A method for configuring capacity of a light storage public bus charging station considering light storage operation conditions

Abstract

The application specifically relates to a photovoltaic and energy storage bus charging station capacity configuration method considering photovoltaic and energy storage operation conditions, characterized by comprising the following steps: firstly, considering bus charging station charging load characteristics, determining energy storage charging and discharging state threshold values through a moving boundary method, and constructing more optimal photovoltaic and energy storage bus charging station operation conditions and operation conditions; secondly, taking maximization of photovoltaic utilization rate and minimization of system investment and operation cost as targets, adopting a particle swarm algorithm for optimization and solution, and determining optimal photovoltaic and energy storage capacities of the photovoltaic and energy storage bus charging station, so that more reasonable photovoltaic and energy storage capacities can be obtained under the same configuration condition by considering load characteristics and operation conditions.

Description

Technical Field

[0001] This invention specifically relates to a method for configuring the capacity of photovoltaic-storage bus charging stations, taking into account the operating conditions of photovoltaic-storage systems. Background Technology

[0002] Integrated photovoltaic (PV) and energy storage (ESD) charging stations, combining bus charging stations with PV energy storage systems, offer better economic benefits and lower carbon emissions. However, the economic benefits vary significantly depending on the capacity matching of different PV and ESD systems. There are two main strategies for addressing the economics of setting the capacity of PV-ESD bus charging stations. The first is to fix the PV capacity, finding the optimal ESD capacity based on the overall equipment cost. The second is to simultaneously optimize the PV and ESD capacities, using parameters such as PV utilization and penetration rate to describe the matching degree between PV and ESD capacities, thus determining the optimal PV-ESD capacity.

[0003] Currently, most methods for photovoltaic and energy storage new energy bus charging stations do not consider the impact of the operation strategies of photovoltaic and energy storage systems, or the characteristics of the load on the capacity of photovoltaic energy storage. Summary of the Invention

[0004] The purpose of this invention is to provide a method for configuring the capacity of photovoltaic and energy storage bus charging stations that takes into account the operating conditions of photovoltaic and energy storage, so that the charging stations can find a more suitable and economical photovoltaic and energy storage capacity for the load from the perspective of economy and efficiency.

[0005] This invention is achieved through the following technical solution:

[0006] Specifically, 1. A method for configuring the capacity of a photovoltaic-storage bus charging station considering the operating conditions of photovoltaic and energy storage, characterized by including the following steps:

[0007] Step 1: Obtain charging station load data and bus scheduling data through the charging pile cloud platform;

[0008] Step 2: Discretize the daily power demand curve by Y hours and represent it using interval symbols:

[0009]

[0010] Step 3: Based on the load characteristics analysis of the bus charging station, construct a new energy storage float charging-charging boundary variable B for the photovoltaic-storage bus charging station operation using the moving boundary method. fv and discharge-float charge boundary variable B pf ;

[0011] The daily load of the 24-hour photovoltaic charging station is L l ={L l1 ,L l2 ,…,L l24}, the daily power generation of the photovoltaic system is L p = {L p1 , L p2 , …, L p24}, the power difference value L = {L1, L2, …, L 24} is obtained, and the new set L r = {L r1 , L r2 , …, L r24} is obtained after the elements of the set are arranged in ascending order;

[0012] The energy storage float charging boundary variable B fv and the discharge-float boundary variable B pf are two elements in the set L r and satisfy B fv < B pf , the daily load sequence mean square distance F(B fv , B pf ) is taken as the objective function, and the minimum value of the objective function is found by moving B fv and B pf in the position of L r , and the mathematical model is as follows:

[0013]

[0014] In the formula, m represents the time period of the energy storage system float charging, charging and discharging; i = 1, 2, …, 24 represents the time point; represents the load sequence clustering center in the m period, which is calculated by the following formula:

[0015]

[0016] In the formula, N m represents the number of loads in the period m;

[0017] The optimal solution B fv and B pf obtained by the method are respectively taken as the charging threshold and the discharging threshold of the energy storage system, when the daily load is less than or equal to the charging threshold B fv , it is determined that the energy storage system needs to be charged at the current time; when the daily load is greater than the discharging threshold B pf , it is determined that the energy storage system needs to be discharged at the current time;

[0018] Step four: according to the energy storage charging and discharging threshold of step three, the operation condition of photovoltaic energy storage and power grid is determined;

[0019] The operation of the integrated photovoltaic energy storage and charging station of the bus is divided into off-grid and grid-connected operation, and in the grid-connected state, the power grid and the charging station perform bidirectional energy interaction;

[0020] The off-grid operation has the following working conditions:

