Optimal dispatching method for regional integrated energy system considering fuel characteristics of plug-in hybrid electric vehicles

Abstract

The present invention is a regional integrated energy system optimization scheduling method based on the fuel characteristics of plug-in hybrid electric vehicles, and the method is characterized in that: the method simultaneously considers the main power system and the slave power system of the plug-in hybrid electric vehicle power system, in the main power system, the battery pack stores electric energy under the action of a charging device, and the electric energy is transmitted to the electric motor for utilization, and in the slave power system, the engine utilizes the chemical energy of gasoline in the fuel tank; the power consumption characteristics and fuel characteristics of the two power systems cooperate with each other, and the driving law of the vehicle and the charging load of the vehicle are more flexibly changed; the fuel characteristics of the plug-in hybrid electric vehicle are considered in the regional integrated energy system, the interests and needs of both the integrated energy system and the new energy vehicle users are taken into account, the comprehensive energy utilization rate and the renewable energy consumption rate are improved, the load volatility is reduced, and the system power supply reliability is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of energy systems, and in particular to a method for optimizing the scheduling of a regional integrated energy system based on the fuel characteristics of a plug-in hybrid electric vehicle. Background Art

[0002] The charging behavior of new energy vehicles is random in time and space. A series of safety and reliability issues will be brought to the integrated energy system with the disorderly access of a large number of new energy vehicles. Making full use of the potential of new energy vehicle demand response has become a new hot spot in the global automotive industry and energy field. Based on the traditional new energy vehicle demand response, technicians in this field have conducted extensive research on this. In the current research, one is to propose to gather large-scale electric vehicles together and perform cluster scheduling on them; the other is to consider the randomness of electric vehicle loads and the volatility of wind power generation, and create a random economic scheduling model considering electric vehicle charging and discharging; and the other is to consider minimizing system losses and propose a coordination method for online charging of plug-in hybrid vehicles. However, the existing coordination method for online charging of plug-in hybrid vehicles is limited to the response of plug-in hybrid vehicle power loads, without considering fuel characteristics, ignoring the possibility of fuel response, resulting in loss of response flexibility. So far, there has been no literature report or practical application of the regional integrated energy system optimization scheduling method for plug-in hybrid vehicle fuel characteristics related to the present invention. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention proposes a regional integrated energy system optimization scheduling method based on the fuel characteristics of plug-in hybrid electric vehicles, which can fully consider the power system characteristics of plug-in hybrid electric vehicles, effectively improve the problem of poor demand response flexibility, and improve the utilization rate of comprehensive energy; and can comprehensively consider the power consumption characteristics and fuel characteristics, perform fuel response and charging response, enhance the system power supply reliability, reduce load volatility, and improve the absorption rate of renewable energy.

[0004] In order to achieve the purpose of the present invention, the technical scheme adopted is a regional integrated energy system optimization scheduling method for plug-in hybrid electric vehicle fuel characteristics, which includes: a regional integrated energy system composed of an upper main grid, a wind turbine, a photovoltaic unit, a natural gas source, a gasoline source, an electric storage device, a heat storage device, an electric boiler, a cogeneration unit, an electric load, a heat load, and a new energy vehicle load, wherein the upper main grid, the wind turbine, the photovoltaic unit, and the electric storage device are all connected to the electric load and the new energy vehicle load; the new energy vehicle load includes the electric vehicle load and the plug-in hybrid electric vehicle load, and the gasoline source is connected to the plug-in hybrid electric vehicle load; the cogeneration unit includes a micro-gas turbine and a bromide refrigeration machine, and the micro-gas turbine is connected to the natural gas. The power source, electric load and new energy vehicle load are connected, the waste heat flue gas discharged by the micro-turbine is sent to the connected bromide refrigeration machine, and the bromide refrigeration machine is connected to the heat load; the electric boiler is connected to the electric load, the new energy vehicle load, the heat load and the heat storage device; the power system structure of the plug-in hybrid electric vehicle includes a slave power system, a clutch, a main power system, a continuously variable transmission and a reducer; in the slave power system, the engine utilizes the chemical energy of the gasoline in the fuel tank; the clutch is connected to the engine and the motor; in the main power system, the battery pack stores the electric energy under the action of the charging device, and the electric energy is transmitted to the motor for utilization; the continuously variable transmission is connected to the motor and the reducer, characterized in that the content of its optimization scheduling method includes:

[0005] In the regional integrated energy system optimization scheduling of plug-in hybrid electric vehicle fuel characteristics, P and ΔP represent the electric energy transmitted and transferred by the system, F represents the amount of fuel, G represents the amount of fuel per 100 kilometers of plug-in hybrid electric vehicles, R represents the daily mileage of plug-in hybrid electric vehicles, N represents the number of plug-in hybrid electric vehicle users, η represents the user response, and α represents the proportionality coefficient; in the parameter subscript, PHEV represents plug-in hybrid electric vehicle, s, en, m represent pure electric mode, internal combustion engine mode, and hybrid drive mode, lim represents compensation limit, and gre represents plug-in hybrid electric vehicle participation in fuel response; the parameter superscript t represents the time period; the fuel characteristics and power consumption characteristics of the main power system and the slave power system of the plug-in hybrid electric vehicle are considered to be coordinated with each other;

