Methods and devices for clearing out the power peak-shaving ancillary services market

Abstract

This application discloses a method and apparatus for clearing the power peak-shaving ancillary service market. It includes: obtaining the peak-shaving demand for electricity for the next day; determining the dispatchable capacity of L load aggregators at the target time and the corresponding node clearing price; obtaining the operating boundary constraints of controllable generating units; constructing a day-ahead pre-clearing model based on the operating boundary constraints with the goal of minimizing electricity cost; solving the model to obtain the pre-clearing capacity and marginal clearing price of controllable generating units at the target time; determining M load aggregators as winners based on the node clearing prices of the L load aggregators and the marginal clearing prices of controllable generating units; and determining N controllable generating units as winners based on the difference between the total dispatchable capacity of the M load aggregators at the target time, the pre-clearing capacity of a single controllable generating unit, and the peak-shaving demand for electricity. This application solves the technical problem of energy curtailment caused by the lack of bidding for renewable energy generation entities in the relevant peak-shaving ancillary service market.

Description

Technical Field

[0001] This application relates to the field of power system dispatching and operation technology, and more specifically, to a method and apparatus for clearing the power peak-shaving ancillary service market. Background Technology

[0002] With the increasing severity of global environmental problems, there is a need to continuously increase the installed capacity and grid connection of renewable energy. The intermittent nature of renewable energy output necessitates the participation of backup or controllable resources in auxiliary peak-shaving services to better support the consumption of green electricity. The participation of demand-side resources, represented by electric vehicles, in the electricity market aggregation will provide the power system with a large amount of adjustable resources, alleviating the problem of system balance.

[0003] However, currently, the participation of new energy sources in the electricity market for peak shaving is not subject to bidding and quotation as other conventional power sources. Instead, it is mostly guaranteed purchase or bidding without quotation. This approach makes it difficult to attract electric vehicle owners to voluntarily join the electric vehicle grid, resulting in energy curtailment and resource waste.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a method and apparatus for clearing the power peak-shaving ancillary service market, which at least solves the technical problem of energy curtailment caused by the failure of the relevant peak-shaving ancillary service market to submit bids for renewable energy power generation entities.

[0006] According to one aspect of the embodiments of this application, a method for clearing the power peak-shaving ancillary service market is provided, comprising: obtaining the peak-shaving demand energy for the next day; determining the dispatchable capacity that L load aggregators can participate in regulation at a target time and the node clearing price corresponding to the dispatchable capacity, wherein L is a positive integer greater than or equal to 1; obtaining the operating boundary constraints of controllable generating units, constructing a day-ahead pre-clearing model based on the operating boundary constraints with the goal of minimizing energy cost, and obtaining the pre-clearing capacity and marginal clearing price of controllable generating units at the target time by solving the day-ahead pre-clearing model; determining M load aggregators as winners based on the node clearing prices of the L load aggregators and the marginal clearing prices of controllable generating units, and determining N controllable generating units as winners based on the difference between the total dispatchable capacity that the M winning load aggregators can participate in regulation at the target time, the pre-clearing capacity of a single controllable generating unit, and the peak-shaving demand energy for the next day, wherein M is a positive integer less than or equal to L, and N is a non-negative integer.

[0007] Optionally, the load aggregator is used to aggregate the electrical energy of multiple electric vehicles. Determining the dispatchable capacity that M load aggregators can participate in regulating at a target time and the node clearing price corresponding to the dispatchable capacity includes: obtaining the first adjustable power of a single electric vehicle at the target time; determining the second adjustable power obtained by each load aggregator aggregating the first adjustable power of multiple electric vehicles at the target time based on the first adjustable power; and obtaining the node clearing price corresponding to the dispatchable capacity reported by each load aggregator, wherein the second adjustable power is the dispatchable capacity that the load aggregator can participate in regulating at the target time.

[0008] Optionally, obtaining the first adjustable power of a single electric vehicle at a target time includes: obtaining the maximum and minimum state of charge (SOC) of the single electric vehicle after charging and discharging; obtaining the real-time SOC of the single electric vehicle at the target time, and calculating the maximum charging power at the target time based on the maximum SOC and the real-time SOC, and calculating the maximum discharging power at the target time based on the minimum SOC and the real-time SOC; obtaining the real-time charging and discharging power of the single electric vehicle at the target time, and calculating the adjustable power at the target time based on the maximum charging power and the real-time charging and discharging power, and calculating the adjustable power at the target time based on the maximum discharging power and the real-time charging and discharging power; and determining the first adjustable power of the single electric vehicle at the target time based on the adjustable power at the target time and the adjustable power at the target time.

[0009] Optionally, the controllable units include at least one of the following: thermal power units, gas power units, hydropower units, and nuclear power units.

[0010] Optionally, the operating boundary constraints of the controllable generating unit are obtained, including: when the controllable generating unit is a thermal power unit, the operating boundary constraints include at least one of the following: demand balance constraint, thermal power unit output range constraint, thermal power unit ramping constraint, thermal power unit minimum start-up time constraint, and thermal power unit minimum shutdown time constraint; when the controllable generating unit is a gas power unit, the operating boundary constraints include at least one of the following: demand balance constraint, gas power unit output range constraint, gas power unit ramping constraint, and gas power unit minimum shutdown time constraint; when the controllable generating unit is a hydropower unit, the operating boundary constraints include at least one of the following: demand balance constraint and hydropower unit operating characteristic constraint; when the controllable generating unit is a nuclear power unit, the operating boundary constraints include at least one of the following: demand balance constraint, nuclear power unit output range constraint, nuclear power unit ramping constraint, and nuclear power unit minimum shutdown time constraint.

