Novel electric power spot market operation method

Abstract

The invention discloses a novel electric power spot market operation method, and belongs to the field of electric power system operation and control. Electric vehicle clusters are equivalently regarded as energy storage power stations, and electric vehicles and the energy storage power stations are comprehensively considered; according to the real-time electricity price under the participation of the electric motor car, the capacity change of the energy storage power station, the operation mechanism of the frequency modulation market and the operation mechanism of the peak regulation market, the minimum scheduling cost under the participation of the electric motor car in the electric power spot market, the minimum energy consumption cost of the thermal power generating unit, the minimum frequency modulation operation cost and the minimum peak regulation operation cost are obtained, and the above costs are analyzed. According to the method, an electric power spot market operation scheme with the minimum cost sum is searched, and energy scheduling is performed on each unit in the electric power spot market based on the operation scheme, so that the problem of too high operation cost of the electric power spot market with participation of electric vehicles is solved, resource scheduling is more flexible, the pressure of resource scheduling is reduced, and the resource scheduling efficiency is improved. And the resource utilization rate is further improved.

Description

Technical Field

[0001] The present invention relates to the field of power system operation and control, and in particular to a novel power spot market operation method. Background Art

[0002] Nowadays, clean energy has gradually become the main source of energy growth and optimized the industrial structure. However, clean energy represented by wind power and photovoltaic power is random, volatile, and intermittent. In addition, with the increasing development of offshore wind power in the future, the impact of its anti-peak characteristics will further increase. It is difficult for the new power market that only participates in the dispatch of traditional thermal power units to achieve a balance between power supply and demand, which puts pressure on the flexible dispatch of the power grid. Considering the rapid growth of energy storage power stations and electric vehicles in the future, only through the centralized application of single energy storage to participate in the power spot market will gradually fail to adapt to the development of diversified power market entities. Fully tapping the regulation capabilities of different market entities has become an important factor in the operation of the new power spot market. To this end, by analyzing and summarizing the energy storage application scenarios and market characteristics designed by different scholars at home and abroad, a solution for energy storage power stations and electric vehicles to participate in the power spot market is proposed. It is necessary to model electric vehicles and energy storage power stations on the basis of considering different energy storage characteristics, and to build a joint operation mechanism for the spot electricity and ancillary service market under the participation of electric vehicles and energy storage, so as to provide solutions for different entities to participate in the spot electricity market and the ancillary service market.

[0003] Chinese patent, publication number: CN115689607A, publication date: February 3, 2023, discloses a method for operating an energy storage power station and participating in the electricity spot market. The method first generates a day-ahead market settlement electricity price forecast curve based on the day-ahead market electricity price forecast curve and the day-ahead market settlement rules; then generates a future day full-full-full-release arbitrage sequence combination set of the energy storage power station based on the day-ahead market settlement electricity price forecast curve; finally, the full-full-full-release arbitrage sequence combination with the highest arbitrage difference is selected from the future day full-full-release arbitrage sequence combination set as the target full-full-full-release arbitrage sequence combination of the energy storage power station in the future day, and controls the energy storage power station to operate according to the target full-full-full-release arbitrage sequence combination, but does not take into account the impact of electric vehicles on the electricity spot market. Summary of the invention

[0004] The present invention aims at the problems of high operating cost and great resource dispatching pressure caused by the participation of electric vehicles in the existing electricity spot market, and proposes a novel electricity spot market operation method. By treating the electric vehicle cluster as equivalent to an energy storage power station, the electric vehicles and the energy storage power station are comprehensively considered together, and the minimum dispatching cost, the minimum energy consumption cost of thermal power units, the minimum frequency regulation operation cost, and the minimum peak-shaving operation cost under the participation of electric vehicles in the electricity spot market are obtained according to the real-time electricity price under the participation of electric locomotives, the capacity change of the energy storage power station, the operation mechanism of the frequency regulation market, and the operation mechanism of the peak-shaving market. The above costs are analyzed to find an electricity spot market operation plan with the minimum sum of the above costs, and energy dispatch is performed on each unit in the electricity spot market based on the operation plan, which solves the problem of excessively high operating cost of the electricity spot market under the participation of electric vehicles, makes resource dispatch more flexible, reduces the pressure of resource dispatch, and further improves resource utilization.

[0005] In a first aspect, a technical solution provided in an embodiment of the present invention is: a novel power spot market operation method, comprising the following steps: S1. Construct an electric vehicle dispatching model based on the driving characteristics of electric vehicles; construct an energy storage power station dispatching model based on the operating characteristics of energy storage power stations; S2. Obtain the total power consumption of electric vehicles and the remaining battery power based on the electric vehicle scheduling model, update the electricity price based on the total power consumption of electric vehicles and the remaining battery power and consider the daily load fluctuation to obtain the real-time electricity price; obtain the capacity change of the energy storage power station during the scheduling process based on the energy storage power station scheduling model; S3. Construct a spot electricity market model based on real-time electricity prices, capacity changes during the energy storage power station dispatch process, the operating mechanism of the frequency regulation market, and the operating mechanism of the peak regulation market to obtain the minimum dispatch cost under the participation of electric vehicles; obtain the minimum energy consumption cost of thermal power units based on the minimum dispatch cost under the participation of electric vehicles; S4. Analyze the frequency regulation market characteristics of energy storage power stations and thermal power units to obtain the minimum frequency regulation operation cost with the participation of energy storage power stations and thermal power units; analyze the peak regulation market characteristics of thermal power units to obtain the minimum peak regulation operation cost with the participation of thermal power units; S5. With the goal of minimizing the sum of the minimum dispatching cost, the minimum energy consumption cost of thermal power units, the minimum frequency regulation operation cost, and the minimum peak regulation operation cost, the electricity spot market model is solved to obtain the electricity spot market operation plan.

[0006] In this scheme, in order to obtain the operation scheme with the minimum operation cost of the electricity spot market under the participation of electric vehicles, it is necessary to first analyze the energy storage equipment in the electricity spot market. Electric vehicles can be equivalent to energy storage power stations to a certain extent, but electric vehicles have driving characteristics and randomness compared to energy storage power stations, which are different from static energy storage power stations. Therefore, an electric vehicle scheduling model is constructed to analyze the changes in electricity prices under the participation of electric vehicles, so as to facilitate the subsequent calculation of the operation cost of the electricity spot market based on the changes in electricity prices; in order to facilitate the subsequent calculation of the frequency regulation market operation cost and peak regulation operation cost, an energy storage power station scheduling model is constructed to obtain the capacity changes of the energy storage power station during the scheduling process, and based on the capacity changes of the energy storage power station, the subsequent thermal power units and the operation of the electricity spot market under the participation of electric vehicles are analyzed. Analysis can reduce operating costs while ensuring stable operation of the electricity spot market. In order to obtain the minimum dispatching cost under the participation of electric vehicles, the electric vehicle power consumption, energy storage power station capacity changes and electricity prices obtained in the electric vehicle dispatching model are considered, and the minimum dispatching cost is calculated through comprehensive analysis. Based on the minimum dispatching cost, the energy consumption of thermal power units is analyzed to obtain the minimum energy consumption cost. Then, the thermal power units are frequency-regulated and peak-regulated to obtain the minimum frequency-regulation operating cost and the minimum peak-regulation operating cost. The above costs are comprehensively analyzed to obtain the electricity spot market operation plan with the minimum total cost. Based on the plan, energy dispatch is performed on each unit in the electricity spot market, which can significantly reduce the operating cost of electric vehicles participating in the electricity spot market, make resource dispatch more flexible, reduce the pressure of resource dispatch, and further improve resource utilization.

