Optimization method and device for electric vehicle virtual energy storage supporting multi-peak load auxiliary service

Abstract

A method and device for optimizing multi-peak load-shaving auxiliary services supported by electric vehicle virtual energy storage, the method comprising: according to the charging urgency of electric vehicles, the electric vehicle virtual energy storage dispatches electric vehicles to charge in an orderly manner to participate in peak load-shaving, obtains compensation for peak load-shaving auxiliary services, and ensures that the charging amount of electric vehicles before and after dispatch remains unchanged; taking wind farms and photovoltaic power stations as peak load-shaving demand parties, sharing the compensation for peak load-shaving auxiliary services of thermal power units and electric vehicle virtual energy storage as peak load-shaving suppliers; with the goal of minimizing the operating cost of the power system including the peak load-shaving auxiliary service compensation cost of electric vehicle virtual energy storage, the comprehensive operating cost of thermal power units, and the penalty for abandoning renewable energy, a power system peak load-shaving optimization model is established. The present invention not only effectively reduces the total operating cost of the power system, but also improves the peak load margin of the power system, and realizes effective peak load-shaving of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a method and device for optimizing multi-peak load-shaving auxiliary services supported by virtual energy storage of electric vehicles. Background Art

[0002] As the peak-to-valley difference of the net load of the power system continues to increase, the "duck curve" of the net load in some areas has even turned into a steeper "canyon curve", and the peak-shaving pressure faced by the power system is increasing day by day. At the same time, in order to strengthen the integration and interaction between new energy vehicles and the power grid, it is necessary to give full play to the flexible adjustment capabilities of electric vehicles (EVs) as controllable loads or mobile energy storage. Guiding electric vehicles to participate in the peak-shaving of the power system through a reasonable ancillary service market mechanism can provide support for the efficient and economical operation of the power system.

[0003] The existing peak-shaving auxiliary services ignore the diversity of peak-shaving auxiliary services, and do not include various forms of peak-shaving auxiliary services such as virtual energy storage and demand response in the cost sharing considerations. At the same time, the participation of electric vehicles in the peak-shaving auxiliary services of the power system has problems such as the actual dispatching effect is inconsistent with the expectation and the peak-shaving compensation is unclear. The above problems not only make the power system operation cost higher, but also make the power system peak-shaving effect poor. Summary of the invention

[0004] The purpose of the present invention is to overcome the above-mentioned defects and problems existing in the prior art, and to provide an optimization method and device for electric vehicle virtual energy storage supporting multiple peak-shaving auxiliary services, which not only effectively reduces the total operating cost of the power system, but also improves the peak-shaving margin of the power system and realizes effective peak-shaving of the electric system.

[0005] To achieve the above objectives, the technical solution of the present invention is:

[0006] An optimization method for electric vehicle virtual energy storage supporting multiple peak load auxiliary services, comprising:

[0007] According to the charging urgency of electric vehicles, the electric vehicle virtual energy storage dispatches electric vehicles to charge in an orderly manner to participate in peak load regulation, obtains compensation for peak load ancillary services, and ensures that the charging volume of electric vehicles remains unchanged before and after dispatch;

[0008] Wind farms and photovoltaic power stations are regarded as peak-shaving demand parties, and the peak-shaving auxiliary service compensation costs of thermal power units and electric vehicle virtual energy storage, which are peak-shaving suppliers, are shared;

[0009] A power system peak-shaving optimization model is established with the goal of minimizing the power system operating cost, which includes the peak-shaving ancillary service compensation costs of electric vehicles’ virtual energy storage, the comprehensive operating cost of thermal power units, and the penalty for abandonment of renewable energy.

[0010] Compensation cost C for peak load ancillary services obtained by electric vehicle virtual energy storage VES for:

[0011]

[0012] In the formula, N is the compensation fee for the peak load auxiliary service of the i-th electric vehicle; EV represents the number of electric vehicles; represents the peak load-shaving contribution of the i-th electric vehicle; is the charging urgency index of the i-th electric vehicle, indicates that the i-th electric vehicle is regarded as an emergency charging electric vehicle, Indicates that the i-th electric vehicle is regarded as an electric vehicle for non-emergency charging; Compensation discount for electric vehicles participating in peak load regulation; is charging prices for electric vehicles; represents the state of charge of the i-th electric vehicle when it leaves; E represents the charge state of the i-th electric vehicle when it arrives; EV Represents the battery capacity of electric vehicles; η EV Indicates the charging efficiency of electric vehicles.

[0013] The charging urgency index of the i-th electric vehicle for:

[0014]

[0015] In the formula, Indicates the fast charging power of electric vehicles; represents the state of charge requirement when the i-th electric vehicle leaves; T i Re It represents the number of time periods during which the i-th electric vehicle is connected to the grid, that is, the time period between the arrival and departure of the electric vehicle; Δt is the length of the scheduling period.