[0011] Optionally, determining the winning bidders for M load aggregators based on the node clearing prices of the L load aggregators and the marginal clearing prices of the controllable units includes: determining the relationship between the node clearing prices of the L load aggregators and the marginal clearing prices of the controllable units; determining the winning bidders for the L load aggregators when all node clearing prices of the L load aggregators are lower than the marginal clearing prices of the controllable units; determining the winning bidders for the M load aggregators when the node clearing prices reported by the M load aggregators are lower than the marginal clearing prices of the controllable units; and determining the winning bidders for zero load aggregators when all node clearing prices reported by the L load aggregators are higher than the marginal clearing prices of the controllable units.

[0012] Optionally, N controllable generating units are selected based on the total dispatchable capacity that the M load aggregators can participate in regulating at the target time, the pre-cleared capacity of a single controllable generating unit, and the difference between the peak-shaving demand energy of the next day. This includes: determining the total dispatchable capacity based on the dispatchable capacity that the M load aggregators can participate in regulating at the target time; determining the difference between the total dispatchable capacity and the peak-shaving demand energy of the next day; and determining the N controllable generating units based on the difference between the pre-cleared energy of a single controllable generating unit and the peak-shaving demand energy.

[0013] According to another aspect of the embodiments of this application, a power peak-shaving ancillary service market clearing device is also provided, comprising: an acquisition module for acquiring the peak-shaving demand power energy of the next day; a first clearing module for determining the dispatchable capacity that L load aggregators can participate in regulation at a target time and the node clearing price corresponding to the dispatchable capacity, wherein L is a positive integer greater than or equal to 1; a second clearing module for acquiring the operating boundary constraints of controllable units, constructing a day-ahead power energy pre-clearing model based on the operating boundary constraints with the goal of minimizing power energy cost, and obtaining the marginal clearing price of controllable units at the target time by solving the day-ahead power energy pre-clearing model; and a joint clearing module for determining M load aggregators as the winning bidders based on the node clearing prices of the L load aggregators and the marginal clearing prices of controllable units, and determining N controllable units as the winning bidders based on the difference between the total dispatchable capacity that the M winning load aggregators can participate in regulation at the target time, the pre-clearing capacity of a single controllable unit, and the peak-shaving demand power energy of the next day, wherein M is a positive integer less than or equal to L, and N is a non-negative integer.

[0014] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-described power peak shaving ancillary service market clearing method by running the computer program.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, the electronic device including: a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-described power peak shaving ancillary service market clearing method through the computer program.

[0016] In this embodiment, the peak-shaving demand for electricity on the next day is obtained; the dispatchable capacity of L load aggregators that can participate in regulation at the target time and the corresponding node clearing price are determined, where L is a positive integer greater than or equal to 1; the operating boundary constraints of controllable units are obtained, and based on the operating boundary constraints, a day-ahead pre-clearing model of electricity is constructed with the goal of minimizing electricity cost. The pre-clearing capacity and marginal clearing price of controllable units at the target time are obtained by solving the day-ahead pre-clearing model; based on the node clearing prices of the L load aggregators and the marginal clearing prices of controllable units, M load aggregators are selected as the winning bidders, and N controllable units are selected as the winning bidders based on the difference between the total dispatchable capacity of the M winning load aggregators that can participate in regulation at the target time, the pre-clearing capacity of a single controllable unit, and the peak-shaving demand for electricity on the next day, where M is a positive integer less than or equal to L, and N is a non-negative integer. This solves the technical problem of energy curtailment caused by the lack of bidding for renewable energy generation entities in the relevant peak-shaving ancillary service market. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 It is a hardware structure block diagram of a computer terminal (or mobile device) for implementing a market clearing method for power peak shaving ancillary services, based on relevant technologies;

[0019] Figure 2 This is a flowchart illustrating an optional market clearing method for power peak shaving ancillary services according to an embodiment of this application;

[0020] Figure 3 This is a schematic diagram of an optional process for determining the first adjustable power according to an embodiment of this application;

[0021] Figure 4 This is a schematic diagram of an optional power peak shaving ancillary service market clearing device according to an embodiment of this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] Furthermore, all information and data (including but not limited to user device information, user personal information, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties. For example, this system has an interface with the relevant user or organization. Before obtaining relevant information, it needs to send an acquisition request to the aforementioned user or organization through the interface, and obtain the relevant information after receiving consent from the aforementioned user or organization.

[0025] Example 1

[0026] According to an embodiment of this application, an embodiment of a method for clearing the power peak-shaving ancillary service market is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] The methods and embodiments provided in this application can be executed on mobile terminals, computer terminals, or similar computing devices. Figure 1 A hardware block diagram of a computer terminal (or mobile device) for implementing a market clearing method for power peak shaving ancillary services is shown. Figure 1As shown, the computer terminal 10 (or mobile device 10) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0028] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0029] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the power peak shaving ancillary service market clearing method in this embodiment of the application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned power peak shaving ancillary service market clearing method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0030] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0031] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0032] Under the above operating environment, Figure 2 This is a flowchart illustrating an optional market clearing method for power peak shaving ancillary services according to an embodiment of this application, as shown below. Figure 2 As shown, the method includes at least steps S202-S208, wherein:

[0033] Step S202: Obtain the peak-shaving demand for electricity for the next day.

[0034] The aforementioned peak-shaving demand energy refers to the electrical energy required to meet the load demand during peak load periods when the load demand exceeds the basic load capacity of the power system.

[0035] Step S204: Determine the schedulable capacity that L load aggregators can participate in regulation at the target time and the node clearing price corresponding to the schedulable capacity, where L is a positive integer greater than or equal to 1.

