EV charging and integrated energy system capacity configuration optimization method considering seasonal characteristics

Abstract

The invention discloses an EV charging and integrated energy system capacity configuration optimization method considering seasonal characteristics. The method comprises the following steps: constructing an electric vehicle load access and departure IES time model and a time slot division model; on the basis of the established IES time model and time slot division model, introducing a dynamic urgency index, and establishing an electric vehicle coordinated charging model with a minimum load peak-valley difference as a target; constructing an IES equipment model and constraints, and generating a source-load seasonal scene; on the basis of the established IES equipment model and constraints, a two-stage optimization model with the minimum IES equipment capacity configuration cost as the target is established by adopting an electric load obtained by an electric vehicle coordinated charging model; and carrying out optimization solution on the established two-stage optimization model by considering coordinated charging of the electric vehicle. According to the method, the peak-valley difference of the load can be effectively reduced, and the light abandoning amount and the carbon dioxide emission amount of the system are effectively reduced; the configuration capacity of each device in the integrated energy system IES is reduced, and the device construction cost of the integrated energy system IES is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system optimization and scheduling, and in particular to a method for optimizing EV charging and integrated energy system capacity configuration taking seasonal characteristics into consideration. Background Art

[0002] Developing clean, efficient, and economical energy supply methods and achieving sustainable energy development have become key priorities in the energy sector. Integrated energy systems, leveraging the complementary characteristics of different energy sources to organically coordinate and optimize energy generation, transmission, distribution, conversion, storage, and consumption, enable the coupled conversion of multiple heterogeneous energy flows and achieve cascaded energy utilization. This represents a key direction for future energy system development.

[0003] As a new type of load in integrated energy systems (IES), electric vehicles (EVs) hold enormous potential for application. EVs consume electricity and emit virtually no pollutants, making them eco-friendly. Therefore, EVs offer an alternative for developing cleaner transportation systems and hold significant potential for sustainable transport development. EVs can also be more easily integrated into intelligent transportation systems, enhancing intelligent transportation services. However, EV development also faces challenges. In addition to technical challenges, management and operational issues can affect the interaction between EVs and the main grid or microgrids, hindering the widespread adoption of EVs. For example, when large numbers of EVs are connected to an integrated energy system (IES), uncoordinated charging can lead to significant peak-to-valley load variations, compromising power system security and stability while reducing the utilization of renewable energy. To address these issues, optimizing EV charging within microgrids is necessary. Summary of the Invention

[0004] The present invention provides a method for optimizing the capacity configuration of EV charging and integrated energy systems taking into account seasonal characteristics. The method can effectively reduce the peak-to-valley difference in load, effectively reduce the system's abandoned solar power and carbon dioxide emissions; reduce the configuration capacity of each device in the integrated energy system (IES), reduce the equipment construction cost of the integrated energy system (IES) and improve the net profit of the system.

[0005] The technical solution adopted by the present invention is:

[0006] The method for optimizing EV charging and integrated energy system capacity configuration considering seasonal characteristics includes the following steps:

[0007] Step 1: Construct the electric vehicle load access and leave IES time model and the electric vehicle load access and leave IES time slot division model;

[0008] Step 2: Based on the IES time model and time slot division model established in Step 1, a dynamic urgency index is introduced to establish an electric vehicle coordinated charging model with the goal of minimizing the load peak-valley difference, adding electric vehicle charging constraints and IES equipment output constraints;

[0009] Step 3: Build IES equipment model and constraints to generate source-load seasonality scenarios;

[0010] Step 4: Based on the IES equipment model and constraints established in step 3, the electric load obtained by the electric vehicle coordinated charging model in step 2 is used to establish a two-stage optimization model for IES equipment capacity configuration with the goal of minimizing the IES equipment capacity configuration cost;

[0011] Step 5: Considering the coordinated charging of electric vehicles, the two-stage optimization model of IES equipment capacity configuration established in step 4 is optimized and solved.

[0012] In step 1, in the electric vehicle load access and exit time model, the time when the electric vehicle accesses and exits the IES follows a normal distribution, as follows:

[0013] This paper mainly analyzes the charging behavior of private car users on weekdays. EV owners start charging when they arrive at work in the morning and complete charging after get off work. c and time t of leaving the charging station dis Although the distribution is normal, the travel patterns of residents in different seasons have different characteristics: in summer, the days are long and the nights are short, so residents’ travel ends later; in winter, the days are short and the nights are long, so residents’ travel ends earlier. Take spring as an example:

[0014] 1) Electric vehicle access time t c and departure time t dis Following the normal distribution, the time t at which electric vehicles are connected to IES c As shown in formula (1):

[0015]

[0016] In formula (1): f(t c ) is t c The distribution function of t c Connect IES time for electric vehicles; is the mean of the normal distribution; The value is 8.5; is the standard deviation of the normal distribution; The value of is 3.3; π is the ratio of the circumference of a circle to the circumference of a circle; exp(·) is the natural exponential function; 12 represents the 12th hour, and 24 represents the 24th hour.

[0017] 2) The time t when the electric vehicle leaves the IES dis As shown in formula (2):

[0018]

[0019] In formula (2): f(t dis ) is t dis The distribution function of t dis The time when the electric vehicle leaves the IES; is the mean of the normal distribution; The value is 17.5; is the standard deviation of the normal distribution; The value is 3.24;

[0020] In step 1, in the IES time slot division model for electric vehicle load access and departure, during the EV scheduling process, 24 hours a day is divided into 96 time slots, each of which is 15 minutes long. When an EV accesses the IES at the middle of a time slot, charging can be scheduled after the next integer time slot. When an EV disconnects from the IES at the middle of a time slot, the EV can only be scheduled before the end of the previous integer time slot. The time slot for each electric vehicle to access the IES is shown in Equation (3):

[0021]

[0022] In formula (3), N is the total number of EVs; i is the sequence number of EVs, i = 1, 2, ..., N; J is the sequence number of the time slot in a day, J = 1, 2, ..., 96; Indicates the time slot number when the i-th EV accesses the IES; is the next integer greater than the result of the division operation; represents the time when the i-th EV accesses the IES; ΔT represents the length of the time slot.

[0023] The time slot when each electric vehicle leaves the IES is shown in formula (4):

[0024]

[0025] In formula (4): represents the time slot number when the i-th EV leaves the IES, is the previous integer smaller than the result of the division operation; represents the time when the i-th EV leaves the IES.

[0026] The number of remaining time slots when EV accesses IES is defined as follows:

[0027]

[0028] In formula (5): Ti Rem It represents the number of remaining time slots that the i-th EV can continue to access the IES. This value is meaningful only when the EV is in the time slot of accessing the IES. The charging behavior and charging scheduling strategy are arranged within these remaining time slots.

[0029] In step 2, a dynamic urgency index is introduced to reflect the urgency of the EV's charging demand in each charging time slot, which is defined as shown in the following formula (6):

[0030]

[0031] In formula (6): DUI i,J is the urgency index of the i-th EV in the J-th time slot. The value of this index is meaningful only when the EV is in the time slot of accessing the IES; is the slow charging power of EV; η EV For EV charging efficiency; is the minimum SOC requirement of the owner of the i-th electric vehicle; When the i-th EV is connected to IES SOC ; SOC i,J For the i-th vehicle EV The SOC at the beginning of the Jth time slot is meaningful only when the EV is in the IES time slot. i Rem represents the number of remaining time slots that the i-th EV can continue to access the IES; express EV Battery capacity; P i,n-1 For the i-th vehicle EV The charging power in the n-1th time slot; n is the summation variable in the summation symbol,

[0032] When DUI i,J ＜0 When EV The charging demand in this time slot is urgent; when DUI i,J ≥ 0 When , it means that the charging demand of the i-th EV in this time slot is not urgent.