[0007] As a preferred embodiment, in S1, the formula of the electric vehicle scheduling model is expressed as follows: in, is the maximum number of electric vehicles stopped in time period t, P t is the probability of electric vehicles stopping in period t, N is the total number of electric vehicles in the planning area, E dpc (t) is the total power consumption of all electric vehicles during the period t, R d (t) is the driving distance of the electric vehicle during period t, PC EV is the power consumption of electric vehicles per 100 kilometers, f(R d ) is the daily driving pattern distribution of electric vehicles, μ D represents the mathematical expectation, σ D represents the standard deviation, E EV (t) is the remaining battery capacity of the electric vehicle at time t, η C is the charging efficiency of electric vehicles, η D is the discharge efficiency of the electric vehicle, is the charging power of the electric vehicle during period t, is the discharge power of the electric vehicle during period t.

[0008] In this scheme, since electric vehicles can be regarded as equivalent to energy storage power stations in the electricity spot market, but they have characteristics such as driving characteristics and randomness, which are different from the static energy storage power stations. Therefore, it is necessary to analyze the impact of electric vehicles on the electricity spot market. Since electric vehicles are in a parked state most of the time in a day, the number of electric vehicles that can be dispatched in each time period is calculated through the probability distribution of electric vehicle parking, and then the total power consumption of electric vehicles in the corresponding time period and the remaining battery power are calculated according to the maximum number of electric vehicles traveling at each moment. This can be analogous to the charging and discharging amount and remaining power of the energy storage power station. Electric vehicles play the role of energy storage power stations. Compared with the construction of energy storage power stations, the cost is smaller and more energy can be dispatched, thereby reducing the operating cost of the electricity spot market.

[0009] As a preferred embodiment, in S1, the formula of the energy storage power station scheduling model is as follows: Among them, E EES,FM (t) is the remaining capacity of the energy storage power station during the dispatching process, n C,t is the binary variable for switching the charging state of the energy storage power station, n D,t is a binary variable for switching the discharge state of the energy storage power station, For the charging efficiency of energy storage power stations, is the discharge efficiency of the energy storage power station, P i ESS,up (t) is the frequency regulation output of the energy storage power station in period t, P i ESS,down (t) is the frequency regulation output of the energy storage power station in period t.

[0010] In this scheme, since electric vehicles cannot completely replace the role of energy storage power stations in the electricity spot market, it is still necessary to analyze the energy storage power stations. The remaining capacity of the energy storage power stations in each time period is calculated through the maximum and minimum capacities allowed by the energy storage power stations during the scheduling process and the up and down frequency modulation outputs in each time period. In the electricity spot market, it is only necessary to keep the remaining capacity of the energy storage power station within a reasonable range to avoid accelerated damage to the energy storage power station due to deep charging and discharging. Energy storage power stations are built within this range, and the rest of the energy is stored and scheduled through electric vehicles. This can reduce the construction cost of the energy storage power station, thereby reducing the operating cost of the electricity spot market.

[0011] Preferably, in S2, the total power consumption of the electric vehicle and the remaining battery power are obtained based on the electric vehicle scheduling model, and the electricity price is updated based on the total power consumption of the electric vehicle and the remaining battery power and taking into account daily load fluctuations to obtain the real-time electricity price, including the following steps: Setting electric vehicle dispatch model constraints for the electric vehicle dispatch model, wherein the electric vehicle dispatch model constraints at least include electric vehicle charging and discharging total amount constraints, electric vehicle charging and discharging power constraints in different time periods, electric vehicle charge state constraints, and time-of-use electricity price constraints; Based on the constraints of the electric vehicle scheduling model, the electric vehicle scheduling model is solved to obtain the total power consumption of the electric vehicle and the remaining battery power; The total power consumption of electric vehicles, the remaining battery power, and the daily load fluctuations are analyzed, and the peak-valley flat pricing of electricity prices is carried out by dynamically dividing the different peak and valley periods of the day. The formula is as follows: Among them C t is the electricity price, c p is the peak electricity price, c f is the flat electricity price, c v is the valley electricity price, P av is the load mean, P ptv is the peak-to-valley difference of equivalent load, and ω is the fluctuation range of electricity price during peak and valley periods.

[0012] In this plan, in order to allow electric vehicles to participate in the electric energy market, time-of-use electricity prices are formulated to allow electric vehicles to participate. Since electric vehicles are mobile energy storage devices on the user side, compared with energy storage power stations, there is no need to consider cost recovery issues. The use of electric vehicles to participate in energy storage scheduling can utilize electricity price differences to achieve arbitrage, peak shaving and valley filling, and reduce energy abandonment. The purpose also reduces the demand for deep peak regulation, and the improvement effect increases with the increase in the number of electric vehicles participating in the electricity spot market. The output of deep peak regulation units can be reduced from the source, which not only improves the energy utilization efficiency, but also reduces the operating cost of the electricity spot market.

[0013] Preferably, in S2, obtaining the capacity change in the energy storage power station scheduling process based on the energy storage power station scheduling model includes the following steps: Setting energy storage power station scheduling model constraints for the energy storage power station scheduling model, wherein the energy storage power station scheduling model constraints at least include energy storage power station SOC initial and final state constraints, energy storage power station charging and discharging state constraints, and energy storage power station up and down frequency regulation state constraints; Based on the constraints of the energy storage power station scheduling model, the energy storage power station scheduling model is solved to obtain the remaining capacity in the energy storage power station scheduling process.

[0014] In this scheme, in order to calculate the capacity change of the energy storage power station during the dispatching process, and to ensure that the energy storage power station will not be damaged rapidly due to deep charging and discharging during the dispatching process, the energy storage power station SOC initial and final state constraints are constructed by making the SOC state of the energy storage power station equal at the beginning and end of the day. Then, the charging, discharging and frequency modulation states of the energy storage power station are analyzed and constrained to calculate the remaining capacity of the energy storage power station during the dispatching process. Under the premise that the energy storage power station can work normally, the layout of the energy storage power station is reduced, the construction cost of the energy storage power station is reduced, and thus the operating cost of the electricity spot market is reduced.

[0015] As a preferred method, in S3, a power spot market model is constructed based on the real-time electricity price, the capacity change during the energy storage power station dispatching process, the operating mechanism of the frequency regulation market, and the operating mechanism of the peak regulation market to obtain the minimum dispatching cost under the participation of electric vehicles, including the following steps: Setting wind power output constraints, wherein the wind power output constraints at least include upper and lower limit loads of wind power output and equivalent load variance constraints; The charging and discharging amount of electric vehicles and the remaining power of electric vehicles are calculated based on the electric vehicle scheduling model, and the minimum scheduling cost under the participation of electric vehicles is calculated with wind power output constraints as constraints.

[0016] In this scheme, since electric vehicle users are parked 90% of the time in a day, their application characteristics are roughly complementary to the output of wind power, that is, at night when wind power is high, most users charge their vehicles in a concentrated manner; at noon when wind power is low, most users stop driving and discharge their vehicles, which can better absorb wind power. In order to obtain the minimum dispatching cost of energy dispatching with the participation of electric vehicles, the wind power operation and maintenance costs, wind abandonment penalty costs, vehicle group battery aging costs, charging costs and discharge compensation costs are analyzed, and then the upper and lower limits of wind power output are constrained. Then, the charge and discharge amount of electric vehicles is analyzed to obtain the load demand of the power grid in various time periods, and then the minimum dispatching cost of energy dispatching with the participation of electric vehicles is calculated based on the load demand and the electricity price. By combining electric vehicles with wind power, the load curve of the power grid can be smoothed and valley filled, which can reduce energy abandonment and improve energy utilization.

[0017] Preferably, in S3, obtaining the minimum energy consumption cost of the thermal power unit based on the minimum dispatching cost under the participation of electric vehicles includes the following steps: Based on the wind power output constraint and equivalent load variance constraint in the minimum dispatch cost under the participation of electric vehicles, an optimization curve of wind power output equivalent load is constructed, and the quotation curve of thermal power units in the electric energy market is obtained based on the optimization curve of wind power output equivalent load; the operation constraints of thermal power units and the power supply and demand balance constraints in the electric energy market are set, and the minimum energy consumption cost of thermal power units is obtained based on the operation constraints of thermal power units and the power supply and demand balance constraints in the electric energy market.

