Energy dispatching methods based on the electricity spot market

Abstract

This invention discloses an energy dispatching method based on the electricity spot market, belonging to the field of power system operation and control. By treating electric vehicle clusters as equivalent energy storage power stations, and comprehensively considering electric vehicles and energy storage power stations together, the minimum dispatching cost, minimum energy consumption cost, minimum frequency regulation operation cost, and minimum peak shaving operation cost of electric vehicles participating in the electricity spot market are obtained based on the real-time electricity price with electric locomotives participating, the capacity change of energy storage power stations, the operation mechanism of the frequency regulation market, and the operation mechanism of the peak shaving market. The minimum dispatching cost, minimum energy consumption cost, minimum frequency regulation operation cost, and minimum peak shaving operation cost of thermal power units are obtained. The above costs are analyzed, and the energy dispatching scheme with the minimum sum of the above costs is found. Based on the scheme, energy dispatching is carried out on each unit in the electricity spot market. This solves the problem of excessively high energy dispatching costs in the electricity spot market with electric vehicle participation, makes resource dispatching more flexible, reduces the pressure of resource dispatching, and further improves resource utilization.

Description

Technical Field

[0001] This invention relates to the field of power system operation and control, and more specifically, to an energy dispatching method based on the background of the electricity spot market. Background Technology

[0002] Today, clean energy is gradually becoming the main source of incremental energy, optimizing the industrial structure. However, clean energy, represented by wind power and photovoltaics, is characterized by randomness, volatility, and intermittency. Furthermore, with the increasing development of offshore wind power in the future, its anti-peak-shaving characteristics will have a greater impact. Electricity markets relying solely on traditional thermal power units for dispatch will struggle to achieve supply and demand balance, putting pressure on the grid's flexible dispatch capabilities. Considering the rapid growth of energy storage power stations and electric vehicles in the future, relying solely on centralized applications of single energy storage systems in the electricity spot market will gradually become insufficient to accommodate the diversified development of electricity market participants. Fully leveraging the regulatory capabilities of different market participants has become an indispensable factor in energy dispatch within the electricity spot market. Therefore, this paper analyzes and summarizes the energy storage application scenarios and market characteristics designed by various domestic and international scholars, proposing solutions for energy storage power stations and electric vehicles participating in the electricity spot market. This requires modeling electric vehicles and energy storage power stations based on different energy storage characteristics, constructing a joint operation mechanism for the spot electricity energy and ancillary services market with the participation of electric vehicles and energy storage, and providing solutions for different participants in the spot electricity energy and ancillary services market.

[0003] Chinese Patent, Publication No. CN115689607A, Publication Date: February 3, 2023, discloses an energy storage power station and an energy storage operation method participating in the electricity spot market. This method first generates a day-ahead market settlement price forecast curve based on the day-ahead market price forecast curve and the day-ahead market settlement rules; then, based on the day-ahead market settlement price forecast curve, it generates a set of future day full-load-release arbitrage sequence combinations for the energy storage power station; finally, it selects the full-load-release arbitrage sequence combination with the highest arbitrage price difference from the future day full-load-release arbitrage sequence combination as the target full-load-release arbitrage sequence combination for the energy storage power station in the future day, and controls the energy storage power station to operate according to the target full-load-release arbitrage sequence combination. However, it does not consider the impact of electric vehicles participating in the electricity spot market. Summary of the Invention

[0004] This invention addresses the high operating costs and significant resource dispatch pressure resulting from the participation of electric vehicles in the current electricity spot market. It proposes an energy dispatch method based on the electricity spot market context. By treating electric vehicle clusters as equivalent energy storage power stations, and considering both electric vehicles and energy storage power stations together, the method obtains the minimum dispatch cost, minimum energy consumption cost, minimum frequency regulation operating cost, and minimum peak-shaving operating cost of thermal power units in the electricity spot market based on real-time electricity prices with electric vehicle participation, capacity changes of energy storage power stations, and the operating mechanisms of the frequency regulation and peak-shaving markets. Analyzing these costs, the method seeks the energy dispatch scheme that minimizes the sum of these costs. Based on this operating scheme, energy dispatch is performed on each unit in the electricity spot market. This solves the problem of excessively high energy dispatch costs in the electricity spot market with electric vehicle participation, making resource dispatch more flexible, reducing resource dispatch pressure, and further improving resource utilization.

[0005] In a first aspect, one technical solution provided in this embodiment of the invention is: an energy dispatching method based on the background of the electricity spot market, comprising the following steps:

[0006] S1. Construct an electric vehicle scheduling model based on the driving characteristics of electric vehicles; construct an energy storage power station scheduling model based on the operating characteristics of energy storage power stations.

[0007] S2. Based on the electric vehicle scheduling model, obtain the total power consumption of electric vehicles and the remaining battery power. Based on the total power consumption of electric vehicles and the remaining battery power, and considering daily load fluctuations, update the electricity price to obtain the real-time electricity price. Based on the energy storage power station scheduling model, obtain the capacity changes during the energy storage power station scheduling process.

[0008] S3. Based on real-time electricity prices, capacity changes during energy storage power station scheduling, the operation mechanism of the frequency regulation market, and the operation mechanism of the peak shaving market, an energy dispatch model is constructed to obtain the minimum dispatch cost with the participation of electric vehicles; based on the minimum dispatch cost with the participation of electric vehicles, the minimum energy consumption cost of thermal power units is obtained.

[0009] S4. Conduct a frequency regulation market characteristic analysis on 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; conduct a peak shaving market characteristic analysis on thermal power units to obtain the minimum peak shaving operating cost with the participation of thermal power units.

[0010] S5. With the goal of minimizing the sum of minimum dispatch cost, minimum energy consumption cost of thermal power units, minimum frequency regulation operation cost, and minimum peak shaving operation cost, the energy dispatch model is solved to obtain the energy dispatch scheme under the background of electricity spot market.

[0011] In this scheme, to obtain the operational plan with minimum energy dispatch cost in the electricity spot market with the participation of electric vehicles, the energy storage devices in the electricity spot market must first be analyzed. Electric vehicles can be considered equivalent to energy storage power stations to some extent; however, electric vehicles have characteristics such as driving behavior and randomness compared to energy storage power stations, which differ from stationary energy storage power stations. Therefore, an electric vehicle dispatch model is constructed to analyze electricity price changes under electric vehicle participation, facilitating subsequent calculation of energy dispatch costs in the electricity spot market based on electricity price changes. To facilitate subsequent calculation of frequency regulation market operating costs and peak shaving operating costs, an energy storage power station dispatch model is constructed to obtain the capacity changes of energy storage power stations during the dispatch process. Based on the capacity changes of energy storage power stations, the subsequent energy dispatch of thermal power units and electric vehicles is analyzed. Analysis shows that operating costs can be reduced while ensuring stable energy dispatch in the electricity spot market. To obtain the minimum dispatch cost with electric vehicles involved, the power consumption of electric vehicles, changes in the capacity of energy storage stations, and electricity prices obtained from the electric vehicle dispatch model are considered. The minimum dispatch cost is calculated through comprehensive analysis. Based on the minimum dispatch cost, the energy consumption of thermal power units is analyzed to obtain the minimum energy consumption cost. Then, frequency regulation and peak shaving of thermal power units are performed to obtain the minimum frequency regulation operating cost and the minimum peak shaving operating cost. The above costs are comprehensively analyzed to obtain the energy dispatch scheme with the minimum total cost. Based on this scheme, energy dispatch of each unit in the electricity spot market can be carried out, which can significantly reduce the energy dispatch cost due to the participation of electric vehicles in the electricity spot market, make resource dispatch more flexible, reduce the pressure on resource dispatch, and further improve resource utilization.