[0016] In the case of non-emergency charging, the charge state of the i-th electric vehicle when it leaves is:

[0017]

[0018] In the formula, Indicates the slow charging power of electric vehicles; is the slow charging state variable of the i-th electric vehicle in period j; Indicates the fast charging power of electric vehicles; is the fast charging state variable of the i-th electric vehicle in period j; and are the sequence numbers of the arrival and departure periods of the i-th electric vehicle respectively; Δt is the length of the scheduling period;

[0019] In non-emergency charging situations, the charging capacity of electric vehicles before and after dispatching meets the following requirements:

[0020]

[0021] In the formula, It indicates the charging period required to meet the state of charge requirements when electric vehicles are charged in an unordered manner; Is the previous integer that is less than the result of the division operation.

[0022] If a day is discretized into multiple time periods, the sequence number of the arrival and departure time periods of each electric vehicle is:

[0023]

[0024] In the formula, and are the arrival time and departure time of the i-th electric car respectively; is the next integer greater than the result of the division operation; Is the previous integer that is less than the result of the division operation.

[0025] The compensation costs for peak load ancillary services shared by the wind farm and photovoltaic power station are:

[0026]

[0027] In the formula, They are the compensation costs for peak load ancillary services shared by wind farms and photovoltaic power stations at time t; are the maximum theoretical outputs of the wind farm and photovoltaic power station at time t; N WT is the total number of wind farms; N PV is the total number of photovoltaic power stations; is the sum of deep peak load compensation expenditures of all thermal power units at time t; C ss is the start-up and shutdown cost of thermal power units; C VES Compensation for peak-shaving ancillary services obtained by virtual energy storage for electric vehicles.

[0028] Quantify their willingness to participate in peak load-shaving auxiliary services through the profits obtained by wind farms, photovoltaic power plants and thermal power units from participating in peak load-shaving auxiliary services;

[0029] The profit of peak load ancillary service of wind farm is:

[0030]

[0031] The profit of the peak load auxiliary service of the photovoltaic power station is:

[0032]

[0033] In the formula, The profit of peak load auxiliary service of wind farm; WT The on-grid electricity price for wind power; is the wind farm output power at time t; The cost of peak load regulation of wind farm at time t is shared; is the wind power abandoned at time t; θ is the penalty coefficient for wind and solar power abandonment; Profits from the peak load auxiliary services of photovoltaic power stations; PV The photovoltaic grid-connected electricity price; is the output power of the photovoltaic power station at time t; The peak load sharing cost of the photovoltaic power station at time t; is the abandoned optical power at time t;

[0034] The profit of peak load ancillary service of thermal power units is:

[0035]

[0036] In the formula, Profits from peak load-shaving auxiliary services for thermal power units; The deep peak regulation compensation fee for the first thermal power unit; The deep peak regulation cost of thermal power units; They are the loss cost and oil input cost of deep peak regulation of thermal power units respectively; The coal-saving benefits of thermal power units due to deep peak regulation.

[0037] The objective function of the power system peak load optimization model is:

[0038] min f=C VES +C MT +C CUR ;

[0039] In the formula, C VES Compensation for peak load ancillary services obtained by virtual energy storage for electric vehicles; C MT is the comprehensive operating cost of thermal power units; C CUR Penalties for renewable energy abandonment.

[0040] The constraints of the power system peak load optimization model include power balance constraints, thermal power unit output constraints and line transmission capacity constraints.

[0041] An electric vehicle virtual energy storage supporting multi-peak load auxiliary service optimization device, which is applied to the above-mentioned method, comprises:

[0042] The electric vehicle virtual energy storage peak-shaving auxiliary service model construction module is used to dispatch electric vehicles to charge in an orderly manner to participate in peak-shaving according to the charging urgency of electric vehicles, obtain peak-shaving auxiliary service compensation fees, and ensure that the charging amount of electric vehicles remains unchanged before and after scheduling;

[0043] A module for building a multi-element peak-shaving auxiliary service cost sharing model is used to share the peak-shaving auxiliary service compensation costs of thermal power units and electric vehicle virtual energy storage as peak-shaving suppliers, taking wind farms and photovoltaic power stations as peak-shaving demand parties;

[0044] The power system peak-shaving optimization model building module is used to establish a power system peak-shaving optimization model with the goal of minimizing the power system operating cost including the peak-shaving auxiliary service compensation fee of electric vehicle virtual energy storage, the comprehensive operating cost of thermal power units and the penalty for renewable energy abandonment.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] In the optimization method and device of multi-peak load-shaving auxiliary service supported by electric vehicle virtual energy storage of the present invention, the multi-peak load-shaving auxiliary service subjects include wind farms, photovoltaic power stations, thermal power units and electric vehicle virtual energy storage; the electric vehicle virtual energy storage dispatches electric vehicles to orderly charge and participate in peak load-shaving according to the charging urgency of electric vehicles, and obtains compensation for peak load-shaving auxiliary services, while ensuring that the charging amount of electric vehicles before and after dispatch remains unchanged; wind farms and photovoltaic power stations, as peak load-shaving demand parties, share the compensation for peak load-shaving auxiliary services of thermal power units and electric vehicle virtual energy storage as peak load-shaving suppliers; with the goal of minimizing the peak load-shaving auxiliary service compensation costs including the electric vehicle virtual energy storage, the comprehensive operating costs of thermal power units and the penalty for abandoning renewable energy, a peak load-shaving optimization model for the power system is established. The present invention not only effectively reduces the total operating cost of the power system, but also improves the peak load margin of the power system, and realizes effective peak load-shaving of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of an optimization method for multi-peak load-shaving auxiliary services supported by virtual energy storage of electric vehicles according to the present invention.