[0033] In step 2, a coordinated charging model for electric vehicles is established with the goal of minimizing the peak-to-valley difference in load, as shown in equations (7) to (10);

[0034] In the coordinated charging model of electric vehicles with the goal of minimizing the peak-to-valley difference in load, two charging modes of EVs are considered: fast charging and slow charging mode:

[0035] Define the state variable x i,J , by the state variable xi,J and Dynamic Urgency Indicator (DUI) i,J Jointly decide on the i-th vehicle EV The charging power in the Jth time slot is as shown in the following formula (7):

[0036]

[0037] In formula (7), x i,J is a state variable.

[0038] The choice of charging mode for a time slot is determined by the dynamic urgency index (DUI), as shown in formula (8):

[0039]

[0040] In formula (8), P i,J For the i-th vehicle EV Charging power in the Jth time slot; For fast charging power, Slow charging power; DUI i,J is the urgency index of the i-th EV in the J-th time slot.

[0041] The total load in the Jth time slot of the electric vehicle coordinated charging model is shown in formula (9):

[0042]

[0043] In formula (9), represents the total load in the Jth time slot of the coordinated charging scheduling model; Represents the basic electrical load in the Jth time slot.

[0044] The goal of charging scheduling is to reduce the peak-valley load difference of IES, as shown in formula (10):

[0045]

[0046] In formula (10), and Represent the maximum and minimum electrical load power respectively.

[0047] The time slot when the electric vehicle stops charging is determined by the time when the EV leaves the IES and the time slot that meets the maximum SOC requirement. The charging constraint of the EV is shown in Equation (11):

[0048]

[0049] In formula (11): x i,J is a state variable; Indicates the highest SOC to ensure the safety of electric vehicle batteries; is the SOC of the i-th EV when it is connected to the IES.

[0050] In order to constrain IES, add formula (12) to limit the increase of peak value:

[0051]

[0052] In formula (12), is the maximum value of the total electric load under the non-coordinated charging mode that meets the maximum state of charge requirement of EV within 96 time slots; Indicates the maximum electrical load power.

[0053] In step 3, the IES device model is as follows:

[0054] The actual power generated by a photovoltaic (PV) device at time t is mainly affected by the intensity of solar radiation and the area of the PV panel. The power generated by the PV device at time t is shown in Equation (13):

[0055]

[0056] In formula (13): t is time; P PVr is the total rated power of the photovoltaic equipment; P PV (t) is the power generated by the photovoltaic panel at time t; S PV is the total installation area of photovoltaic panels; P SE (t) is the solar energy available per unit area of photovoltaic panels at time t; PV is the conversion efficiency of the photovoltaic panel; k is the conversion coefficient of the output power and area of the photovoltaic panel per unit area when the solar radiation level is 1kW / ㎡, in kW / ㎡; is the abandoned power of the photovoltaic equipment at time t.

[0057] Due to the limitations of actual site space and construction costs, there is a maximum area limit for configuring photovoltaic panels, as shown in formula (14):

[0058]

[0059] In formula (14): S PV is the total installed area of PV panels; The maximum area for configuring photovoltaic panels.

[0060] The actual power generation of the wind turbine (WT) at time t is mainly determined by the real-time wind speed and the rated power of the wind turbine. The calculation model is shown in formula (15):

[0061]

[0062] In formula (15): P WT (t) is the actual power generation of the wind turbine at time t; V(t) is the actual wind speed at time t; V in V is the cut-in wind speed, which is 3m / s in the present invention; out For the cut-out wind speed, the present invention takes the value as 24m / s; V r is the rated wind speed, which is 12m / s in the present invention; P wtr is the rated power of the fan; W is the number of configured fans.

[0063] Like photovoltaic generators, wind turbines are also subject to limitations such as actual site space and construction costs. There is a maximum limit on the number of wind turbines that can be configured, as shown in formula (16):

[0064] 0≤W≤W max (16);

[0065] In formula (16): W is the number of fans configured; W max The maximum number of fans that can be configured.

[0066] Power to gas (P2G) equipment mainly includes electrolyzers (EL) and methane synthesis equipment (MSE). The operation models and constraints of the electrolyzers EL and the methane synthesis equipment MSE are shown in Equations (17) and (18):

[0067]

[0068] In formula (17): P EL (t) and P MSE (t) are the EL hydrogen production at time t Synthesis of methane with MSE Power consumption; and are the power consumption coefficients of EL and MSE respectively; and is the chemical reaction equilibrium coefficient; and The composite volume at time t The volumes of CO2 and H2 required for methane.

[0069]

[0070] Formula (18) is the ramp power constraint of the electrolyzer device EL and the methane synthesis equipment MSE, where: P EL,fluc (t) and P MSE,fluc(t) are the power fluctuation values of EL and MSE at time t; M EL and M MSE are the upper limits of the climbing power of EL and MSE respectively. EL,max 、P EL,min are the upper and lower limits of EL power consumption respectively; P MSE,max 、P MSE,min They are the upper and lower limits of MSE power consumption respectively.

[0071] The operation model and constraint model of thermal power units and carbon capture and storage (CCS) equipment are shown in Equations (19) and (20):

[0072]

[0073] In formula (19): P PGU (t) are the carbon emissions and power generation of the thermal power unit at time t; ε PGU is the carbon emission intensity of thermal power units; are the actual CO2 capture and dissipation of CCS; δ CCS is the capture efficiency of CCS; P CCS (t) is the power consumption of CCS at time t; is the power consumption coefficient corresponding to the unit CO2 captured by CCS.

[0074]

[0075] Formula (20) is the ramp power constraint of the thermal power unit and the power constraint of the CCS, where: P PGU (t) is the power generation capacity of the thermal power unit at time t; are the upper and lower limits of the power generation capacity of thermal power units respectively; P PGU,fluc (t) is the power fluctuation value of the thermal power unit at time t; M PGU P is the upper limit of the ramp power of the thermal power unit. rCCS is the rated power of CCS.

[0076] The operation model and constraints of hydrogen storage equipment (HSE) can be expressed by formula (21):

[0077]

[0078] In formula (21): V HSE (t) is the hydrogen storage value of HSE at time t; is the self-release rate of HSE; are the hydrogen storage and release powers of HSE at time t respectively; are the hydrogen storage and release efficiencies of HSE respectively; HSE 、C HSE are the energy rate and configuration capacity of HSE respectively; x HSE is the charge and discharge state variable of HSE, which is a Boolean variable; △t is the unit time step, which is 1h in the present invention;

[0079] Similar to HSE equipment, the operation model and constraints of carbon dioxide storage (CDS) can be expressed as Equation (22):

[0080]

[0081] In formula (22): V CDS (t) is the CO2 storage value of CDS at time t; is the self-release rate of CDS; are the CO2 storage and release power of CDS at time t respectively; are the storage and release efficiency of CO2 of CDS respectively; CDS 、C CDS are the energy rate and configuration capacity of CDS respectively; x CDS It is the charge and discharge state variable of CDS, which is a Boolean variable.

[0082] In step 3, the source-load seasonality scenario is generated as follows:

[0083] In real life, there are great differences in the intensity of sunlight and wind speed in the four seasons. WT and PV are affected by the sunlight and wind speed of the construction site, resulting in seasonal characteristics of wind and solar output. At the same time, users' travel is affected by the climate, and the travel patterns of EVs also change with the change of seasons. Therefore, when optimizing IES, it is more practical to consider the seasonal characteristics of wind and solar output and EV travel patterns. The flow chart of the typical scene generation of EV travel patterns and wind and solar power in this invention is summarized as follows: Figure 3 and Figure 4 shown.

[0084] First, based on 8760 hours of wind speed and total horizontal radiation data throughout the year, the year is divided into four seasons: March to May is spring; June to August is summer; September to November is autumn; and December to February is winter. The 8760 wind speed and total horizontal radiation data throughout the year are divided into four parts according to time: spring, summer, autumn and winter. The K-means clustering algorithm is used to cluster each part into scenarios, and then the probabilistic scenario analysis method is used to select the scenario with the highest probability in each season to represent the typical day of that season. The specific flow chart is as follows Figure 4 The present invention uses Monte Carlo Simulation (MCS) to simulate the randomness of 300 EVs.

