Optical storage charging station multi-objective optimization configuration method based on full life cycle carbon emission

Abstract

The invention relates to an optical storage charging station multi-objective optimization configuration method based on full-life-cycle carbon emission, and belongs to the technical field of electric power planning and design. The method comprises the following steps: S1, based on a full life cycle evaluation method, identifying a carbon emission boundary of the optical storage charging station, and constructing a carbon emission refined model of the optical storage charging station; s2, constructing a multi-target optical storage capacity optimal configuration mathematical model of the optical storage charging station by taking the maximum annual economic benefit and the minimum annual carbon emission as optimization targets; and S3, solving the optimal configuration mathematical model by adopting a multi-target particle swarm optimization algorithm to obtain a Pareto solution set, and selecting an optimal solution of the Pareto solution set by adopting an entropy weight TOPSIS method. According to the method, the full-life-cycle carbon emission of the optical storage charging station can be accurately calculated, an optical storage charging station capacity configuration scheme considering economical efficiency and low-carbon property is obtained, and theoretical support and technical reference can be provided for investment construction and operation of the optical storage charging station.

Description

Technical Field

[0001] The present invention belongs to the technical field of power planning and design, and relates to a multi-objective optimal configuration method for a photovoltaic energy storage charging station based on refined modeling of carbon emissions over the entire life cycle. Background Art

[0002] As a new type of energy supply facility, a photovoltaic energy storage charging station integrates a photovoltaic power generation system and an energy storage battery system, utilizes clean energy to a certain extent, reduces the dependence on the external power grid, and thus reduces the overall carbon emission level of the power station. However, the following key problems restricting development exist in the prior art:

[0003] (1) Lack of modeling of carbon emissions over the entire life cycle: The current capacity optimization configuration methods mainly focus on the initial investment cost of the system and the analysis of operation economy, and have not established a carbon emission accounting model covering equipment production and manufacturing, transportation and installation, operation and maintenance, and scrapping and recycling. Existing research only simplifies and estimates the carbon emissions during the operation stage, resulting in the carbon emission reduction potential in links such as equipment material selection and energy scheduling strategies not being fully exploited.

[0004] (2) Insufficient quantification of dynamic coupling relationships: There is a strong coupling relationship between the photovoltaic output fluctuation, the energy storage charge and discharge strategy, and the time-varying characteristics of the charging load during the operation of the system. However, the existing static configuration model is difficult to accurately quantify the impact of the dynamic interaction of the three on carbon emissions. Especially in the multi-time scale scenario, dynamic factors such as the cycle life attenuation of the energy storage battery and the efficiency degradation of the photovoltaic module are not included in the carbon emission calculation framework.

[0005] (3) Weak adaptability of carbon constraint boundary conditions: The existing configuration schemes lack the ability to respond to policy constraints such as the carbon quota trading mechanism and the dynamic changes of the carbon emission factor of the regional power grid. With the improvement of the maturity of the global carbon trading market, the optimization methods without establishing an internal carbon cost model will be difficult to meet the compliance requirements of future policy tools such as carbon tariffs and green certificate quotas.

[0006] The above technical defects lead to the incapability of the capacity optimization configuration scheme aiming at carbon emission reduction to balance the economic and environmental protection indicators, resulting in the disconnection between the theoretical optimization results and the actual engineering requirements. This not only restricts the release of carbon emission reduction benefits of the photovoltaic energy storage charging station over the entire life cycle, but also is difficult to adapt to the increasingly strict carbon emission supervision system under the goals of carbon peak and carbon neutrality. Summary of the Invention

[0007] In view of this, the purpose of the present invention is to provide a multi-objective optimal configuration method for a photovoltaic energy storage charging station based on refined modeling of carbon emissions over the entire life cycle, so as to solve the problems of insufficient accuracy of carbon emission accounting of the existing photovoltaic energy storage charging station and the deviation of the optimization configuration results aiming at carbon emission reduction from the actual situation.

[0008] To achieve the above purpose, the present invention provides the following technical solutions:

[0009] A multi-objective optimal configuration method for a photovoltaic-storage charging station based on refined modeling of carbon emissions throughout the life cycle. The method specifically includes the following steps:

[0010] S1: Based on the life cycle assessment method, identify the carbon emission boundary of the photovoltaic-storage charging station and construct a refined model of the carbon emissions of the photovoltaic-storage charging station.

[0011] S2: Construct a multi-objective mathematical model for optimizing the configuration of the photovoltaic-storage capacity of the photovoltaic-storage charging station with the maximum annual economic benefit and the minimum annual carbon emissions as the optimization objectives.

[0012] S3: Use the multi-objective particle swarm optimization algorithm to solve the optimization configuration mathematical model to obtain the Pareto solution set, and use the entropy weight TOPSIS method to select the optimal solution of the Pareto solution set.

