A campus energy management method considering the low-carbon charging and discharging response of electric vehicles

Abstract

The present application discloses a park energy management method that takes into account the low-carbon charging and discharging response of electric vehicles, which relates to the field of energy optimization management; the method includes: obtaining setting information data of the target park; constructing an EV multi-trip mobile charging and discharging model; constructing an EV low-carbon charging and discharging response model; calculating the initial carbon flow index data corresponding to each node in the target park according to the initial operation strategy and the park carbon flow calculation model; determining the carbon content per kilowatt-hour corresponding to multiple electric vehicles in the target park; determining the low-carbon incentive factor for charging and discharging according to the carbon content per kilowatt-hour and the initial carbon flow index data, and then determining the park operation cost objective function; solving the park operation cost objective function using constraint conditions to obtain the optimal energy management plan; the present application can provide a stable low-carbon charging and discharging response and realize optimized scheduling management.

Description

Technical Field

[0001] The present application relates to the field of energy optimization management, and in particular to a campus energy management method that takes into account the low-carbon charging and discharging response of electric vehicles. Background Art

[0002] With the advancement of the "carbon peak and carbon neutrality" goals, low-carbon transformation has become a development trend in the power system. Promoting efficient energy utilization and guiding users to energy conservation and emission reduction are key tasks. The park has abundant flexible resources, such as photovoltaic units and energy storage equipment, which can assist the distribution network in promoting carbon emission reduction and serve as a key technical means to build a low-carbon and efficient energy management system. Through park energy optimization management, energy consumption within the park can be monitored and controlled, and the operating output of various equipment can be coordinated to improve the park's operational efficiency.

[0003] In recent years, electric vehicles have become an essential means of transportation and have been widely researched and applied within industrial park systems. Compared to traditional vehicles, electric vehicles offer zero pollution, coordinated renewable energy generation, and low-carbon potential, promoting clean energy consumption. This contributes to reducing fossil fuel combustion and lowering CO2 emissions. Furthermore, electric vehicles possess energy storage properties, storing charged energy in batteries and discharging it as needed to pump it back into the grid, enabling vehicle-grid interaction, providing demand response for the power grid, and improving grid dispatch flexibility. However, large-scale, disorderly charging and discharging of electric vehicles poses safety risks to the distribution network and reduces its reliability. By guiding their orderly charging and discharging, electric vehicles can provide stable demand response while fully realizing their low-carbon potential and promoting carbon emission reduction. This application aims to study charging and discharging response strategies that fully leverage the low-carbon characteristics of electric vehicles, thereby better assisting industrial parks in energy optimization management and further reducing their carbon emissions. Carbon emission flow theory enables accurate allocation of generator power and carbon flow, providing an important theoretical basis for improving the development of carbon trading markets and promoting carbon emission reduction in electricity. Applying carbon emission flow theory to industrial park energy optimization management can guide user participation in energy conservation and emission reduction by tracing and allocating carbon flow.

[0004] Among existing energy optimization management technologies for parks with electric vehicles, research on EVs primarily focuses on analyzing, modeling, and expanding their application. The charging and discharging characteristics of electric vehicles (EVs) with multiple trips, based on personalized user experiences, have garnered widespread attention. Developing charging and discharging strategies based on EV characteristics can efficiently and orderly achieve EV charging and discharging. Existing EV charging and discharging strategy research mostly employs cost incentives. These approaches build EV charging and discharging models based on grid electricity transaction cost information to determine EV charging and discharging scheduling driven by electricity costs. Alternatively, they consider the charging and discharging cost coefficients set by park EV agents based on user-provided electricity purchases, thereby guiding EV charging and discharging within the park. However, these approaches, which use cost coefficient incentives to guide EV charging and discharging, have limited impact on park energy optimization. Exploiting and fully utilizing the low-carbon characteristics of EVs can improve the economic operation of parks while effectively improving their environmental performance. For example, leveraging the shared energy storage capabilities of EVs to fully exploit the dispatchable potential of EV clusters has led to the development of low-carbon park operation strategies that utilize EVs as energy storage devices, reducing carbon emissions within the park. However, existing technical solutions rarely introduce carbon perspectives and combine carbon factors to further develop the low-carbon potential of EVs. The application of EV characteristics also mainly utilizes its charging and discharging energy storage characteristics to assist the park in energy coordination, and the carbon emission reduction effect of EVs is insufficient.

[0005] Furthermore, while existing research has explored ways to integrate EVs with carbon emissions, relatively few studies have incorporated carbon emission flow theory into the EV charging and discharging process. Some studies have considered the uncertainty of carbon emissions and proposed a low-carbon energy utilization strategy for industrial parks based on a dynamic carbon emission factor, treating electric vehicles as controllable devices in the calculation of the industrial park's carbon emission factor. Other studies have proposed a low-carbon operation control model for industrial parks that takes into account the shared energy storage characteristics of electric vehicles, introduces a tiered carbon trading mechanism, and uses the difference in carbon emissions between EVs and fuel vehicles under the same mileage as the equivalent carbon emission reductions for EVs in carbon trading. However, most existing studies have integrated EV operation and carbon emissions through indirect means, primarily by incorporating EVs into carbon emission constraints, treating EVs as simple energy devices and including them in carbon emission factor calculations, or using crude estimates of EV equivalent carbon emissions to include in carbon trading. However, the carbon emission flows associated with EV charging and discharging, and the role of carbon flow information in guiding vehicle charging and discharging, are often not considered in these studies. Further development and application of the low-carbon capabilities of electric vehicles is urgently needed.

