CN112529622A - Virtual power plant-based method for clearing multiple micro-micro main bodies participating in spot market - Google Patents

Virtual power plant-based method for clearing multiple micro-micro main bodies participating in spot market Download PDF

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CN112529622A
CN112529622A CN202011443256.1A CN202011443256A CN112529622A CN 112529622 A CN112529622 A CN 112529622A CN 202011443256 A CN202011443256 A CN 202011443256A CN 112529622 A CN112529622 A CN 112529622A
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尹硕
王世谦
杨萌
宋大为
金曼
柴喆
杨钦臣
郭兴五
路尧
陈兴
张钧钊
姜欣
韩丁
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Zhengzhou University
State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
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State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
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Abstract

The invention provides a virtual power plant-based clearing method for a multi-element small micro-subject participating spot market, which solves the risk problem brought to VPP income by the uncertainty of a competitor in a trading decision game process. The method comprises the following steps: firstly, constructing a double-layer clearing model considering the uncertainty of the electricity price and a constraint condition of the double-layer clearing model based on the VPP parameters of the power grid side and the information of the electric energy buyer and the electric energy seller; secondly, solving the double-layer output model based on the constraint conditions of the double-layer output model to obtain the optimal bidding result and the optimal income of the day-ahead energy and duration frequency modulation market; and finally, obtaining the successful bidding condition, the optimal quotation and the clearing price of each main body of the market according to the optimal bidding result and the optimal income. According to the invention, through the two-stage double-layer clearing model and the two-stage double-layer constraint condition established by the clearing method, the visual benefit analysis and bidding decision of the peak shaving market in which the power grid side energy storage participates can be simply and conveniently realized by considering the uncertainty of the electricity price.

Description

Virtual power plant-based method for clearing multiple micro-micro main bodies participating in spot market
Technical Field
The invention relates to the technical field of data processing, in particular to a virtual power plant-based method for clearing a plurality of small micro-subjects participating in spot market.
Background
Environmental issues have led to an increase in the penetration of renewable energy sources in several power systems around the world over the last few years. However, the integration of renewable energy power generation presents many challenges to the operation of power systems, particularly due to the limited predictability and high variability of the primary "fuels" (i.e., wind, solar radiation), which make renewable energy power plants non-dispatchable.
To date, a great deal of research has been conducted to find the best way to incorporate renewable energy power plants into existing power systems within the concept of smart grids. Among them, Demand Response (DR) and distributed energy storage (DS) have proven to be the most important key factors for smart grid deployment so far, as their incorporation into the demand side of the grid can alleviate the challenges of large-scale demand response penetration and improve system reliability. The use of Advanced Metering Infrastructure (AMI) can provide consumers with real-time electricity prices, can increase their active participation, and help to efficiently integrate intermittent resources into the grid. In the same direction, the fast response characteristic of distributed energy storage makes it a valuable regulatory resource, and can balance the negative effects of intermittently regenerated resources.
At present, a Virtual Power Plant (VPP) is one of the most effective and potential technologies recognized in the world for dealing with large-scale renewable energy grid connection. With the continuous promotion of the progress of the electric power market in China, the advantages of VPPs (virtual private Power Point) as independent bodies participating in the energy market and frequency modulation are widely accepted, but a complete market transaction mechanism and an effective model are not provided for exciting and standardizing the exertion of the energy storage peak regulation capability. The VPP is an important frequency modulation means as a high-quality bidirectional modulation resource with mature large-scale application technical conditions, can relieve the influence of the increase of the frequency modulation demand on the conventional unit, and reduces the loss of the conventional unit caused by frequent output reduction. However, even if the VPP is added to the fm auxiliary service, the mechanism problem of the fm auxiliary service market still cannot be solved, and the commercial application thereof is more dependent on the solution of the market mechanism problem.
In the market mechanism problem, the electricity price is the basic and core factor of the electric power market transaction, the electricity price is determined by the market, the rationalization and the validation of the electricity price in the transaction link can be guaranteed, and the electricity price can be guaranteed to correctly reflect the actual value of the electric power in the production and the transaction. The day-ahead electricity price prediction can provide decision basis for the bidding of power generators and electricity selling companies on spot market, and the accurate day-ahead electricity price prediction can greatly reduce the price risk of market participants and bring stable benefits to the market participants. In the current electric power market, the electricity purchase price is obtained by the trading center by integrating the quotation of the power generation party, the market demand and the condition of the electric power system. The electricity purchase price is determined by the value of the power commodity and is influenced by the supply and demand relation, unlike the past power control period. Therefore, the price of electricity should contain abundant information, such as the cost of the network unit, the supply and demand status of the market, and the health status of the system (including the transmission network and the generator). However, the price of electricity in the electricity market is uncertain due to its dynamic changes, resulting in higher bidding risk and failure to obtain stable profit. Therefore, the above factors need to be taken into account when formulating a storage bidding strategy. There is currently a few literature studying the bidding strategy of VPPs participating in the spot-market, and the influence of the randomness of self-quote and the uncertainty of competitor strategy in the bidding decision process on the bidding strategy.
Disclosure of Invention
Aiming at the technical problems that the risk brought by the uncertainty of a competitor in the process of introducing a multi-scenario description transaction decision game to the VPP income cannot ensure that the VPP reasonably participates in market-oriented transaction, the invention provides a clearing method of a virtual power plant-based multi-element small micro-subject participating spot market from the participation of the multi-element small micro-subject in the spot market, and provides a two-stage double-layer combined bidding model; firstly, in the first stage, a plurality of small micro bodies are aggregated into a VPP according to the clustering idea and are scheduled by a unified operation mechanism, and secondly, the whole VPP is preprocessed according to the analysis of the output characteristics of the plurality of small micro bodies, so that the upper limit and the lower limit of the whole VPP for charging and discharging can be determined; the second stage is that the VPP participates in a master-slave game model of a joint bidding strategy of the day-ahead energy and real-time balance market, and the goal of the master-slave game model is that the day-ahead profit maximization is combined with the minimization of the expected real-time production and consumption unbalance cost; the two-stage double-layer clearing model and the two-stage double-layer constraint condition established by the clearing method can simply and conveniently realize the visual benefit analysis and bidding decision of the peak shaving market in which the power grid side energy storage participates, considering the uncertainty of the electricity price.
The technical scheme of the invention is realized as follows:
a virtual power plant-based method for clearing a plurality of small micro-subjects participating in spot market includes the following steps:
the method comprises the following steps: constructing a double-layer clearing model considering the uncertainty of the electricity price based on the VPP parameters of the power grid side and the information of the electric energy buyer and the electric energy seller;
step two: constructing a constraint condition of a double-layer clearing model based on the VPP parameters of the power grid side, the information of both electric energy buyers and sellers and the double-layer clearing model;
step three: solving the double-layer clearing model based on the constraint conditions of the double-layer clearing model to obtain the optimal bidding result and the optimal profit of the day-ahead frequency modulation market of the day-ahead energy and duration;
step four: and obtaining the successful bidding condition, the optimal quotation and the clearing price of each main body of the market according to the optimal bidding result and the optimal income.
The double-layer clearing model comprises a first-stage model, a second-stage inner-layer model and a second-stage outer-layer model, wherein the first-stage model is a VPP coordination optimization scheduling model, the second-stage inner-layer model is a VPP participation day-ahead market income maximization model, and the second-stage outer-layer model is a full social welfare maximization model; the constraint conditions of the double-layer output model comprise the constraint conditions of a first-stage model, the constraint conditions of a second-stage inner-layer model and the constraint conditions of a second-stage outer-layer model; the constraint conditions of the first section model comprise VPP internal power balance constraint, wind power constraint, photovoltaic constraint, distributed energy storage constraint and adjustable load constraint; the constraint conditions of the second-stage inner layer model comprise VPP competitive bidding electric quantity constraint, VPP up-down capacity constraint and VPP up-down mileage constraint; and the constraint conditions of the second-stage outer layer model comprise energy market power balance constraint, network safety constraint, system frequency modulation capacity constraint, system frequency modulation mileage constraint and conventional unit power constraint.
