CN117314485A - Electric power peak regulation auxiliary service market clearing method and device - Google Patents
Electric power peak regulation auxiliary service market clearing method and device Download PDFInfo
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Abstract
The application discloses a method and a device for clearing an electric power peak shaving auxiliary service market. Comprising the following steps: obtaining peak regulation required electric energy of the next day; determining the schedulable capacity of the L load aggregators at the target moment and the node clear price corresponding to the schedulable capacity; acquiring operation boundary constraint of the controllable unit, constructing a daily electric energy pre-clearing model by taking the lowest electric energy cost as a target based on the operation boundary constraint, and obtaining pre-clearing capacity and marginal clearing price of the controllable unit at a target moment by solving the model; and determining the bid-winning of the M load aggregators based on the node bid-winning prices of the L load aggregators and the marginal bid-winning prices of the controllable units, and determining the bid-winning of the N controllable units based on the dispatchable total capacity of the M load aggregators at the target moment, the pre-bid-winning capacity of the single controllable unit and the difference of peak shaving required electric energy. The technical problem that the related peak shaving auxiliary service market does not offer to the new energy power generation main body to cause energy abandonment is solved.
Description
Technical Field
The application relates to the technical field of power system dispatching operation, in particular to a method and a device for clearing an auxiliary service market of power peak shaving.
Background
Along with the increasing severity of global environmental problems, the grid-connected capacity of the renewable energy installation needs to be continuously improved, and the output intermittence of the renewable energy installation needs to be standby or adjustable resources to participate in auxiliary peak regulation service so as to better support green electricity consumption. The electric vehicle-represented demand side resources participate in the electric power market aggregation, so that a large amount of adjustable resources can be provided for the electric power system, and the system is balanced to run.
However, at present, the new energy is involved in the peak regulation of the electric power market, and the metering quotation is not carried out like other conventional power supplies, but is mostly guaranteed purchase or metering is not carried out, so that the mode is difficult to attract an electric automobile owner to voluntarily add into the electric automobile grid connection, thereby energy abandonment is generated, and resource waste is caused.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the application provides a method and a device for clearing an electric power peak shaving auxiliary service market, which are used for at least solving the technical problem that energy is abandoned because the related peak shaving auxiliary service market does not offer a price for a new energy power generation main body.
According to an aspect of the embodiments of the present application, there is provided a method for electric power peak shaving auxiliary service market clearing, including: obtaining peak regulation required electric energy of the next day; determining schedulable capacity which can participate in regulation and control at a target moment and node clearing prices corresponding to the schedulable capacity by L load aggregators, wherein L is a positive integer greater than or equal to 1; acquiring operation boundary constraint of the controllable unit, constructing a daily electric energy pre-clearing model by taking the lowest electric energy cost as a target based on the operation boundary constraint, and obtaining pre-clearing capacity and marginal clearing price of the controllable unit at a target moment by solving the daily electric energy pre-clearing model; and determining the winning bid 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 bid of N controllable units based on the difference between the schedulable total capacity of the M load aggregators in the winning bid, the pre-clearing capacity of a single controllable unit and the peak shaving required electric energy of the next day, wherein M is a positive integer less than or equal to L, and N is a non-negative integer.
Optionally, the load aggregator is configured to aggregate electric energy of a plurality of electric vehicles, where determining a schedulable capacity that M load aggregators can participate in regulation at a target moment and a node price corresponding to the schedulable capacity includes: acquiring first adjustable power of a single electric automobile at a target moment; and determining second adjustable power obtained by aggregating the first adjustable powers of the plurality of electric vehicles at the target moment by each load aggregator based on the first adjustable power, and acquiring node clear prices corresponding to the schedulable capacity reported by each load aggregator, wherein the second adjustable power is the schedulable capacity which can be participated in the regulation and control by the load aggregator at the target moment.
Optionally, obtaining the first adjustable power of the single electric automobile at the target time includes: acquiring the maximum charge state and the minimum charge state of a single electric automobile after charging and discharging; acquiring a real-time state of charge of a single electric automobile at a target moment, calculating the maximum charging power at the target moment based on the maximum state of charge and the real-time state of charge, and calculating the maximum discharging power at the target moment based on the minimum state of charge and the real-time state of charge; acquiring real-time charge and discharge power of a single electric automobile at a target moment, calculating the adjustable power at the target moment based on the maximum charge power and the real-time charge and discharge power, and calculating the adjustable power at the target moment based on the maximum discharge power and the real-time charge and discharge power; and determining the first adjustable power of the single electric automobile at the target moment by the adjustable power and the adjustable power of the single electric automobile at the target moment.
Optionally, the controllable unit includes at least one of the following: thermal power generating unit, gas electric unit, hydroelectric generating unit and nuclear power generating unit.
Optionally, obtaining an operation boundary constraint of the controllable unit includes: when the controllable unit is a thermal power unit, the operation boundary constraint comprises at least one of the following: demand balance constraint, thermal power unit output range constraint, thermal power unit climbing constraint, thermal power unit minimum start-up time constraint and thermal power unit minimum stop time constraint; when the controllable unit is a gas-electric unit, the operation boundary constraint comprises at least one of the following: demand balance constraint, gas motor set output range constraint, gas motor set climbing constraint, and gas motor set minimum downtime constraint; when the controllable unit is a hydroelectric unit, the operation boundary constraint comprises at least one of the following: requirement balance constraint and hydropower unit operation characteristic constraint; when the controllable unit is a nuclear power unit, the operation boundary constraint comprises at least one of the following: demand balance constraint, nuclear power unit output range constraint, nuclear power unit climbing constraint and nuclear power unit minimum downtime constraint.