[0006] The working modes of plug-in hybrid electric vehicles are divided into pure electric mode, internal combustion engine mode, and hybrid drive mode. The vehicle fuel consumption is related to the daily mileage, the proportion of working modes, and the energy consumption value of each mode. The expression is:

[0007]

[0008] G 100s =α en ·G 100en +α m ·G 100m

[0009] Among them, F PHEV is the fuel volume of the plug-in hybrid vehicle, R i is the daily mileage of the i-th plug-in hybrid vehicle, N a is the number of plug-in hybrid electric vehicles, G 100s , G 100en , G 100m are the fuel consumption per 100 kilometers under the comprehensive working conditions of plug-in hybrid vehicles, internal combustion engine mode, and hybrid driving mode, α en is the proportional coefficient of the internal combustion engine mode, α m is the proportional coefficient of the hybrid driving mode;

[0010] In the agreement signed between the plug-in hybrid vehicle owner and the load aggregator, the charging load transfer information, the fuel response limit mileage and the fuel response compensation unit price information must be set. When the user's driving mileage reaches the fuel response limit mileage, if the user chooses to drive the internal combustion engine, the system will give the user fuel compensation. The fuel response amount is related to the fuel response mileage, and its expression is:

[0011] F gre =R gre ·G 100en

[0012]

[0013]

[0014] Among them, F gre is the fuel response of the plug-in hybrid vehicle, R gre is the fuel efficiency mileage, R gre,h is the fuel response mileage of the hth plug-in hybrid vehicle, R h is the daily mileage of the hth plug-in hybrid vehicle, R lim To compensate for the limited mileage, N b The number of plug-in hybrid electric vehicles that have signed agreements with load aggregators and participate in dispatch;

[0015] The unit price of fuel response compensation is estimated based on the BP neural network prediction algorithm. After training, six influencing indicators are used as input, including peak electricity price, oil price, transfer electricity load, internal combustion engine mode ratio, daily mileage and compensation limit mileage, and the unit price of fuel response compensation is used as output. Through prediction, the unit price of fuel response compensation is obtained;

[0016] In the charging scenario, the actual participation of car owners in scheduling varies, so the user responsiveness is defined to reveal the proportion of vehicles actually participating in scheduling. The expression of the plug-in hybrid vehicle load transfer model is:

[0017]

[0018] in, is the transfer load power of the plug-in hybrid vehicle during period t, is the charging power of the plug-in hybrid vehicle during period t, α PHEV is the proportion coefficient of plug-in hybrid electric vehicle contract users, η PHEV User responsiveness for plug-in hybrid electric vehicles;

[0019] In a dispatch cycle, the operating cost of the regional integrated energy system is C RIES :

[0020]

[0021] Among them, T is a scheduling period; They are the system energy purchase cost, unit maintenance cost, wind curtailment penalty cost, demand response purchase cost, and load fluctuation cost during period t; is the power purchased by the system from the main grid during period t; are the power generation of electric boiler, micro-turbine, photovoltaic unit, wind turbine and energy storage equipment during period t; K eb , K mt , K pv , K w , K es They are the maintenance unit prices of electric boilers, micro-turbines, photovoltaic units, wind turbines and energy storage equipment; K is the abandoned wind power of the wind turbine in period t; wd Penalty fees for wind curtailment units; is the system electric load after demand response in period t; are the charging power transferred by new energy vehicles and electric vehicles during period t respectively; is the new energy vehicle load after demand response in period t; are the system electricity purchase price and demand response unit payment during period t, respectively; is the gas consumption of the micro-turbine during period t; μ f is the natural gas price; μ gl is the price of No. 92 gasoline; K gre , α gre are the fuel compensation unit price and elasticity coefficient respectively; is the system electric load during period t; λ1 is the economic conversion coefficient;

[0022] In a dispatch cycle, the payment fee of the new energy vehicle owner is C v0 :

[0023]

[0024] in, C gl , C gre They are the charging cost, charging load dispatching income, fuel cost, and fuel response compensation income of the new energy vehicle owner during period t; K cp is the charging pile service fee; uidr N is the elasticity coefficient of the owner's demand response compensation; c is the number of plug-in hybrid electric vehicles that did not participate in the response; R j , R k are the daily mileage of the jth and kth plug-in hybrid vehicles respectively; G gl,k , R gre,k are the fuel quantity and fuel response mileage of the kth plug-in hybrid vehicle respectively;

[0025] At the same time, the operating cost of the regional integrated energy system and the payment fees of new energy vehicle owners are considered, and the conversion between multi-objective function and single objective function is carried out, and the weighted coefficient method is used to achieve:

[0026]

[0027] Among them, C is the single objective function of transformation; ω1 and ω2 are the weights of the two objective functions respectively; C1, C 1,max are the actual value and maximum value of objective function 1 respectively; C2, C 2,max are the actual value and maximum value of objective function 2 respectively;

[0028] The constraints of the regional integrated energy system include the output constraints of electric boilers, micro-turbines, wind turbines, photovoltaic units, purchased electricity, energy storage, and charge balance. The constraints of new energy vehicles include transfer power, operating mode, charging load balance, and user response benefit constraints.

