Power dispatching method and device for balancing charging load

Abstract

The invention discloses a power dispatching method and device for balancing a charging load, and relates to the technical field of power dispatching, and the method comprises the steps: calculating and obtaining a battery charge state at a trip end moment based on a probability distribution condition of daily mileage; according to the probability distribution condition of the initial charging moment, the initial charging moment is obtained through random extraction; calculating to obtain a charging load corresponding to each electric vehicle, and accumulating to obtain a charging load curve; identifying to obtain a charging load curve crest value and a charging load curve trough value; and based on the charging load curve, the crest value of the charging load curve and the trough value of the charging load curve, taking the minimum mean square error of the charging load curve as a target to carry out power optimization scheduling. Statistical analysis is performed based on the charging time and the charging parameters of the electric vehicle, the charging load curve is obtained, the minimum mean square error of the charging load curve serves as the target, a data basis is provided for charging optimization scheduling, and it is guaranteed that the stability of power grid operation is not affected.

Description

Technical Field

[0001] The present application relates to the technical field of power dispatching, and specifically relates to a power dispatching method and device for balancing charging load. Background Art

[0002] Currently, with the development of electric vehicles, the charging demand for electric vehicles is increasing day by day. In order to meet the charging demand of electric vehicles, service facilities such as charging stations are gradually put into use in urban construction, which also brings new challenges to the power load and power dispatching of urban power grids. How to optimize dispatching based on the charging situation of electric vehicles is a problem that needs to be solved currently.

[0003] Therefore, to meet the actual needs, a power dispatching technology for balancing charging load is provided now. Summary of the Invention

[0004] Aiming at the defects existing in the prior art, the purpose of the present application is to provide a power dispatching method and device for balancing charging load. Based on the charging time and charging parameters of electric vehicles, statistical analysis is carried out to obtain a charging load curve, and taking the minimum mean square deviation of the charging load curve as the goal, data basis is provided for charging optimization dispatching to ensure that the stability of power grid operation is not affected.

[0005] To achieve the above purpose, the technical solution adopted by the present application is as follows:

[0006] In the first aspect, the present application provides a power dispatching method for balancing charging load, and the method includes the following steps:

[0007] Based on the probability distribution of daily driving mileage, calculate and obtain the state of charge of the battery at the end of the trip;

[0008] According to the probability distribution of the starting charging time, randomly select and obtain the starting charging time;

[0009] Based on the charging power, starting charging time, and state of charge of the battery at the end of the trip corresponding to each electric vehicle, calculate and obtain the charging load corresponding to each electric vehicle;

[0010] Based on the charging load corresponding to each electric vehicle, accumulate to obtain a charging load curve, where the abscissa of the charging load curve is time and the ordinate is the charging load;

[0011] Based on the charging load curve, identify the peak value and trough value of the charging load curve;

[0012] Based on the charging load curve, the peak value of the charging load curve, and the trough value of the charging load curve, taking the minimum mean square deviation of the charging load curve as the goal, carry out power optimization dispatching.

[0013] Based on the above technical solution, the method is configured with a power optimization scheduling objective formula. The power optimization scheduling objective formula takes the minimum mean square deviation of the charging load curve as the objective function, and the power optimization scheduling objective formula is:

[0014] Wherein,

[0015] P0 is the inherent load of the power grid in the t-th time period, and P EV is the charging load of the electric vehicle in the t-th time period, is the average value of the total load of the power grid including the charging load of electric vehicles in T time periods.