Optionally, determining the winning bid of the M load aggregators based on the node clearing prices of the L load aggregators and the marginal clearing prices of the controllable unit includes: judging the size relation between the node clearing prices of the L load aggregators and the marginal clearing prices of the controllable unit; when node clearing prices of the L load aggregators are lower than marginal clearing prices of the controllable unit, determining that the L load aggregators bid; when the node clearing price reported by M load aggregators is lower than the marginal clearing price of the controllable unit, determining the winning bid of the M load aggregators; and when the node clearing prices reported by the L load aggregators are higher than the marginal clearing price of the controllable unit, determining zero load aggregators to bid.
Optionally, determining the winning bid in the N controllable units based on the difference between the schedulable total capacity of the winning M load aggregators that can participate in the regulation at the target time, the pre-clearing capacity of the single controllable unit, and the peak shaving required electric energy of the next day, includes: determining schedulable total capacity based on schedulable capacity which can participate in regulation and control at target time by the winning M load aggregators; determining a peak shaver demand electrical energy difference value based on the schedulable total capacity and the peak shaver demand electrical energy of the next day; and determining the winning of the N controllable units based on the difference value of the pre-output clear electric energy and the peak shaving required electric energy of the single controllable unit.
According to another aspect of the embodiments of the present application, there is also provided an electric power peak shaving auxiliary service market clearing apparatus, including: the acquisition module is used for acquiring peak shaving required electric energy in the next day; the first clearing module is used for determining schedulable capacity which can be regulated and controlled by L load aggregators at target time and node clearing price corresponding to the schedulable capacity, wherein L is a positive integer greater than or equal to 1; the second clearing module is used for acquiring the operation boundary constraint of the controllable unit, constructing a daily electric energy pre-clearing model by taking the lowest electric energy cost as a target based on the operation boundary constraint, and obtaining the marginal clearing price of the controllable unit at the target moment by solving the daily electric energy pre-clearing model; the combined clearing module is used for determining the winning bid of M load aggregators based on node clearing prices of the L load aggregators and marginal clearing prices of the controllable units, and determining the winning bid of N controllable units based on the schedulable total capacity of the winning bid M load aggregators which can participate in regulation at the target moment, the pre-clearing capacity of a single controllable unit and the peak shaving required electric energy difference of the next day, wherein M is a positive integer less than or equal to L, and N is a non-negative integer.
According to another aspect of the embodiments of the present application, there is further provided a nonvolatile storage medium, where the nonvolatile storage medium includes a stored computer program, and a device where the nonvolatile storage medium is located executes the above-mentioned method for market clearing of power peak shaving auxiliary service by running the computer program.
According to another aspect of the embodiments of the present application, there is also provided an electronic device including: the system comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the electric power peak shaving auxiliary service market clearing method through the computer program.
In the embodiment of the application, peak shaving required electric energy in the next day is obtained; determining schedulable capacity which can participate in regulation and control at a target moment and node clearing prices corresponding to the schedulable capacity by L load aggregators, wherein L is a positive integer greater than or equal to 1; acquiring operation boundary constraint of the controllable unit, constructing a daily electric energy pre-clearing model by taking the lowest electric energy cost as a target based on the operation boundary constraint, and obtaining pre-clearing capacity and marginal clearing price of the controllable unit at a target moment by solving the daily electric energy pre-clearing model; and determining the winning bid 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 bid of N controllable units based on the difference between the schedulable total capacity of the M load aggregators in the winning bid, the pre-clearing capacity of a single controllable unit and the peak shaving required electric energy of the next day, wherein M is a positive integer less than or equal to L, and N is a non-negative integer. And further solves the technical problem that the related peak shaving auxiliary service market does not offer for the new energy power generation main body, so that energy is abandoned.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
fig. 1 is a block diagram of a hardware structure of a computer terminal (or mobile device) for implementing a power peak shaving auxiliary service market clearing method according to the related art;
FIG. 2 is a flow chart of an alternative power peak shaving auxiliary service market clearing method according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of an alternative method for determining a first adjustable power according to an embodiment of the present application;
fig. 4 is a schematic structural view of an alternative electric peak shaving aid market clearing device according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In addition, the related information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for presentation, analyzed data, etc.) referred to in this application are information and data authorized by the user or sufficiently authorized by the parties. For example, an interface is provided between the system and the relevant user or institution, before acquiring the relevant information, the system needs to send an acquisition request to the user or institution through the interface, and acquire the relevant information after receiving the consent information fed back by the user or institution.
Example 1
In accordance with embodiments of the present application, there is provided an embodiment of a power peak shaving aid market clearing method, it being noted that the steps illustrated in the flowchart of the figures may be performed in a computer system, such as a set of computer executable instructions, and that although a logical sequence is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in a different order than what is illustrated herein.