[0006] In the context of "carbon peak and carbon neutrality", the operation of electric vehicles in the park needs to take carbon emissions into consideration on the basis of orderly charging and discharging, so as to better serve the low-carbon energy management of the park. However, the existing technology still has the following problems and shortcomings:

[0007] 1) Existing park energy management methods lack precise descriptions of the carbon emissions characteristics of electric vehicle charging and discharging. Calculation methods are crude, and carbon flow calculation tools are not used to improve data and calculation accuracy. The carbon emission flows associated with electrical energy during EV charging and discharging, and the role of this carbon flow information in guiding EV charging and discharging, have not been studied or applied in existing park energy management methods. Further development of the low-carbon performance of electric vehicles is urgently needed.

[0008] 2) Among existing park energy management methods, the regulation of electric vehicles is relatively simple. Most EV charging and discharging strategies adopt the cost coefficient incentive method, that is, guiding the charging and discharging response of electric vehicles through the charging and discharging cost coefficient based on electricity prices. This method has limited promoting effect on park energy optimization management, and does not consider the carbon emission reduction benefits that electric vehicles can bring through charging and discharging. There is a lack of reasonable ways to exert the carbon emission reduction effect of EVs. Summary of the Invention

[0009] The purpose of this application is to provide a campus energy management method that takes into account the low-carbon charging and discharging response of electric vehicles, which can provide a stable low-carbon charging and discharging response and achieve optimized scheduling management.

[0010] To achieve the above objectives, this application provides the following solutions:

[0011] The present application provides a campus energy management method that takes into account the low-carbon charging and discharging response of electric vehicles. The campus energy management method that takes into account the low-carbon charging and discharging response of electric vehicles includes:

[0012] Obtaining setting information data for the target park; the setting information data includes: initial operation strategy and basic parameters; the basic parameters include: fuel unit parameters, energy storage device parameters, photovoltaic output data and load data;

[0013] Constructing an EV multi-trip mobile charging and discharging model; the EV multi-trip mobile charging and discharging model is constructed based on the trips of multiple electric vehicles within the target park, as well as the continuity and correlation of charging and discharging; the trip is the process from the start of the electric vehicle's driving phase to the completion of parking and charging and discharging in the target park;

[0014] Constructing an EV low-carbon charging and discharging response model; the EV low-carbon charging and discharging response model is determined by performing charging and discharging and energy analysis based on the EV multi-trip mobile charging and discharging model using carbon emission flow theory; the EV low-carbon charging and discharging response model includes: a park carbon flow calculation model and an EV carbon flow model; the park carbon flow calculation model is a mathematical model determined based on carbon flow index data; the carbon flow index data includes: branch carbon flow rate, network loss carbon flow rate, branch carbon flow density, and node carbon potential;

[0015] Calculating initial carbon flow index data corresponding to each node in the target park according to the initial operation strategy and the park carbon flow calculation model;

[0016] Determining the carbon content per kilowatt-hour of electricity corresponding to a plurality of electric vehicles in the target park based on the EV carbon flow model and the EV multi-trip mobile charging and discharging model;

[0017] For any of the electric vehicles, determining a low-carbon incentive factor for charging and discharging based on the carbon content per kilowatt-hour and the initial carbon flow index data;

[0018] Determining a park operation cost objective function based on the basic parameters and all the charging and discharging low-carbon incentive factors;

[0019] The park operation cost objective function is solved using constraint conditions to obtain the optimal energy management plan for the target park; the optimal energy management plan is used to manage and schedule the output of each unit in the target park, the EV charging and discharging power, and the carbon emission reduction.

[0020] Optionally, the calculation formula for the branch carbon flow rate is:

[0021]

[0022] Among them, R ij is the branch carbon flow rate from the i-th branch to the j-th branch; P i is the active power flowing through the i-th node; is a column vector; H u is the power flow distribution matrix corresponding to the power network; P G is the active power column vector of the unit; E G is the column vector of the unit’s carbon emission intensity; P ij is the active power transmitted from the i-th branch to the j-th branch; i and j are both serial numbers; diag(P G ) means to convert P G Convert to a diagonal matrix.

[0023] Optionally, the calculation formula for the network loss carbon flow rate is:

[0024]

[0025] in, is the active power loss from the i-th branch to the j-th branch; is the network loss carbon flow rate from the i-th branch to the j-th branch.

[0026] Optionally, the calculation formula for the branch carbon flow density is:

[0027]

[0028] Among them, ρ ij is the carbon flow density from the i-th branch to the j-th branch.