[0018] In this scheme, in order to obtain the minimum energy consumption cost of the thermal power unit, where the energy consumption cost includes startup cost, no-load cost, shutdown cost, fuel cost and market electricity purchase cost, the goal is to minimize the energy consumption cost of the thermal power unit. Based on the wind power output equivalent load optimization curve, the quotation curve of the thermal power unit in the electric energy market is obtained, and the upper and lower limits of the output of the thermal power unit in each time period are constrained. Under the premise of ensuring the positive profit of the thermal power unit, the operating reserve capacity constraint of the thermal power unit, the continuous start / stop time constraint of the thermal power unit, the start / stop cost constraint, the climbing / slipping constraint and the operating electricity purchase cost constraint are set. Based on these constraints, the minimum energy consumption cost of the thermal power unit is calculated to ensure that the thermal power unit can operate optimally and economically, effectively absorb wind power, improve energy utilization efficiency, and reduce energy abandonment.

[0019] As a preferred embodiment, in S4, the frequency regulation market characteristics of the energy storage power station and the thermal power unit are analyzed to obtain the minimum frequency regulation operation cost under the participation of the energy storage power station and the thermal power unit, including the following steps: Set frequency regulation demand constraints and frequency regulation operation constraints for thermal power units; Based on the frequency regulation service costs of thermal power units, the operation and maintenance costs of thermal power units, the frequency regulation service costs of energy storage power stations, the operation service costs of energy storage power stations and the life cycle costs, and with the frequency regulation demand constraints and the frequency regulation operation constraints of thermal power units as constraints, the minimum frequency regulation operation cost with the participation of energy storage power stations and thermal power units is calculated.

[0020] In this scheme, in order to analyze the frequency regulation market and obtain the minimum frequency regulation operation cost, the thermal power units are taken as the main body to construct the frequency regulation demand constraints and the frequency regulation operation constraints of the thermal power units. Based on the above constraints, the up / down frequency regulation status, up / down frequency regulation capacity, up / down frequency regulation electricity price of the thermal power units, the income of the thermal power units participating in the electric energy market without reserving frequency regulation capacity, and the income obtained after reserving a certain frequency regulation capacity are comprehensively considered to calculate the minimum frequency regulation operation cost under the participation of the thermal power units. In the frequency regulation market, the market regulation effect of the thermal power units participating in the electricity spot market in coordination with energy storage power stations and electric vehicles is better than that of the single type of energy storage participating. By analyzing the characteristics and combinations of different types of energy storage, it is possible to provide high-quality flexible regulation resources for the electricity spot market and fully tap the scheduling potential of energy storage resources.

[0021] As a preferred embodiment, in S4, the peak load regulation market characteristics of the thermal power units are analyzed to obtain the minimum peak load regulation operation cost under the participation of the thermal power units, including the following steps: Set peak load demand constraints, peak load operation constraints of thermal power units, and coupling constraints of thermal power units; Based on the fuel cost, life loss cost, oil investment cost, pollution emission cost, operation and maintenance cost of deep peak-shaving of thermal power units, and the payment cost of deep peak-shaving of power system, and with the peak-shaving demand constraint, the peak-shaving operation constraint of thermal power units and the coupling constraint of thermal power units as constraints, the minimum peak-shaving operation cost of thermal power units is calculated.

[0022] In this plan, in order to analyze the peak-shaving market and obtain the minimum peak-shaving operating cost, thermal power units are taken as the main body to construct peak-shaving demand constraints, peak-shaving operating constraints of thermal power units and coupling constraints of thermal power units. Based on the above constraints, the deep peak-shaving fuel cost, life loss cost (deep peak-shaving without oil and with oil), oil investment cost, pollution emission cost, operation and maintenance cost and deep peak-shaving payment cost of thermal power units are comprehensively analyzed to calculate the minimum peak-shaving operating cost of thermal power units; the peak-shaving of thermal power units includes three stages: conventional peak-shaving, peak-shaving without oil investment and peak-shaving with oil investment. When there is wind abandonment, thermal power units can be appropriately allowed to reduce the minimum technical output limit to absorb wind power. This process includes the minimum stable combustion output without oil investment and the maximum output with oil investment. Compared with conventional peak-shaving, the life loss cost, oil investment cost and the pollution cost attached to the oil investment output are increased. Therefore, in order to maximize the absorption of wind power, these increased costs must be taken into account.

[0023] The beneficial effects of the present invention are as follows: (1) The present invention uses energy storage power stations to coordinate thermal power units and electric vehicles to participate in the electricity spot market. Compared with the electricity spot market in which a single type of energy storage participates, the market regulation effect of the present invention using multiple types of energy storage participating is more excellent; (2) By analyzing the characteristics and combinations of different types of energy storage, the present invention can make the electricity spot market more flexible in regulating resources, reduce the pressure of resource scheduling, and further improve resource utilization; (3) The present invention helps optimize the resource allocation of the power system, alleviate the scheduling pressure of traditional thermal power units, promote the consumption of wind power, reduce energy waste, and improve resource utilization by building a power spot market that includes energy storage power stations, electric vehicles and traditional energy.

[0024] The above invention content is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0026] Figure 1 This is a flow chart of a novel power spot market operation method of the present invention; Figure 2 The load, wind power prediction and equivalent load curve diagram in Example 1 of the present invention; Figure 3 This is a comparison diagram of equivalent load curves at different scales in Example 1 of the present invention; Figure 4 This is a schematic diagram of the charging and discharging power at a scale of 500,000 electric vehicles in Example 1 of the present invention; Figure 5 This is a comparison diagram before and after equivalent load constraint in Example 1 of the present invention; Figure 6 This is a diagram showing the impact of a single charge and discharge change of an energy storage power station on the charge and discharge behavior of a frequency modulation market in Example 1 of the present invention; Figure 7 This is a charge-discharge and charge state diagram of a 20MW energy storage power station for a single charge-discharge in Example 1 of the present invention; Figure 8 This is a diagram showing the influence of the energy storage charging and discharging price difference on the charging and discharging behavior in Example 1 of the present invention; Fig. 9 This is a comparison diagram of equivalent load curves under different modes in Example 1 of the present invention; Fig.10 This is a graph showing the wind abandonment volume with and without the influence of electric vehicles in Example 1 of the present invention. DETAILED DESCRIPTION

[0027] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0029] Example: Figure 1As shown, in order to solve the problem of high operating costs and high resource scheduling pressure caused by the participation of electric vehicles in the existing electricity spot market, an embodiment of the present invention provides a new electricity spot market operation method, including the following steps: S1: Build an electric vehicle dispatching model based on the driving characteristics of electric vehicles; build an energy storage power station dispatching model based on the operating characteristics of the energy storage power station.

[0030] In this embodiment, the formula of the electric vehicle scheduling model is as follows: in, is the maximum number of electric vehicles stopped in time period t, P t is the probability of electric vehicles stopping in period t, N is the total number of electric vehicles in the planning area, E dpc (t) is the total power consumption of all electric vehicles during the period t, R d (t) is the driving distance of the electric vehicle during period t, PC EV is the power consumption of electric vehicles per 100 kilometers, f(R d ) is the daily driving pattern distribution of electric vehicles, μ D represents the mathematical expectation, σ D represents the standard deviation, E EV (t) is the remaining battery capacity of the electric vehicle at time t, η C is the charging efficiency of electric vehicles, η D is the discharge efficiency of the electric vehicle, is the charging power of the electric vehicle during period t, is the discharge power of the electric vehicle during period t.

