CN113888209A - Collaborative bidding method for virtual power plant participating in power market and carbon trading market - Google Patents
Collaborative bidding method for virtual power plant participating in power market and carbon trading market Download PDFInfo
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Abstract
The invention relates to a collaborative bidding method for a virtual power plant to participate in an electric power market and a carbon trading market, which is characterized by comprising the following steps of: the method comprises four steps of analyzing the operation mechanism and structure of the virtual power plant; bidding strategies of the virtual power plant in the market and the adjustment market in the day ahead; the virtual power plant bidding model and the virtual power plant robust optimization bidding model. Has the advantages that: the method can provide technical support for the virtual power plant to participate in the collaborative bidding of the power market and the carbon trading market, solves the problems of unclear cost benefit structure and risk preference faced by the virtual power plant collaborative bidding, meets the risk-benefit balance principle, is suitable for the scenario that multiple risk preference virtual power plants participate in the collaborative bidding of the power market and the carbon trading market, and has wide practicability.
Description
Technical Field
The invention belongs to the technical field of power markets, and particularly relates to a collaborative bidding method for a virtual power plant to participate in a power market and a carbon trading market.
Background
The diversity of loads and the access of high-permeability renewable energy sources bring new challenges to the economic operation, safety and stability of the traditional power system. As a novel distributed power supply coordination control and energy management technology, a Virtual Power Plant (VPP) can aggregate various distributed power supplies in a region into a whole based on an internet communication technology, and participate in bidding of an electric power market and a carbon trading market uniformly. The multizone virtual power plant can also realize that the energy flow between the region is complementary, reduces the electric wire netting and dispatches the degree of difficulty, improves renewable energy's utilization efficiency and consumption level, promotes the electric wire netting and is changed to diversified energy consumption structure by single energy consumption structure.
Various distributed power sources, controllable loads, and energy storage devices may be aggregated in a virtual power plant. The virtual power plant has multiple functions in the power market by combining various market price information and the output characteristics of various units. The electric energy selling system not only can be used as an electric energy supplier to participate in market bidding to sell electric energy, but also can be used as a load to purchase electric energy from the market to meet the electric energy demand of the electric energy supplier. The virtual power plant participates in adjusting market bidding by means of diversified energy structures, flexible energy supply forms and various energy storage devices and by scheduling the distributed power supplies, the controllable loads and the energy storage devices, and provides reserve capacity for the power system.
At present, more bidding strategies are researched for the wind turbine generator to participate in the electric power market, and a collaborative bidding method considering the wind turbine generator and a fixed energy storage device is provided. The electric automobile has commuting and energy storage functions, which makes the electric automobile have certain cost advantages as an energy storage device. The utilization of the energy storage function of electric vehicles to provide reserve capacity for the power system is a promising business operation service mode, namely, the network access (V2G) mode of electric vehicles. However, there are few joint bidding studies considering electric vehicles and wind turbines.
Due to the intermittency and randomness of renewable energy power generation, a virtual power plant with high permeability renewable energy will be at high trading risk in the power market. Domestic and foreign research shows that the utilization of demand response resources as substitute resources of a power supply side is an important means for relieving transaction risks of virtual power plants in the power market. In recent years, the demand response market has also been considered to be an important component of the future power market and has attracted widespread attention. In view of the uncertainty that the output of the renewable power supply can be stabilized in the demand response transaction, the transaction risk is reduced, and the economic benefit is improved. The existing research at home and abroad uses demand response as a substitute resource of a power supply side of a virtual power plant, and establishes a virtual power plant double-layer random scheduling optimization model. Patent document CN 112465248A discloses a method and system for optimizing operation and revenue allocation of a virtual power plant in consideration of carbon trading. The method comprises the following steps: establishing a virtual power plant optimization model with the maximum sum of the carbon market income and the electric power market income of the virtual power plant as a target; solving the virtual power plant optimization model to obtain the competitive bidding amount of the market in the day ahead; determining the income distribution of the members of the virtual power plant by adopting a Shapril value calculation method according to the competitive bidding amount of the market at the day before; the virtual power plant members comprise a gas turbine set, an energy storage device, a wind turbine set and a photovoltaic set. The method and the system can provide theoretical guidance for behavior decision of the multivariate demand response resources participating in the electricity market and the carbon market.
In addition, the virtual power plant may also participate in bidding in the carbon trading market. At present, a bidding strategy of a virtual power plant participating in a carbon trading market is researched at home and abroad, a virtual power plant economic scheduling model considering a carbon trading mechanism is provided, and the purpose of maximizing the income of the virtual power plant is achieved.
From the above analysis, the output, load and market price of the virtual power plant are uncertain, and these three uncertainties are inevitable. When researching the participation of the virtual power plant in the power market bidding strategy, the uncertainty of the number of dispatchable electric vehicles and the output of the wind generating set needs to be considered. Robust optimization as a method for processing uncertain factors draws extensive attention in the fields of natural science, engineering technology, economic management and the like in recent years, and the application field of the robust optimization is more and more extensive. At present, bidding strategies of virtual power plants are mostly based on power trading analysis models of power markets, and carbon emission rights are not introduced into the power markets to be considered together. With the increase of the permeability of the distributed power supply and the improvement of the load controllability, the controllable resources of the virtual power plant are fully excavated, and a collaborative bidding strategy model of the virtual power plant participating in the power market and the carbon trading market is constructed, so that the method has important significance for improving the operation economy of the virtual power plant. In addition, the application research of the demand response resources in the virtual power plant mainly focuses on the aspects of the economic operation and the optimized scheduling of the virtual power plant, and the research considering the influence of the demand response market on the bidding strategy of the virtual power plant is lacked.
Disclosure of Invention
The invention aims to overcome the defects of the technology, and provides a collaborative bidding method for a virtual power plant to participate in an electric power market and a carbon trading market, which can participate in a day-ahead electric power market, a day-in demand response market, an adjustment market, a real-time electric power market and a carbon trading market at the same time, effectively improve the profit of the virtual power plant, reduce the risks caused by the uncertainty of the power generation, the load and the electricity price of renewable energy sources, and meet the collaborative bidding strategies of the virtual power plants with different risk preferences.
In order to achieve the purpose, the invention adopts the following technical scheme: a collaborative bidding method for a virtual power plant to participate in an electric power market and a carbon trading market is characterized by comprising the following steps: comprises four steps of the following steps of,
1. virtual power plant operation mechanism and structure analysis: the method comprises the steps that a virtual power plant with an operation structure as a power generation unit, an energy storage unit and a demand response unit is taken as an object, and a robust optimization bidding strategy is provided when the virtual power plant participates in a day-ahead power market, a day-in demand response market, an adjustment market, a real-time power market and a carbon trading market; by analyzing the integrated characteristics of the virtual power plant business mode, participation in the power market bidding planning and the power market and carbon trading market, the composition of the multivariate income and the cost of the virtual power plant participating in the power market and carbon trading market collaborative bidding is determined;
2. bidding strategies for virtual power plant market and regulatory market in the future: by establishing a revenue cost model of a wind turbine generator, an electric automobile and a gas turbine generator, determining bidding strategies of a virtual power plant participating in a day-ahead power market and adjusting the market; establishing a quantitative model of the uncertainty of the virtual power plant, and reducing the bidding risk of the virtual power plant caused by the uncertainty of the renewable energy power generation through a demand response market;
3. virtual power plant bidding model: introducing a carbon trading mechanism, and optimizing a collaborative bidding strategy of the virtual power plant participating in the power market and the carbon trading market by taking the profit maximization of the virtual power plant as a target;
4. the virtual power plant robust optimization bidding model comprises the following steps: and considering different risk preferences of the virtual power plant bidding, establishing a robust optimization model of the collaborative bidding strategy of the virtual power plant participating in the power market and the carbon trading market, and further improving the collaborative bidding profits of the virtual power plant participating in the power market and the carbon trading market with different risk preferences.
Further, the specific steps of the operation mechanism and the structure analysis of the virtual power plant are as follows:
(1) power distribution market business model for virtual power plant
The power distribution market business model of a virtual power plant includes four phases,
according to the prediction information of the distributed renewable energy sources, the virtual power plant aggregates the internal resources according to the output range and the operation cost to obtain an output curve and cost characteristics of the aggregated flexible resources;
the virtual power plant submits the price and the quantity of various electric power commodities to an operator of the power distribution network;
thirdly, the power distribution network operators collect bidding information of each operator, take the bidding output and cost characteristics of various units into consideration, put forward a market clearing strategy and determine the winning price and the winning quantity of the virtual power plant according to the demands of various power commodities and the safe operation constraint of the system;
the virtual power plant optimizes the internal resource scheduling plan according to the market clearing result and follows the scheduling instruction issued by the power distribution network operator, the virtual power plant designs the scheduling plan of the internal flexible resources according to the scheduling plan curve issued by the power distribution network operator and the prices of various electric power commodities and tracks the scheduling instruction issued by the power distribution network operator,
(2) bidding rule for virtual power plant participating in power market
The bidding rule of the virtual power plant is as follows: based on the participation of a virtual power plant containing high-permeability renewable energy in the day-ahead market, the day-in demand response market, the adjustment market and the real-time market,
the virtual power plant needs to predict the actual power generation amount 12-36 hours in advance, and submits bidding information of 24 hours in the future to a power distribution network operator before closing the market in the day, namely a power generation price-power generation capacity curve; setting the bidding price of the virtual power plant to be 0, and optimizing the bidding electric quantity of the virtual power plant only according to the predicted market settlement price; the method comprises the steps that an adjusting market is closed one hour before a real-time market is opened, during the period, a virtual power plant submits bidding prices of an adjusting standby power and a rotating standby power to a power distribution network operator, after the electricity market transaction is finished, the actual power generation deviation of the virtual power plant is settled according to the real-time electricity price, the bidding strategy of the virtual power plant is researched by adopting bidding power generation power, and the power generation power per hour of the virtual power plant have the same data, namely the power generation power per hour is multiplied by 1 hour;
(3) power market and carbon trading market integrated feature with participation of virtual power plant
In the operation structure of the virtual power plant: the power generation unit mainly comprises a wind turbine generator and a gas turbine generator, the energy storage unit is an electric automobile, the demand response unit is a local load and external demand response provider, each unit transmits daily operation data to a control center of a virtual power plant through a data layer, the control center uniformly makes a strategy and schedules the output of power generation unit equipment, the response capacity of the energy storage unit and the response capacity of the demand response unit according to the current profit and the carbon emission target, the power generation unit, the energy storage unit and the demand response unit in the aggregation area of the virtual power plant are the basis for constructing the integration of a power market and a carbon trading market, and each unit in the virtual power plant participates in the market trading process and is also the process for simultaneously transferring the power consumption and the carbon emission right;
2) virtual power plant day-ahead market and market-adjusted bidding strategy
(1) Income function of wind turbine generator
After winning the bid in the market before the day, when the wind turbine generator is scheduled to generate electricity in the real-time market, the competitive bidding income R of the wind turbine generator in the market before the day is in the T periodWT,daAs shown in equation (1)
In the formula, i is the node number of the power distribution network; b is a power distribution network node set; lambda [ alpha ]da,e,tIs the day-ahead electricity price; is a wind turbine set of an access node i; pi,wt,tThe day-ahead competitive bidding output of the wind turbine generator at the moment t; Δ t represents a time interval;
(2) electric automobile provides reserve capacity for distribution network
First, revenue function
When the electric automobile participates in the adjustment of market bidding, the bidding capacities of the up-adjustment standby, the down-adjustment standby and the rotation standby are respectively expressed as
And
when the electric automobile provides the spare capacity, the expected dispatching ratios of the up-regulation spare capacity, the down-regulation spare capacity and the rotation spare capacity of the electric automobile are respectively expressed as
And
as shown in equations (6-8)
In the formula (I), the compound is shown in the specification,
and
the actual scheduling output of the up-regulation reserve capacity, the down-regulation reserve capacity and the rotation reserve capacity of the electric automobile which is accessed to the node i at the time t in the real-time market is respectively carried out;
and
the ev electric automobile which is accessed to the node i at the time t is provided with an up-regulation reserve capacity, a down-regulation reserve capacity and a rotation reserve capacity respectively; e (-) represents the expected value;
exchange power P between electric vehicle and power distribution networkEV,tAs shown in equation (9)
In the formula (I), the compound is shown in the specification,
the charging power is required for meeting the driving requirement of the electric automobile;
and
respectively is the total charge and discharge power of the electric automobile;
virtual power plant lambda according to electricity priceev,subCharging fee is charged for the schedulable electric automobile, and the total income R of the electric automobile in the adjustment market in the T time periodEV,rg，
As shown in the equation (11),
in the formula (I), the compound is shown in the specification,
charging power required by the ev quantity of electric vehicles accessing the node i at the moment t is met; lambda [ alpha ]RD,t，λRU,t，λRR,tCapacity prices for down-regulation reserve, up-regulation reserve and rotation reserve of the regulation market are respectively;
second, cost function
The cost of electric vehicle participation in adjusting market bids involves two parts, as shown in equation (12),
CEV＝CEV,pur+CEV,los (12)
firstly, the electricity purchasing cost C for charging the electric automobile by the virtual power plantEV,purAs shown in equation (13)
In the formula, λrt,e,tIs the real-time electricity price.