[0048] Figure 2 It is a schematic diagram of the results of electric vehicle virtual energy storage participating in peak load scheduling in an embodiment of the present invention.

[0049] Figure 3 It is a schematic diagram of the optimization scheduling results of each unit under scenario 1 in an embodiment of the present invention.

[0050] Figure 4 It is a schematic diagram of the optimization scheduling results of each unit under scenario 2 in an embodiment of the present invention.

[0051] Figure 5It is a schematic diagram of the optimization scheduling results of each unit under scenario 3 in an embodiment of the present invention.

[0052] Figure 6 It is a schematic diagram of the optimization scheduling results of each unit under scenario 4 in an embodiment of the present invention.

[0053] Figure 7 It is a structural block diagram of an electric vehicle virtual energy storage supporting multi-peak load auxiliary service optimization device of the present invention.

[0054] Figure 8 It is a structural block diagram of an electric vehicle virtual energy storage supporting multi-peak load auxiliary service optimization device of the present invention. DETAILED DESCRIPTION

[0055] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0056] See also Figure 1 The present invention provides an optimization method for electric vehicle virtual energy storage supporting multi-peak load-shaving auxiliary services, comprising:

[0057] S1. According to the charging urgency of electric vehicles, the electric vehicle virtual energy storage dispatches electric vehicles to charge in an orderly manner to participate in peak load regulation, obtains compensation for peak load ancillary services, and ensures that the charging volume of electric vehicles remains unchanged before and after dispatch.

[0058] Large-scale electric vehicles are aggregated into virtual energy storage with considerable capacity. The electric vehicle virtual energy storage rationally arranges the charging of individual electric vehicles, pays peak-shaving compensation to each electric vehicle based on its specific peak-shaving contribution, and participates in the diversified peak-shaving auxiliary service market as a peak-shaving supplier.

[0059] S2. Take wind farms and photovoltaic power stations as peak-shaving demand parties, and share the peak-shaving auxiliary service compensation costs of thermal power units and electric vehicle virtual energy storage as peak-shaving suppliers.

[0060] S3. Establish a power system peak-shaving optimization model with the goal of minimizing the power system operating cost, including the compensation for peak-shaving auxiliary services of electric vehicle virtual energy storage, the comprehensive operating cost of thermal power units, and the penalty for abandonment of renewable energy.

[0061] In another embodiment of the present application, based on the information collected by the charging pile, including the arrival time and departure time, the state of charge (SOC) when the electric vehicle is connected to the power system, and the state of charge requirement when the electric vehicle ends the charging behavior, the electric vehicle virtual energy storage can dispatch the electric vehicle to charge in an orderly manner to participate in the peak load regulation. First, the compensation fee for the electric vehicle virtual energy storage peak load regulation auxiliary service is stipulated; second, the length and number of time periods are divided according to the distribution of electric vehicle charging time; third, the electric vehicle charging urgency index is defined to distinguish whether the charging demand of the electric vehicle is urgent, and the owner is allocated two charging modes: emergency charging and non-emergency charging; finally, the electric vehicle virtual energy storage power balance model is established to ensure that the charging amount of the electric vehicle remains unchanged before and after the dispatch.

[0062] Furthermore, the electric vehicle virtual energy storage transfers part of the electric vehicle charging load demand from the peak period to the valley period, thereby alleviating the peak load pressure of the power system. In this process, the peak load auxiliary service compensation fee C obtained by the electric vehicle virtual energy storage is VES for:

[0063]

[0064] In the formula, N is the compensation fee for the peak load auxiliary service of the i-th electric vehicle; EV represents the number of electric vehicles; represents the peak load-shaving contribution of the i-th electric vehicle; is the charging urgency index of the i-th electric vehicle, indicates that the i-th electric vehicle is regarded as an emergency charging electric vehicle, Indicates that the i-th electric vehicle is regarded as an electric vehicle for non-emergency charging; Compensation discount for electric vehicles participating in peak load regulation; is charging prices for electric vehicles; represents the state of charge of the i-th electric vehicle when it leaves; E represents the charge state of the i-th electric vehicle when it arrives; EV Represents the battery capacity of electric vehicles; η EV Indicates the charging efficiency of electric vehicles.