[0021] Working condition one: photovoltaic as the energy source of the bus charging station, electric bus and energy storage as the consumption object of photovoltaic energy;

[0022] Working condition two: photovoltaic and energy storage as the energy source of the bus charging station, electric bus as the consumption object of photovoltaic and energy storage energy;

[0023] The grid-connected operation mode has the following working conditions:

[0024] Working condition three: the grid as the energy source of the bus charging station, electric bus and energy storage as the consumption object of electric energy;

[0025] Working condition four: the grid and photovoltaic as the energy source of the bus charging station, electric bus and energy storage as the consumption object of electric energy;

[0026] Working condition five: the grid, photovoltaic and energy storage as the energy source of the bus charging station, electric bus as the consumption object of electric energy;

[0027] Working condition six: photovoltaic as the energy source of the bus charging station, electric bus, energy storage and grid as the consumption object of electric energy;

[0028] Step five: taking the lowest comprehensive daily operation cost of the photovoltaic and energy storage charging station, the highest photovoltaic penetration rate and the highest consumption degree as the objective function of the optimal configuration of the photovoltaic and energy storage capacity of the photovoltaic and energy storage bus charging station, taking the power balance, the energy storage charge and discharge rate limit and the photovoltaic power limit as the constraint conditions, taking the photovoltaic and energy storage capacity as the optimization quantity, and obtaining the optimal photovoltaic capacity and the matched energy storage capacity through the particle swarm optimization algorithm;

[0029] The objective function is as follows:

[0030] (1) Taking the lowest comprehensive daily operation cost of the photovoltaic and energy storage charging station as the first objective function:

[0031] minF1=C1+C2+C3-C4

[0032] In the formula: C1 is the initial daily equivalent cost of the bus charging station photovoltaic and energy storage; C2 is the daily maintenance cost of the bus charging station photovoltaic and energy storage; C3 is the daily power purchase cost of the bus charging station to the grid; and C4 is the daily income of the photovoltaic and energy storage system;

[0033] The calculation of each cost value is as follows:

[0034]

[0035] C buttery =C e E+C p P b.max

[0036]

[0037]

[0038]

[0039]

[0040] wherein C pv is the cost of photovoltaic, C buttery is the cost of energy storage system, r is the discount rate, m1 is the service life of charging station, m2 is the service life of energy storage battery; C v , C e , C p are the prices of photovoltaic cell module, unit capacity energy storage battery and power conversion device respectively, E, P b.max are the maximum power generation of photovoltaic system, the capacity of energy storage system and the maximum charge-discharge power of energy storage system respectively, k but is the annual operation and maintenance coefficient of energy storage system, x t is the electricity price at time t, P t is the equivalent power purchased from the grid at time t, T is the number of daily time periods with a sampling interval of Δt, is the discharge power of energy storage system at time t, is the direct utilization power of photovoltaic at time t, including the power used for selling electricity to the grid and charging electric vehicles;

[0041] (2) Taking the highest photovoltaic penetration rate as the second objective function:

[0042]

[0043] wherein P t pv is the power generation of photovoltaic module at time t; P t load is the load power size of charging station demand at time t;

[0044] (3) Taking the photovoltaic consumption degree as the third objective function:

[0045]

[0046] Constraint conditions:

[0047] (1) Power balance constraint;

[0048] The power of photovoltaic, energy storage, grid and vehicle charging of the photovoltaic and energy storage public transit charging station needs to maintain power balance under each operating condition;

[0049]

[0050] (2) Energy storage charge and discharge rate limit;

[0051] The charge and discharge capacity of the energy storage system is subject to the capacity of the matched power converter, and too high charge and discharge rate will directly affect the service life of the energy storage system, in order to maintain the normal use of the energy storage, the upper limit of the charge and discharge rate is set to 0.4C, and the constraint condition of the energy storage is as follows:

[0052]

[0053] In the formula: k is the energy storage charge and discharge rate;

[0054] (3) Photovoltaic power constraint;

[0055] The installed area of photovoltaic is limited, and the photovoltaic power generation needs to be within the limited range, and the formula is as follows:

[0056]

[0057] The particle swarm algorithm described in the application is a search algorithm, and the computer automatically searches for the optimal solution in the feasible domain according to the objective function and the constraint condition.