[0029] Optionally, the calculation formula for the node carbon potential is:

[0030]

[0031] in, is the node carbon potential of the jth node; P j is the active power flowing through the jth node; U j is the set of downstream nodes of the j-th node.

[0032] Optionally, the mathematical expression of the EV carbon flow model is:

[0033]

[0034] Among them, κ m,k,t+1 is the carbon content of electricity consumed by EV numbered k in the mth trip during the t+1 period; κ m,k,t is the carbon content of electricity consumed by EV numbered k during the mth trip in period t; C EV is the rated capacity of the EV battery; is the carbon emissions injected by EV numbered k in the mth trip during the t+1 period; is the carbon emissions of EV numbered k in the mth trip during the t+1 period; is the state of charge of EV numbered k in the mth trip during period t; m,t+1 is the driving status of the EV during the t+1 period of the m-th trip; is the average change in the state of charge of EV numbered k in the mth trip; is the state of charge of EV numbered k in the mth trip at time t+1.

[0035] Optionally, the charging and discharging low-carbon incentive factor includes: a charging low-carbon incentive factor and a discharging low-carbon incentive factor;

[0036] The expression of the charging low-carbon incentive factor is:

[0037]

[0038] The expression of the discharge low-carbon incentive factor is:

[0039]

[0040] in, is the low-carbon charging incentive factor corresponding to the EV numbered k in the target park during period t; is the cost coefficient of electricity sold to the grid; is the low-carbon incentive factor for discharge corresponding to the EV numbered k in the target park during period t; is the electricity purchase cost coefficient of the power grid; is the dynamic carbon emission factor of the charging station node in period t, The calculation formula is:

[0041]

[0042] in, is the node carbon potential of the charging station node in period t.

[0043] Optionally, determining a park operation cost objective function based on the basic parameters and all the charging and discharging low-carbon incentive factors specifically includes:

[0044] Determining a total charge and discharge cost function based on all of the charge and discharge low-carbon incentive factors;

[0045] The park operation cost objective function is determined based on the basic parameters and the total charge and discharge cost function.

[0046] Optionally, the park operation cost objective function is expressed as:

[0047]

[0048] in, is the park operation cost objective function; is the operating cost of the fuel unit in period t; is the operating cost of the energy storage unit in period t; is the total cost function of charging and discharging; is the electricity transaction cost between the park and the power grid; a c ,b c ,c c are the fuel cost coefficients of the units; P g,t is the active power of the fuel unit in period t; K ESB is the unit charge and discharge cost coefficient of the energy storage unit; P cha,t is the charging power of the energy storage during period t; P dis,t is the discharge power of the energy storage in period t; η cha is the charging efficiency of the energy storage in period t; η dis is the discharge efficiency of the energy storage in period t; P t Ubuy The target park purchases electricity from the power grid; P t Usell The electricity sold by the park to the power grid.

[0049] Optionally, the constraints include: charging and discharging power and energy constraints, energy constraints between two trips, power balance constraints, fuel unit operation constraints and energy storage unit operation constraints.

[0050] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0051] The present application provides a park energy management method that takes into account the low-carbon charging and discharging response of electric vehicles, so as to guide the low-carbon charging and discharging management of each EV in the park, motivate EV users to improve the enthusiasm for vehicle-grid interaction, and reduce the carbon emissions of the park. First, the multi-trip charging and discharging characteristics of EV are considered, and the EV carbon content per kilowatt-hour is proposed based on the carbon emission flow theory, that is, the EV carbon flow model. The low-carbon charging and discharging response model of EV, which considers the gap between the EV carbon content per kilowatt-hour and the dynamic carbon emission factor of the charging station to set a personalized EV charging and discharging low-carbon incentive factor, and proposes an EV low-carbon charging and discharging decision guided by the personalized low-carbon incentive factor and the dynamic carbon emission factor of the charging station, with the goal of minimizing the operating cost of the park, to achieve optimized scheduling of various equipment in the park, and to improve the enthusiasm of EV users to participate in vehicle-grid interaction. The present application can provide a stable low-carbon charging and discharging response and achieve optimized scheduling management. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0053] Figure 1 This is a flow chart of a campus energy management method that takes into account the low-carbon charging and discharging response of electric vehicles in an embodiment of the present application;

[0054] Figure 2 A schematic diagram of a specific framework corresponding to the campus energy management method provided in an embodiment of the present application;

[0055] Figure 3 A schematic diagram of a detailed charging and discharging process provided in an embodiment of the present application, including the charging and discharging process after the vehicle is parked at a charging station in a park and the charging and discharging process is completed;

[0056] Figure 4 A schematic diagram of the energy management solution optimization process provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0058] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0059] like Figure 1 As shown, an embodiment of the present application provides a campus energy management method that takes into account the low-carbon charging and discharging response of electric vehicles, the method comprising:

[0060] Step 100: Obtain the target park's set information data. The set information data includes: initial operation strategy and basic parameters; basic parameters include: fuel unit parameters, energy storage device parameters, photovoltaic output data and load data.