[0065] Furthermore, the current low midday electricity price for industrial and commercial use is beneficial to the consumption of new energy. Based on the load characteristics of the power grid, the output characteristics of new energy, and the price signals of the power auxiliary service market, it is more feasible for electric vehicles in parking lots and commercial buildings with high penetration and dispatchable potential to participate in virtual energy storage peak-shaving auxiliary services. Therefore, this application selects electric vehicles in parking lots and commercial buildings as the research objects. The owners start charging when they arrive at the workplace in the morning and complete charging after get off work. Electric vehicle arrival time t c and departure time tdis It also obeys the normal distribution and can be expressed as:

[0066]

[0067] In the formula, μ c =8.5;σ c =3.3; μ dis =17.5;σ dis =3.24.

[0068] Furthermore, the scheduling plan is usually executed according to integer time periods. A day is discretized into 96 time periods, and the time period length Δt is 15 minutes. Then the sequence number of the arrival and departure time periods of each electric vehicle is:

[0069]

[0070]

[0071] In the formula, and Respectively represent the sequence number of the arrival and departure time periods of the i-th electric vehicle; and are the arrival time and departure time of the i-th electric car respectively; is the next integer greater than the result of the division operation; is the previous integer that is less than the result of the division operation; N EV Represents the number of electric vehicles.

[0072] Furthermore, the charging urgency index is defined To reflect the urgency of the electric vehicle charging demand, its value is equal to the difference between the electric vehicle charging power since arrival and the electric vehicle demand power. It means that the electric vehicle charging demand cannot be met when leaving; if This means that the charging needs of electric vehicles can be met when you leave, and there is a certain regulation margin.

[0073] Charging urgency index of the i-th electric vehicle for:

[0074]

[0075] In the formula, Indicates the fast charging power of electric vehicles; represents the state of charge requirement when the i-th electric vehicle leaves; T i Re It represents the number of time periods during which the i-th electric vehicle is connected to the grid, that is, the time period between the arrival and departure of the electric vehicle; Δt is the length of the scheduling period.

[0076] Furthermore, the EV virtual energy storage allocates charging modes to all EVs according to their charging urgency. When , the i-th electric vehicle is regarded as an emergency charging electric vehicle, and the electric vehicle is fast charged during the grid-connected period, ensuring that the emergency charging electric vehicle can charge more power at the charging time. The constraints of the emergency charging electric vehicle are expressed as:

[0077]

[0078] In the case of emergency charging, the charge state of the i-th electric vehicle when it leaves is:

[0079]

[0080] Furthermore, when When , the i-th electric vehicle is regarded as a non-emergency charging electric vehicle. There is a certain scheduling margin in the charging process of the electric vehicle, and fast charging or slow charging can be performed:

[0081]

[0082] When an electric vehicle is off the grid (before arriving or after leaving), the scheduling plan cannot be executed, and charging behavior can only be scheduled during the period when the electric vehicle is connected to the grid; therefore, when a non-emergency charging electric vehicle is off the grid, the charging status Must be equal to 0;

[0083]

[0084] In the case of non-emergency charging, the charge state of the i-th electric vehicle when it leaves is:

[0085]

[0086] In the formula, Indicates the slow charging power of electric vehicles; is the slow charging state variable of the i-th electric vehicle in period j; Indicates the fast charging power of electric vehicles; is the fast charging state variable of the i-th electric vehicle in period j; and are the sequence numbers of the arrival and departure periods of the i-th electric vehicle respectively; Δt is the length of the scheduling period.

[0087] Electric vehicle load aggregation participates in peak load regulation as virtual energy storage, and should ensure that the charging amount of electric vehicles remains unchanged before and after dispatch in non-emergency charging situations:

[0088]

[0089] In the formula, It indicates the charging period required to meet the state of charge requirements when electric vehicles are charged in an unordered manner; It is the previous integer that is less than the result of the division operation, avoiding overcharging of electric vehicles in the next integer.

[0090] In another embodiment of the present application, wind farms and photovoltaic power stations jointly share the compensation costs for peak load auxiliary services of thermal power units and electric vehicle virtual energy storage. In order to further increase the enthusiasm of various entities to participate in power generation, unlike the electricity sharing mechanism, the share of wind farms and photovoltaic power stations in the capacity sharing mechanism is determined according to their maximum theoretical output ratio.

[0091] The compensation costs for peak load-shaving auxiliary services shared by wind farms and photovoltaic power stations are:

[0092]

[0093] In the formula, They are the compensation costs for peak load ancillary services shared by wind farms and photovoltaic power stations at time t; are the maximum theoretical outputs of the wind farm and photovoltaic power station at time t; N WT is the total number of wind farms; N PV is the total number of photovoltaic power stations; is the sum of deep peak load compensation expenditures of all thermal power units at time t; C ss is the start-up and shutdown cost of thermal power units; C VES Compensation for peak-shaving ancillary services obtained by virtual energy storage for electric vehicles.

