CN111754076A - Method and equipment suitable for electric power wholesale market mode evaluation - Google Patents

Method and equipment suitable for electric power wholesale market mode evaluation Download PDF

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CN111754076A
CN111754076A CN202010452968.3A CN202010452968A CN111754076A CN 111754076 A CN111754076 A CN 111754076A CN 202010452968 A CN202010452968 A CN 202010452968A CN 111754076 A CN111754076 A CN 111754076A
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王晓蔚
赵瑞娜
孙广辉
杨立波
马斌
鲁鹏
曹欣
吕昊
王亚军
李一鹏
栗纬勋
袁龙
胡聪
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State Grid Hebei Electric Power Co Ltd
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Abstract

The utility model belongs to the technical field of power market, and provides a method suitable for power wholesale market mode evaluation, namely, more than two market modes are simulated and cleared for a target power market of a power grid in the same area, and a comparison result is output according to a comparison index and a simulation clearing result of each market mode; the comparison index includes one or more of inter-provincial market engagement, day-ahead optimization space, total day power generation cost, and clean energy consumption. The disclosure also provides an output device, a storage device and a computing device using the method. The present disclosure provides a specific power market mode selection evaluation method and apparatus for regional power spot market construction in our country.

Description

Method and equipment suitable for electric power wholesale market mode evaluation
Technical Field
The disclosure belongs to the technical field of power markets, and particularly relates to a method and equipment for evaluating a power wholesale market mode.
Background
The electric power market comprises an electric power wholesale market and an electric power retail market, and according to the requirements of technical standards, the actual electric power market of each region needs to be combined with development situation data to select a proper electric power market mode. At present, the typical power market modes are mainly distributed power market mode and centralized power market mode. The distributed power market mode mainly takes medium and long term real object contract data as basic data for analysis and processing, in the processing flow, a transmitting party and a using party determine a daily power generation curve at a day-ahead stage, and the deviation electric quantity is adjusted through day-ahead and real-time balance transactions; the centralized electric power market mode mainly manages market risk indexes by medium-long term price difference contract data and adopts full-electric quantity centralized bidding in cooperation with spot transaction.
The prior art considers that the biggest characteristics of the market mode of the distributed power are as follows: allowing one market subject in the power market structure to sign out-of-site medium-long term physical contracts and autonomously determining an on-off plan and a power generation and utilization plan; the market main body can voluntarily participate in the spot transactions of day ahead, day in and the like of the organization of the electric power trading center according to the increment (or decrement) power generation demand except the medium-long term physical contract; the centralized electric power market mode has the greatest characteristics that medium-long term contracts are mainly financial electric power price difference contracts, electric power futures, options and the like, the financial contracts do not need physical delivery and occupy physical power transmission channels, and the start-stop plan and the power generation and utilization plan of a market main body must be determined through concentrated bidding of spot markets.
The prior art research on two power market modes is mature, but for how to select a power market mode in a specific region, no related method exists in the prior art for evaluating the technical effect of the selected power market mode before implementation.
Disclosure of Invention
The disclosure aims to provide a specific power market mode selection evaluation method and device for regional power spot market construction in China.
The first aspect of the disclosure provides a method suitable for electric power wholesale market mode evaluation, which includes performing simulation clearing of more than two market modes on a target electric power market of a same regional power grid, and outputting a comparison result according to a comparison index according to a simulation clearing result of each market mode; the comparison index includes one or more of inter-provincial market engagement, day-ahead optimization space, total day power generation cost, and clean energy consumption.
Further, the method comprises the following steps: step S10, configuring a market mode; step S20, selecting a comparison index; step S30, reading power grid operation data; step S40, clear simulation; step S50, analyzing the comparison index; and step S60, outputting the evaluation result.
Further, the step S10 includes a configuration step of: s11, acquiring medium and long term trading electric quantity and base electric quantity distributed by all units in the target electric power market, and decomposing the medium and long term trading electric quantity curve according to time intervals to form a medium and long term trading decomposition curve; s12, configuring declaration principles of each unit; and S13, configuring the spot shipment model selected by the market mode.