[0016] In a second aspect, the present application provides a power scheduling device for balancing the charging load. The device includes:

[0017] A state of charge acquisition module for calculating and obtaining the state of charge at the end of the trip based on the probability distribution of the daily driving mileage;

[0018] A starting charging time acquisition module for randomly extracting the starting charging time according to the probability distribution of the starting charging time;

[0019] A charging load calculation module for calculating and obtaining the charging load corresponding to each electric vehicle based on the charging power, the starting charging time, and the state of charge of the battery at the end of the trip corresponding to each electric vehicle;

[0020] A charging load curve acquisition module for accumulating to obtain a charging load curve based on the charging load corresponding to each electric vehicle. The abscissa of the charging load curve is time, and the ordinate is the charging load;

[0021] A charging load curve analysis module for identifying and obtaining the peak value of the charging load curve and the valley value of the charging load curve based on the charging load curve;

[0022] A charging optimization scheduling module for performing power optimization scheduling with the minimum mean square deviation of the charging load curve as the objective based on the charging load curve, the peak value of the charging load curve, and the valley value of the charging load curve.

[0023] Based on the above technical solution, the method is configured with a power optimization scheduling objective formula. The power optimization scheduling objective formula takes the minimum mean square deviation of the charging load curve as the objective function, and the power optimization scheduling objective formula is:

[0024] Wherein,

[0025] $P_0$ is the inherent load of the power grid during the time period $t$, and $P$ EV is the charging load of electric vehicles during the time period $t$, and $\overline{P}$ is the average value of the total load of the power grid including the charging load of electric vehicles over $T$ time periods.

[0026] Compared with the prior art, the advantages of the present application are as follows:

[0027] Based on the charging time and charging parameters of electric vehicles, the present application conducts statistical analysis to obtain the charging load curve, and takes the minimum mean square deviation of the charging load curve as the goal, providing a data basis for charging optimization scheduling and ensuring that the stability of the power grid operation is not affected. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0029] Figure 1 FIG.

[0030] Figure 2 is a flowchart of the steps of the power dispatching method for balancing the charging load according to the embodiment of the present application;

[0031] Figure 3 FIG.

[0032] Figure 4 is a flowchart of the technical principle of the power dispatching method for balancing the charging load according to the embodiment of the present application;

[0033] Figure 5 FIG.

[0034] Figure 6 is a schematic diagram of the charging load prediction curve of 2000 electric vehicles in the power dispatching method for balancing the charging load according to the embodiment of the present application; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Apparently, the described embodiments are some, but not all, of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the scope of protection of this application.

[0036] The embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0037] An embodiment of this application provides a power dispatching method and device for balancing charging loads. Based on the charging time and charging parameters of electric vehicles, statistical analysis is performed to obtain a charging load curve. With the minimum mean square deviation of the charging load curve as the objective, it provides a data basis for optimized charging dispatching and ensures that the stability of the power grid operation is not affected.

[0038] To achieve the above technical effects, the overall idea of this application is as follows:

[0039] A power dispatching method for balancing charging loads, the method includes the following steps:

[0040] S1. Based on the probability distribution of the daily driving mileage, calculate and obtain the state of charge of the battery at the end of the trip.

[0041] S2. According to the probability distribution of the starting charging time, randomly extract the starting charging time.

[0042] S3. Based on the charging power, starting charging time, and state of charge of the battery at the end of the trip corresponding to each electric vehicle, calculate and obtain the charging load corresponding to each electric vehicle.

[0043] S4. Based on the charging loads corresponding to each electric vehicle, accumulate to obtain a charging load curve, where the abscissa of the charging load curve is time and the ordinate is the charging load.

[0044] S5. Based on the charging load curve, identify the peak value and trough value of the charging load curve.

[0045] S6. Based on the charging load curve, peak value of the charging load curve, and trough value of the charging load curve, with the minimum mean square deviation of the charging load curve as the objective, perform power optimization dispatching.

[0046] The embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0047] In the first aspect, see Figures 1 to 5As shown in the figure, an embodiment of the present application provides a power scheduling method for balancing the charging load. The method includes the following steps:

[0048] S1. Calculate and obtain the state of charge of the battery at the end of the trip based on the probability distribution of the daily driving mileage.

[0049] S2. Randomly select the starting charging time according to the probability distribution of the starting charging time.

[0050] S3. Calculate and obtain the charging load corresponding to each electric vehicle based on the charging power, starting charging time, and state of charge of the battery at the end of the trip corresponding to each electric vehicle.