[0013] Furthermore, in step S1, based on the life cycle assessment method, identify the carbon emission boundary of the photovoltaic-storage charging station and construct a refined life cycle carbon emission model of the photovoltaic-storage charging station. Its expression is:

[0014]

[0015] Among them, EQ is the equipment type, and PV, ES, CP, and C are photovoltaic, energy storage battery, charging pile, and inverter respectively; E EQ 、E EQ,geg 、E EQ,peg 、E EQ,teg 、E EQ,oeg 、E EQ,reg are the carbon emissions of the PSCIS equipment EQ in the whole life cycle, raw material acquisition link, production and manufacturing link, transportation link, operation link, and waste recycling link respectively; PSCIS represents the photovoltaic energy storage charging station;

[0016] The carbon emission model E EQ,geg of the raw material acquisition link of the PSCIS equipment EQ is:

[0017]

[0018] Among them, Q e,i is the carbon emission coefficient of the i-th raw material; g EQ,i,a is the weight of the i-th raw material required for the production of each unit of the equipment EQ component; N EQ is the number of equipment EQ components; P EQ,a is the rated output power of the unit equipment EQ component; n is the number of types of raw materials required for the production of the photovoltaic-storage charging station;

[0019] The carbon emission model E EQ,peg of the production and manufacturing link of the PSCIS equipment EQ is:

[0020]

[0021] Among them, Q j,l is the carbon emission coefficient of the l-th auxiliary material in the j-th process; g EQ,j,l,a is the consumption of the l-th auxiliary material in the j-th process in the production process of each unit of equipment EQ component; e EQ,j is the power consumption of the j-th process in the production process of each unit of equipment EQ component; EF is the carbon emission factor of the regional power grid; m is the total number of process flows in the production and manufacturing links of each device in the photovoltaic energy storage charging station; z is the number of types of auxiliary materials required for the production of the photovoltaic energy storage charging station;

[0022] The carbon emission amount E of the transportation link of the PSCIS device EQ EQ,teg The model is:

[0023]

[0024] Among them, Q r is the carbon emission intensity of transporting an object with a unit weight per unit distance using the r-th type of vehicle; g EQ,a is the weight of each unit of equipment EQ component; L EQ,r is the transportation distance of each unit of equipment EQ component using the r-th type of vehicle;

[0025] The carbon emission amount E of the operation link of the PSCIS device EQ EQ,oeg The model is:

[0026] E EQ,oeg = e oeg EF (5)

[0027] Among them, e oeg is the electricity purchase amount from the superior power grid during the whole life cycle of the PSCIS;

[0028] The carbon emission amount E of the scrapping and recycling link of the PSCIS device EQ EQ,reg The model is:

[0029]

[0030] Among them, Q l is the carbon emission coefficient of the l-th auxiliary material used in material recycling and waste disposal; g EQ,l,a is the consumption of the l-th auxiliary material used in material recycling and waste disposal of each unit of equipment EQ component; g EQ,i,re,a is the recoverable amount of the i-th raw material of each unit of equipment EQ component.

[0031] Furthermore, in step S2, a mathematical model for maximizing the economic benefits of the photovoltaic-storage charging station is constructed, specifically including: The annual economic benefit objective function of the photovoltaic-storage charging station is mainly composed of the annual operating cost and the annual economic income. Among them, the annual economic income is composed of the annual charging income, the annual value of the charging pile installation subsidy, etc.; the annual operating cost is composed of the annual value investment cost, the annual operation and maintenance cost, and the annual power purchase cost from the superior power grid. As shown in the following formula:

[0032] maxR=max(R m +R g -C b -C i -C m ) (7)

[0033] Among them, R, R m 、R g are the annual economic benefits, the annual charging service income, and the annual value of the charging pile installation subsidy of the photovoltaic-storage charging station respectively; C i 、C m 、C b are the annual value investment cost, the annual operation and maintenance cost, and the annual power purchase cost from the superior power grid of the photovoltaic-storage charging station respectively;

[0034] The mathematical model of the annual charging service income R m of the photovoltaic-storage charging station is:

[0035]

[0036] Among them, l(t) is the charging unit price of electric vehicles per unit time t; P CP (t) is the output power of the charging pile per unit time t; T is the charging time of electric vehicles.

[0037] The mathematical model of the annual value R g of the charging pile installation subsidy of the photovoltaic-storage charging station is:

[0038]

[0039] Among them, p is the subsidy amount per charging pile; N CP is the number of charging piles configured; y CP is the service life of the charging pile; r is the discount rate;

[0040] The mathematical model of the annual value investment cost C i of the photovoltaic-storage charging station is:

[0041]

[0042] Among them, C EQ,a is the unit price of the EQ component of the unit device; y is the EQ operation life of the PSCIS device;

[0043] Annual operation and maintenance cost C of the photovoltaic and energy storage charging station m The mathematical model is as follows:

[0044]

[0045] Among them, β EQ is the operation and maintenance cost per unit power of the equipment EQ of the photovoltaic and energy storage charging station; P EQ (t) is the output power of the equipment EQ per unit time t;

[0046] Annual power purchase cost C of the photovoltaic and energy storage charging station from the superior power grid b The mathematical model is as follows:

[0047]

[0048] Among them, g(t) is the electricity price for purchasing electricity from the superior power grid per unit time t; P buy (t) is the power of the photovoltaic and energy storage charging station for purchasing electricity from the superior power grid per unit time t.

[0049] Furthermore, in step S2, a mathematical model for minimizing the annual carbon emissions of the photovoltaic and energy storage charging station is constructed, specifically including: calculating the total life cycle carbon emissions of the photovoltaic and energy storage charging station based on the established total life cycle carbon emission model of the photovoltaic and energy storage charging station, and using the equal annual value method to calculate the equal annual value carbon emissions of the total life cycle of the photovoltaic and energy storage charging station, as shown in the following formula:

[0050]

[0051] Among them, E k is the equal annual value carbon emissions of the PSCIS equipment EQ for the total life cycle.