Further, the step S13 spot shipment model is based on the constraint condition of the SCUC, and the constraint condition includes that the objective function is to minimize the electricity purchasing cost.
Further, the constraint conditions comprise one or more of system load balance constraint, system positive and negative spare capacity constraint, unit output upper and lower limit constraint, unit climbing constraint, unit minimum continuous start-stop time constraint, section flow constraint and radix decomposition power physics execution constraint.
Further, when one market mode configured for the target power market in step S10 is the distributed power market mode, the spot inventory model of the market mode includes the radix decomposition power physics execution constraint.
Further, the principle declared in step S12 includes that the output between the minimum technical output and the rated capacity of one unit is divided into more than two monotonically increasing value curves for declaration.
The second aspect of the present disclosure provides an output device, which includes visual output data, wherein the visual output data includes part or all of the data for simulating the result of a target power market in a market mode, which is obtained by the method.
A third aspect of the present disclosure provides a storage device, which can read storage data, at least one part of which is used to compose part or all of data showing that a target electric power market obtained by the above method simulates a result in a market mode.
A fourth aspect of the present disclosure provides a computing device configured to execute the above method according to an instruction, or control an output device to output part or all of data of a target power market obtained by the above method to simulate a result in a market mode, or read/write stored data of the storage device provided in the third aspect.
Benefits of the present disclosure include, but are not limited to: the method is suitable for electric power wholesale market mode evaluation, and comprises the steps of firstly setting different market modes, secondly designing comparison indexes, then selecting power grid data of a certain area for market clearing, and finally comparing and analyzing market clearing results and outputting a conclusion. According to the method, the power market data of a certain region are adopted for simulation, the method suitable for power wholesale market mode evaluation provided by the patent is verified, and the method is beneficial to auxiliary analysis on how to select a market mode suitable for localization in the power spot market construction process of a certain regional power grid.
Drawings
FIG. 1 is a schematic flow chart of one embodiment of a method of the present disclosure;
FIG. 2 is a flow diagram of a process for solving according to a configured spot shipment model in one embodiment of the present disclosure;
FIG. 3 is a medium-long term trade decomposition curve of a centralized power market model for a target power market in an embodiment of the present disclosure;
fig. 4 is a schematic diagram of a predicted load curve of the system output in the step S300 of the target power market in the embodiment of the disclosure;
FIG. 5 is a schematic diagram of a day-ahead optimization space in two market modes of a target power market in an embodiment of the disclosure;
FIG. 6 is a schematic diagram of the power generation cost of the target power market in two market modes throughout the day in an embodiment of the disclosure;
fig. 7 is a schematic diagram illustrating the total daily output ratio of different types of units in a centralized power market mode of a target power market in an embodiment of the present disclosure;
fig. 8 is a schematic diagram of the total daily output ratio of different types of units in the decentralized power market mode of the target power market in the embodiment of the disclosure.
Detailed Description
It should be noted that, when the embodiment of the method for evaluating the electric wholesale market mode of the present disclosure shown in fig. 1 is executed on a computer system, the data processing flow thereof includes the following steps S10 to S60.
And S10, configuring a market mode. I.e., a market mode selected for a particular target power market configured for subsequent analysis, in some embodiments the configuration includes creating a new data object and assigning values to parameters of the data object. This step includes three configuration steps S11 to S13: s11, acquiring medium and long term trading electric quantity and base electric quantity distributed by all units in the target electric power market, and decomposing the medium and long term trading electric quantity curve according to time intervals to form a medium and long term trading decomposition curve; s12, configuring declaration principles of each unit; s13, configuring the SCUC (safety restraint unit combination) -based spot shipment clearing model selected by the market mode.
Specifically, the spot-out model in step S13 includes one or more of the following constraints.
The first constraint condition is that the objective function is to minimize the electricity purchasing cost:
Figure BDA0002507464930000051
wherein N is the total number of the units; t is the time granularity of the market; pi,tThe output of the unit i in the time period t is obtained; ci,t(Pi,t)、
Figure BDA0002507464930000052
Respectively the operation cost and the starting cost of the unit i in the time period t, and the sum of the operation cost and the starting cost of the unit i; m is a network power flow constraint relaxation penalty factor;
Figure BDA0002507464930000053
respectively setting forward and reverse tide relaxation variables of the section S; n is a radical ofSThe total number of sections.