[0031] In this embodiment, electric vehicles can be regarded as equivalent to energy storage power stations in the electricity spot market, but they have characteristics such as driving characteristics and randomness, which are different from the static energy storage power stations. Therefore, it is necessary to analyze the impact of electric vehicles on the electricity spot market. Since electric vehicles are in a stopped state most of the time in a day, the number of electric vehicles that can be dispatched in each time period is calculated through the probability distribution of electric vehicle stopping, and then the total power consumption of electric vehicles in the corresponding time period and the remaining battery power are calculated according to the maximum number of electric vehicles traveling at each moment. This can be compared to the charging and discharging amount and the remaining power of the energy storage power station, but the electric vehicles play the role of the energy storage power station, which is less expensive than building an energy storage power station and can dispatch more energy, thereby reducing the operating cost of the electricity spot market.

[0032] In this embodiment, the formula of the energy storage power station scheduling model is as follows: Among them, E EES,FM(t) is the remaining capacity of the energy storage power station during the dispatching process, n C,t is the binary variable for switching the charging state of the energy storage power station, n D,t is a binary variable for switching the discharge state of the energy storage power station, For the charging efficiency of energy storage power stations, is the discharge efficiency of the energy storage power station, P i ESS,up (t) is the frequency regulation output of the energy storage power station in period t, P i ESS,down (t) is the frequency regulation output of the energy storage power station in period t.

[0033] In this embodiment, since electric vehicles cannot completely replace the role of energy storage power stations in the electricity spot market, it is still necessary to analyze the energy storage power stations. The remaining capacity of the energy storage power stations in each time period is calculated through the maximum and minimum capacities allowed by the energy storage power stations during the scheduling process and the up and down frequency modulation outputs in each time period. In the electricity spot market, it is only necessary to keep the remaining capacity of the energy storage power station within a reasonable range to avoid accelerated damage to the energy storage power station due to deep charging and deep discharging. Energy storage power stations are built within this range, and the rest of the energy is stored and scheduled through electric vehicles. This can reduce the construction cost of the energy storage power station, thereby reducing the operating cost of the electricity spot market.

[0034] S2: Based on the electric vehicle scheduling model, the total power consumption of the electric vehicle and the remaining battery power are obtained. Based on the total power consumption of the electric vehicle and the remaining battery power and taking into account the daily load fluctuations, the electricity price is updated to obtain the real-time electricity price; based on the energy storage power station scheduling model, the capacity changes during the energy storage power station scheduling process are obtained.

[0035] In this embodiment, the total power consumption of the electric vehicle and the remaining battery power are obtained based on the electric vehicle scheduling model, and the electricity price is updated based on the total power consumption of the electric vehicle and the remaining battery power and taking into account the daily load fluctuation to obtain the real-time electricity price, including the following steps: The electric vehicle scheduling model constraint conditions are set for the electric vehicle scheduling model. The electric vehicle scheduling model constraint conditions at least include the total amount constraint of electric vehicle charging and discharging, the charging and discharging power constraint of electric vehicles in different time periods, the state of charge constraint of electric vehicles and the time-of-use electricity price constraint. The formulas are as follows: C min ≤C t ≤C max in is the total daily charging volume of electric vehicles, is the total daily discharge of electric vehicles, E EVA.max(t) is the total capacity of electric vehicles in period t, P EVA,max (t) is the upper limit of the electric vehicle charging and discharging power in period t, which is related to the number of electric vehicles that are not in service. υ is the fixed proportional coefficient between the upper limit of the electric vehicle power and its capacity. EVA is the 0-1 variable for EVA charging and discharging, S max and S min is the state of charge, C max and C min The upper and lower limits of the time-of-use electricity price; Based on the constraints of the electric vehicle scheduling model, the electric vehicle scheduling model is solved to obtain the total power consumption of the electric vehicle and the remaining battery power; The total power consumption of electric vehicles, the remaining battery power, and the daily load fluctuations are analyzed, and the peak-valley flat pricing of electricity prices is carried out by dynamically dividing the different peak and valley periods of the day. The formula is as follows: Among them C t is the electricity price, c p is the peak electricity price, c f is the flat electricity price, c v is the valley electricity price, P av is the load mean, P ptv is the peak-to-valley difference of equivalent load, ω is the fluctuation range of electricity price during peak and valley periods, when the equivalent load value is greater than P av +ωP ptv When the equivalent load value is less than P av -ωP ptv The off-peak electricity price is determined as

[0036] In order to allow electric vehicles to participate in the electric energy market, this embodiment allows electric vehicles to participate by formulating time-of-use electricity prices. Since electric vehicles are mobile energy storage devices on the user side, they do not need to consider cost recovery issues compared to energy storage power stations. The use of electric vehicles to participate in energy storage scheduling can utilize electricity price differences to achieve the purposes of arbitrage, peak shaving and valley filling, and reduce energy abandonment, and also reduce the demand for deep peak regulation. The improvement effect increases with the increase in the number of electric vehicles participating in the electricity spot market, and the output of deep peak regulation units can be reduced from the source, which not only improves the energy utilization efficiency, but also reduces the operating cost of the electricity spot market.

[0037] In this embodiment, obtaining the capacity change during the energy storage power station scheduling process based on the energy storage power station scheduling model includes the following steps: The energy storage power station scheduling model constraint conditions are set for the energy storage power station scheduling model. The energy storage power station scheduling model constraint conditions at least include the energy storage power station SOC initial and final state constraints, the energy storage power station charging and discharging state constraints, and the energy storage power station up and down frequency regulation state constraints. The formula is as follows: in, is the maximum remaining capacity allowed by the energy storage power station during the dispatch process. is the minimum remaining capacity allowed by the energy storage power station during the dispatch process, E EES,FM (t) is the remaining capacity of the energy storage power station during the dispatching process, P i ESS ,up (t) and P i ESS,down (t) are the upper and lower frequency regulation outputs of the energy storage power station during the dispatching process, P i EES,max and P i EES ,min They are the upper and lower limits of the maximum declared capacity of the energy storage power station at a time. and They are the charge states at the initial and end times of the energy storage power station; Based on the constraints of the energy storage power station scheduling model, the energy storage power station scheduling model is solved to obtain the remaining capacity in the energy storage power station scheduling process.

[0038] In order to calculate the capacity change of the energy storage power station during the dispatching process and to ensure that the energy storage power station will not be damaged rapidly due to deep charging and discharging during the dispatching process, this embodiment constructs the SOC start-end state constraint of the energy storage power station by making the SOC state of the energy storage power station equal at the beginning and end of the day, and then analyzes and constrains the charge, discharge and frequency modulation state of the energy storage power station to calculate the remaining capacity of the energy storage power station during the dispatching process, so that the layout of the energy storage power station can be reduced under the premise that the energy storage power station can work normally, thereby reducing the construction cost of the energy storage power station and thus reducing the operating cost of the electricity spot market.

[0039] S3: Construct an electricity spot market model based on real-time electricity prices, capacity changes during the dispatch process of energy storage power stations, the operating mechanism of the frequency regulation market, and the operating mechanism of the peak regulation market to obtain the minimum dispatch cost with the participation of electric vehicles; obtain the minimum energy consumption cost of thermal power units based on the minimum dispatch cost with the participation of electric vehicles.

[0040] In this embodiment, a power spot market model is constructed based on real-time electricity prices, capacity changes during the energy storage power station dispatching process, the operating mechanism of the frequency regulation market, and the operating mechanism of the peak regulation market to obtain the minimum dispatching cost under the participation of electric vehicles, including the following steps: Set wind power output constraints, which at least include upper and lower limit loads of wind power output and equivalent load variance constraints, and the formula is as follows: Among them, P Wind(t) is the actual dispatch output of wind power in period t, and are the upper and lower limits of wind power output, P Load (t) is the load demand in period t, P ave is the equivalent load mean, C is the electricity price; Based on the electric vehicle dispatch model, the charging and discharging amount of electric vehicles and the remaining power of electric vehicles are calculated, and the minimum dispatch cost minF under the participation of electric vehicles is calculated with wind power output constraints as constraints. A , the formula is as follows: in, is the operation and maintenance cost coefficient, λ Wind is the wind abandonment penalty coefficient, is the predicted wind power output in period t, k EV is the unit charge and discharge loss cost of the battery, is the discharge compensation coefficient.