Secondly, schedulable electric vehicles of the virtual power plant use the same type of battery, and the loss cost of the electric vehicle battery is shown in equation (14).
In the formula, PEV,batIs the rated capacity of the battery of the electric automobile; cbatIs the purchase cost coefficient of the batteries of the electric automobile, yuan/kWh. EtadcIs the discharge efficiency of the electric vehicle battery;
(3) gas engine set
First, revenue function
The gas turbine set which wins the bid in the day-ahead market is scheduled to generate power in the real-time market, and the bidding income R of the gas turbine set in the day-ahead market in the T periodGT,daAs shown in equation (16)
In the formula (I), the compound is shown in the specification,
the gas turbine set is an access node i; pi,gt,tThe day-ahead competitive bidding output of the gas turbine set at each time interval t;
when the gas turbine unit participates in adjusting market bidding, the bidding forces of up-adjustment standby, down-adjustment standby and rotation standby are respectively used
And
it is shown that,
and
respectively an up-regulation actual regulation value, a down-regulation actual regulation value and a rotation standby actual regulation value for the gas turbine unit,
after the gas turbine unit wins the bid in the adjustment market, the total income R obtained by the gas turbine unit in the adjustment marketGT,rgAs shown in equation (17)
Second, cost function
Cost C of virtual power plant for purchasing natural gas from gas market for gas turbine setGT,purAs shown in equation (18).
In the formula, λgasIs the natural gas price; etai,gt,tThe power generation efficiency of the gas engine set is obtained; LHV is the low calorific value of natural gas, kWh/m3；
3) Virtual power plant bidding model
(1) Modeling of uncertainty factors
Uncertainty factors include wind turbine output, local user load demand, and market prices, including day-ahead market electricity prices, adjusted market prices, demand response market prices for the day, and real-time market electricity prices,
wind power
In the day-ahead market, the upper and lower output limits of the wind turbine generator are set by using an interval constraint method, and the wind turbine generator at the time t is subjected to bidding and outputGo out of PWT,tAre respectively defined by an uncertain parameter Pup,WT,tAnd Plow,WT,tRepresents; at any time t, only one uncertain parameter is in the upper and lower limit constraints output by the wind turbine unit bidding, and the parameters are respectively Pup,WT,tAnd Plow,WT,t(ii) a The upper and lower limits of the wind turbine output follow normal distribution, as shown in equations (19-21)
σWind＝{σup,WT,t,σlow,WT,t}μWind＝{μup,WT,t,μlow,WT,t} (21)
In the formula, muup,WT,t，σup,WT,tIs Pup,WT,tExpected value and standard deviation of; mu.slow,WT,t，σlow,WT,tIs Plow,WT,tExpected value and standard deviation of;
the opportunity constraint can be used to convert the wind turbine generator bidding output constraint into an inequality constraint, as shown in equations (22-23)
pr{PWT,t≤Pup,WT,t}≥εup,WT (22)
pr{PWT,t≥Plow,WT,t}≥εlow,WT (23)
In the formula, epsilonup,WTAnd εlow,WTA probability of satisfying an opportunity constraint;
local user load demand
The virtual power plant needs to meet the load demand of local users, and the prediction is carried out through historical load data, as shown in equation (24)
In the formula (I), the compound is shown in the specification,
predicting local user load through a differential autoregressive moving average model based on historical load data;
respectively representing the upper limit and the lower limit of the prediction error and following normal distribution;
(iii) market trading price
Forecasting the current day price, demand response market price, reserve capacity price and real-time electricity price of each hour in intervals according to historical data
An internal variation wherein, in addition,
a predicted value representing a price;
representing the radius of a price fluctuation interval;
(2) objective function
The goal of the virtual plant bidding strategy is to maximize the virtual plant profit I, as shown in equation (25)
maxI＝Rrev-CWT,pl-CEV-CGT,pur-CDR (25)
Rrev＝RWT,da+RGT,da+REV,rg+RGT,rg+RLU+RCT (26)
In the formula, RrevIs revenue generated by the virtual power plant, including: wind and gas turbine revenue in the day-ahead market of equations (1) and (16), electric and gas turbine revenue in the adjustment market of equations (11) and (17), sales electricity revenue to local users of equation (27), and carbon emission rights revenue in the carbon trading market of equation (28); cWT,plIs caused by the deviation between the actual output of the wind turbine and the bid output of equation (2)Economic penalty, CEVCost of electric vehicle, C, of equation (12)GT,purCost to purchase natural gas, C, for a gas train of equation (18)DRIs the purchase cost of the demand response load reduction in the market for the day of equation (30);
(3) constraint conditions
First, wind turbine generator operation constraints
The bid output and actual output of the wind turbine should be between its minimum and maximum output powers as shown in equations (39-40)
In the formula (I), the compound is shown in the specification,
and
respectively the minimum and maximum output power of the wind turbine;
second, electric vehicle operation constraints
The state of the electric vehicle, including charging, discharging, and standby, needs to satisfy the constraint condition of equation (41)
In the formula udc,tIs the electric vehicle discharge state. u. ofc,tIs the charging state of the electric automobile
At the time t, the SOC of the electric vehicle is shown as an equation (42), and the SOC of the electric vehicle needs to meet the upper and lower capacity limit constraints as shown as an equation (43)
In the formula: SOCi,ev,tThe SOC value, SOC of the ev electric vehicle which is the access node i in the time period ti,ev,0In order to start the SOC of the electric automobile,
respectively the upper and lower limits, P, of the SOC of the electric vehiclei,ev,batThe rated capacity of the ev electric vehicle battery of the access node i;
the electric vehicle also needs to satisfy the maximum charge and discharge power PEV, max constraint, as shown in equation (44-45)
Third, gas turbine unit operational constraints
The competitive bidding output of the gas turbine set meets the upper and lower limits of the output power and the maximum climbing rate constraint, as shown in equations (46-49)
In the formula (I), the compound is shown in the specification,
is the maximum output power of the gas turbine;
the competitive bidding capacity for standby under the gas turbine set;
and
the maximum upward/downward climbing rate of the gas turbine unit is respectively;
fourth, line constraint
In the formula (I), the compound is shown in the specification,
is the injection power of node i at time t; pij,max、Pij,minRespectively, the upper and lower power limits of line ij; h isijIs the line ij power distribution variation coefficient;
4) virtual power plant robust optimization bidding model
Firstly, converting a virtual power plant bidding model into a linear programming problem, and then converting the virtual power plant bidding model into a robust linear programming model; modifying the constraint equations (3, 4, 9) by introducing decision variables to form new constraint equations (52-55)
Converting equation (42) into a linear function equation (56) for calculating the SOC of the electric vehicle,
converting the linear programming model into a virtual power plant robust optimization bidding model according to a robust optimization principle, which is detailed as follows
max IROM (58)
-y≤P≤y (73)
Wherein P is a decision variable vector; y is an auxiliary decision variable vector introduced by dual transformation; z is a radical ofεIs the upper quantile of the standard normal distribution; mu.sa,W,tAnd σa,W,tAre respectively
The expected value and the standard deviation of the measured values,
subject to a normal distribution of the signals,
in the virtual power plant bidding robust optimization bidding model, the charging and discharging power constraint of the electric automobile meets an equation (54) and an equation (55), and the SOC constraint of the electric automobile meets an equation (56); equations (77) and (78) are upper and lower limit constraints of the competitive bidding output of the wind turbine generator, and equations (79) and (80) are positive and negative deviation constraints of the competitive bidding output of the wind turbine generator; other constraints are shown in the equation (43-51).
Further, a cost function of the wind turbine set in the distribution market business model of the virtual power plant
When the competitive bidding output is deviated from the actual output of the wind turbine generator, the economic penalty is suffered, as shown in the equation (2)
When the electric vehicle is charged, the wind turbine generator preferentially charges the electric vehicle, the residual electric quantity is sold in the real-time market, and the electric vehicle is charged when the residual electric quantity is charged
During the process, the deviation between the bidding output and the actual output of the wind turbine generator is reduced by controlling the discharge of the electric automobile and reducing the purchase load of the market from the demand response transaction, the output fluctuation of the wind turbine generator is stabilized,
in the formula (I), the compound is shown in the specification,
and
respectively determining the actual output is greater than or less than the bidding output deviation of the wind turbine; u. ofec,t、ubl,tRespectively outputting state variables of positive deviation/negative deviation for the fan;
and
the charging and discharging power of the electric automobile is used for reducing the output deviation of the wind turbine generator; omegawt,ecIs the penalty coefficient, omega, of the positive deviation of the fan outputwt,ec<1；ωwt,blIs the penalty coefficient of negative deviation of fan output, omegawt,bl>1
Is the actual output of the fan;
is the electric vehicle set of the access node i; gamma rayDRIs a collection of demand response providers; pdr,tIs the load reduction amount purchased from the dr demand response supplier.
Furthermore, when the electric vehicles in the commercial mode of the power distribution market of the virtual power plant cannot be scheduled according to a plan, the charging and discharging power of other electric vehicles needs to be increased to make up for the capacity loss. For a certain electric vehicle, when other electric vehicles cannot be scheduled at time t, the charging and discharging power adjustment coefficient is as shown in equation (15).
In the formula (I), the compound is shown in the specification,
in order to adjust the coefficient, when the total number of the electric vehicles reaches 10000 or more, the electric vehicles can be regarded as a constant; that the ev electric vehicle accessing the node i at the time t cannot be scheduledProbability of being scheduled.