A second constraint, a system load balancing constraint. For each time period t, there is:
Figure BDA0002507464930000054
wherein, Tj,tPlanned power for the off-area link j during time t; n is a radical ofTThe total number of links outside the area; dtFor system load of time period t, by D in an applied embodiment with a constraint of selecting the second conditiontAnd the system prediction load curve is provided by an information system from a power grid dispatching department.
And a third constraint, namely a system positive and negative spare capacity constraint. For each period t:
Figure BDA0002507464930000055
Figure BDA0002507464930000056
wherein, αi,tStarting and stopping a unit i at a time t;
Figure BDA0002507464930000057
respectively representing the output limit of the unit i in the time period t;
Figure BDA0002507464930000058
positive and negative spare capacity requirements of the system for the t period, respectively.
And a fourth constraint condition, unit output upper and lower limit constraint. For each unit i, time period t:
Figure BDA0002507464930000059
wherein the variables are defined by equation (4).
And a fifth constraint condition, unit climbing constraint.
Figure BDA00025074649300000510
Figure BDA00025074649300000511
Wherein the content of the first and second substances,
Figure BDA0002507464930000061
the maximum climbing rate and the maximum descending rate of the unit i are respectively.
A sixth constraint condition, the minimum continuous start-stop time constraint of the unit:
Figure BDA0002507464930000062
Figure BDA0002507464930000063
wherein, TU、TDThe minimum continuous starting time and the minimum continuous stopping time of the unit are obtained;
Figure BDA0002507464930000064
the time that the unit i is continuously started and the time that the unit i is continuously stopped in the t period are shown.
And a seventh constraint condition, section flow constraint.
Figure BDA0002507464930000065
Wherein the content of the first and second substances,
Figure BDA0002507464930000066
respectively the tidal current transmission limit of the section s; gs-iThe generator output power of the section s is transferred to a distribution factor for the node where the unit i is located; gs-kThe generator output power transfer distribution factor is node k to section s.
Figure BDA0002507464930000067
Forward and reverse current relaxation of section s respectivelyAnd (4) variable quantity.
The eighth constraint, the radix split power physics enforcement constraint, is:
Figure BDA0002507464930000068
wherein the content of the first and second substances,
Figure BDA0002507464930000069
and decomposing the long-term electric quantity in the unit in the step S11 into a base electric quantity in a period.
By way of example, fig. 3 provides the steps of a solution algorithm for the day-ahead SCUC spot shipment model. These steps can be implemented in combination with the method provided by wang, xiaqing, kangjie "identification method of integer variables acting in a unit combination algorithm" (chinese electro-mechanical engineering, press 2010, 30 (13): 46-51).
And S20, selecting a comparison index. That is, the indexes for comparing the target market patterns, which are obtained after the simulation of the step S40, of the target market patterns to be compared in the step S50 include one centimeter or more of four comparison parameters, i.e., inter-provincial market engagement, day-ahead optimization space, power generation cost all day long, and clean energy consumption.
Exemplarily, the calculation or comparison methods of the four alignment parameters are S21 to S24:
and S21, linking the provinces in the market, and quantitatively evaluating the size of the adjustable range of the transmission power of the province tie lines, wherein the larger the adjustable range of the transmission power of the province tie lines is, the more flexible the inter-province market linking is, and the better the inter-province market linking is.
And S22, quantitatively evaluating the day-ahead optimization space through a specific unit optimization space calculation formula as an example:
the unit optimization space calculation formula of the distributed power market mode is as follows:
Figure BDA0002507464930000071
wherein the content of the first and second substances,
Figure BDA0002507464930000072
to optimize space for the spot market time t period in the day ahead,
Figure BDA0002507464930000073
for the predicted load for the time period t of the whole network,
Figure BDA0002507464930000074
for inter-provincial link power for the t period,
Figure BDA0002507464930000075
respectively is the planned output of a new energy unit and a hydroelectric generating unit in the time period t,
Figure BDA0002507464930000076
the medium-long term trade electric quantity decomposition output and the base electric quantity decomposition output of the coal electric unit in the t period are respectively obtained.