[0036] The aforementioned load aggregators refer to service providers that aggregate customer-side electricity loads to achieve more efficient power operation and utilization. Their services primarily include load forecasting, dispatching, and control. Through technological means, they aggregate dispersed, small-scale electricity loads to enable large-scale electricity trading and assist users in participating in electricity market competition. Typically, load aggregators can be charging station operators, integrating charging station resources and corresponding charging users to access the electricity market and participate in demand-side response.

[0037] As an optional implementation, in the technical solution provided in step S204 above, the load aggregator is used to aggregate the electrical energy of multiple electric vehicles, and the method may include: obtaining the first adjustable power of a single electric vehicle at a target time; determining the second adjustable power obtained by each load aggregator aggregating the first adjustable power of multiple electric vehicles at the target time based on the first adjustable power; and obtaining the node clearing price reported by each load aggregator corresponding to the schedulable capacity, wherein the second adjustable power is the schedulable capacity that the load aggregator can participate in regulating at the target time.

[0038] In this embodiment, the adjustable power of a single electric vehicle is calculated by determining its adjustable power at a target time, and its adjustable power downwards. Then, a second adjustable power is calculated by aggregating the first adjustable power of multiple electric vehicles from different load aggregators. The relevant bid for the additional second adjustable power provided by each load aggregator is also calculated, i.e., the node clearing price. This second schedulable capacity is the schedulable capacity that the load aggregator can participate in peak shaving regulation. In other words, each load aggregator establishes a clearing queue for electric vehicles participating in the peak shaving ancillary service market by submitting a bid. The node clearing price for each load aggregator can be denoted as [λ]. EV1 ,λ EV2 ,λ EV3 ,…,λ EVL ].

[0039] Optionally, Figure 3 This is a schematic diagram of an optional process for determining the first adjustable power according to an embodiment of this application, as shown below. Figure 3 As shown, the method may include the following steps S31-S34, wherein:

[0040] Step S31: Obtain the maximum and minimum state of charge of a single electric vehicle after charging and discharging.

[0041] Step S32: Obtain the real-time state of charge of a single electric vehicle at the target time, and calculate the maximum charging power at the target time based on the maximum state of charge and the real-time state of charge, and calculate the maximum discharging power at the target time based on the minimum state of charge and the real-time state of charge.

[0042] Step S33: Obtain the real-time charging and discharging power of a single electric vehicle at the target time, and calculate the adjustable power at the target time based on the maximum charging power and the real-time charging and discharging power, and calculate the adjustable power at the target time based on the maximum discharging power and the real-time charging and discharging power.

[0043] Step S34: Determine the first adjustable power of a single electric vehicle at the target time based on the adjustable power and adjustable power of the single electric vehicle at the target time.

[0044] Specifically, the maximum allowable battery capacity (i.e., maximum state of charge, SoC) and the minimum allowable battery capacity (i.e., minimum state of charge) of a single electric vehicle after charging and discharging are first calculated. Since different types of electric vehicles have different battery characteristics, in this embodiment, the maximum and minimum state of charge of each electric vehicle after charging and discharging are denoted as SoC. i,min SoC i,max , where i represents the vehicle number.

[0045] Next, based on the real-time state of charge (SOC) of each electric vehicle... i,t and minimum state of charge SoC i,max The minimum state of charge is used to calculate the maximum charging power of a single electric vehicle at this point, denoted as P. i,cmax,t =min{(SoC i,max -SoC i,t ) / Δt,P cmax The maximum discharge power is denoted as P. i,dmax,t =min{(SoC i,t -SoC i,min ) / Δt,P dmax}, where min represents the minimum value between two numbers, Δt represents the scheduling time, Pcmax represents the maximum charging power that the charging pile can provide, and Pdmax represents the maximum discharging power that the charging pile can provide.

[0046] Then, based on the real-time charging and discharging power P of electric vehicle i at time t... i,t and the maximum charging power P of a single electric vehicle at this time i,cmax,t Maximum discharge power P i,dmax,t The real-time adjustable power and real-time adjustable power of a single electric vehicle are calculated, where the real-time adjustable power P is... i,up,t =P i,dmax,t -P i,t Real-time adjustable power P i,down,t =P i,cmax,t -P i,t Furthermore, the charging power is negative, and the discharging power is positive.

[0047] Finally, based on the adjustable power P of electric vehicle i at time t, i,up,t Adjustable power P i,down,t This allows us to obtain the first adjustable power of electric vehicle i at time t.

[0048] Furthermore, based on the first adjustable power of a single electric vehicle i at time t, the second adjustable power obtained by each load aggregator aggregating the first adjustable power of multiple electric vehicles at time t is determined, and the node clearing price corresponding to the schedulable capacity reported by each load aggregator is obtained.

[0049] Specifically, the adjustable power P of different types of electric vehicles with different states of charge that can participate in regulation at the same time is calculated sequentially. i,up,t Adjustable power P i,down,t (i.e., the first adjustable power); then, the adjustable power of various types of electric vehicles is aggregated and calculated to obtain the total adjustable power of electric vehicles that can participate in regulation (i.e., the second adjustable power), where the total adjustable power at time t is P. up,t =∑P i,up,t The total adjustable power is P. down,t =∑P i,down,t .

[0050] Step S206: Obtain the operating boundary constraints of the controllable units. Based on the operating boundary constraints, construct a day-ahead pre-clearing model of electrical energy with the goal of minimizing the cost of electrical energy. Then, obtain the pre-clearing capacity and marginal clearing price of the controllable units at the target time by solving the day-ahead pre-clearing model of electrical energy.