Further, the objective function specific income of the virtual power plant bidding model
Income for selling electricity to local users
Relationship between electricity consumption and electricity price of local users: the revenue of selling electricity to the local user during the period T is the product of the electricity usage and the electricity price, as shown in equation (27)
In the formula ulu,tIs the ratio of the electricity price of the local user to the real-time electricity price, and u is more than or equal to 0i,lu,t≤1；
Is a set of local users of access node i;
the power consumption of the lu-th user at the moment t;
second, carbon trade market revenue
The revenue of the virtual power plant selling carbon emissions in the carbon trading market is shown in equation (28)
In the formula, λc,tIs the price coefficient, yuan/MW, of the carbon emission right at time t; pi,c,tThe competitive bidding output of the wind turbine generator and the gas turbine generator which are connected to the node i at the moment t in the carbon trading market;
load reduction purchase cost of demand response market
In the demand response market, the virtual power plant purchases a load reduction amount through a demand response provider in a mode of bilateral negotiation and centralized bidding, and the purchase cost is shown as equation (30)
In the formula, gammaDRIs a collection of demand response providers; cDR,cenCost of purchasing load reduction through centralized bidding transaction; cDR,biThe cost of purchasing load reduction through bilateral negotiation is adopted;
the purchase cost reduced by the load of the centralized bidding method is shown in equation (31)
λcen,dr,t＝θcen,drλrt,e,t (32)
In the formula, gammaDRIs a collection of demand response providers; lambda [ alpha ]cen,dr,tIs the bid price of the dr th demand response supplier at time t; pcen,dr,tIs a demand response bid amount corresponding to the demand response bid price; thetacen,drThe adjustment can be carried out by the operator of the power distribution network for adjusting the coefficient; qDR,cen,tIs the total demand response provided by the centralized bidding mode at time t;
is the maximum value of the dr th demand response supplier bid amount;
purchase of cost of load reduction through bilateral negotiation mode, as shown in equation (35)
In the formula, λ bi, dr, t is a demand response bilateral negotiation price, which can be agreed and determined by the supplier and the supplier in advance; pbi, dr, t is the demand response purchase amount corresponding to the demand response bilateral negotiation price; QDR, Bi, t is the total demand response provided by bilateral negotiation mode at time t,
the total load reduction purchased by the virtual power plant should satisfy the DRcap constraint on the demand response capability that the demand response supplier can provide, as shown in equation (37)
In the formula, DRcapDemand response capabilities that can be provided for demand response providers; pdr,tIs the load reduction amount purchased from the dr demand response supplier.
Further, in the virtual plant robust optimization bidding model, when the electric vehicle is charged, the equation (56) is the same as the equation (42), and when the electric vehicle is discharged, the electric vehicle discharge amount calculated by the equation (56) is smaller than the actual discharge amount, and the Δ err is usedi,ev,tIndicating the deviation of discharge capacity, and the charge-discharge efficiency eta of the electric vehiclec＝ηdcWhen equal to 0.95,. DELTA.erri,ev,tOnly occupying the actual discharge capacity
9.75% of (d), when etac＝ηdc1.0,. DELTA.err i,ev,t0, by equation (56)Equation (42).
Further, in the virtual power plant robust optimization bidding model, in order to enable the constraint violation probability not to exceed kappa, a robust control coefficient gamma is used1The constraint equation (81) needs to be satisfied
In the formula phi-1An inverse function of a cumulative distribution function that is a standard normal distribution; n isnpThe number of uncertain parameters contained in the constraint equation (59).
Further, under the environment that the CPU is Inter (R) core (TM) i7-8250U and the dominant frequency is 1.8GHz, a robust optimization model for the virtual power plant to participate in the cooperative bidding of the power market and the carbon trading market is constructed by adopting AMPL/CPLEX based on an MATLAB R2018b platform, and is solved.
Has the advantages that: the method can provide technical support for the virtual power plant to participate in the collaborative bidding of the power market and the carbon trading market, solves the problems of uncertain cost benefit structure and risk preference faced by the virtual power plant collaborative bidding, meets the risk-benefit balance principle, is suitable for the scenario that multiple risk preference virtual power plants participate in the collaborative bidding of the power market and the carbon trading market, and has wide practicability; the method has the advantages that the method can fully integrate the characteristics of the commercial mode of the virtual power plant, participation in the competitive price planning of the power market, and integration of the power market and the carbon trading market, and provides the multivariate income and cost composition of the virtual power plant participating in the cooperative bidding of the power market and the carbon trading market, so that the direction is indicated for the bidding strategy of the virtual power plant; the bidding risk caused by renewable energy power generation is relieved through an electric vehicle energy storage and demand response market, and bidding strategies of virtual power plants participating in a day-ahead power market, a day-in demand response market and a regulation market are determined; a carbon trading mechanism is introduced, different risk preferences of virtual power plant bidding are considered, a robust optimization model of a virtual power plant participating power market and carbon trading market collaborative bidding strategy is established with the goal of maximizing the profit of the virtual power plant, and the finally obtained virtual power plant participating power market and carbon trading market collaborative bidding strategy is high in universality, good in economic and social benefits and strong in robustness, can better balance risks and profits, reduces investment risks caused by renewable energy power generation, load and power price uncertainty, promotes renewable energy consumption, reduces the carbon dioxide emission amount of power generation, promotes low-carbon transformation of an energy structure, and helps carbon to achieve peak early, and has more academic significance and engineering value.
Drawings
FIG. 1 is a logical architecture of a virtual power plant participating in collaborative bidding in an electricity market and a carbon trading market;
FIG. 2a power distribution market business model architecture with virtual power plants;
FIG. 2b is a virtual plant internal operation mode;
FIG. 3 is a power distribution market structure and trading timeframes including a virtual power plant;
FIG. 4 is an integrated electricity market and carbon trading market operating feature with virtual power plant participation;
FIG. 5 is a wind power output constraint and an actual output expected value;
FIG. 6a is a power market price prediction curve;
FIG. 6b is a power market dispatch scale expectation curve;
FIG. 7a is a local user load prediction curve;
FIG. 7b is a step curve of electricity price versus electricity power for a local user;
FIG. 8a is a diagram of a demand response provider bilateral negotiation price;
FIG. 8b is a bid price-bid amount diagram for a demand response provider in a centralized bidding mode;
FIG. 9 is a fan bidding output variation curve;
FIG. 10a is a graph of the charging and discharging power of an electric vehicle to smooth out the fluctuation of the fan power output;
FIG. 10b is a graph of the spinning reserve bid capacity of an electric vehicle in the regulatory market;
FIG. 10c is a graph of the electric vehicle's bid capacity for adjustment on the adjustment market;
FIG. 10d is a graph of electric vehicle stand-by bidding capacity at a reduced regulatory market;
FIG. 11a is aWind＝0.1μWindA curve graph of the influence of the wind turbine generator output standard deviation and the demand response capacity DRcap on the day-ahead market competitive electric quantity of the wind turbine generator;
FIG. 11b is σWind＝0.2μWindA curve graph of the influence of the wind turbine generator output standard deviation and the demand response capacity DRcap on the day-ahead market competitive electric quantity of the wind turbine generator;
FIG. 12a is a graph showing the influence of the standard deviation of the wind turbine output and the demand response capacity DRcap on the competitive bidding power generation capacity of the wind turbine;
FIG. 12b is a graph of the effect of the wind turbine output standard deviation and the demand response capacity DRcap on the virtual plant profit;
FIG. 13 is a graph showing the effect of DRcap on the electricity revenue of local users under different wind turbine generator output standard deviations;
FIG. 14a is a graph of gas turbine unit bid output (day ahead market bid output power) for a carbon free trade constraint;
FIG. 14b is a graph of gas turbine unit bid output (regulating market bid output power) for a carbon-free trading constraint;
FIG. 15a is a graph of gas turbine unit bid output (at day-ahead market bid output power) with carbon trading constraints;
FIG. 15b shows the competitive power of the gas turbine when there is a carbon trade restriction (when the competitive output power of the market is adjusted)
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments thereof which are illustrated in the appended drawings. In addition, the embodiments and features of the embodiments of the present application may be combined with each other without conflict. In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention, and the described embodiments are merely a subset of the embodiments of the present invention, rather than a complete embodiment. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The embodiments and figures provide detailed descriptions of the collaborative bidding strategy of the virtual power plant of the present invention for participating in the electricity market and the carbon trading market.
Currently, there are uncertainties in the output, load and market price of virtual power plants, and these uncertainties in three areas are inevitable. When researching the participation of the virtual power plant in the power market bidding strategy, the uncertainty of the number of dispatchable electric vehicles and the output of the wind generating set needs to be considered. Moreover, the bidding strategy of the virtual power plant is mostly based on the power trading analysis model of the power market, and the carbon emission right is not introduced into the power market to be considered together. With the increase of the permeability of the distributed power supply and the improvement of the load controllability, the controllable resources of the virtual power plant are fully excavated, and a collaborative bidding strategy model of the virtual power plant participating in the power market and the carbon trading market is constructed, so that the method has important significance for improving the operation economy of the virtual power plant. In addition, the application research of the demand response resources in the virtual power plant mainly focuses on the aspects of the economic operation and the optimized scheduling of the virtual power plant, and the research considering the influence of the demand response market on the bidding strategy of the virtual power plant is lacked. Therefore, the virtual power plant containing the wind turbine generator, the electric automobile, the gas turbine generator and the demand response load is taken as a research object, the uncertainty of renewable energy power generation, load and market price is further considered by analyzing the integrated characteristics of the power market and the carbon trading market, and a bidding strategy robust optimization model of the virtual power plant participating in the day-ahead market, the day-in demand response trading market, the regulation market, the real-time market and the carbon trading market is constructed. The collaborative bidding strategy of the virtual power plant participating in the electric power market and the carbon trading market is more practical, the output fluctuation of the renewable energy power generation is reduced, the utilization of the renewable energy is promoted, the bidding output of the high-carbon-emission generator set is obviously reduced, and the economic and social benefits of the virtual power plant are improved.
The collaborative bidding strategy of the virtual power plant participating in the power market and the carbon trading market is divided into four steps, as shown in figure 1. The method comprises the following steps: 1) the method comprises four steps of virtual power plant operation mechanism and structure analysis, 2) virtual power plant bidding strategy in the market and the adjustment market in the future, 3) virtual power plant bidding model, and 4) virtual power plant robust optimization bidding model, which are detailed as follows.
1) Virtual power plant operation mechanism and structure analysis
(1) Power distribution market business model considering virtual power plant
Consider a power distribution market business model architecture of a virtual power plant as shown in fig. 2. The business model includes three types of bodies and two interaction layers. The three main bodies comprise power distribution network operators, virtual power plants and flexible resource equipment managers. The three types of bodies can be divided into two interacting layers. The first interaction layer is an information interaction and management control layer between the virtual power plant and a flexible resource equipment manager. The second interaction layer is the market bidding and trading layer between the distribution network operator and the virtual power plant.
The power distribution market business model considering virtual power plants can be divided into the following four phases. Firstly, according to the prediction information of the distributed renewable energy sources, the virtual power plant aggregates the internal resources according to the output range and the operation cost to obtain the output curve and the cost characteristic of the aggregated flexible resources. Second, the virtual power plant submits the bid prices and quantities of the various power commodities to the distribution network operator. Thirdly, the power distribution network operators collect bidding information of each operator, take the bidding output and cost characteristics of various units into consideration, put forward a market clearing strategy and determine the bid price and the bid amount of the virtual power plant according to various power commodity requirements and system safety operation constraints. And fourthly, the virtual power plant optimizes an internal resource scheduling plan according to the market clearing result and follows scheduling instructions issued by the power distribution network operator.