[0012] As a preferred option, the formula for the electric vehicle scheduling model in S1 is expressed as follows:

[0013]

[0014]

[0015] in, This represents the maximum number of electric vehicles that can be taken off the road within time period t. Let t be the probability that an electric vehicle will be out of service during time period t. To determine the total number of electric vehicles in the planned area, Let be the total electricity consumption of all electric vehicles during time period t. Let t be the distance traveled by the electric vehicle during time period t. Electricity consumption per 100 kilometers for electric vehicles. The daily driving pattern distribution of electric vehicles, Represents the mathematical expectation. Indicates standard deviation, Let t represent the remaining battery power of the electric vehicle during time period t. For electric vehicle charging efficiency, For the discharge efficiency of electric vehicles. The amount of electricity charged for the electric vehicle during period t. This represents the discharge charge of the electric vehicle during period t.

[0016] In this scheme, electric vehicles can be considered equivalent to energy storage stations in the electricity spot market. However, they have characteristics such as driving and randomness, which are different from those of stationary energy storage 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 for most of the day, the number of dispatchable electric vehicles in each time period is calculated by the probability distribution of electric vehicle stoppage. Then, based on the maximum number of electric vehicles driving at each moment, the total power consumption of electric vehicles in the corresponding time period and the remaining battery power are calculated. This can be compared with the charging and discharging capacity and remaining power of energy storage stations. Electric vehicles play the role of energy storage stations. Compared with building energy storage stations, the cost is lower and more energy can be dispatched, thereby reducing the energy dispatch cost in the electricity spot market.

[0017] As a preferred option, the formula for the energy storage power station scheduling model in S1 is as follows:

[0018]

[0019] in, This represents the remaining capacity of the energy storage power station during time period t in the dispatching process. Binary variables for switching charging states of energy storage power stations. This is a binary variable representing the switching of the discharge state of the energy storage power station. To improve the charging efficiency of energy storage power stations. For the discharge efficiency of energy storage power stations, To increase the frequency regulation output of the energy storage power station during time period t, This is to regulate the frequency output of the energy storage power station during time period t.

[0020] In this plan, 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. By calculating the maximum and minimum allowable capacity of the energy storage power stations during the dispatch process and the up and down frequency regulation output at various times, the remaining capacity of the energy storage power stations at each time period can be determined. In the electricity spot market, it is only necessary for the remaining capacity of the energy storage power stations to be within a reasonable range to avoid accelerated damage to the energy storage power stations caused by deep charging and deep discharging. Building energy storage power stations within this range and using electric vehicles for energy storage and dispatch can reduce the construction costs of building energy storage power stations, thereby reducing the energy dispatch costs in the electricity spot market.

[0021] Preferably, in S2, the total electricity consumption of electric vehicles and the remaining battery power are obtained based on the electric vehicle scheduling model. The real-time electricity price is then updated based on the total electricity consumption of electric vehicles and the remaining battery power, taking into account daily load fluctuations. This includes the following steps:

[0022] The electric vehicle scheduling model is set with constraints, which include at least the total charging and discharging capacity of electric vehicles, the charging and discharging power of electric vehicles at different time periods, the state of charge of electric vehicles, and the time-of-use electricity price.

[0023] Based on the constraints of the electric vehicle scheduling model, the total power consumption of electric vehicles and the remaining battery power are obtained by solving the electric vehicle scheduling model.

[0024] The total electricity consumption of electric vehicles, the remaining battery capacity, and daily load fluctuations are analyzed. Peak-valley pricing is then applied by dynamically dividing different peak and valley periods each day. The formula is as follows:

[0025]

[0026] in For electricity price, For peak electricity pricing, To stabilize electricity prices, Off-peak electricity pricing, This is the average load. For the equivalent load peak-valley difference, This refers to the range of electricity price fluctuations during peak and off-peak hours.

[0027] In this scheme, time-of-use pricing is used to allow electric vehicles to participate in the electricity market. 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. Using electric vehicles to participate in energy storage dispatch can achieve arbitrage, peak shaving and valley filling, and reduce energy curtailment by utilizing electricity price differences. It also reduces the need for deep dispatch, and the improvement effect increases with the number of electric vehicles participating in the electricity spot market. It can reduce the output of deep peak shaving units from the source, which not only improves energy utilization efficiency but also reduces energy dispatch costs under the electricity spot market.

[0028] Preferably, in S2, the capacity changes during the energy storage power station scheduling process are obtained based on the energy storage power station scheduling model, including the following steps:

[0029] The energy storage power station scheduling model is set with constraints, which include at least the energy storage power station SOC start and end 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.

[0030] The remaining capacity of the energy storage power station during the scheduling process is obtained by solving the energy storage power station scheduling model based on the constraints of the energy storage power station scheduling model.

[0031] In this scheme, in order to calculate the capacity changes of the energy storage power station during the dispatching process and to ensure that the energy storage power station will not suffer accelerated damage due to deep charging and discharging during the dispatching process, the SOC state of the energy storage power station is 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 regulation 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. This reduces the number of energy storage power stations required while ensuring normal operation, thereby reducing the construction cost of energy storage power stations and thus reducing the energy dispatching cost under the electricity spot market.

[0032] As a preferred option, in S3, an energy dispatching 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-shaving market to obtain the minimum dispatching cost with the participation of electric vehicles, including the following steps:

[0033] Set wind power output constraints, which include at least upper and lower limits of wind power output and equivalent load variance constraints;

[0034] The electric vehicle (EV) charging and discharging capacity and remaining power of the EVs are calculated based on the EV scheduling model, and the minimum scheduling cost with EV participation is calculated with wind power output constraints as the constraint condition.

[0035] In this scheme, since electric vehicle users are in a 90% off-peak state throughout the day, their application characteristics are roughly complementary to wind power output. That is, during the peak wind power period at night, most users are charging; during the low wind power period at noon, most users are discharging, which can effectively absorb wind power. In order to obtain the minimum dispatch cost of energy dispatch with the participation of electric vehicles, the scheme analyzes the wind power operation and maintenance costs, wind curtailment penalty costs, vehicle battery aging costs, charging costs, and discharge compensation costs. Then, it constrains the upper and lower limits of wind power output, analyzes the charging and discharging of electric vehicles to obtain the load demand of the power grid at various times, and calculates the minimum dispatch cost of energy dispatch with the participation of electric vehicles based on the load demand and electricity price. By combining electric vehicles and wind power, the load curve of the power grid can be peak-shaving and valley-filling, which can reduce energy curtailment and improve energy utilization.

[0036] As a preferred embodiment, in S3, obtaining the minimum energy consumption cost of thermal power units based on the minimum scheduling cost with the participation of electric vehicles includes the following steps:

[0037] Based on the wind power output constraints and equivalent load variance constraints in the minimum dispatch cost under electric vehicle participation, an optimization curve for wind power output equivalent load is constructed, and the bidding curve of thermal power units in the electric energy market is obtained based on the optimization curve for wind power output equivalent load.