[0051] In the technical solution provided in step S206, this embodiment of the application calculates the day-ahead electrical energy pre-out model of the controllable unit to predict the pre-out capacity and marginal clearing price of a single controllable unit at the target time.

[0052] Optionally, the aforementioned controllable units include at least one of the following: thermal power units, gas power units, hydropower units, and nuclear power units.

[0053] As an optional implementation, in the technical solution provided in step S206 above, obtaining the operating boundary constraints of the controllable unit may include:

[0054] When the controllable unit is a thermal power unit, the operating boundary constraints include at least one of the following: demand balance constraint, thermal power unit output range constraint, thermal power unit ramp constraint, thermal power unit minimum start-up time constraint, and thermal power unit minimum shutdown time constraint.

[0055] When the controllable unit is a gas turbine generator, the operating boundary constraints include at least one of the following: demand balance constraint, gas turbine generator output range constraint, gas turbine generator ramp constraint, and gas turbine generator minimum downtime constraint.

[0056] When the controllable generating unit is a hydropower unit, the operating boundary constraints include at least one of the following: demand balance constraints and hydropower unit operating characteristic constraints.

[0057] When the controllable unit is a nuclear power unit, the operating boundary constraints include at least one of the following: demand balance constraints, nuclear power unit output range constraints, nuclear power unit ramp-up constraints, and nuclear power unit minimum downtime constraints.

[0058] Typically, a thermal power unit refers to a coal-fired power unit. The following explanation uses a thermal power unit as an example to illustrate the acquisition of the operating boundary constraints for a thermal power unit, where:

[0059] Peak-shaving demand balancing constraints: Among them, P n,t R represents the electrical energy declared by thermal power unit n at time t, N represents the total number of units participating in the peak-shaving market, and R represents the total electrical energy declared by unit n at time t. t This represents the total peak-shaving demand at time t;

[0060] Peak-shaving unit output constraint: P n,min ≤P n,t ≤P n,max , where P n,min ,P n,max This indicates the upper and lower limits of peak shaving for thermal power unit n;

[0061] Peak-shaving unit ramp-up constraint: P n,t -P n,t-1 ≤ΔP n,up ;P n,t-1 -P n,t ≤ΔP n,down , where ΔP n,up ΔP represents the maximum upward climbing power of thermal power unit n. n,down This represents the maximum downward ramp power of thermal power unit n; minimum continuous start-up constraint: And minimum consecutive downtime constraint: Among them, u n,τ y represents the switching state variable of a thermal power unit. n,τ z represents the operating state variable of thermal power unit n during time period t. n,τ This represents the shutdown state variable of thermal power unit n during time period t.

[0062] Furthermore, after obtaining the operating boundary constraints of the adjustable generating units, a day-ahead pre-clearing model is constructed based on these constraints, with the objective of minimizing the cost of electricity. The objective function of this day-ahead pre-clearing model is... Where, λ n,t P represents the price quoted by thermal power unit n at time t. n,t Δt represents the report volume of unit n at time t, and Δt represents the length of each time interval.

[0063] The objective function is to obtain the minimum value, so the boundary conditions need to be determined. That is, the constraint equations need to be established based on the operating boundary constraints of the adjustable unit. Once the constraint conditions are determined, the day-ahead electrical energy pre-clearing model can be solved by calling the optimization solver to obtain the minimum value of the objective function. This minimum value is the lowest cost.

[0064] In addition, the constraints on the operating characteristics of hydropower units specifically include: establishing the water consumption relationship, power generation head relationship, tailwater level relationship, head loss relationship, and reservoir capacity-water level relationship of hydropower units; and establishing water storage level constraints, hydropower plant output constraints, power generation flow constraints, outflow constraints, and initial and final water level constraints of hydropower stations.

[0065] Step S208: Based on the node clearing prices of L load aggregators and the marginal clearing prices of controllable units, determine the winning bidders of M load aggregators, and based on the difference between the total dispatchable capacity that the winning M load aggregators can participate in regulation at the target time, the pre-clearing capacity of a single controllable unit, and the peak-shaving demand power energy of the next day, determine the winning bidders of N controllable units, where M is a positive integer less than or equal to L, and N is a non-negative integer.

[0066] As an optional implementation, in the technical solution provided in step S208 above, the method for determining the M winning load aggregators includes:

[0067] Determine the relationship between the node clearing price of L load aggregators and the marginal clearing price of controllable units;

[0068] When the node clearing prices of all L load aggregators are lower than the marginal clearing prices of the controllable units, the L load aggregators are determined to be the winning bidders.

[0069] When the node clearing prices reported by M load aggregators are lower than the marginal clearing prices of controllable units, the M load aggregators are determined to be the winning bidders.

[0070] When the node clearing prices reported by L load aggregators are all higher than the marginal clearing price of the controllable units, zero load aggregators are determined to be the winners.

[0071] Specifically, firstly, the marginal clearing prices of the controllable units reported by the M load aggregators are sorted to obtain a clearing price queue, where the node clearing price is denoted as λ. EV The marginal clearing price is denoted as λ. MC Among them, in λ MC >λ EV_max When the marginal clearing price of the controllable unit is higher than the node clearing price reported by L load aggregators, it means that all L load aggregators have won the bid; at λ EV_min <λ MC <λ EV_maxWhen this occurs, it means that only a portion (i.e., M) of the load aggregators report node clearing prices lower than the marginal clearing price of the controllable units; in this case, only these load aggregators win the bid. At λ EV_min >λ MC If the marginal clearing price of the controllable unit is lower than the node clearing price reported by L load aggregators, it means that none of the L load aggregators won the bid.