The unit in the centralized power market mode can be freely optimized in the day ahead, and the calculation formula of the unit optimization space is as follows:
Figure BDA0002507464930000077
wherein the content of the first and second substances,
Figure BDA0002507464930000078
the optimization space of the concentrated market in the time period t of the spot market before the day.
And S23, generating cost all day, wherein the generating cost of a single unit can be used as the generating cost of all units in the whole network. Specifically, the calculation formula of the power generation cost of the single unit is as follows:
Figure BDA0002507464930000079
wherein, CiFor the generating cost of unit i, pi,tThe output of the unit i in the time period t, ci,AVGFor average power generation of unit iCost, T is the number of time segments in the whole day.
The total daily power generation cost of all the units in the whole network can be obtained from the power generation cost of a single unit, namely:
Figure BDA0002507464930000081
wherein, CGeneral assemblyTotal power generation cost for the whole grid, CiAnd n is the number of generator sets in the whole network.
S24, clean energy consumption, as an example, in the embodiments of the present disclosure, the duty ratio in the clean energy unit of the target electric power market in the selected market mode, which is output after clearing, is simulated in step S40, and the duty ratios in the clean energy units of a plurality of different target electric power markets are quantitatively analyzed and compared in step S50 to reflect the clean energy consumption condition, where the comparison standard is configured such that the larger the duty ratio in the clean energy unit is, the better the clean energy consumption is. In various embodiments, the clean energy unit includes a hydro-power unit and a new energy unit.
And S30, reading the power grid operation data, and reading the actual power grid operation data of each unit of the regional power grid of a target power market, wherein the actual power grid operation data comprises system load prediction data, unit start-stop states, output limits, climbing constraints, system positive spare and negative spare capacity requirements, section flow transmission limits, unit medium and long term transaction electric quantity and the like, and the data is obtained from an information system of a power grid dispatching department.
And S40, clearing is simulated, namely, clearing is simulated for the regional power grid described in the step S30 under more than two market modes respectively according to the unit declaration principle described in the step S20 and the spot clearing model and algorithm configured in the step S10 by a target power market, and clearing results of each market mode are obtained.
And S50, analyzing the comparison indexes, namely comparing and analyzing the clearing results of the target power market in more than two market modes according to the comparison indexes selected in the step S40.
And S60, outputting an evaluation conclusion, and performing conclusive evaluation on a plurality of market modes of the target power market of the regional power grid based on the comparison result of the step S50.
By way of example, the present disclosure also provides a system embodiment adapted to implement the above method embodiment, including one or more storage devices for storing all or part of the configuration data of steps S10 to S60, an arithmetic device for completing all or part of the arithmetic process of steps S10 to S60 according to the data read from the storage devices, and a display device for displaying the output results of one or more of steps S10 to S60, where the display device is a visual output device, and in an embodiment having a computing device configured with a preset audio driver, the audio output device may also be an output device of the system embodiment.
The present disclosure provides a specific application embodiment based on the above method embodiment and system embodiment, so as to fully illustrate the application of the technical solution of the present disclosure, and facilitate the implementation and improvement of those skilled in the art.
According to the application embodiment, the target power market of a specific regional power grid is configured according to two market modes, namely a distributed power market mode and a centralized power market mode, and the target power market is simulated respectively, so that the conclusion that the target power market of the specific regional power grid is more suitable for the centralized power market mode on four comparison indexes is finally made. The using steps include the following steps S100 to S600.
Step S100, creating, by the computing device, an evaluation item in the storage device, the evaluation item being specific to a target power market of a specific regional power grid, the regional power grid being a provincial regional power grid, and creating a specific centralized power market mode and a distributed power market mode thereof according to step S10, respectively.