[0085] Since the travel patterns of EV users in spring, summer, autumn and winter are somewhat different, Monte Carlo simulation (MCS) is used to simulate the EV travel conditions in the four seasons and obtain the EV travel patterns in the four seasons. The EV travel patterns of the four seasons are generated by equations (1) and (2) in sequence. The mean and standard deviation of the normal distribution in each season are different. The values are 17.48, 18.00, 17.26, and 17.10 respectively; the standard deviation The values are 3.60, 3.59, 3.60, and 3.62 respectively.

[0086] In step 4, the IES equipment capacity configuration two-stage optimization model is as follows:

[0087] In this method, the electrical load is optimized using the results of an EV coordinated charging model, taking into account the seasonal characteristics of renewable energy and load demand. The first phase involves EV coordinated charging optimization. Building on this first phase, the second phase uses the electrical load results from the first phase to optimize the IES equipment capacity configuration. This establishes a two-stage optimization model for IES equipment capacity configuration.

[0088] A quadratic function is used to describe the energy satisfaction of energy users (EU) within the IES. The value itself has no practical meaning and only reflects the advantages and disadvantages of the two strategies when compared. As shown in formula (23):

[0089]

[0090] In formula (23): U UT (t) is the energy efficiency of the user in IES at time t; ρ ele , τ ele is the electricity preference coefficient within IES, and ρ in the present invention ele Take 7, τ ele Take 0.008; P ele (t) is the actual electrical load in IES at time t.

[0091] Interest demands of users within IES LA , as shown in formula (24):

[0092]

[0093] In formula (24): U LA For the comprehensive benefits of users within IES; U LA (t) The comprehensive benefits of users in IES at time t; cele (t) is the electricity price at time t; P ele (t) is the actual electrical load in IES at time t.

[0094] In the entire IES system, wind turbines, photovoltaics, and thermal power units are power generation equipment, while CCS, EL, and MSE are power consumption equipment. The user's power load demand is also taken into account. The system's power balance constraint is expressed by Equation (25):

[0095] P PV (t)+P PUG (t)+P WT (t) = P CCS (t)+P EL (t)+P MSE (t)+P ele (t) (25);

[0096] In formula (25): P PV (t) is the power generated by the photovoltaic panel at time t; P PGU (t) is the power generation power of the thermal power unit at time t; P WT (t) is the actual power generation of the wind turbine at time t; P CCS (t) is the power consumption of CCS at time t; P EL (t) and P MSE (t) are the EL hydrogen production at time t Synthesis of methane with MSE Power consumption; P ele (t) is the actual electrical load in IES at time t.

[0097] In the entire IES system, the equipment that produces H2 is EL, the equipment that consumes H2 is MSE, and HSE can both input H2 from the system and output H2 to the system. The hydrogen balance constraint of the system can be expressed by Equation (26):

[0098]

[0099] In formula (26): is the volume of hydrogen produced by EL at time t; The composite volume at time t The volume of H2 required for methane; are the hydrogen storage and release powers of HSE at time t respectively.

[0100] In the entire system, the equipment that produces CO2 is CCS, the equipment that consumes CO2 is MSE, and CDS can both input CO2 from the system and output CO2 to the system. The CO2 balance constraint of the system can be expressed by equation (27):

[0101]

[0102] In formula (27): is the actual amount of CO2 captured by CCS; are the CO2 storage and release power of CDS at time t respectively; The composite volume at time t The volume of CO2 required for methane.

[0103] In order to achieve economical and low-carbon operation of IES, from the perspective of maximizing the benefits of the system throughout its life cycle, that is, minimizing the cost, a two-stage optimization model for IES equipment capacity configuration is constructed, whose objective function is shown in formula (28):

[0104]

[0105] In formula (28): F D is the total comprehensive cost; y and z represent the 0-1 variable and continuous variable involved in the model respectively; B is the set of equipment involved in capacity optimization in IES, B = {PV, WT, CCS, HSE, CDS, MSE}; C inv is the investment cost of each device in the system; C OM is the operation and maintenance cost of each device in the system; C tari The resource acquisition cost during the system operation process, which is composed of the cost of purchasing coal and the income from selling electricity and natural gas; C tax is the system carbon emission cost; ν b 、 φ b are the unit capacity investment cost, equal installment capital recovery coefficient, and rated capacity of equipment b; the ratio of operation and maintenance cost to the present value of its investment cost; and c fuel (t), c ele (t), c NG (t), are the coal purchase price, electricity price, natural gas price and carbon tax at time t respectively; P ele (t), V NG (t) are the coal purchase quantity, electricity load, and natural gas sales quantity at time t; is the CO2 dissipation of CCS.

[0106] Equal installment capital recovery coefficient ν for equipment b b The calculation formula is shown in formula (29):

[0107]

[0108] In formula (29), 10% is the annual interest rate; n is the service life of equipment b.

[0109] In step 5, the two-stage optimization solution is divided into two stages:

[0110] Phase 1: Using Equation (26) as the objective function, the dynamic urgency index (DUI) is used to select the charging power of each EV in each time slot to minimize the objective function. A model is built in MATLAB / YALMIP and solved using the CPLEX solver. Four typical day loads are generated, and the load usage of the 96 time slots is converted into a full-day load profile with a step size of 1 hour.

[0111] Phase 2: Using the electric load after coordinated charging of EVs in Phase 1, considering the seasonal characteristics of wind and solar power, and taking Equation (28) as the objective function, the CPLEX solver is used to solve the MILP problem in Phase 2.

[0112] The two-stage optimization solution diagram is as follows Figure 8 The process includes the following steps:

[0113] Step 1: Generate charging information for 300 EVs based on the MCS. The charging information includes the time the EV connects to the microgrid, the time the EV is expected to leave the microgrid, the minimum SOC requirement of the EV user, and the maximum SOC requirement of the EV.

[0114] Step 2: Calculate the DUI dynamic urgency index of each EV in each time slot based on the EV information generated in Step 1;

[0115] Step 3: Select the charging power of each EV in each time slot through the dynamic urgency index DUI to minimize the objective function (26) and obtain the optimal combination of charging power and charging time slot;

[0116] Step 4: Obtain the optimal electric load from Step 1 to Step 2 above. Considering the seasonal characteristics of wind and solar power, optimize the IES equipment capacity configuration based on the objective function of minimizing the IES life cycle cost, based on the IES satisfying the power balance constraints, hydrogen balance constraints, and carbon dioxide balance constraints.

[0117] The present invention provides a method for optimizing EV charging and integrated energy system capacity configuration considering seasonal characteristics, and the technical effects are as follows:

[0118] 1) In order to better connect electric vehicles to the integrated energy system, improve the operating economy of the integrated energy system, increase the wind and solar power absorption rate, and enable users in the microgrid to use energy more economically and comfortably. Based on the consideration of the seasonal characteristics of renewable energy and load-side electricity demand, the present invention uses an optimized scheduling method that takes into account EV coordinated charging for the electric vehicle load in the IES. Using the coordinated charging method proposed by the present invention for electric vehicles can greatly reduce the peak-to-valley difference of the load, reduce the load power fluctuation within a day, effectively reduce the power consumption pressure of the microgrid during peak hours, and enable electric vehicles (EV) to better connect to the IES; while reducing user costs, it effectively reduces the system's abandoned light and carbon dioxide emissions, reduces the configuration capacity of each device in the IES, reduces the equipment construction cost of the IES, and improves the net income of the system.

[0119] 2) The optimization strategy of the present invention introduces EV load coordinated charging into an integrated energy system that takes into account the seasonal characteristics of renewable energy and load demand. This can reduce the capacity configuration of the IES system on the original basis, thereby reducing the construction cost of the IES. While reducing user costs, it also increases the profit of the IES, greatly improves the energy efficiency of users, and effectively reduces the system's abandoned solar power and carbon dioxide emissions.