[0052] Furthermore, in step S2, the constraint conditions of the multi-objective optimal configuration mathematical model of the photovoltaic and energy storage capacity of the photovoltaic and energy storage charging station include:

[0053] (1) Equipment quantity constraint conditions of the photovoltaic and energy storage charging station, as shown in the following formula:

[0054]

[0055] Among them, N pv,max 、N pv,min 、N sto,max 、N sto,min are the upper and lower limits of the quantity of the photovoltaic power generation system and the energy storage battery system components respectively;

[0056] (2) Charge and discharge constraint conditions of the energy storage battery system of the photovoltaic and energy storage charging station, including state of charge constraint, charge and discharge power constraint, charging constraint, discharging constraint and self-discharge constraint;

[0057] The state of charge constraint of the energy storage battery system is as shown in the following formula:

[0058] 0.3 ≤ SOC sto (t) ≤ 0.9 (15)

[0059] Among them, SOC sto (t) is the state of charge of the energy storage battery system per unit time t;

[0060] The charge-discharge power constraint of the energy storage battery system is shown in the following formula:

[0061] -P sto,max (t) ≤ P sto (t) ≤ P sto,max (t) (16)

[0062] Among them, P sto,max (t) is the maximum charge-discharge power of the energy storage battery system per unit time t, and P sto (t) is the charge-discharge power of the energy storage battery system within unit time t;

[0063] The charging constraint of the energy storage battery system is shown in the following formula:

[0064] E sto (t + 1) = E sto (t)(1 - θ) Δt +P sto (t + 1)Δt (17)

[0065] Among them, E sto (t + 1) is the state of charge of the energy storage battery system at the next moment; θ is the charge retention ability of the energy storage battery system; Δt is the adjacent unit time interval; P sto (t + 1) is the charge-discharge power of the energy storage battery system at the next moment;

[0066] The discharging constraint of the energy storage battery system is shown in the following formula:

[0067] E sto (t + 1) = E sto (t)(1 - θ) Δt -P sto (t)Δt (18)

[0068] The self-discharge constraint of the energy storage battery system is shown in the following formula:

[0069] E sto (t + 1) = E sto (t)(1 - θ) Δt (19)

[0070] Furthermore, step S3 specifically includes the following steps:

[0071] S31: Set the basic optimization parameters and the upper and lower limits of the decision variables;

[0072] S32: Initialize the population and randomly generate the number of decision variables;

[0073] S33: Perform population iteration operations to obtain the Pareto solution set of the model;

[0074] First, calculate the fitness values of the initial population, then determine the objective function values of individuals and the population, and obtain the objective function values of the individuals in the new population; then, update the velocities and positions of the particles, and update the optimal values according to the dominance relationship; finally, update the non-dominated solution set of the archive.

[0075] S34: After obtaining the Pareto solution set of the model, use the entropy weight TOPSIS method to select the optimal solution of the Pareto solution set;

[0076] First, positiveize all elements in the Pareto solution set; then, standardize the positiveized Pareto solution set; finally, calculate the scores of the elements in the standardized Pareto solution set and normalize them to obtain the optimal solution among them.

[0077] The beneficial effects of the present invention are as follows: The method of the present invention takes the main component systems of the photovoltaic charging station, such as the photovoltaic power generation system, the energy storage power generation system, and the charging pile system, as the main body, conducts refined modeling of the carbon emissions of the photovoltaic charging station based on the full life cycle assessment method, and on this basis, establishes a multi-objective optimization configuration mathematical model for the photovoltaic and energy storage capacity of the photovoltaic charging station with the minimum annual carbon emissions and the minimum annual operating cost as the objectives. The present invention can improve the accuracy of the carbon emissions accounting of the photovoltaic charging station in the whole life cycle, and obtain a capacity configuration plan for the photovoltaic charging station that takes into account both economy and low carbon, and can provide technical guidance and data reference for the current investment and construction of the photovoltaic charging station.

[0078] Other advantages, objectives and features of the present invention will be described to some extent in the subsequent description, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, where:

[0080] Figure 1 is the flow chart of the multi-objective optimization configuration method for the photovoltaic charging station based on refined modeling of the carbon emissions in the whole life cycle of the present invention;

[0081] Figure 2 is the carbon emission boundary diagram for identifying the photovoltaic charging station of the present invention;

[0082] Figure 3Flow chart of the multi-objective particle swarm optimization algorithm used in the present invention;

[0083] Figure 4 Distribution diagram of the Pareto solution set in the embodiment of the present invention;

[0084] Figure 5 Typical daily scheduling of each configuration result in the embodiment of the present invention;

[0085] Figure 6 Composition diagram of the annualized carbon emissions of the whole life cycle in the embodiment of the present invention;

[0086] Figure 7 Percentage diagram of carbon emissions in each link of the whole life cycle under the compromise solution result in the embodiment of the present invention. Detailed implementation manners

[0087] The following uses specific specific examples to illustrate the implementation manners of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0088] Please refer to Figures 1 to 7 , the present invention provides a multi-objective optimal configuration method for a photovoltaic and energy storage charging station based on refined modeling of the whole life cycle carbon emissions. As Figure 1 shown, the method specifically includes the following steps:

[0089] S1: Based on the whole life cycle assessment method, identify the carbon emission boundary of the photovoltaic and energy storage charging station, and construct a refined model of the whole life cycle carbon emissions of the photovoltaic and energy storage charging station. Its expression is:

[0090]

[0091] Among them, EQ is the equipment type, and PV, ES, CP, and C are photovoltaic, energy storage battery, charging pile, and inverter respectively; E EQ , E EQ,geg , E EQ,peg , E EQ,teg , E EQ,oeg , E EQ,reg are the carbon emissions of the whole life cycle, raw material acquisition link, production and manufacturing link, transportation link, operation link, and waste recycling link of the PSCIS equipment EQ respectively;

[0092] The carbon emissions E of the raw material acquisition link of the photovoltaic and energy storage charging station EQ,gegThe model is as follows:

[0093]

[0094] Among them, Q e,i is the carbon emission coefficient of the i-th raw material; g EQ,i,a is the weight of the i-th raw material required for the production of each unit of the EQ component of the equipment; N EQ is the number of EQ components of the equipment; P EQ,a is the rated output power per unit of the EQ component of the equipment; n is the number of types of raw materials required for the production of the optical storage charging station;

[0095] The carbon emission amount E EQ,peg in the production and manufacturing process of the EQ of the PSCIS equipment is modeled as:

[0096]

[0097] Among them, Q j,l is the carbon emission coefficient of the l-th auxiliary material in the j-th process; g EQ,j,l,a is the consumption of the l-th auxiliary material in the j-th process in the production process of each unit of the EQ component of the equipment; e EQ,j is the power consumption of the j-th process in the production process of each unit of the EQ component of the equipment; EF is the carbon emission factor of the regional power grid; m is the total number of production processes in the production and manufacturing process of each equipment of the optical storage charging station; z is the number of types of auxiliary materials required for the production of the optical storage charging station;

[0098] The carbon emission amount E EQ,teg in the transportation process of the EQ of the PSCIS equipment is modeled as:

[0099]

[0100] Among them, Q r is the carbon emission intensity of transporting a unit weight object per unit distance using the r-th type of vehicle; g EQ,a is the weight of each unit of the EQ component of the equipment; L EQ,r is the transportation distance of each unit of the EQ component of the equipment using the r-th type of vehicle;

[0101] The carbon emission amount E EQ,oeg in the operation process of the EQ of the PSCIS equipment is modeled as:

[0102] E EQ,oeg = e oeg EF (5)

[0103] Among them, e oeg is the electricity purchase amount from the superior power grid during the whole life cycle of the PSCIS;

[0104] The carbon emission amount E EQ,reg in the scrapping and recycling process of the EQ of the PSCIS equipment is modeled as:

[0105]

[0106] Among them, Q l is the carbon emission coefficient of the l-th auxiliary material used in material recycling and waste disposal; g EQ,l,a is the amount of the l-th auxiliary material used in material recycling and waste disposal per unit of equipment EQ component; g EQ,i,re,a is the recyclable amount of the i-th raw material per unit of equipment EQ component.

[0107] S2: Based on the refined carbon emission model of the PV-storage charging station, construct a multi-objective mathematical model for optimizing the PV-storage capacity of the PV-storage charging station with the maximum annual economic benefit and the minimum annual carbon emission as the optimization objectives, specifically including:

[0108] 1) Construction of the mathematical model for maximizing the economic benefit of the PV-storage charging station;

[0109] Construct a mathematical model for maximizing the economic benefit of the PV-storage charging station, which consists of the annual operating cost and the annual economic income. Among them, the annual economic income consists of the annual charging income, the annual value of the charging pile installation subsidy, etc.; the annual operating cost consists of the annual equivalent investment cost, the annual operation and maintenance cost, and the annual power purchase cost from the superior power grid. As shown in the following formula:

[0110] maxR = max(R m + R g - C b - C i - C m ) (7)

[0111] Among them, R, R m , R g are the annual economic benefit, the annual charging service income, and the annual value of the charging pile installation subsidy of the PV-storage charging station respectively; C i , C m , C b are the annual equivalent investment cost, the annual operation and maintenance cost, and the annual power purchase cost from the superior power grid of the PV-storage charging station respectively;

[0112] The mathematical model of the annual charging service income R m of the PV-storage charging station is:

[0113]

[0114] Among them, l(t) is the charging unit price of electric vehicles per unit time t; P CP (t) is the output power of the charging pile per unit time t; T is the charging time of electric vehicles.

[0115] The mathematical model of the annual value R g of the charging pile installation subsidy of the PV-storage charging station is:

[0116]

[0117] Where: p is the subsidy amount for each charging pile; N CP is the number of charging piles configured; y CP is the service life of the charging pile; r is the discount rate;

[0118] The equivalent annual value investment cost C of a photovoltaic-storage charging station, etc. i The mathematical model is:

[0119]

[0120] Where: C EQ,a is the unit price of the EQ component of the unit equipment; y is the EQ operation life of the PSCIS equipment;

[0121] The annual operation and maintenance cost C of a photovoltaic-storage charging station m The mathematical model is:

[0122]

[0123] Among them, β EQ is the operation and maintenance cost per unit power of the EQ equipment of the photovoltaic-storage charging station; P EQ (t) is the output power of the EQ equipment per unit time t;

[0124] The annual power purchase cost C of a photovoltaic-storage charging station from the superior power grid b The mathematical model is:

[0125]

[0126] Among them, g(t) is the electricity price for purchasing electricity from the superior power grid per unit time t; P buy (t) is the power of purchasing electricity from the superior power grid by the photovoltaic-storage charging station per unit time t.