[0038] Set operating constraints for thermal power units and power supply and demand balance constraints in the power market, and obtain the minimum energy consumption cost of thermal power units based on these constraints.

[0039] In this scheme, to obtain the minimum energy consumption cost of thermal power units, which includes start-up cost, no-load cost, shutdown cost, fuel cost, and market electricity purchase cost, the scheme aims to minimize the energy consumption cost of thermal power units. Based on the wind power output equivalent load optimization curve, the scheme obtains the price curve of thermal power units in the electricity market. Constraints are imposed on the upper and lower limits of the output of thermal power units in various time periods. Under the premise of ensuring positive revenue of thermal power units, constraints are set on the operating reserve capacity of thermal power units, continuous start / stop time of thermal power units, start / stop cost constraints, ramp / slippage constraints, and operating electricity purchase cost constraints. Based on these constraints, the minimum energy consumption cost of thermal power units is calculated, ensuring that thermal power units can operate in an optimal and economical manner, effectively absorbing wind power, improving energy utilization efficiency, and reducing energy curtailment.

[0040] As a preferred option, in S4, a frequency regulation market characteristic analysis is performed on 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, including the following steps:

[0041] Set frequency regulation demand constraints and thermal power unit frequency regulation operation constraints;

[0042] 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 constraints of frequency regulation demand and thermal power unit frequency regulation operation, the minimum frequency regulation operation cost involving energy storage power stations and thermal power units is calculated.

[0043] In this scheme, to analyze the frequency regulation market and obtain the minimum frequency regulation operating cost, thermal power units are used as the main body to construct frequency regulation demand constraints and thermal power unit frequency regulation operation constraints. Based on the above constraints, the minimum frequency regulation operating cost under the participation of thermal power units is calculated by comprehensively considering the frequency regulation status, frequency regulation capacity, frequency regulation price, the revenue of thermal power units participating in the electricity market without reserving frequency regulation capacity, and the revenue obtained after reserving a certain frequency regulation capacity. In the frequency regulation market, the market regulation effect of thermal power units participating in the electricity spot market in conjunction with energy storage power stations and electric vehicles is better than that of single-type energy storage participation. By analyzing the characteristics and combinations of different types of energy storage, high-quality flexible regulation resources can be provided for the electricity spot market, and the dispatch potential of energy storage resources can be fully explored.

[0044] As a preferred option, in S4, a peak-shaving market characteristic analysis of thermal power units is performed to obtain the minimum peak-shaving operating cost with the participation of thermal power units, including the following steps:

[0045] Set peak-shaving demand constraints, thermal power unit peak-shaving operation constraints, and thermal power unit coupling constraints;

[0046] Based on the fuel cost, life loss cost, oil injection cost, pollution emission cost, operation and maintenance cost, and power system deep peak shaving payment cost of thermal power units, and with the constraints of peak shaving demand, thermal power unit peak shaving operation constraints, and thermal power unit coupling constraints as constraints, the minimum peak shaving operation cost of thermal power units is calculated.

[0047] In this scheme, to analyze the peak-shaving market and obtain the minimum peak-shaving operating cost, thermal power units are used as the main body to construct peak-shaving demand constraints, thermal power unit peak-shaving operation constraints, and thermal power unit coupling constraints. Based on the above constraints, the minimum peak-shaving operating cost of thermal power units is calculated by comprehensively analyzing the fuel cost, life loss cost (deep peak-shaving without oil injection and with oil injection), oil injection cost, pollution emission cost, operation and maintenance cost, and power system deep peak-shaving payment cost. The peak-shaving of thermal power units includes three stages: conventional peak-shaving, peak-shaving without oil injection, and peak-shaving with oil injection. When wind curtailment occurs, thermal power units can be appropriately allowed to reduce the minimum technical output limit to absorb wind power. This process includes the minimum stable fuel output without oil injection and the maximum output with oil injection. Compared with conventional peak-shaving, it increases the life loss cost, oil injection cost, and pollution cost associated with oil injection output. Therefore, in order to maximize the absorption of wind power, these increased costs must be taken into account.

[0048] The beneficial effects of the present invention are: (1) The present invention enables energy storage power stations to participate in the electricity spot market in conjunction with thermal power units and electric vehicles. Compared with the electricity spot market with a single type of energy storage, the present invention has a better market regulation effect by using multiple types of energy storage.

[0049] (2) By analyzing the characteristics and combinations of different types of energy storage, this invention enables the electricity spot market to adjust resources more flexibly, reduces the pressure of resource scheduling, and further improves resource utilization.

[0050] (3) By constructing an electricity spot market that includes energy storage power stations, electric vehicles and traditional energy, this invention helps to optimize the allocation of power system resources, alleviate the dispatch pressure of traditional thermal power units and promote the consumption of wind power, reduce energy waste and improve resource utilization.

[0051] The above description of the invention is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0052] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings. The drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0053] Figure 1 This is a flowchart of the energy dispatching method based on the electricity spot market background of the present invention;

[0054] Figure 2 This is a graph showing the load, wind power forecast, and equivalent load in Embodiment 1 of the present invention;

[0055] Figure 3 This is a comparison chart of equivalent load curves under different scales in Embodiment 1 of the present invention;

[0056] Figure 4 This is a schematic diagram of the charging and discharging power under the scale of 500,000 electric vehicles in Embodiment 1 of the present invention;

[0057] Figure 5 This is a comparison diagram of the equivalent load constraint before and after in Embodiment 1 of the present invention;

[0058] Figure 6 This is a diagram illustrating the impact of single charge and discharge variations of an energy storage power station on the charging and discharging behavior of the frequency regulation market in Embodiment 1 of the present invention.

[0059] Figure 7 This is a charge / discharge and state-of-charge diagram of a 20MW energy storage power station in Embodiment 1 of the present invention.

[0060] Figure 8 This is a diagram illustrating the impact of the price difference between energy storage charging and discharging on charging and discharging behavior in Embodiment 1 of the present invention.

[0061] Figure 9 This is a comparison diagram of equivalent load curves under different modes in Embodiment 1 of the present invention;

[0062] Figure 10 This is a graph showing the amount of wind curtailment in Embodiment 1 of the present invention, with and without the influence of electric vehicles. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only one preferred embodiment of this invention and are only used to explain this invention. They do not limit the scope of protection of this invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0064] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations (or steps) can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but it may also have additional steps not included in the figures; the process may correspond to a method, function, procedure, subroutine, subroutine, etc.

[0065] Example: Figure 1 As shown, in order to address the problems of high operating costs and significant resource scheduling pressure caused by the participation of electric vehicles in the existing electricity spot market, this invention provides an energy scheduling method based on the electricity spot market, including the following steps:

[0066] S1: Construct an electric vehicle scheduling model based on the driving characteristics of electric vehicles; construct an energy storage power station scheduling model based on the operating characteristics of energy storage power stations.