[0072] Furthermore, after determining the M winning load aggregators, i.e., the third-party clearing market, the total dispatchable capacity can be determined based on the dispatchable capacity that the M winning load aggregators can participate in regulation at the target time; the difference between the total dispatchable capacity and the peak-shaving demand energy of the next day can be determined; and the N controllable units can be determined based on the difference between the pre-clearing energy of a single controllable unit and the peak-shaving demand energy.

[0073] In other words, based on the difference between the peak-shaving demand power energy of the next day and the total dispatchable capacity of the M load aggregators, a secondary clearing is carried out for the controllable generating units. That is, the N controllable generating units are determined to win the bid based on the difference between the pre-cleared power energy of a single controllable generating unit and the peak-shaving demand power energy. Subsequently, the clearing result of the power system is formed based on the M load aggregators and the N controllable generating units.

[0074] Based on the scheme defined in steps S202 to S208 above, it can be understood that in this embodiment, the peak-shaving demand for electricity on the next day is obtained; the dispatchable capacity that L load aggregators can participate in regulation at the target time and the node clearing price corresponding to the dispatchable capacity are determined, where L is a positive integer greater than or equal to 1; the operating boundary constraints of controllable units are obtained, and based on the operating boundary constraints, a day-ahead pre-clearing model of electricity is constructed with the goal of minimizing electricity cost, and the pre-clearing capacity and marginal clearing price of controllable units at the target time are obtained by solving the day-ahead pre-clearing model; based on the node clearing prices of L load aggregators and the marginal clearing prices of controllable units, M load aggregators are determined to win bids, and N controllable units are determined to win bids based on the difference between the total dispatchable capacity that the M load aggregators can participate in regulation at the target time, the pre-clearing capacity of a single controllable unit, and the peak-shaving demand for electricity on the next day, where M is a positive integer less than or equal to L, and N is a non-negative integer.

[0075] Therefore, the technical solution of this application embodiment determines the winning load aggregator by judging the node clearing price reported by the load aggregator and the marginal clearing price of the controllable units. Then, based on the difference between the winning load aggregator and the peak-shaving demand, further joint clearing is conducted with the controllable units. This prioritizes power output through load aggregators, reduces the output of controllable units, and absorbs more power resources from electric vehicles. This solves the technical problem of energy curtailment caused by the lack of bidding for renewable energy generation in the relevant peak-shaving ancillary service market.

[0076] Example 2

[0077] Based on Embodiment 1 of this application, an embodiment of a power peak-shaving ancillary service market clearing device is also provided. This device executes the power peak-shaving ancillary service market clearing method described in the above embodiment during operation. Wherein, Figure 4 This is a schematic diagram of an optional power peak-shaving ancillary service market clearing device according to an embodiment of this application, as shown below. Figure 4 As shown, the power peak-shaving ancillary service market clearing device includes at least an acquisition module 41, a first clearing module 42, a second clearing module 43, and a joint clearing module 44, wherein:

[0078] The acquisition module 41 is used to acquire the peak-shaving demand for electricity for the next day.

[0079] The aforementioned peak-shaving demand energy refers to the electrical energy required to meet the load demand during peak load periods when the load demand exceeds the basic load capacity of the power system.

[0080] The first clearing module 42 is used to determine the schedulable capacity that L load aggregators can participate in regulation at the target time and the node clearing price corresponding to the schedulable capacity, where L is a positive integer greater than or equal to 1.

[0081] Among them, the load aggregator is used to aggregate the electrical energy of multiple electric vehicles.

[0082] As an optional implementation, the first clearing module 42 is also used to obtain the first adjustable power of a single electric vehicle at the target time; determine the second adjustable power obtained by each load aggregator aggregating the first adjustable power of multiple electric vehicles at the target time based on the first adjustable power; and obtain the node clearing price reported by each load aggregator corresponding to the schedulable capacity, wherein the second adjustable power is the schedulable capacity that the load aggregator can participate in regulating at the target time.

[0083] In this embodiment, the adjustable power of a single electric vehicle is calculated by determining its adjustable power at a target time, and then a second adjustable power is calculated by aggregating the first adjustable power of multiple electric vehicles from different load aggregators. The relevant bid from each load aggregator for its additional second adjustable power is also calculated, i.e., the node clearing price. This second schedulable capacity is the schedulable capacity that the load aggregator can participate in peak shaving regulation. In other words, each load aggregator establishes a clearing queue for electric vehicles participating in the peak shaving ancillary service market by submitting a quantity and price bid.

[0084] Optionally, the first adjustable power can be determined as follows: obtain the maximum and minimum state of charge (SOC) of a single electric vehicle after charging and discharging; obtain the real-time SOC of a single electric vehicle at a target time, and calculate the maximum charging power at the target time based on the maximum SOC and the real-time SOC, and calculate the maximum discharging power at the target time based on the minimum SOC and the real-time SOC; obtain the real-time charging and discharging power of a single electric vehicle at the target time, and calculate the adjustable power at the target time based on the maximum charging power and the real-time charging and discharging power, and calculate the adjustable power at the target time based on the maximum discharging power and the real-time charging and discharging power; determine the first adjustable power of a single electric vehicle at the target time based on the adjustable power at the target time and the adjustable power at the target time.

[0085] The second clearing module 43 is used to obtain the operating boundary constraints of the controllable units. Based on the operating boundary constraints, a day-ahead pre-clearing model of electrical energy is constructed with the goal of minimizing the cost of electrical energy. The marginal clearing price of the controllable units at the target time is obtained by solving the day-ahead pre-clearing model of electrical energy.

[0086] Optionally, the aforementioned controllable units include at least one of the following: thermal power units, gas power units, hydropower units, and nuclear power units.

