Charging station electricity price adjustment method based on solving load sales model

Abstract

The invention discloses a charging station electricity price adjustment method based on solving a load sales model, and the method comprises the steps: building a function which aims at reducing the power generation cost of a power plant and the total social charging cost through setting related parameters, such as an electrical load, an electricity price and the like; the known electricity utilization cost function, the number of charging stations, the existing electricity price, the electricity utilization load, the electricity price influence coefficient and the like are used as input, time variables in the target function are discretized and converted into a matrix form, and the electricity utilization load and the sales electricity price are solved and output by means of an interior point method. According to the method, historical data and electricity price influence factors are fully combined, through time-of-use electricity price regulation and control, on the premise that the total charging amount of new energy is not changed, user expenses are reduced, power plant profits are improved, power grid loads and traffic flow can be optimized, and remarkable economic and social benefits are achieved.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicle charging, and in particular to a method for adjusting electricity prices at charging stations based on solving a load sales model. Background Art

[0002] Against the backdrop of the booming global new energy vehicle industry and the continued advancement of smart grid construction, vehicle-grid interaction, as an emerging technology, is crucial for alleviating grid operational pressure and improving energy efficiency. With the rapid growth of electric vehicle ownership, load fluctuations caused by their charging behavior have become a key factor affecting the stable operation of the power grid. The centralized charging of large numbers of electric vehicles, especially during peak hours, significantly increases grid power supply pressure, posing significant challenges to the dispatch and operation of the power system.

[0003] Academia and industry have conducted extensive research on EV charging load analysis, proposing various methods. Horizontal comparison methods primarily infer load trends by comparing charging load data across different regions or time periods. However, this approach ignores regional differences in economic development, infrastructure, and user travel habits, and fails to factor in electricity prices, a key influencing factor. Consequently, the resulting load forecasts deviate significantly from actual conditions.

[0004] While the elasticity coefficient method considers the relationship between electricity prices and load, it typically uses a fixed elasticity coefficient to describe the relationship. In real-world applications, the market environment is complex and volatile, and user charging behavior is influenced by a variety of factors, including time of day, weather, and travel demand. Fixed elasticity coefficients cannot accurately reflect the dynamic impact of electricity price adjustments on charging load, making it difficult to meet real-time and accuracy requirements.

[0005] Expert system principles rely on the empirical knowledge of domain experts to construct models, a method that is significantly influenced by the subjective judgment of experts. Furthermore, with the rapid changes in the market environment and the continuous innovation of power technology, the updating speed of expert empirical knowledge lags behind, making it difficult for models to adapt to new market and technical conditions, and their versatility and timeliness are significantly insufficient.

[0006] More critically, most existing methods fail to effectively integrate historical data with factors influencing electricity prices. When analyzing charging loads, they neither fully exploit the user behavior patterns contained in historical charging data nor deeply explore the inherent connection between factors such as dynamic fluctuations in electricity prices, tiered electricity price policies, and real-time market electricity prices and changes in charging loads. This makes it impossible for existing methods to achieve multi-objective optimization in practical applications, such as reducing power plant power generation costs, lowering user charging expenses, and increasing power plant profits. This makes it difficult to meet the actual needs of efficient and economical operation of the power system in the context of vehicle-grid interaction. Therefore, an innovative charging station electricity price adjustment method is urgently needed to solve the above technical problems. Summary of the Invention

[0007] The purpose of the present invention is to provide a charging station electricity price adjustment method based on solving the load sales model. By reasonably regulating the time-of-use electricity price, the power plant's power generation cost is reduced, the total social charging cost is lowered, and at the same time, the user's total expenditure is ensured not to increase compared with the existing solution, and the power plant's profit is not reduced compared with the existing solution.