[0067] In this embodiment, the formula for the electric vehicle scheduling model is expressed as follows:

[0068]

[0069]

[0070] in, This represents the maximum number of electric vehicles that can be taken off the road within time period t. Let t be the probability that an electric vehicle will be out of service during time period t. To determine the total number of electric vehicles in the planned area, Let be the total electricity consumption of all electric vehicles during time period t. Let t be the distance traveled by the electric vehicle during time period t. Electricity consumption per 100 kilometers for electric vehicles. The daily driving pattern distribution of electric vehicles, Represents the mathematical expectation. Indicates standard deviation, Let t represent the remaining battery power of the electric vehicle during time period t. For the charging efficiency of electric vehicles. For the discharge efficiency of electric vehicles. The amount of electricity charged for the electric vehicle during period t. This represents the discharge charge of the electric vehicle during period t.

[0071] In this embodiment, electric vehicles can be considered equivalent to energy storage stations in the electricity spot market. However, they have characteristics such as driving and randomness, which are different from those of stationary energy storage 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 for most of the day, the number of dispatchable electric vehicles in each time period is calculated by the probability distribution of electric vehicle stoppage. Then, based on the maximum number of electric vehicles driving at each moment, the total power consumption of electric vehicles in the corresponding time period and the remaining battery power are calculated. This can be compared to the charging and discharging capacity and remaining power of an energy storage station. However, using electric vehicles to play the role of an energy storage station is less expensive than building an energy storage station and can dispatch more energy, thereby reducing the energy dispatch cost in the electricity spot market.

[0072] In this embodiment, the formula for the energy storage power station scheduling model is as follows:

[0073]

[0074] in, This represents the remaining capacity of the energy storage power station during time period t in the dispatching process. Binary variables for switching the charging state of energy storage power stations. This is a binary variable representing the switching of the discharge state of the energy storage power station. To improve the charging efficiency of energy storage power stations. For the discharge efficiency of energy storage power stations, To increase the frequency regulation output of the energy storage power station during time period t, This is to regulate the frequency output of the energy storage power station during time period t.

[0075] Since electric vehicles cannot completely replace the role of energy storage power stations in the electricity spot market, this embodiment still requires analysis of energy storage power stations. By calculating the maximum and minimum allowable capacity of energy storage power stations during dispatching, as well as the up- and down-frequency regulation output at various times, the remaining capacity of energy storage power stations at each time period can be determined. In the electricity spot market, it is only necessary for the remaining capacity of energy storage power stations to be within a reasonable range to avoid accelerated damage to energy storage power stations caused by deep charging and deep discharging. Building energy storage power stations within this range, and using electric vehicles for energy storage and dispatching, can reduce the construction costs of building energy storage power stations, thereby reducing the energy dispatching costs in the electricity spot market.

[0076] S2: Based on the electric vehicle scheduling model, obtain the total power consumption of electric vehicles and the remaining battery power. Based on the total power consumption of electric vehicles and the remaining battery power, and considering daily load fluctuations, update the electricity price to obtain the real-time electricity price. Based on the energy storage power station scheduling model, obtain the capacity changes during the energy storage power station scheduling process.

[0077] In this embodiment, the total electricity consumption of electric vehicles and the remaining battery power are obtained based on the electric vehicle scheduling model. The real-time electricity price is then updated based on the total electricity consumption of electric vehicles and the remaining battery power, taking into account daily load fluctuations. This includes the following steps:

[0078] The electric vehicle scheduling model is subject to constraints, which include at least the total charging and discharging capacity constraints, charging and discharging power constraints at different time periods, state of charge constraints, and time-of-use pricing constraints. The formulas for these constraints are as follows:

[0079]

[0080]

[0081]

[0082]

[0083] in The total daily charging volume for electric vehicles, This refers to the total daily discharge of electric vehicles. Let be the total capacity of electric vehicles during time period t. The upper limit of electric vehicle charging and discharging power during time period t is related to the number of electric vehicles that are not in use. This is a fixed proportional coefficient for the power limit and capacity of electric vehicles. For charging and discharging EVA, use 0-1 variables. and In a charged state, and These are the upper and lower limits of time-of-use electricity pricing;

[0084] Based on the constraints of the electric vehicle scheduling model, the total power consumption of electric vehicles and the remaining battery power are obtained by solving the electric vehicle scheduling model.

[0085] The total electricity consumption of electric vehicles, the remaining battery capacity, and daily load fluctuations are analyzed. Peak-valley pricing is then applied by dynamically dividing different peak and valley periods each day. The formula is as follows:

[0086]

[0087] in For electricity price, For peak electricity pricing, To stabilize electricity prices, Off-peak electricity pricing, This is the average load. For the equivalent load peak-valley difference, This refers to the fluctuation range of electricity prices during peak and off-peak hours. When the equivalent load value is greater than... When the peak electricity price is determined, the equivalent load value is less than the peak price. At that time, the off-peak electricity price was determined.

[0088] This embodiment allows electric vehicles to participate in the electricity market through time-of-use pricing. 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. Using electric vehicles to participate in energy storage dispatch can achieve arbitrage, peak shaving and valley filling, and reduce energy curtailment by utilizing electricity price differences. It also reduces the need for deep dispatch, and the improvement effect increases with the number of electric vehicles participating in the electricity spot market. It can reduce the output of deep peak shaving units from the source, which not only improves energy utilization efficiency but also reduces energy dispatch costs under the electricity spot market.

[0089] In this embodiment, the capacity changes during the energy storage power station scheduling process are obtained based on the energy storage power station scheduling model, including the following steps:

[0090] The scheduling model of the energy storage power station is subject to constraints, which include at least the initial and final state constraints of the energy storage power station's State of Charge (SOC), the charging and discharging state constraints, and the up- and down-frequency regulation state constraints. The formulas are as follows:

[0091]

[0092] in, This refers to the maximum remaining capacity allowed for the energy storage power station during dispatching. This refers to the minimum remaining capacity allowed for energy storage power stations during dispatching. This represents the remaining capacity of the energy storage power station during time period t in the dispatching process. and These represent the up- and down-frequency regulation outputs of the energy storage power station during time period t in the dispatching process. and These are the upper and lower limits of the maximum application capacity for a single energy storage power station. and These represent the initial and final states of charge of the energy storage power station, respectively.

[0093] The remaining capacity of the energy storage power station during the scheduling process is obtained by solving the energy storage power station scheduling model based on the constraints of the energy storage power station scheduling model.

[0094] In order to calculate the capacity changes of the energy storage power station during the dispatching process and to ensure that the energy storage power station will not suffer accelerated damage due to deep charging and discharging during the dispatching process, this embodiment constructs the SOC start and end state constraints of the energy storage power station by making the SOC state of the energy storage power station equal at the start and end of the day. Then, the charging, discharging and frequency regulation 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. This allows the energy storage power station to be deployed less while still being able to operate normally, thereby reducing the construction cost of the energy storage power station and thus reducing the energy dispatching cost under the electricity spot market.

[0095] S3: Construct an energy dispatch model based on real-time electricity prices, capacity changes during energy storage power station dispatch, the operation mechanism of the frequency regulation market, and the operation mechanism of the peak shaving market to obtain the minimum dispatch cost with electric vehicles participating; obtain the minimum energy consumption cost of thermal power units based on the minimum dispatch cost with electric vehicles participating.

[0096] In this embodiment, an energy dispatching model is constructed based on real-time electricity prices, capacity changes during energy storage power station dispatching, the operating mechanism of the frequency regulation market, and the operating mechanism of the peak-shaving market to obtain the minimum dispatching cost with the participation of electric vehicles, including the following steps:

[0097] Wind power output constraints are set, which include at least upper and lower limits of wind power output and equivalent load variance constraints, as expressed in the following formula:

[0098]

[0099]

[0100] in, The actual wind power output during time period t. and These represent the upper and lower limits of wind power output. For the load demand in time period t, C is the equivalent load average, and C is the electricity price.