The method embodiments provided by the embodiments of the present application may be performed in a mobile terminal, a computer terminal, or similar computing device. Fig. 1 shows a block diagram of a hardware architecture of a computer terminal (or mobile device) for implementing a power peak shaving auxiliary service market clearing method. As shown in fig. 1, the computer terminal 10 (or mobile device 10) may include one or more (shown as 102a, 102b, … …,102 n) processors 102 (the processors 102 may include, but are not limited to, a microprocessor MCU, a programmable logic device FPGA, etc. processing means), a memory 104 for storing data, and a transmission means 106 for communication functions. In addition, the method may further 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 the BUS), a network interface, a power supply, and/or a camera. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
It should be noted that the one or more processors 102 and/or other data processing circuits described above may be referred to generally herein as "data processing circuits. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. Furthermore, the data processing circuitry may be a single stand-alone processing module, or incorporated, in whole or in part, into any of the other elements in the computer terminal 10 (or mobile device). As referred to in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of the path of the variable resistor termination to interface).
The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the electric power peak shaving auxiliary service market clearing method in the embodiments of the present application, and the processor 102 executes the software programs and modules stored in the memory 104 to perform various functional applications and data processing, that is, implement the electric power peak shaving auxiliary service market clearing method described above. 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 examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission means 106 is arranged to receive or transmit data via a network. The specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 10 (or mobile device).
In the above operating environment, fig. 2 is a schematic flow chart of an alternative method for market clearing of power peak shaving auxiliary service according to an embodiment of the present application, as shown in fig. 2, the method at least includes steps S202-S208, where:
step S202, peak shaving required electric energy in the next day is obtained.
The peak regulation demand energy refers to the peak load period, and because the load demand exceeds the base load capacity of the power system, the peak regulation means is needed to meet the electric energy required by the load demand.
Step S204, determining schedulable capacity which can be regulated and controlled by L load aggregators at target time and node clearing price corresponding to the schedulable capacity, wherein L is a positive integer greater than or equal to 1.
The load aggregator refers to a service provider for aggregating electricity loads on a client side to achieve more efficient electric power operation and utilization, and mainly comprises load prediction, scheduling, control and the like. The distributed small electricity loads are aggregated through the technical means, so that large-scale electric power transaction is realized, and the users are assisted to participate in electric power market competition. Usually, the load aggregator can be a charging pile operator, and can access the power market through integrating charging pile resources and corresponding charging users to participate in the demand side response.
As an alternative embodiment, in the solution provided in step S204, the load aggregator is configured to aggregate electric energy of a plurality of electric vehicles, and the method may include: acquiring first adjustable power of a single electric automobile at a target moment; and determining second adjustable power obtained by aggregating the first adjustable powers of the plurality of electric vehicles at the target moment by each load aggregator based on the first adjustable power, and acquiring node clear prices corresponding to the schedulable capacity reported by each load aggregator, wherein the second adjustable power is the schedulable capacity which can be participated in the regulation and control by the load aggregator at the target moment.
In this embodiment, in this embodiment of the present application, the up-adjustable power and the down-adjustable power of a single electric automobile at a target moment are calculated to obtain a first adjustable power of the single electric automobile, and then second adjustable powers obtained by aggregating the first adjustable powers of a plurality of electric automobiles by different load aggregators are calculated, and each load aggregation calculates a price for a node corresponding to the second adjustable power provided by the load aggregation, where the second schedulable capacity is a schedulable capacity of the load aggregation that can participate in peak regulation. That is, each load aggregator establishes a pay-out queue of electric vehicles participating in peak shaving auxiliary service markets in the form of a bid amount, wherein the node pay-out price of each load aggregator can be recorded as [ lambda ] EV1 ,λ EV2 ,λ EV3 ,…,λ EVL ]。
Optionally, fig. 3 is a schematic flow chart of an alternative method for determining the first adjustable power according to an embodiment of the present application, as shown in fig. 3, the method may include the following steps S31-S34, where:
step S31, obtaining the maximum charge state and the minimum charge state of a single electric automobile after charging and discharging;
step S32, acquiring the real-time charge state of a single electric automobile at a target moment, calculating the maximum charge power at the target moment based on the maximum charge state and the real-time charge state, and calculating the maximum discharge power at the target moment based on the minimum charge state and the real-time charge state;
Step S33, acquiring real-time charge and discharge power of a single electric automobile at a target moment, calculating down-adjustable power at the target moment based on the maximum charge power and the real-time charge and discharge power, and calculating up-adjustable power at the target moment based on the maximum discharge power and the real-time charge and discharge power;
step S34, determining the first adjustable power of the single electric automobile at the target moment by the adjustable power and the adjustable power of the single electric automobile at the target moment.
Specifically, firstly, the maximum allowable battery power, i.e. the maximum State of Charge (SoC), and the minimum allowable battery power, i.e. the minimum State of Charge (SoC), of a single electric vehicle are counted, wherein, because the battery characteristics of different types of electric vehicles are different, the maximum State of Charge and the minimum State of Charge of each electric vehicle after charging and discharging are respectively recorded as SoC in the embodiment of the present application i,min ,SoC i,max Where i represents a vehicle number.
Then, according to the real-time state of charge SoC of each electric automobile i,t Minimum state of charge SoC i,max Calculating the minimum charge state to obtain the maximum charging power of the single electric automobile at the moment, and recording the maximum charging power 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 And (2) min represents the minimum value between two numbers, delta t represents the scheduling time, pcmax represents the maximum charging power which can be provided by the charging pile, and Pdmax represents the maximum discharging power which can be provided by the charging pile.