[0041] In this embodiment, since electric vehicle users are parked for 90% of the day, their application characteristics are roughly complementary to the output of wind power, that is, at night when wind power is high, most users charge their vehicles in a concentrated manner; at noon when wind power is low, most users stop driving and discharge their vehicles, which can better absorb wind power. In order to obtain the minimum dispatching cost for energy dispatching with the participation of electric vehicles, the wind power operation and maintenance costs, wind abandonment penalty costs, vehicle group battery aging costs, charging costs and discharge compensation costs are analyzed, and then the upper and lower limits of wind power output are constrained, and then the charge and discharge amount of electric vehicles is analyzed to obtain the load demand of the power grid in each period, and then the minimum dispatching cost for energy dispatching with the participation of electric vehicles is calculated based on the load demand and the electricity price. By combining electric vehicles with wind power, the load curve of the power grid can be smoothed and valley filled, which can reduce energy abandonment and improve energy utilization.

[0042] In this embodiment, obtaining the minimum energy consumption cost of a thermal power unit based on the minimum dispatching cost under the participation of electric vehicles includes the following steps: Based on the wind power output constraint and equivalent load variance constraint in the minimum dispatch cost under the participation of electric vehicles, the wind power output equivalent load optimization curve is constructed, and the quotation curve of the thermal power unit in the electric energy market is obtained based on the wind power output equivalent load optimization curve; the operation constraints of the thermal power unit are set, and the formula is expressed as follows: Among them, P i TPU,max (t) and P i TPU,min(t) represents the upper and lower limits of the output of the thermal power unit numbered i in time period t, ρ by P dx (t) represents the positive and negative operating reserve capacity that the system needs to reserve during period t, ρ by is the operation reserve factor, T i TPU,on and T i TPU,off are the start / stop time of the thermal power unit numbered i in time period t, T i on (t) and T i off (t) are the minimum start / stop time of the thermal power unit numbered i in time period t, and are the minimum start / stop costs of the thermal power unit numbered i in time period t, U i (t) is a 0 / 1 variable to determine the unit operating status, P i TPU,u and P i TPU,d are the maximum climbing / slipping rates of thermal power unit i, is the revenue of thermal power unit i in period t, for The first derivative of ; Assuming the power supply and demand balance constraint in the electric energy market, the formula is as follows: Among them, P i TPU (t) represents the output of the thermal power unit numbered i in time period t Obtain the minimum energy cost minF of thermal power units based on the operation constraints of thermal power units and the power supply and demand balance constraints of the electric energy market B , the formula is as follows: Among them, f i TPU (P i TPU (t)) is the price quote curve of the thermal power unit numbered i in the electric energy market, is the startup cost of thermal power units, is the no-load cost of thermal power units, is the shutdown cost of thermal power units, is the fuel cost of thermal power units.

[0043] In order to obtain the minimum energy consumption cost of the thermal power unit, the energy consumption cost includes startup cost, no-load cost, shutdown cost, fuel cost and market electricity purchase cost, and to minimize the energy consumption cost of the thermal power unit, this embodiment obtains the quotation curve of the thermal power unit in the electric energy market based on the wind power output equivalent load optimization curve, constrains the upper and lower limits of the output of the thermal power unit in each time period, and sets the operating reserve capacity constraint of the thermal power unit, the continuous start / stop time constraint of the thermal power unit, the start / stop cost constraint, the climbing / slipping constraint and the operating electricity purchase cost constraint on the premise of ensuring the positive profit of the thermal power unit. Based on these constraints, the minimum energy consumption cost of the thermal power unit is calculated to ensure that the thermal power unit can operate optimally and economically, effectively absorb wind power, improve energy utilization efficiency, and reduce energy abandonment.

[0044] S4: Analyze the frequency regulation market characteristics of energy storage power stations and thermal power units to obtain the minimum frequency regulation operating cost with the participation of energy storage power stations and thermal power units; analyze the peak regulation market characteristics of thermal power units to obtain the minimum peak regulation operating cost with the participation of thermal power units.

[0045] In this embodiment, the frequency regulation market characteristics of energy storage power stations and thermal power units are analyzed to obtain the minimum frequency regulation operation cost under the participation of energy storage power stations and thermal power units, including the following steps: Set the frequency modulation demand constraint, the formula is as follows: Among them, P i TPU* (t) The winning bid output of the thermal power unit numbered i in the electric energy market is used to update the capacity range of the thermal power units participating in the electric energy market and participating in the frequency regulation market. and are the upward and downward frequency modulation requirements in period t, and is a 0-1 variable, representing the winning state of the up / down frequency regulation of the thermal power unit numbered i, P i TPU,u (t) and P i TPU,d (t) are the up / down frequency regulation capacity of the thermal power unit numbered i, P i TPU,max (t) is the upper limit of the frequency regulation capacity of the thermal power unit numbered i, P i TPU,min (t)P i TPU,max (t) is the lower limit of the frequency regulation capacity of the thermal power unit numbered i; The frequency regulation operation constraints of thermal power units are set, and the formula is as follows: in, Representing thermal power units that won bids in the electric energy market; Based on the frequency regulation service cost of thermal power units, the operation and maintenance cost of thermal power units, the frequency regulation service cost of energy storage power stations, the operation service cost of energy storage power stations and the life cost, and with the frequency regulation demand constraint and the frequency regulation operation constraint of thermal power units as the constraints, the minimum frequency regulation operation cost minF under the participation of energy storage power stations and thermal power units is calculated. C , the formula is as follows: in, and They are the up / down frequency adjustment electricity prices of thermal power units, is the unit operation and maintenance factor, is the daily frequency regulation capacity of thermal power units, and They are respectively the income of thermal power units participating in the electricity energy market without reserving frequency regulation capacity and the income obtained after reserving a certain frequency regulation capacity.

[0046] In order to analyze the frequency regulation market and obtain the minimum frequency regulation operation cost, this embodiment takes thermal power units as the main body, constructs frequency regulation demand constraints and thermal power unit frequency regulation operation constraints, and based on the above constraints, comprehensively considers the up / down frequency regulation state, up / down frequency regulation capacity, up / down frequency regulation electricity price, the income of the thermal power unit participating in the electric energy market without reserving frequency regulation capacity, and the income obtained after reserving a certain frequency regulation capacity, and calculates the minimum frequency regulation operation cost with the participation of the thermal power unit. In the frequency regulation market, the market regulation effect of thermal power units participating in the electric power spot market in coordination with energy storage power stations and electric vehicles is better than that of a single type of energy storage. By analyzing the characteristics and combinations of different types of energy storage, it is possible to provide high-quality flexible regulation resources for the electric power spot market, and fully tap the scheduling potential of energy storage resources.

[0047] In this embodiment, the peak load regulation market characteristics of the thermal power units are analyzed to obtain the minimum peak load regulation operation cost under the participation of the thermal power units, including the following steps: Set the peak demand constraint, the formula is as follows: in, is the deep peak load demand in period t, P Wind* (t) is the winning bid output of the wind turbine in period t, P EV* (t) is the winning bid output of the electric vehicle in period t, P i TPU* (t) is the winning bid output of the thermal power unit during period t; Set the peak load operation constraints of thermal power units and the coupling constraints of thermal power units. The formula is as follows: Among them, Pi DP P is the minimum stable combustion output of the thermal power unit without adding oil. i DPO To provide the thermal power unit with the maximum output of fuel injection and stable combustion. and Respectively represent the winning status of the up and down frequency regulation of thermal power units; Based on the deep peak-shaving fuel cost, life loss cost, oil investment cost, pollution emission cost, operation and maintenance cost and deep peak-shaving payment cost of the power system, and with the peak-shaving demand constraint, the peak-shaving operation constraint of the thermal power unit and the coupling constraint of the thermal power unit as the constraint conditions, the minimum peak-shaving operation cost minF of the thermal power unit is calculated. D , the formula is as follows: Among them, δ is the loss coefficient of the thermal power unit, N t is the number of cycles of rotor cracking in thermal power units, which is related to the output of thermal power units. oil is the fuel consumption, P oil is the oil price, τ is the pollution coefficient, is the operation and maintenance cost coefficient of thermal power units, is the daily peak load capacity of thermal power units, φ i (t) is a 0-1 variable, representing the winning bid status of the thermal power unit participating in peak load regulation in period t; and are the deep peak load shaving quotations of the thermal power unit numbered i in the non-oil and oil-added stages; P i DPn (t) and P i DPo (t) are the winning bid capacity without oil and with oil for the thermal power unit numbered i in period t.