[0029] While considering the charging load demand response, the present invention fully considers the fuel characteristics of plug-in hybrid vehicles. Through the fuel response method, the fuel mileage of plug-in hybrid vehicles is improved, and the power consumption mileage is reduced. At the same time, the user responsiveness of plug-in hybrid vehicles is improved, the demand response capacity is improved, and the optimization effect is improved; the load volume during the low electricity price period is increased, which creates conditions for the further output of wind turbines, and cooperates with electric boilers and cogeneration units to achieve better wind power consumption effects. Fuel response is taken into account in the system to give full play to the advantages of fuel characteristics; the operating cost of the integrated energy system is low, the load volatility is small, and the utilization rate of renewable energy is high. At the same time, the user's responsiveness is improved, and the interests and needs of both the integrated energy system and new energy vehicle users are taken into account; therefore, the regional integrated energy system optimization scheduling of plug-in hybrid vehicle fuel characteristics is of great significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A block diagram of the regional integrated energy system structure for the fuel characteristics of a plug-in hybrid electric vehicle;

[0031] Figure 2 A schematic diagram of the power system structure of a plug-in hybrid electric vehicle fuel characteristics;

[0032] Figure 3 This is a framework diagram for predicting fuel compensation unit price based on BP neural network;

[0033] Figure 4 Schematic diagram of predicted power curves for electrical load, thermal load, wind turbine, photovoltaic unit, electric vehicle and plug-in hybrid vehicle in each time period;

[0034] Figure 5 Electric load power curves and electric load transfer power diagrams for four scenarios;

[0035] Figure 6 Wind turbine output diagrams for four scenarios;

[0036] Figure 7 This is the output diagram of the electric load unit in scenario 2;

[0037] Figure 8 This is the heat load unit output diagram for scenario 2;

[0038] Fig. 9 This is the output diagram of the electric load unit in scenario 3;

[0039] Fig.10 This is the heat load unit output diagram for scenario 3;

[0040] Fig.11 Electric load power curves and electric load transfer power diagrams for four scenarios with different fuel compensation unit prices;

[0041] Fig.12 This is the output diagram of the electric load unit in scenario 4;

[0042] Fig.13 Scenario 4 thermal load unit output diagram. DETAILED DESCRIPTION

[0043] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0044] At present, the scheduling of new energy vehicles mainly considers the power consumption characteristics of the vehicles. With the diversity and popularization of new energy vehicles, the power characteristics of the vehicles will bring great challenges to the flexibility of their scheduling. This invention mainly considers the power characteristics of plug-in hybrid vehicles to conduct relevant research on improving the flexibility of charging scheduling. At the same time, the main power system and the slave power system in the power system are considered, such as Figure 1 As shown. In the main power system, the battery pack stores electrical energy under the action of the charging device, and the electric motor utilizes the electrical energy. In the secondary power system, the engine utilizes the chemical energy of the gasoline in the fuel tank. The fuel characteristics and power consumption characteristics of the two power systems cooperate with each other, which can more flexibly change the vehicle's driving pattern and dispatch the vehicle's charging load.

[0045] Reference Figure 2, the fuel characteristics of a plug-in hybrid electric vehicle of the present invention are used to optimize the charging load of the vehicle in the regional integrated energy system. The upper main grid, wind turbine, photovoltaic unit and power storage equipment in the regional integrated energy system are all connected to the electric load and the new energy vehicle load; the cogeneration unit includes a micro-gas turbine and a bromide refrigeration machine, the micro-gas turbine is connected to the natural gas source, the electric load and the new energy vehicle load, the waste heat flue gas discharged by the micro-gas turbine is sent to the connected bromide refrigeration machine, and the bromide refrigeration machine is connected to the heat load; the electric boiler is connected to the electric load, the new energy vehicle load, the heat load and the heat storage equipment. When electric vehicles and plug-in hybrid electric vehicles are connected to the system, if they are not regulated, they will produce a series of negative effects as disordered charging loads. Introduce load aggregators, through the time-of-use electricity price and the agreement of load aggregators, new energy vehicle users participate in the optimal scheduling of the system through comprehensive demand response. In order to alleviate the power supply pressure of the system peak load, new energy vehicles adopt an orderly charging method to participate in load transfer, and transfer part of the charging load during the peak and valley periods of electricity prices. In order to further reduce the power consumption of new energy vehicles during driving and further improve the user responsiveness of plug-in hybrid vehicles, the fuel characteristics of plug-in hybrid vehicles are fully considered, and the actual driving fuel volume is determined according to the proportion of different working modes. Through the determination of the fuel compensation unit price and the fuel compensation limit mileage, plug-in hybrid vehicles will participate in the fuel response in an orderly manner, further improving the capacity and optimization effect of demand response. While reducing peak load, valley load demand is increased. In conjunction with electric boilers, cogeneration units and energy storage equipment, the output space of wind power is increased, and the absorption rate of renewable energy is increased.