[0127] 2) Construction of the mathematical model for minimizing the annual carbon emissions of a photovoltaic-storage charging station;

[0128] Based on the established full-life cycle carbon emission model of the photovoltaic-storage charging station, calculate the full-life cycle carbon emissions of the photovoltaic-storage charging station, and use the equivalent annual value method to calculate the equivalent annual value carbon emissions of the full-life cycle of the photovoltaic-storage charging station, and construct the mathematical model for minimizing the annual carbon emissions of the photovoltaic-storage charging station, as shown in the following formula:

[0129]

[0130] Among them, E k is the equivalent annual value carbon emissions of the EQ equipment of the PSCIS equipment over the full life cycle.

[0131] 3) Constraints of the mathematical model for optimizing the configuration of the optical storage capacity of a multi-objective optical storage charging station;

[0132] To ensure the effective solution of the mathematical model for optimizing the configuration of the optical storage capacity of a multi-objective optical storage charging station, constraints should be imposed on the number of its devices, the charge and discharge of the energy storage battery system, and the power balance.

[0133] Establish the constraint conditions for the number of devices in the optical storage charging station, as shown in Equation (14).

[0134]

[0135] Among them, N pv,min 、N pv,max 、N sto,min 、N sto,max are the upper and lower limits of the number of components of the photovoltaic power generation system and the energy storage battery system, respectively.

[0136] Establish the charge and discharge constraint conditions of the energy storage battery system in the optical storage charging station, including the state of charge constraint, charge and discharge power constraint, charging constraint, discharging constraint, and self-discharge constraint.

[0137] The state of charge constraint of the energy storage battery system is shown in Equation (25).

[0138] 0.3 ≤ SOC sto (t) ≤ 0.9 (15)

[0139] Among them, SOC sto (t) is the state of charge of the energy storage battery system per unit time t.

[0140] The charge and discharge power constraint of the energy storage battery system is shown in Equation (26).

[0141] -P sto,max (t) ≤ P sto (t) ≤ P sto,max (t) (16)

[0142] Among them, P sto,max (t) is the maximum charge and discharge power of the energy storage battery system per unit time t.

[0143] The charging constraint of the energy storage battery system is shown in Equation (27).

[0144] E sto (t + 1) = E sto (t)(1 - θ) Δt + P sto (t + 1)Δt (17)

[0145] Among them, E sto(t + 1) is the state of charge of the energy storage battery system at the next moment; θ is the charge retention capacity of the energy storage battery system; Δt is the adjacent unit time interval; P sto (t + 1) is the charging and discharging power of the energy storage battery system at the next moment.

[0146] The discharge constraint of the energy storage battery system is shown in Equation (28).

[0147] E sto (t + 1) = E sto (t)(1 - θ) Δt -P sto (t)Δt (18)

[0148] The self-discharge constraint of the energy storage battery system is shown in Equation (29).

[0149] E sto (t + 1) = E sto (t)(1 - θ) Δt (19)

[0150] S3: Use the multi-objective particle swarm optimization algorithm to solve the optimization configuration mathematical model to obtain the Pareto solution set, and use the entropy weight TOPSIS method to select the optimal solution of the Pareto solution set, which specifically includes the following steps:

[0151] S31: Set the basic optimization parameters and the upper and lower limits of the decision variables;

[0152] S32: Initialize the population and randomly generate the number of decision variables;

[0153] S33: Start the iterative operation. First, calculate the fitness value of the initial population, and then determine the objective function values of the individuals and the population, and obtain the objective function values of the individuals in the new population; then, update the velocity and position of the particles, and update the optimal value according to the dominance relationship; finally, update the non-dominated solution set of the archive;

[0154] S34: After the iterative operation ends, use the entropy weight TOPSIS method to select the optimal solution of the Pareto solution set.

[0155] Example:

[0156] Suppose to purchase equipment such as TOPCon photovoltaic modules, lithium iron phosphate energy storage batteries, 7kW public AC charging piles, and a two-way inverter of a certain company to build a PSCIS, and carry out a case study according to the light resources and EV charging load requirements of a residential area in a certain place in the north.

[0157] To verify the effectiveness of the multi-objective optimization configuration model proposed in this paper, a study on the capacity optimization configuration of PSCIS is carried out. The results of the capacity optimization configuration are as Figure 4As shown, three representative configuration cases are selected, namely the economically optimal solution, the compromise solution, and the solution with the least carbon emissions, and they are compared and analyzed with the conventional EV charging station. The corresponding indicators are shown in Table 1, and the typical daily scheduling of each configuration result is as Figure 5 shown.

[0158] Table 1 Optimization results under each configuration

[0159]

[0160]

[0161] From Figure 4 it can be seen that the maximum annual economic benefit and the minimum annual carbon emissions are two conflicting goals. If we want to reduce carbon emissions, it is necessary to increase the proportion of photovoltaic power generation and improve the photovoltaic installed capacity. At the same time, to effectively absorb photovoltaic power, it is also necessary to add energy storage devices, which will significantly increase the operating cost of PSCIS and lead to a decline in the economic benefits of PSCIS.

