CN117974210A - Virtual power plant capacity configuration optimization method considering multi-transaction market - Google Patents
Virtual power plant capacity configuration optimization method considering multi-transaction market Download PDFInfo
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Abstract
The invention belongs to the field of power system market transaction and power supply optimal configuration, and discloses a virtual power plant capacity configuration optimizing method considering a multi-transaction market, which divides a virtual power plant into SEVPP and DRVPP according to the functionality of the virtual power plant and models the resources contained in the virtual power plant in sequence; establishing a master-slave game model through an electricity-carbon-green combined transaction market mechanism and a DRVPP demand response mechanism for participating in market transaction, and obtaining an optimal capacity configuration scheme of SEVPP and an optimal response electric quantity scheme of DRVPP through solving; the capacity ratios of different investors in the virtual power plant are distributed to SEVPP and DRVPP so as to achieve the optimal capacity allocation scheme of each investor. The invention can simultaneously improve the benefits SEVPP and DRVPP, effectively improve the enthusiasm of various investors for participating in virtual power plant aggregation, and further explore the potential of energy conservation and emission reduction of the virtual power plant.
Description
Technical Field
The invention belongs to the field of power system market trading and power supply optimal configuration, and particularly relates to a virtual power plant capacity configuration optimization method considering multiple trading markets.
Background
Along with the large-scale construction of the distributed power supply, the power system is transformed towards a novel power system for generating power with high-proportion new energy, and the distributed new energy which is scattered often encounters the difficult problem that the capacity of the distributed power system is too small to meet the requirement of grid-connected capacity if the distributed new energy is required to be integrated into a large power grid for generating power. Therefore, the concept of the virtual power plant has been developed, and with the development of advanced measurement technology and communication technology, the virtual power plant can perform unified and centralized scheduling management on large-scale distributed power sources, and meanwhile, the virtual power plant can be used as one of main bodies of market participation to perform bidding on daily and daily electric power markets with the traditional power plant due to the aggregation of the large-scale distributed power sources. Meanwhile, in order to achieve the double-carbon target, the whole electric power industry is performing energy conservation and emission reduction, and the aggregation characteristics of the virtual power plant prove that the virtual power plant has excellent energy conservation and emission reduction potential, and the new energy power generation equipment aggregated inside the virtual power plant can participate in the carbon trade market and can also participate in the green certificate trade market, so that a strong power is added for pushing the realization of the double-carbon target.
At present, virtual power plants can be divided into two major types of electricity selling virtual power plants (SELL ENERGY virtual power plant, SEVPP) and demand response type virtual power plants (demand response virtual power plant, DRVPP) according to different aggregate resource types, and the two types of virtual power plants have obvious complementary characteristics when participating in an electricity-carbon-green combined trading market, but the capacity configuration of the virtual power plants is too little or too much to cause the reduction of benefits of the virtual power plants, so that the prior art lacks an effective method for carrying out optimal capacity configuration for the two types of virtual power plants, and the benefit maximization and the co-win effect of the two types of virtual power plants are difficult to realize.
Disclosure of Invention
In order to solve the problems, the invention provides a virtual power plant capacity allocation optimization method considering a multi-trading market, which is characterized in that by modeling power output of SEVPP and DRVPP internal resources and a reasonable 'electricity-carbon-green' combined trading market mechanism and a DRVPP demand response mechanism participating in market trading, the specific optimal capacity allocation of SEVPP and DRVPP internal aggregate resources is finally obtained, so that the maximum benefit can be achieved when the virtual power plant operates.