[0061] Step 200: Construct an EV multi-trip mobile charging and discharging model. The EV multi-trip mobile charging and discharging model is constructed based on the trips of multiple electric vehicles within the target park, as well as the continuity and correlation of charging and discharging. A trip is the process from the start of the electric vehicle's driving phase to the completion of charging and discharging after parking in the target park.

[0062] Step 300: Construct an EV low-carbon charging and discharging response model. The EV low-carbon charging and discharging response model is determined based on carbon emission flow theory and a multi-trip mobile charging and discharging model, performing charging and discharging and energy analysis. The EV low-carbon charging and discharging response model includes a campus carbon flow calculation model and an EV carbon flow model. The campus carbon flow calculation model is a mathematical model determined based on carbon flow index data. Carbon flow index data includes branch carbon flow rate, grid loss carbon flow rate, branch carbon flow density, and node carbon potential.

[0063] Step 400: Calculate the initial carbon flow index data corresponding to each node in the target park according to the initial operation strategy and the park carbon flow calculation model.

[0064] Step 500: Based on the EV carbon flow model and the EV multi-trip mobile charging and discharging model, determine the carbon content per kilowatt-hour corresponding to multiple electric vehicles in the target park.

[0065] Step 600: For any electric vehicle, determine a low-carbon incentive factor for charging and discharging based on the carbon content per kilowatt-hour and initial carbon flow index data.

[0066] Step 700: Determine the park operation cost objective function based on basic parameters and all charging and discharging low-carbon incentive factors.

[0067] Step 800: Solve the park operation cost objective function using constraints to obtain the optimal energy management plan for the target park. The optimal energy management plan is used to manage and schedule the output of each unit in the target park, the charging and discharging power of EVs, and the carbon emission reduction.

[0068] The calculation formula of branch carbon flow rate is:

[0069]

[0070] Among them, R ij is the branch carbon flow rate from the i-th branch to the j-th branch; P i is the active power flowing through the i-th node; is a column vector; H u is the power flow distribution matrix corresponding to the power network; P G is the active power column vector of the unit; E G is the column vector of the unit’s carbon emission intensity; P ij is the active power transmitted from the i-th branch to the j-th branch; i and j are both serial numbers; diag(P G ) means to convert P G Convert to a diagonal matrix.

[0071] The calculation formula of network loss carbon flow rate is:

[0072]

[0073] in, is the active power loss from the i-th branch to the j-th branch; is the network loss carbon flow rate from the i-th branch to the j-th branch.

[0074] The calculation formula of branch carbon flow density is:

[0075]

[0076] Among them, ρ ij is the carbon flow density from the i-th branch to the j-th branch.

[0077] The calculation formula of the node carbon potential is:

[0078]

[0079] in, is the node carbon potential of the jth node; P j is the active power flowing through the jth node; U j is the set of downstream nodes of the j-th node.

[0080] The mathematical expression of the EV carbon flow model is:

[0081]

[0082] Among them, κ m,k,t+1 is the carbon content of electricity consumed by EV numbered k in the mth trip during the t+1 period; κ m,k,t is the carbon content of electricity consumed by EV numbered k during the mth trip in period t; C EV is the rated capacity of the EV battery; is the carbon emissions injected by EV numbered k in the mth trip during the t+1 period; is the carbon emissions of EV numbered k in the mth trip during the t+1 period; is the state of charge of EV numbered k in the mth trip during period t; m,t+1 is the driving status of the EV during the t+1 period of the m-th trip; is the average change in the state of charge of EV numbered k in the mth trip; is the state of charge of EV numbered k in the mth trip at time t+1.

[0083] The charging and discharging low-carbon incentive factors include: charging low-carbon incentive factors and discharging low-carbon incentive factors.

[0084] The expression of the charging low-carbon incentive factor is:

[0085]

[0086] The expression of the discharge low-carbon incentive factor is:

[0087]

[0088] in, is the low-carbon charging incentive factor corresponding to the EV numbered k in the target park during period t; is the cost coefficient of electricity sold to the grid; is the low-carbon incentive factor for discharge corresponding to the EV numbered k in the target park during period t; is the electricity purchase cost coefficient of the power grid;

[0089] is the dynamic carbon emission factor of the charging station node in period t, and its calculation formula is:

[0090]

[0091] in, is the node carbon potential of the charging station node in period t.

[0092] In one embodiment, the park operation cost objective function is determined based on the basic parameters and all charging and discharging low-carbon incentive factors, specifically including:

[0093] Based on all the low-carbon incentive factors for charging and discharging, the total cost function of charging and discharging is determined; based on the basic parameters and the total cost function of charging and discharging, the objective function of the park operation cost is determined.

[0094] The expression of the park operation cost objective function is:

[0095]

[0096]

[0097] in, is the park operation cost objective function; is the operating cost of the fuel unit in period t; is the operating cost of the energy storage unit in period t; is the total cost function of charging and discharging; is the electricity transaction cost between the park and the power grid; a c ,b c ,c c are the fuel cost coefficients of the units; P g,t is the active power of the fuel unit in period t; K ESB is the unit charge and discharge cost coefficient of the energy storage unit; P cha,t is the charging power of the energy storage during period t; P dis,t is the discharge power of the energy storage in period t; η cha is the charging efficiency of the energy storage in period t; η dis is the discharge efficiency of the energy storage in period t; P t Ubuy The target park purchases electricity from the power grid; P t Usell The electricity sold by the park to the power grid.