Then, according to the electric automobile i, the charging and discharging power P is real-time at the time t i,t Maximum charging power P of single electric automobile at this time i,cmax,t Maximum discharge power P i,dmax,t Calculating to obtain the real-time power up-adjustable and real-time power down-adjustable of the single electric automobile, wherein the real-time power P up-adjustable i,up,t ＝P i,dmax,t -P i,t Real-time power P can be adjusted downwards i,down,t ＝P i,cmax,t -P i,t And the charging power is negative, and the discharging power is positive.
Finally, according to the obtained power P which can be adjusted upwards at the moment t of the electric automobile i i,up,t Power P can be adjusted downwards i,down,t The first adjustable power of the electric automobile i at the time t can be obtained.
Further, based on the first adjustable power of the single electric automobile i at the time t, determining second adjustable power obtained by each load aggregator in the process of aggregating the first adjustable powers of the plurality of electric automobiles at the time t, and obtaining node clearing prices corresponding to the schedulable capacity, reported by each load aggregator.
Specifically, the up-adjustable power P of electric vehicles of different types and different states of charge which can participate in regulation and control at the same moment is sequentially calculated i,up,t Power P can be adjusted downwards i,down,t (i.e., a first adjustable power); and then the up-adjustable power and the down-adjustable power of various electric vehicles are obtained to carry out aggregation calculation to obtain the total adjustable power (namely second adjustable power) of the electric vehicles which can participate in regulation and control, wherein the total up-adjustable power at the time t is P up,t ＝∑P i,up,t The total power can be adjusted down to P down,t ＝∑P i,down,t 。
And S206, acquiring operation boundary constraint of the controllable unit, constructing a daily electric energy pre-clearing model by taking the lowest electric energy cost as a target based on the operation boundary constraint, and obtaining the pre-clearing capacity and marginal clearing price of the controllable unit at the target moment by solving the daily electric energy pre-clearing model.
In the technical solution provided in step S206, in the embodiment of the present application, the pre-request capacity and the marginal price of the single controllable unit at the target moment are predicted by calculating the pre-request model of the daily electric energy of the controllable unit.
Optionally, the controllable unit includes at least one of the following: thermal power generating unit, gas electric unit, hydroelectric generating unit and nuclear power generating unit.
As an optional implementation manner, in the technical solution provided in step S206, obtaining the operation boundary constraint of the controllable unit may include:
when the controllable unit is a thermal power unit, the operation boundary constraint comprises at least one of the following: demand balance constraint, thermal power unit output range constraint, thermal power unit climbing constraint, thermal power unit minimum start-up time constraint and thermal power unit minimum stop time constraint;
when the controllable unit is a gas-electric unit, the operation boundary constraint comprises at least one of the following: demand balance constraint, gas motor set output range constraint, gas motor set climbing constraint, and gas motor set minimum downtime constraint;
When the controllable unit is a hydroelectric unit, the operation boundary constraint comprises at least one of the following: requirement balance constraint and hydropower unit operation characteristic constraint;
when the controllable unit is a nuclear power unit, the operation boundary constraint comprises at least one of the following: demand balance constraint, nuclear power unit output range constraint, nuclear power unit climbing constraint and nuclear power unit minimum downtime constraint.
In general, the thermal power generating unit is a coal electric unit, and the thermal power generating unit is taken as an example to describe the operation boundary constraint of the thermal power generating unit, wherein:
peak shaving demand balance constraint:wherein P is n,t Represents the electric energy declared by the thermal power generating unit N at the time t, N represents the total number of units participating in peak regulation market, R t The total peak regulation demand at the time t is represented;
output constraint of peak shaving unit: p (P) n,min ≤P n,t ≤P n,max Wherein P is n,min ,P n,max Indicating firePeak regulation upper and lower limits of the motor group n;
peak shaver set climbing constraint: p (P) n,t -P n,t-1 ≤ΔP n,up ；P n,t-1 -P n,t ≤ΔP n,down Wherein ΔP n,up Represents the upward maximum climbing power of the thermal power unit n, delta P n,down Representing the downward maximum climbing power of the thermal power unit n; minimum continuous power-on constraint:and minimum continuous shutdown constraint: /> Wherein u is n,τ Represents the switch state variable, y of the thermal power unit n,τ A starting state variable z representing the time period t of the thermal power generating unit n n,τ And the shutdown state variable of the thermal power generating unit n in the period t is represented.
Further, after obtaining the operation boundary constraint of the adjustable unit, based on the operation boundary constraint, constructing a day-ahead electric energy pre-clearing model with the lowest electric energy cost as a target, wherein the objective function of the day-ahead electric energy pre-clearing model is thatWherein lambda is n,t Representing the quotation of the thermal power generating unit n at the time t, P n,t Indicating the quantum of the unit n at time t, Δt indicating the length of time for each time period.
The objective function is to obtain the minimum value, so that the boundary condition is to be determined, namely, a constraint equation is to be established according to the operation boundary constraint of the adjustable unit, and after the constraint condition is determined, the optimization solver can be called to solve the day-ahead electric energy pre-clearing model to obtain the minimum value of the objective function, wherein the minimum value is the minimum cost.