[0094] In another embodiment of the present application, a peak-shaving initiative model is constructed to quantify the willingness of multiple entities to participate in peak-shaving auxiliary services. The peak-shaving initiative is quantified by the additional profit obtained by each entity from participating in the peak-shaving auxiliary services. When the profit is positive, each entity is willing to participate in the peak-shaving auxiliary services to ensure the fairness of the peak-shaving auxiliary service compensation fee and the compensation fee sharing mechanism.

[0095] The willingness of wind farms, photovoltaic power stations and thermal power units to participate in peak-shaving auxiliary services is quantified through the profits obtained from their participation in peak-shaving auxiliary services.

[0096] The profit of peak load ancillary service of wind farm is:

[0097]

[0098] The profit of the peak load auxiliary service of the photovoltaic power station is:

[0099]

[0100] In the formula, The profit of peak load auxiliary service of wind farm; WT The on-grid electricity price for wind power; is the wind farm output power at time t; is the cost of peak load sharing of wind farm l at time t; is the wind power abandoned at time t; θ is the penalty coefficient for wind and solar power abandonment; Profits from the peak load auxiliary services of photovoltaic power stations; PV The photovoltaic grid-connected electricity price; is the output power of photovoltaic power station m at time t; is the peak load sharing cost of photovoltaic power station m at time t; is the abandoned optical power at time t;

[0101] The profit of the peak-shaving auxiliary service of thermal power units is the difference between the deep peak-shaving compensation and the deep peak-shaving cost, where the deep peak-shaving cost includes the loss cost of the thermal power units, the oil investment cost, and the coal-saving benefits of the less coal used due to deep peak-shaving. The profit of the peak-shaving auxiliary service of thermal power units is:

[0102]

[0103] In the formula, Profits from peak load-shaving auxiliary services for thermal power units; is the deep peak regulation compensation cost of the i-th thermal power unit; The deep peak regulation cost of thermal power units; They are the loss cost and oil input cost of deep peak regulation of thermal power units respectively; The coal-saving benefits of thermal power units due to deep peak regulation.

[0104] In another embodiment of the present application, the objective function of the power system peak load optimization model is:

[0105] min f=C VES +C MT +C CUR ;

[0106] In the formula, C VES Compensation for peak load ancillary services obtained by virtual energy storage for electric vehicles; C MT is the comprehensive operating cost of thermal power units; C CUR Penalties for renewable energy abandonment.

[0107] Furthermore, the comprehensive operating cost of thermal power units includes the basic operating cost of thermal power units, the deep peak-shaving loss cost of thermal power units and the deep peak-shaving combustion-supporting oil investment cost of thermal power units.

[0108] Basic operating costs of thermal power units:

[0109] C basic =C coal +C ss ;

[0110]

[0111] In the formula, C basic is the basic operating cost of thermal power units; C coal , C ss are the coal consumption cost and start-up and shutdown cost of thermal power units respectively; a i , b i and c i are the secondary, primary and constant coefficients of the consumption of the i-th thermal power unit respectively; Output power for thermal power units; It is the start and stop state variable of the thermal power unit; is the startup cost of the i-th thermal power unit; is the shutdown cost of the i-th thermal power unit.

[0112] Calculating the loss cost of deep peak load regulation of thermal power units using the Manson-Coffin formula for:

[0113]

[0114] In the formula, β is the operation influence coefficient of thermal power unit; is the purchase cost of the i-th thermal power unit; The number of rotor cracking cycles.

[0115] Linear fitting is performed on the actual thermal power unit loss data to obtain the linear formula of the thermal power unit loss under different outputs, so as to reduce the difficulty of solution and improve the solution rate.

[0116] Oil input cost for deep peak load regulation of thermal power units for:

[0117]

[0118] In the formula, is the oil input of the i-th thermal power unit in the deep peak regulation stage at time t; p oil For oil prices.

[0119] The comprehensive operating cost of thermal power units is expressed in different segments according to different operating states:

[0120]

[0121] Where P i G，max , P i G，min are the maximum output of the i-th thermal power unit and the minimum output of conventional peak load regulation; P i b It is the extreme output of deep peak regulation and fuel injection for thermal power units; Pi a It is the deep peak regulation of thermal power units without adding oil to assist combustion;

[0122] Penalty for Renewable Energy Curtailment C CUR for:

[0123]

[0124] Where, θ is the penalty coefficient for abandoning wind and solar power; are the wind power and solar power abandonment at time t respectively; Δt is the length of the scheduling period.

[0125] Furthermore, the constraints of the power system peak load optimization model include power balance constraints, thermal power unit output constraints and line transmission capacity constraints.

[0126] The power balance constraint is:

[0127] P t VES =P t UC -P t CC ;

[0128]

[0129] Where P t LD is the system user load; P t VES P is the virtual energy storage equivalent power of electric vehicles; t UC The disorderly charging load of electric vehicles; P t CC Orderly charging load for electric vehicles;

[0130] The output constraints of thermal power units are:

[0131]

[0132] In the formula, r i is the maximum ramp rate of the thermal power unit;

[0133] The line transmission capacity constraint is:

[0134]

[0135] In the formula, B i，j is the susceptance between nodes i and j; θ i，t ,θ j，t are the voltage phase angles of nodes i and j respectively; It is the per-unit value of the maximum transmission power allowed by the line between nodes i and j.