[0058] The application considers the operation strategy of the photovoltaic and energy storage system and the characteristics of the load which affect the optimization of the system cost, optimizes the operation condition of the light and storage charging station, and makes the charging station find more suitable and economic photovoltaic and energy storage capacity for the load. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 Flow chart of photovoltaic and energy storage capacity optimization of light and storage bus charging station. DETAILED DESCRIPTION

[0060] The embodiments of the technical scheme of the application will be described in detail below with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical scheme of the application, and therefore only serve as examples, and cannot limit the protection scope of the application. It should be noted that, unless otherwise specified, the technical terms or scientific terms used in the application should be understood as the usual meaning understood by the skilled person in the art to which the application belongs.

[0061] Step one: obtain the charging station load data and obtain the bus vehicle scheduling data through the charging pile cloud platform data;

[0062] Step two: since the power demand is a continuous value with time, the optimization needs to be discretized, and 96 data points are discretized according to 0.25 hour intervals for 24 hours, and the expression form is as follows

[0063] [P need,t1,P need,t2 ,P need,t3 ,...,P need,t96 ]

[0064] Step 3: Based on the load characteristics analysis of the bus charging station, construct a new energy storage float charging-charging boundary variable B for the photovoltaic-storage bus charging station operation using the moving boundary method. fv and discharge-float charge boundary variable B pf Assume the daily load of the 24-hour photovoltaic charging station is L. l ={L l1 ,L l2 ,…,L l24 The daily power generation of the photovoltaic system is L. p ={L p1 ,L p2 ,…,L p24}, thus obtaining the power difference L = {L1, L2, ..., L 24}, sorting the elements of this set in ascending order yields a new set L. r ={L r1 ,L r2 ,…,L r24 This method assumes that the energy storage float charging-charging boundary variable B fv and discharge-float charge boundary variable B pf Let B be two elements in set Lr that satisfy B fv pf The mean square distance F(B) of the daily load sequence fv B pf Let B be the objective function, and by moving B... fv and B pf In L r The mathematical model for finding the minimum value of the objective function at the position in the matrix is ​​as follows:

[0065]

[0066] In the formula: m represents the time period of float charging, charging and discharging of the energy storage system; i = 1, 2, ..., 24 represents the time point; The cluster center of the load sequence in time period m can be calculated by the following formula:

[0067]

[0068] In the formula, N m This represents the number of loads in time period m.

[0069] The optimal solution B is obtained using this method. fv and B pf These are respectively used as the charging threshold and discharging threshold for the energy storage system. If the daily load is less than or equal to the charging threshold B... fv ​When the current time needs to be charged by the energy storage system; when the daily load is greater than the discharge threshold B pf When the current time needs to be discharged by the energy storage system.

[0070] Step four: according to the energy storage charge and discharge threshold of step three, the operation condition of photovoltaic energy storage and power grid is determined.

[0071] The operation of the integrated photovoltaic energy storage and charging station for public transport is mainly divided into off-grid and grid-connected operation, wherein in the grid-connected state, the power grid can interact with the charging station in a bidirectional manner.

[0072] The off-grid operation mainly has the following working conditions:

[0073] Working condition one: photovoltaic energy is used as the energy source of the public transport charging station, and electric buses and energy storage are used as the energy consumption objects of the photovoltaic energy.

[0074] Working condition two: photovoltaic energy and energy storage are used as the energy source of the public transport charging station, and electric buses are used as the energy consumption objects of the photovoltaic energy and energy storage.

[0075] The grid-connected operation mainly has the following working conditions:

[0076] Working condition three: the power grid is used as the energy source of the public transport charging station, and electric buses and energy storage are used as the energy consumption objects of the electric energy.

[0077] Working condition four: the power grid and photovoltaic energy are used as the energy source of the public transport charging station, and electric buses and energy storage are used as the energy consumption objects of the electric energy.

[0078] Working condition five: the power grid, photovoltaic energy and energy storage are used as the energy source of the public transport charging station, and electric buses are used as the energy consumption objects of the electric energy.

[0079] Working condition six: photovoltaic energy is used as the energy source of the public transport charging station, and electric buses, energy storage and the power grid are used as the energy consumption objects of the electric energy.

[0080] Step five: on the basis of the public transport charging station, the installation cost of photovoltaic energy and energy storage needs to be considered, as well as the income ability under the capacity of photovoltaic energy and energy storage, and the photovoltaic energy consumption. The present application takes the lowest comprehensive daily operation cost, the highest photovoltaic penetration rate and the highest consumption degree of the integrated photovoltaic energy storage and charging station for public transport as the objective function of the optimal configuration of the photovoltaic energy storage capacity, takes the power balance, the energy storage charge and discharge rate limit, and the photovoltaic power limit as the constraint condition, takes the photovoltaic energy storage capacity as the optimization quantity, and obtains the optimal photovoltaic capacity and the matching energy storage capacity through the particle swarm optimization algorithm;

[0081] The objective function is as follows:

[0082] (1) The first objective function is to minimize the comprehensive daily operation cost of the photovoltaic energy storage and charging station:

[0083] minF1=C1+C2+C3-C4

[0084] In the formula: C1 is the initial daily equivalent cost of photovoltaic and energy storage construction for the bus charging station; C2 is the daily maintenance cost of photovoltaic and energy storage for the bus charging station; C3 is the daily electricity purchase cost from the grid for the bus charging station; and C4 is the daily revenue of the photovoltaic and energy storage system.