[0051] S4. Based on the charging loads corresponding to each electric vehicle, accumulate to obtain a charging load curve. The abscissa of the charging load curve is time, and the ordinate is the charging load.

[0052] S5. Identify the peak value and trough value of the charging load curve based on the charging load curve.

[0053] S6. Based on the charging load curve, peak value of the charging load curve, and trough value of the charging load curve, perform power optimization scheduling with the goal of minimizing the mean square error of the charging load curve.

[0054] It should be noted that the technical solution of the embodiment of the present application can be applied to a charging station equipped with multiple charging piles, a charging station management platform, a charging parking lot of a building, or a power grid power supply management system of a certain area, for performing power scheduling according to the situation of electric vehicles under its charge responsibility to ensure that the stability of the power grid operation in the affiliated area is not affected.

[0055] In the embodiment of the present application, statistical analysis is performed based on the charging time and charging parameters of electric vehicles to obtain a charging load curve, and with the goal of minimizing the mean square error of the charging load curve, it provides a data basis for charging optimization scheduling to ensure that the stability of the power grid operation is not affected.

[0056] Furthermore, the method is configured with a power optimization scheduling target formula. The power optimization scheduling target formula takes the minimum mean square error of the charging load curve as the objective function, and the power optimization scheduling target formula is:

[0057] Among them,

[0058] P0 is the inherent load of the power grid in the t-th period, P EV is the charging load of the electric vehicle in the t-th period, is the average value of the total power grid load including the charging load of electric vehicles in T periods.

[0059] As shown in the accompanying drawings of the specification Figure 2 shown, it is a flowchart of the technical principle of the embodiment of the present application. The technical solution of the embodiment of the present application is mainly based on the Monte Carlo method for charging load prediction. The Monte Carlo method is also known as the statistical simulation method, which obtains an approximate answer to the problem by random sampling, associates the problem to be solved with a probability model, and uses the computer as a tool for realizing random sampling or statistical simulation. The electric vehicle charging load prediction based on Monte Carlo sampling is based on Monte Carlo random sampling. According to the real traffic travel behavior data, by imitating the driving habits of electric vehicle owners, a model with random characteristics is established to predict the charging load demand of electric vehicles.

[0060] Specifically, the embodiment of the present application is also configured with an electric vehicle charging load prediction simulation process, which is as follows

[0061] Select 2000 household pure electric vehicles as samples for charging power prediction. In the embodiment of the present application, the slow charging power of electric vehicles is selected as 3kW, the charging efficiency is 0.9, the power consumption per kilometer of electric vehicles is 0.5kWh, and the electric vehicles start charging immediately after completing the last return trip and charge at a constant power until the full charge state. Through MATLAB software simulation, the unordered charging load prediction results of 2000 electric vehicles are obtained. As shown in the accompanying drawings of the specification Figure 3 shown, it is the charging load prediction curve of 2000 electric vehicles.

[0062] It can be seen from the electric vehicle charging load prediction curve that the daily charging load of electric vehicles shows obvious peak-valley characteristics. In the time period from 16:00 to 24:00, a large number of electric vehicles are charging, but in the time period from 6:00 to 10:00, the charging load demand of electric vehicles is relatively low, which has a very close relationship with the living driving habits of the owners. The peak-valley difference of the unordered charging load curve of electric vehicles is very large, which is harmful to the stable operation of the power system. If the charging of electric vehicles is not optimized and scheduled, it will cause large power fluctuations in the power grid and affect the normal operation of other electrical equipment in the power system. Therefore, it is necessary to optimize and schedule electric vehicles to reduce the impact on the power grid caused by their unordered charging.

[0063] Specifically, the embodiment of the present application is also configured with an electric vehicle electricity price-guided charging method analysis process, which is as follows

[0064] There are many factors that can affect the charging load demand of electric vehicle owners, such as weather, production mode, electricity price, etc. To optimize the charging load of electric vehicles, it is necessary to start from these factors that can affect the charging load demand of the owners. Among them, the most operable factor is the electricity price.