[0162] Observing the optimization results of the three configurations of PSCIS in Table 1, it can be seen that from Configuration 1 to Configuration 3, the configured capacities of photovoltaic and energy storage battery systems gradually increase significantly, resulting in a decrease in the power purchased from the grid by PSCIS and a significant decrease in the annual carbon emissions. The annual carbon emissions of Configuration 3 are reduced by 68.31% compared with Configuration 1. At the same time, the annual operating cost of the system gradually increases, and the economic benefit of Configuration 3 is reduced by 280,500 yuan compared with Configuration 1, showing a negative benefit situation. This indicates that there is a certain difference in the change ranges of economic and low-carbon indicators. In addition, by comparing the three configuration schemes with the conventional EV charging station, it can be obtained that the current economic efficiency of PSCIS is still relatively low. Only when a small amount of photovoltaic is configured in Configuration 1 can the carbon emissions be reduced by 9.5% without affecting the economic benefits. The above results show that the popularization of PSCIS awaits the further reduction of the costs of photovoltaic and energy storage devices, or the introduction of new subsidy policies by the government.

[0163] From Figure 5It can be seen that in the working-day scenario, there is no PV output during the period from 00:00 to 08:00, and the EV charging load demand is relatively low. At this time, the PSCIS energy storage system is in the standby state, and the PSCIS mainly relies on purchased electricity to meet the load demand. During the period from 08:00 to 18:00, the PV output gradually climbs to the peak. However, due to its volatility and intermittency, there is an obvious peak-valley difference from the load demand. Therefore, the energy storage system needs to work in the charging state to absorb the surplus electric energy and improve the in-situ PV consumption rate. During the period from 18:00 to 24:00, the PV output drops significantly, and the working-day load demand reaches the peak. The energy storage system needs to switch to the discharging state to supplement the power gap and reduce the dependence on purchased electricity. In the rest-day scenario, the EV charging load demand is lower than that in the working-day scenario. During the period from 00:00 to 08:00, the EV charging load demand is low and there is no PV output. At this time, the PSCIS is in the low-load operation state. During the period from 08:00 to 18:00, the PV output gradually climbs to the peak, and the EV charging load demand on rest days also reaches the highest point. At this time, the PV can be effectively consumed, and the surplus electric energy can be consumed by the energy storage system. During the period from 18:00 to 24:00, the PV output and the EV charging load demand drop significantly. At this time, the energy storage system can be switched to the discharging state to reduce the amount of purchased electricity.

[0164] In addition, Table 1 and Figure 5 reveal significant differences in the PV power consumption capacity and the dependence on external power among different configuration schemes. In the case of Configuration 1, the number of PV modules configured is small, and the EV charging load demand can effectively consume the PV power without energy storage equipment. At this time, the economic benefit of the PSCIS is high, but the demand for purchased electricity is high and the carbon emissions are large. In the case of Configuration 2, a certain capacity of PV and energy storage equipment is configured, which not only significantly reduces the demand for purchased electricity and carbon emissions, but also avoids the excessive investment costs of PV and energy storage, achieving a good balance between economic benefits and carbon emissions. In the case of Configuration 3, the configured capacity of PV and energy storage reaches the highest, the demand for purchased electricity reaches the lowest, and the carbon emissions are significantly reduced. However, due to the substantial increase in investment costs brought about by the expansion of PV and energy storage, the economy of the PSCIS declines. Generally speaking, Configuration 2 performs the best in the multi-objective optimization, achieving the best trade-off between economy and low carbon.

[0165] It can be seen from the above analysis that the capacity optimization configuration model of the PV-storage charging station established in this paper can comprehensively consider the economic and low-carbon requirements of the PV-storage charging station and obtain a relatively reasonable multi-objective optimization configuration result.

[0166] In addition, to discuss the impact of carbon emissions in each link of the full life cycle of a PV-storage charging station on its low-carbon operation and reveal the carbon emission laws of each device in the full life cycle of the PV-storage charging station, an analysis of the carbon emissions in the full life cycle of the PV-storage charging station is carried out based on the optimization configuration results in the previous section, and an analysis of the carbon emissions in each link of the full life cycle of each device for the compromise solution is carried out. The annualized carbon emissions in each link of the full life cycle of the PSCIS under the three configuration results are as Figure 6 shown, and the diagram of the proportion of carbon emissions in each link of the full life cycle of each device of the compromise solution PSCIS is as Figure 7 shown.

[0167] Observation Figure 6 shows that there are significant differences in the carbon emissions in the full life cycle of the PSCIS under the three configuration results. Among them, compared with the result of Configuration 1, the carbon emissions in the operation link of Configuration 3 are reduced by 77.63% year-on-year, while there are certain increases in the carbon emissions in other links of the full life cycle, such as raw material acquisition, production and manufacturing, transportation, and end-of-life recycling, which are 92.46%, 92.89%, 76.04%, and 89.48% respectively. This result indicates that the increase in the capacity of PV and energy storage can significantly reduce the grid power purchase demand in the operation link of the PSCIS, but to a certain extent, it will increase the carbon emissions in other links of the full life cycle.