The VPP coordination optimization scheduling model is as follows:
Figure BDA0002823243960000031
wherein the content of the first and second substances,
Figure BDA0002823243960000032
representing the amount of power VPP delivered to the grid at time t,
Figure BDA0002823243960000033
and (4) predicting the load quantity in the power grid in the period t.
The VPP participation day-ahead market profit maximization model is as follows:
Figure BDA0002823243960000034
where R denotes the scenario of a competitor's offer, probrRepresenting the probability of occurrence of a scenario R, I representing the investment portfolio parameter set of the VPP, D representing the internal load parameter set of the virtual power plant, T representing the time set of 24h,
Figure BDA0002823243960000035
the system marginal liquidity price representing the energy market at day-ahead in scenario R and time t,
Figure BDA0002823243960000036
representing the amount of electricity reported by the ith unit in the market trading day before and accepted by the ISO in the scenario R and time t,
Figure BDA0002823243960000037
representing the load declaration curve that the d-th load is reported in the market before the day and is accepted by ISO in the scene R and the time t,
Figure BDA0002823243960000038
represents the clearing price of the day-ahead service market capacity in scenario R and time t,
Figure BDA0002823243960000039
representing the up-modulation capacity reported by the ith unit in the service market at the day before and accepted by ISO in the scenario R and time t,
Figure BDA00028232439600000310
represents the lower frequency modulation capacity reported by the ith unit in the service market at the day before and accepted by ISO in the scene and time t,
Figure BDA00028232439600000311
represents the clearing price of the service market fm mileage at scene R and time t,
Figure BDA00028232439600000312
representing the frequency-up mileage reported by the ith unit in the service market in the day-ahead and accepted by ISO in the scene R and the time t,
Figure BDA00028232439600000313
and (3) indicating the frequency-down mileage which is reported by the ith unit in the service market at the day before and accepted by ISO in the scene R and the time t.
The social welfare maximization model is as follows:
Figure BDA00028232439600000314
wherein the content of the first and second substances,
Figure BDA00028232439600000315
represents the quotation of the discharge of a plurality of small micro-bodies j in a virtual power plant in the scene R and the time t in the energy market,
Figure BDA00028232439600000316
at scene R andsimulating the price of charging the multielement micro-body j in the power plant in the energy market in the interval t,
Figure BDA00028232439600000317
representing the quoted frequency modulation capacity of a plurality of small micro-agents j in a virtual power plant in the scene R and the time t in the auxiliary service market,
Figure BDA0002823243960000041
represents the quotation of the multiple small micro-main bodies in the virtual power plant for the day-ahead frequency modulation market mileage in the scene R and the time t,
Figure BDA0002823243960000042
representing the quoted capacity of the conventional unit g in the auxiliary service market for the scenario R and time t,
Figure BDA0002823243960000043
representing the offer of mileage in the fm market for the conventional unit g in the scenario R and time t,
Figure BDA0002823243960000044
representing the amount of electricity reported by the multivariate micro agent j in the market trading day before and accepted by the ISO in the scenario R and time t,
Figure BDA0002823243960000045
representing the load declaration curve that the load is reported in the market for the day-ahead trading and accepted by the ISO in the scenario R and the time t,
Figure BDA0002823243960000046
the up-modulation capacity reported by the multivariate micro-agent j in the service market at the day before and accepted by ISO in the scene R and time t,
Figure BDA0002823243960000047
the down-tuning capacity reported by the multivariate micro-agent j in the service market in the day ahead and accepted by ISO in the scenario R and time t,
Figure BDA0002823243960000048
representing the up-frequency mileage reported by the multivariate micro-agent j in the service market in the day before and accepted by ISO in the scene R and the time t,
Figure BDA0002823243960000049
represents the down-frequency mileage reported by the multivariate micro-agent j in the service market in the day before and accepted by ISO in the scene R and the time t,
Figure BDA00028232439600000410
represents the lower frequency-modulation capacity reported by the conventional unit g in the service market in the day ahead and accepted by the ISO in the scene and time t,
Figure BDA00028232439600000411
represents the lower frequency-modulation capacity reported by the conventional unit g in the service market in the day ahead and accepted by the ISO in the scene and time t,
Figure BDA00028232439600000412
representing the up-tuned mileage reported by the conventional unit g in the day-ahead service market and accepted by ISO within the scenario R and time t,
Figure BDA00028232439600000413
represents the lower frequency-modulation mileage reported by the conventional unit g in the service market in the day ahead and accepted by the ISO in the scene R and the time t.
The VPP internal power balance constraint is:
Figure BDA00028232439600000414
wherein the content of the first and second substances,
Figure BDA00028232439600000415
the output of the wind generating set in the virtual power plant in the time t is shown,
Figure BDA00028232439600000416
photovoltaic power generation in virtual power plant within representation time tThe magnitude of the electric output force is large,
Figure BDA00028232439600000417
representing the amount of stored energy in the virtual power plant during time t,
Figure BDA00028232439600000418
represents the amount of stored energy and discharge in a virtual power plant within the time t, PL,shRepresenting the maximum load transferable during time t, LtRepresenting the amount of load in the grid over time t,
Figure BDA00028232439600000419
representing the electric quantity of wind power, photovoltaic and stored energy to the VPP internal load within the time t;
the wind power constraint is as follows:
Figure BDA00028232439600000420
wherein the content of the first and second substances,
Figure BDA00028232439600000421
representing the electric quantity delivered to the power grid by the wind generating set at the time of the VPP at t,
Figure BDA00028232439600000422
representing the amount of power supplied by the wind turbines to the loads in the virtual power plant during time t,
Figure BDA00028232439600000423
representing the amount of power supplied by the wind turbine to the energy storage unit over time t,
Figure BDA00028232439600000424
representing the maximum power generation of the wind turbine generator set within time t;
the photovoltaic constraints are:
Figure BDA0002823243960000051
wherein the content of the first and second substances,
Figure BDA0002823243960000052
the amount of power delivered to the grid by the photovoltaic power generation in the VPP at time t,
Figure BDA0002823243960000053
in time t, the electric quantity supplied to the load in the virtual power plant by the photovoltaic power generation,
Figure BDA0002823243960000054
representing the amount of power supplied by the photovoltaic power generation to the energy storage unit during time t,
Figure BDA0002823243960000055
representing the maximum power generation of the wind turbine generator set over time t;
the distributed energy storage constraints are:
Figure BDA0002823243960000056
wherein, PBSS,dRepresenting the amount of stored energy and discharge in the virtual power plant at time t, PBSS,cRepresenting the amount of stored energy in the virtual power plant at time t,
Figure BDA0002823243960000057
representing the size of the energy storage capacity in the virtual power plant at the moment t,
Figure BDA0002823243960000058
the energy storage capacity in the virtual power plant at the time t-1 is shown, delta t represents the time interval of charging and discharging,
Figure BDA0002823243960000059
are all binary variables, and are provided with the following functions,
Figure BDA00028232439600000510
indicating that the energy storage unit is in a charging state,
Figure BDA00028232439600000511
indicating that the energy storage unit is not in a charging state,
Figure BDA00028232439600000512
indicating that the energy storage unit is in a discharge state,
Figure BDA00028232439600000513
indicating that the energy storage unit is not in the discharge state, etaBSSIndicating the charging efficiency of the energy storage unit, EminIndicating minimum storage capacity of energy storage plant, EmaxRepresenting the maximum storage capacity of the energy storage power station;
the adjustable load constraints are:
Figure BDA00028232439600000514
wherein the content of the first and second substances,
Figure BDA00028232439600000515
representing the power of the internal load of the virtual power plant at time t,
Figure BDA00028232439600000516
representing the amount of power purchased by the load in the VPP to the grid at time t,
Figure BDA00028232439600000517
representing the amount of load reduction during the time t,
Figure BDA00028232439600000518
which indicates the amount of increase of the load during the time t,
Figure BDA00028232439600000519
are all binary variables, and are provided with the following functions,
Figure BDA00028232439600000520
representing a reduction in the virtual plant load over time t,
Figure BDA00028232439600000521
representing a virtual plant load increase over time t, otherwise
Figure BDA00028232439600000522
PL,sh,totRepresenting the maximum load allowed to be transferred during the entire scheduling.