In addition, the internal operation mode of the virtual power plant plays an important role in the entire power distribution market. The virtual power plant has various flexible resources and can provide various auxiliary services for the power grid. The virtual power plants are aggregated by considering the coupling relationship between the internal various demand side and power supply side according to the characteristics of the flexibility resources of the demand side and the power supply side, and a bidding scheme is formulated so as to obtain a profit in the power market. After the market is cleared, the virtual power plant designs a scheduling plan of internal flexible resources according to a scheduling plan curve and various power commodity prices issued by a power distribution network operator, and tracks a scheduling instruction issued by the power distribution network operator.
(2) Bidding rule for virtual power plant participating in power market
Considering that a virtual power plant generally contains a large amount of distributed renewable energy, the research work of the invention is carried out on the basis that the virtual power plant containing high-permeability renewable energy participates in the day-ahead market, the day-ahead demand response market, the regulation market and the real-time market, and the power distribution market containing the virtual power plant is structured as shown in fig. 3. The virtual power plant bidding rules are detailed below.
The actual power generation amount of the virtual power plant needs to be predicted 12-36 hours in advance, and bidding information of 24 hours in the future is submitted to a power distribution network operator before the market is closed in the day ahead, namely a power generation price-power generation capacity curve. Due to uncertainty in the renewable energy generation output, it is difficult for the virtual power plant to control the deviation between the bid output and the actual output in the day-ahead market. It is assumed that the bidding strategy of the virtual power plant does not substantially affect the market price, i.e., the virtual power plant is the price recipient. In order to ensure that the bidding electric quantity can bid a bid, the virtual power plant can set the bidding price to be 0 and optimize the bidding electric quantity according to the predicted market settlement price.
Over time, the relevant predictions will become progressively more accurate and the virtual power plant may update its predicted power generation. According to the updated generated energy predicted value, the virtual power plant can rearrange its scheduling plan and participate in the demand response market in the day to ensure that it is not penalized due to the deviation between the actual output and the bid output. That is, by responding to the market purchase load reduction amount from the demand in the day, the output fluctuation of the renewable energy power generation is stabilized. Demand response markets were shut down one hour prior to the real-time market within the day. Although the output power prediction at this time is more accurate than the day-ahead market prediction, the prediction error is still unavoidable. Therefore, the virtual power plant can also utilize the energy storage device of the virtual power plant to stabilize the output fluctuation of the renewable energy power generation.
The regulatory market was closed one hour before the real-time market was open. During this time, the virtual power plant may submit bids to the distribution network operator to adjust the backup and spin the backup. To ensure that the bid reserve capacity can bid a bid, the virtual power plant may set the associated bid to 0. And after the electric power market transaction is finished, the actual power generation deviation of the virtual power plant is settled according to the real-time electricity price.
In consideration of the fact that bidding of the virtual power plant in the power market and the regulation market is a 1-hour bidding mode, the hourly generated power and the hourly generated energy generated by the virtual power plant have the same data. That is, the amount of power generated per hour is the generated power per hour × 1 hour. Therefore, the bidding strategy of the virtual power plant is researched by adopting the bidding power generation power.
(3) Power market and carbon trading market integrated feature with participation of virtual power plant
The operation structure of the virtual power plant in the invention is shown in figure 4. The power generation unit mainly comprises a wind turbine generator and a gas turbine generator, the energy storage unit is an electric automobile, and the demand response unit is a local load and external demand response provider. In recent years, electric vehicles have been rapidly developed. Although the electric automobile is mainly responsible for transportation, the electric automobile still has an energy storage function. The electric automobile can be used as an energy storage device to stabilize the output fluctuation of renewable energy power generation and participate in adjusting market bidding. Each unit transmits daily operation data to a control center of a virtual power plant through a data layer, and then the control center uniformly makes strategies for the output of power generation unit equipment, the energy storage unit and the response capacity of a demand response unit according to the current profit and the carbon emission target and schedules the strategies. Therefore, the power generation unit, the energy storage unit and the demand response unit in the virtual power plant aggregation area are the basis for building the integration of the power market and the carbon trading market.
In the carbon trading market, carbon emission rights are used as commodities to participate in market trading. How to measure the carbon emission rights is a key link, and avoiding the repeated use of the carbon emission rights is a difficult implementation point. The integration of the electric power market and the carbon trading market is not only a circulation and trading center of electric energy, but also a collection and trading center of carbon emission rights. In order to effectively measure the carbon emission rights, the users trade the carbon emission rights of the users simultaneously in the electricity trading process and obtain corresponding income.
The virtual power plant participates in the integrated trading of the electric power market and the carbon trading market as shown in fig. 4. Each unit in the virtual power plant participates in the market trading process, and the electricity utilization right and the carbon emission right are simultaneously transferred, so that the virtual power plant has the following trading characteristics. First, the virtual power plant needs to purchase the corresponding carbon emission rights for the gas turbine set while scheduling the gas turbine set to generate power. And secondly, the wind turbine generator set does not generate carbon emission during power generation, and the virtual power plant can obtain corresponding carbon emission rights. The virtual power plant may assign carbon emissions to gas turbine units or sell them on a carbon trading market to obtain revenue. Third, the carbon emissions of the energy storage unit may be traded concurrently with the electrical energy.
2) Virtual power plant day-ahead market and market-adjusted bidding strategy
In order to maximize profits, the virtual power plant wind turbine generator, the electric vehicle and the gas turbine generator participate in the bidding of the day-ahead market and the adjustment market, and the corresponding bidding strategy is detailed as follows.
(1) Wind turbine generator system
First, revenue function
After the bid is successful in the market before the day, the wind turbine generator is scheduled to generate electricity in the real-time market. Bidding income R of wind turbine generators in the market before day in the period of TWT,daAs shown in equation (1).
In the formula, i is the node number of the power distribution network; b is a power distribution network node set; lambda [ alpha ]da,e,tIs the day-ahead electricity price;
wind turbine set being access node iCombining; pi,wt,tThe day-ahead competitive bidding output of the wind turbine generator at the moment t; Δ t represents a time interval.
Second, cost function
When bidding out force PWT,tActual output of wind turbine generator
There is a deviation that will be subject to economic penalties as shown in equation (2). With the increase of the number of schedulable electric vehicles, the electric vehicles can be used as energy storage devices of virtual power plants. When in use
In time, the wind turbine generator set can preferentially charge the electric automobile, and the residual electric quantity is sold in the real-time market. When in use
In the process, the deviation between the competitive bidding output and the actual output of the wind turbine generator set can be effectively reduced and the output fluctuation of the wind turbine generator set is stabilized by controlling the discharge of the electric automobile and reducing the purchase load of the market from demand response trading.
In the formula (I), the compound is shown in the specification,
and
respectively determining the actual output is greater than or less than the bidding output deviation of the wind turbine; u. ofec,t、ubl,tRespectively outputting state variables of positive deviation/negative deviation for the fan;
and
the charging and discharging power of the electric automobile is used for reducing the output deviation of the wind turbine generator; omegawt,ecIs the penalty coefficient, omega, of the positive deviation of the fan outputwt,ec<1；ωwt,blIs the penalty coefficient of negative deviation of fan output, omegawt,bl>1；
Is the actual output of the fan;
is the electric vehicle set of the access node i; gamma rayDRIs a collection of demand response providers; pdr,tIs the load reduction amount purchased from the dr demand response supplier.
(2) Electric automobile
As a controllable load and a controllable power supply, the electric automobile can provide reserve capacity for a power distribution network by adjusting the self charge and discharge power on the basis of meeting the travel requirement of the electric automobile.
First, revenue function
When the electric automobile participates in the adjustment of market bidding, the bidding capacities of the up-adjustment standby, the down-adjustment standby and the rotation standby are respectively expressed as
And
when the electric automobile provides the spare capacityThey cannot predict in advance the output that will actually be scheduled in the real-time market. Therefore, it is necessary to perform statistical analysis by the scheduling ratio of the historical scheduling data to the spare capacity. The expected dispatching ratios of the up-regulation reserve capacity, the down-regulation reserve capacity and the rotation reserve capacity of the electric automobile are respectively expressed as
And
as shown in equations (6-8).
In the formula (I), the compound is shown in the specification,
and
the actual scheduling output of the up-regulation reserve capacity, the down-regulation reserve capacity and the rotation reserve capacity of the electric automobile which is accessed to the node i at the time t in the real-time market is respectively carried out;
and
the ev electric automobile which is accessed to the node i at the time t is provided with an up-regulation reserve capacity, a down-regulation reserve capacity and a rotation reserve capacity respectively; e (-) denotes the expected value.
Exchange power P between electric vehicle and power distribution networkEV,tAs shown in equation (9).
In the formula (I), the compound is shown in the specification,
the charging power is required for meeting the driving requirement of the electric automobile;
and
respectively the total charge and discharge power of the electric automobile.
In order to attract users to connect electric vehicles to the charging facility at a given time and obey the scheduling of the virtual power plant, the users need to be given a discount on the charging fee. I.e. virtual power plant by electricity price lambdaev,subCharging fees are charged to the schedulable electric vehicle. After the wining is carried out in the adjustment market, the total income R of the electric automobile in the adjustment market in the T periodEV,rgAs shown in equation (11).
In the formula (I), the compound is shown in the specification,
charging power required by the ev quantity of electric vehicles accessing the node i at the moment t is met; lambda [ alpha ]RD,t，λRU,t，λRR,tCapacity prices for down standby, up standby, and spinning standby, respectively, for the regulation market.
Second, cost function
The cost of electric vehicle participation in adjusting market bids is comprised of two parts, as shown in equation (12).
CEV＝CEV,pur+CEV,los (12)
First, the electricity purchase cost C for charging the electric vehicle by the virtual power plantEV,purAs shown in equation (13).
In the formula, λrt,e,tIs the real-time electricity price.
Secondly, according to the relevant experimental data, the battery loss cost and the battery purchase cost of the electric automobile have the following relationship: the battery loss cost for an electric vehicle battery purchase cost of 3 ten thousand yuan is 0.252 yuan/kWh. The schedulable electric vehicles of the virtual power plant in the invention use the same type of battery, and the loss cost of the electric vehicle battery is shown in equation (14).
In the formula, PEV,batIs the rated capacity of the battery of the electric automobile; cbatIs the purchase cost coefficient of the batteries of the electric automobile, yuan/kWh. EtadcIs the discharge efficiency of the battery of the electric automobile.
Although the virtual power plant may determine the number of dispatchable electric vehicles in the future, there are certain reasons to change the number of dispatchable electric vehicles. When the electric automobile can not be scheduled according to a plan, the charging and discharging power of other electric automobiles needs to be increased to make up for capacity loss. For a certain electric vehicle, when other electric vehicles cannot be scheduled at time t, the charging and discharging power adjustment coefficient is as shown in equation (15).
In the formula (I), the compound is shown in the specification,
in order to adjust the coefficient, when the total number of the electric vehicles reaches 10000 or more, the electric vehicles can be regarded as a constant;
is the probability that the ev electric car of the access node i at time t cannot be scheduled.
(3) Gas engine set
First, revenue function
After the market wins day before, the gas turbine set is scheduled to generate electricity in the real-time market. Bidding income R of gas turbine unit in day-ahead market in T periodGT,daAs shown in equation (16).
In the formula (I), the compound is shown in the specification,
the gas turbine set is an access node i; pi,gt,tThe day-ahead competitive bidding output of the gas turbine set in each time period t.
Similar to the electric automobile, when the gas turbine unit participates in adjusting market bidding, the bidding forces of the up-regulation standby, the down-regulation standby and the rotation standby are respectively used
And
and (4) showing.
And
respectively an actual regulating value for up regulation and an actual regulating value for down regulation of the gas turbine unitThe actual modulation value is switched to.