[0098] The constraints include: charging and discharging power and energy constraints, energy constraints between two trips, power balance constraints, fuel unit operation constraints and energy storage unit operation constraints.

[0099] In practical applications, such as Figure 2 The following is a schematic diagram of the specific framework of this method. The implementation steps of this method framework specifically include:

[0100] Step 1: Establish a multi-trip mobile charging and discharging model for EV.

[0101] Users may make multiple trips throughout the day and, depending on real-time conditions, may need to charge and discharge their EVs multiple times. Therefore, when modeling the charging and discharging of EVs in the target park, the continuity and correlation of charging and discharging associated with multiple user trips must be considered to provide an accurate model foundation for steps 2 and 3.

[0102] Figure 3 Where w and p represent the driving phase and parking phase indexes respectively. A complete EV trip starts from the driving phase and ends after parking at the charging station in the park for charging and discharging (that is, until the beginning of the driving phase of the next trip). The subscript m represents the trip number; t represents the time period number; and k represents the EV number. m,k,t and To represent the EV time period status and the user's charging and discharging willingness. The EV charging and discharging process considering multi-trip mobility is modeled as follows:

[0103]

[0104] Where: and are the charging and discharging power of EV respectively; and They are the upper limits of EV’s charging and discharging power; and are the state of charge of the EV and the upper and lower limits of the state of charge allowed by the battery during the charging and discharging process. Formulas (1) to (5) describe the charging and discharging power and energy constraints of the EV in each charging period and in the overall optimization.

[0105]

[0106] Where: A m,k is the power consumption of EV's driving range; C EV is the rated capacity of the EV battery; are the EV access period and grid connection period length respectively; η EV,c ,η EV,d is the energy conversion efficiency in the EV charging and discharging mode; T = 24h is the total dispatching time; Δt represents the time interval. Formula (6) reflects the charge state change process during EV driving. Formula (8) ensures that the EV power level during the user's subsequent trip is not lower than the power level before participating in the park V2G control, which helps alleviate the user's mileage anxiety during subsequent trips and prevents EV over-discharge to pursue economic benefits. If formula (8) is 0, it means that the EV did not charge or discharge during this grid-connected phase, or only provided power regulation services for the park. Formula (9) is the energy constraint between the two EV trips.

[0107] Step 2: Establish an EV low-carbon charging and discharging response model.

[0108] To achieve carbon-driven low-carbon EV charging and discharging management, it is necessary to utilize carbon flow calculation tools to obtain carbon flow information for the park. Furthermore, it is necessary to utilize carbon emission flow theory to analyze energy changes such as EV charging and discharging, and driving power consumption, thereby establishing an EV carbon flow model to provide a model foundation for low-carbon charging and discharging decision-making.

[0109] Establish a carbon flow calculation model for the park.

[0110] Carbon flow information is usually quantified using carbon flow indicators, and the calculation of various carbon flow indicators in a park together constitutes its carbon flow calculation model. Commonly used indicators include:

[0111] 1) Branch carbon flow rate, that is, the equivalent carbon emissions per unit time of a branch with energy flow:

[0112]

[0113] P G =[P g,1 P g,2 … P g,i P ES,k … P g,n ] T (11)

[0114] E G =[E g,1 E g,2 … E g,i γ k … E g,n ] T (12)

[0115]

[0116] γ t =(γ t-1 S oc,t-1 D c +Q cha,t -Q dis,t ) / (S oc,t D c )(18)

[0117]

[0118] Among them, R ij is the branch carbon flow rate of branch ij, that is, the branch carbon flow rate from the i-th branch to the j-th branch; H u is the power flow distribution matrix corresponding to the calculated power network, through which the power distribution of the units can be realized; P ji is the active power transmitted on branch ji; P iis the active power flowing through the i-th node; W j is the set of downstream nodes of node j; P G is the active power column vector of the unit; P g,i is the active output power of the unit connected to node i; P ES,k is the discharge power of the kth energy storage device; E G is the column vector of the unit’s carbon emission intensity; E g,i is the carbon emission intensity of unit i, which is calculated as shown in formula (13)-formula (14); E G is the column vector of the unit’s carbon emission intensity; δ i and a i ,b i ,c i are the fuel consumption per unit of electricity and characteristic parameters of unit i; h i is the correction factor; and M C are the molar masses of carbon dioxide and carbon, respectively; η i and k i are the carbon content and carbon oxidation rate of the unit fuel respectively; is a column vector whose i-th component is 1 and the rest are 0; k is the carbon density of the k-th electric energy storage unit, which represents the equivalent carbon emissions contained in the unit electric energy stored in the electric energy storage unit. It is calculated using formula (15)-formula (18); Q cha,t and Q dis,t are the carbon emissions of the injected and discharged electric energy storage, respectively; γ t is the carbon density of the electric energy storage during period t; R is the charging branch set of the electric energy storage; and ρ i,t are the amount of electricity flowing into the i-th charging branch and its branch carbon flow density; P cha,t 、P dis,t ,η cha and η dis are the charging and discharging power and charging and discharging efficiency of the energy storage in period t respectively; S is the external carbon emission density of the energy storage device during discharge. oc,t D is the state of charge of the energy storage during period t; c Indicates the rated capacity of the electrical energy storage.