[0087] As an optional implementation, the second clearing module 43 may acquire the operating boundary constraints of the controllable unit, including:

[0088] When the controllable unit is a thermal power unit, the operating boundary constraints include at least one of the following: demand balance constraint, thermal power unit output range constraint, thermal power unit ramp constraint, thermal power unit minimum start-up time constraint, and thermal power unit minimum shutdown time constraint.

[0089] When the controllable unit is a gas turbine generator, the operating boundary constraints include at least one of the following: demand balance constraint, gas turbine generator output range constraint, gas turbine generator ramp constraint, and gas turbine generator minimum downtime constraint.

[0090] When the controllable generating unit is a hydropower unit, the operating boundary constraints include at least one of the following: demand balance constraints and hydropower unit operating characteristic constraints.

[0091] When the controllable unit is a nuclear power unit, the operating boundary constraints include at least one of the following: demand balance constraints, nuclear power unit output range constraints, nuclear power unit ramp-up constraints, and nuclear power unit minimum downtime constraints.

[0092] The joint clearing module 44 is used to determine the winning bids of M load aggregators based on the node clearing prices of L load aggregators and the marginal clearing prices of controllable units, and to determine the winning bids of N controllable units based on the difference between the total dispatchable capacity that the winning M load aggregators can participate in regulation at the target time, the pre-clearing capacity of a single controllable unit, and the peak-shaving demand power energy of the next day, where M is a positive integer less than or equal to L, and N is a non-negative integer.

[0093] As an optional implementation, the joint clearing module 44 is also used to determine the M winning load aggregators according to the following method:

[0094] Determine the relationship between the node clearing price of L load aggregators and the marginal clearing price of controllable units;

[0095] When the node clearing prices of all L load aggregators are lower than the marginal clearing prices of the controllable units, the L load aggregators are determined to be the winning bidders.

[0096] When the node clearing prices reported by M load aggregators are lower than the marginal clearing prices of controllable units, the M load aggregators are determined to be the winning bidders.

[0097] When the node clearing prices reported by L load aggregators are all higher than the marginal clearing price of the controllable units, zero load aggregators are determined to be the winners.

[0098] Furthermore, after determining the M winning load aggregators, i.e., the third-party clearing market, the joint clearing module 44 can determine the total dispatchable capacity based on the dispatchable capacity that the M winning load aggregators can participate in regulation at the target time; determine the difference between the peak-shaving demand energy based on the total dispatchable capacity and the peak-shaving demand energy of the next day; and determine the winning bids for N controllable units based on the difference between the pre-clearing energy and the peak-shaving demand energy of a single controllable unit.

[0099] In this embodiment, the peak-shaving demand for electricity for the next day is obtained; the dispatchable capacity of L load aggregators that can participate in regulation at the target time and the node clearing price corresponding to the dispatchable capacity are determined, where L is a positive integer greater than or equal to 1; the operating boundary constraints of controllable units are obtained, and based on the operating boundary constraints, a day-ahead pre-clearing model of electricity is constructed with the goal of minimizing electricity cost, and the pre-clearing capacity and marginal clearing price of controllable units at the target time are obtained by solving the day-ahead pre-clearing model; based on the node clearing prices of the L load aggregators and the marginal clearing prices of controllable units, M load aggregators are determined to win bids, and N controllable units are determined to win bids based on the difference between the total dispatchable capacity of the M load aggregators that can participate in regulation at the target time, the pre-clearing capacity of a single controllable unit, and the peak-shaving demand for electricity for the next day, where M is a positive integer less than or equal to L, and N is a non-negative integer.

[0100] Therefore, the technical solution of this application embodiment determines the winning load aggregator by judging the node clearing price reported by the load aggregator and the marginal clearing price of the controllable units. Then, based on the difference between the winning load aggregator and the peak-shaving demand, further joint clearing is conducted with the controllable units. This prioritizes power output through load aggregators, reduces the output of controllable units, and absorbs more power resources from electric vehicles. This solves the technical problem of energy curtailment caused by the lack of bidding for renewable energy generation in the relevant peak-shaving ancillary service market.

[0101] It should be noted that each module in the aforementioned power peak shaving ancillary service market clearing device can be a program module (e.g., a set of program instructions to implement a specific function) or a hardware module. For the latter, it can take the following forms, but is not limited to them: each of the above modules is represented by a processor, or the functions of each of the above modules are implemented by a processor.

[0102] Example 3

[0103] According to an embodiment of this application, a non-volatile storage medium is also provided, which stores a program, wherein when the program runs, it controls the device where the non-volatile storage medium is located to execute the power peak shaving ancillary service market clearing method in Embodiment 1.

[0104] Optionally, the device containing the non-volatile storage medium executes the following steps by running the program: obtaining the peak-shaving demand for electricity for the next day; determining the dispatchable capacity of L load aggregators that can participate in regulation at the target time and the node clearing price corresponding to the dispatchable capacity, where L is a positive integer greater than or equal to 1; obtaining the operating boundary constraints of the controllable units, constructing a day-ahead pre-clearing model based on the operating boundary constraints with the goal of minimizing the electricity cost, and obtaining the pre-clearing capacity and marginal clearing price of the controllable units at the target time by solving the day-ahead pre-clearing model; determining the winning bidders of M load aggregators based on the node clearing prices of the L load aggregators and the marginal clearing prices of the controllable units, and determining the winning bidders of N controllable units based on the difference between the total dispatchable capacity of the M winning load aggregators that can participate in regulation at the target time, the pre-clearing capacity of a single controllable unit, and the peak-shaving demand for electricity for the next day, where M is a positive integer less than or equal to L, and N is a non-negative integer.