The VPP bidding electric quantity constraint is as follows:
Figure BDA00028232439600000523
wherein the content of the first and second substances,
Figure BDA0002823243960000061
for the amount of power sold by the VPP during the day-ahead energy market time t,
Figure BDA0002823243960000062
the method comprises the following steps that the electricity purchasing quantity of a VPP in the day-ahead energy market time t is shown, and G is the maximum competitive bidding electricity quantity of an electricity market;
the VPP drop-down capacity constraint is:
Figure BDA0002823243960000063
wherein the content of the first and second substances,
Figure BDA0002823243960000064
representing the upper frequency modulation capacity reported by the ith unit in the service market at the moment in the scene R and the time t,
Figure BDA0002823243960000065
the lower frequency modulation capacity reported by the ith unit in the service market at the moment in the scene R and the time t is shown,
Figure BDA0002823243960000066
representing the electricity sales amount reported by the ith unit in the energy market in the scene R and the time t,
Figure BDA0002823243960000067
the power purchasing quantity reported by the ith unit in the energy market in the scene R and the time t,
Figure BDA0002823243960000068
represents the maximum capacity of the ith unit in the VPP,
Figure BDA0002823243960000069
represents the maximum turndown capacity of the ith unit in the VPP,
Figure BDA00028232439600000610
indicating the maximum charging power of the VPP market at the day-ahead,
Figure BDA00028232439600000611
represents the maximum discharge power of the VPP on the market at the day-ahead;
the VPP up-down mileage constraint is as follows:
Figure BDA00028232439600000612
wherein the content of the first and second substances,
Figure BDA00028232439600000613
showing the frequency-up mileage reported by the ith unit in the service market in the time t and the scene R,
Figure BDA00028232439600000614
showing the lower frequency-modulation mileage, epsilon, reported by the ith unit in the service market in the time t and in the scene RgA mileage multiplier representing a multivariate micro-body g.
The energy market power balance constraint is:
Figure BDA00028232439600000615
in the formula: pL,tExpressed as the magnitude of the system load over time t;
the network security constraints are:
pl,min≤pl,t≤pl,max
wherein p isl,tFor the flow of the line in time t, pl,maxFor maximum transmission capacity of the line, pl,minRespectively, the minimum transmission capacity of the line;
the system frequency modulation capacity constraint is as follows:
Figure BDA0002823243960000071
wherein the content of the first and second substances,
Figure BDA0002823243960000072
representing the up-modulation capacity of the system over time t,
Figure BDA0002823243960000073
representing the down-modulation capacity of the system over time t,
Figure BDA0002823243960000074
indicating the up-conversion capacity in VPP during time t,
Figure BDA0002823243960000075
indicating the down-scaled capacity in VPP over time t,
Figure BDA0002823243960000076
represents the upper frequency modulation capacity of the conventional unit in time t,
Figure BDA0002823243960000077
expressing the lower frequency-reducing capacity of the bid in the conventional unit within time t;
the system frequency modulation mileage constraint is as follows:
Figure BDA0002823243960000078
wherein the content of the first and second substances,
Figure BDA0002823243960000079
represents the up-modulated mileage of the system over time t,
Figure BDA00028232439600000710
represents the lower frequency-modulated mileage of the system over time t,
Figure BDA00028232439600000711
represents the up-frequency-modulated mileage in the VPP within the time t,
Figure BDA00028232439600000712
represents the lower frequency-reduction mileage in VPP within time t,
Figure BDA00028232439600000713
represents the bidding mileage of the conventional unit within the time t,
Figure BDA00028232439600000714
expressing the lower frequency-regulating mileage of the conventional unit within time t;
the conventional unit power constraint is as follows:
Figure BDA00028232439600000715
wherein, Pg,t,rRepresenting the output value, P, of the conventional unit g over time tg,maxIs the maximum output of the conventional unit g, Pg,minThe minimum output of the conventional unit g.
The method for obtaining the optimal bidding result and the optimal profit of the day-ahead energy and duration day-ahead frequency modulation market comprises the following steps:
step 3.1, carrying out data preprocessing on the VPP after the multiple small micro-main bodies are gathered, and generating multiple bidding strategies k by taking the maximum profit of the VPP as an objective function;
step 3.2, generating a typical quotation scene r of the competitor according to the marginal cost of the competitor multiplied by a curve random scale factor [0.75,1.25 ];
step 3.3, the energy market and the frequency modulation market are cleared in a combined mode in the day ahead;
and 3.4, calculating the probability weighted average of all power price prediction scenes to obtain clearing prices in each market time t in the day, and calculating the total profit of the VPP under each strategy.
The bid-winning condition of each main body in the market comprises a bid-winning condition of each main body in the day-ahead energy market of the market, a bid-winning condition of the day-ahead auxiliary market frequency modulation capacity of each main body in the market and a bid-winning condition of day-ahead auxiliary market frequency modulation mileage of each main body in the market; the optimal quotation comprises the optimal quotation of the VPP in each market and the optimal report of the VPP in each market; the clearing price refers to the clearing price of a day-ahead energy market and a day-ahead frequency modulation market.
The beneficial effect that this technical scheme can produce:
1. the invention can establish a power price bidding model fully considering the uncertainty of the price of electricity in the day-ahead market and day-ahead frequency modulation, and provides a reference basis for VPP to carry out market bidding, operation decision and energy storage economic feasibility assessment.
2. The method can construct a clearing model of the day-ahead energy and day-ahead frequency modulation market, and enables the VPP on the power grid side to be used as a buyer and seller to participate in the unified clearing process of the day-ahead energy and day-ahead frequency modulation market. Therefore, the VPP can be effectively stimulated to participate in frequency modulation auxiliary service, more benefits are obtained, the loss caused by the output power of the thermal power generating unit is reduced frequently, the electricity purchasing cost of a demand party is reduced, and the benefit requirements of supply and demand parties are met.
3. The method can realize the related algorithm through programming, obtain the output result by reading the input data, and visually display the output result in a data and graph mode by carrying out visual interface design on each module interface, thereby more simply and conveniently realizing the participation of the VPP in the frequency modulation auxiliary service market transaction process.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a game model architecture for VPP participation in spot market bidding in accordance with the present invention;
FIG. 3 is a flow chart of model solution of the present invention;
FIG. 4 is a schematic diagram of visualized charge and discharge power of the energy storage power station in each time period according to the present invention;
FIG. 5 is a diagram illustrating the power consumption after VPP internal load optimization according to the present invention;
FIG. 6 is a diagram of system load power prediction in accordance with the present invention;
FIG. 7 is a chart of bid winning status in the day-ahead energy market of each subject of the market according to the present invention;
FIG. 8 is a plot of the bid amount of the FM capacity of the day-ahead auxiliary market for each market subject of the present invention;
FIG. 9 is a chart of bid-winning status of the frequency-modulated mileage of each market subject in the day-ahead auxiliary market of the present invention;
FIG. 10 is a diagram of the best bid in each market for the VPP of the present invention;
FIG. 11 is a graph of the optimal volume of the VPP of the present invention in various markets;
FIG. 12 is a plot of the day-ahead energy market consolidated clearing price profile and total load curve of the present invention;
FIG. 13 is a plot of the day-ahead auxiliary market FM capacity distribution and FM mileage clearing price curves of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a virtual power plant-based method for clearing a plurality of small micro-subjects participating in spot market, which includes the following specific steps:
the method comprises the following steps: constructing a double-layer clearing model considering the uncertainty of the electricity price based on the VPP parameters of the power grid side and the information of the electric energy buyer and the electric energy seller;
in this embodiment, a day-ahead energy and day-ahead auxiliary service market trading platform may be provided, and based on an energy and frequency modulation market trading mode of day-ahead centralized bidding trading provided in the trading platform, the electric energy buyer and seller may claim related information, so that the trading platform may form a day-ahead energy and frequency modulation market for centralized bidding and unified clearing of electricity prices.