[0119] 2) Network loss carbon flow rate, that is, the equivalent carbon emissions of network loss per unit time of a branch:

[0120]

[0121] in, and are the branch active power loss and network loss carbon flow rate of branch ij respectively.

[0122] 3) Branch carbon flow density, that is, the equivalent carbon emissions contained in the unit of electricity transmitted by a branch:

[0123]

[0124] Among them, ρ ij is the carbon flow density of branch ij.

[0125] 4) Node carbon potential, that is, the equivalent carbon emissions when a node consumes a unit of electricity:

[0126]

[0127] is the nodal carbon potential of node j.

[0128] Establish an EV carbon flow model.

[0129] According to the carbon emission flow theory, all electric energy flowing through the system is accompanied by equivalent carbon emissions. Therefore, during the charging and discharging process, EVs will also inject or outflow equivalent carbon emissions with the charging pile as the node. During the driving process of the EV, as the battery energy is consumed, the total amount of equivalent carbon emissions carried by the EV will also decrease. In order to quantify the equivalent carbon emission content in EVs, the concept of EV carbon ratio of unit electricity (CRUE) is proposed to characterize the equivalent carbon emissions contained in the unit electric energy stored in the EV battery. It is represented by the symbol κ and the unit is kg / kWh. Based on CRUE, the carbon emission flow of EVs at each stage of the scheduling cycle can be modeled.

[0130]

[0131] κ m,k,t is the CRUE of EV number k in the mth trip at time period t; and are the carbon emissions injected into and out of EV respectively; S and are the set of branches supplying power to the charging station and the carbon flow density of the sth power supply branch; is the external carbon emission density of EV during discharge; C EV is the rated capacity of the EV battery.

[0132] Establish a personalized EV low-carbon incentive factor formulation mechanism and a low-carbon charging and discharging decision-making model.

[0133] The dynamic carbon emission factor of the node where the charging station is located can be calculated through carbon emission flow theory and applied to the low-carbon charging and discharging response of EVs. Users arrange EV charging and discharging plans based on travel needs and the dynamic carbon emission factor information of the charging station to actively participate in low-carbon emission reduction actions. The dynamic carbon emission factor is:

[0134]

[0135] Where: and Dynamic carbon emission factor and node carbon potential of charging station nodes in period t.

[0136] To further encourage EV users to participate in the park's carbon emission reduction efforts, the park has developed a personalized EV low-carbon incentive factor. Based on the grid's electricity cost coefficient, it considers the difference between the EV's carbon content per kilowatt-hour and the charging station's dynamic carbon emission factor. This guides EVs to consume low-carbon green electricity and helps promote the clean energy distribution network. The park's personalized EV low-carbon incentive factor is formulated as follows:

[0137]

[0138] Where: and is the electricity purchase and sale cost coefficient of the power grid; and is the low-carbon incentive factor for EV charging and discharging in the park during period t.

[0139] The park's goal of guiding EV users to participate in low-carbon charging and discharging responses is to minimize the total single-cycle charging and discharging cost of all EV users in the park. This includes the equivalent carbon emission reduction benefits that users gain from EV charging and discharging. The cost function is expressed as:

[0140]

[0141] is the total equivalent carbon emission reduction of EVs in the park during period t; p CM Subsidy for unit carbon emission reduction in the park.

[0142] The total cost of single-cycle charging and discharging for all EV users in this park The corresponding constraints are formula (2)-formula (5) and formula (9)-formula (10) in step 1.

[0143] Step 3: Establish a park optimization decision model and corresponding constraints.

[0144] Since the charging cost and discharge subsidy of EV in step 2 need to be borne by the park, the total operating cost during the park scheduling cycle is Including the total single-cycle charging and discharging cost of all EV users in the park constructed in step 2 The operating costs of fuel units and energy storage equipment, as well as the electricity transaction costs between the park and the power grid The operating cost objective function is as follows:

[0145]

[0146] Where: are the operating costs of the fuel unit and the energy storage unit in time period t respectively; P g,t is the active power of the fuel unit in period t; a c ,b c ,c c is the fuel cost coefficient of the unit; K ESB is the unit charge and discharge cost coefficient of the energy storage unit; P t Ubuy and P t Usell The two are respectively the electricity purchased and sold by the park from the power grid.