[0105] According to an embodiment of this application, a processor is also provided for running a program, wherein the program executes the power peak shaving ancillary service market clearing method in embodiment 1 during runtime.

[0106] Optionally, the program executes the following steps during runtime: Obtain the peak-shaving demand for electricity for the next day; determine the dispatchable capacity of L load aggregators that can participate in regulation at the target time and the node clearing price corresponding to the dispatchable capacity, where L is a positive integer greater than or equal to 1; obtain the operating boundary constraints of controllable units, construct a day-ahead pre-clearing model based on the operating boundary constraints with the goal of minimizing electricity cost, and obtain the pre-clearing capacity and marginal clearing price of controllable units at the target time by solving the day-ahead pre-clearing model; determine the winning bidders for M load aggregators based on the node clearing prices of the L load aggregators and the marginal clearing prices of controllable units, and determine the winning bidders for N controllable units based on the difference between the total dispatchable capacity of the M winning load aggregators that can participate in regulation at the target time, the pre-clearing capacity of a single controllable unit, and the peak-shaving demand for electricity for the next day, where M is a positive integer less than or equal to L, and N is a non-negative integer.

[0107] According to an embodiment of this application, an electronic device is also provided, wherein the electronic device includes one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are configured to run the programs, wherein the programs are configured to execute the power peak shaving ancillary service market clearing method in Embodiment 1 above.

[0108] Optionally, the processor is configured to execute the following steps via a computer program: obtain the peak-shaving demand for electricity for the next day; determine the dispatchable capacity of L load aggregators that can participate in regulation at the target time and the node clearing price corresponding to the dispatchable capacity, where L is a positive integer greater than or equal to 1; obtain the operating boundary constraints of controllable units, construct a day-ahead pre-clearing model based on the operating boundary constraints with the goal of minimizing electricity cost, and obtain the pre-clearing capacity and marginal clearing price of controllable units at the target time by solving the day-ahead pre-clearing model; determine the winning bidders of M load aggregators based on the node clearing prices of the L load aggregators and the marginal clearing prices of controllable units, and determine the winning bidders of N controllable units based on the difference between the total dispatchable capacity of the M winning load aggregators that can participate in regulation at the target time, the pre-clearing capacity of a single controllable unit, and the peak-shaving demand for electricity for the next day, where M is a positive integer less than or equal to L and N is a non-negative integer.

[0109] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0110] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0112] The units described as separate components may or may not be physically separate. Similarly, the components shown as units may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0113] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0114] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0115] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for clearing the market for power peak-shaving ancillary services, characterized in that, include: Obtain the peak-shaving electrical energy demand for the next day; Determine the schedulable capacity that L load aggregators can participate in regulation at a target time and the node clearing price corresponding to the schedulable capacity, wherein the load aggregators are used to aggregate the electrical energy of multiple electric vehicles, and L is a positive integer greater than or equal to 1; Obtain the operating boundary constraints of the controllable unit, construct a day-ahead pre-clearing model of electrical energy based on the operating boundary constraints with the goal of minimizing the cost of electrical energy, and obtain the pre-clearing capacity and marginal clearing price of the controllable unit at the target time by solving the day-ahead pre-clearing model of electrical energy; Based on the node clearing prices of the L load aggregators and the marginal clearing prices of the controllable units, M load aggregators are determined to be the winning bidders. Based on the difference between the total dispatchable capacity that the M winning load aggregators can participate in regulation at the target time, the pre-clearing capacity of a single controllable unit, and the peak-shaving demand power energy of the next day, N controllable units are determined to be the winning bidders. Here, M is a positive integer less than or equal to L, and N is a non-negative integer. The step of determining the schedulable capacity that L load aggregators can participate in regulating at a target time and the node clearing price corresponding to the schedulable capacity includes: obtaining the first adjustable power of a single electric vehicle at the target time; determining the second adjustable power obtained by the L load aggregators aggregating the first adjustable power of multiple electric vehicles at the target time based on the first adjustable power; and obtaining the node clearing price corresponding to the schedulable capacity reported by the L load aggregators, wherein the second adjustable power is the schedulable capacity that the load aggregators can participate in regulating at the target time; The step of obtaining the first adjustable power of a single electric vehicle at the target time includes: obtaining the maximum and minimum state of charge of the single electric vehicle after charging and discharging; obtaining the real-time state of charge of the single electric vehicle at the target time, and calculating the maximum charging power at the target time based on the maximum state of charge and the real-time state of charge, and calculating the maximum discharging power at the target time based on the minimum state of charge and the real-time state of charge; obtaining the real-time charging and discharging power of the single electric vehicle at the target time, and calculating the adjustable power at the target time based on the maximum charging power and the real-time charging and discharging power, and calculating the adjustable power at the target time based on the maximum discharging power and the real-time charging and discharging power; and determining the first adjustable power of the single electric vehicle at the target time based on the adjustable power at the target time and the adjustable power at the target time. The step of determining the winning bids for M load aggregators based on the node clearing prices of the L load aggregators and the marginal clearing prices of the controllable generating units includes: determining the relationship between the node clearing prices of the L load aggregators and the marginal clearing prices of the controllable generating units; determining the winning bids for L load aggregators when the node clearing prices of the L load aggregators are all lower than the marginal clearing prices of the controllable generating units; determining the winning bids for M load aggregators when the node clearing prices reported by the M load aggregators are lower than the marginal clearing prices of the controllable generating units; and determining the winning bids for zero load aggregators when the node clearing prices reported by the L load aggregators are all higher than the marginal clearing prices of the controllable generating units. The determination of N controllable generating units based on the difference between the total dispatchable capacity that the M load aggregators that won the bid can participate in regulating at the target time, the pre-cleared capacity of a single controllable generating unit, and the peak-shaving demand energy of the next day includes: determining the total dispatchable capacity based on the dispatchable capacity that the M load aggregators that won the bid can participate in regulating at the target time; determining the difference between the total dispatchable capacity and the peak-shaving demand energy of the next day; and determining N controllable generating units based on the difference between the pre-cleared energy of a single controllable generating unit and the peak-shaving demand energy.