[0085] The calculation of each cost value is shown in the following formula:

[0086]

[0087] C buttery =C e E+C p P b.max

[0088]

[0089]

[0090]

[0091]

[0092] In the formula: C pv For photovoltaic costs, C buttery Where C is the cost of the energy storage system, r is the discount rate, m1 is the service life of the charging station, and m2 is the lifespan of the energy storage battery; v C e C p These are the prices of photovoltaic cell modules (ten thousand yuan / kW), energy storage batteries per unit capacity (yuan / kWh), and power conversion devices (yuan / kW). E, P b.max These represent the maximum power generation of the photovoltaic system (kW), the capacity of the energy storage system (kWh), and the maximum charge / discharge power of the energy storage system, respectively; k but x is the annual operation and maintenance factor for the energy storage system. t Let P be the grid electricity price at time t. t P represents the equivalent power purchased from the grid at time t, where T is the number of time periods per day with a sampling interval of Δt; t buttery Let P be the discharge power of the energy storage system at time t. t pv1 Let t be the direct power utilized by photovoltaics, including power sold to the grid and power used for charging electric vehicles.

[0093] (2) The second objective function is to take the highest photovoltaic penetration rate:

[0094]

[0095] P = Ppv + Pch + Pgrid + Pveh t pv P is the power of the photovoltaic module at time t; P t load P is the load power size of the demand charge of the charging station at time t.

[0096] (3) The photovoltaic consumption degree is taken as a third objective function:

[0097]

[0098] Constraint:

[0099] (1) Power balance constraint. The power of photovoltaic, energy storage, power grid and vehicle charging of the light and storage public transportation charging station needs to maintain power balance under each operating condition.

[0100]

[0101] (2) Energy storage charge and discharge rate limit. The charge and discharge capacity of the energy storage system is subject to the capacity of the matched power converter, and too high charge and discharge rate will directly affect the service life of the energy storage system. In order to maintain the normal use of the energy storage, the upper limit of the charge and discharge rate is set to 0.4C, and the constraint condition of the energy storage is as follows:

[0102]

[0103] In the formula, k is the charge and discharge rate of the energy storage.

[0104] (3) Photovoltaic power constraint. The photovoltaic cell module is mainly composed of silicon material, and its processing is mainly related to the intensity of the sun. The public transportation charging station is transformed into a light and storage charging station, and the photovoltaic needs a certain area for installation, most of which are installed in the high places such as roof and direct sunlight, so the area of the photovoltaic that can be installed is limited, and the photovoltaic power needs to be within the limited range, and the formula is as follows:

[0105]

[0106] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the specification of the present application.