Generally, the information of the buyer and the seller of the electric energy includes price information and electric quantity information of each buyer and the seller, such as parameters of different quotes of the conventional generator set and the VPP in different bidding periods, the price of the outgoing clear electricity in the day, the profit of the outgoing clear electricity in the day, the electric quantity sold and the electric quantity purchased.
It should be noted that, the conventional generator set is an electric energy seller, and the VPP may be used as an electric energy seller in the market or an electric energy buyer in the market. Which can be profitable by low-buying high-selling through the two-stage double-deck pull-out model described herein according to time-of-use pricing.
Meanwhile, the VPP parameter on the grid side generally includes a parameter related to the energy storage unit and a parameter related to the renewable energy unit. For example, the bid amount of the energy storage unit and the renewable energy source, the number of the energy storage unit participating in frequency modulation and the demand side response user, the cyclic loss cost of one day of energy storage, the number of charging and discharging cycles of the energy storage full life cycle, the energy storage charging and discharging depth and the like.
Generally, in a mature market, the price quoted for electricity generation should be able to represent the cost of electricity generation. Therefore, in the present embodiment, the compensation fee for the settlement of the quoted price can be approximately regarded as the power generation cost of the power generation manufacturer. If the power generation manufacturer quotes based on the power generation cost or the energy storage cost, a clearing model can be constructed based on the uncertainty of the electricity price according to the VPP parameters of the power grid side and the information of the electric energy buyers and sellers.
As shown in fig. 2, the double-layer export model includes a first-stage model, a second-stage inner-layer model and a second-stage outer-layer model, wherein the first-stage model is a VPP coordinated optimization scheduling model, the second-stage inner-layer model is a VPP participation day-ahead market profit maximization model, and the second-stage outer-layer model is a social welfare maximization model.
The VPP coordinated optimization scheduling model coordinates and schedules each small micro main body in the main body by taking the maximum target of the goodness of fit between the output characteristic curve of each main body and the load curve of the power grid. And taking the optimized output curve as the basis of the next-stage system operation optimization, wherein the target function is as follows:
Figure BDA0002823243960000091
wherein the content of the first and second substances,
Figure BDA0002823243960000092
representing the amount of power VPP delivered to the grid at time t,
Figure BDA0002823243960000093
and (4) predicting the load quantity in the power grid in the period t.
The VPP participation day-ahead market profit maximization model is as follows:
Figure BDA0002823243960000101
where R denotes the scenario of a competitor's offer, probrRepresenting the probability of occurrence of a scenario R, I representing the investment portfolio parameter set of the VPP, D representing the internal load parameter set of the virtual power plant, T representing the time set of 24h,
Figure BDA0002823243960000102
the system marginal liquidity price representing the energy market at day-ahead in scenario R and time t,
Figure BDA0002823243960000103
representing the amount of electricity reported by the ith unit in the market trading day before and accepted by the ISO in the scenario R and time t,
Figure BDA0002823243960000104
representing the load declaration curve that the d-th load is reported in the market before the day and is accepted by ISO in the scene R and the time t,
Figure BDA0002823243960000105
represents the clearing price of the day-ahead service market capacity in scenario R and time t,
Figure BDA0002823243960000106
representing the up-modulation capacity reported by the ith unit in the service market at the day before and accepted by ISO in the scenario R and time t,
Figure BDA0002823243960000107
represents the lower frequency modulation capacity reported by the ith unit in the service market at the day before and accepted by ISO in the scene and time t,
Figure BDA0002823243960000108
represents the clearing price of the service market fm mileage at scene R and time t,
Figure BDA0002823243960000109
representing the frequency-up mileage reported by the ith unit in the service market in the day-ahead and accepted by ISO in the scene R and the time t,
Figure BDA00028232439600001010
and (3) indicating the frequency-down mileage which is reported by the ith unit in the service market at the day before and accepted by ISO in the scene R and the time t.
The social welfare maximization model is as follows:
Figure BDA00028232439600001011
wherein the content of the first and second substances,
Figure BDA00028232439600001012
represents the quotation of the discharge of a plurality of small micro-bodies j in a virtual power plant in the scene R and the time t in the energy market,
Figure BDA00028232439600001013
simulating the price of charging the multi-element micro main body j in the power plant in the scene R and the time t in the energy market,
Figure BDA00028232439600001014
representing the quoted frequency modulation capacity of a plurality of small micro-agents j in a virtual power plant in the scene R and the time t in the auxiliary service market,
Figure BDA00028232439600001015
represents the quotation of the multiple small micro-main bodies in the virtual power plant for the day-ahead frequency modulation market mileage in the scene R and the time t,
Figure BDA00028232439600001016
representing the quoted capacity of the conventional unit g in the auxiliary service market for the scenario R and time t,
Figure BDA00028232439600001017
representing the offer of mileage in the fm market for the conventional unit g in the scenario R and time t,
Figure BDA00028232439600001018
representing the amount of electricity reported by the multivariate micro agent j in the market trading day before and accepted by the ISO in the scenario R and time t,
Figure BDA00028232439600001019
representing the load declaration curve that the load is reported in the market for the day-ahead trading and accepted by the ISO in the scenario R and the time t,
Figure BDA00028232439600001020
at scene R and timethe up-modulation capacity reported by the multivariate micro-agent j in the service market at the day before and accepted by ISO within t,
Figure BDA0002823243960000111
the down-tuning capacity reported by the multivariate micro-agent j in the service market in the day ahead and accepted by ISO in the scenario R and time t,
Figure BDA0002823243960000112
representing the up-frequency mileage reported by the multivariate micro-agent j in the service market in the day before and accepted by ISO in the scene R and the time t,
Figure BDA0002823243960000113
represents the down-frequency mileage reported by the multivariate micro-agent j in the service market in the day before and accepted by ISO in the scene R and the time t,
Figure BDA0002823243960000114
represents the lower frequency-modulation capacity reported by the conventional unit g in the service market in the day ahead and accepted by the ISO in the scene and time t,
Figure BDA0002823243960000115
represents the lower frequency-modulation capacity reported by the conventional unit g in the service market in the day ahead and accepted by the ISO in the scene and time t,
Figure BDA0002823243960000116
representing the up-tuned mileage reported by the conventional unit g in the day-ahead service market and accepted by ISO within the scenario R and time t,
Figure BDA0002823243960000117
represents the lower frequency-modulation mileage reported by the conventional unit g in the service market in the day ahead and accepted by the ISO in the scene R and the time t.
Step two: constructing a constraint condition of a double-layer clearing model based on the VPP parameters of the power grid side, the information of both electric energy buyers and sellers and the double-layer clearing model;
the constraint conditions of the double-layer output model comprise the constraint conditions of a first-stage model, the constraint conditions of a second-stage inner-layer model and the constraint conditions of a second-stage outer-layer model; the constraint conditions of the first section model comprise VPP internal power balance constraint, wind power constraint, photovoltaic constraint, distributed energy storage constraint and adjustable load constraint; the constraint conditions of the second-stage inner layer model comprise VPP competitive bidding electric quantity constraint, VPP up-down capacity constraint and VPP up-down mileage constraint; and the constraint conditions of the second-stage outer layer model comprise energy market power balance constraint, network safety constraint, system frequency modulation capacity constraint, system frequency modulation mileage constraint and conventional unit power constraint.
The VPP internal power balance constraint comprises absorption quantities of a plurality of small micro bodies with output equal to load and stored energy, and the expression is as follows:
Figure BDA0002823243960000118
wherein the content of the first and second substances,
Figure BDA0002823243960000119
the output of the wind generating set in the virtual power plant in the time t is shown,
Figure BDA00028232439600001110
represents the magnitude of the photovoltaic power generation output in the virtual power plant within the time t,
Figure BDA00028232439600001111
representing the amount of stored energy in the virtual power plant during time t,
Figure BDA00028232439600001112
represents the amount of stored energy and discharge in a virtual power plant within the time t, PL,shRepresenting the maximum load transferable during time t, LtRepresenting the amount of load in the grid over time t,
Figure BDA00028232439600001113
electric quantity for expressing wind power, photovoltaic and energy storage to supply internal load of VPP within time t。
The wind power constraint is that the output power of the wind power is equal to the sum of the internal load given by the wind power and the internal load of the virtual power plant and is constrained by the maximum power, and the expression is as follows:
Figure BDA00028232439600001114
wherein the content of the first and second substances,
Figure BDA0002823243960000121
representing the electric quantity delivered to the power grid by the wind generating set at the time of the VPP at t,
Figure BDA0002823243960000122
representing the amount of power supplied by the wind turbines to the loads in the virtual power plant during time t,
Figure BDA0002823243960000123
representing the amount of power supplied by the wind turbine to the energy storage unit over time t,
Figure BDA0002823243960000124
representing the maximum power production of the wind park over time t.