[0120] 3) After the optimization strategy of the present invention uses a coordinated charging method for EV loads, the user's power load is significantly reduced, and the peak-shaving and valley-filling effects are obvious; considering the seasonal characteristics of the renewable energy end and the load end, the configuration capacity of the power equipment in the IES will change by more than 10%, which is more in line with reality. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] The present invention will be further described below with reference to the accompanying drawings and examples:

[0122] Figure 1 Schematic diagram of the basic structure and energy flow of the integrated energy system.

[0123] Figure 2 This is the basic electrical load diagram.

[0124] Figure 3 Generate a flow chart for a typical daily scenario considering the seasonal characteristics of EVs.

[0125] Figure 4 Generate a flow chart for a typical day scenario considering the seasonal characteristics of wind and solar power.

[0126] Figure 5 The probability density function diagram of the time when EV connects to and leaves the microgrid.

[0127] Figure 6 Four typical daily graphs of total horizontal radiation and wind speed.

[0128] Figure 7Figure 2 is the result diagram of coordinated and two non-coordinated charging loads.

[0129] Figure 8 Block diagram for the two-stage optimization solution.

[0130] Figure 9 The charging process diagram of 10 random EVs.

[0131] Figure 10 This is the result diagram of power dispatch optimization.

[0132] Figure 11 The electric load diagrams for EV coordinated charging and non-coordinated charging on four typical days. DETAILED DESCRIPTION

[0133] The method for optimizing EV charging and integrated energy system capacity configuration considering seasonal characteristics includes the following steps:

[0134] Step 1: Construct a time model for electric vehicle loads to access and leave the integrated energy system and a time slot division model;

[0135] Step 2: Based on the IES (integrated energy system, IES) time model and time slot division model for electric vehicle load access and departure established in Step 1, a dynamic urgency index is introduced to establish an electric vehicle coordinated charging model with the goal of minimizing the peak-to-valley difference in load;

[0136] Step 3: Build IES equipment model and constraints to generate source-load seasonality scenarios;

[0137] Step 4: Based on the IES equipment model and constraints established in step 3, the electric load obtained from the electric vehicle coordinated charging model in step 2 is used to establish a two-stage optimization model with the goal of minimizing the IES equipment capacity configuration cost;

[0138] Step 5: Considering the seasonal characteristics of renewable energy and load demand, carry out two-stage optimization of electric vehicle coordinated charging and IES capacity configuration. Considering electric vehicle coordinated charging, the optimization solution is performed based on the two-stage optimization model established in step 4.

[0139] Step six: Set up two scenarios and compare them with the method used in the present invention in terms of capacity configuration, abandoned light, carbon emissions and total cost. The results show that in an integrated energy system, considering the seasonal characteristics of renewable energy and load demand at the load end and coordinated charging of EV loads can reduce the construction cost of IES. The rationality and effectiveness of the proposed scheme are demonstrated through case analysis. The profit of the IES system and the comprehensive benefits and energy costs of each microgrid user are improved. For the coordinated charging method of electric vehicles, two non-coordinated charging methods are set up for comparison. The peak value, valley value, peak-valley difference and variance of the electric load of the three charging methods are compared to analyze the impact of the coordinated charging method of electric vehicles on the electric load. The following is a detailed description of the preferred example with reference to the accompanying drawings:

[0140] 1. Before proposing the dynamic urgency index, the charging time of EVs is modeled first, and the IES time model and time slot division model of electric vehicle load access and departure are constructed. The specific process is shown in formulas (1) to (5).

[0141] Electric vehicle access IES time t c and departure time t dis Following the normal distribution, the time t at which electric vehicles are connected to IES c As shown in formula (1):

[0142]

[0143] In formula (1), f(t c ) is t c The distribution function of t c Connect IES time for electric vehicles; The value is 8.5; The value of is 3.3; π is the ratio of circumference to circumference of a circle; exp(·) is the natural exponential function.

[0144] The time t at which the electric vehicle leaves the IES dis As shown in formula (2):

[0145]

[0146] In formula (2), f(t dis ) is t dis The distribution function of t dis The time when the electric vehicle leaves the IES; The value is 17.5; The value is 3.24; π is the ratio of circumference to circumference of a circle; exp(·) is the natural exponential function.

[0147] In the time slot partitioning model, during the EV scheduling process, 24 hours a day is divided into 96 time slots, each of which is 15 minutes long. When an EV connects to the IES at the middle of a time slot, charging can be scheduled after the next integer time slot. When an EV disconnects from the IES at the middle of a time slot, the EV can only be scheduled before the end of the previous integer time slot. The time slot for each electric vehicle to connect to the IES is shown in Equation (3):

[0148]

[0149] In formula (3), N is the total number of EVs; i is the sequence number of EVs, i = 1, 2, ..., N; J is the sequence number of the time slot in a day, J = 1, 2, ..., 96; represents the time slot number when the i-th EV accesses the IES, is the next integer greater than the result of the division operation; represents the time when the i-th EV accesses the IES; ΔT represents the length of the time slot.

[0150] The time slot when each electric vehicle leaves the IES is shown in formula (4):

[0151]

[0152] In formula (4), N is the total number of EVs; i is the sequence number of EVs, i = 1, 2, ..., N; J is the sequence number of the time slot in a day, J = 1, 2, ..., 96; represents the time slot number when the i-th EV leaves the IES, is the previous integer smaller than the result of the division operation; represents the time when the i-th EV leaves the IES; ΔT represents the length of the time slot.

[0153] A dynamic urgency index is defined to reflect the urgency of EV charging demand in each charging time slot.

[0154] Define the number of remaining time slots when EV accesses IES, as shown in formula (5)

[0155]

[0156] In formula (5): T i Rem The number of remaining time slots that the i-th EV can continue to access the IES is significant only when the EV is in the time slot of accessing the IES. Charging behavior and charging scheduling strategies are arranged within these remaining time slots. N is the total number of EVs. i is the EV number, i = 1, 2, ..., N. J is the number of time slots in a day, J = 1, 2, ..., 96. represents the time slot number when the i-th EV leaves the IES; Indicates the time slot number when the i-th EV accesses the IES.

[0157] 2. Introducing a dynamic urgency index, establishing an electric vehicle coordinated charging model with the goal of minimizing the load peak-valley difference, adding electric vehicle charging constraints and IES equipment output constraints. The specific process is shown in formulas (6) to (12):

[0158] The dynamic urgency index is introduced and defined as shown in the following formula (6):

[0159]

[0160] In formula (6), N is the total number of EVs; i is the serial number of EV, i = 1, 2, ..., N; J is the serial number of the time slot in a day, J = 1, 2, ..., 96; DUI i,J is the urgency index of the i-th EV in the J-th time slot. The value of this index is meaningful only when the EV is in the time slot of the IES. △T represents the length of the time slot. is the slow charging power of EV; η EV For EV charging efficiency; is the minimum SOC requirement of the owner of the i-th electric vehicle; is the SOC of the i-th EV when it is connected to IES; SOC i,J is the SOC of the i-th EV at the beginning of the J-th time slot. The value of this indicator is only meaningful when the EV is in the time slot of accessing the IES; T i Rem represents the number of remaining time slots that the i-th EV can continue to access the IES; Indicates the battery capacity of the EV; and are the time slot numbers when the i-th EV enters and leaves the IES; P i,n-1 is the charging power of the i-th EV in the n-1th time slot; n is the summation variable in the summation symbol,

[0161] When DUI i,J <0, it means that the charging demand of the i-th EV in this time slot is urgent; when DUI i,J When ≥0, it means that the charging demand of the i-th EV in this time slot is not urgent;

[0162] In the coordinated charging model of electric vehicles with the goal of minimizing the load peak-valley difference, two charging modes of EVs are considered: fast charging and slow charging mode;

[0163] Define the state variable x i,J , by the state variable x i,J and Dynamic Urgency Indicator (DUI) i,JThe charging power of the i-th EV in the J-th time slot is jointly determined as shown in the following formula (7):

[0164]

[0165] In formula (7), x i,J is a state variable; N is the total number of EVs; i is the sequence number of EVs, i = 1, 2, ..., N; J is the sequence number of the time slot in a day, J = 1, 2, ..., 96; and They represent the time slot numbers when the i-th EV enters and leaves the IES.