[0136] The market entities of the multi-faceted peak-shaving auxiliary service in the present invention include wind farms, photovoltaic power stations, thermal power units and electric vehicle virtual energy storage. In order to solve the problem of unclear cost sharing subjects, a cost sharing mechanism for the participation of electric vehicle virtual energy storage in auxiliary service compensation is designed. Wind farms, photovoltaic power stations and other peak-shaving demand parties share the auxiliary service compensation costs required by thermal power units and electric vehicle virtual energy storage and other peak-shaving suppliers according to their respective power generation capacity ratios, ensuring that the peak-shaving costs of multiple entities can be reasonably channeled. In order to solve the compensation for electric vehicle peak-shaving auxiliary services, each electric vehicle is compensated according to its specific contribution to peak-shaving, fully reflecting the value of electric vehicle owners participating in the power auxiliary service market as energy storage, and mobilizing their enthusiasm for participating in peak-shaving. Finally, a peak-shaving optimization model for the power system is constructed with the goal of minimizing the total cost of power system operation. The simulation analysis of the example proves that the proposed model effectively reduces the total cost of power system operation, while improving the peak-shaving margin of the power system and promoting the sustainable development of flexible resources such as electric vehicle virtual energy storage.

[0137] Depend on Figure 2 It can be seen that the peak and valley periods of the net load of the power system are concentrated and single, with the peak period concentrated from 22:00 to 2:00 the next day, and the valley period concentrated from 10:00 to 14:00. Car owners start charging immediately after arriving at work in the morning. The original peak of the disorderly charging load of electric vehicles is concentrated at 8:30. At this time, the net load of the power system is still in the normal period. This part of the charging load of electric vehicles needs to be dispatched to other periods to smooth out. In non-emergency charging mode, the virtual energy storage of electric vehicles can delay the charging demand of electric vehicles and shift the peak of electric vehicle charging to around 12:00, corresponding to the net load valley period, which is also the period of high photovoltaic power generation. This shows that the proposed peak-shaving method can make full use of the complementary characteristics of the public charging load of electric vehicles and photovoltaics. The peak-shaving and valley-filling function of the original physical energy storage can be realized by the virtual energy storage of electric vehicles through load transfer. The equivalent load curve of the virtual energy storage of electric vehicles can follow the net load curve well to achieve peak-shaving of the power system.

[0138] In order to verify the effectiveness of the model proposed in this invention, four scenarios are designed, as shown in Table 1.

[0139] Table 1 Simulation scenario settings

[0140]

[0141] Scenario 1 is the benchmark scenario, including basic elements such as deep peak regulation, peak regulation initiative and peak regulation subject; Scenario 2 removes the deep peak regulation capability of thermal power units relative to Scenario 1, which is used to verify the effectiveness of the flexibility transformation of thermal power units; Scenario 3 removes the electric vehicle virtual energy storage resources relative to Scenario 1 to verify the effectiveness of the proposed electric vehicle virtual energy storage participating in peak regulation; Scenario 4 removes photovoltaic and other resources relative to Scenario 1, aiming to verify the adaptability of the proposed peak regulation method under emergency conditions. The above four scheduling scenarios are simulated and analyzed, and the output curves and optimization results of each subject are shown in Tables 2, 3 and Figure 3 , Figure 4 , Figure 5 , Figure 6 shown.

[0142] Table 2 Optimization results under different scenarios

[0143]

[0144] Table 3 Peak load regulation costs and benefits of different thermal power units under different scenarios

[0145]

[0146] Compared with scenario 2, scenario 1 considers deep peak regulation (DPR) of thermal power units. Although the deep peak regulation cost of thermal power units is increased, deep peak regulation reduces the total cost by 36,000 yuan and the wind and solar abandonment cost by 10,000 yuan under the premise of ensuring the basic peak regulation margin of the system. As can be seen from Table 3, the costs related to deep peak regulation of each thermal power unit are sufficiently compensated. After the introduction of the peak regulation initiative constraint, the unit is still willing to perform deep peak regulation, which verifies the effectiveness of the paid peak regulation compensation scheme proposed in this invention. In general, deep peak regulation of thermal power units has significant effects in improving system operating costs and renewable energy consumption.

[0147] By comparing Scenario 3 and Scenario 1, it can be seen that the addition of electric vehicle virtual energy storage has significantly improved the consumption of renewable energy. By guiding electric vehicles to charge in an orderly manner and optimizing the load curve, the cost of wind and solar abandonment has been reduced from 137,000 yuan to 0. In addition, after participating in the peak-shaving auxiliary service, the electric vehicle virtual energy storage is compensated according to the method of the present invention, and the peak-shaving income obtained is 92,000 yuan, but the total cost is reduced by 8.5%, which proves that the introduction of electric vehicle virtual energy storage in the peak-shaving market can effectively reduce the peak-to-valley difference of the system net load and alleviate the peak-shaving pressure of thermal power units.