The photovoltaic constraint is that the output power of the photovoltaic is equal to the sum of the internal load given by the photovoltaic and the internal load of the virtual power plant and is constrained by the maximum power, and the expression is as follows:
Figure BDA0002823243960000125
wherein the content of the first and second substances,
Figure BDA0002823243960000126
the amount of power delivered to the grid by the photovoltaic power generation in the VPP at time t,
Figure BDA0002823243960000127
in time t, the electric quantity supplied to the load in the virtual power plant by the photovoltaic power generation,
Figure BDA0002823243960000128
representing the amount of power supplied by the photovoltaic power generation to the energy storage unit during time t,
Figure BDA0002823243960000129
representing the maximum power generation of the wind park over time t.
The distributed energy storage constraint comprises charge-discharge constraint, capacity constraint and state constraint of all energy storage power stations, and the expression is as follows:
Figure BDA00028232439600001210
wherein, PBSS,dRepresenting the amount of stored energy and discharge in the virtual power plant at time t, PBSS,cRepresenting the amount of stored energy in the virtual power plant at time t,
Figure BDA00028232439600001211
representing the size of the energy storage capacity in the virtual power plant at the moment t,
Figure BDA00028232439600001212
the energy storage capacity in the virtual power plant at the time t-1 is shown, delta t represents the time interval of charging and discharging,
Figure BDA00028232439600001213
are all binary variables, and are provided with the following functions,
Figure BDA00028232439600001214
indicating that the energy storage unit is in a charging state,
Figure BDA00028232439600001215
indicating that the energy storage unit is not in a charging state,
Figure BDA00028232439600001216
indicating that the energy storage unit is in a discharge state,
Figure BDA00028232439600001217
indicating that the energy storage unit is not in the discharge state, etaBSSIndicating the charging efficiency of the energy storage unit, EminIndicating minimum storage capacity of energy storage plant, EmaxRepresenting the maximum storage capacity of the energy storage power station;
the adjustable load constraints comprise all adjustable load up-down capacity constraints and state constraints, and the expression is as follows:
Figure BDA00028232439600001218
wherein the content of the first and second substances,
Figure BDA00028232439600001219
representing the power of the internal load of the virtual power plant at time t,
Figure BDA00028232439600001220
representing the amount of power purchased by the load in the VPP to the grid at time t,
Figure BDA00028232439600001221
representing the amount of load reduction during the time t,
Figure BDA00028232439600001222
which indicates the amount of increase of the load during the time t,
Figure BDA00028232439600001223
are all binary variables, and are provided with the following functions,
Figure BDA00028232439600001224
representing a reduction in the virtual plant load over time t,
Figure BDA00028232439600001225
representing a virtual plant load increase over time t, otherwise
Figure BDA0002823243960000131
PL,sh,totThe VP representing the maximum load allowed to be transferred during the entire schedulingThe P bidding electric quantity constraint comprises VPP and main network transmission power limitation, and the bidding electric quantity of the VPP in the electric power market needs to meet the following constraint formula:
Figure BDA0002823243960000132
wherein the content of the first and second substances,
Figure BDA0002823243960000133
for the amount of power sold by the VPP during the day-ahead energy market time t,
Figure BDA0002823243960000134
the electricity quantity purchased by the VPP in the energy market time t in the day ahead, and G is the maximum competitive bidding electricity quantity of the electricity market.
The VPP up-down capacity constraint comprises a maximum up-down capacity constraint and a maximum charge-discharge constraint, and the expression is as follows:
Figure BDA0002823243960000135
wherein the content of the first and second substances,
Figure BDA0002823243960000136
representing the upper frequency modulation capacity reported by the ith unit in the service market at the moment in the scene R and the time t,
Figure BDA0002823243960000137
the lower frequency modulation capacity reported by the ith unit in the service market at the moment in the scene R and the time t is shown,
Figure BDA0002823243960000138
representing the electricity sales amount reported by the ith unit in the energy market in the scene R and the time t,
Figure BDA0002823243960000139
the power purchasing quantity reported by the ith unit in the energy market in the scene R and the time t,
Figure BDA00028232439600001310
represents the maximum capacity of the ith unit in the VPP,
Figure BDA00028232439600001311
represents the maximum turndown capacity of the ith unit in the VPP,
Figure BDA00028232439600001312
indicating the maximum charging power of the VPP market at the day-ahead,
Figure BDA00028232439600001313
indicating the maximum discharge power of the VPP market at the day-ahead.
The VPP up-down mileage constraint comprises a maximum up-down history constraint, and the expression is as follows:
Figure BDA00028232439600001314
wherein the content of the first and second substances,
Figure BDA00028232439600001315
showing the frequency-up mileage reported by the ith unit in the service market in the time t and the scene R,
Figure BDA00028232439600001316
showing the lower frequency-modulation mileage, epsilon, reported by the ith unit in the service market in the time t and in the scene RgA mileage multiplier representing a multivariate micro-body g.
The energy market power balance constraint comprises that the discharge power of the virtual power plant and the discharge power of the conventional unit are equal to the total load required by the system, and the expression is as follows:
Figure BDA00028232439600001317
in the formula: pL,tExpressed as the magnitude of the system load over time t.
The network security constraint comprises maximum and minimum transmission capacity constraints of a line, and the expression is as follows:
pl,min≤pl,t≤pl,max
wherein p isl,tFor the flow of the line in time t, pl,maxFor maximum transmission capacity of the line, pl,minRespectively the minimum transmission capacity of the line.
The system frequency modulation capacity constraint comprises that the total up-down regulation capacity of the system is equal to the sum of the up-down regulation capacity of the virtual power plant and the up-down regulation capacity of the unit, and the expression is as follows:
Figure BDA0002823243960000141
wherein the content of the first and second substances,
Figure BDA0002823243960000142
representing the up-modulation capacity of the system over time t,
Figure BDA0002823243960000143
representing the down-modulation capacity of the system over time t,
Figure BDA0002823243960000144
indicating the up-conversion capacity in VPP during time t,
Figure BDA0002823243960000145
indicating the down-scaled capacity in VPP over time t,
Figure BDA0002823243960000146
represents the upper frequency modulation capacity of the conventional unit in time t,
Figure BDA0002823243960000147
and (4) indicating the lower frequency-reducing capacity of the conventional unit during the time t.
The system frequency modulation mileage constraint comprises that the total upper and lower mileage of the system is equal to the sum of the upper and lower mileage of the virtual power plant and the upper and lower mileage of the unit, and the expression is as follows:
Figure BDA0002823243960000148
wherein the content of the first and second substances,
Figure BDA0002823243960000149
represents the up-modulated mileage of the system over time t,
Figure BDA00028232439600001410
represents the lower frequency-modulated mileage of the system over time t,
Figure BDA00028232439600001411
represents the up-frequency-modulated mileage in the VPP within the time t,
Figure BDA00028232439600001412
represents the lower frequency-reduction mileage in VPP within time t,
Figure BDA00028232439600001413
represents the bidding mileage of the conventional unit within the time t,
Figure BDA00028232439600001414
and (4) indicating the lower frequency-regulating mileage in the conventional unit within the time t.