[0101] Based on the electric vehicle scheduling model, the electric vehicle charging and discharging capacity and remaining electric vehicle power are calculated, and the minimum scheduling cost with electric vehicle participation is calculated using wind power output constraints. The formula is expressed as follows:

[0102]

[0103] in, This is the operation and maintenance cost coefficient. This is the wind curtailment penalty coefficient. For the predicted wind power output in time period t, Cost per unit charge / discharge loss of the battery. This is the discharge compensation coefficient.

[0104] In this embodiment, since electric vehicle users are in a 90% off-peak state throughout the day, their application characteristics are roughly complementary to wind power output. That is, during the peak wind power period at night, most users are charging; during the low wind power period at noon, most users are discharging, which can effectively absorb wind power. In order to obtain the minimum dispatch cost of energy scheduling with the participation of electric vehicles, the operation and maintenance costs of wind power, wind curtailment penalty costs, battery aging costs of vehicle fleets, charging costs, and discharge compensation costs are analyzed. Then, the upper and lower limits of wind power output are constrained, and the charging and discharging of electric vehicles are analyzed to obtain the load demand of the power grid at various times. Based on the load demand and electricity price, the minimum dispatch cost of energy scheduling with the participation of electric vehicles is calculated. By combining electric vehicles and wind power, the load curve of the power grid can be peak-shaving and valley-filling, which can reduce energy curtailment and improve energy utilization.

[0105] In this embodiment, obtaining the minimum energy consumption cost of a thermal power unit based on the minimum scheduling cost with the participation of electric vehicles includes the following steps:

[0106] Based on the wind power output constraints and equivalent load variance constraints in the minimum dispatch cost under electric vehicle participation, an optimization curve for wind power output equivalent load is constructed, and the bidding curve of thermal power units in the electric energy market is obtained based on the optimization curve for wind power output equivalent load.

[0107] The operating constraints for thermal power units are set, and the formula is expressed as follows:

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115] in, and The upper and lower limits of the output of thermal power unit numbered i during time period t; The positive and negative operational reserve capacity that the system needs to reserve during the time period t; For operational reserve coefficient, and These represent the start / stop times of thermal power unit numbered i during time period t; and These represent the minimum start / stop times for thermal power unit numbered i during time period t. and These represent the minimum start-up / shutdown costs for thermal power unit numbered i during time period t. These are 0 / 1 variables used to determine the unit's operating status; and These represent the maximum ramp / slope rates of the thermal power unit numbered i. Let i be the revenue of the thermal power unit numbered i in time period t. for The first derivative;

[0116] The electricity supply and demand balance constraint in the electricity market is set, and the formula is expressed as follows:

[0117]

[0118] in, This represents the output of thermal power unit numbered i during time period t.

[0119] The minimum energy consumption cost of thermal power units is obtained based on the operating constraints of thermal power units and the power supply and demand balance constraints of the electricity market. The formula is expressed as follows:

[0120]

[0121] in, This is the price quote curve for thermal power unit numbered i in the electricity market. For the start-up cost of thermal power units, For the no-load cost of thermal power units, For the cost of thermal power unit shutdown, Fuel cost for thermal power units.

[0122] This embodiment aims to obtain the minimum energy consumption cost of thermal power units, which includes start-up cost, no-load cost, shutdown cost, fuel cost, and market electricity purchase cost. With the goal of minimizing the energy consumption cost of thermal power units, the bidding curve of thermal power units in the electricity market is obtained based on the wind power output equivalent load optimization curve. Constraints are placed on the upper and lower limits of the output of thermal power units in various time periods. Under the premise of ensuring positive revenue for thermal power units, constraints are set on the operating reserve capacity, continuous start / stop time, start / stop cost, ramp-up / slippage, and operating electricity purchase cost of thermal power units. Based on these constraints, the minimum energy consumption cost of thermal power units is calculated, ensuring optimal economic operation of thermal power units, effectively absorbing wind power, improving energy utilization efficiency, and reducing energy curtailment.

[0123] S4: Conduct a frequency regulation market characteristic analysis for 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; conduct a peak shaving market characteristic analysis for thermal power units to obtain the minimum peak shaving operating cost with the participation of thermal power units.

[0124] In this embodiment, frequency regulation market characteristic analysis is performed on 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, including the following steps:

[0125] The frequency modulation demand constraint is set, and the formula is expressed as follows:

[0126]

[0127] in, The winning output of thermal power unit numbered i in the electricity market will be used to update the capacity range for thermal power units participating in the electricity market to re-participate in the frequency regulation market. and These represent the up- and down-frequency modulation demands for time period t. and This is a 0-1 variable, representing the upper and lower frequency regulation bidding status of thermal power unit numbered i. and These represent the upper / lower frequency regulation capacity of thermal power unit numbered i. This represents the upper limit of the frequency regulation capacity for thermal power unit numbered i. This is the lower limit of the frequency regulation capacity of the thermal power unit numbered i;

[0128] The constraints for frequency regulation operation of thermal power units are set, and the formula is expressed as follows:

[0129]

[0130] in, This represents thermal power units that have won bids in the electricity market;

[0131] 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 and service costs of energy storage power stations, and the life cycle costs, and with frequency regulation demand constraints and thermal power unit frequency regulation operation constraints as constraints, the minimum frequency regulation operation cost involving energy storage power stations and thermal power units is calculated. The formula is expressed as follows:

[0132]

[0133] in, and These are the on / off frequency regulation electricity prices for thermal power units. This refers to the unit's operation and maintenance coefficient. For the daily frequency regulation capacity of thermal power units, and These are the revenues of thermal power units participating in the electricity market without reserving frequency regulation capacity and the revenues obtained after reserving a certain amount of frequency regulation capacity, respectively.

[0134] In this embodiment, to analyze the frequency regulation market and obtain the minimum frequency regulation operating cost, thermal power units are used as the main body to construct frequency regulation demand constraints and thermal power unit frequency regulation operation constraints. Based on the above constraints, the minimum frequency regulation operating cost under the participation of thermal power units is calculated by comprehensively considering the frequency regulation status of thermal power units, frequency regulation capacity, frequency regulation price, the revenue of thermal power units participating in the electricity market without reserving frequency regulation capacity, and the revenue obtained after reserving a certain frequency regulation capacity. In the frequency regulation market, the market regulation effect of thermal power units participating in the electricity spot market in conjunction with energy storage power stations and electric vehicles is better than that of single-type energy storage participation. By analyzing the characteristics and combinations of different types of energy storage, high-quality flexible regulation resources can be provided for the electricity spot market, and the dispatch potential of energy storage resources can be fully explored.

[0135] In this embodiment, the minimum peak-shaving operating cost of thermal power units is obtained by analyzing the peak-shaving market characteristics of thermal power units, including the following steps:

[0136] The peak-shaving demand constraint is set, and the formula is expressed as follows:

[0137]

[0138] in, For the deep peak shaving demand in time period t. The winning bid output of the wind turbine units during time period t. The power output for the electric vehicles in time period t. The winning bid output of the thermal power units during time period t;

[0139] The constraints for peak-shaving operation of thermal power units and the coupling constraints of thermal power units are set, and the formulas are expressed as follows:

[0140]

[0141] in, To achieve the minimum stable combustion output of thermal power units without oil injection. To ensure stable combustion and maximum output of thermal power units by injecting oil. and These represent the bidding status for frequency regulation of thermal power units, respectively.