[0046] In the regional integrated energy system optimization scheduling of plug-in hybrid electric vehicle fuel characteristics, P and ΔP represent the electric energy transmitted and transferred by the system, F represents the amount of fuel, G represents the amount of fuel per 100 kilometers of the plug-in hybrid electric vehicle, R represents the daily mileage of the plug-in hybrid electric vehicle, N represents the number of plug-in hybrid electric vehicle users, η represents the user responsiveness, and α represents the proportionality coefficient; in the parameter subscript, PHEV represents plug-in hybrid electric vehicle, s, en, and m represent pure electric mode, internal combustion engine mode, and hybrid drive mode, lim represents compensation limit, and gre represents the plug-in hybrid electric vehicle participating in fuel response; the parameter superscript t represents the time.

[0047] Use lognormal distribution to approximate the daily mileage R of new energy vehicles d , as shown in formula (1):

[0048]

[0049] in, is the probability density function of daily mileage; σ m is the standard deviation of the logarithm of daily mileage; μ mis the expected logarithm of daily mileage.

[0050] The relationship between the initial charging state of charge and the daily mileage of a new energy vehicle when it is connected to a charging pile is shown in formula (2):

[0051]

[0052] Among them, S0 is the grid load when the vehicle is stopped; S e is the expected charge of the vehicle when it is off-grid; P 100 It is the power consumption of new energy vehicles per 100 kilometers. v is the capacity of the battery pack.

[0053] Use the normal distribution function to approximate the time t that new energy vehicles will be connected to the grid in , as shown in formula (3):

[0054]

[0055] in, is the probability density function of network access time; σ c is the standard deviation of network access time; μ c Expected time to join the network.

[0056] The charging time of new energy vehicles is T c , as shown in formula (4).

[0057]

[0058] Among them, P nvc is the charging power of new energy vehicles; η nc For the charging efficiency of new energy vehicles.

[0059] Monte Carlo prediction is performed based on the above function to obtain the load of new energy vehicles within the scheduling period.

[0060] The vehicle fuel consumption is related to the daily mileage, the proportion of working modes and the energy consumption of each mode, as shown in equations (6)-(7).

[0061]

[0062] G 100s =α en ·G 100en +α m ·G 100m (7)

[0063] Among them, F PHEV is the fuel volume of the plug-in hybrid vehicle; N a is the number of plug-in hybrid electric vehicles; R iis the daily mileage of the i-th plug-in hybrid vehicle; G 100s , G 100en , G 100m are the fuel consumption per 100 kilometers under the comprehensive working conditions of plug-in hybrid vehicles, internal combustion engine mode, and hybrid driving mode, α en , α m are the proportional coefficients of internal combustion engine mode and hybrid drive mode respectively;

[0064] The agreement signed between the load aggregator and the electric vehicle user takes into account the transfer period, the transferred period and the transfer compensation of the charging load. The agreement signed with the plug-in hybrid vehicle user also sets the fuel response limit mileage and the fuel response compensation unit price information. When the user's driving mileage reaches the fuel response limit mileage, he can choose to drive in the internal combustion engine mode. The system will give the user a certain amount of fuel compensation. The fuel response amount is related to the fuel response mileage, as shown in equations (8)-(10).

[0065] F gre =R gre ·G 100en (8)

[0066]

[0067] Among them, F gre is the fuel response of the plug-in hybrid vehicle; R gre R is the fuel response mileage; gre,h is the fuel response mileage of the hth plug-in hybrid vehicle; R h is the daily mileage of the h-th plug-in hybrid vehicle; R lim To compensate for the limited mileage; N b The number of plug-in hybrid electric vehicles that have signed agreements with load aggregators and participate in dispatch.

[0068] The architecture diagram of predicting fuel compensation unit price based on BP neural network is as follows Figure 3 After the training is completed, six influencing indicators are used as input, including peak electricity price, oil price, transfer electricity load, internal combustion engine mode ratio, daily mileage and compensation limit mileage, and the fuel response compensation unit price is used as output. Through prediction, the fuel response compensation unit price is obtained;

[0069] The response degree of new energy vehicle users to scheduling is different. The fuzzy inference system is used to estimate the user's response willingness, thereby obtaining the transfer load model of new energy vehicles, as shown in equations (11)-(12):

[0070]

[0071] in, are the transfer load power of electric vehicle and plug-in hybrid vehicle in period t; η EV , η PHEV are the user responsiveness of electric vehicles and plug-in hybrid vehicles in period t; α EV , α PHEV are the proportion coefficients of contracted users of electric vehicles and plug-in hybrid vehicles respectively; are the charging powers of electric vehicles and plug-in hybrid vehicles during period t respectively.