The conventional unit power constraint comprises maximum up-down regulation power constraint, and the expression is as follows:
Figure BDA00028232439600001415
wherein, Pg,t,rRepresenting the output value, P, of the conventional unit g over time tg,maxIs the maximum output of the conventional unit g, Pg,minThe minimum output of the conventional unit g. The conventional unit constraint also comprises other constraints such as climbing constraint, declared frequency modulation capacity and mileage constraint and the like. Wherein, the declared frequency modulation capacity and mileage constraint are the same as the energy storage form. The restriction of the bid amount includes that the sum of the bid amounts in each time interval of the conventional unit does not exceed the sum of the bid amounts in each time interval of the conventional unitThe sum of the rated capacity and the bid amount in each energy storage period does not exceed the rated capacity and the rated power.
Step three: solving the double-layer clearing model based on the constraint conditions of the double-layer clearing model to obtain the optimal bidding result and the optimal profit of the day-ahead frequency modulation market of the day-ahead energy and duration;
as shown in fig. 3, the method for obtaining the optimal bidding result and the optimal profit of the day-ahead energy and duration day-ahead frequency modulation market includes:
step 3.1, carrying out data preprocessing on the VPP after the multiple small micro-main bodies are gathered, and generating multiple bidding strategies k by taking the maximum profit of the VPP as an objective function; in step 3.1, due to uncertainty and randomness of output of renewable energy sources (wind power, photovoltaic and the like) and distribution universality, scheduling of the renewable energy sources is difficult. With the development of the power internet of things and the data communication technology, the multiple small micro-bodies can be aggregated into one VPP and scheduled by a unified operator. After a VPP is aggregated, an operator needs to perform data preprocessing on the day-ahead energy and frequency modulation market quotation information input by each main body based on methods such as abnormal value screening and supplementing, feature selection, optimized parameter configuration and the like to generate a quotation and report curve for 24h in the future, so that various bidding strategies of the day-ahead energy and frequency modulation market are regenerated.
Step 3.2, generating a typical quotation scene r of the competitor according to the marginal cost of the competitor multiplied by a curve random scale factor [0.75,1.25 ]; in step 3.2, since the multi-step bidding strategy is generated in step 3.1, but the bidding strategies of other competitors, such as the bidding strategies of the traditional unit and the bidding strategies of other VPPs, are also considered, the ARMA is adopted to fit the data, a series of data is obtained through Monte Carlo sampling, and the scene is cut down by adopting the rapid antecedent method, so that a typical target scene set of the competitors is obtained.
Step 3.3, the energy market and the frequency modulation market are cleared in a combined mode in the day ahead; in step 3.3, the VPP is used as a leader in a competitive game to ensure that the energy of the VPP and the income of the frequency modulation market are maximized in the day ahead, the ISO is used as a follower in the competitive game to listen to the quoted price and the quoted volume of each unit, the maximization of the social welfare is taken as a target, namely the minimum electricity purchasing cost is taken as a target, the combined clearing of the energy market and the frequency modulation market in the day ahead is carried out, and the competitive electric quantity and the competitive price of each unit for 24 hours are obtained.
And 3.4, calculating the probability weighted average of all power price prediction scenes to obtain clearing prices in each market time t in the day, and calculating the total profit of the VPP under each strategy. In step 3.4, considering the uncertainty of the scenes, each scene corresponds to a probability, the successful bidding electric quantity and the bidding price of each main body in each scene are obtained, and the clearing price of each market in the day-ahead time t period in each scene is obtained through the weighted average. And obtaining the best quotation strategy of the VPP participating in the day-ahead energy market and the day-ahead frequency modulation market by comparing the total profit of the VPP in each scene.
Through the steps 3.1 to 3.4, the optimal bidding result and the optimal income of the seller in the market can be obtained according to the two-stage double-layer clearing model and the two-stage double-layer constraint condition.
Step four: and obtaining the successful bidding condition, the optimal quotation and the clearing price of each main body of the market according to the optimal bidding result and the optimal income. The bid-winning condition of each main body in the market comprises a bid-winning condition of each main body in the day-ahead energy market of the market, a bid-winning condition of the day-ahead auxiliary market frequency modulation capacity of each main body in the market and a bid-winning condition of day-ahead auxiliary market frequency modulation mileage of each main body in the market; the optimal quotation comprises the optimal quotation of the VPP in each market and the optimal report of the VPP in each market; the clearing price refers to the clearing price of a day-ahead energy market and a day-ahead frequency modulation market. The embodiment outputs a bidding situation diagram of each main body day-ahead energy market, a bidding capacity bidding situation diagram of each main body day-ahead auxiliary market, a bidding mileage bidding situation diagram of each main body day-ahead auxiliary market, a different market main body profit comparison table, an optimal quotation diagram of VPP in each market, and a clearing price diagram of the day-ahead energy market and the day-ahead frequency modulation market based on the regional dimension and the time dimension in a visual mode.
By encapsulating the related algorithms involved in the steps one to three, the process of clearing can be displayed through a simple and convenient visual interface. For example, a Graphical User Interface (GUI) module in MATLAB may be employed to package the relevant algorithms.
In the embodiment, an example is constructed by a VPP formed by a wind turbine generator, a photovoltaic generator, an energy storage generator and 4000 families, and the output parameters of each small micro main body in the VPP are shown in figures 4 and 5; the total load situation of the system is shown in figure 6.
As can be seen from fig. 7, in the energy market at day time, the conventional units G1, G2 provide the vast majority of energy, whereas VPP is mainly 1: the energy is provided during the periods of 00-6: 00 and 13: 00-15: 00, because the wind power provides larger output at night, the sunlight has stronger illumination capability at noon, and the photovoltaic provides larger output at the moment. In the following step 9: 00-11: 00 and 17: 00-19: during 00 the VPP needs to purchase power from the grid due to the large power usage by the load within the VPP (see fig. 4).
Comparing fig. 8 and 9, the VPP assumes the main fm capacity and fm mileage tasks of the system. This is because when the same modulation capacity is provided as compared with the modulation output of the conventional unit, the VPP unit, which includes the distributed energy storage device, responds to the modulation signal more frequently in the upward and downward directions, and thus can provide more frequency modulation mileage than the conventional unit, and is preferentially called by the system in the frequency modulation market. The revenue situation of different market entities in each market is shown in table 1.
TABLE 1 comparison table of different market subject profits
Figure BDA0002823243960000161
As can be seen from table 1, the main gains of the conventional units G1, G2 and G6 mainly come from the day-ahead energy market, and the main gains of the VPP mainly include the day-ahead energy market gains and the frequency modulation capacity gains.
As can be seen from FIG. 10, the VPP discharge price is always higher than the charge price because the VPP earns profit from the price difference. The VPP FM capacity quote, although fluctuating, remains entirely on a straight line, whereas the VPP FM mileage quote is a straight line.
As can be seen more intuitively from fig. 11, the VPP charges its internal energy storage during periods of low load, and discharges it when the load is high. The VPP frequency modulation capability is higher, so the frequency modulation capacity and the medium-rate rating of the frequency modulation mileage are higher.
As can be seen from fig. 12, the clear electricity price of the energy market is similar to the load trend, and shows a variation trend of low night and high day, because the conventional units quote according to the power generation cost, the quoted price is linearly related to the power generation amount, and the quoted price trend is similar to the load trend.
As can be seen from fig. 13, the fm capacity and the odds price of fm mileage of the aftermarket fluctuate over time. They all follow a law: 4: 00-6: price of 00 out is lowest, 12: 00-14: 00. 20: 00-23: the 00 clearing price is the highest, but the overall clearing price is not greatly fluctuated, and is maintained at an approximate level.