[0168] Figure 7Reveals the carbon emission composition distribution characteristics of key links in the full life cycle of each device. Among them, in the raw material acquisition link, the carbon emission of the photovoltaic due to the acquisition of the brackets required for installation is relatively high, resulting in the photovoltaic accounting for 73.1% in the raw material acquisition link. The carbon emission of the electrolyte of the energy storage battery raw material and the battery chip slurry is relatively high during acquisition, but the required amount during production is small. Therefore, the carbon emission of the energy storage battery in the raw material acquisition link only accounts for 15.7%. The inverter accounts for 7.1% because the manufacturing process of the required electronic components is relatively complex. Although the charging pile requires more raw materials for production, its configuration number is relatively small compared to other devices, so it only accounts for 4.1%. In the production and manufacturing link, due to the current thickness factor of monocrystalline silicon wafers, the carbon emission of silicon wafer production is relatively high, accounting for 77.3%. Devices such as energy storage batteries, inverters, and charging piles mainly consume electricity in the production and manufacturing link, accounting for 18.6%, 0.7%, and 3.4% respectively. In the transportation link, the carbon emissions mainly depend on the weight of the device itself and the transportation distance. The carbon emission proportions of the photovoltaic, energy storage battery, inverter, and charging pile in this link are 45.9%, 12.4%, 5.1%, and 36.7% respectively. In the end-of-life recycling link, due to the recyclability of the photovoltaic brackets and the use of pyrolysis technology to recycle the photovoltaic panels, the carbon offset benefit is relatively large, accounting for as high as 70.7%. Without considering the scenario of cascade utilization, assuming mechanical crushing and wet recycling are used to recycle metal materials for energy storage batteries, the carbon offset accounts for 14.2%. For end-of-life recycling of power transmission devices such as inverters and charging piles, only the recycling of the outer shell and the internal metal is considered, and the recycling amount is small. The carbon offset proportions of the inverter and the charging pile are 10.1% and 5% respectively.

[0169] The above analysis results show that the key to achieving low-carbon operation of the photovoltaic energy storage charging station lies in reasonably configuring the photovoltaic and energy storage capacities, optimizing the energy supply structure of the photovoltaic energy storage charging station, increasing the proportion of green power in the photovoltaic energy storage charging station, reducing the dependence on the external power grid, and improving the power supply cleanliness of the photovoltaic energy storage charging station.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the present technical solution, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A multi-objective optimization configuration method for photovoltaic charging stations based on full life cycle carbon emissions, characterized in that: The method specifically comprises the following steps: S1: Based on the full life cycle assessment method, identify the carbon emission boundary of the photovoltaic and energy storage charging station, and build a refined carbon emission model of the photovoltaic and energy storage charging station; S2: Based on the refined model of carbon emissions of photovoltaic and storage charging stations, a mathematical model for optimizing the photovoltaic and storage capacity of photovoltaic and storage charging stations with multi-objective optimization goals of maximizing annual economic benefits and minimizing annual carbon emissions is constructed; S3: The multi-objective particle swarm optimization algorithm is used to solve the optimal configuration mathematical model to obtain the Pareto solution set, and the entropy weight TOPSIS method is used to select the optimal solution of the Pareto solution set.

2. The multi-objective optimization configuration method of the photovoltaic charging station according to claim 1 is characterized in that: In step S1, a refined model of carbon emissions over the entire life cycle of a photovoltaic and energy storage charging station is constructed, and its expression is: Among them, EQ is the type of equipment, PV, ES, CP, and C are photovoltaic, energy storage battery, charging pile, and inverter respectively; E EQ 、E EQ,geg 、E EQ,peg 、E EQ,teg 、E EQ,oeg 、E EQ,reg They are the carbon emissions of the entire life cycle, raw material acquisition, manufacturing, transportation, operation, and waste recycling of PSCIS equipment EQ; PSCIS stands for photovoltaic energy storage charging station; Carbon emissions from raw material acquisition of PSCIS equipment EQ E EQ,geg The model is: Among them, Q e,i is the carbon emission coefficient of the i-th raw material; g EQ,i,a N is the weight of the i-th raw material required to produce each unit of equipment EQ component; EQ is the number of EQ components in the device; P EQ,a is the rated output power of the EQ component of the unit equipment; n is the number of types of raw materials required for the production of the photovoltaic storage charging station; Carbon emissions from the production and manufacturing of PSCIS equipment EQ EQ,peg The model is: Among them, Q j,l is the carbon emission coefficient of the lth auxiliary material in the jth process; g EQ,j,l,a is the amount of the lth auxiliary material used in the jth process in the production process of each unit of equipment EQ component; e EQ,j is the power consumption of the jth process in the production process of each unit of equipment EQ component; EF is the carbon emission factor of the regional power grid; m is the total number of process flows in the production and manufacturing links of each equipment of the photovoltaic storage charging station; z is the number of types of auxiliary materials required for the production of the photovoltaic storage charging station; Carbon emissions from transportation of PSCIS equipment EQ E EQ,teg The model is: Among them, Q r is the carbon emission intensity of transporting unit weight of objects per unit distance by the r-type vehicle; g EQ,a is the weight of EQ component per unit of equipment; L EQ,r The transportation distance of each unit of equipment EQ component using the rth type of vehicle; Carbon emissions from the operation of PSCIS equipment EQ EQ,oeg The model is: AND EQ,oeg =and oeg EF (5) Among them, e oeg Purchase electricity from the higher-level power grid during the entire life cycle of PSCIS; Carbon emissions from the end-of-life recycling of PSCIS equipment EQ EQ,reg The model is: Among them, Q l The carbon emission coefficient of the first auxiliary material used in material recycling and waste disposal; g EQ,l,a The amount of the first auxiliary material used for material recovery and waste disposal of each unit of equipment EQ component; g EQ,i,re,a is the recyclable amount of the i-th raw material per unit of equipment EQ component.

