t, U v represents the utility function of electric vehicle users, Expressed as charging cost; The electric vehicle power demand constraint and the air conditioning load demand are used as the second constraint condition. The electric vehicle power demand constraint is that the electric vehicle capacity is kept between the minimum driving range power set for the electric vehicle and the battery rated capacity. The air conditioning load demand includes: active power balance constraint and air conditioning load aggregation power constraint; A lower model for achieving a dynamic balance among electricity price, load demand and power supply capacity in the two-layer programming model is constructed according to the second objective function and the second constraint condition.

5. The optimization method of a photovoltaic energy storage charging station according to claim 1, characterized in that: The specific steps of solving the bi-level programming model include: The lower model is piecewise linearized by piecewise linear function; By incorporating the piecewise linearized lower model into the upper model through the Karush-Kuhn-Tucker condition (KKT), the bi-level programming model used to characterize the bi-level optimization problem can be transformed into an equivalent single-level optimization problem. The single-layer optimization problem is linearized by a linear approximation method, and the model results are obtained using a commercial solver for processing optimization problems.

6. A photovoltaic energy storage charging station optimization method as claimed in claim 4, characterized in that: The charging cost specifically includes: the total cost of the sum of the operating cost of the photovoltaic energy storage charging station and the compensation cost of the air-conditioning load and the basic flexible load response.

7. A photovoltaic energy storage charging station optimization method as claimed in claim 5, characterized in that: The piecewise linearizing the lower model by means of a piecewise linear function specifically includes: piecewise linearizing the electric vehicle user interests in the lower model into a user utility function for measuring consumer satisfaction by means of a piecewise linear function.

8. The optimization method of a photovoltaic energy storage charging station according to claim 5, characterized in that: The method of obtaining the model result by using a commercial solver for processing the optimization problem comprises the following specific steps: According to user needs, photovoltaic power generation forecast data and historical load data, the initial parameters of the two-level planning model are set, and the structure of the two-level planning model is adjusted according to multiple scenarios as the problem initialization stage of the two-level planning model. The multiple scenarios include changes in the intensity of photovoltaic power generation, time differences between peak and trough electricity consumption, and multiple electricity price policies; By linearizing the nonlinear part of the bi-level programming model structure, it serves as the linearization stage of the bi-level programming model; The upper model of the two-layer programming model is solved to determine the capacity configuration plan; the upper plan results are input into the lower model to optimize the real-time scheduling strategy, which serves as the two-layer solution stage of the two-layer programming model.

9. An optimization system for a photovoltaic energy storage charging station, characterized in that: include: A data acquisition module, used to acquire the electric vehicle charging demand associated with the photovoltaic energy storage charging station and the user's air conditioning load demand; A model building module is used to build a two-level planning model for photovoltaic energy storage charging stations: taking the maximum benefit of photovoltaic energy storage charging stations as the first objective function, and taking investment, balance and safety as the first constraint conditions, an upper-level model for determining the optimal capacity configuration and pricing of photovoltaic energy storage charging stations in the two-level planning model is built; taking the maximization of benefits of electric vehicle users and air-conditioning users as the second objective function, and taking the electric vehicle power demand constraint and air-conditioning load demand as the second constraint conditions, a lower-level model for achieving a dynamic balance among electricity prices, load demand and power supply capacity in the two-level planning model is built; The model analysis module is used to solve the double-layer programming model and obtain the optimization results that can optimize the operation of the photovoltaic energy storage charging station.

10. A computer device comprising: Memory and processor; The memory stores a computer program, wherein the processor implements a photovoltaic energy storage charging station optimization method according to any one of claims 1 to 8 when executing the computer program.

11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, an optimization method for a photovoltaic energy storage charging station according to any one of claims 1 to 8 is implemented.

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