[0148] In order to explore the impact of different renewable energy penetration rates on system peak regulation, scenarios 1 and 4 are compared. The results show that in scenario 4, as the penetration rate of renewable energy decreases, the operating cost increases significantly. This is because the thermal power units are in the conventional peak regulation stage, and they need to face more coal consumption costs to replace renewable energy to provide more electricity. Despite the increase in the cost of thermal power units, the system can still operate well in the low penetration rate renewable energy system, showing good adaptability.

[0149] See also Figure 7 The present invention also provides an electric vehicle virtual energy storage supporting multiple peak-shaving auxiliary service optimization device, which is applied to the above-mentioned electric vehicle virtual energy storage supporting multiple peak-shaving auxiliary service optimization method, and the device includes:

[0150] The electric vehicle virtual energy storage peak-shaving auxiliary service model construction module is used to dispatch electric vehicles to charge in an orderly manner to participate in peak-shaving according to the charging urgency of electric vehicles, obtain peak-shaving auxiliary service compensation fees, and ensure that the charging amount of electric vehicles remains unchanged before and after scheduling;

[0151] A module for building a multi-element peak-shaving auxiliary service cost sharing model is used to share the peak-shaving auxiliary service compensation costs of thermal power units and electric vehicle virtual energy storage as peak-shaving suppliers, taking wind farms and photovoltaic power stations as peak-shaving demand parties;

[0152] The power system peak-shaving optimization model building module is used to establish a power system peak-shaving optimization model with the goal of minimizing the power system operating cost including the peak-shaving auxiliary service compensation fee of electric vehicle virtual energy storage, the comprehensive operating cost of thermal power units and the penalty for renewable energy abandonment.

[0153] See also Figure 8 , the present invention also provides an electric vehicle virtual energy storage supporting multi-peak load auxiliary service optimization device, including a memory and a processor;

[0154] The memory is used to store computer program code and transmit the computer program code to the processor;

[0155] The processor is used to execute the above-mentioned method for optimizing multi-peak load-shaving auxiliary services supported by virtual energy storage of electric vehicles according to the instructions in the computer program code.

[0156] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the above-mentioned method for optimizing multi-peak load-shaving auxiliary services supported by virtual energy storage of electric vehicles is implemented.

[0157] Generally speaking, the computer instructions for implementing the method of the present invention may be carried by any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media, except for the signal itself that is temporarily propagating.

[0158] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0159] Computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer or to an external computer (for example, using an Internet service provider to connect via the Internet) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0160] The above-mentioned devices and non-temporary computer-readable storage media can refer to the specific description of an electric vehicle virtual energy storage supporting multi-peak load auxiliary service optimization method and its beneficial effects, which will not be repeated here.

[0161] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. An optimization method for multi-peak load-shaving auxiliary services supported by electric vehicle virtual energy storage, characterized in that: include: According to the charging urgency of electric vehicles, the electric vehicle virtual energy storage dispatches electric vehicles to charge in an orderly manner to participate in peak load regulation, obtains compensation for peak load ancillary services, and ensures that the charging volume of electric vehicles remains unchanged before and after dispatch; Compensation cost C for peak load ancillary services obtained by electric vehicle virtual energy storage VES for: In the formula, N is the compensation fee for the peak load auxiliary service of the i-th electric vehicle; EV represents the number of electric vehicles; represents the peak load-shaving contribution of the i-th electric vehicle; is the charging urgency index of the i-th electric vehicle, indicates that the i-th electric vehicle is regarded as an emergency charging electric vehicle, Indicates that the i-th electric vehicle is regarded as an electric vehicle for non-emergency charging; Compensation discount for electric vehicles participating in peak load regulation; is charging prices for electric vehicles; represents the state of charge of the i-th electric vehicle when it leaves; E represents the charge state of the i-th electric vehicle when it arrives; EV Represents the battery capacity of electric vehicles; η EV Indicates the charging efficiency of electric vehicles; The charging urgency index of the i-th electric vehicle for: In the formula, Indicates the fast charging power of electric vehicles; represents the state of charge requirement when the i-th electric vehicle leaves; represents the number of grid-connected periods of the i-th electric vehicle, i.e., the period between the arrival and departure of the electric vehicle; Δt is the length of the scheduling period; Wind farms and photovoltaic power stations are regarded as peak-shaving demand parties, and the peak-shaving auxiliary service compensation costs of thermal power units and electric vehicle virtual energy storage, which are peak-shaving suppliers, are shared; A power system peak-shaving optimization model is established with the goal of minimizing the power system operating cost, which includes the peak-shaving ancillary service compensation costs of electric vehicles’ virtual energy storage, the comprehensive operating cost of thermal power units, and the penalty for renewable energy abandonment.