In addition, the operation characteristic constraint of the hydroelectric generating set specifically comprises: establishing a water consumption relational expression, a power generation water purification head relational expression, a tail water level relational expression, a head loss relational expression and a reservoir capacity-water level relational expression of the hydroelectric generating set; and establishing a water storage level constraint, a hydropower plant output constraint, a power generation flow constraint, a warehouse outlet flow constraint and a hydropower station initial and final water level constraint.
And step S208, determining the winning bid 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 bid of N controllable units based on the schedulable total capacity of the winning M load aggregators which can participate in regulation at the target moment, the pre-clearing capacity of a single controllable unit and the peak shaving required electric energy difference of the next day, wherein M is a positive integer less than or equal to L, and N is a non-negative integer.
As an optional implementation manner, in the technical solution provided in step S208, the method for determining the winning M load aggregators includes:
judging the size relation between the node clearing prices of the L load aggregators and the marginal clearing prices of the controllable unit;
when node clearing prices of the L load aggregators are lower than marginal clearing prices of the controllable unit, determining that the L load aggregators bid;
when the node clearing price reported by M load aggregators is lower than the marginal clearing price of the controllable unit, determining the winning bid of the M load aggregators;
and when the node clearing prices reported by the L load aggregators are higher than the marginal clearing price of the controllable unit, determining zero load aggregators to bid.
Specifically, firstly, sorting marginal clearing prices of node clearing price controllable units reported by M load aggregators to obtain a clearing price queue, wherein the node clearing prices are recorded as lambda EV The marginal price is recorded as lambda MC . Wherein at lambda MC >λ EV_max When the marginal clearing price of the controllable unit is higher than the node clearing price reported by the L load aggregators, the L load aggregators are marked; at lambda EV_min <λ MC <λ EV_max At this time, only part (i.e., M) of nodes reported by load aggregators are describedThe clearing price is lower than the marginal clearing price of the controllable unit, and only the part of load aggregators bid at the moment; at lambda EV_min >λ MC And when the marginal clearing price of the controllable unit is lower than the node clearing price reported by the L load aggregators, the L load aggregators are not marked.
Further, after determining the winning M load aggregators, that is, after the form third party market out, the schedulable total capacity can be determined based on schedulable capacities of the winning M load aggregators that can participate in regulation and control at the target moment; determining a peak shaver demand electrical energy difference value based on the schedulable total capacity and the peak shaver demand electrical energy of the next day; and determining the winning of the N controllable units based on the difference value of the pre-output clear electric energy and the peak shaving required electric energy of the single controllable unit.
That is, according to the difference between the peak shaving required electric energy and the schedulable total capacity of the M load aggregators in the next day, secondary clearing is performed for the adjustable unit, that is, the bid in the N controllable units is determined according to the difference between the pre-clearing electric energy and the peak shaving required electric energy of the single controllable unit, and then the clearing result of the power system is formed according to the M load aggregators and the N controllable units.
Based on the schemes defined in the steps S202 to S208, it can be known that, in the embodiment, the peak shaver required electric energy in the next day is obtained; determining schedulable capacity which can participate in regulation and control at a target moment and node clearing prices corresponding to the schedulable capacity by L load aggregators, wherein L is a positive integer greater than or equal to 1; acquiring operation boundary constraint of the controllable unit, constructing a daily electric energy pre-clearing model by taking the lowest electric energy cost as a target based on the operation boundary constraint, and obtaining pre-clearing capacity and marginal clearing price of the controllable unit at a target moment by solving the daily electric energy pre-clearing model; and determining the winning bid 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 bid of N controllable units based on the difference between the schedulable total capacity of the M load aggregators in the winning bid, the pre-clearing capacity of a single controllable unit and the peak shaving required electric energy of the next day, wherein M is a positive integer less than or equal to L, and N is a non-negative integer.
Therefore, according to the technical scheme of the embodiment of the application, the winning load aggregate with lower price is obtained by judging the node clearing price reported by the load aggregate and the marginal clearing price of the controllable unit, and then the winning load aggregate is further combined with the controllable unit according to the difference between the winning load aggregate and the peak shaving required electric energy, so that the output is preferentially carried out through the load aggregate, the output of the controllable unit is reduced, and more electric power resources of the electric automobile are consumed. Therefore, the technical problem that the energy is abandoned because the related peak shaving auxiliary service market does not offer the new energy power generation main body is solved.
Example 2
Based on embodiment 1 of the present application, there is also provided an embodiment of a power peak shaving auxiliary service market shaving apparatus, which executes the above power peak shaving auxiliary service market shaving method of the above embodiment when running. Fig. 4 is a schematic structural diagram of an optional electric power peak shaving auxiliary service market clearing device according to an embodiment of the present application, and as shown in fig. 4, the electric power peak shaving auxiliary service market clearing device at least includes an obtaining module 41, a first clearing module 42, a second clearing module 43 and a combined clearing module 44, where:
the obtaining module 41 is configured to obtain peak shaving required electric energy in the next day.
The peak regulation demand energy refers to the peak load period, and because the load demand exceeds the base load capacity of the power system, the peak regulation means is needed to meet the electric energy required by the load demand.
The first clearing module 42 is configured to determine schedulable capacities of L load aggregators that can participate in regulation and control at a target moment and node clearing prices corresponding to the schedulable capacities, where L is a positive integer greater than or equal to 1.
The load aggregate is used for aggregating electric energy of a plurality of electric vehicles.