[0048] In order to analyze the peak-shaving market and obtain the minimum peak-shaving operation cost, this embodiment takes thermal power units as the main body, constructs peak-shaving demand constraints, peak-shaving operation constraints of thermal power units and coupling constraints of thermal power units. Based on the above constraints, the deep peak-shaving fuel cost of the thermal power units, life loss cost (deep peak-shaving without oil investment and with oil investment), oil investment cost, pollution emission cost, operation and maintenance cost and payment cost of deep peak-shaving of the power system are comprehensively analyzed to calculate the minimum peak-shaving operation cost of the thermal power units; the peak-shaving of thermal power units includes three stages: conventional peak-shaving, peak-shaving without oil investment and peak-shaving with oil investment. When there is wind abandonment, the thermal power units can be appropriately allowed to reduce the minimum technical output limit to absorb wind power. This process includes the minimum stable combustion output without oil investment and the maximum output with oil investment. Compared with conventional peak-shaving, the life loss cost, oil investment cost and the pollution cost attached to the oil investment output are increased. Therefore, in order to maximize the absorption of wind power, these increased costs must be taken into account.

[0049] S5: With the goal of minimizing the sum of the minimum dispatching cost with the participation of electric vehicles, the minimum energy consumption cost of thermal power units, the minimum frequency regulation operation cost with the participation of energy storage power stations and thermal power units, and the minimum peak regulation operation cost of thermal power units, the electricity spot market model is solved to obtain the electricity spot market operation plan.

[0050] In order to obtain the electricity spot market operation plan with the minimum operation cost, this embodiment first needs to analyze the energy storage equipment in the electricity spot market. Electric vehicles can be equivalent to energy storage power stations to a certain extent, but compared with energy storage power stations, electric vehicles have driving characteristics and randomness, which are different from static energy storage power stations. Therefore, an electric vehicle scheduling model is constructed to analyze the changes in electricity prices under the participation of electric vehicles, so as to facilitate the subsequent calculation of the operation cost of the electricity spot market based on the changes in electricity prices; in order to facilitate the subsequent calculation of the frequency regulation market operation cost and peak regulation operation cost, an energy storage power station scheduling model is constructed to obtain the capacity changes of the energy storage power station during the scheduling process, and based on the capacity changes of the energy storage power station, the subsequent operation of the electricity spot market under the participation of thermal power units and electric vehicles is analyzed. Analysis can reduce operating costs while ensuring stable operation of the electricity spot market. In order to obtain the minimum dispatching cost with the participation of electric vehicles, the electric vehicle power consumption, energy storage power station capacity changes and electricity prices obtained in the electric vehicle dispatching model are considered, and the minimum dispatching cost is calculated through comprehensive analysis. Based on the minimum dispatching cost, the energy consumption of thermal power units is analyzed to obtain the minimum energy consumption cost. Then, the thermal power units are frequency-regulated and peak-regulated to obtain the minimum frequency-regulation operating cost and the minimum peak-regulation operating cost. The above costs are comprehensively analyzed to obtain the electricity spot market operation plan with the minimum total cost. Based on the plan, energy dispatch of each unit in the electricity spot market is carried out, which can significantly reduce the operating cost of the electricity spot market, make resource dispatch more flexible, reduce the pressure of resource dispatch, and further improve resource utilization.

[0051] As a further supplement to this embodiment, the following scenario is taken as an example to further illustrate this solution: This embodiment selects a typical power load curve of a provincial power grid as a reference. The system includes 182 thermal power units with an installed capacity of 106204MW. The load demand, wind power forecast output and initial equivalent load curve are as follows: Figure 2As shown, the wind power forecast deviation is set to 25%; the frequency regulation and deep peak regulation electricity prices of thermal power units refer to the auxiliary service market, the frequency regulation capacity demand is 0.7% of the load, and the quotation types in the deep peak regulation market are classified into two levels: non-oil output and oil-assisted peak regulation output interval, the output range is [40%, 50%) and [30%, 40%) of the rated capacity respectively, and the pricing is based on the unit's quotation curve, and the marginal electricity price is cleared; the spare capacity reserved by the unit in the electric energy market is set to 20% of the rated capacity, and the start-up and shutdown cost ranges are set to 56,000-616,000 yuan and 388,160-426,985 yuan; Set to 0.0946 (yuan / kW); Cap oil , P oil and τ are determined according to the peak-shaving capabilities of different units.

[0052] First, set the parameters of the electric vehicle. The specific parameters are shown in the following table: Set various parameters of the energy storage power station. The specific parameters are shown in the following table: In order to minimize the overall cost of the electric energy market, frequency regulation market and deep peak regulation market, F A 、F B 、F C and F D The four objective functions are solved jointly, and the calculations are made from the two mechanisms of sequential clearing and joint clearing. The relevant costs are shown in the following table: By comparing the two different mechanisms, it is found that the total cost of the sequential clearing mechanism is 0.9% higher than that of the joint clearing, and the difference between the two is not large. Both can achieve the optimal operation of the electricity spot market. The various links of the joint clearing are reduced compared with the sequential clearing, and they have decreased by 8.4%, 1.2%, 2.9% and 7.6% respectively, indicating that the joint clearing mechanism is better in terms of equivalent load optimization cost and deep peak regulation cost. However, the current electricity spot market is in the early stages of construction and there are no clear market operation constraints. The line constraints and complexity involved in the joint operation mechanism are much more than those of the sequential clearing. For this reason, this embodiment should ensure the operating efficiency of various market entities by clarifying the clearing mechanisms of various markets.

[0053] Take the example of electric vehicles of different sizes participating in the electric energy market and adopting a sequential clearing mechanism. Figure 3As shown in Figure 2, by comparing the optimization effects of 0, 100,000, 200,000, 300,000, 400,000, and 500,000 electric vehicles participating in the power grid, it is found that the expansion of the scale of electric vehicles can effectively smooth the load curve. The EVA load variance of the 500,000 scale is reduced by 49.56%, 44.19%, 36.82%, 26.89%, 15.56%, and 25.11% respectively compared with the other four scales and disordered electric vehicles, and the peak-to-valley difference is reduced by 43.13%, 39.10%, 30.09%, 21.03%, 9.27%, and 61.22% respectively. The charging and discharging capacity of electric vehicles of this scale is shown in Figure 2. Figure 4 As shown in the figure, in this mode, electric vehicles are charged and discharged in a centralized manner, and the timing of charging and discharging is related to the personal living habits of electric vehicle users and the anti-peak characteristics of wind power output, which proves the rationality of this mode; Figure 5 The figure shows the comparison before and after with / without equivalent load variance constraint. It can be seen that adding equivalent load variance constraint under the same target can make the equivalent load curve smoother, effectively reduce the pressure of flexible scheduling of thermal power units, and prove the advantages of reasonable scheduling of electric vehicles.