Claims

1. A method for configuring the capacity of photovoltaic-storage bus charging stations considering photovoltaic-storage operating conditions, characterized in that... Includes the following steps: Step 1: Obtain charging station load data and bus scheduling data through the charging pile cloud platform; Step 2: Discretize the daily power demand curve by Y hours and represent it using interval symbols: Step 3: Based on the load characteristics analysis of the bus charging station, construct a new energy storage float charging-charging boundary variable B for the photovoltaic-storage bus charging station operation using the moving boundary method. fv and discharge-float charge boundary variable B pf ; The daily load of the 24-hour photovoltaic charging station is L l ={L l1 ,L l2 ,…,L l24 The daily power generation of the photovoltaic system is L. p ={L p1 ,L p2 ,…,L p24 }, thus obtaining the power difference L = {L1, L2, ..., L 24 }, sorting the elements of this set in ascending order yields a new set L. r ={L r1 ,L r2 ,…,L r24 }; Energy storage float charging - charging boundary variable B fv and discharge-float charge boundary variable B pf For set L r Two elements in the array that satisfy B fv pf The mean square distance of the daily load sequence F(B) fv B pf Let B be the objective function, and by moving B... fv and B pf In L r The mathematical model for finding the minimum value of the objective function at the position in the matrix is ​​as follows:​ In the formula: m represents the time period of float charging, charging and discharging of the energy storage system; i = 1, 2, ..., 24 represents the time point; The cluster center of the load sequence in time period m is represented by the following formula: In the formula, N m The number of loads in time period m; The optimal solution B is obtained using this method. fv and B pf These are respectively used as the charging threshold and discharging threshold of the energy storage system. When the daily load is less than or equal to the charging threshold B... fv When it is determined that the energy storage system needs to be charged at the current moment; when the daily load is greater than the discharge threshold B. pf At that time, it is determined that the energy storage system needs to discharge. Step 4: Determine the operating conditions of photovoltaic energy storage and the power grid based on the energy storage charging and discharging thresholds determined in Step 3. The operation of the photovoltaic-storage-charging integrated bus charging station is divided into off-grid and grid-connected operation. In the grid-connected state, the power grid and the charging station carry out bidirectional energy interaction. Off-grid operation has the following operating conditions: Operating Condition 1: Photovoltaics serve as the energy source for bus charging stations, while electric buses and energy storage are the objects that consume photovoltaic power. Operating Scenario 2: Photovoltaics and energy storage serve as the energy source for bus charging stations, while electric buses are the recipients of photovoltaic and energy storage energy. The grid-connected operation modes include the following operating conditions: Operating Condition 3: The power grid serves as the energy source for bus charging stations, while electric buses and energy storage are the objects of electricity consumption; Operating Condition 4: The power grid and photovoltaics serve as the energy source for bus charging stations, while electric buses and energy storage serve as the objects of electricity consumption; Operating Condition 5: The power grid, photovoltaics, and energy storage serve as the energy sources for bus charging stations, while electric buses are the recipients of the electricity. Operating Condition 6: Photovoltaics serve as the energy source for bus charging stations, while electric buses, energy storage, and the power grid act as the recipients of the electricity. Step 5: The objective function for optimizing the configuration of photovoltaic and energy storage capacity of the photovoltaic and energy storage bus charging station is to achieve the lowest comprehensive daily operating cost, the highest photovoltaic penetration rate, and the highest absorption rate. With power balance, energy storage charge and discharge rate limit, and photovoltaic power limit as constraints, the photovoltaic and energy storage capacity is used as the optimization quantity. The optimal photovoltaic capacity and its matching energy storage capacity are obtained through particle swarm optimization algorithm. The objective function is as follows: (1) The first objective function is to minimize the overall daily operating cost of the photovoltaic-storage charging station: minF1=C1+C2+C3-C4 In the formula: C1 is the initial daily equivalent cost of photovoltaic and energy storage construction for bus charging stations; C2 is the daily maintenance cost of photovoltaic and energy storage for bus charging stations; C3 is the daily electricity purchase cost from the grid for bus charging stations; C4 is the daily revenue of the photovoltaic and energy storage system. The calculation of each cost value is shown in the following formula: C buttery =C e E+C p P b.max In the formula: C pv For photovoltaic costs, C buttery Where C is the cost of the energy storage system, r is the discount rate, m1 is the service life of the charging station, and m2 is the lifespan of the energy storage battery; v C e C p These are the prices of photovoltaic cell modules, energy storage batteries per unit capacity, and power conversion devices. E, P b.max These represent the maximum power generation of the photovoltaic system, the capacity of the energy storage system, and the maximum charge / discharge power of the energy storage system, respectively, k. but x is the annual operation and maintenance factor of the energy storage system. t Let P be the grid electricity price at time t. t Let P be the equivalent power purchased from the grid at time t, where T is the number of time periods per day with a sampling interval of Δt. t buttery Let P be the discharge power of the energy storage system at time t. t pv1 The direct power utilized by photovoltaics at time t includes the power sold to the grid and the power used for charging electric vehicles. (2) The second objective function is to take the highest photovoltaic penetration rate: In the formula: P t pv P represents the power output of the photovoltaic module at time t. t load The magnitude of the load power required to charge the charging station at time t; (3) Using the photovoltaic absorption rate as the third objective function: Constraints: (1) Power balance constraints; The power of photovoltaic, energy storage, grid and vehicle charging in photovoltaic-storage bus charging stations needs to be balanced under every operating condition; (2) Limitation on energy storage charge / discharge rate; The charging and discharging capacity of an energy storage system is limited by the capacity of its power converter, and excessively high charging and discharging rates can directly affect the lifespan of the energy storage system. To ensure the normal operation of energy storage, the upper limit of the charging and discharging rate is set at 0.4C. The constraints on energy storage are as follows: Where: k is the energy storage charge / discharge rate; (3) Photovoltaic power constraint; There are certain limitations on the area that can be installed with photovoltaic systems, and the photovoltaic power generation capacity must be within these limitations, as shown in the following formula:

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