[0008] To achieve the above objectives, the present invention adopts the following technical means:

[0009] A method for adjusting charging station electricity prices based on solving a load sales model includes the following steps:

[0010] Parameter setting: Assume that price fluctuations do not change the total charging capacity of renewable energy within a sufficiently long period of time; set the function x(t) of the power plant's renewable energy load x changing with time t, and the power load of the i-th renewable energy charging station at time t is x i (t), and satisfies Where N represents the number of new energy charging stations; clarify the existing electricity load data and Indicates that p(t) represents the existing sales electricity price per unit of electricity charge at time t, and the electricity prices of new energy sites at different power stations at a fixed time are the same. The existing sales electricity price data is used Indicates; setting the existing electricity price of new energy charging stations and power load All are known; determine the influence coefficient of electricity price on the electricity load of the i-th new energy charging station at time t as k i (t), and x i (t) can be expressed as The electricity cost function f(t,x) of a power plant is known. When the time t is fixed, the power generation cost increases with the increase of the electricity load, that is, The rate at which power generation costs increase increases with the increase in electricity load, i.e.

[0011] Objective function construction: The objective function is constructed as:

[0012]

[0013] Algorithm process: Input electricity cost function f(t,x), the number of new energy charging stations N, and the electricity price of existing new energy charging stations The power load of each existing charging station Coefficient k i (t); discretize the time t in the objective function, convert the objective function into a matrix form, and solve it using the interior point method; output the electricity load x and the sales electricity price p(t).

[0014] A further solution of the present invention is that the power load of the i-th new energy charging station at time t is affected by the electricity price coefficient k i (t), determined by correlation analysis between historical charging data and electricity price data.

[0015] A further solution of the present invention is that the electricity cost function f(t,x) of the power plant is constructed based on the equipment parameters, fuel costs, and operation and maintenance cost factors of the power plant.

[0016] A further solution of the present invention is that the objective function is converted into a smooth-constrained optimization problem by discretizing the time t, and then solved using an interior point method.

[0017] Beneficial effects of the present invention:

[0018] 1. Precise multi-objective optimization: This method deeply integrates historical data and factors affecting electricity prices to innovatively construct an objective function based on the load sales model. Compared with traditional methods, by mining and analyzing historical charging data, it accurately grasps user behavior patterns and closely links factors such as dynamic fluctuations in electricity prices and tiered electricity pricing policies with changes in charging load. This enables the method to more accurately achieve multi-objective collaborative optimization to reduce power plant power generation costs, lower user charging expenses, and increase power plant profits in a complex and changing market environment, breaking through the technical bottleneck of existing methods that make it difficult to simultaneously take into account the interests of multiple parties.

[0019] 2. Efficient time-of-use electricity price regulation: Discretization processing and the interior point method are used to solve the objective function, transforming it into an effectively manageable optimization problem. In this way, time-of-use electricity price regulation can be achieved dynamically and accurately based on the real-time operating status of the power grid and user charging needs. On the premise of ensuring that the total charging capacity of new energy sources remains unchanged over a period of time, on the one hand, the charging load of electric vehicles can be shifted from peak hours to off-peak hours, effectively alleviating the power supply pressure of the power grid during peak hours, reasonably adjusting the power grid load curve, and reducing power generation costs; on the other hand, the power price lever is used to guide electric vehicle owners to charge in a decentralized manner, reducing traffic congestion caused by centralized charging, while reducing users' electricity costs, achieving a dual optimal allocation of power and transportation resources, and having significant economic and social benefits.

[0020] 3. Strong adaptability and versatility: This method fully considers the various complex factors in real-world application scenarios and is independent of specific regional conditions, user groups, or electricity market models. Whether in regions with varying levels of economic development, facing diverse user charging needs, or changing electricity market policies, it can flexibly adapt to different application environments by adjusting relevant parameters and input data. This method has strong versatility and broad application prospects, providing reliable technical support for the optimized operation of power systems in the context of vehicle-grid interaction. DETAILED DESCRIPTION

[0021] The technical solution of the present invention will be described clearly and completely below. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0022] Example

[0023] A method for adjusting charging station electricity prices based on solving a load sales model includes the following steps:

[0024] Parameter settings:

[0025] Assume that price fluctuations do not change the total amount of renewable energy charged over a sufficiently long period of time;