As an alternative embodiment, the first clearing module 42 is further configured to obtain a first adjustable power of the single electric automobile at the target time; and determining second adjustable power obtained by aggregating the first adjustable powers of the plurality of electric vehicles at the target moment by each load aggregator based on the first adjustable power, and acquiring node clear prices corresponding to the schedulable capacity reported by each load aggregator, wherein the second adjustable power is the schedulable capacity which can be participated in the regulation and control by the load aggregator at the target moment.
In this embodiment, in this embodiment of the present application, the up-adjustable power and the down-adjustable power of a single electric automobile at a target moment are calculated to obtain a first adjustable power of the single electric automobile, and then second adjustable powers obtained by aggregating the first adjustable powers of a plurality of electric automobiles by different load aggregators are calculated, and each load aggregation calculates a price for a node corresponding to the second adjustable power provided by the load aggregation, where the second schedulable capacity is a schedulable capacity of the load aggregation that can participate in peak regulation. That is, each load aggregator establishes a dequeue of electric vehicles participating in the peak shaving auxiliary service market in the form of a bid amount.
Alternatively, the first adjustable power may be determined as follows: acquiring the maximum charge state and the minimum charge state of a single electric automobile after charging and discharging; acquiring a real-time state of charge of a single electric automobile at a target moment, calculating the maximum charging power at the target moment based on the maximum state of charge and the real-time state of charge, and calculating the maximum discharging power at the target moment based on the minimum state of charge and the real-time state of charge; acquiring real-time charge and discharge power of a single electric automobile at a target moment, calculating the adjustable power at the target moment based on the maximum charge power and the real-time charge and discharge power, and calculating the adjustable power at the target moment based on the maximum discharge power and the real-time charge and discharge power; and determining the first adjustable power of the single electric automobile at the target moment by the adjustable power and the adjustable power of the single electric automobile at the target moment.
The second clearing module 43 is configured to obtain an operation boundary constraint of the controllable unit, construct a daily electric energy pre-clearing model with the lowest electric energy cost as a target based on the operation boundary constraint, and obtain a marginal clearing price of the controllable unit at a target moment by solving the daily electric energy pre-clearing model.
Optionally, the controllable unit includes at least one of the following: thermal power generating unit, gas electric unit, hydroelectric generating unit and nuclear power generating unit.
As an alternative embodiment, the second clearing module 43 may obtain the operation boundary constraint of the controllable unit, which may include:
when the controllable unit is a thermal power unit, the operation boundary constraint comprises at least one of the following: demand balance constraint, thermal power unit output range constraint, thermal power unit climbing constraint, thermal power unit minimum start-up time constraint and thermal power unit minimum stop time constraint;
when the controllable unit is a gas-electric unit, the operation boundary constraint comprises at least one of the following: demand balance constraint, gas motor set output range constraint, gas motor set climbing constraint, and gas motor set minimum downtime constraint;
when the controllable unit is a hydroelectric unit, the operation boundary constraint comprises at least one of the following: requirement balance constraint and hydropower unit operation characteristic constraint;
When the controllable unit is a nuclear power unit, the operation boundary constraint comprises at least one of the following: demand balance constraint, nuclear power unit output range constraint, nuclear power unit climbing constraint and nuclear power unit minimum downtime constraint.
The joint clearing module 44 is configured to determine winning bid of M load aggregators based on node clearing prices of the L load aggregators and marginal clearing prices of the controllable units, and determine winning bid of N controllable units based on a difference between a schedulable total capacity of the winning M load aggregators that can participate in regulation at a target time, a pre-clearing capacity of a single controllable unit, and peak shaving required electric energy of the next day, where M is a positive integer less than or equal to L, and N is a non-negative integer.
As an alternative embodiment, the joint clearing module 44 is further configured to determine that the winning M load aggregators include:
judging the size relation between the node clearing prices of the L load aggregators and the marginal clearing prices of the controllable unit;
when node clearing prices of the L load aggregators are lower than marginal clearing prices of the controllable unit, determining that the L load aggregators bid;
when the node clearing price reported by M load aggregators is lower than the marginal clearing price of the controllable unit, determining the winning bid of the M load aggregators;
And when the node clearing prices reported by the L load aggregators are higher than the marginal clearing price of the controllable unit, determining zero load aggregators to bid.
Further, after determining the winning M load aggregators, i.e., the formal third party, to market, the joint market module 44 may determine a schedulable total capacity based on schedulable capacities that the winning M load aggregators may participate in regulation at the target moment; determining a peak shaver demand electrical energy difference value based on the schedulable total capacity and the peak shaver demand electrical energy of the next day; and determining the winning of the N controllable units based on the difference value of the pre-output clear electric energy and the peak shaving required electric energy of the single controllable unit.
In an embodiment, peak shaving required electric energy of the next day is obtained; determining schedulable capacity which can participate in regulation and control at a target moment and node clearing prices corresponding to the schedulable capacity by L load aggregators, wherein L is a positive integer greater than or equal to 1; acquiring operation boundary constraint of the controllable unit, constructing a daily electric energy pre-clearing model by taking the lowest electric energy cost as a target based on the operation boundary constraint, and obtaining pre-clearing capacity and marginal clearing price of the controllable unit at a target moment by solving the daily electric energy pre-clearing model; and determining the winning bid 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 bid of N controllable units based on the difference between the schedulable total capacity of the M load aggregators in the winning bid, the pre-clearing capacity of a single controllable unit and the peak shaving required electric energy of the next day, wherein M is a positive integer less than or equal to L, and N is a non-negative integer.