[0065] The time-of-use electricity price strategy is an effective means to influence users' demand response. Different electricity prices are divided by analyzing the pressure of the stable operation of the power system, and a day is divided into three periods: low valley, normal, and peak. The change in electricity price can directly affect the change in the charging load of electric vehicle owners. By formulating different electricity prices for different periods, the role of electricity price in users' load demand is fully exerted, enabling electric vehicle users to participate in peak shaving and valley filling, so that the power grid can maintain stable operation, and at the same time, the charging cost of electric vehicles can be reduced.

[0066] To simplify the calculation, only the factor of electricity price is considered in this embodiment of the application to affect the charging load demand of vehicle owners, without considering the influence of other factors. To represent the impact of electricity price changes on the change in users' charging load demand, the concept of price elasticity is introduced in this embodiment of the application. Price elasticity refers to the phenomenon that under the condition that other influencing factors remain unchanged, only the price factor changes, which causes the change in user demand. Price elasticity is a sensitivity index to measure the change in demand caused by price changes. Under the action of price elasticity, users change their previous electric vehicle charging habits, so that more electric vehicles are connected to the power grid for charging during the price valley period, and fewer electric vehicles are connected for charging during the price peak period, achieving the effect of peak shaving and valley filling, and ensuring the stability of the power grid operation.

[0067] Comprehensively considering the output curve of new energy power generation and the charging load demand of electric vehicle users, 24 hours are divided into three periods: valley, normal, and peak. The division of peak and valley periods is shown in Table 1 below:

[0068]

[0069] Table 1

[0070] Taking the goal of achieving peak shaving and valley filling and minimizing the mean square error of the total power grid load as the objective function, the following formula can be established:

[0071] Among them,

[0072] P0 is the inherent load of the power grid in the t-th period, and P EV is the charging load of electric vehicles in the t-th period, is the average value of the total power grid load including the charging load of electric vehicles in T periods.

[0073] At the same time, a minimum charging cost model for electric vehicle users is established:

[0074]

[0075] P EV is the charging load of electric vehicles in the t-th period, and π t is the charging electricity price of electric vehicles in the t-th period.

[0076] Specifically, the embodiment of the present application is also configured with an optimization scheduling process for electric vehicles based on a genetic algorithm, which is as follows:

[0077] In the 1970s, the genetic algorithm was first proposed by John Holland in the United States. Influenced by Darwin's theory of biological evolution, the genetic algorithm realizes random search and optimization by mimicking the evolution of biological populations. The genetic algorithm adopts the principle of survival of the fittest, transforms the problem-solving through coding into phenomena such as gene crossover and mutation in the process of biological evolution, and through gene mutation, the evolution of individuals appears in the population, so that the population can better adapt to the environment. The genetic algorithm finds the optimal solution according to the principle of survival of the fittest, and only retains the relatively optimal solutions as the basis for genetic operations to find better solutions. This cycle continues until the optimal solution is found.

[0078] The embodiment of the present application solves the optimization problem with multiple objectives and multiple constraints, and there may be a phenomenon of conflicting objectives during the operation. In order to achieve the purpose of simultaneously achieving the optimal solutions for multiple objectives, the embodiment of the present application adopts the parallel selection method as the method for solving multiple objectives.

[0079] The basic idea of the parallel selection genetic algorithm is to equally divide all individuals in the population into different groups according to the number of objective functions. Different groups correspond to different objective functions respectively. Select the individuals with high fitness for the objective function from each group, and merge these selected individuals into a new group. Perform operations such as crossover and mutation in the new group to make it more adaptable to the objective function. This cycle continues until the optimal solution that satisfies all objective functions is finally generated. As shown in the Figure 4 figure in the specification, it is the flow chart of the parallel selection genetic algorithm.