[0072] The introduction of the economic conversion coefficient λ1 projects the load fluctuation suppression target into the economic dimension, taking into account both the economy and stability of the system. In a dispatch cycle, the operating cost of the regional integrated energy system is C RIES , as shown in equations (13)-(15). It contains the system energy purchase cost for period t Unit maintenance cost Wind curtailment penalty costs Demand response purchasing costs Cost of load fluctuation

[0073]

[0074] Among them, T is a scheduling period; is the power purchased by the system from the main grid during period t; are the power generation of electric boiler, micro-turbine, photovoltaic unit, wind turbine and energy storage equipment during period t; K eb , K mt , K pv , K w , K es They are the maintenance unit prices of electric boilers, micro-turbines, photovoltaic units, wind turbines and energy storage equipment; K is the abandoned wind power of the wind turbine in period t; wd Penalty fees for wind curtailment units; is the system electric load after demand response in period t; is the charging power transferred by new energy vehicles during period t; is the new energy vehicle load after demand response in period t; are the system electricity purchase price and demand response unit payment during period t, respectively; is the gas consumption of the micro-turbine during period t; μ f is the natural gas price; μ gl is the price of No. 92 gasoline; K gre , α gre are the fuel compensation unit price and elasticity coefficient respectively; is the system electrical load during period t.

[0075] In a dispatch cycle, the payment fee of the new energy vehicle owner is C v0 , as shown in equations (16)-(17). It contains the charging cost of new energy vehicle owners in period t Charging load dispatch revenue Fuel costs for new energy vehicle owners C gl and fuel response compensation benefit C gre .

[0076]

[0077]

[0078] Among them, K cp is the charging pile service fee; uidr N is the elasticity coefficient of the owner's demand response compensation; c is the number of plug-in hybrid electric vehicles that did not participate in the response; R j , R k are the daily mileage of the jth and kth plug-in hybrid vehicles respectively; G gl,k , R gre,k are the fuel volume and fuel response mileage of the kth plug-in hybrid vehicle respectively.

[0079] The conversion between multi-objective functions and single-objective functions is achieved by using the weighted coefficient method, as shown in equations (18)-(19).

[0080]

[0081] ω1+ω2=1 (19)

[0082] Among them, C is the single objective function of transformation; ω1 and ω2 are the weights of the two objective functions; C1 and C 1,max are the actual value and maximum value of objective function 1; C2 and C 2,max are the actual value and maximum value of objective function 2 respectively.

[0083] The constraints on the unit output include the output constraints of electric boilers, micro-turbines, wind turbines, photovoltaic units, purchased power, energy storage, and charge balance, as shown in formula (20):

[0084]

[0085] in, They are the upper and lower limits of electric boiler output respectively; They are the upper and lower limits of the micro-turbine output respectively; They are the upper and lower limits of the output of the upper main network respectively; are the predicted outputs of photovoltaic units and wind turbines during period t; SOC t is the charge of the energy storage device during period t; SOC 0 , SOC T are the charges at the beginning and end of the energy storage device dispatch period; SOC min , SOC max They are the upper and lower limits of the charge of the energy storage equipment respectively; are the charging and discharging power of the power storage device during period t respectively; are the maximum charging and discharging power of the energy storage device, respectively. is the heating capacity of the bromide refrigeration machine during period t; is the heating power of the electric boiler during period t; are the charging and discharging power of the heat storage equipment during period t respectively; is the system heat load during period t.

[0086] The constraints of new energy vehicles include transfer power constraints, working mode constraints, charging load balance constraints, and user response benefit constraints, as shown in formula (21):

[0087]

[0088] Among them, ε1 and ε2 are the upper limit ratio coefficients of load transfer for electric vehicles and plug-in hybrid vehicles respectively; α b is the pure electric mode proportional coefficient; R p is the daily mileage of the pth plug-in hybrid vehicle; They are the loads of electric vehicles and plug-in hybrid vehicles after demand response in period t.

[0089] Using GAMS optimization software, the feasibility of the regional integrated energy system optimization scheduling method for plug-in hybrid electric vehicle fuel characteristics is simulated and analyzed. The parameters of new energy vehicles are shown in Table 1.

[0090] Table 1 New Energy Vehicle Parameters

[0091] Electric Vehicles Plug-in hybrid electric vehicles Power consumption per 100 kilometers (KWh / 100km) 12.8 25.7 Fuel consumption per 100 kilometers (L / 100km) 0 6.1 Battery capacity / KWh 52 18 Fuel tank capacity / L 0 48 Slow charging power / KW 3.5 3.5 Charging efficiency / % 90 90

[0092] Load forecast power curve Figure 4 In order to analyze the necessity of plug-in hybrid vehicle scheduling and the impact of the implementation of the fuel response method on the system and user benefits, four scenarios are set for comparative analysis:

[0093] Scenario 1: Both EVs and PHEVs are charged out of order.