After the gas turbine unit wins the bid in the adjustment market, the total income R obtained by the gas turbine unit in the adjustment marketGT,rgAs shown in equation (17).
Second, cost function
Cost C of virtual power plant for purchasing natural gas from gas market for gas turbine setGT,purAs shown in equation (18).
In the formula, λgasIs the natural gas price; etai,gt,tThe power generation efficiency of the gas engine set is obtained; LHV is the low calorific value of natural gas, kWh/m3。
3) Virtual power plant bidding model
(1) Modeling of uncertainty factors
The invention mainly considers three uncertain factors of wind turbine generator output, local user load demand and market price. The market prices include day-ahead market electricity prices, adjusted market prices, demand response market prices for the day, and real-time market electricity prices.
First, the fan output
Although various wind turbine generator output prediction software has been developed at home and abroad, due to factors such as errors of numerical weather forecast (NWP), the prediction error of the short-term wind turbine generator output is still 10% -15%, and accurate prediction is difficult to achieve. In the day-ahead market, the upper and lower limits of the wind turbine output can be given by using an interval constraint method so as to represent the uncertainty of the wind turbine output. Wind turbine generator bidding output P at time tWT,tAre respectively defined by an uncertain parameter Pup,WT,tAnd Plow,WT,tAnd (4) showing. The robust optimization method is suitable for the optimization problem of uncertain parameters under the constraint condition. At any time t, the upper and lower limit constraints of the wind turbine generator bidding outputOnly one uncertain parameter, respectively Pup,WT,tAnd Plow,WT,t. The upper and lower limits of the wind turbine output follow a normal distribution, as shown in equations (19-20).
σWind＝{σup,WT,t,σlow,WT,t}μWind＝{μup,WT,t,μlow,WT,t} (21)
In the formula, muup,WT,t，σup,WT,tIs Pup,WT,tExpected value and standard deviation of; mu.slow,WT,t，σlow,WT,tIs Plow,WT,tExpected value and standard deviation of.
The opportunity constraints may be used to convert the wind turbine bid output constraints into inequality constraints, as shown in equations (22-23).
pr{PWT,t≤Pup,WT,t}≥εup,WT (22)
pr{PWT,t≥Plow,WT,t}≥εlow,WT (23)
In the formula, epsilonup,WTAnd εlow,WTProbability of satisfying the opportunity constraint.
Second, local user load demand
Assuming that the virtual plant needs to meet the load demand of the local customer, the prediction can be made from historical load data, as shown in equation (24).
In the formula (I), the compound is shown in the specification,
predicting local user load through a differential autoregressive moving average model based on historical load data;
the upper and lower limits of the prediction error are respectively in accordance with normal distribution.
Third, market trading price
The day-ahead electricity price, the demand response market price, the reserve capacity price and the real-time electricity price of each hour can be predicted according to historical data
An internal variation. Wherein the content of the first and second substances,
a predicted value representing a price;
representing the price fluctuation interval radius.
(2) Objective function
The objective of the virtual plant bidding strategy in the present invention is to maximize the virtual plant profit I, as shown in equation (25).
maxI＝Rrev-CWT,pl-CEV-CGT,pur-CDR (25)
Rrev＝RWT,da+RGT,da+REV,rg+RGT,rg+RLU+RCT (26)
In the formula, RrevIs revenue generated by the virtual power plant, including: wind and gas turbine revenue in the day-ahead market of equations (1) and (16), electric and gas turbine revenue in the adjustment market of equations (11) and (17), sales of electricity to local users of equation (27), and carbon emission rights revenue in the carbon trading market of equation (28). CWT,plIs the economic penalty due to the deviation between the actual output of the wind turbine and the bid output of equation (2). CEVIs the electric vehicle cost of equation (12). CGT,purIs a squareA cost of purchasing natural gas for the gas train of pass (18). CDRIs the purchase cost of the demand response load reduction in the market for the day of equation (30).
First, revenue to sell electricity to local users
Since the load demand of the local user shows a flexible response to electricity rates, the relationship between the electricity usage of the local user and the electricity rates can be approximately described by a stepped price-demand curve, as shown in fig. 7 (b). The revenue of selling electricity to the local users during the period T is the product of the amount of electricity used and the electricity price, as shown in equation (27).
In the formula ulu,tIs the ratio of the electricity price of the local user to the real-time electricity price, and u is more than or equal to 0i,lu,t≤1；
Is a set of local users of access node i;
the power consumption of the lu-th user at the moment t.
Second, carbon trading market revenue
The introduction of a carbon trading mechanism realizes the quantification of carbon emission rights, so that the carbon emission rights can participate in market trading as commodities. The introduction of carbon trading costs will change the operating costs of the gas turbine. When the carbon emission of the gas turbine plant is excessively high, it is necessary to purchase the carbon emission right from the carbon trading market, which increases the operating cost of the gas turbine plant. When the profit of the virtual power plant is affected by the excessively high gas turbine set operation cost, the virtual power plant adjusts the operation strategy to limit the competitive bidding output of the gas turbine set. Therefore, depending on market mechanisms, carbon emissions can be limited, improving energy consumption structure. High-carbon-emission enterprises need to pay high cost for purchasing the carbon emission rights in the carbon trading market, and low-carbon-emission enterprises can sell the carbon emission rights in the carbon trading market to obtain certain benefits. And a carbon transaction mechanism is introduced to promote the transformation of power generation enterprises to green low carbon. The virtual power plant sells the revenue of the carbon emissions right in the carbon trading market as shown in equation (28).
In the formula, λc,tIs the price coefficient, yuan/MW, of the carbon emission right at time t; pi,c,tAnd the competitive bidding output of the wind turbine generator and the gas turbine generator which are connected to the node i at the moment t in the carbon trading market.
Third, the load reduction purchase cost of the demand response market
In the demand response marketplace, the virtual power plant may purchase load reduction in a mode where the demand response providers employ bilateral negotiations and centralized bidding, with the purchase costs shown in equation (30).
In the formula, gammaDRIs a collection of demand response providers; cDR,cenCost of purchasing load reduction through centralized bidding transaction; cDR,biIs the cost of trading the purchase load reduction through bilateral negotiation.
In the centralized bidding mode, the bid price-bid amount curve for the demand response provider is shown in FIG. 8 (b). The centralized bidding method reduces the purchase cost by the load, as shown in equation (31).
λcen,dr,t＝θcen,drλrt,e,t (32)
In the formula, gammaDRIs a collection of demand response providers; lambda [ alpha ]cen,dr,tIs the bid price of the dr th demand response supplier at time t; pcen,dr,tIs a demand response bid amount corresponding to the demand response bid price; thetacen,drThe adjustment can be carried out by the operator of the power distribution network for adjusting the coefficient; qDR,cen,tIs the total demand response provided by the centralized bidding mode at time t;
is the maximum value of the dr th demand response supplier bid amount.
The cost of the load reduction is purchased through the bilateral negotiation mode as shown in equation (35).
In the formula, λbi,dr,tThe price is negotiated for the demand response on two sides, and the price can be agreed and determined by the supply and demand parties in advance; pbi,dr,tThe demand response purchase amount corresponding to the demand response bilateral negotiation price; qDR,Bi,tIs the total demand response provided by the bilateral negotiation mode at time t.
The total load reduction purchased by the virtual power plant should meet the demand response capability DR that the demand response supplier can providecapConstraint, as shown in equation (37).
In the formula, DRcapDemand response capabilities that can be provided for demand response providers; pdr,tIs the load reduction amount purchased from the dr demand response supplier.
(3) Constraint conditions
First, wind turbine generator operation constraints
The bid output and actual output of the wind turbine should be between its minimum and maximum output power as shown in equations (39-40).
In the formula (I), the compound is shown in the specification,
and
respectively the minimum and maximum output power of the wind turbine.
Second, electric vehicle operation constraints
The state of the electric vehicle can be divided into three types, i.e., charge, discharge, and standby, and the constraint condition of equation (41) needs to be satisfied.
In the formula udc,tIs the electric vehicle discharge state. u. ofc,tIs the charging state of the electric vehicle.
The SOC of the electric vehicle at the time t is as shown in equation (42). In order to extend the battery life of an electric vehicle as much as possible, it is desirable to avoid complete discharge of the battery. Due to the aging of the batteries of the electric automobile and the like, a certain rated capacity loss exists. Therefore, the electric vehicle SOC needs to satisfy the capacity upper and lower limit constraints as shown in equation (43).
In the formula: SOCi,ev,tThe SOC value of the ev electric vehicle which is the access node i in the time period t. SOCi,ev,0And the initial SOC of the electric automobile.
Respectively are the upper and lower limits of the SOC of the electric automobile. Pi,ev,batThe rated capacity of the ev electric vehicle battery of the access node i.
The electric automobile also needs to satisfy the maximum charge-discharge power PEV,maxConstraints, as shown in equations (44-45).
Third, gas turbine unit operational constraints
The competitive bidding output of the gas turbine set meets the upper and lower output power limits and the maximum climbing rate constraint, as shown in equations (46-49).
In the formula (I), the compound is shown in the specification,
is the maximum output power of the gas turbine;
the competitive bidding capacity for standby under the gas turbine set;
and
the maximum upward/downward ramp rates of the gas turbine units are respectively.
Fourth, line constraint
In the formula (I), the compound is shown in the specification,
is the injection power of node i at time t; pij,max、Pij,minRespectively, the upper and lower power limits of line ij; h isijIs the line ij power profile variation coefficient.
4) Virtual power plant robust optimization bidding model
The virtual plant bidding model is a mixed integer planning model. This section first converts it into a linear programming problem and then into a robust linear programming model. To convert the bidding model of the virtual plant to a linear programming problem, the 0-1 variable in the model needs to be eliminated. The constraint equations (3-4, 9) are modified in view of the introduction of decision variables to form new constraint equations (52-55).
According to equation (42), the charging and discharging efficiency of the electric vehicle is different, so that the energy loss in the charging and discharging process of the battery is different. And converting the equation (42) into a linear function equation (56) for calculating the SOC of the electric automobile.
Equation (56) is the same as equation (42) when the electric vehicle is charging. When the electric vehicle is discharged, the electric vehicle discharge amount calculated by equation (56) is smaller than the actual discharge amount due to linearization, using Δ erri,ev,tIndicating the deviation of the discharge amount. When the electric automobile is charged and dischargedElectrical efficiency ηc＝ηdcWhen equal to 0.95,. DELTA.erri,ev,tOnly occupying the actual discharge capacity
9.75 percent of the total weight. When etac＝ηdcWhen 1.0, Δ erri,ev,t0. Therefore, it is feasible to replace equation (42) with equation (56).
And converting the linear programming model into a virtual power plant robust optimization bidding model according to a robust optimization principle, which is detailed as follows.
max IROM (58)
-y≤P≤y (73)
Wherein P is a decision variable vector; y is an auxiliary decision variable vector introduced by dual transformation; z is a radical ofεIs the upper quantile of the standard normal distribution; mu.sa,W,tAnd σa,W,tAre respectively
The expected value and the standard deviation of the measured values,
obey a normal distribution.
In the virtual power plant bidding robust optimization bidding model, the charging and discharging power constraint of the electric vehicle meets an equation (54) and an equation (55), and the SOC constraint of the electric vehicle meets an equation (56). Equations (77) and (78) are upper and lower limit constraints of the competitive bidding output of the wind turbine generator, and equations (79) and (80) are positive and negative deviation constraints of the competitive bidding output of the wind turbine generator. Other constraints are shown in the equation (43-51).
In order to make the constraint violation probability not exceed k, the robust control coefficient Γ1The constraint equation (81) needs to be satisfied.