[0166] The choice of charging mode for the time slot is determined by DUI, as shown in formula (8):

[0167]

[0168] In formula (8), N is the total number of EVs; i is the sequence number of EVs, i = 1, 2, ..., N; J is the sequence number of the time slot in a day, J = 1, 2, ..., 96; x i,J is the state variable; P i,J is the charging power of the i-th EV in the J-th time slot; For fast charging power, Slow charging power; DUI i,J is the urgency index of the i-th EV in the J-th time slot.

[0169] The coordinated charging model of electric vehicles in step 2 is as follows:

[0170] The total load in the Jth time slot of the electric vehicle coordinated charging model is shown in formula (9):

[0171]

[0172] In formula (9): represents the total load in the Jth time slot of the coordinated charging scheduling model; represents the basic electric load in the Jth time slot; P i,J is the charging power of the i-th EV in the J-th time slot; N is the total number of EVs; i is the sequence number of EV, i = 1, 2, ..., N; J is the sequence number of the time slot in a day, J = 1, 2, ..., 96.

[0173] The goal of charging scheduling is to reduce the peak-valley load difference of IES, as shown in formula (10):

[0174]

[0175] In formula (10), and Represent the maximum and minimum electrical load power respectively.

[0176] The time slot when the electric vehicle stops charging is determined by the time when the EV leaves the IES and the time slot that meets the maximum SOC requirement. The charging constraint of the EV is shown in Equation (11):

[0177]

[0178] In formula (11): x i,J is a state variable; N is the total number of EVs; i is the sequence number of EVs, i = 1, 2, ..., N; J is the sequence number of the time slot in a day, J = 1, 2, ..., 96; Indicates the highest SOC to ensure the safety of electric vehicle batteries; is the SOC of the i-th EV when it is connected to the IES.

[0179] In order to constrain IES, add formula (12) to limit the increase of peak value:

[0180]

[0181] In formula (12), is the maximum value of the total electric load under the non-coordinated charging mode that meets the maximum state of charge requirement of EV within 96 time slots; Indicates the maximum electrical load power.

[0182] 3. Generate the source-load seasonality scenario and build the IES device model and constraints according to the following steps:

[0183] Taking into account seasonal characteristics and EV charging, the integrated energy system capacity configuration optimization strategy is proposed. A dynamic urgency index is proposed to reflect the charging demand in different time slots. This index is used to select the charging mode of electric vehicles in the time slot. In the first stage, a coordinated charging scheduling optimization model with the goal of minimizing the overall load difference is established, and the randomness of electric vehicles is simulated using Monte Carlo simulation. Taking into account the seasonal characteristics of renewable energy and load demand, the K-means clustering algorithm is used to characterize the seasonal characteristics of wind and solar power, and four typical days representing wind and solar power output in spring, summer, autumn and winter are generated. In the second stage, mixed integer linear programming is used to optimize the capacity configuration of each device in IES. Finally, through examples, it is verified that the two-stage optimization strategy proposed in this invention can effectively improve the overall economy and user benefits of IES, promote the consumption of renewable energy, reduce the peak-valley difference of electricity load and the overall carbon emissions of the IES system.

[0184] The actual power generation of the photovoltaic device at time t is mainly affected by the intensity of solar radiation and the area of the photovoltaic panel. The power generated by the photovoltaic device at time t is shown in formula (13):

[0185]

[0186] In formula (13): t is time; P PVr is the total rated power of the photovoltaic equipment; P PV (t) is the power generated by the photovoltaic panel at time t; S PV is the total installation area of photovoltaic panels; P SE (t) is the solar energy available per unit area of photovoltaic panels at time t; PV is the conversion efficiency of the photovoltaic panel; k is the conversion coefficient of the output power and area of the photovoltaic panel per unit area when the solar radiation level is 1kW / ㎡, in kW / ㎡; is the abandoned power of the photovoltaic equipment at time t.

[0187] Due to the limitations of actual site space and construction costs, there is a maximum area limit for configuring photovoltaic panels, as shown in formula (14):

[0188]

[0189] In formula (14): S PV is the total installed area of PV panels; The maximum area for configuring photovoltaic panels.

[0190] The actual power generation of the wind turbine at time t is mainly determined by the real-time wind speed and the rated power of the wind turbine. The calculation model is shown in formula (15):

[0191]

[0192] In formula (15): t is time; P WT (t) is the actual power generation of the wind turbine at time t; V(t) is the actual wind speed at time t; V in V is the cut-in wind speed, which is 3m / s in the present invention; out For the cut-out wind speed, the present invention takes the value as 24m / s; V r is the rated wind speed, which is 12m / s in the present invention; P wtr is the rated power of the fan; W is the number of configured fans.

[0193] Like photovoltaic generators, wind turbines are also subject to limitations such as actual site space and construction costs. There is a maximum limit on the number of wind turbines that can be configured, as shown in formula (16):

[0194] 0≤W≤W max (16);

[0195] In formula (16): W is the number of fans configured; W max The maximum number of fans that can be configured.

[0196] The P2G device of the present invention mainly includes EL and MSE. The operation model and constraints of EL and MSE are shown in formula (17)-formula (18):

[0197]

[0198] In formula (17): t is time; P EL (t) and P MSE (t) are the EL hydrogen production at time t Synthesis of methane with MSE Power consumption; and are the power consumption coefficients of EL and MSE respectively; and is the chemical reaction equilibrium coefficient; and The composite volume at time t The volumes of CO2 and H2 required for methane.

[0199]

[0200] Formula (18) is the ramp power constraint of EL and MSE, where t is time; P EL (t) and P MSE (t) are the EL hydrogen production at time t Synthesis of methane V with MSE CH4 (t) power consumption; P EL,fluc (t) and P MSE,fluc (t) are the power fluctuation values of EL and MSE at time t; M EL and M MSE are the upper limits of the climbing power of EL and MSE respectively. EL,max 、P EL,min are the upper and lower limits of EL power consumption respectively; P MSE,max 、P MSE,min They are the upper and lower limits of MSE power consumption respectively.

[0201] The operation model and constraint model of thermal power units and CCS equipment are shown in Equations (19) to (20):

[0202]

[0203] In formula (19): t is time; P PGU (t) are the carbon emissions and power generation of the thermal power unit at time t; ε PGU is the carbon emission intensity of thermal power units; are the actual CO2 capture and dissipation of CCS; δ CCS is the capture efficiency of CCS; PCCS (t) is the power consumption of CCS at time t; is the power consumption coefficient corresponding to the unit CO2 captured by CCS.

[0204]

[0205] Formula (20) is the ramp power constraint of the thermal power unit and the power constraint of the CCS, where t is the time; P PGU (t) is the power generation capacity of the thermal power unit at time t; P is the upper and lower limits of the power generation capacity of the thermal power unit; PGU,fluc (t) is the power fluctuation value of the thermal power unit at time t; M PGU P is the upper limit of the ramp power of the thermal power unit. rCCS is the rated power of CCS.

[0206] The HSE operation model and constraints can be expressed as follows:

[0207]

[0208] In formula (21): t is time; V HSE (t) is the hydrogen storage value of HSE at time t; is the self-release rate of HSE; are the hydrogen storage and release powers of HSE at time t respectively; are the hydrogen storage and release efficiencies of HSE respectively; HSE 、C HSE are the energy rate and configuration capacity of HSE respectively; x HSE is the charge and discharge state variable of HSE, which is a Boolean variable; △t is the unit time step, which is 1h in the present invention.