Wherein the centralized power market mode is configured to: corresponding to step S11, all the units of the regional power grid are allocated to certain medium and long term trading power and base power. The new energy unit in the target power market generates power preferentially, consumption is guaranteed, the report of the hydroelectric generating set is not quoted, and the new energy unit only participates in deviation settlement in a settlement link. And the medium and long term transaction and the base electric quantity of the coal-electricity unit are only settled by finance and do not need to be physically executed. Due to the uncertainty of the medium and long term trading curve decomposition, the daily average base electric quantity and the daily average medium and long term trading electric quantity are evenly distributed to each time interval in one day, specifically, 24h of the whole day is divided into 96 time intervals, the daily average base electric quantity is Q1, and the daily average medium and long term trading electric quantity is Q2, so that the electric quantity in each time interval is (Q1+ Q2)/96, and the medium and long term trading decomposition curve of the target electric power market in the centralized electric power market mode shown in FIG. 3 can be formed and stored in the storage device.
Corresponding to the step S12, only the coal electric unit participates in spot bid declaration, the contribution between the minimum technical contribution and the rated capacity of the coal electric unit is declared as three sections of monotonically increasing volume price curves, and the declared price of each unit is output according to the online electricity price of each unit multiplied by a cost coefficient calculation device given according to the unit historical quotation, so as to ensure that the simulation clear result is close to the actual situation as much as possible. In the embodiment of the application, the minimum technical output of the target electric power market unit G1 is 75MW, the rated capacity is 150MW, the online electricity price is 300 yuan/MWh, the quotation cost coefficients are 0.85, 0.90 and 0.95 respectively, the declaration principle is configured, the quotation curve is declaration capacity [75,100] MW, and the quotation is 255 yuan/MWh; declare capacity [100,125] MW, quoted price is 270 yuan/MWh; declared capacity [125,150] MW, quoted 280 Yuan/MWh.
Corresponding to step S13, the first to sixth constraints are selected as the SCUC spot shipment model of the centralized electric power market mode and parameters of the constraints in the model are configured according to the target electric power market index of the regional power grid.
The decentralized power market model is configured to: corresponding to the step S11, all the units are allocated to certain medium and long term trading electric quantity and base electric quantity as in the centralized electric power market mode; the clean energy unit only carries out deviation settlement, but the medium and long term trade and the base electric quantity of the coal-electric unit are selected to be physically executed. The actual output of the unit is configured according to zero quoted price, and the surplus electricity space is configured to be the competitive price of the coal-electricity unit. The medium-and-long-term trading decomposition curve of the market mode is consistent with a centralized market, the daily average base electricity quantity and the daily average medium-and-long-term trading electricity quantity are evenly distributed to each time interval, and assuming that 24h of the whole day is divided into 96 time intervals, the daily average base electricity quantity is Q1, and the daily average medium-and-long-term trading electricity quantity is Q2, the electricity quantity in each time interval is (Q1+ Q2)/96, and the medium-and-long-term trading decomposition curve is consistent with the graph shown in FIG. 3.
Corresponding to the step S12, the bidding space and declaration principle of the coal-electric machine set are configured to be the same as those in the centralized power market mode, that is, the market mode adopts three monotone increasing pricing curves with the same centralized power market mode to declare prices, and as the same regional power grid is selected, the cost coefficient is the same as the data of the machine set and the centralized power market mode configured above, that is, the minimum technology output of the machine set G1 is 75MW, the rated capacity is 150MW, the power price on the internet is 300 yuan/MWh, and the quoted cost coefficients are 0.85, 0.90 and 0.95 respectively, according to the declaration principle provided by the patent, the quoted curve is declaration capacity [75,100] MW, and the quoted price is 255 yuan/MWh; declare capacity [100,125] MW, quoted price is 270 yuan/MWh; declared capacity [125,150] MW, quoted 280 Yuan/MWh.
Corresponding to step S13, different from the centralized electric power market mode, the first to seventh constraints are selected as the SCUC spot shipment model of the centralized electric power market mode and parameters of the constraints in the model are configured according to the target electric power market indicator of the regional power grid.