[0142] Based on the fuel cost, life loss cost, oil injection cost, pollution emission cost, operation and maintenance cost, and power system deep peak shaving payment cost of thermal power units, and with peak shaving demand constraints, thermal power unit peak shaving operation constraints, and thermal power unit coupling constraints as constraints, the minimum peak shaving operation cost of thermal power units is calculated. The formula is expressed as follows:

[0143]

[0144] in, This represents the loss coefficient of the thermal power unit. The number of cycles that cause rotor cracking in thermal power units is related to the unit's output. For fuel consumption, For oil prices, The pollution coefficient, This is the coefficient for the operation and maintenance costs of thermal power units. For the daily peak-shaving capacity of thermal power units, The variable is 0-1, representing the winning bid status of the thermal power unit in peak shaving during time period t; and These are the deep peak-shaving quotations for thermal power unit numbered i during both the non-oil-injection and oil-injection phases, respectively. and These represent the winning bid capacity for thermal power unit numbered i during time period t, with and without oil supply.

[0145] In this embodiment, to analyze the peak-shaving market and obtain the minimum peak-shaving operating cost, thermal power units are used as the main body. Peak-shaving demand constraints, thermal power unit peak-shaving operation constraints, and thermal power unit coupling constraints are constructed. Based on the above constraints, the minimum peak-shaving operating cost of thermal power units is calculated by comprehensively analyzing the fuel cost, life loss cost (deep peak-shaving without oil injection and with oil injection), oil injection cost, pollution emission cost, operation and maintenance cost, and power system deep peak-shaving payment cost. The peak-shaving of thermal power units includes three stages: conventional peak-shaving, peak-shaving without oil injection, and peak-shaving with oil injection. When wind curtailment occurs, 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 injection and the maximum output with oil injection. Compared with conventional peak-shaving, it increases the life loss cost, oil injection cost, and pollution cost associated with oil injection output. Therefore, in order to maximize the absorption of wind power, these increased costs must be taken into account.

[0146] S5: The energy dispatch scheme is obtained by solving the energy dispatch model with the goal of minimizing the sum of the minimum dispatch 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 shaving operation cost of thermal power units.

[0147] To obtain the energy dispatch scheme with the lowest operating cost, this embodiment first analyzes energy storage devices in the electricity spot market. Electric vehicles can be considered equivalent to energy storage power stations to some extent; however, electric vehicles have different characteristics and randomness compared to stationary energy storage power stations. Therefore, an electric vehicle dispatch model is constructed to analyze electricity price changes under electric vehicle participation, facilitating subsequent calculation of energy dispatch costs in the electricity spot market based on electricity price changes. To facilitate subsequent calculations of frequency regulation and peak shaving market operating costs, an energy storage power station dispatch model is constructed to obtain the capacity changes of energy storage power stations during the dispatch process. Based on the capacity changes of energy storage power stations, the subsequent energy dispatch of thermal power units and electric vehicles is analyzed. Analysis can reduce operating costs while ensuring stable energy dispatch. To obtain the minimum dispatch cost with electric vehicles involved, the power consumption of electric vehicles, changes in the capacity of energy storage stations, and electricity prices obtained from the electric vehicle dispatch model are considered. The minimum dispatch cost is calculated through comprehensive analysis. Based on the minimum dispatch cost, the energy consumption of thermal power units is analyzed to obtain the minimum energy consumption cost. Then, frequency regulation and peak shaving of thermal power units are performed to obtain the minimum frequency regulation operating cost and the minimum peak shaving operating cost. The above costs are comprehensively analyzed to obtain the energy dispatch scheme with the minimum total cost. Based on this scheme, energy dispatch is carried out on each unit in the electricity spot market, which can significantly reduce the energy dispatch cost in the electricity spot market, make resource dispatch more flexible, reduce the pressure of resource dispatch, and further improve resource utilization.

[0148] As a further supplement to this embodiment, the following scenario will be used as an example to further illustrate this solution:

[0149] 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 106,204 MW. The load demand, wind power forecast output, and initial equivalent load curves are as follows: Figure 2 As shown, the wind power forecast deviation is set at 25%; the frequency regulation and deep peak shaving electricity prices for thermal power units are referenced to the ancillary service market, with the frequency regulation capacity demand being 0.7% of the load. The deep peak shaving market has two tiers of pricing types: no oil injection output and oil injection peak shaving output range, with output ranges of [40%, 50%) and [30%, 40%) of the rated capacity, respectively. Pricing is based on the unit's pricing curve, and the units are cleared at the marginal electricity price; the reserve capacity reserved by the units in the electricity market is set at 20% of the rated capacity, and the start-up and shutdown cost ranges are set at 56,000~616,000 yuan and 388,160~426,985 yuan, respectively. , Set to 0.0946 (yuan / kW); , , and It is determined based on the peak-shaving capacity of different generating units.

[0150] First, set the parameters for the electric vehicle. The specific parameters are shown in the table below:

[0151]

[0152] Set the parameters for the energy storage power station. The specific parameters are shown in the table below:

[0153]

[0154] To minimize the overall costs of the electricity market, frequency regulation market, and deep peak shaving market, , , and The four objective functions are solved jointly, and the calculations are performed using both sequential clearing and joint clearing mechanisms. The relevant costs are shown in the table below:

[0155]

[0156] A comparison of the two different mechanisms reveals that the total cost of sequential clearing is 0.9% higher than that of joint clearing, but the difference is not significant. Both mechanisms can achieve optimal energy dispatch in the electricity spot market. The costs of each stage of joint clearing are lower than those of sequential clearing, specifically by 8.4%, 1.2%, 2.9%, and 7.6%, respectively. This indicates that the joint clearing mechanism is more effective in terms of equivalent load optimization costs and deep peak-shaving costs. However, the current electricity spot market is in its early stages of development and lacks clear market operation constraints. The joint operation mechanism involves far more line constraints and complexity than sequential clearing. Therefore, this embodiment should clarify the clearing mechanisms of each market to ensure the operational efficiency of all market participants.

[0157] Taking electric vehicles of different sizes participating in the electric energy market and adopting a sequential clearing mechanism as an example, such as Figure 3 As shown, 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 was found that expanding the scale of electric vehicles can effectively smooth the load curve. The load variance of 500,000 EVA vehicles is reduced by 49.56%, 44.19%, 36.82%, 26.89%, 15.56%, and 25.11% respectively compared to the other four scales and disordered electric vehicles. 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 capacities of electric vehicles at this scale are as follows: Figure 4 As shown, in this mode, electric vehicles undergo concentrated charging and discharging, and the timing of these charges and discharges is related to the individual lifestyle habits of electric vehicle users and the anti-peak-shaving characteristics of wind power output, thus proving the rationality of this mode; Figure 5 The comparison shown is before and after the equivalent load variance constraint. It can be seen that under the same objective, adding the equivalent load variance constraint can make the equivalent load curve smoother, effectively reducing the pressure of flexible scheduling of thermal power units, and proving the advantages of reasonable scheduling of electric vehicles.