2. According to claim 1, a method for optimizing multi-peak load-shaving auxiliary services supported by virtual energy storage for electric vehicles is characterized in that: In the case of non-emergency charging, the charge state of the i-th electric vehicle when it leaves is: In the formula, Indicates the slow charging power of electric vehicles; is the slow charging state variable of the i-th electric vehicle in period j; Indicates the fast charging power of electric vehicles; is the fast charging state variable of the i-th electric vehicle in period j; and are the sequence numbers of the arrival and departure periods of the i-th electric vehicle respectively; Δt is the length of the scheduling period; In non-emergency charging situations, the charging capacity of electric vehicles before and after dispatching meets the following requirements: In the formula, It indicates the charging period required to meet the state of charge requirements when electric vehicles are charged in an unordered manner; Is the previous integer that is less than the result of the division operation.

3. The method for optimizing multi-peak load-shaving auxiliary services supported by virtual energy storage for electric vehicles according to claim 2 is characterized in that: If a day is discretized into multiple time periods, the sequence number of the arrival and departure time periods of each electric vehicle is: In the formula, and are the arrival time and departure time of the i-th electric car respectively; is the next integer greater than the result of the division operation; Is the previous integer that is less than the result of the division operation.

4. The method for optimizing multi-peak load-shaving auxiliary services supported by virtual energy storage for electric vehicles according to claim 1 is characterized in that: The compensation costs for peak load ancillary services shared by the wind farm and photovoltaic power station are: In the formula, They are the compensation costs for peak load ancillary services shared by wind farms and photovoltaic power stations at time t; are the maximum theoretical outputs of the wind farm and photovoltaic power station at time t; N WT is the total number of wind farms; N PV is the total number of photovoltaic power stations; is the sum of deep peak load compensation expenditures of all thermal power units at time t; C ss is the start-up and shutdown cost of thermal power units; C VES Compensation for peak-shaving ancillary services obtained by virtual energy storage for electric vehicles.

5. The method for optimizing multi-peak load-shaving auxiliary services supported by virtual energy storage for electric vehicles according to claim 1 is characterized in that: Quantify their willingness to participate in peak load-shaving auxiliary services through the profits obtained by wind farms, photovoltaic power plants and thermal power units from participating in peak load-shaving auxiliary services; The profit of peak load ancillary service of wind farm is: The profit of the peak load auxiliary service of the photovoltaic power station is: In the formula, The profit of peak load auxiliary service of wind farm; WT The on-grid electricity price for wind power; is the wind farm output power at time t; The cost of peak load sharing of wind farm at time t; is the wind power abandoned at time t; θ is the penalty coefficient for wind and solar power abandonment; Profits from the peak load auxiliary services of photovoltaic power stations; PV The photovoltaic grid-connected electricity price; is the output power of the photovoltaic power station at time t; The peak load sharing cost of the photovoltaic power station at time t; is the abandoned optical power at time t; The profit of peak load ancillary service of thermal power units is: In the formula, Profits from peak load-shaving auxiliary services for thermal power units; is the deep peak regulation compensation cost of the i-th thermal power unit; The deep peak regulation cost of thermal power units; They are the loss cost and oil input cost of deep peak regulation of thermal power units respectively; The coal-saving benefits of thermal power units due to deep peak regulation.

6. The method for optimizing multi-peak load-shaving auxiliary services supported by electric vehicle virtual energy storage according to claim 1 is characterized in that: The objective function of the power system peak load optimization model is: min f=C VES +C MT +C CUR ; In the formula, C VES Compensation for peak load ancillary services obtained by virtual energy storage for electric vehicles; C MT is the comprehensive operating cost of thermal power units; C CUR Penalties for renewable energy abandonment.

7. The method for optimizing multi-peak load-shaving auxiliary services supported by virtual energy storage for electric vehicles according to claim 6 is characterized in that: The constraints of the power system peak load optimization model include power balance constraints, thermal power unit output constraints and line transmission capacity constraints.

8. An electric vehicle virtual energy storage supporting multi-peak load auxiliary service optimization device, characterized in that: The device is applied to the method described in any one of claims 1 to 7, and the device comprises: The electric vehicle virtual energy storage peak-shaving auxiliary service model construction module is used to dispatch electric vehicles to charge in an orderly manner to participate in peak-shaving according to the charging urgency of electric vehicles, obtain peak-shaving auxiliary service compensation fees, and ensure that the charging amount of electric vehicles remains unchanged before and after scheduling; A module for building a multi-element peak-shaving auxiliary service cost sharing model is used to share the peak-shaving auxiliary service compensation costs of thermal power units and electric vehicle virtual energy storage as peak-shaving suppliers, taking wind farms and photovoltaic power stations as peak-shaving demand parties; The power system peak-shaving optimization model building module is used to establish a power system peak-shaving optimization model with the goal of minimizing the power system operating cost including the peak-shaving auxiliary service compensation fee of electric vehicle virtual energy storage, the comprehensive operating cost of thermal power units and the penalty for renewable energy abandonment.

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