According to the invention, by establishing a clearing model and constraint conditions, visual benefit analysis and bidding decision analysis can be carried out on the current energy and frequency modulation market in which VPP participates on the basis of considering the uncertainty of the electricity price. The invention can establish the electric power price bidding model fully considering the uncertainty of the price of electricity in the market in the day ahead, and provides a reference basis for market bidding, operation decision and energy storage economic feasibility evaluation of VPPs. The method can construct a clearing model of the day-ahead energy and frequency modulation market, and enables the VPP on the power grid side to be used as a buyer and a seller to participate in the unified clearing process of the day-ahead energy and frequency modulation market. Therefore, the VPP can be effectively stimulated to participate in the peak shaving auxiliary service, more benefits are obtained, the loss caused by the output power of the thermal power generating unit is reduced frequently, the electricity purchasing cost of a demand party is reduced, and the benefit requirements of supply and demand parties are met. The invention can realize the related algorithm through programming, obtain the output result through reading the input data, carry on the visual interface design to each module interface so as to show the output result through the way of data and figure visualization, realize VPP to the participation of the energy and frequency modulation market transaction process before the day more simply, conveniently.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. A virtual power plant-based method for clearing a plurality of small micro-subjects participating in spot market is characterized by comprising the following steps:
the method comprises the following steps: constructing a double-layer clearing model considering the uncertainty of the electricity price based on the VPP parameters of the power grid side and the information of the electric energy buyer and the electric energy seller;
step two: constructing a constraint condition of a double-layer clearing model based on the VPP parameters of the power grid side, the information of both electric energy buyers and sellers and the double-layer clearing model;
step three: solving the double-layer clearing model based on the constraint conditions of the double-layer clearing model to obtain the optimal bidding result and the optimal profit of the day-ahead frequency modulation market of the day-ahead energy and duration;
step four: and obtaining the successful bidding condition, the optimal quotation and the clearing price of each main body of the market according to the optimal bidding result and the optimal income.
2. The virtual power plant-based multi-element micro-subject spot market clearing method according to claim 1, wherein the double-layer clearing model comprises a first-stage model, a second-stage inner-layer model and a second-stage outer-layer model, wherein the first-stage model is a VPP coordination optimization scheduling model, the second-stage inner-layer model is a VPP participation day-ahead market profit maximization model, and the second-stage outer-layer model is a full social welfare maximization model; the constraint conditions of the double-layer output model comprise the constraint conditions of a first-stage model, the constraint conditions of a second-stage inner-layer model and the constraint conditions of a second-stage outer-layer model; the constraint conditions of the first section model comprise VPP internal power balance constraint, wind power constraint, photovoltaic constraint, distributed energy storage constraint and adjustable load constraint; the constraint conditions of the second-stage inner layer model comprise VPP competitive bidding electric quantity constraint, VPP up-down capacity constraint and VPP up-down mileage constraint; and the constraint conditions of the second-stage outer layer model comprise energy market power balance constraint, network safety constraint, system frequency modulation capacity constraint, system frequency modulation mileage constraint and conventional unit power constraint.
3. The virtual power plant based multi-element micro-agent spot market clearing method according to claim 2, wherein the VPP coordinated optimization scheduling model is:
Figure FDA0002823243950000011
wherein the content of the first and second substances,
Figure FDA0002823243950000012
representing the amount of power VPP delivered to the grid at time t,
Figure FDA0002823243950000013
and (4) predicting the load quantity in the power grid in the period t.
4. The virtual power plant based export method of multivariate micro-agents participating in spot market of claim 2, wherein the VPP participation day-ahead market profit maximization model is:
Figure FDA0002823243950000014
where R denotes the scenario of a competitor's offer, probrRepresenting the probability of occurrence of a scenario R, I representing the investment portfolio parameter set of the VPP, D representing the internal load parameter set of the virtual power plant, T representing the time set of 24h,
Figure FDA0002823243950000015
the system marginal liquidity price representing the energy market at day-ahead in scenario R and time t,
Figure FDA0002823243950000021
representing the amount of electricity reported by the ith unit in the market trading day before and accepted by the ISO in the scenario R and time t,
Figure FDA0002823243950000022
representing the load declaration curve that the d-th load is reported in the market before the day and is accepted by ISO in the scene R and the time t,
Figure FDA0002823243950000023
represents the clearing price of the day-ahead service market capacity in scenario R and time t,
Figure FDA0002823243950000024
representing the up-modulation capacity reported by the ith unit in the service market at the day before and accepted by ISO in the scenario R and time t,
Figure FDA0002823243950000025
represents the lower frequency modulation capacity reported by the ith unit in the service market at the day before and accepted by ISO in the scene and time t,
Figure FDA0002823243950000026
represents the clearing price of the service market fm mileage at scene R and time t,
Figure FDA0002823243950000027
representing the frequency-up mileage reported by the ith unit in the service market in the day-ahead and accepted by ISO in the scene R and the time t,
Figure FDA0002823243950000028
and (3) indicating the frequency-down mileage which is reported by the ith unit in the service market at the day before and accepted by ISO in the scene R and the time t.
5. The virtual power plant based multi-element micro-agent spot market clearing method according to claim 4, wherein the social welfare maximization model is:
Figure FDA0002823243950000029
wherein the content of the first and second substances,
Figure FDA00028232439500000210
represents the quotation of the discharge of a plurality of small micro-bodies j in a virtual power plant in the scene R and the time t in the energy market,
Figure FDA00028232439500000211
simulating the price of charging the multi-element micro main body j in the power plant in the scene R and the time t in the energy market,
Figure FDA00028232439500000212
representing the quoted frequency modulation capacity of a plurality of small micro-agents j in a virtual power plant in the scene R and the time t in the auxiliary service market,
Figure FDA00028232439500000213
represents the quotation of the multiple small micro-main bodies in the virtual power plant for the day-ahead frequency modulation market mileage in the scene R and the time t,
Figure FDA00028232439500000214
representing the quoted capacity of the conventional unit g in the auxiliary service market for the scenario R and time t,
Figure FDA00028232439500000215
representing the offer of mileage in the fm market for the conventional unit g in the scenario R and time t,
Figure FDA00028232439500000216
showing that multiple micro-subjects j are reported in the market of the day-ahead trade in the scene R and the time tAnd the amount of power accepted by the ISO,
Figure FDA00028232439500000217
representing the load declaration curve that the load is reported in the market for the day-ahead trading and accepted by the ISO in the scenario R and the time t,
Figure FDA00028232439500000218
the up-modulation capacity reported by the multivariate micro-agent j in the service market at the day before and accepted by ISO in the scene R and time t,
Figure FDA00028232439500000219
the down-tuning capacity reported by the multivariate micro-agent j in the service market in the day ahead and accepted by ISO in the scenario R and time t,
Figure FDA00028232439500000220
representing the up-frequency mileage reported by the multivariate micro-agent j in the service market in the day before and accepted by ISO in the scene R and the time t,
Figure FDA00028232439500000221
represents the down-frequency mileage reported by the multivariate micro-agent j in the service market in the day before and accepted by ISO in the scene R and the time t,
Figure FDA0002823243950000031
represents the lower frequency-modulation capacity reported by the conventional unit g in the service market in the day ahead and accepted by the ISO in the scene and time t,
Figure FDA0002823243950000032
represents the lower frequency-modulation capacity reported by the conventional unit g in the service market in the day ahead and accepted by the ISO in the scene and time t,
Figure FDA0002823243950000033
shows that the conventional unit g reports in the service market at the day before the scene R and the time tThe up-tuned mileage accepted by ISO,
Figure FDA0002823243950000034
represents the lower frequency-modulation mileage reported by the conventional unit g in the service market in the day ahead and accepted by the ISO in the scene R and the time t.