[0158] Taking the impact of energy storage power stations on the electricity spot market as an example, such as Figure 6 As shown in the figure, the impact of single charge and discharge variations of energy storage power stations on the charging and discharging behavior of the frequency regulation market is illustrated in the following table: The resulting operating costs of thermal power units, energy storage power stations, and total costs are shown in the following table:

[0159]

[0160] As the single-charge and discharge capacity of energy storage power stations continues to increase, the amount of electricity they can bid for also continues to increase, while the operating costs and total costs of thermal power units in the frequency regulation market continue to decrease; for example Figure 7 As shown, the charge / discharge and state-of-charge diagrams of a 20MW energy storage power station in a single charge / discharge cycle are presented, ensuring the state of charge at the beginning and end of the dispatch process; as shown... Figure 8As shown, the impact of energy storage power station bidding behavior on its charging and discharging behavior was analyzed. When the price difference is within 120%, the charging and discharging capacity of the energy storage power station is not affected. When the bidding price of the energy storage power station for charging is as high as 120% of the benchmark price, the bidding price difference reaches its 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 required to use thermal power units as the main frequency regulation unit 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 frequency regulation unit.

[0161] In this embodiment, the following four scenarios are compared and analyzed with the background that the energy storage power station does not participate in multiple markets: Scenario 1: The electricity spot market with / without electric vehicles clears out in sequence (Mode 1 / Mode 1 (without electric vehicles) / Mode 1 (disordered electric vehicle cluster)).

[0162] Scenario 2: Sequential clearing of the electricity spot market with / without energy storage participation (Mode 1 / Mode 2 (without energy storage)); specific data are shown in the table below, where the electricity market includes equivalent load optimization costs and operating costs of thermal power units and the system:

[0163]

[0164] Comparing the various models in Scenario 1, it was found that when electric vehicles participate in the electricity market, the total cost is slightly higher than in the model without electric vehicle participation. This increased cost mainly comes from the operating costs of the electricity market. However, the cost of deep peak shaving is 9.7% lower than in the model without electric vehicle participation. This indicates that the model without electric vehicles lacks flexible energy storage, resulting in poor flexible dispatching capabilities of thermal power units. To absorb wind power, thermal power units actively reduce their output range, leading to reduced participation in the electricity market and increased deep peak shaving operating costs. Model 1 (disorderly electric vehicle cluster) saves on the cost of managing electric vehicles but increases the dispatching pressure on thermal power units, with costs 3.7% higher than in Model 1. The increased costs are mainly borne by the deep peak shaving market, with deep peak shaving costs 24.3% higher than in Model 1. This suggests that, compared to the model without electric vehicles, unmanaged electric vehicle dispatching is more harmful. Figure 9 A comparison of the optimized equivalent load curves for Mode 1 (black line), Mode 1 (no electric vehicles) (green line), and Mode 1 (disordered electric vehicle cluster) (yellow line) shows that Mode 1 (disordered electric vehicle cluster) has the largest equivalent load fluctuation, which is not conducive to stable unit output. Mode 1 is smoother than the other two. Combined with... Figure 10It can be observed that the amount of wind curtailment is significantly lower than in the mode without electric vehicles, with a total wind curtailment reduction of 66.1% compared to the mode without electric vehicles, demonstrating the advantage of electric vehicles in absorbing wind power. By introducing electric vehicles, it was found that the increase in the cost of the electricity 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 electricity market and reduce their participation in the deep peak shaving market.

[0165] A comparison between Mode 2 and Mode 1 reveals that the cost changes in each market mainly originate from the frequency regulation market. The participation of energy storage power stations can effectively reduce the operating costs of the frequency regulation market, by 3.8% compared to Mode 2.

[0166] In this embodiment, the following four scenarios are compared and analyzed, taking the participation of energy storage power stations in the electricity market as the background:

[0167] Scenario 3: (1) 0 MWh energy storage in the electricity market & 200 MWh energy storage in the frequency regulation market; (2) 200 MWh energy storage in the electricity market & 0 MWh energy storage in the frequency regulation market;

[0168] Scenario 4: 100MWh energy storage in the power market & 100MWh energy storage in the frequency regulation market (comparison with / without / without disordered EVA participation);

[0169] Scenario 5: 200MWh of energy storage in the electricity market & 200MWh of energy storage in the frequency regulation market;

[0170] Scenario 6: 400MWh of energy storage in the electricity market & 200MWh of energy storage in the frequency regulation market (because the demand in the electricity market is larger than that in the frequency regulation market, and the growth rate of the demand in the electricity market is faster than that in the frequency regulation market, the proportion of energy storage power stations in the electricity market should gradually increase).

[0171] The operating cost data for each market are shown in the table below:

[0172]

[0173] 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 indicates that energy storage power stations can effectively reduce the deep peak shaving cost of thermal power units. However, under the same capacity, when participating in a single market with the goal of minimizing costs, it is more suitable to participate in the frequency regulation market.

[0174] Scenario 3(1) and Scenario 4 are used to compare the costs of various market players participating in multiple markets under the same capacity of energy storage power stations. The frequency regulation operation cost of thermal power units in Scenario 4 (with / without / disordered electric vehicles) increases by 0.3%, 0.2%, and 0.1% respectively compared with Scenario 3(1), indicating that the increase of energy storage power station capacity can reduce the operating cost of a certain market. The energy market cost in Scenario 4 (without / disordered electric vehicles) is lower than that in Scenario 3 because the system's flexible adjustment capability is insufficient due to the influence of the degree of participation of electric vehicles and the capacity of energy storage power stations. The units can only frequently enter the deep peak shaving market, and the deep peak shaving cost increases by 9.1% and 30.89% compared with Scenario 3(1). In Scenario 4 (with electric vehicles), except for the deep peak shaving market operation cost which is lower than that in Scenario 3(1), the costs of other markets all increase to a certain extent. This indicates that the participation of energy storage power stations in each market is not high, the market cost is not significantly optimized, and the purpose of alleviating the pressure of peak shaving or frequency regulation of the units is not achieved. Moreover, low-capacity energy storage power stations are more effective in participating in a single market than in participating in multiple markets.

[0175] The comparison between Scenario 4 (with / without electric vehicles) is used to verify the impact of different energy storage combinations on the total operating cost of the market. The deep adjustment cost of the disordered electric vehicle model increased by 32.26% compared to the model with electric vehicles, and the total operating cost was higher than that of the electric vehicle model, indicating that the combination of electric vehicles and energy storage power stations has a better market regulation effect than energy storage alone.

[0176] By comparing the operating costs of energy storage power stations with different capacities participating simultaneously in the electricity and frequency regulation markets under scenarios 3, 4 (with electric vehicles), 5, and 6, it was found that as the capacity of energy storage power stations increases, the total energy dispatch cost in the electricity spot market is effectively reduced. The main cost reduction comes from deep peak shaving costs, while the cost in the electricity market increases only slightly, and the increase in frequency regulation market costs is negligible compared to the electricity market. This indicates that increasing the capacity of energy storage power stations can effectively solve the problem of increased costs associated with participating in multiple markets and alleviate the pressure on peak shaving and frequency regulation of generating units.