[0094] Scenario 2: The load aggregator signs an agreement with electric vehicle users, and electric vehicles are charged in an orderly manner, while plug-in hybrid vehicles are charged in an unordered manner.

[0095] Scenario 3: The load aggregator signs an agreement with the PHEV user, the PHEV performs orderly charging and fuel response, and the EV performs disorderly charging.

[0096] Scenario 4: Load aggregators sign agreements with both EV users and PHEV users. EVs are charged in an orderly manner, and PHEVs are charged in an orderly manner and respond to fuel consumption.

[0097] The present invention considers the operation of the system under the above four scenarios, analyzes the advantages of fuel response strategy and joint scheduling of new energy vehicles, and the specific optimization results are shown in Table 2.

[0098] Table 2 System optimization results for four scenarios

[0099] Scenario 1 Scenario 2 Scene 3 Scene 4 System operating cost / yuan 16118.82 13324.77 13763.03 12717.84 Fees paid by the car owner / yuan 7274.58 6684.99 6436.30 5846.72 Wind abandonment rate / % 13.27 7.66 5.43 0 <![CDATA[Load variance / KW 2 h]]> 59720.57 36175.86 26879.77 12725.69 Load peak-valley difference / KWh 774.28 609.25 528.78 417.69

[0100] Figure 5 The load power curves and load transfer power diagrams for four scenarios are shown below. Figure 6 The following are wind turbine output diagrams for four scenarios. As can be seen from the table and the figure, the system operation cost, payment fee paid by new energy vehicle owners, wind abandonment rate, load variance and load peak-valley difference in scenario 1 are much higher than those in the other three scenarios. It can be seen that when new energy vehicles are charged in an unordered manner, the charging behavior of the owners is based on the charging needs of the vehicles, resulting in the accumulation of charging load on the original peak load in the area, affecting the safe and stable operation of the system.

[0101] Scenario 2 and Scenario 3 are both single-type vehicle dispatch scenarios. Under the effect of comprehensive demand response, the system operation costs and owner payment fees of Scenario 2 and Scenario 3 are greatly reduced. At the same time, compared with Scenario 1, the wind abandonment rate of Scenario 2 and Scenario 3 was reduced by 42.28% and 59.08% respectively, the load variance was reduced by 39.42% and 54.99% respectively, and the load peak-valley difference was reduced by 21.31% and 31.71% respectively. It can be seen that comprehensive demand response can guide new energy vehicle users to reasonably transfer loads, effectively utilize the peak-valley time difference and coupling relationship between electric energy and thermal energy, improve the impact caused by the anti-peak regulation characteristics of wind power and the disordered charging behavior of users, promote the abandonment of wind power during the low-peak period of power load, and improve energy utilization efficiency and system operation stability.

[0102] Scenario 4 is a full dispatch scenario for two types of vehicles. Scenario 4 has the lowest system operating costs and owner payment fees, the largest amount of charging load transfer, and the full absorption of wind power in the system, and the load variance and load peak-valley difference are further reduced. It can be concluded that as the number of new energy vehicles that sign the agreement increases, the load capacity actually participating in the comprehensive demand response increases, and the system optimization effect is better.

[0103] Figure 7 , Figure 8 This is the power and heat load unit output diagram for scenario 2. Fig. 9 , Fig.10 The output diagram of the electric and thermal load units in scenario 3. As can be seen from the figure, compared with scenario 2, the load of new energy vehicles in scenario 3 is reduced. Under the action of comprehensive demand response, more charging loads participate in load transfer, and the peak load is further reduced. At the same time, the valley load is further increased, the output space of wind turbines is improved, and better wind power consumption effect is achieved in conjunction with electric boilers and cogeneration units. The load variance and load peak-valley difference in scenario 3 are both reduced compared with scenario 2. This is because after the introduction of the fuel response strategy, plug-in hybrid vehicles have improved fuel mileage and reduced electric mileage. At the same time, the user responsiveness of plug-in hybrid vehicles has increased, and the capacity of demand response has increased, so the optimization effect has improved. The results show that during the optimized operation, compared with electric vehicles that only consider orderly charging, plug-in hybrid vehicles that consider both orderly charging and fuel response are more beneficial to suppress load fluctuations, improve system stability and user economy.