[0054] Take the impact of energy storage power stations on the electricity spot market as an example. Figure 6 As shown, the impact of single charge and discharge changes of energy storage power stations on the charging and discharging behavior of the frequency modulation market is shown. The operating costs of thermal power units, energy storage power stations and total costs are shown in the following table: As the single charge and discharge capacity of energy storage power stations continues to increase, the standard power volume is also increasing, and the operating cost and total cost of thermal power units in the frequency regulation market are constantly decreasing; Figure 7 As shown in the figure, the charge and discharge and charge state diagram of a 20MW energy storage power station with a single charge and discharge is given to ensure the charge state from the beginning to the end of the dispatch; Figure 8 As shown in the figure, the impact of the bidding behavior of the energy storage power station on its charging and discharging behavior is analyzed. When the price difference is changed within 120%, the charging and discharging amount of the energy storage power station is not affected. When the charging electricity price reported by the energy storage power station is as high as 120% of the benchmark electricity price, the reported price difference reaches the maximum, which can ensure the maximum profit of the energy storage power station. When the price difference of the energy storage power station reaches 130%, the frequency regulation market no longer selects the energy storage power station. Therefore, when the price difference of the energy storage power station reaches a certain level, the cost of using thermal power units as the main body of frequency regulation is less than that of the energy storage power station. Therefore, when the price difference of the energy storage power station reaches 130%, thermal power units are selected as the main body of frequency regulation.

[0055] In this embodiment, the following four scenarios are assumed to be comparatively analyzed with the background that the energy storage power station does not participate in multiple markets: Scenario 1: sequential clearing of the electricity spot market with / without the participation of electric vehicles (mode 1 / mode 1 (no electric vehicles) / mode 1 (disordered electric vehicle clusters)); Scenario 2: Sequential clearing of the electricity spot market with / without energy storage (Mode 1 / Mode 2 (without energy storage)); the specific data is shown in the following table, where the electricity energy market includes equivalent load optimization costs and thermal power units and system operation costs: By comparing the modes in scenario 1, it is found that when electric vehicles participate in the electricity energy market, the total cost increases slightly compared with the mode without electric vehicles. The increase in cost mainly comes from the operating cost of the electricity energy market, but the deep peak-shaving cost is reduced by 9.7% compared with the mode without electric vehicles. This shows that the mode without electric vehicles does not have the existence of flexible energy storage, and the flexibility of thermal power units is poor. In order to absorb wind power, thermal power units actively reduce their output range, which leads to a decrease in the participation in the electricity energy market and increases the operating cost of deep peak-shaving. Mode 1 (disordered electric vehicle cluster) saves the cost of managing electric vehicles, but increases the scheduling pressure of thermal power units. The cost is 3.7% higher than that of mode 1. The main increase in cost is borne by the deep peak-shaving market. The cost of deep peak-shaving is 24.3% higher than that of mode 1, indicating that compared with the mode without electric vehicles, the dispatch of unmanaged electric vehicles is more harmful. Fig. 9 Comparing the optimized equivalent load curves of Mode 1 (black line), Mode 1 (no electric vehicles) (green line), and Mode 1 (disordered electric vehicle cluster) (yellow line), it can be seen that the equivalent load fluctuation of Mode 1 (disordered electric vehicle cluster) is the largest, which is not conducive to the stable output of the unit. Mode 1 is smoother than the other two. Fig.10 It can be found that the amount of wind abandoned is significantly lower than that without electric vehicles, and the total amount of wind abandoned is reduced by 66.1% compared with the mode without electric vehicles, which proves the advantage of electric vehicles in absorbing wind power. By introducing electric vehicles, it is found that the increase in the cost of the electric energy market is far less than the reduction in the deep peak-shaving market, which can effectively mobilize the enthusiasm of thermal power units to participate in the electric energy market and reduce the participation in the deep peak-shaving market.

[0056] By comparing Mode 2 with Mode 1, it can be found that the cost changes in each market mainly come from the frequency regulation market. The participation of energy storage power stations can effectively reduce the operating costs of the frequency regulation market, and it is reduced by 3.8% compared with Mode 2.

[0057] In this embodiment, the following four scenarios are assumed to be comparatively analyzed with the participation of energy storage power stations in the electric energy market as the background: Scenario 3: (1) 0MWh energy storage in the electric energy market & 200MWh energy storage in the frequency regulation market; (2) 200MWh energy storage in the electric energy market & 0MWh energy storage in the frequency regulation market; Scenario 4: 100MWh energy storage in the electric energy market & 100MWh energy storage in the frequency regulation market (comparison of with / without / disordered EVA participation); Scenario 5: 200MWh energy storage in the electric energy market & 200MWh energy storage in the frequency regulation market; Scenario 6: 400MWh energy storage in the electric energy market & 200MWh energy storage in the frequency regulation market (because the demand in the electric energy market is larger than that in the frequency regulation market, and the demand in the electric energy market is growing faster than that in the frequency regulation market, the share of energy storage power stations in the electric energy market should gradually increase).

[0058] The operating cost data of each market is shown in the following table: In the table, the operating cost of the deep peak-shaving market in scenario 3(2) is lower than that in scenario 3(1), but the total cost is higher than that in scenario 3(1). This shows that energy storage power stations can effectively reduce the deep peak-shaving costs of thermal power units. However, under the same capacity, when participating in a single market with the lowest cost as the goal, they are more suitable to participate in the frequency regulation market.

[0059] Scenario 3(1) and Scenario 4 are used to compare the costs of various market players when energy storage power stations with the same capacity participate in multiple markets. The frequency regulation operation costs of thermal power units in Scenario 4 (with / without / disordered electric vehicles) increase by 0.3%, 0.2%, and 0.1% respectively compared with Scenario 3(1), indicating that the increase in the capacity of energy storage power stations can reduce the operating costs of a certain market. The market cost of electricity in Scenario 4 (without / disordered electric vehicles) is lower than that in Mode 3 because of the influence of the degree of participation of electric vehicles and the capacity of energy storage power stations, resulting in insufficient flexible regulation of the system. The units can only frequently enter the deep peak regulation market, and the deep regulation cost increases by 9.1% and 30.89% compared with Scenario 3(1). In Scenario 4 (with electric vehicles), except for the deep regulation market operation cost which is lower than that in Scenario 3(1), the other market costs increase to a certain extent, indicating that the participation of energy storage power stations in each market is not high, the market cost has not been significantly optimized, and the purpose of alleviating the pressure of unit peak regulation or frequency regulation has not been achieved. In addition, low-capacity energy storage power stations participating in a single market are more effective than participating in multiple markets.

[0060] The comparison between scenario 4 (with / without electric vehicles) is used to verify the impact of different energy storage combinations on the total market operating costs. The deep regulation cost of disordered electric vehicles increased by 32.26% compared with the electric vehicle mode, and the total operating cost was higher than that of electric vehicles, indicating that the combination of electric vehicles and energy storage power stations has a better market regulation effect than single energy storage.

[0061] The operating costs of energy storage stations of different capacities participating in the electricity and frequency regulation markets are compared through scenarios 3, 4 (with electric vehicles), 5, and 6. When the capacity of energy storage stations increases, the total operating costs of the electricity spot market are effectively reduced, and the main cost reduction comes from the deep peak regulation costs. The cost of the electricity market has a small increase, and the increase in the cost of the frequency regulation market is negligible relative to the electricity market. This shows that with the increase in the capacity of energy storage stations, it can effectively solve the cost increase problem of energy storage stations participating in multiple markets and alleviate the peak regulation and frequency regulation pressure of units.

[0062] It can be seen from the above embodiments that at least the following substantial effects are achieved: (1) The present invention uses energy storage power stations to coordinate thermal power units and electric vehicles to participate in the electricity spot market. Compared with the electricity spot market in which a single type of energy storage participates, the market regulation effect of the present invention using multiple types of energy storage participating is more excellent; (2) By analyzing the characteristics and combinations of different types of energy storage, the present invention can make the electricity spot market more flexible in regulating resources, reduce the pressure of resource scheduling, and further improve resource utilization; (3) The present invention helps optimize the resource allocation of the power system, alleviate the scheduling pressure of traditional thermal power units, promote the consumption of wind power, reduce energy waste, and improve resource utilization by building a power spot market that includes energy storage power stations, electric vehicles and traditional energy.