Step S200, corresponding to step S20, selects the evaluation index of the evaluation item created in step S100, including all four comparison parameters. The calculation formula of the unit optimization space of the distributed power market mode is set as an expression (12), and the calculation formula of the unit optimization space of the centralized power market mode is set as an expression (13); the total-day power generation cost is set to be obtained according to the formula (14) and the formula (15); the clean energy consumption configuration is the duty ratio in the clean energy unit.
Step S300, which corresponds to step S30 specifically, the total number of the regional power grids selected in the target power market of the specific regional power grid is 147, and the 147 includes 30 coal-fired units, 94 hydroelectric units, 5 wind power plants, 3 photovoltaic power plants and 15 biomass units, and only the coal-fired units are bid and cleared. Specifically, the second constraint is selected for both the two market modes in the embodiment of the present application, and the selection of the other actual power grid operation data can be obtained from the power grid dispatching department according to the constraint condition selected in step 11, as shown in fig. 4, for the day-ahead load prediction data in the actual operation data of the regional power grid.
Step S400, starting an operation instruction of the computing device, enabling the computing device to simultaneously simulate and output requests according to different market models configured in the step S100 and conditions of the step S40, namely respectively simulating and outputting the regional power grid power market described in the step S30 in two market modes by adopting the unit declaration principle, the spot shipment output model and the algorithm described in the step S20, and obtaining output results.
Step S500, corresponding to step S50, the computing device is configured to respectively compute the evaluation indexes selected in step S20 according to the clearing results of the target power market in the two market modes in step S40, as follows:
specifically, the provincial regional power grid has the practical situation that the provincial coal-electricity shortage exists and the provincial water-electricity needs to be taken away, the distributed power market mode needs to determine and maintain the tie line power in advance, and the tie line adjustable power range is (P1-P2) assuming that the power which can be transmitted by all the tie lines is P1 and the tie line power which needs to be determined and maintained in advance is P2(P2> 0). In the medium-long term trading and the base number contract of the centralized electric power market mode, inter-provincial junctor is not considered, contract signing and trading are free, actual implementation conditions of the market in the day-ahead are not affected, and the junctor adjustable power range is P1 on the assumption that all junctors can transmit power P1 and junctor power which needs to be determined and maintained in advance is 0. The comparison strategy and result of inter-provincial market join is that, because of P1> (P1-P2), the adjustable range of the transmission power of the inter-provincial links in the centralized market mode is larger than that in the distributed market mode, so that the centralized market mode is more beneficial to the inter-provincial market join, and is more flexible and better.
The comparison for the optimization space before the day is: specifically, the results of the simulation of the optimization space of the provincial regional power grid actual data, the centralized market and the distributed market in the spot market before the day are shown in fig. 5, and the bidding space of the spot market before the day in two market modes, namely the optimization space before the day, wherein the optimization space of the spot market before the day in the centralized power market mode is 347599.27MW, the optimization space of the spot market before the day in the distributed power market mode is 112512.55MW, in the evaluation project, the unit priority output of the spot market before the day in the centralized power market mode is much higher, and the unit bidding space is also larger than that of the distributed power market mode. The optimization space of the centralized market is easily obtained to be larger than that of the distributed market, and the social welfare maximization is easily realized.
The comparison of the power generation cost throughout the day is: the total power generation cost of the whole network in the two market modes is calculated from the daily winning power ratio in step S20, and as a result, as shown in fig. 6, the total power generation cost of the whole network in the two market modes is 144.76 ten thousand yuan higher than that in the centralized power market mode in one day, and is about 4% of the total power generation cost of the centralized power market mode in the whole day. The situation that the distributed power market mode in the region causes waste due to unreasonable resource allocation or other reasons is explained, and when the centralized power market mode is adopted, the power generation cost is lower, and energy conservation, emission reduction and electricity price reduction can be effectively realized.
The comparison of clean energy consumption is: the target power markets in the two market modes are respectively subjected to clearing simulation, so that the all-day output ratio of the three types of units in the two market modes shown in fig. 7 and 8 can be obtained, wherein the all-day output ratio of the different types of units in the centralized power market mode is up to 37.07% in the clean energy (new energy + water power), the all-day output ratio of the clean energy (new energy + water power) in the distributed power market mode is up to 32.82%, and the all-day output ratio of the clean energy in the centralized power market mode is higher than that in the distributed power market mode.