[0209] Similar to HSE equipment, the CDS operation model and constraints can be expressed as follows:

[0210]

[0211] In formula (22): t is time; V CDS (t) is the CO2 storage value of CDS at time t; is the self-release rate of CDS; are the CO2 storage and release power of CDS at time t respectively; are the storage and release efficiency of CO2 of CDS respectively; CDS 、C CDS are the energy rate and configuration capacity of CDS respectively; x CDS is the charge and discharge state variable of CDS, which is a Boolean variable; △t is the unit time step, which is 1h in the present invention.

[0212] This paper uses a quadratic function to describe the energy satisfaction of users (energy users, EU) within the IES. The value itself has no practical meaning and only reflects the advantages and disadvantages of the two strategies when compared. As shown in formula (23):

[0213]

[0214] In formula (23): t is time; U UT is the energy efficiency of users in IES; ρ ele , τ ele is the electricity preference coefficient within IES, and ρ in the present invention ele Take 7, τ ele Take 0.008; P ele (t) is the actual electrical load in IES at time t.

[0215] Interest demands of users within IES LA , as shown in formula (24):

[0216]

[0217] In formula (24): t is time; U LA For the comprehensive benefits of users within IES; c ele (t) is the electricity price at time t; U UT Energy efficiency of users in IES; P ele (t) is the actual electrical load in IES at time t.

[0218] In the entire IES system, wind turbines, photovoltaics, and thermal power units are power generation equipment, while CCS, EL, and MSE are power consumption equipment. The user's power load demand is also taken into account. The system's power balance constraint is expressed by Equation (25):

[0219] P PV (t)+P PUG (t)+P WT (t) = P CCS (t)+P EL (t)+P MSE (t)+P ele (t)(25);

[0220] In formula (25): t is time; P PV (t) is the power generated by the photovoltaic panel at time t; P PGU (t) is the power generation power of the thermal power unit at time t; P WT (t) is the actual power generation of the wind turbine at time t; P CCS (t) is the power consumption of CCS at time t; P EL(t) and P MSE (t) are the EL hydrogen production at time t Synthesis of methane with MSE Power consumption; P ele (t) is the actual electrical load in IES at time t.

[0221] In the entire IES system, the equipment that produces H2 is EL, the equipment that consumes H2 is MSE, and HSE can both input H2 from the system and output H2 to the system. The hydrogen balance constraint of the system can be expressed by Equation (26):

[0222]

[0223] In formula (26): t is time; is the volume of hydrogen produced by EL at time t; The composite volume at time t The volume of H2 required for methane. are the hydrogen storage and release powers of HSE at time t respectively.

[0224] In the entire system, the equipment that produces CO2 is CCS, the equipment that consumes CO2 is MSE, and CDS can both input CO2 from the system and output CO2 to the system. The CO2 balance constraint of the system can be expressed by equation (27):

[0225]

[0226] In formula (27): t is time; is the actual amount of CO2 captured by CCS; are the CO2 storage and release power of CDS at time t respectively; The composite volume at time t The volume of CO2 required for methane.

[0227] 4. Establish a two-stage optimization model with the goal of minimizing the IES equipment capacity configuration cost as shown in Equations (28) to (29):

[0228] In order to achieve economical and low-carbon operation of IES, from the perspective of maximizing the benefits of the system throughout its life cycle, that is, minimizing the cost, the objective function of the IES equipment capacity configuration optimization model is constructed as shown in formula (28):

[0229]

[0230] In formula (28): t is time; F Dis the total comprehensive cost; y and z represent the 0-1 variable and continuous variable involved in the model respectively; B is the set of equipment involved in capacity optimization in IES, B = {PV, WT, CCS, HSE, CDS, MSE}; C inv is the investment cost of each device in the system; C OM is the operation and maintenance cost of each device in the system; C tari The resource acquisition cost during the system operation process, which is composed of the cost of purchasing coal and the income from selling electricity and natural gas; C tax is the system carbon emission cost; ν b 、 φ b are the unit capacity investment cost, equal installment capital recovery coefficient, and rated capacity of equipment b; the ratio of operation and maintenance cost to the present value of its investment cost; and c fuel (t), c ele (t), are the coal purchase price, electricity price, natural gas price and carbon tax at time t respectively; P ele (t), V NG (t) are the coal purchase quantity, electricity load, and natural gas sales quantity at time t; is the CO2 dissipation of CCS.

[0231] Equal installment capital recovery coefficient ν for equipment b b The calculation formula is shown in formula (29):

[0232]

[0233] In formula (29), 10% is the annual interest rate; n is the service life of equipment b.

[0234] The technical effects of the present invention will be further illustrated by examples below:

[0235] For the EV coordinated charging model, the model is established in MATLAB / YALMIP and solved using the CPLEX solver. The basic structure and energy flow of the integrated energy system are as follows: Figure 1 As shown in Figure 1. Assume that when connected to the microgrid, the SOC follows a continuous uniform distribution between 0.1 and 0.3, the minimum SOC requirement to meet the user follows a uniform distribution between 0.4 and 0.6, and the maximum SOC requirement to protect the health of the EV battery follows a uniform distribution between 0.8 and 1.0. The basic electrical load in IES is as follows: Figure 2 As shown in the figure, the typical day scene generation flow chart considering the seasonal characteristics of EV and the typical day scene generation flow chart considering the seasonal characteristics of wind and solar are shown in the figure. Figure 3 and Figure 4The probability density function of the time when EVs connect to and leave the microgrid is shown in the figure below. Figure 5 The total horizontal radiation and wind speed for four typical days are shown in Figure 2. Figure 6 As shown. The slow charging power and fast charging power are set to 2.5kW and 7kW respectively, the non-coordinated charging power is 5kW, the charging efficiency of the electric vehicle is 0.9, and the capacity of an electric vehicle is 30kWh. The electric load results of the coordinated and two non-coordinated charging methods are shown as follows Figure 7 shown.

[0236] The two-stage optimization solution diagram is as follows Figure 8 The solution process of the present invention is divided into two stages:

[0237] 1) Phase 1: Using Equation (26) as the objective function, the DUI selects the charging power for each EV in each time slot to minimize the objective function. A model is built in MATLAB / YALMIP and solved using the CPLEX solver. Four typical day loads are generated, and the load usage for each of the 96 time slots is converted to a full-day load profile with a step size of 1 hour.

[0238] Phase 2: Using the electric load after coordinated charging of EVs in 1), considering the seasonal characteristics of wind and solar power, and taking Equation (28) as the objective function, the CPLEX solver is used to solve the second-stage MILP problem.

[0239] In order to illustrate the rationality and feasibility of the optimization strategy of the present invention, four scenarios are set up for comparison as follows:

[0240] 1) Scenario 1: Considering the seasonal characteristics of renewable energy and load demand, and considering the coordinated charging of EV loads, the IES capacity configuration optimization after the two-stage optimization proposed in this invention is used.

[0241] 2) Scenario 2: Considering the seasonal characteristics of renewable energy and load demand, without considering EV coordinated charging, EV charging meets maxSOC, and then the capacity configuration of equipment in IES is optimized.

[0242] 3) Scenario 3: Considering the seasonal characteristics of renewable energy and load demand, without considering EV coordinated charging, EV charging meets the minSOC, and then the capacity configuration of equipment in IES is optimized.

[0243] 4) Scenario 4: Without considering the seasonal characteristics of renewable energy and load demand, considering the coordinated charging of EV loads, the IES capacity configuration optimization after the two-stage optimization proposed in this invention is used.