In the formula phi-1An inverse function of a cumulative distribution function that is a standard normal distribution; n isnpThe number of uncertain parameters contained in the constraint equation (59).
5) As a modeling language for describing and solving large-scale optimization problems, AMPL does not directly solve the optimization problem, but rather obtains an optimal solution by invoking appropriate external solvers (e.g., CPLEX, minnos, IPOPT, SNOPT, KNITRO, etc.). IBM ILOG CPLEX12.2 is widely applied to the optimization field as a commercial solver for solving quadratic programming and mixed integer linear programming. The collaborative bidding strategy for the virtual power plant to participate in the power market and the carbon trading market is to construct a robust optimization model for the virtual power plant to participate in the collaborative bidding of the power market and the carbon trading market by adopting AMPL/CPLEX based on an MATLAB R2018b platform under the environment that a CPU is Inter (R) core (TM) i7-8250U and a dominant frequency is 1.8GHz, and solve the robust optimization model.
Best mode for carrying out the invention
The optimal embodiment takes a virtual power plant demonstration area in a certain place of China as an example, a robust optimization model for the virtual power plant to participate in the collaborative bidding of the power market and the carbon trading market is established, and the collaborative bidding strategy for the virtual power plant to participate in the power market and the carbon trading market provided by the invention is obtained.
1) Basic data
The invention takes a virtual power plant demonstration area as an example to carry out example analysis, takes hours as time intervals, has 24 time intervals every day, and delta t is 1 h. The virtual power plant mainly comprises two gas units, two wind power units and twenty thousand electric vehicles. Local power consumers are classified into residential, commercial, and industrial categories. To minimize the penalty of actual power generation deviations, the virtual power plant may purchase a load reduction from the demand response market in the day, with the load reduction not exceeding the demand response capability DR of the demand response providercap。
The expected value and the actual output of the wind turbine output constraint are shown in fig. 5. The penalty coefficient of the positive deviation of the fan output is 0.95. The penalty coefficient of the negative deviation of the fan output is 1.05. The gas turbine set power parameters are shown in table 1. The charge and discharge parameters and the schedulable characteristics of the electric vehicle are shown in tables 2 and 3. Price information of the day-ahead market, the adjustment market, and the real-time market, as shown in fig. 6 (a). The real-time market adjustment/rotation reserve capacity scheduling ratio is expected as shown in fig. 6 (b). The trade price of the carbon emission rights is 148.41 yuan/MW. Book (I)The load demand prediction curve of the ground user is shown in fig. 7 (a). A staircase curve of the relationship between local user load demand and electricity prices, as shown in fig. 7 (b). The prediction error in both fig. 6(a) and fig. 7(a) was set to ± 15%. In the demand response marketplace, the demand response provider provides a bilateral negotiation curve for load reduction, as shown in FIG. 8 (a); the collective bidding curve is shown in fig. 8 (b). Demand response capability DR of demand response providercapHas a variation range of [0,25WM ]]。
TABLE 1 gas turbine units electrical parameters
TABLE 2 electric vehicle charging and discharging parameters
TABLE 3 unintended departure probability and charging power of electric vehicle
2) Analysis of influence of robust control coefficient change on bidding result
When demand response capability DRcap10MW, standard deviation σ of upper/lower fan output and actual outputWindTaking the expected value mu Wind10%, analyzing the virtual power plant bidding result by robust control coefficient gamma1The effect of the change. According to the robust optimization model of the virtual power plant, the constraint violation probability can be obtained through the gamma1To adjust. Different constraint violation probabilities correspond to the economic risk of various decisions, and the smaller the constraint violation probability, the smaller the economic risk assumed by the decision maker. The values of the relevant parameters corresponding to the different constraint violation probabilities and the optimal value of the virtual plant profit, as shown in Table 4Shown in the figure. Probability epsilon in which chance constraint holdsup,WT，εlow,WT，εpl,ec，εpl,blThe values are the same and are all epsilon.
TABLE 4 relevant parameter values and optimization results under different constraint violation probabilities
The 8 scenarios in table 4 correspond to different risk preferences of the decision maker. From scenario 1 to scenario 8, the robust control coefficient gradually increases, and the constraint violation probability gradually decreases. Meanwhile, the probability of establishment of the opportunity constraint is increased, the risk that the bidding result of the virtual power plant violates the constraint is gradually reduced, the risk aversion degree of a decision maker is continuously improved, and the profit optimal value of the virtual power plant is reduced. In 8 scenes, the virtual power plant wind turbine generator and the electric automobile participate in market bidding, as shown in fig. 9 and 10.
As can be seen from fig. 9, as the risk aversion degree of the decision maker is increased, the competitive bidding output of the wind turbine generator is reduced. Accordingly, the charge and discharge power provided by the electric vehicle to stabilize the output fluctuation of the wind turbine is also reduced, as shown in fig. 10 (a). When the electric automobile in the virtual power plant participates in adjusting market bidding, the adjustment standby and the rotation standby can be provided at the same time. Under the influence of the regulated reserve and the spinning reserve prices, the electric vehicles need to coordinate various reserves when participating in the regulated market bidding to achieve maximum revenue. Therefore, although the change of the bidding strategy of the electric vehicle under different scenes can be found from fig. 10, a certain change trend is not presented.
3) Analysis of influence of electric vehicle quantity and wind turbine generator output change on bidding result
In the virtual power plant bidding problem, the virtual power plant bidding strategy is influenced by the changes of the number of electric vehicles and the output of fans. When demand response capability DRcap＝10MW，σWind＝0.1μWindRobust control coefficient Γ 120,. epsilon. 98.09%, other parameters and "2) robust control coefficient variation versus bidding outcomeAnd when the influence analysis is the same, the influence of the change of the number of the electric vehicles and the output of the wind generating set on the bidding result is analyzed. When the number of electric vehicles and the output of the wind turbine generator are changed, the bidding result of the virtual power plant is shown in table 5. Wherein N isEVThe number of the electric automobiles is ten thousands. Mu.sup,WT,tAnd mulow,WT,tRespectively, expected values of the competitive bidding output upper limit/lower limit of the wind turbine generator set. The relative change amount is N relative to the original caseEVScenario 7 in table 4, which is the variation at 2.
TABLE 5 Bidding strategy results for electric vehicle and Fan output variation
As can be seen from table 5, when the relative variation amounts of the number of electric vehicles and the output of the wind turbine generator are the same, the influence of the variation of the number of electric vehicles on the bidding result of the virtual power plant is larger. That is, the sensitivity of the virtual power plant bidding strategy to the number of electric vehicles is greater than the sensitivity to the output of the wind turbine. When the number of the electric automobiles in the system is increased, the electric automobiles can provide standby for the wind turbine generator and can also be used as an energy storage device to participate in adjusting market bidding to obtain benefits. When the number of the wind turbine generators in the system is increased, whether the redundant output of the wind turbine generators can participate in power market bidding depends on whether the stored energy of the electric automobile can provide a reserve for the output fluctuation of the wind turbine generators, and therefore the influence of the output of the wind turbine generators on the bidding result of the virtual power plant is limited. Therefore, the virtual plant bidding strategy is relatively insensitive to changes in wind turbine output.
The result shows that the virtual power plant bidding strategy model provided by the invention can reasonably reflect the economic risk of decision-makers in different scenes and the coordination relationship of the electric vehicles and the wind turbine generators in virtual power plant bidding, and can correspondingly provide bidding strategies of the electric vehicles in the regulation market and the wind turbine in the market before the day. It is worth noting that when the virtual power plant robust optimization bidding model established by the method is used for making a decision, parameter values of robust control coefficients and opportunity constraints are properly selected according to risk preferences of a decision maker.
4) Analysis of influence of wind turbine generator output standard deviation and demand response capacity change on bidding result
While robust control coefficient gamma1When the output standard deviation of the wind turbine generator is 20 percent and the epsilon is 98.09 percent, analyzing the output standard deviation of the wind turbine generator and the demand response capacity DRcapThe effect of the change on bid outcome is shown in fig. 11 and 12.
As can be seen from FIG. 11, the day-ahead market competitive power and DR of the wind turbine generatorcapAnd the standard deviation of the output of the wind turbine generator. When the output standard deviations of the wind turbine generators are the same, DRcapThe higher the competitive bidding output of the wind turbine generator is, the larger the competitive bidding output of the wind turbine generator is. As can be seen from FIGS. 11 and 12(a), when DR is reachedcapMeanwhile, with the increase of the standard deviation of the output of the wind turbine generator, the competitive bidding total electric quantity of the wind turbine generator in the market at the day before is increased. The reason for this is that the purchase load reduction cost is low, and when the demand response capability of the demand response supplier is enough to make up for the output fluctuation of the wind turbine generator, the virtual power plant can increase the competitive output of the wind turbine generator in the market at the present time for effectively utilizing the wind turbine generator.
As can be seen from fig. 12(b), the virtual plant profit can be significantly improved by purchasing a load reduction amount from the demand response market. However, when DRcapAfter reaching a certain level, following DRcapThe virtual plant profit begins to decrease. At this time, the virtual power plant settles the deviation of the actual power generation amount at the real-time electricity price to obtain a larger profit than the purchase load reduction amount. Although the standard deviation of the wind turbine output changes, the standard deviation is changed along with the DRcapThe virtual power plant profit is gradually increased. But at DRcapWhen smaller, the virtual plant profit increases as the wind turbine output standard deviation decreases. When DRcapAt greater time (e.g. DR)cap25MWh), the larger the standard deviation of the wind turbine output, the more the virtual power plant profit.
When the standard deviation of the output of the wind turbine generator changes, the electricity income of local users is subjected to DRcapThe effect of (2) is shown in fig. 13. Day-ahead market bidding efficiency with DR due to virtual power plantcapIncreased, so that the virtual power plant is decreased to increase the local user's power consumptionThe electricity price of the local user is lowered. The relative change of electricity price of local user is shown in table 6. Meanwhile, the local user access position also influences the electricity price of the user. Since user 1 is directly connected to the gas turbine rather than being powered by the wind turbine, user 1 in table 6 has the highest commercial utility price.
TABLE 6 electricity price ratio of local user
5) Effect of introducing carbon trading on bid outcome
Robust control coefficient gamma when demand response capability1＝20，ε＝98.09％，σWind＝0.1μWind，DRcapAt 10MW, the impact of the introduction of carbon transactions on virtual plant bid results was analyzed. Compared with a wind turbine generator, the gas turbine generator has the characteristics of stable output, small fluctuation and the like, and has good economy. When the carbon emission right cost is not considered, in order to meet the load demand and improve the economy, the virtual power plant can fully call the gas turbine unit to participate in market bidding output. At this time, the optimal value of virtual plant profit is 316806.6 yuan. Regardless of the carbon emission right cost, the gas turbine unit participates in the electricity market bidding output situation, as shown in fig. 14.
As can be seen from fig. 14, the gas turbine set has good economy and output stability, and its 24h bid output is almost at full load. However, on the background that the carbon trading market is increasingly perfected, the gas turbine set needs to pay high carbon emission right purchase cost for power generation, and the improvement of the economy of the virtual power plant is not facilitated. Therefore, the carbon trading mechanism is introduced to play a key role in formulating the virtual power plant bidding strategy and adjusting the energy structure. When considering the carbon emission right cost, the gas turbine unit participates in the electricity market bidding output situation, as shown in fig. 15.