[0104] Table 3 Optimization results of four fuel compensation unit price systems

[0105] 0.1 / (yuan / L) 0.3 / (yuan / L) 0.5 / (yuan / L) 0.7 / (yuan / L) System operating cost / yuan 15995.68 13794.58 13763.03 14008.09 Fees paid by the car owner / yuan 7117.85 6485.91 6436.30 6287.88 Wind abandonment rate / % 12.93 6.22 5.43 2.57 <![CDATA[Load variance / KW 2 h]]> 51940.98 28517.23 26879.77 22364.59 Load transfer amount / KWh 137.66 701.22 755.35 941.24

[0106] Table 3 shows the optimization results of different fuel compensation unit price systems. Fig.11 The electric load power curve and electric load transfer power diagram for different fuel compensation unit prices are shown in Table 3 and Fig.11 It can be seen that after increasing the fuel compensation unit price, the user responsiveness of plug-in hybrid vehicles is improved, the charging load transfer volume is increased, and the user fuel compensation income and charging load transfer income are both improved, so the owner's payment fee, system wind abandonment rate, and load variance are all reduced. With the improvement of the user responsiveness of plug-in hybrid vehicles, the demand response capacity gradually reaches the upper limit of regulation, the load response flexibility decreases, and the fuel compensation cost continues to increase, so the system operating cost first shows a downward trend and then an upward trend. It can be seen that within a certain adjustment range, appropriately increasing the fuel compensation unit price can take into account the needs and interests of the system and users, improve the system power supply reliability, reduce load volatility, and increase the absorption rate of renewable energy.

[0107] Fig.12 , Fig.13 This is the output diagram of the electric and thermal load units in scenario 4. As can be seen from the figure, during the period of low electricity prices, the system controls the output of the cogeneration unit and the electric boiler to supply electricity and heat. At this time, the power storage equipment is in a charging state, and the heat storage equipment is in a flexible charging and discharging state. A large number of electric vehicles and plug-in hybrid electric vehicle loads participate in the comprehensive demand response to perform "valley filling". The wind turbine unit achieves full power generation with the coordination of the electric boiler and the energy storage equipment, which improves the utilization rate of renewable energy. During the period of normal electricity prices, the thermal load is reduced, the total heat supply of the system is reduced, and the cost of purchasing electricity is increased. The output of the electric boiler is reduced, the output of wind power is reduced, and the power purchased by the system from the power grid increases. At the same time, part of the new energy vehicle load is transferred during this period, which has a certain effect on relieving the pressure on the power grid. During the peak electricity price period, heating is also at its peak. At this time, the cogeneration units increase their output due to the influence of heat-to-electricity. This period is the main load transfer period for electric vehicles and plug-in hybrid vehicles. A large number of charging loads participate in the "peak shaving" task, reducing the electricity demand during this period and reducing the power supply pressure of the system.

[0108] Although the present invention has been described above in conjunction with the accompanying drawings, the present invention is not limited to the above-mentioned specific embodiments, which are illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can make many forms without departing from the purpose of the invention, which are all within the protection of the present invention.