[0080] Specifically, the embodiment of the present application is also configured with an optimization scheduling modeling process for electric vehicle charging, which is as follows:

[0081] First, price analysis of time-of-use electricity price strategy:

[0082] The embodiment of the present application takes the actual daily load data of a certain regional power grid as a condition, assuming that a total of 2,000 electric vehicles participate in optimized charging. Before optimization, the disorderly charging electricity price of electric vehicles is 1 yuan / kWh, and the lowest charging electricity price cannot be lower than the comprehensive power generation cost electricity price of 0.25 yuan / kWh, and at the same time cannot be higher than 2 yuan / kWh. By referring to the reference literature, the price elasticity matrix of the peak-valley flat periods of electric vehicles can be obtained, which is as follows:

[0083]

[0084] Second, modeling of orderly charging of electric vehicles:

[0085] Using the parallel selection method to solve, an optimized scheduling model for the charging load of electric vehicles with multiple objectives and multiple constraints is obtained. The ordered charging load curve results guided by the electricity price of electric vehicles are as shown in the attached drawings of the specification Figure 5 as shown;

[0086] At the same time, the electricity prices for each period of valley, flat, and peak after iterative optimization are obtained, as shown in Table 2:

[0087]

[0088] Table 2

[0089] Comparing the results before and after optimization, as shown in Table 3 below:

[0090]

[0091] Table 3

[0092] Analyzing the optimized load results obtained from the simulation, it can be known that due to the implementation of the time-of-use electricity price strategy, more electric vehicles are connected to the grid for charging during the valley and flat periods, resulting in an increase in the grid load during the valley and flat periods; fewer electric vehicles are connected during the peak period, and the grid load decreases. This optimized scheduling strategy for electric vehicles, on the one hand, guides electric vehicle owners to change their original charging habits and choose the orderly charging method, which plays a great role in peak shaving and valley filling. Moreover, the optimized grid load curve fits better with the wind and light output curves, reducing the occurrence of abandoned electricity, and at the same time maintaining the stability of the grid operation; on the other hand, users can generate less charging costs by choosing to charge their electric vehicles during the valley period and normal times. Adopting the time-of-use electricity price strategy to guide electric vehicle owners to participate in charging optimization scheduling safeguards the interests of multiple parties.

[0093] Second, as shown in Figure 6 the embodiments of the present application provide a power scheduling device for balancing the charging load. The device includes:

[0094] A battery state of charge acquisition module, which is used to calculate and obtain the battery state of charge at the end of the trip based on the probability distribution of the daily driving mileage;

[0095] A starting charging time acquisition module, which is used to randomly extract the starting charging time according to the probability distribution of the starting charging time;

[0096] A charging load calculation module, which is used to calculate and obtain the charging load corresponding to each electric vehicle based on the charging power, starting charging time, and battery state of charge at the end of the trip corresponding to each electric vehicle;

[0097] A charging load curve acquisition module, which is used to accumulate and obtain a charging load curve based on the charging loads corresponding to the electric vehicles, where the abscissa of the charging load curve is time and the ordinate is the charging load;

[0098] A charging load curve analysis module, which is used to identify and obtain the peak value and the trough value of the charging load curve based on the charging load curve;

[0099] A charging optimization scheduling module, which is used to perform power optimization scheduling with the goal of minimizing the mean square deviation of the charging load curve based on the charging load curve, the peak value of the charging load curve, and the trough value of the charging load curve.

[0100] It should be noted that the technical solution of the embodiment of the present application can be applied to a charging station equipped with multiple charging piles, a charging station management platform, a charging parking lot of a building, or a power grid power supply management system of a certain area, and is used to perform power scheduling according to the situation of the electric vehicles it is responsible for charging, so as to ensure that the stability of the power grid operation in the affiliated area is not affected.

[0101] In the embodiment of the present application, based on the charging time and charging parameters of the electric vehicles, statistical analysis is carried out to obtain a charging load curve, and with the goal of minimizing the mean square deviation of the charging load curve, it provides a data basis for charging optimization scheduling and ensures that the stability of the power grid operation is not affected.