[0063] The specific implementation described above is a preferred implementation of a new type of electricity spot market operation method of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A novel method for operating a power spot market, characterized in that: The following steps are involved: S1. Construct an electric vehicle dispatching model based on the driving characteristics of electric vehicles; construct an energy storage power station dispatching model based on the operating characteristics of energy storage power stations; S2. Obtain the total power consumption of electric vehicles and the remaining battery power based on the electric vehicle scheduling model, update the electricity price based on the total power consumption of electric vehicles and the remaining battery power and consider the daily load fluctuation to obtain the real-time electricity price; obtain the capacity change of the energy storage power station during the scheduling process based on the energy storage power station scheduling model; S3. Construct a spot electricity market model based on real-time electricity prices, capacity changes during the energy storage power station dispatch process, the operating mechanism of the frequency regulation market, and the operating mechanism of the peak regulation market to obtain the minimum dispatch cost under the participation of electric vehicles; obtain the minimum energy consumption cost of thermal power units based on the minimum dispatch cost under the participation of electric vehicles; S4. Analyze the frequency regulation market characteristics of energy storage power stations and thermal power units to obtain the minimum frequency regulation operation cost with the participation of energy storage power stations and thermal power units; analyze the peak regulation market characteristics of thermal power units to obtain the minimum peak regulation operation cost with the participation of thermal power units; S5. With the goal of minimizing the sum of the minimum dispatching cost, the minimum energy consumption cost of thermal power units, the minimum frequency regulation operation cost, and the minimum peak regulation operation cost, the electricity spot market model is solved to obtain the electricity spot market operation plan.

2. A novel power spot market operation method according to claim 1, characterized in that: In S1, the formula of the electric vehicle dispatch model is as follows: in, is the maximum number of electric vehicles stopped in time period t, P t is the probability of electric vehicles stopping in period t, N is the total number of electric vehicles in the planning area, E dpc (t) is the total power consumption of all electric vehicles during the period t, R d (t) is the driving distance of the electric vehicle during period t, PC EV is the power consumption of electric vehicles per 100 kilometers, f(R d ) is the daily driving pattern distribution of electric vehicles, μ D represents the mathematical expectation, σ D represents the standard deviation, E EV (t) is the remaining battery capacity of the electric vehicle at time t, η C is the charging efficiency of electric vehicles, η D is the discharge efficiency of the electric vehicle, is the charging power of the electric vehicle during period t, is the discharge power of the electric vehicle during period t.

3. A novel power spot market operation method according to claim 1, characterized in that: In S1, the formula of the energy storage power station dispatch model is as follows: Among them, E EES,FM (t) is the remaining capacity of the energy storage power station during the dispatching process, n C,t is the binary variable for switching the charging state of the energy storage power station, n D,t is a binary variable for switching the discharge state of the energy storage power station, For the charging efficiency of energy storage power stations, is the discharge efficiency of the energy storage power station, P i ESS,up (t) is the frequency regulation output of the energy storage power station in period t, P i ESS,down (t) is the frequency regulation output of the energy storage power station in period t.

4. A novel power spot market operation method according to claim 1, characterized in that: In S2, the total power consumption of the electric vehicle and the remaining battery power are obtained based on the electric vehicle scheduling model, and the electricity price is updated based on the total power consumption of the electric vehicle and the remaining battery power and taking into account the daily load fluctuation to obtain the real-time electricity price, including the following steps: Setting electric vehicle dispatch model constraints for the electric vehicle dispatch model, wherein the electric vehicle dispatch model constraints at least include electric vehicle charging and discharging total amount constraints, electric vehicle charging and discharging power constraints in different time periods, electric vehicle charge state constraints, and time-of-use electricity price constraints; The electric vehicle dispatching model is solved based on the constraints of the electric vehicle dispatching model to obtain the total power consumption of the electric vehicle and the remaining power of the battery; The total power consumption of electric vehicles, the remaining battery power, and the daily load fluctuations are analyzed, and the peak-valley flat pricing of electricity prices is carried out by dynamically dividing the different peak and valley periods of the day. The formula is as follows: Among them C t is the electricity price, c p is the peak electricity price, c f is the flat electricity price, c v is the valley electricity price, P av is the load mean, P ptv is the peak-to-valley difference of equivalent load, and ω is the fluctuation range of electricity price during peak and valley periods.

5. A novel power spot market operation method according to claim 1, characterized in that: In S2, the capacity change during the energy storage power station scheduling process is obtained based on the energy storage power station scheduling model, including the following steps: Setting energy storage power station scheduling model constraints for the energy storage power station scheduling model, wherein the energy storage power station scheduling model constraints at least include energy storage power station SOC initial and final state constraints, energy storage power station charging and discharging state constraints, and energy storage power station up and down frequency regulation state constraints; Based on the constraints of the energy storage power station scheduling model, the energy storage power station scheduling model is solved to obtain the capacity changes during the energy storage power station scheduling process.

6. A novel power spot market operation method according to claim 1, characterized in that: In S3, a power spot market model is constructed based on real-time electricity prices, capacity changes during the dispatching process of energy storage power stations, the operating mechanism of the frequency regulation market, and the operating mechanism of the peak regulation market to obtain the minimum dispatching cost under the participation of electric vehicles, including the following steps: setting wind power output constraints, wherein the wind power output constraints at least include upper and lower limit loads of wind power output and equivalent load variance constraints; The charging and discharging amount of electric vehicles and the remaining power of electric vehicles are calculated based on the electric vehicle scheduling model, and the minimum scheduling cost under the participation of electric vehicles is calculated with wind power output constraints as constraints.

7. A novel power spot market operation method according to claim 1, characterized in that: In S3, the minimum energy consumption cost of the thermal power unit is obtained based on the minimum dispatch cost with the participation of electric vehicles, including the following steps: constructing a wind power output equivalent load optimization curve based on the wind power output constraint and the equivalent load variance constraint in the minimum dispatch cost with the participation of electric vehicles, and obtaining the quotation curve of the thermal power unit in the electric energy market based on the wind power output equivalent load optimization curve; setting the operation constraints of the thermal power unit and the power supply and demand balance constraints in the electric energy market, and obtaining the minimum energy consumption cost of the thermal power unit based on the operation constraints of the thermal power unit and the power supply and demand balance constraints in the electric energy market.

8. A novel power spot market operation method according to claim 1, characterized in that: In S4, the frequency regulation market characteristics of energy storage power stations and thermal power units are analyzed to obtain the minimum frequency regulation operation cost under the participation of energy storage power stations and thermal power units, including the following steps: Set frequency regulation demand constraints and frequency regulation operation constraints for thermal power units; Based on the frequency regulation service costs of thermal power units, the operation and maintenance costs of thermal power units, the frequency regulation service costs of energy storage power stations, the operation service costs of energy storage power stations and the life cycle costs, and with the frequency regulation demand constraints and the frequency regulation operation constraints of thermal power units as constraints, the minimum frequency regulation operation cost with the participation of energy storage power stations and thermal power units is calculated.

9. A novel power spot market operation method according to claim 1, characterized in that: In S4, the peak-shaving market characteristics of the thermal power units are analyzed to obtain the minimum peak-shaving operation cost under the participation of the thermal power units, including the following steps: Set peak load demand constraints, peak load operation constraints of thermal power units, and coupling constraints of thermal power units; Based on the fuel cost, life loss cost, oil investment cost, pollution emission cost, operation and maintenance cost of deep peak-shaving of thermal power units, and the payment cost of deep peak-shaving of power system, and with the peak-shaving demand constraint, the peak-shaving operation constraint of thermal power units and the coupling constraint of thermal power units as constraints, the minimum peak-shaving operation cost with the participation of thermal power units is calculated.

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