[0026] The power load x of the renewable energy source of the power plant changes with time t, which is represented by the function x(t). i (t) represents the power load of the i-th new energy charging station at time t, and satisfies Where N represents the number of new energy charging stations;

[0027] Existing electricity load data and express;

[0028] p(t) represents the existing sales electricity price per unit of electricity charge at time t. It is assumed that the electricity prices of different power stations at new energy sites are the same at a fixed time. The existing sales electricity price data is used express;

[0029] Assuming the existing electricity price of new energy charging stations and power load All are known;

[0030] The influence coefficient of electricity price on the electricity load of the i-th new energy charging station at time t is k i (t), assuming that the coefficient is known and x i (t) can be expressed as That is, the higher the price of the i-th charging station, the lower the charge, and its power consumption is affected by the coefficient k i (t) impact;

[0031] The power cost function f(t,x) of the power plant is known. This function represents the power generation cost of the power plant with a renewable energy power load of x at a certain time t, and satisfies the condition that when the time t is fixed, the power generation cost increases with the increase of power load, that is, The rate at which power generation costs increase increases with the increase in electricity load, i.e.

[0032] Objective function construction:

[0033] The objective function is constructed as:

[0034]

[0035] The objective function constraints are:

[0036]

[0037]

[0038] By discretizing the continuous time period [t0, t1] into n equal parts, the number of nodes is N = n + 1, and the step size is Note 0j =t0+j*h,x(t 0j )=x j ,p(t 0j )=p j ,f(t 0j ,x(t 0j ))=f j , Then the objective function can be written in matrix form:

[0039]

[0040] Since x(t) and p(t) have equality constraints, we can consider that there is only one unknown vector. The same discretization process is performed for the constraint conditions:

[0041]

[0042]

[0043] In this case, methods such as sequential quadratic programming or the interior point method can be used to solve the problem. This allows us to solve for p(t), and then, based on the equation, solve for x(t). This method can reduce user costs while maintaining the same total electricity consumption over a period of time, while also ensuring that the power plant's profits are comparable to those of existing solutions.

[0044] It aims to reduce the power generation cost of power plants and reduce the total charging cost for society.

[0045] Algorithm flow:

[0046] Input: electricity cost function f(t,x), number of new energy charging stations N, electricity prices of existing new energy charging stations The power load of each existing charging station Coefficient k i (t);

[0047] Discretization and solution: Discretize the time t in the objective function, convert the objective function into a matrix form, and solve it using the interior point method;

[0048] Output: electricity load x, sales electricity price p(t).

[0049] The principle is to discretize the time t, transform the objective function into a smooth constrained optimization problem, and then solve it using an interior point method. By regulating time-of-use electricity prices, the goal is to reduce user expenses (at least, the user's total expenses should not increase compared to the existing solution) while maintaining the same total electricity consumption over a period of time, and to ensure that power plant profits do not decrease compared to the existing solution.

[0050] Example 2

[0051] Electricity price adjustment for charging stations in a certain city area

[0052] In a second-tier city, there are 10 new energy charging stations (i.e. N=10). Collect electricity load data for the past three months. and sales electricity price data And through data analysis, the influence coefficient k of electricity price on the power load of each charging station at different times is determined. i(t). At the same time, the electricity cost function f(t,x) of the local power plant is obtained. This function is constructed based on the power plant's equipment operating parameters, coal procurement costs, and human operation and maintenance costs.

[0053] Set the time interval (t0, t1) to one day (24 hours), and discretize the 24 hours into 96 time points with a time interval of 15 minutes. Take the collected electricity cost function f(t, x), the number of new energy charging stations N = 10, and the existing new energy charging station electricity price The power load of each existing charging station Coefficient k i (t) as input data.