[0102] Furthermore, the method is configured with a power optimization scheduling target formula, and the power optimization scheduling target formula takes the minimum mean square deviation of the charging load curve as the objective function. The power optimization scheduling target formula is:

[0103] Wherein,

[0104] P0 is the inherent load of the power grid in the t period, P EV is the charging load of the electric vehicle in the t period, is the average value of the total load of the power grid including the charging load of the electric vehicle in T periods.

[0105] It should be noted that the power scheduling device for balancing the charging load mentioned in the embodiment of the present application is similar in technical problems, technical solutions, and technical effects to the technical principle of the power scheduling method for balancing the charging load mentioned in the first aspect, and will not be elaborated here.

[0106] In the description of the present application, it should be noted that the orientation or positional relationship indicated by terms such as "upper" and "lower" is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present application. Unless otherwise clearly specified and defined, the terms "installed", "connected" and "coupled" shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0107] It should be noted that in the present application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

[0108] The above are only specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined in the embodiments of the present application can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown in the embodiments of the present application, but will conform to the broadest scope consistent with the principles and novel features claimed in the embodiments of the present application.

Claims

1. A power dispatching method for balancing charging loads, characterized in that, The method includes the following steps: Based on the probability distribution of the daily driving mileage, calculate the state of charge of the battery at the end of the trip; According to the probability distribution of the starting charging time, randomly select the starting charging time; Based on the charging power corresponding to each electric vehicle, the starting charging time, and the state of charge of the battery at the end of the trip, calculate the charging load corresponding to each electric vehicle; Based on the charging loads corresponding to each of the electric vehicles, accumulate to obtain a charging load curve, where the abscissa of the charging load curve is time and the ordinate is the charging load; Based on the charging load curve, identify the peak value and the trough value of the charging load curve; Based on the charging load curve, the peak value of the charging load curve, and the trough value of the charging load curve, with the minimum mean square error of the charging load curve as the goal, perform power optimization scheduling.

2. The power dispatching method for balancing charging loads according to claim 1, characterized in that The method is configured with a power optimization scheduling target formula. The power optimization scheduling target formula takes the minimum mean square error of the charging load curve as the objective function, and the power optimization scheduling target formula is: Among them, $P_0$ is the inherent load of the power grid during period $t$, and $P$ EV is the charging load of electric vehicles during period $t$, and $\overline{P}$ is the average value of the total load of the power grid including the charging load of electric vehicles over $T$ periods.

3. A power dispatching device for balancing charging loads, characterized in that, The device includes: A state of charge acquisition module, which is used to calculate the state of charge of the battery at the end of the trip based on the probability distribution of the daily driving mileage; A starting charging time acquisition module, which is used to randomly select the starting charging time according to the probability distribution of the starting charging time; A charging load calculation module, which is used to calculate the charging load corresponding to each electric vehicle based on the charging power corresponding to each electric vehicle, the starting charging time, and the state of charge of the battery at the end of the trip; A charging load curve acquisition module, which is used to accumulate to obtain a charging load curve based on the charging loads corresponding to each of the electric vehicles, where the abscissa of the charging load curve is time and the ordinate is the charging load; A charging load curve analysis module, which is used to identify the peak value and the trough value of the charging load curve based on the charging load curve; A charging optimization scheduling module, which is used to perform power optimization scheduling based on the charging load curve, the peak value of the charging load curve, and the trough value of the charging load curve, with the minimum mean square error of the charging load curve as the goal.

4. The power dispatching device for balancing charging loads according to claim 3, wherein, The method is configured with a power optimization scheduling target formula. The power optimization scheduling target formula takes the minimum mean square error of the charging load curve as the objective function, and the power optimization scheduling target formula is: Among them, $P_0$ is the inherent load of the power grid during period $t$, and $P$ EV is the charging load of electric vehicles during period $t$, and $\overline{P}$ is the average value of the total load of the power grid including the charging load of electric vehicles over $T$ periods.

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