[0177] As can be seen from the above embodiments, it has at least the following substantial effects:

[0178] (1) This invention enables energy storage power stations to participate in the electricity spot market in collaboration with thermal power units and electric vehicles. Compared with the electricity spot market with a single type of energy storage, the market regulation effect of this invention using multiple types of energy storage is better.

[0179] (2) By analyzing the characteristics and combinations of different types of energy storage, this invention enables the electricity spot market to adjust resources more flexibly, reduces the pressure of resource scheduling, and further improves resource utilization.

[0180] (3) By constructing an electricity spot market that includes energy storage power stations, electric vehicles and traditional energy, this invention helps to optimize the allocation of power system resources, alleviate the dispatch pressure of traditional thermal power units and promote the consumption of wind power, reduce energy waste and improve resource utilization.

[0181] The specific embodiments described above are preferred embodiments of the energy dispatching method based on the background of the electricity spot market of the present invention, and are not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to the specific embodiments described above. 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. An energy dispatching method based on the background of the electricity spot market, characterized by: Includes the following steps: S1. Construct an electric vehicle scheduling model based on the driving characteristics of electric vehicles; construct an energy storage power station scheduling model based on the operating characteristics of energy storage power stations. The formula for the electric vehicle scheduling model is as follows: in, This represents the maximum number of electric vehicles that can be taken off the road within time period t. Let t be the probability that an electric vehicle will be out of service during time period t. To determine the total number of electric vehicles in the planned area, Let be the total electricity consumption of all electric vehicles during time period t. Let t be the distance traveled by the electric vehicle during time period t. Electricity consumption per 100 kilometers for electric vehicles. The daily driving pattern distribution of electric vehicles, Represents the mathematical expectation. Indicates standard deviation, Let t represent the remaining battery power of the electric vehicle during time period t. For the charging efficiency of electric vehicles. For the discharge efficiency of electric vehicles. The amount of electricity charged for the electric vehicle during period t. The discharge amount of the electric vehicle during period t; S2. Based on the electric vehicle scheduling model, obtain the total power consumption of electric vehicles and the remaining battery power. Update the electricity price based on the total power consumption of electric vehicles and the remaining battery power, taking into account daily load fluctuations, to obtain the real-time electricity price. Based on the energy storage power station scheduling model, obtain the capacity changes during the energy storage power station scheduling process, including the following steps: The electric vehicle scheduling model is set with constraints, which include at least the total charging and discharging capacity of electric vehicles, the charging and discharging power of electric vehicles at different time periods, the state of charge of electric vehicles, and the time-of-use electricity price. Based on the constraints of the electric vehicle scheduling model, the total power consumption of electric vehicles and the remaining battery power are obtained by solving the electric vehicle scheduling model. The total electricity consumption of electric vehicles, the remaining battery capacity, and daily load fluctuations are analyzed. Peak-valley pricing is then applied by dynamically dividing different peak and valley periods each day. The formula is as follows: in For electricity price, For peak electricity pricing, To stabilize electricity prices, Off-peak electricity pricing, This is the average load. For the equivalent load peak-valley difference, This refers to the range of electricity price fluctuations during peak and off-peak hours. S3. Based on real-time electricity prices, capacity changes during energy storage power station scheduling, the operation mechanism of the frequency regulation market, and the operation mechanism of the peak shaving market, an energy dispatch model is constructed to obtain the minimum dispatch cost with the participation of electric vehicles; based on the minimum dispatch cost with the participation of electric vehicles, the minimum energy consumption cost of thermal power units is obtained. S4. Conduct a frequency regulation market characteristic analysis on 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; conduct a peak shaving market characteristic analysis on thermal power units to obtain the minimum peak shaving operating cost with the participation of thermal power units. S5. With the goal of minimizing the sum of minimum dispatch cost, minimum energy consumption cost of thermal power units, minimum frequency regulation operation cost, and minimum peak shaving operation cost, the energy dispatch model is solved to obtain the energy dispatch scheme under the background of electricity spot market.

2. The energy dispatching method based on the electricity spot market as described in claim 1, characterized in that: In S1, the formula for the energy storage power station scheduling model is as follows: in, This represents the remaining capacity of the energy storage power station during time period t in the dispatching process. Binary variables for switching charging states of energy storage power stations. This is a binary variable representing the switching of the discharge state of the energy storage power station. To improve the charging efficiency of energy storage power stations. For the discharge efficiency of energy storage power stations, To increase the frequency regulation output of the energy storage power station during time period t, This is to regulate the frequency output of the energy storage power station during time period t.

3. The energy dispatching method based on the electricity spot market as described in claim 1, characterized in that: In S2, the capacity changes during the energy storage power station scheduling process are obtained based on the energy storage power station scheduling model, including the following steps: The energy storage power station scheduling model is set with constraints, which include at least the energy storage power station SOC start and end 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. Based on the constraints of the energy storage power station scheduling model, the capacity changes during the energy storage power station scheduling process are obtained by solving the energy storage power station scheduling model.

4. The energy dispatching method based on the electricity spot market as described in claim 1, characterized in that: In S3, an energy dispatching model is constructed based on real-time electricity prices, capacity changes during energy storage power station dispatching, the operating mechanism of the frequency regulation market, and the operating mechanism of the peak-shaving market to obtain the minimum dispatching cost with the participation of electric vehicles. The model includes the following steps: Set wind power output constraints, which include at least upper and lower limits of wind power output and equivalent load variance constraints; The electric vehicle (EV) charging and discharging capacity and remaining power of the EVs are calculated based on the EV scheduling model, and the minimum scheduling cost with EV participation is calculated with wind power output constraints as the constraint condition.

5. The energy dispatching method based on the electricity spot market as described in claim 1, characterized in that: In S3, the minimum energy consumption cost of thermal power units is obtained based on the minimum scheduling cost with the participation of electric vehicles, including the following steps: Based on the wind power output constraints and equivalent load variance constraints in the minimum dispatch cost under electric vehicle participation, an optimization curve for wind power output equivalent load is constructed, and the bidding curve of thermal power units in the electric energy market is obtained based on the optimization curve for wind power output equivalent load. Set operating constraints for thermal power units and power supply and demand balance constraints in the electricity market, and obtain the minimum energy consumption cost of thermal power units based on these constraints.

6. The energy dispatching method based on the electricity spot market as described in claim 1, characterized in that: In S4, a frequency regulation market characteristic analysis is performed on 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, including the following steps: Set frequency regulation demand constraints and thermal power unit frequency regulation operation constraints; 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 constraints of frequency regulation demand and thermal power unit frequency regulation operation, the minimum frequency regulation operation cost involving energy storage power stations and thermal power units is calculated.

7. The energy dispatching method based on the electricity spot market as described in claim 1, characterized in that: In S4, a peak-shaving market characteristic analysis of thermal power units is performed to obtain the minimum peak-shaving operating cost with the participation of thermal power units, including the following steps: Set peak-shaving demand constraints, thermal power unit peak-shaving operation constraints, and thermal power unit coupling constraints; Based on the fuel cost, life loss cost, oil injection cost, pollution emission cost, operation and maintenance cost, and power system deep peak shaving payment cost of thermal power units, and with the constraints of peak shaving demand, thermal power unit peak shaving operation constraints, and thermal power unit coupling constraints, the minimum peak shaving operation cost with the participation of thermal power units is calculated.

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