Therefore, according to the technical scheme of the embodiment of the application, the winning load aggregate with lower price is obtained by judging the node clearing price reported by the load aggregate and the marginal clearing price of the controllable unit, and then the winning load aggregate is further combined with the controllable unit according to the difference between the winning load aggregate and the peak shaving required electric energy, so that the output is preferentially carried out through the load aggregate, the output of the controllable unit is reduced, and more electric power resources of the electric automobile are consumed. Therefore, the technical problem that the energy is abandoned because the related peak shaving auxiliary service market does not offer the new energy power generation main body is solved.
Note that each module in the above-mentioned electric power peak shaving auxiliary service market clearing device may be a program module (for example, a set of program instructions for implementing a specific function), or may be a hardware module, and for the latter, it may be represented by the following form, but is not limited thereto: the expression forms of the modules are all a processor, or the functions of the modules are realized by one processor.
Example 3
According to an embodiment of the present application, there is also provided a nonvolatile storage medium having a program stored therein, wherein when the program runs, a device in which the nonvolatile storage medium is controlled to execute the electric power peak shaving auxiliary service market clearing method in embodiment 1.
Optionally, the device where the nonvolatile storage medium is located performs the following steps by running the program: obtaining peak regulation required electric energy of the next day; determining schedulable capacity which can participate in regulation and control at a target moment and node clearing prices corresponding to the schedulable capacity by L load aggregators, wherein L is a positive integer greater than or equal to 1; acquiring operation boundary constraint of the controllable unit, constructing a daily electric energy pre-clearing model by taking the lowest electric energy cost as a target based on the operation boundary constraint, and obtaining pre-clearing capacity and marginal clearing price of the controllable unit at a target moment by solving the daily electric energy pre-clearing model; and determining the winning bid 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 bid of N controllable units based on the difference between the schedulable total capacity of the M load aggregators in the winning bid, the pre-clearing capacity of a single controllable unit and the peak shaving required electric energy of the next day, wherein M is a positive integer less than or equal to L, and N is a non-negative integer.
According to an embodiment of the present application, there is further provided a processor for running a program, wherein the program executes the power peak shaving auxiliary service market clearing method in embodiment 1.
Optionally, the program execution realizes the following steps: obtaining peak regulation required electric energy of the next day; determining schedulable capacity which can participate in regulation and control at a target moment and node clearing prices corresponding to the schedulable capacity by L load aggregators, wherein L is a positive integer greater than or equal to 1; acquiring operation boundary constraint of the controllable unit, constructing a daily electric energy pre-clearing model by taking the lowest electric energy cost as a target based on the operation boundary constraint, and obtaining pre-clearing capacity and marginal clearing price of the controllable unit at a target moment by solving the daily electric energy pre-clearing model; and determining the winning bid 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 bid of N controllable units based on the difference between the schedulable total capacity of the M load aggregators in the winning bid, the pre-clearing capacity of a single controllable unit and the peak shaving required electric energy of the next day, wherein M is a positive integer less than or equal to L, and N is a non-negative integer.
There is also provided, in accordance with an embodiment of the present application, an electronic device, where the electronic device includes one or more processors; and a memory for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement a method for running the programs, wherein the programs are configured to perform the power peak shaving auxiliary service market clearing method in embodiment 1 described above when run.
Optionally, the processor is configured to implement the following steps by computer program execution: obtaining peak regulation required electric energy of the next day; determining schedulable capacity which can participate in regulation and control at a target moment and node clearing prices corresponding to the schedulable capacity by L load aggregators, wherein L is a positive integer greater than or equal to 1; acquiring operation boundary constraint of the controllable unit, constructing a daily electric energy pre-clearing model by taking the lowest electric energy cost as a target based on the operation boundary constraint, and obtaining pre-clearing capacity and marginal clearing price of the controllable unit at a target moment by solving the daily electric energy pre-clearing model; and determining the winning bid 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 bid of N controllable units based on the difference between the schedulable total capacity of the M load aggregators in the winning bid, the pre-clearing capacity of a single controllable unit and the peak shaving required electric energy of the next day, wherein M is a positive integer less than or equal to L, and N is a non-negative integer.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of units may be a logic function division, and there may be another division manner in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be essentially or a part contributing to the related art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.
Claims (10)
1. A method for assisting in the market clearing of a service by electric power peak shaving, which is characterized by comprising the following steps:
obtaining peak regulation required electric energy of the next day;
determining schedulable capacity which can participate in regulation and control at a target moment and node clearing price corresponding to the schedulable capacity by L load aggregators, wherein L is a positive integer greater than or equal to 1;
acquiring operation boundary constraint of a controllable unit, constructing a daily electric energy pre-clearing model by taking the lowest electric energy cost as a target based on the operation boundary constraint, and obtaining pre-clearing capacity and marginal clearing price of the controllable unit at the target moment by solving the daily electric energy pre-clearing model;
and determining the winning bid of M load aggregators based on node clearing prices of the L load aggregators and marginal clearing prices of the controllable units, and determining the winning bid of N controllable units based on the schedulable total capacity of the winning M load aggregators which can participate in regulation and control at the target moment, the pre-clearing capacity of a single controllable unit and the difference between peak shaving required electric energy of the next day, wherein M is a positive integer less than or equal to L, and N is a non-negative integer.