[0054] The objective function, converted to matrix form, is solved using the interior point method. After multiple iterations, the sales electricity price p(t) and the corresponding electricity load x are calculated for each time point throughout the day. For example, during peak hours between 6 PM and 8 PM, the calculated electricity price is higher than the original price, encouraging some electric vehicle owners to adjust their charging schedule to between 12 PM and 5 PM, when electricity prices are lower. This approach, while maintaining the city's total daily renewable energy charging capacity, reduces power plant generation costs by 8%, reduces users' total charging expenses by 5%, and increases power plant profits by 6%.

[0055] Example 3

[0056] Charging station electricity price adjustment during the peak summer period of the power grid

[0057] During the peak summer electricity consumption period, a certain province's power grid faces a great pressure on power supply. The province has a total of 50 new energy charging stations (N=50). Sales electricity price data Combined with meteorological data, user travel patterns, etc., a more accurate coefficient k of the impact of electricity load on electricity prices is determined. i At the same time, the electricity cost function f(t,x) is updated based on the increased equipment loss and reduced fuel efficiency caused by high temperatures in the summer at power plants.

[0058] Set the time interval (t0, t1) to one week (7 days), and discretize the 24 hours of each day into 48 time points with a 30-minute interval, for a total of 336 time points. Input the relevant data into the system and solve the objective function using the interior point method.

[0059] The solution yields the electricity price p(t) and electricity load x at each time point during the week. During the afternoon hours of 2:00 PM, when temperatures are high and electricity demand is high, charging station prices are significantly increased, prompting a large number of electric vehicle owners to charge between 2:00 AM and 6:00 AM. After a week of price adjustments, the provincial power grid successfully reduced peak charging load by 12%, while maintaining the total amount of renewable energy charging, lowered power plant generation costs by 10%, and reduced average user charging costs by 7%. This ensured stable profits for power plants during this critical period.

[0060] The present invention is provided as an example, not as a limitation of the embodiments. Those skilled in the art will appreciate that other variations or modifications may be made based on the above description. It is not necessary and impossible to enumerate all embodiments here, and obvious variations or modifications derived therefrom remain within the scope of protection of the present invention.

Claims

1. A method for adjusting charging station electricity prices based on solving a load sales model, characterized in that: The following steps are involved: Parameter setting: Assume that price fluctuations do not change the total charging capacity of renewable energy within a sufficiently long period of time; set the function x(t) of the power plant's renewable energy load x changing with time t, and the power load of the i-th renewable energy charging station at time t is x i (t), and satisfies Where N represents the number of new energy charging stations; clarify the existing electricity load data and Indicates that p(t) represents the existing sales electricity price per unit of electricity charge at time t, and the electricity prices of new energy sites at different power stations at a fixed time are the same. The existing sales electricity price data is used Indicates; setting the existing electricity price of new energy charging stations and power load All are known; determine the influence coefficient of electricity price on the electricity load of the i-th new energy charging station at time t as k i (t), and x i (t) can be expressed as The electricity cost function f(t,x) of a power plant is known. When the time t is fixed, the power generation cost increases with the increase of the electricity load, that is, The rate at which power generation costs increase increases with the increase in electricity load, i.e. Objective function construction: The objective function is constructed as: Algorithm process: Input electricity cost function f(t,x), the number of new energy charging stations N, and the electricity price of existing new energy charging stations The power load of each existing charging station Coefficient k i (t); discretize the time t in the objective function, convert the objective function into a matrix form, and solve it using the interior point method; output the electricity load x and the sales electricity price p(t).

2. The method for adjusting charging station electricity prices based on solving a load sales model according to claim 1, characterized in that: The influence coefficient k of the electricity price on the electricity load of the i-th new energy charging station at time t i (t), determined by correlation analysis between historical charging data and electricity price data.

3. The method for adjusting charging station electricity prices based on solving a load sales model according to claim 1, characterized in that: The electricity cost function f(t,x) of the power plant is constructed based on the equipment parameters, fuel costs, and operation and maintenance cost factors of the power plant.

4. The method for adjusting charging station electricity prices based on solving a load sales model according to claim 1, characterized in that: The objective function is converted into a smooth constrained optimization problem by discretizing the time t and then solved using the interior point method.

Images