6. The virtual power plant based multivariate small micro-agent spot market clearing method according to claim 2 or 3, wherein the VPP internal power balance constraint is:
Figure FDA0002823243950000035
wherein the content of the first and second substances,
Figure FDA0002823243950000036
the output of the wind generating set in the virtual power plant in the time t is shown,
Figure FDA0002823243950000037
represents the magnitude of the photovoltaic power generation output in the virtual power plant within the time t,
Figure FDA0002823243950000038
representing the amount of stored energy in the virtual power plant during time t,
Figure FDA0002823243950000039
represents the amount of stored energy and discharge in a virtual power plant within the time t, PL,shRepresenting the maximum load transferable during time t, LtRepresenting the amount of load in the grid over time t,
Figure FDA00028232439500000310
representing the electric quantity of wind power, photovoltaic and stored energy to the VPP internal load within the time t;
the wind power constraint is as follows:
Figure FDA00028232439500000311
wherein the content of the first and second substances,
Figure FDA00028232439500000312
representing the electric quantity delivered to the power grid by the wind generating set at the time of the VPP at t,
Figure FDA00028232439500000313
representing the amount of power supplied by the wind turbines to the loads in the virtual power plant during time t,
Figure FDA00028232439500000314
representing the amount of power supplied by the wind turbine to the energy storage unit over time t,
Figure FDA00028232439500000315
representing the maximum power generation of the wind turbine generator set within time t;
the photovoltaic constraints are:
Figure FDA00028232439500000316
wherein the content of the first and second substances,
Figure FDA00028232439500000317
the amount of power delivered to the grid by the photovoltaic generation in the VPP at the time,
Figure FDA00028232439500000318
in time t, the electric quantity supplied to the load in the virtual power plant by the photovoltaic power generation,
Figure FDA00028232439500000319
representing the amount of power supplied by the photovoltaic power generation to the energy storage unit during time t,
Figure FDA00028232439500000320
representing the maximum power generation of the wind turbine generator set over time t;
the distributed energy storage constraints are:
Figure FDA0002823243950000041
wherein, PBSS,dRepresenting the amount of stored energy and discharge in the virtual power plant at time t, PBSS,cRepresenting the amount of stored energy in the virtual power plant at time t,
Figure FDA0002823243950000042
representing the size of the energy storage capacity in the virtual power plant at the moment t,
Figure FDA0002823243950000043
the energy storage capacity in the virtual power plant at the time t-1 is shown, delta t represents the time interval of charging and discharging,
Figure FDA0002823243950000044
are all binary variables, and are provided with the following functions,
Figure FDA0002823243950000045
indicating that the energy storage unit is in a charging state,
Figure FDA0002823243950000046
indicating that the energy storage unit is not in a charging state,
Figure FDA0002823243950000047
indicating that the energy storage unit is in a discharge state,
Figure FDA0002823243950000048
indicating that the energy storage unit is not in the discharge state, etaBSSIndicating the charging efficiency of the energy storage unit, EminIndicating minimum storage capacity of energy storage plant, EmaxRepresenting the maximum storage capacity of the energy storage power station;
the adjustable load constraints are:
Figure FDA0002823243950000049
wherein the content of the first and second substances,
Figure FDA00028232439500000410
representing the power of the internal load of the virtual power plant at time t,
Figure FDA00028232439500000411
representing the amount of power purchased by the load in the VPP to the grid at time t,
Figure FDA00028232439500000412
representing the amount of load reduction during the time t,
Figure FDA00028232439500000413
which indicates the amount of increase of the load during the time t,
Figure FDA00028232439500000414
are all binary variables, and are provided with the following functions,
Figure FDA00028232439500000415
representing a reduction in the virtual plant load over time t,
Figure FDA00028232439500000416
representing a virtual plant load increase over time t, otherwise
Figure FDA00028232439500000417
PL,sh,totRepresenting the maximum load allowed to be transferred during the entire scheduling.
7. The virtual power plant-based multi-element micro-agent spot market clearing method according to claim 2 or 4, wherein the VPP bidding power constraint is as follows:
Figure FDA00028232439500000418
wherein the content of the first and second substances,
Figure FDA00028232439500000419
for the amount of power sold by the VPP during the day-ahead energy market time t,
Figure FDA00028232439500000420
the method comprises the following steps that the electricity purchasing quantity of a VPP in the day-ahead energy market time t is shown, and G is the maximum competitive bidding electricity quantity of an electricity market;
the VPP drop-down capacity constraint is:
Figure FDA0002823243950000051
wherein the content of the first and second substances,
Figure FDA0002823243950000052
representing the upper frequency modulation capacity reported by the ith unit in the service market at the moment in the scene R and the time t,
Figure FDA0002823243950000053
the lower frequency modulation capacity reported by the ith unit in the service market at the moment in the scene R and the time t is shown,
Figure FDA0002823243950000054
representing the electricity sales amount reported by the ith unit in the energy market in the scene R and the time t,
Figure FDA0002823243950000055
the power purchasing quantity reported by the ith unit in the energy market in the scene R and the time t,
Figure FDA0002823243950000056
represents the maximum capacity of the ith unit in the VPP,
Figure FDA0002823243950000057
represents the maximum turndown capacity of the ith unit in the VPP,
Figure FDA0002823243950000058
indicating the maximum charging power of the VPP market at the day-ahead,
Figure FDA0002823243950000059
represents the maximum discharge power of the VPP on the market at the day-ahead;
the VPP up-down mileage constraint is as follows:
Figure FDA00028232439500000510
wherein the content of the first and second substances,
Figure FDA00028232439500000511
showing the frequency-up mileage reported by the ith unit in the service market in the time t and the scene R,
Figure FDA00028232439500000512
showing the lower frequency-modulation mileage, epsilon, reported by the ith unit in the service market in the time t and in the scene RgA mileage multiplier representing a multivariate micro-body g.
8. The virtual power plant based multi-element micro-agent spot market clearing method according to claim 7, wherein the energy market power balance constraint is:
Figure FDA00028232439500000513
in the formula: pL,tExpressed as the magnitude of the system load over time t;
the network security constraints are:
pl,min≤pl,t≤pl,max
wherein p isl,tFor the flow of the line in time t, pl,maxFor maximum transmission capacity of the line, pl,minRespectively, the minimum transmission capacity of the line;
the system frequency modulation capacity constraint is as follows:
Figure FDA00028232439500000514
wherein the content of the first and second substances,
Figure FDA0002823243950000061
representing the up-modulation capacity of the system over time t,
Figure FDA0002823243950000062
representing the down-modulation capacity of the system over time t,
Figure FDA0002823243950000063
indicating the up-conversion capacity in VPP during time t,
Figure FDA0002823243950000064
indicating the down-scaled capacity in VPP over time t,
Figure FDA0002823243950000065
represents the upper frequency modulation capacity of the conventional unit in time t,
Figure FDA0002823243950000066
expressing the lower frequency-reducing capacity of the bid in the conventional unit within time t;
the system frequency modulation mileage constraint is as follows:
Figure FDA0002823243950000067
wherein the content of the first and second substances,
Figure FDA0002823243950000068
represents the up-modulated mileage of the system over time t,
Figure FDA0002823243950000069
represents the lower frequency-modulated mileage of the system over time t,
Figure FDA00028232439500000610
represents the up-frequency-modulated mileage in the VPP within the time t,
Figure FDA00028232439500000611
represents the lower frequency-reduction mileage in VPP within time t,
Figure FDA00028232439500000612
represents the bidding mileage of the conventional unit within the time t,
Figure FDA00028232439500000613
expressing the lower frequency-regulating mileage of the conventional unit within time t;
the conventional unit power constraint is as follows:
Figure FDA00028232439500000614
wherein, Pg,t,rRepresenting the output value, P, of the conventional unit g over time tg,maxIs the maximum output of the conventional unit g, Pg,minThe minimum output of the conventional unit g.
9. The virtual power plant-based ex-clearing method for multivariate micro-subjects participating in spot market according to claim 6, wherein the method for obtaining optimal bidding results and optimal profits of day-ahead energy and duration day-ahead frequency-modulated market is as follows:
step 3.1, carrying out data preprocessing on the VPP after the multiple small micro-main bodies are gathered, and generating multiple bidding strategies k by taking the maximum profit of the VPP as an objective function;
step 3.2, generating a typical quotation scene r of the competitor according to the marginal cost of the competitor multiplied by a curve random scale factor [0.75,1.25 ];
step 3.3, the energy market and the frequency modulation market are cleared in a combined mode in the day ahead;
and 3.4, calculating the probability weighted average of all power price prediction scenes to obtain clearing prices in each market time t in the day, and calculating the total profit of the VPP under each strategy.
10. The virtual power plant-based method for coming out of the spot market by participation of multiple small micro-agents in the market according to claim 1, wherein the bid-winning condition of each agent in the market comprises a day-ahead energy market bid-winning condition of each agent in the market, a day-ahead auxiliary market frequency modulation capacity bid-winning condition of each agent in the market, and a day-ahead auxiliary market frequency modulation mileage bid-winning condition of each agent in the market; the optimal quotation comprises the optimal quotation of the VPP in each market and the optimal report of the VPP in each market; the clearing price refers to the clearing price of a day-ahead energy market and a day-ahead frequency modulation market.
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