2. The method of claim 1, wherein the load aggregator is configured to aggregate electrical energy of a plurality of electric vehicles, and determining a schedulable capacity that M load aggregators can participate in regulation at a target time and a node price-out corresponding to the schedulable capacity comprises:
Acquiring first adjustable power of a single electric automobile at the target moment;
and determining second adjustable power obtained by aggregating the first adjustable powers of the electric vehicles at the target moment by each load aggregator based on the first adjustable power, and acquiring node clear prices corresponding to the schedulable capacity reported by each load aggregator, wherein the second adjustable power is the schedulable capacity which the load aggregator can participate in regulation and control at the target moment.
3. The method of claim 2, wherein obtaining a first adjustable power of a single electric vehicle at the target time comprises:
acquiring the maximum charge state and the minimum charge state of the electric automobile after charging and discharging;
acquiring a real-time state of charge of the electric automobile at the target moment, calculating the maximum charging power at the target moment based on the maximum state of charge and the real-time state of charge, and calculating the maximum discharging power at the target moment based on the minimum state of charge and the real-time state of charge;
acquiring real-time charging and discharging power of the electric automobile at the target moment, calculating the adjustable power at the target moment based on the maximum charging power and the real-time charging and discharging power, and calculating the adjustable power at the target moment based on the maximum discharging power and the real-time charging and discharging power;
And determining the first adjustable power of the electric automobile at the target moment by the adjustable power of the electric automobile at the target moment and the adjustable power.
4. The method of claim 1, wherein the controllable unit comprises at least one of: thermal power generating unit, gas electric unit, hydroelectric generating unit and nuclear power generating unit.
5. The method of claim 4, wherein obtaining operational boundary constraints for the controllable unit comprises:
when the controllable unit is a thermal power unit, the operation boundary constraint comprises at least one of the following: demand balance constraint, thermal power unit output range constraint, thermal power unit climbing constraint, thermal power unit minimum start-up time constraint and thermal power unit minimum stop time constraint;
when the controllable unit is a gas-electric unit, the operation boundary constraint comprises at least one of the following: demand balance constraint, gas motor set output range constraint, gas motor set climbing constraint, and gas motor set minimum downtime constraint;
when the controllable unit is a hydroelectric unit, the operation boundary constraint comprises at least one of the following: requirement balance constraint and hydropower unit operation characteristic constraint;
When the controllable unit is a nuclear power unit, the operation boundary constraint comprises at least one of the following: demand balance constraint, nuclear power unit output range constraint, nuclear power unit climbing constraint and nuclear power unit minimum downtime constraint.
6. The method of claim 1, wherein determining M load aggregators for bid based on node bid prices of L load aggregators and marginal bid prices of the controllable unit comprises:
judging the size relation between the node clearing prices of the L load aggregators and the marginal clearing prices of the controllable unit;
when node clearing prices of the L load aggregators are lower than marginal clearing prices of the controllable unit, determining that the L load aggregators bid;
when the node clearing prices reported by the M load aggregators are lower than the marginal clearing prices of the controllable unit, determining the winning bid of the M load aggregators;
and when the node clearing prices reported by the L load aggregators are higher than the marginal clearing price of the controllable unit, determining zero bid-winning bid in the load aggregators.
7. The method of claim 1, wherein determining the winning bid for the N controllable units based on the difference between the sum of the schedulable capacities of the winning M load aggregators available for regulation at the target time, the pre-purge capacity of a single controllable unit, and the peak shaver demand electrical energy for the next day, comprises:
Determining the schedulable total capacity based on schedulable capacities of the winning M load aggregators which can participate in regulation and control at the target moment;
determining a peak shaver demand electrical energy difference value based on the schedulable total capacity and the peak shaver demand electrical energy of the next day;
and determining the bid-winning of the N controllable units based on the difference value of the pre-output clear electric energy and the peak shaving required electric energy of the single controllable unit.
8. An electric peak shaving auxiliary service market clearing device, comprising:
the acquisition module is used for acquiring peak shaving required electric energy in the next day;
the first clearing module is used for determining schedulable capacity which can participate in regulation and control at target time and node clearing price corresponding to the schedulable capacity of L load aggregators, wherein L is a positive integer greater than or equal to 1;
the second clearing module is used for acquiring the operation boundary constraint of the controllable unit, constructing a day-ahead electric energy pre-clearing model by taking the lowest electric energy cost as a target based on the operation boundary constraint, and obtaining the marginal clearing price of the controllable unit at the target moment by solving the day-ahead electric energy pre-clearing model;
the joint clearing module is used for determining the winning bid 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 bid of N controllable units based on the schedulable total capacity of the winning M load aggregators which can participate in regulation and control at the target moment, the pre-clearing capacity of a single controllable unit and the difference between peak shaving required electric energy of the next day, wherein M is a positive integer less than or equal to L, and N is a non-negative integer.
9. A non-volatile storage medium, wherein a computer program is stored in the non-volatile storage medium, and wherein a device in which the non-volatile storage medium is located executes the electric power peak shaving auxiliary service market clearing method according to any one of claims 1 to 7 by running the computer program.
10. An electronic device, comprising: a memory and a processor for executing a program stored in the memory, wherein the program is executed to perform the power peak shaving auxiliary service market clearing method of any one of claims 1 to 7.
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