3. The multi-objective optimization configuration method of photovoltaic charging stations according to claim 1 is characterized in that: In step S2, a mathematical model for maximizing the economic benefits of the photovoltaic and energy storage charging station is constructed, specifically including: the annual economic benefit objective function of the photovoltaic and energy storage charging station includes annual operating costs and annual economic income; among which, the annual economic income includes annual charging income and charging pile installation subsidies; the annual operating cost includes equal annual investment costs, annual operation and maintenance costs, and annual electricity purchase costs from the superior power grid; as shown in the following formula: maxR=max(R m +R g -C b -C i -C m ) (7) Among them, R, R m , R g They are the annual economic benefits of the photovoltaic storage charging station, the annual income from providing charging services and the subsidy for charging pile installation; C i , C m , C b They are the annual investment cost of photovoltaic storage charging stations, annual operation and maintenance costs, and annual electricity purchase costs from the upper-level power grid; The annual charging service income of the photovoltaic charging station is R m The mathematical model is: Where l(t) is the charging price of electric vehicles per unit time t; P CP (t) is the output power of the charging pile per unit time t; T is the charging time of the electric vehicle; Solar storage charging station charging pile installation subsidy and other annual value R g The mathematical model is: Among them, p is the subsidy amount for each charging pile; N CP Configure the number of charging piles; y CP is the useful life of the charging pile; r is the discount rate; Annual investment cost of photovoltaic storage charging station C i The mathematical model is: Where: C EQ,a is the unit price of the EQ component of the unit equipment; y is the operating life of the PSCIS equipment EQ; N EQ is the number of EQ components of the device; Annual operation and maintenance cost of solar-storage charging station C m The mathematical model is: Among them, β EQ P is the unit power operation and maintenance cost of the photovoltaic charging station equipment EQ; EQ (t) is the output power of the device EQ per unit time t; The annual cost of electricity purchased by the photovoltaic charging station from the upper grid is C b The mathematical model is: Among them, g(t) is the price of electricity purchased from the upper power grid within unit time t; P buy (t) is the power purchased by the solar-storage charging station from the upper power grid per unit time t.

4. The multi-objective optimization configuration method of the photovoltaic charging station according to claim 2 is characterized in that: In step S2, a mathematical model for minimizing annual carbon emissions of a photovoltaic charging station is constructed, specifically including: calculating the carbon emissions of the photovoltaic charging station over its entire life cycle according to the refined model of carbon emissions of the photovoltaic charging station over its entire life cycle, and using the equal annual value method to calculate the equal annual value carbon emissions of the photovoltaic charging station over its entire life cycle, as shown in the following formula: Among them, E k is the annual carbon emission of the entire life cycle of the PSCIS equipment EQ, r is the discount rate, and y is the operating life of the PSCIS equipment EQ.

5. The multi-objective optimization configuration method of photovoltaic charging stations according to claim 1 is characterized in that: In step S2, the constraints of the mathematical model for optimizing the configuration of the photovoltaic storage capacity of the multi-objective photovoltaic storage charging station include: (1) The number of solar-storage charging station equipment constraints is as follows: Among them, N pv,max 、N pv,min 、N sto,max 、N sto,min They are the upper and lower limits of the number of components of the photovoltaic power generation system and the energy storage battery system; (2) The charging and discharging constraints of the energy storage battery system of the photovoltaic charging station, including state of charge constraints, charging and discharging power constraints, charging constraints, discharging constraints and self-discharge constraints; The state of charge constraint of the energy storage battery system is as follows: 0.3≤SOC sto (t)≤0.9 (15) Where, SOC sto (t) is the state of charge of the energy storage battery system per unit time t; The charging and discharging power constraints of the energy storage battery system are as follows: -P sto,max (t)≤P sto (t)≤P sto,max (t) (16) Among them, P sto,max (t) is the maximum charge and discharge power of the energy storage battery system per unit time t, P sto (t) is the charge and discharge power of the energy storage battery system per unit time t; The charging constraints of the energy storage battery system are as follows: E sto (t+1)=E sto (t)(1-θ) Δt +P sto (t+1)Δt (17) Among them, E sto (t+1) is the charge of the energy storage battery system at the next moment; θ is the charge retention capacity of the energy storage battery system; Δt is the time interval between adjacent units; P sto (t+1) is the charging and discharging power of the energy storage battery system at the next moment; The discharge constraint of the energy storage battery system is as follows: E sto (t+1)=E sto (t)(1-θ) Δt -P sto (t)Δt (18) The self-discharge constraint of the energy storage battery system is as follows: E sto (t+1)=E sto (t)(1-θ) Δt (19)。 6. The multi-objective optimization configuration method of photovoltaic charging stations according to claim 1 is characterized in that: Step S3 specifically includes the following steps: S31: Setting optimization basic parameters and upper and lower limits of decision variables; S32: Initialize the population and randomly generate the number of decision variables; S33: Perform population iteration operation to obtain the Pareto solution set of the model; First, calculate the fitness value of the initial population, and then determine the objective function values ​​of individuals and populations, and obtain the objective function values ​​of individuals in the new population; then, update the speed and position of the particles, and update the optimal value according to the dominance relationship; finally, update the archived non-inferior solution set; S34: After obtaining the Pareto solution set of the model, the entropy weight TOPSIS method is used to select the optimal solution of the Pareto solution set; First, all elements in the Pareto solution set are normalized; then, the normalized Pareto solution set is standardized; finally, the scores of each element in the standardized Pareto solution set are calculated and normalized to obtain the optimal solution.

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