[0244] Table 1 Comparison of optimization results of scenarios 1 to 3

[0245]

[0246]

[0247] Table 2 Comparison of optimization results between scenario 1 and scenario 4

[0248]

[0249] As shown in Table 1, compared with Scenario 2 and Scenario 3, PV capacity decreased by 399 kW and 234 kW, respectively; curtailed solar power decreased by 2,483 kW and 1,532 kW, respectively; carbon emissions decreased by 759 tons and 497 tons, respectively; and total costs decreased by 140,000 yuan and 60,000 yuan, respectively. Net profits increased by 60,000 yuan and 220,000 yuan, significantly improving overall user benefits. Compared with Scenario 2, Scenario 3 shows a reduction in the configured capacity of each device and the total cost of the IES, as well as reduced curtailed solar power and carbon dioxide emissions. However, due to the reduction in total residential load power, IES electricity sales revenue also decreased significantly, demonstrating that simply reducing peak load is insufficient to improve IES net profit. The proposed two-stage optimization scheme of EV coordinated charging and IES capacity configuration optimization can effectively reduce curtailed solar power and carbon dioxide emissions. This is because EV coordinated charging significantly reduces the peak-to-valley variation in user load, increasing the utilization rate of photovoltaic power generation and reducing the output of thermal power units. This reduces coal purchase costs while also reducing carbon dioxide emissions. The two-stage optimization strategy proposed in this paper increases the IES's net profit while reducing the total electricity load. This is because, under the EV uncoordinated charging mode, although the increase in total electricity load increases the IES's total revenue, the severe peak-to-valley difference leads to an increase in the construction cost of the IES's internal equipment, coal purchases, and carbon taxes, which significantly increases the IES's total cost and reduces its annual net profit.

[0250] Comparing the capacity allocation optimization results in Table 2, we see that when seasonality is considered for both renewable energy and loads, wind turbine capacity increases by 15.34%, photovoltaic capacity by 19.21%, electrolyzer capacity by 17.62%, methanation capacity by 21.73%, and carbon capture capacity by 16.95%, compared to when seasonality is not considered. However, energy storage capacity decreases. Analysis reveals that seasonal variations in wind and solar resources and load demand necessitate an appropriate increase in the capacity allocation of power equipment. The electrolyzer and methanation equipment in the P2G link are highly flexible and have a certain ability to cope with seasonal variations. Therefore, considering seasonality results in a more realistic change in the capacity of each power equipment component in the IES by over 10%.

[0251] The above analysis demonstrates that a two-stage optimization scheduling strategy that incorporates coordinated EV charging and optimized IES capacity allocation can effectively reduce system curtailment and CO2 emissions, reduce the allocated capacity of each device in the IES, lower IES equipment construction costs, and increase the system's net benefits. A comparative analysis of Schemes 2-5 and the scheme employed in this invention shows that the introduction of coordinated EV charging within a multi-microgrid integrated energy system with power exchange and cooling and heating load demand response can further improve the operational economics of the multi-microgrid system. Taking into account the seasonal characteristics of both renewable energy and loads can result in a more realistic variation in the allocated capacity of power devices in the IES by over 10%.

Claims

1. An optimization method for EV charging and integrated energy system capacity configuration considering seasonal characteristics, characterized by The following steps are involved: Step 1: Construct the electric vehicle load access and leave IES time model and the electric vehicle load access and leave IES time slot division model; Step 2: Based on the IES time model and time slot division model established in step 1, a dynamic urgency index is introduced to establish an electric vehicle coordinated charging model with the goal of minimizing the load peak-valley difference; Step 3: Build IES equipment model and constraints to generate source-load seasonality scenarios; Step 4: Based on the IES equipment model and constraints established in step 3, the electric load obtained by the electric vehicle coordinated charging model in step 2 is used to establish a two-stage optimization model for IES equipment capacity configuration with the goal of minimizing the IES equipment capacity configuration cost; Step 5: Considering the coordinated charging of electric vehicles, the two-stage optimization model of IES equipment capacity configuration established in step 4 is optimized and solved.

2. The method for optimizing EV charging and integrated energy system capacity configuration considering seasonal characteristics according to claim 1, characterized in that: In step 1, electric vehicle loads are connected to and leave the IES time model. 1) Electric vehicle access time t c and departure time t dis Following the normal distribution, the time t at which electric vehicles are connected to IES c As shown in formula (1): In formula (1): f(t c ) is t c The distribution function of t c Connect IES time for electric vehicles; is the mean of the normal distribution; is the standard deviation of the normal distribution; π is the ratio of pi; exp(·) is the natural exponential function; 2) The time t when the electric vehicle leaves the IES dis As shown in formula (2): In formula (2): f(t dis ) is t dis The distribution function of t dis The time when the electric vehicle leaves the IES; is the mean of the normal distribution; is the standard deviation of the normal distribution.

3. The method for optimizing EV charging and integrated energy system capacity configuration considering seasonal characteristics according to claim 2, characterized in that: In step 1, in the EV load access and exit IES time slot division model, during the EV scheduling process, 24 hours a day is divided into 96 time slots, each time slot is 15 minutes long. When an EV accesses the IES at the middle of a time slot, the charging behavior is scheduled after the next integer time slot. When an EV disconnects from the IES at the middle of a time slot, the EV can only be scheduled before the end of the previous integer time slot. The time slot of each EV accessing the IES is shown in formula (3): In formula (3), N is the total number of EVs; i is the sequence number of EVs, i = 1, 2, ..., N; J is the sequence number of the time slot in a day, J = 1, 2, ..., 96; Indicates the time slot number when the i-th EV accesses the IES; represents the time when the i-th EV accesses the IES; △T represents the length of the time slot; The time slot when each electric vehicle leaves the IES is shown in formula (4): In formula (4): represents the time slot number when the i-th EV leaves the IES; represents the time when the i-th EV leaves the IES; The number of remaining time slots when EV accesses IES is defined as follows: In formula (5): It represents the number of remaining time slots that the i-th EV can continue to access the IES, and the charging behavior and charging scheduling strategy are arranged within these remaining time slots.

4. The method for optimizing EV charging and integrated energy system capacity configuration considering seasonal characteristics according to claim 2, characterized in that: In step 2, a dynamic urgency index is introduced to reflect the urgency of the EV's charging demand in each charging time slot, which is defined as shown in the following formula (6): In formula (6): DUI i,J is the urgency index of the i-th EV at the J-th time slot; Slow charging power for EV; η EV For EV charging efficiency; is the minimum SOC requirement of the owner of the i-th electric vehicle; is the SOC of the i-th EV when it is connected to IES; SOC i,J is the SOC of the i-th EV at the beginning of the J-th time slot; represents the number of remaining time slots that the i-th EV can continue to access the IES; Indicates the battery capacity of EV; P i,n-1 is the charging power of the i-th EV in the n-1th time slot; n is the summation variable in the summation symbol, When DUI i,J <0, it means that the charging demand of the i-th EV in this time slot is urgent; when DUI i,J When ≥0, it means that the charging demand of the i-th EV in this time slot is not urgent.

5. The method for optimizing EV charging and integrated energy system capacity configuration considering seasonal characteristics according to claim 4, characterized in that: A coordinated charging model for electric vehicles is established with the goal of minimizing the peak-to-valley difference in load. In the coordinated charging model for electric vehicles with the goal of minimizing the peak-to-valley difference in load, two charging modes of EVs are considered: fast charging and slow charging mode: Define the state variable x i,J , by the state variable x i,J and Dynamic Urgency Indicator (DUI) i,J The charging power of the i-th EV in the J-th time slot is jointly determined as shown in the following formula (7): In formula (7), x i,J is a state variable; The choice of charging mode for a time slot is determined by the dynamic urgency index, as shown in formula (8): In formula (8), P i,J is the charging power of the i-th EV in the J-th time slot; For fast charging power, Slow charging power; DUI i,J is the urgency index of the i-th EV at the J-th time slot; The total load in the Jth time slot of the electric vehicle coordinated charging model is shown in formula (9): In formula (9), represents the total load in the Jth time slot of the coordinated charging scheduling model; represents the basic electric load in the Jth time slot; The goal of charging scheduling is to reduce the peak-valley load difference of IES, as shown in formula (10): In formula (10), and Represent the maximum and minimum electrical load power respectively.