[0147] The constraints corresponding to the total operating cost function within the park's scheduling cycle include not only the corresponding constraints of the single-cycle charging and discharging total cost function of all EV users in the park, i.e., formulas (2)-(5), (9)-(10) in step 1, but also the following related constraints:

[0148]

[0149] P WT,t and P PV,t Represent the output power of wind turbines and photovoltaic units respectively; ε cha,n,t and ε dis,n,t is a binary variable indicating the charging and discharging status of the energy storage unit; P ES,n,t represents the capacity of the energy storage unit in time period t; S oc,max and S oc,min They represent the state of charge boundaries of the energy storage unit. Formula (35) is the power balance constraint, formula (36) is the fuel unit operation constraint; formula (37)-formula (42) are the energy storage unit operation constraints.

[0150] Step 4: Solve the model to obtain the optimized park operation decisions and benefits.

[0151] Based on the constraints, the park optimization decision model in step 3 is solved to obtain all specific energy decisions in the optimized park, including electric vehicle charging and discharging, as well as the economic benefits and carbon emissions of the park. The optimization solution is implemented in the MATLAB environment with the help of the Cplex toolbox. The specific solution process is as follows: Figure 4 Specifically, the steps are as follows:

[0152] (1) The park operator sets the relevant basic parameters, including the parameters of the fuel unit, energy storage equipment, and photovoltaic output and load data.

[0153] (2) Combining the historical operation strategy of the park system in the same period with the actual operation strategy in recent days, an initial operation strategy is set for the park system. Based on this strategy, the park carbon flow calculation model in step 2 is used to calculate the initial node carbon potential of each node in the park and the initial carbon flow density of the branch.

[0154] (3) The operator collects the carbon content per kilowatt-hour of electricity of each EV, and formulates the corresponding low-carbon charging and discharging incentive factor for each EV based on the carbon flow index data of the park, and publishes it to each EV user.

[0155] (4) Under the current parameters and the low-carbon incentive factor for EV charging and discharging, the operator constructs the operating cost objective function of the park system according to formulas (31) to (34) in step 3.

[0156] (5) Call the Cplex toolbox in the Matlab environment to solve the park system operation cost objective function.

[0157] (6) The optimized output of each device in each time period, the charging and discharging power of EVs and carbon emission reduction, as well as the operating cost and overall carbon emissions of the park are obtained, and the algorithm ends.

[0158] Beneficial effects of this application:

[0159] 1. Considering the multi-trip charging and discharging characteristics of EVs, the concept of EV carbon content per kilowatt-hour is proposed based on the carbon emission flow theory, and an accurate carbon emission flow model for EVs is established.

[0160] 2. A low-carbon charging and discharging response model for EVs is proposed. This model considers the gap between the carbon content of EV electricity and the dynamic carbon emission factor of charging stations to develop a personalized low-carbon incentive factor for EV charging and discharging. A low-carbon charging and discharging decision-making model for EVs is proposed, guided by the personalized low-carbon incentive factor and the dynamic carbon emission factor of charging stations.

[0161] 3. Building on the above work, we propose a park energy optimization management method that considers the low-carbon charging and discharging response of EVs. With the goal of minimizing park operating costs, we optimize the scheduling of various equipment within the park and increase the enthusiasm of EV users to participate in vehicle-grid interaction.

[0162] This application aims to guide the low-carbon charging and discharging management of EVs in the park, motivate EV users to increase their enthusiasm for vehicle-grid interaction, and reduce the park's carbon emissions. First, considering the multi-trip charging and discharging characteristics of EV, the concept of EV carbon content per kilowatt-hour is proposed based on the carbon emission flow theory, and an accurate carbon emission flow model for EV is established. Then, a low-carbon charging and discharging response model for EV is proposed, in which a personalized low-carbon incentive factor for EV charging and discharging is set based on the gap between the EV carbon content per kilowatt-hour and the dynamic carbon emission factor of the charging station, and an EV low-carbon charging and discharging decision-making model guided by the personalized low-carbon incentive factor and the dynamic carbon emission factor of the charging station is proposed. On this basis, a park energy optimization management method considering the low-carbon charging and discharging response of EV is proposed, with the goal of minimizing the operating costs of the park, to achieve optimized scheduling of various equipment in the park, and to improve the enthusiasm of EV users to participate in vehicle-grid interaction.