2. The method according to claim 1, characterized in that, The controllable units include at least one of the following: thermal power units, gas power units, hydropower units, and nuclear power units.

3. The method according to claim 2, characterized in that, Obtain the operating boundary constraints of the controllable unit, including: When the controllable unit is a thermal power unit, the operating boundary constraints include at least one of the following: demand balance constraint, thermal power unit output range constraint, thermal power unit ramp constraint, thermal power unit minimum start-up time constraint, and thermal power unit minimum shutdown time constraint. When the controllable unit is a gas turbine generator, the operating boundary constraints include at least one of the following: demand balance constraint, gas turbine generator output range constraint, gas turbine generator ramp constraint, and gas turbine generator minimum downtime constraint. When the controllable unit is a hydropower unit, the operating boundary constraints include at least one of the following: demand balance constraints and hydropower unit operating characteristic constraints; When the controllable unit is a nuclear power unit, the operating boundary constraints include at least one of the following: demand balance constraints, nuclear power unit output range constraints, nuclear power unit ramping constraints, and nuclear power unit minimum downtime constraints.

4. A market clearing device for power peak-shaving ancillary services, characterized in that, include: The acquisition module is used to acquire the peak-shaving demand for electricity for the next day; The first clearing module is used to determine the schedulable capacity that L load aggregators can participate in regulating at a target time and the node clearing price corresponding to the schedulable capacity, wherein the load aggregators are used to aggregate the electrical energy of multiple electric vehicles, and L is a positive integer greater than or equal to 1. The second clearing module is used to obtain the operating boundary constraints of the controllable unit, construct a day-ahead pre-clearing model of electrical energy based on the operating boundary constraints with the goal of minimizing the cost of electrical energy, and obtain the marginal clearing price of the controllable unit at the target time by solving the day-ahead pre-clearing model of electrical energy. The joint clearing module is used to determine the winning bids of M load aggregators based on the node clearing prices of L load aggregators and the marginal clearing prices of the controllable units, and to determine the winning bids of N controllable units based on the difference between the total dispatchable capacity that the M winning load aggregators can participate in regulation at the target time, the pre-clearing capacity of a single controllable unit, and the peak-shaving demand power energy of the next day, where M is a positive integer less than or equal to L, and N is a non-negative integer; The step of determining the schedulable capacity that L load aggregators can participate in regulating at a target time and the node clearing price corresponding to the schedulable capacity includes: obtaining the first adjustable power of a single electric vehicle at the target time; determining the second adjustable power obtained by the L load aggregators aggregating the first adjustable power of multiple electric vehicles at the target time based on the first adjustable power; and obtaining the node clearing price corresponding to the schedulable capacity reported by the L load aggregators, wherein the second adjustable power is the schedulable capacity that the load aggregators can participate in regulating at the target time; The step of obtaining the first adjustable power of a single electric vehicle at the target time includes: obtaining the maximum and minimum state of charge of the single electric vehicle after charging and discharging; obtaining the real-time state of charge of the single electric vehicle at the target time, and calculating the maximum charging power at the target time based on the maximum state of charge and the real-time state of charge, and calculating the maximum discharging power at the target time based on the minimum state of charge and the real-time state of charge; obtaining the real-time charging and discharging power of the single electric vehicle at the target time, and calculating the adjustable power at the target time based on the maximum charging power and the real-time charging and discharging power, and calculating the adjustable power at the target time based on the maximum discharging power and the real-time charging and discharging power; and determining the first adjustable power of the single electric vehicle at the target time based on the adjustable power at the target time and the adjustable power at the target time. The step of determining the winning bids for M load aggregators based on the node clearing prices of the L load aggregators and the marginal clearing prices of the controllable generating units includes: determining the relationship between the node clearing prices of the L load aggregators and the marginal clearing prices of the controllable generating units; determining the winning bids for L load aggregators when the node clearing prices of the L load aggregators are all lower than the marginal clearing prices of the controllable generating units; determining the winning bids for M load aggregators when the node clearing prices reported by the M load aggregators are lower than the marginal clearing prices of the controllable generating units; and determining the winning bids for zero load aggregators when the node clearing prices reported by the L load aggregators are all higher than the marginal clearing prices of the controllable generating units. The determination of N controllable generating units based on the difference between the total dispatchable capacity that the M load aggregators that won the bid can participate in regulating at the target time, the pre-cleared capacity of a single controllable generating unit, and the peak-shaving demand energy of the next day includes: determining the total dispatchable capacity based on the dispatchable capacity that the M load aggregators that won the bid can participate in regulating at the target time; determining the difference between the total dispatchable capacity and the peak-shaving demand energy of the next day; and determining N controllable generating units based on the difference between the pre-cleared energy of a single controllable generating unit and the peak-shaving demand energy.

5. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores a computer program, wherein the device containing the non-volatile storage medium executes the power peak shaving ancillary service market clearing method according to any one of claims 1 to 3 by running the computer program.

6. An electronic device, characterized in that, include: A memory and a processor, the processor being configured to run a program stored in the memory, wherein the program, when running, executes the power peak-shaving ancillary service market clearing method according to any one of claims 1 to 3.

Images