6. The method for optimizing EV charging and integrated energy system capacity configuration considering seasonal characteristics according to claim 5, characterized in that: The time slot when the electric vehicle stops charging is determined by the time when the EV leaves the IES and the time slot that meets the maximum SOC requirement; the charging constraint of the EV is shown in formula (11): In formula (11): x i,J is a state variable; Indicates the highest SOC to ensure the safety of electric vehicle batteries; is the SOC of the i-th EV when it is connected to the IES; In order to constrain IES, add formula (12) to limit the increase of peak value: In formula (12), is the maximum value of the total electric load under the non-coordinated charging mode that meets the maximum state of charge requirement of EV within 96 time slots; Indicates the maximum electrical load power.

7. The method for optimizing EV charging and integrated energy system capacity configuration considering seasonal characteristics according to claim 1, characterized in that: In step 3, the IES device model is as follows: The actual power generation of the photovoltaic device at time t is affected by the solar radiation intensity and the area of the photovoltaic panel. The power generated by the photovoltaic device at time t is shown in formula (13): In formula (13): t is time; P PVr is the total rated power of the photovoltaic equipment; P PV (t) is the power generated by the photovoltaic panel at time t; S PV is the total installation area of photovoltaic panels; P SE (t) is the solar energy available per unit area of photovoltaic panels at time t; PV is the conversion efficiency of the photovoltaic panel; k is the conversion coefficient of the output power and area of the photovoltaic panel per unit area when the solar radiation level is 1kW / ㎡, in kW / ㎡; is the abandoned power of the photovoltaic equipment at time t; Due to the limitations of actual site space and construction costs, there is a maximum area limit for configuring photovoltaic panels, as shown in formula (14): In formula (14): S PV is the total installed area of PV panels; The maximum area for configuring photovoltaic panels; The actual power generated by the wind turbine (WT) at time t is determined by the real-time wind speed and the rated power of the wind turbine. The calculation model is shown in Equation (15): In formula (15): P WT (t) is the actual power generation of the wind turbine at time t; V(t) is the actual wind speed at time t; V in is the cut-in wind speed; V out is the cut-out wind speed; V r is the rated wind speed; P wtr is the rated power of the fan; W is the number of configured fans; There is a maximum limit on the number of fans that can be configured, as shown in formula (16): 0≤W≤W max (16); In formula (16): W is the number of fans configured; W max The maximum number of fans configured; The power-to-gas (P2G) equipment includes an electrolyzer and a methane synthesis device. The operation models and constraints of the electrolyzer and methane synthesis devices are shown in Equations (17) and (18): In formula (17): P EL (t) and P MSE (t) are the EL hydrogen production at time t Synthesis of methane with MSE Power consumption; and are the power consumption coefficients of EL and MSE respectively; and is the chemical reaction equilibrium coefficient; and The composite volume at time t The volumes of CO2 and H2 required for methane; Formula (18) is the ramp power constraint of the electrolyzer device EL and the methane synthesis equipment MSE, where: P EL,fluc (t) and P MSE,fluc (t) are the power fluctuation values of EL and MSE at time t; M EL and M MSE are the upper limits of the climbing power of EL and MSE respectively; P EL,max 、P EL,min are the upper and lower limits of EL power consumption respectively; P MSE,max 、P MSE,min They are the upper and lower limits of MSE power consumption respectively; The operation model and constraint model of thermal power units and carbon capture and storage equipment are shown in Equations (19) and (20): In formula (19): P PGU (t) are the carbon emissions and power generation of the thermal power unit at time t; ε PGU is the carbon emission intensity of thermal power units; are the actual CO2 capture and dissipation of CCS; δ CCS is the capture efficiency of CCS; P CCS (t) is the power consumption of CCS at time t; is the power consumption coefficient corresponding to the unit CO2 captured by CCS; Formula (20) is the ramp power constraint of the thermal power unit and the power constraint of the CCS, where: P PGU (t) is the power generation capacity of the thermal power unit at time t; are the upper and lower limits of the power generation capacity of thermal power units respectively; P PGU,fluc (t) is the power fluctuation value of the thermal power unit at time t; M PGU is the upper limit of the ramp power of the thermal power unit; P rCCS is the rated power of CCS; The operation model and constraints of the hydrogen storage device are expressed by formula (21): In formula (21): V HSE (t) is the hydrogen storage value of HSE at time t; is the self-release rate of HSE; are the hydrogen storage and release powers of HSE at time t respectively; are the hydrogen storage and release efficiencies of HSE, respectively; HSE 、C HSE are the energy multiplication rate and configuration capacity of HSE respectively; x HSE is the charge and discharge state variable of HSE, which is a Boolean variable; △t is the unit time step; The CO2 storage tank operation model and constraints are expressed by Equation (22): In formula (22): V CDS (t) is the CO2 storage value of CDS at time t; is the self-release rate of CDS; are the CO2 storage and release power of CDS at time t respectively; are the storage and release efficiency of CO2 of CDS respectively; CDS 、C CDS are the energy rate and configuration capacity of CDS respectively; x CDS is the charge and discharge state variable of CDS.

8. The method for optimizing EV charging and integrated energy system capacity configuration considering seasonal characteristics according to claim 2, characterized in that: In step 3, the source-load seasonality scenario is generated as follows: The annual wind speed and total horizontal radiation data were divided into four parts according to time: spring, summer, autumn and winter. The K-means clustering algorithm was used to cluster each part into scenarios. The probabilistic scenario analysis method was then used to select the scenario with the highest probability in each season to represent the typical day of that season. The Monte Carlo simulation (MCS) was used to simulate the randomness of EV. Monte Carlo simulation (MCS) is used to simulate the EV travel conditions in the four seasons and obtain the EV travel patterns in the four seasons. The EV travel patterns in the four seasons are generated by equations (1) and (2) in sequence. The mean and standard deviation of the normal distribution in each season are different.

9. The method for optimizing EV charging and integrated energy system capacity configuration considering seasonal characteristics according to claim 1, characterized in that: In step 4, a two-stage optimization model for IES equipment capacity configuration is established, as follows: In order to achieve economical and low-carbon operation of IES, from the perspective of maximizing the benefits of the system throughout its life cycle, that is, minimizing the cost, a two-stage optimization model for IES equipment capacity configuration is constructed, whose objective function is shown in formula (28): In formula (28): F D is the total comprehensive cost; y and z represent the 0-1 variable and continuous variable involved in the model respectively; B is the set of equipment involved in capacity optimization in IES, B = {PV, WT, CCS, HSE, CDS, MSE}; C inv is the investment cost of each device in the system; C OM is the operation and maintenance cost of each device in the system; C tari The resource acquisition cost during the system operation process, which is composed of the cost of purchasing coal and the income from selling electricity and natural gas; C tax is the system carbon emission cost; ν b 、 φ b are the unit capacity investment cost, equal installment capital recovery coefficient, and rated capacity of equipment b; the ratio of operation and maintenance cost to the present value of its investment cost; and c fuel (t), c ele (t), c NG (t), are the coal purchase price, electricity price, natural gas price and carbon tax at time t respectively; P ele (t), V NG (t) are the coal purchase quantity, electricity load, and natural gas sales quantity at time t; is the CO2 dissipation of CCS; Equal installment capital recovery coefficient ν for equipment b b The calculation formula is shown in formula (29): In formula (29), 10% is the annual interest rate; n is the service life of equipment b.

10. The method for optimizing EV charging and integrated energy system capacity configuration considering seasonal characteristics according to claim 9, characterized in that: In step 5, the two-stage optimization solution is divided into two stages: Stage 1: Equation (26) is used as the objective function; In formula (26): is the volume of hydrogen produced by EL at time t; The composite volume at time t The volume of H2 required for methane; are the hydrogen storage and release powers of HSE at time t respectively; A dynamic urgency metric was used to select the charging power for each EV in each time slot to minimize the objective function. A model was built in MATLAB / YALMIP and solved using the CPLEX solver. Electricity loads for four typical days were generated, and the load profiles for each of the 96 time slots were converted to full-day load profiles with a step size of 1 hour. Phase 2: Using the electric load after EV coordinated charging in Phase 1, considering the seasonal characteristics of wind and solar power, and taking Equation (28) as the objective function; the CPLEX solver is used to solve the MILP problem in Phase 2.

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