As seen from scenario 7 in Table 4, the optimal value of virtual plant profit when considering the cost of carbon emissions is 318442.7 yuan. The profit is slightly increased compared to the optimal value of the virtual plant profit when the cost of carbon emission rights is not considered. By comparing fig. 14 and fig. 15, the following can be concluded.
Firstly, the bidding output of the wind turbine generator is low when the price ratio is 01: 00-10: 00, and the virtual power plant calls the gas turbine generator to meet the power load. When the power ratio is 11: 00-24: 00, the output of the wind turbine generator is gradually increased, and the load requirements of users can be met to a greater extent. The carbon trading mechanism is introduced, so that the operation cost of the gas turbine set is increased, the purchase cost of the carbon emission right is reduced, and the competitive output of the gas turbine set is reduced by the virtual power plant. The total competitive power generation of the gas turbine set is reduced from 921.12MWh to 758.88 MWh.
Secondly, the economy of the gas turbine set obviously slips down by purchasing the carbon emission right, and the output of the wind turbine set needs to be increased in order to improve the optimal profit value of the virtual power plant. In the example, the output of the wind turbine generator is saturated. Therefore, the introduction of the carbon trading mechanism does not have a great influence on the bidding success of the wind turbine.
Thirdly, the renewable energy permeability in the embodiment is high, the income obtained by selling the carbon emission right of the virtual power plant is larger than the cost of purchasing the carbon emission right of the gas turbine unit, and the profit optimal value of the virtual power plant is increased by 1636.1 yuan.
Therefore, the competitive price income of the virtual power plant is more diversified by introducing a carbon trading mechanism, and the virtual power plant can obtain greater economic benefit by reasonably adjusting the competitive price output of the wind turbine generator and the gas turbine generator. Meanwhile, a carbon trading mechanism is introduced to effectively reduce the emission of pollutants and greenhouse gases in the area, and the method has economic and social significance.
The above detailed description of the method for collaborative bidding of virtual power plants to participate in the electricity market and the carbon trading market with reference to the embodiments is illustrative and not restrictive, and several embodiments can be enumerated within the scope of the limitations, so that variations and modifications thereof are within the scope of the present invention without departing from the general concept thereof.
Claims (8)
1. A collaborative bidding method for a virtual power plant to participate in an electric power market and a carbon trading market is characterized by comprising the following steps: comprises four steps of the following steps of,
(1) virtual power plant operation mechanism and structure analysis: a virtual power plant with an operation structure as a power generation unit, an energy storage unit and a demand response unit is taken as an object, and a robust optimization bidding strategy is provided when the virtual power plant participates in a day-ahead power market, a day-in demand response market, an adjustment market, a real-time power market and a carbon trading market; by analyzing the integrated characteristics of the virtual power plant business mode, participation in the power market bidding planning and the power market and carbon trading market, the composition of the multivariate income and the cost of the virtual power plant participating in the power market and carbon trading market collaborative bidding is determined;
(2) bidding strategies for virtual power plant market and regulatory market in the future: by establishing a revenue cost model of a wind turbine generator, an electric automobile and a gas turbine generator, determining bidding strategies of a virtual power plant participating in a day-ahead power market and adjusting the market; establishing a quantitative model of the uncertainty of the virtual power plant, and reducing the bidding risk of the virtual power plant caused by the uncertainty of the renewable energy power generation through a demand response market;
(3) virtual power plant bidding model: introducing a carbon trading mechanism, and optimizing a collaborative bidding strategy of the virtual power plant participating in the power market and the carbon trading market by taking the profit maximization of the virtual power plant as a target;
(4) the virtual power plant robust optimization bidding model comprises the following steps: and considering different risk preferences of the virtual power plant bidding, establishing a robust optimization model of the collaborative bidding strategy of the virtual power plant participating in the power market and the carbon trading market, and further improving the collaborative bidding profits of the virtual power plant participating in the power market and the carbon trading market with different risk preferences.
2. The collaborative bidding method for the virtual power plant to participate in the power market and the carbon trading market according to claim 1, wherein: the operation mechanism and structure analysis of the virtual power plant comprises the following specific steps:
(1) power distribution market business model for virtual power plant
The power distribution market business model of a virtual power plant includes four phases,
according to the prediction information of the distributed renewable energy sources, the virtual power plant aggregates the internal resources according to the output range and the operation cost to obtain an output curve and cost characteristics of the aggregated flexible resources;
the virtual power plant submits the price and the quantity of various electric power commodities to an operator of the power distribution network;
thirdly, the power distribution network operators collect bidding information of each operator, take the bidding output and cost characteristics of various units into consideration, put forward a market clearing strategy and determine the winning price and the winning quantity of the virtual power plant according to the demands of various power commodities and the safe operation constraint of the system;
the virtual power plant optimizes an internal resource scheduling plan according to the market clearing result and follows scheduling instructions issued by the power distribution network operator, and the virtual power plant designs a scheduling plan of internal flexible resources according to a scheduling plan curve and various electric power commodity prices issued by the power distribution network operator and tracks the scheduling instructions issued by the power distribution network operator;
(2) bidding rule for virtual power plant participating in power market
The bidding rule of the virtual power plant is as follows: based on the participation of a virtual power plant containing high-permeability renewable energy in the day-ahead market, the day-in demand response market, the adjustment market and the real-time market,
the virtual power plant needs to predict the actual power generation amount 12-36 hours in advance, and submits bidding information of 24 hours in the future to a power distribution network operator before closing the market in the day, namely a power generation price-power generation capacity curve; setting the bidding price of the virtual power plant to be 0, and optimizing the bidding electric quantity of the virtual power plant only according to the predicted market settlement price; the method comprises the steps that an adjusting market is closed one hour before a real-time market is opened, during the period, a virtual power plant submits bidding prices of an adjusting standby power and a rotating standby power to a power distribution network operator, after the electricity market transaction is finished, the actual power generation deviation of the virtual power plant is settled according to the real-time electricity price, the bidding strategy of the virtual power plant is researched by adopting bidding power generation power, and the power generation power per hour of the virtual power plant have the same data, namely the power generation power per hour is multiplied by 1 hour;
(3) power market and carbon trading market integrated feature with participation of virtual power plant
In the operation structure of the virtual power plant: the power generation unit mainly comprises a wind turbine generator and a gas turbine generator, the energy storage unit is an electric automobile, the demand response unit is a local load and external demand response provider, each unit transmits daily operation data to a control center of a virtual power plant through a data layer, the control center uniformly makes a strategy and schedules the output of power generation unit equipment, the response capacity of the energy storage unit and the response capacity of the demand response unit according to the current profit and the carbon emission target, the power generation unit, the energy storage unit and the demand response unit in the aggregation area of the virtual power plant are the basis for constructing the integration of a power market and a carbon trading market, and each unit in the virtual power plant participates in the market trading process and is also the process for simultaneously transferring the power consumption and the carbon emission right;
2) virtual power plant day-ahead market and market-adjusted bidding strategy
(1) Income function of wind turbine generator
After winning the bid in the market before the day, when the wind turbine generator is scheduled to generate electricity in the real-time market, the competitive bidding income R of the wind turbine generator in the market before the day is in the T periodWT,daAs shown in equation (1)
In the formula, i is the node number of the power distribution network; b is a power distribution network node set; lambda [ alpha ]da,e,tIs the day-ahead electricity price; is a wind turbine set of an access node i; pi,wt,tThe day-ahead competitive bidding output of the wind turbine generator at the moment t; Δ t represents a time interval;
(2) electric automobile provides reserve capacity for distribution network
First, revenue function
When the electric automobile participates in the adjustment of market bidding, the bidding capacities of the up-adjustment standby, the down-adjustment standby and the rotation standby are respectively expressed as
And
when the electric automobile provides the spare capacity, the expected dispatching ratios of the up-regulation spare capacity, the down-regulation spare capacity and the rotation spare capacity of the electric automobile are respectively expressed as
And
as shown in equations (6-8)
In the formula (I), the compound is shown in the specification,
and
the actual scheduling output of the up-regulation reserve capacity, the down-regulation reserve capacity and the rotation reserve capacity of the electric automobile which is accessed to the node i at the time t in the real-time market is respectively carried out;
and
the ev electric automobile which is accessed to the node i at the time t is provided with an up-regulation reserve capacity, a down-regulation reserve capacity and a rotation reserve capacity respectively; e (-) represents the expected value;
exchange power P between electric vehicle and power distribution networkEV,tAs shown in equation (9)
In the formula (I), the compound is shown in the specification,
the charging power is required for meeting the driving requirement of the electric automobile;
and
respectively is the total charge and discharge power of the electric automobile;
virtual power plant lambda according to electricity priceev,subCharging fee is charged for the schedulable electric automobile, and the total income R of the electric automobile in the adjustment market in the T time periodEV,rg，
As shown in the equation (11),
in the formula (I), the compound is shown in the specification,
charging power required by the ev quantity of electric vehicles accessing the node i at the moment t is met; lambda [ alpha ]RD,t，λRU,t，λRR,tCapacity prices for down-regulation reserve, up-regulation reserve and rotation reserve of the regulation market are respectively;
second, cost function
The cost of electric vehicle participation in adjusting market bids consists of two parts, as shown in equation (12), CEV＝CEV,pur+CEV,los (12)
Firstly, the electricity purchasing cost C for charging the electric automobile by the virtual power plantEV,purAs shown in equation (13)
In the formula, λrt,e,tIs the real-time electricity price.
Secondly, schedulable electric vehicles of the virtual power plant use the same type of battery, and the loss cost of the electric vehicle battery is shown in equation (14).
In the formula, PEV,batIs the rated capacity of the battery of the electric automobile; cbatIs the purchase cost coefficient of the batteries of the electric automobile, yuan/kWh. EtadcIs the discharge efficiency of the electric vehicle battery;
(3) gas engine set
First, revenue function
The gas turbine set which wins the bid in the day-ahead market is scheduled to generate power in the real-time market, and the bidding income R of the gas turbine set in the day-ahead market in the T periodGT,daAs shown in equation (16)
In the formula (I), the compound is shown in the specification,
the gas turbine set is an access node i; pi,gt,tThe day-ahead competitive bidding output of the gas turbine set at each time interval t;
gas turbine unit participating in market adjustment competitionDuring the price, the competitive bidding forces of the upper reserve, the lower reserve and the rotary reserve are respectively used
And
it is shown that,
and
respectively an up-regulation actual regulation value, a down-regulation actual regulation value and a rotation standby actual regulation value for the gas turbine unit,
after the gas turbine unit wins the bid in the adjustment market, the total income R obtained by the gas turbine unit in the adjustment marketGT,rgAs shown in equation (17)
Second, cost function
Cost C of virtual power plant for purchasing natural gas from gas market for gas turbine setGT,purAs shown in equation (18).