Step S600, corresponding to step S60, in the present application embodiment, for a specific evaluation item, the following text information or corresponding graphic information may be output through the display device according to each comparison result in step S500 by using the configured template: "1) the adjustable range of the transmission power of the provincial junctor in the regional centralized market mode is larger than that in the distributed market mode, so that the centralized market mode is more beneficial to the provincial market connection and more flexible and better. 2) The optimization space of the regional centralized market is larger than that of the distributed market, and social welfare maximization is easier to realize. 3) The power generation cost of the selected centralized market in the region is lower, and the energy conservation and emission reduction and the electricity price reduction can be more effective. 4) The standard-winning capacity of the clean energy in the regional diversity type market is higher than that in the distributed type market all day, and the centralized type market is favorable for the consumption of the clean energy. In conclusion, the centralized market model has significant advantages in the process of building the regional power market. "
The method and system provided by the present disclosure are not limited to the technical details presented in the above application embodiments, and those skilled in the art can reconstruct the corresponding software module or disperse the software module in different storage units and computing units according to the above description to achieve the same effect through distributed computing, and these solutions should also be considered as system embodiments of the present disclosure. In a system embodiment including all the method steps of the present disclosure, one or more display devices may be included, wherein one or more display devices are in a state of displaying output data of one or more steps of the present disclosure, or one or more readable storage devices for storing output data of one or more steps of the present disclosure are also an apparatus embodiment of the present disclosure.

Claims (10)

1. A method suitable for electric power wholesale market mode assessment is characterized by comprising the following steps: simulating more than two market modes for a target power market of the same regional power grid, and outputting a comparison result according to a comparison index according to a simulation clearing result of each market mode; the comparison index includes one or more of inter-provincial market engagement, day-ahead optimization space, total day power generation cost, and clean energy consumption.
2. The method for electric wholesale market pattern assessment according to claim 1, characterized in that its method steps comprise: step S10, configuring a market mode; step S20, selecting a comparison index; step S30, reading power grid operation data; step S40, clear simulation; step S50, analyzing the comparison index; and step S60, outputting the evaluation result.
3. The method for electric wholesale market pattern assessment according to claim 2, wherein said step S10 comprises the configuration steps of: s11, acquiring medium and long term trading electric quantity and base electric quantity distributed by all units in the target electric power market, and decomposing the medium and long term trading electric quantity curve according to time intervals to form a medium and long term trading decomposition curve; s12, configuring declaration principles of each unit; and S13, configuring the spot shipment model selected by the market mode.
4. The method for electric power wholesale market pattern assessment according to claim 3, wherein said step S13 spot shipment model is based on SCUC constraint condition, said constraint condition comprises objective function being to minimize electricity purchase cost.
5. The method for electric wholesale market pattern assessment according to claim 4, wherein said constraints comprise one or more of system load balancing constraints, system positive and negative spare capacity constraints, unit output upper and lower limit constraints, unit ramp-up constraints, unit minimum continuous on-off time constraints, section flow constraints and radix split electrophysical execution constraints.
6. The method of claim 5, wherein when one market model configured for the target power market in the step S10 is a decentralized power market model, the spot inventory model of the market model comprises radix decomposition power physics execution constraints.
7. The method for electric wholesale market pattern assessment according to claim 3, wherein the principle declared in step S12 comprises declaring the contribution between a unit minimum technical contribution and rated capacity as more than two monotonically increasing rating curves.
8. An output device, characterized by: output data of the plant visualization, the output data comprising part or all of data of a target power market simulating a clearing result in a market mode, obtained by the method of any one of claims 1 to 7.
9. A storage device, characterized by storage data readable by said device, at least one part of which is used to compose part or all of data showing the simulation of a target electricity market in a market mode, obtained by the method of any one of claims 1 to 7.
10. A computing device configured to execute the method of any one of claims 1 to 7 according to an instruction, or control an output device to output part or all of data that a target power market obtained by the method of any one of claims 1 to 7 simulates a clearing result in a market mode, or read/write stored data of the storage device of claim 9.
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