[0163] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0164] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A campus energy management method that takes into account the low-carbon charging and discharging response of electric vehicles, characterized in that: The park energy management method for low-carbon charging and discharging response of electric vehicles includes: Obtaining setting information data for the target park; the setting information data includes: initial operation strategy and basic parameters; the basic parameters include: fuel unit parameters, energy storage device parameters, photovoltaic output data and load data; Constructing an EV multi-trip mobile charging and discharging model; the EV multi-trip mobile charging and discharging model is constructed based on the trips of multiple electric vehicles within the target park, as well as the continuity and correlation of charging and discharging; the trip is the process from the start of the electric vehicle's driving phase to the completion of parking and charging and discharging in the target park; Constructing an EV low-carbon charging and discharging response model; the EV low-carbon charging and discharging response model is determined by performing charging and discharging and energy analysis based on the EV multi-trip mobile charging and discharging model using carbon emission flow theory; the EV low-carbon charging and discharging response model includes: a park carbon flow calculation model and an EV carbon flow model; the park carbon flow calculation model is a mathematical model determined based on carbon flow index data; the carbon flow index data includes: branch carbon flow rate, network loss carbon flow rate, branch carbon flow density, and node carbon potential; Calculating initial carbon flow index data corresponding to each node in the target park according to the initial operation strategy and the park carbon flow calculation model; Determining the carbon content per kilowatt-hour of electricity corresponding to a plurality of electric vehicles in the target park based on the EV carbon flow model and the EV multi-trip mobile charging and discharging model; For any of the electric vehicles, determining a low-carbon incentive factor for charging and discharging based on the carbon content per kilowatt-hour and the initial carbon flow index data; Determining a park operation cost objective function based on the basic parameters and all the charging and discharging low-carbon incentive factors; Solving the park operation cost objective function using constraints to obtain an optimal energy management plan for the target park; the optimal energy management plan is used to manage and schedule the output of each unit in the target park, the charging and discharging power of EVs, and the amount of carbon emission reduction; The calculation formula of the branch carbon flow rate is: Among them, R ij is the branch carbon flow rate from the i-th branch to the j-th branch; P i is the active power flowing through the i-th node; is a column vector; H u is the power flow distribution matrix corresponding to the power network; P G is the active power column vector of the unit; E G is the column vector of the unit’s carbon emission intensity; P ij is the active power transmitted from the i-th branch to the j-th branch; i and j are both serial numbers; diag(P G ) means to convert P G Convert to a diagonal matrix; The calculation formula of the network loss carbon flow rate is: in, is the active power loss from the i-th branch to the j-th branch; is the network loss carbon flow rate from the i-th branch to the j-th branch; The calculation formula of the branch carbon flow density is: Among them, ρ ij is the carbon flow density from the i-th branch to the j-th branch; The calculation formula of the node carbon potential is: in, is the node carbon potential of the jth node; P j is the active power flowing through the jth node; U j is the set of downstream nodes of the j-th node; The mathematical expression of the EV carbon flow model is: Among them, κ m,k,t+1 is the carbon content of electricity consumed by EV numbered k in the mth trip during the t+1 period; κ m,k,t is the carbon content of electricity consumed by EV numbered k during the mth trip in period t; C EV is the rated capacity of the EV battery; is the carbon emissions injected by EV numbered k in the mth trip during the t+1 period; is the carbon emissions of EV numbered k in the mth trip during the t+1 period; is the state of charge of EV numbered k in the mth trip during period t; m,t+1 is the driving status of the EV during the t+1 period of the m-th trip; is the average change in the state of charge of EV numbered k in the mth trip; is the state of charge of EV numbered k in the mth trip at time t+1.

2. The park energy management method taking into account the low-carbon charging and discharging response of electric vehicles according to claim 1 is characterized in that: The charging and discharging low-carbon incentive factors include: a charging low-carbon incentive factor and a discharging low-carbon incentive factor; The expression of the charging low-carbon incentive factor is: The expression of the discharge low-carbon incentive factor is: in, is the low-carbon charging incentive factor corresponding to the EV numbered k in the target park during period t; is the cost coefficient of electricity sold to the grid; is the low-carbon incentive factor for discharge corresponding to the EV numbered k in the target park during period t; is the electricity purchase cost coefficient of the power grid; is the dynamic carbon emission factor of the charging station node in period t; The calculation formula is: in, is the node carbon potential of the charging station node in period t.

3. The park energy management method taking into account the low-carbon charging and discharging response of electric vehicles according to claim 1 is characterized in that: Determine the park operation cost objective function based on the basic parameters and all the charging and discharging low-carbon incentive factors, specifically including: Determining a total charge and discharge cost function based on all of the charge and discharge low-carbon incentive factors; The park operation cost objective function is determined based on the basic parameters and the total charge and discharge cost function.

4. The park energy management method taking into account the low-carbon charging and discharging response of electric vehicles according to claim 1 is characterized in that: The expression of the park operation cost objective function is: in, is the park operation cost objective function; is the operating cost of the fuel unit in period t; is the operating cost of the energy storage unit in period t; is the total cost function of charging and discharging; is the electricity transaction cost between the park and the power grid; a c ,b c ,c c are the fuel cost coefficients of the units; P g,t is the active power of the fuel unit in period t; K ESB is the unit charge and discharge cost coefficient of the energy storage unit; P cha,t is the charging power of the energy storage during period t; P dis,t is the discharge power of the energy storage in period t; η cha is the charging efficiency of the energy storage in period t; η dis is the discharge efficiency of the energy storage in period t; P t Ubuy The target park purchases electricity from the power grid; P t Usell The electricity sold by the park to the grid; is the cost coefficient of electricity sold to the grid; It is the cost coefficient of purchasing electricity from the power grid.

5. The park energy management method taking into account the low-carbon charging and discharging response of electric vehicles according to claim 1 is characterized in that: The constraints include: charging and discharging power and energy constraints, energy constraints between two trips, power balance constraints, fuel unit operation constraints and energy storage unit operation constraints.

Images