In the formula, λgasIs the natural gas price; etai,gt,tThe power generation efficiency of the gas engine set is obtained; LHV is the low calorific value of natural gas, kWh/m3；
3) Virtual power plant bidding model
(1) Modeling of uncertainty factors
Uncertainty factors include wind turbine output, local user load demand, and market prices, including day-ahead market electricity prices, adjusted market prices, demand response market prices for the day, and real-time market electricity prices,
output of fan
In the day-ahead market, the upper and lower output limits of the wind turbine are set by using an interval constraint method, and the wind turbine competitive output P at the moment t is outputWT,tAre respectively defined by an uncertain parameter Pup,WT,tAnd Plow,WT,tRepresents; at any time t, only one uncertain parameter is in the upper and lower limit constraints output by the wind turbine unit bidding, and the parameters are respectively Pup,WT,tAnd Plow,WT,t(ii) a The upper and lower limits of the wind turbine output follow normal distribution, as shown in equations (19-21)
σWind＝{σup,WT,t,σlow,WT,t}μWind＝{μup,WT,t,μlow,WT,t} (21)
In the formula, muup,WT,t，σup,WT,tIs thatPup,WT,tExpected value and standard deviation of; mu.slow,WT,t，σlow,WT,tIs Plow,WT,tExpected value and standard deviation of;
the opportunity constraint can be used to convert the wind turbine generator bidding output constraint into an inequality constraint, as shown in equations (22-23)
pr{PWT,t≤Pup,WT,t}≥εup,WT (22)
pr{PWT,t≥Plow,WT,t}≥εlow,WT (23)
In the formula, epsilonup,WTAnd εlow,WTA probability of satisfying an opportunity constraint;
local user load demand
The virtual power plant needs to meet the load demand of local users, and the prediction is carried out through historical load data, as shown in equation (24)
In the formula (I), the compound is shown in the specification,
predicting local user load through a differential autoregressive moving average model based on historical load data;
respectively representing the upper limit and the lower limit of the prediction error and following normal distribution;
(iii) market trading price
Forecasting the current day price, demand response market price, reserve capacity price and real-time electricity price of each hour in intervals according to historical data
An internal variation wherein, in addition,
a predicted value representing a price;
representing the radius of a price fluctuation interval;
(2) objective function
The goal of the virtual plant bidding strategy is to maximize the virtual plant profit I, as shown in equation (25)
maxI＝Rrev-CWT,pl-CEV-CGT,pur-CDR (25)
Rrev＝RWT,da+RGT,da+REV,rg+RGT,rg+RLU+RCT (26)
In the formula, RrevIs revenue generated by the virtual power plant, including: wind turbine generator and gas turbine generator in day-ahead marketIncome RWT,daAnd RGT,daAdjusting the income R of electric vehicles and gas units in the marketEV,rgAnd RGT,rgIncome R for selling electricity to local usersLUCarbon emission rights revenue R in carbon trading marketCT；CWT,plIs an economic penalty due to the deviation between the actual output of the wind turbine and the bid output, CEVIs the cost of the electric vehicle, CGT,purIs the cost of purchasing natural gas from the gas turbine unit, CDRIs the purchase cost of the load reduction in the demand response market in the day.
(3) Constraint conditions
First, wind turbine generator operation constraints
The bid output and actual output of the wind turbine should be between its minimum and maximum output powers as shown in equations (39-40)
In the formula (I), the compound is shown in the specification,
and
respectively the minimum and maximum output power of the wind turbine;
second, electric vehicle operation constraints
The state of the electric vehicle, including charging, discharging, and standby, needs to satisfy the constraint condition of equation (41)
In the formula udc,tIs electrically drivenVehicle discharge state. u. ofc,tIs the charging state of the electric automobile
At the time t, the SOC of the electric vehicle is shown as an equation (42), and the SOC of the electric vehicle needs to meet the upper and lower capacity limit constraints as shown as an equation (43)
In the formula: SOCi,ev,tThe SOC value, SOC of the ev electric vehicle which is the access node i in the time period ti,ev,0In order to start the SOC of the electric automobile,
respectively the upper and lower limits, P, of the SOC of the electric vehiclei,ev,batThe rated capacity of the ev electric vehicle battery of the access node i;
the electric vehicle also needs to satisfy the maximum charge and discharge power PEV, max constraint, as shown in equation (44-45)
Third, gas turbine unit operational constraints
The competitive bidding output of the gas turbine set meets the upper and lower limits of the output power and the maximum climbing rate constraint, as shown in equations (46-49)
In the formula (I), the compound is shown in the specification,
is the maximum output power of the gas turbine;
the competitive bidding capacity for standby under the gas turbine set;
and
the maximum upward/downward climbing rate of the gas turbine unit is respectively;
fourth, line constraint
In the formula (I), the compound is shown in the specification,
is the injection power of node i at time t;Pij,max、Pij,minrespectively, the upper and lower power limits of line ij; h isijIs the line ij power distribution variation coefficient;
4) virtual power plant robust optimization bidding model
Firstly, converting a virtual power plant bidding model into a linear programming problem, and then converting the virtual power plant bidding model into a robust linear programming model; forming new constraint equations (52-55) by introducing decision variables
Converting equation (42) into a linear function equation (56) for calculating the SOC of the electric vehicle,
converting the linear programming model into a virtual power plant robust optimization bidding model according to a robust optimization principle, which is detailed as follows
max IROM (58)
-y≤P≤y (73)
Wherein P is a decision variable vector; y is an auxiliary decision variable vector introduced by dual transformation; z is a radical ofεIs the upper quantile of the standard normal distribution; mu.sa,W,tAnd σa,W,tAre respectively
The expected value and the standard deviation of the measured values,
subject to a normal distribution of the signals,
in the virtual power plant bidding robust optimization bidding model, the charging and discharging power constraint of the electric automobile meets an equation (54) and an equation (55), and the SOC constraint of the electric automobile meets an equation (56); equations (77) and (78) are upper and lower limit constraints of the competitive bidding output of the wind turbine generator, and equations (79) and (80) are positive and negative deviation constraints of the competitive bidding output of the wind turbine generator; other constraints are shown in the equation (43-51).
3. The collaborative bidding method for the virtual power plant to participate in the power market and the carbon trading market according to claim 2, wherein: the cost function of the wind turbine generator in the commercial mode of the power distribution market of the virtual power plant is subjected to economic penalty when the bidding output is deviated from the actual output of the wind turbine generator, as shown in equation (2), when the cost function is used
When the electric vehicle is charged, the wind turbine generator preferentially charges the electric vehicle, the residual electric quantity is sold in the real-time market, and the electric vehicle is charged when the residual electric quantity is charged
By controlling electric vehicle discharge and purchasing from demand response trading marketsThe load reduction is reduced, the deviation between the competitive power and the actual power of the wind turbine generator is reduced, the power fluctuation of the wind turbine generator is stabilized,
in the formula (I), the compound is shown in the specification,
and
respectively determining the actual output is greater than or less than the bidding output deviation of the wind turbine; u. ofec,t、ubl,tRespectively outputting state variables of positive deviation/negative deviation for the fan;
and
the charging and discharging power of the electric automobile is used for reducing the output deviation of the wind turbine generator; omegawt,ecIs the penalty coefficient, omega, of the positive deviation of the fan outputwt,ec<1；ωwt,blIs the penalty coefficient of negative deviation of fan output, omegawt,bl>1
Is the actual output of the fan;
is the electric vehicle set of the access node i; gamma rayDRIs a collection of demand response providers; pdr,tIs the load reduction amount purchased from the dr demand response supplier.
4. The collaborative bidding method for the virtual power plant to participate in the power market and the carbon trading market according to claim 2, wherein: when the electric automobile in the commercial mode of the power distribution market of the virtual power plant can not be scheduled according to a plan, the charging and discharging power of other electric automobiles needs to be increased to make up for capacity loss. For a certain electric vehicle, when other electric vehicles cannot be scheduled at time t, the charging and discharging power adjustment coefficient is as shown in equation (15).
In the formula (I), the compound is shown in the specification,
in order to adjust the coefficient, when the total number of the electric vehicles reaches 10000 or more, the electric vehicles can be regarded as a constant;
is the probability that the ev electric car of the access node i at time t cannot be scheduled.
5. The collaborative bidding method for the virtual power plant to participate in the power market and the carbon trading market according to claim 2, wherein: objective function specific revenue for the virtual power plant bidding model
Income for selling electricity to local users
Relationship between electricity consumption and electricity price of local users: the revenue of selling electricity to the local user during the period T is the product of the electricity usage and the electricity price, as shown in equation (27)
In the formula ulu,tIs the ratio of the electricity price of the local user to the real-time electricity price, and u is more than or equal to 0i,lu,t≤1；
Is a set of local users of access node i;
the power consumption of the lu-th user at the moment t;
second, carbon trade market revenue
The revenue of the virtual power plant selling carbon emissions in the carbon trading market is shown in equation (28)
In the formula, λc,tIs the price coefficient, yuan/MW, of the carbon emission right at time t; pi,c,tThe competitive bidding output of the wind turbine generator and the gas turbine generator which are connected to the node i at the moment t in the carbon trading market;
load reduction purchase cost of demand response market
In the demand response market, the virtual power plant purchases a load reduction amount through a demand response provider in a mode of bilateral negotiation and centralized bidding, and the purchase cost is shown as equation (30)
In the formula, gammaDRIs a collection of demand response providers; cDR,cenCost of purchasing load reduction through centralized bidding transaction; cDR,biThe cost of purchasing load reduction through bilateral negotiation is adopted;
the purchase cost reduced by the load of the centralized bidding method is shown in equation (31)
λcen,dr,t＝θcen,drλrt,e,t (32)
In the formula, gammaDRIs a collection of demand response providers; lambda [ alpha ]cen,dr,tIs the bid price of the dr th demand response supplier at time t; pcen,dr,tIs a demand response bid amount corresponding to the demand response bid price; thetacen,drThe adjustment can be carried out by the operator of the power distribution network for adjusting the coefficient; qDR,cen,tIs the total demand response provided by the centralized bidding mode at time t;
is the maximum value of the dr th demand response supplier bid amount;
purchase of cost of load reduction through bilateral negotiation mode, as shown in equation (35)
In the formula, λbi,dr,tThe price is negotiated for the demand response on two sides, and the price can be agreed and determined by the supply and demand parties in advance; pbi,dr,tThe demand response purchase amount corresponding to the demand response bilateral negotiation price; qDR,Bi,tIs the total demand response provided by the bilateral negotiation mode at time t,
the total load reduction purchased by the virtual power plant should satisfy the DRcap constraint on the demand response capability that the demand response supplier can provide, as shown in equation (37)
In the formula, DRcapDemand response capabilities that can be provided for demand response providers; pdr,tIs the load reduction amount purchased from the dr demand response supplier.
6. The collaborative bidding method for the virtual power plant to participate in the power market and the carbon trading market according to claim 2, wherein: in the robust optimization bidding model of the virtual power plant, when the electric automobile is charged, the equation (56) is the same as the equation (42), when the electric automobile is discharged, the electric automobile discharge amount calculated by the equation (56) is smaller than the actual discharge amount, and delta err is usedi,ev,tIndicating the deviation of discharge capacity, and the charge-discharge efficiency eta of the electric vehiclec＝ηdcWhen equal to 0.95,. DELTA.erri,ev,tOnly occupying the actual discharge capacity
9.75% of (d), when etac＝ηdc1.0,. DELTA.erri,ev,t0, equation (42) is replaced with equation (56).
7. The collaborative bidding method for the virtual power plant to participate in the power market and the carbon trading market according to claim 2, wherein: in the virtual power plant robust optimization bidding model, in order to enable the constraint violation probability not to exceed kappa, a robust control coefficient gamma1The constraint equation (81) needs to be satisfied
In the formula phi-1An inverse function of a cumulative distribution function that is a standard normal distribution; n isnpThe number of uncertain parameters contained in the constraint equation (59).
8. The collaborative bidding method for the virtual power plant to participate in the power market and the carbon trading market according to claim 1 or 2, wherein: the collaborative bidding strategy for the virtual power plant to participate in the power market and the carbon trading market is to construct a robust optimization model for the virtual power plant to participate in the collaborative bidding of the power market and the carbon trading market by adopting AMPL/CPLEX based on an MATLAB R2018b platform under the environment that a CPU is Inter (R) core (TM) i7-8250U and a dominant frequency is 1.8GHz, and solve the robust optimization model.
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