Claims

1. A regional integrated energy system optimization scheduling method based on the fuel characteristics of plug-in hybrid electric vehicles, comprising: A regional comprehensive energy system consisting of an upper main grid, wind turbines, photovoltaic units, natural gas sources, gasoline sources, power storage equipment, heat storage equipment, electric boilers, cogeneration units, electric loads, heat loads, and new energy vehicle loads. The upper main grid, wind turbines, photovoltaic units, and power storage equipment are all connected to the electric loads and new energy vehicle loads; the new energy vehicle loads include electric vehicle loads and plug-in hybrid electric vehicle loads, and the gasoline source is connected to the plug-in hybrid electric vehicle load; the cogeneration unit includes a micro-gas turbine and a bromide refrigeration machine, the micro-gas turbine is connected to the natural gas source, the electric load, and the new energy vehicle load, and the waste heat flue gas discharged by the micro-gas turbine is sent to The bromide refrigeration machine connected to the heat load; the electric boiler is connected to the electric load, the new energy vehicle load, the heat load, and the heat storage device; the power system structure of the plug-in hybrid vehicle includes a slave power system, a clutch, a main power system, a continuously variable transmission, and a reducer; in the slave power system, the engine utilizes the chemical energy of the gasoline in the fuel tank; the clutch is connected to the engine and the motor; in the main power system, the battery pack stores the electric energy under the action of the charging device, and the electric energy is transmitted to the motor for utilization; the continuously variable transmission is connected to the motor and the reducer, characterized in that the content of its optimization scheduling method includes: In the regional integrated energy system optimization scheduling of plug-in hybrid electric vehicle fuel characteristics, P and ΔP represent the electric energy transmitted and transferred by the system, F represents the amount of fuel, G represents the amount of fuel per 100 kilometers of plug-in hybrid electric vehicles, R represents the daily mileage of plug-in hybrid electric vehicles, N represents the number of plug-in hybrid electric vehicle users, η represents the user response, and α represents the proportionality coefficient; in the parameter subscript, PHEV represents plug-in hybrid electric vehicle, s, en, m represent pure electric mode, internal combustion engine mode, and hybrid drive mode, lim represents compensation limit, and gre represents plug-in hybrid electric vehicle participation in fuel response; the parameter superscript t represents the time period; the fuel characteristics and power consumption characteristics of the main power system and the slave power system of the plug-in hybrid electric vehicle are considered to be coordinated with each other; The working modes of plug-in hybrid electric vehicles are divided into pure electric mode, internal combustion engine mode, and hybrid drive mode. The vehicle fuel consumption is related to the daily mileage, the proportion of working modes, and the energy consumption value of each mode. The expression is: G 100s =a en ·G 100en +a m ·G 100m Among them, F PHEV is the fuel volume of the plug-in hybrid vehicle, R i is the daily mileage of the i-th plug-in hybrid vehicle, N a is the number of plug-in hybrid electric vehicles, G 100s , G 100en , G 100m are the fuel consumption per 100 kilometers under the comprehensive working conditions of plug-in hybrid vehicles, internal combustion engine mode, and hybrid driving mode, α en is the proportional coefficient of the internal combustion engine mode, α m is the proportional coefficient of the hybrid driving mode; In the agreement signed between the plug-in hybrid vehicle owner and the load aggregator, the charging load transfer information, the fuel response limit mileage and the fuel response compensation unit price information must be set. When the user's driving mileage reaches the fuel response limit mileage, if the user chooses to drive the internal combustion engine, the system will give the user fuel compensation. The fuel response amount is related to the fuel response mileage, and its expression is: F gre =R gre ·G 100en Among them, F gre is the fuel response of the plug-in hybrid vehicle, R gre is the fuel efficiency mileage, R gre,h is the fuel response mileage of the hth plug-in hybrid vehicle, R h is the daily mileage of the hth plug-in hybrid vehicle, R lim To compensate for the limited mileage, N b The number of plug-in hybrid electric vehicles that have signed agreements with load aggregators and participate in dispatch; The unit price of fuel response compensation is estimated based on the BP neural network prediction algorithm. After training, six influencing indicators are used as input, including peak electricity price, oil price, transfer electricity load, internal combustion engine mode ratio, daily mileage and compensation limit mileage, and the unit price of fuel response compensation is used as output. Through prediction, the unit price of fuel response compensation is obtained; In the charging scenario, the actual participation of car owners in scheduling varies, so the user responsiveness is defined to reveal the proportion of vehicles actually participating in scheduling. The expression of the plug-in hybrid vehicle load transfer model is: in, is the transfer load power of the plug-in hybrid vehicle during period t, is the charging power of the plug-in hybrid vehicle during period t, α PHEV is the proportion coefficient of plug-in hybrid electric vehicle contract users, η PHEV User responsiveness for plug-in hybrid electric vehicles; In a dispatch cycle, the operating cost of the regional integrated energy system is C RIES : Among them, T is a scheduling period; They are the system energy purchase cost, unit maintenance cost, wind curtailment penalty cost, demand response purchase cost, and load fluctuation cost during period t. is the power purchased by the system from the main grid during period t; are the power generation of electric boiler, micro-turbine, photovoltaic unit, wind turbine and energy storage equipment during period t; K eb , K mt , K pv , K w , K es They are the maintenance unit prices of electric boilers, micro-turbines, photovoltaic units, wind turbines and energy storage equipment; K is the abandoned wind power of the wind turbine in period t; wd Penalty fees for wind curtailment units; is the system electric load after demand response in period t; are the charging power transferred by new energy vehicles and electric vehicles during period t respectively; is the load of new energy vehicles after demand response in period t; are the system electricity purchase price and demand response unit payment during period t, respectively; is the gas consumption of the micro-turbine during period t; μ f is the natural gas price; μ gl is the price of No. 92 gasoline; K gre , α gre are the fuel compensation unit price and elasticity coefficient respectively; is the system electric load during period t; λ1 is the economic conversion coefficient; In a dispatch cycle, the payment fee of the new energy vehicle owner is C v0 : in, C gl , C gre They are the charging cost, charging load dispatching income, fuel cost, and fuel response compensation income of the new energy vehicle owner during period t; K cp is the charging pile service fee; uidr N is the elasticity coefficient of the owner's demand response compensation; c is the number of plug-in hybrid electric vehicles that did not participate in the response; R j , R k are the daily mileage of the jth and kth plug-in hybrid vehicles respectively; G gl,k , R gre,k are the fuel quantity and fuel response mileage of the kth plug-in hybrid vehicle respectively; At the same time, the operating cost of the regional integrated energy system and the payment fees of new energy vehicle owners are considered, and the conversion between multi-objective function and single objective function is carried out, and the weighted coefficient method is used to achieve: Among them, C is the single objective function of transformation; ω1 and ω2 are the weights of the two objective functions respectively; C1, C 1,max are the actual value and maximum value of objective function 1 respectively; C2, C 2,max are the actual value and maximum value of objective function 2 respectively; The constraints of the regional integrated energy system include the output constraints of electric boilers, micro-turbines, wind turbines, photovoltaic units, purchased electricity, energy storage, and charge balance. The constraints of new energy vehicles include transfer power, operating mode, charging load balance, and user response benefit constraints.

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