The invention relates to a virtual power plant capacity configuration optimization method considering multi-transaction markets, which comprises the following steps:
step 1, dividing the virtual power plants into two main types of electricity selling type virtual power plants (SELL ENERGY virtual power plant, SEVPP) and demand response type virtual power plants (demand response virtual power plant, DRVPP) according to the functionality of the virtual power plants, and sequentially modeling output power of wind power investors, photovoltaic investors, thermal power investors, energy storage investors and load aggregators contained in SEVPP, wherein the load aggregators in DRVPP are divided into a type A load aggregator (translatable and uninterruptable load aggregator), a type B load aggregator (interruptable and uninterruptable load aggregator) and a type C load aggregator (interruptable and translatable load aggregator) according to different response time and response depth;
Step 2, according to the established output model, a combined transaction mechanism considering electric market transaction, carbon transaction and green certificate transaction and a step-type excitation type demand response mechanism are provided, so that two game parties play games by taking response price and response electric quantity as decision variables;
step 3, SEVPP and DRVPP are taken as two market main bodies to participate in bidding of daily preload demand, game modeling is carried out on the two market main bodies, a master-slave game method in a game theory is utilized, SEVPP is taken as a leader, DRVPP is taken as a follower, a master-slave game model is established by taking response price and response electric quantity as game quantities, and the model is solved by a distributed algorithm until an optimal casting quantity of Nash equilibrium solutions, namely SEVPP and DRVPP, is obtained;
Step 4, three kinds of load aggregators aggregated in DRVPP take the maximum benefit of the load aggregators as a target, take the total demand side load response electric quantity as a constraint, construct a non-cooperative game model by using a non-cooperative game method, and solve the model by using a heuristic intelligent algorithm until a Nash equilibrium solution is obtained, namely the optimal response electric quantity of each kind of load aggregators;
And 5, SEVPP, internally polymerizing resources including a photovoltaic cell unit, a wind power generation unit, a thermal power unit and an energy storage battery unit, establishing a cooperation alliance for benefit distribution to establish a cooperation game model for internal investors of the virtual power plant by utilizing an improved SHAPLEY value method in cooperation game, and solving the model by utilizing a particle swarm algorithm until a Nash equilibrium solution is obtained, and determining an optimal capacity allocation scheme required to bear various units.
Further, in step 1, the modeling of the daily preload requirement is determined as follows:
The predicted daily load predicted electric quantity needs of the electric power market release are balanced with the virtual power plant projected quantity and the demand response load aggregate response quantity:
PForecasting＝PVPP+PDR (1)，
Wherein: p Forecasting is the predicted power of daily load, P VPP is the bidding power of SEVPP, P DR is the participation response power of DRVPP, For wind turbine generator to participate in response to electric quantity,/>For the photovoltaic unit to participate in responding to the electric quantity,/>For the energy storage unit to participate in responding to the electric quantity,/>For the thermal power generating unit to participate in responding to the electric quantity,/>Responding to power for class A load aggregators,/>Responding to power for class B load aggregators,/>Responding to the power for class C load aggregators.
Further, since the various distributed resources belong to different investors, the modeling of the internal resources in SEVPP is as follows:
The photovoltaic cell utilizes the photoelectric effect to realize photoelectric conversion, so that the solar radiation intensity is a main factor influencing the output of the photovoltaic unit. A plurality of photovoltaic cells are connected in series and parallel to form a photovoltaic unit, the illumination intensity and the ambient temperature influence the output of the photovoltaic cell power generation. The output power equation is as follows:
in the method, in the process of the invention, For the output power of the photovoltaic unit at the time T, N PV is the number of integrated photovoltaic cells, S (T) is the actual solar radiation intensity at the time T, S ref is the reference solar radiation intensity, 1000W/m 2,Pst is generally taken as the output power of the photovoltaic array in the rated state, T q (T) is the actual working point temperature at the time T, and T st is the temperature in the rated state.
The output constraint of the photovoltaic unit is as follows:
in the method, in the process of the invention, The maximum installed capacity of the photovoltaic unit;
Different wind speed grades are set for the wind turbine generator according to the wind speed, including cut-in wind speed, cut-out wind speed, rated wind speed and the like; when the input wind speed is lower than the cut-in wind speed or higher than the cut-out wind speed, the controller controls the output of the fan to be 0 according to the received wind speed information; when the wind speed is between the cut-in wind speed and the rated wind speed, the controller controls positive correlation between the output of the fan and the wind speed; when the wind speed is between the rated wind speed and the cut-out wind speed, the controller sets the constant rated power output of the fan system; the fan output mathematical model is as follows:
in the method, in the process of the invention, The output power of the wind turbine generator set at the time t is v ci、vco、vrated, which is the cut-in wind speed and the cut-out wind speed respectively, the rated wind speed is v (t), the wind speed at the time t is v (t), the rated output power is P rated, and the corresponding system parameters are k 1、k2、k3;
the output constraint of the wind turbine is as follows:
in the method, in the process of the invention, The maximum installed capacity of the wind turbine generator is set;
The energy storage device can well inhibit randomness and fluctuation of new energy power generation, improves the utilization rate of renewable energy sources, and the State of Charge (SOC) represents the residual capacity of the energy of the storage battery, and is an important index of the storage battery. In the charging process, the value of the SOC is continuously increased; the discharging SOC value is continuously reduced, the relation between the SOC value and the charging and discharging power is very tight, and the charging process relation is shown in the following formula:
SOCSC(t)＝(1-ε)SOC(t-1)-PBS.cΔtηc/EBS (8)
The discharge process is as follows:
SOCSD(t)＝(1-ε)SOC(t-1)-PBS.dΔt/(EBSηd) (9)
In the formula, SOC SC(t),SOCSD (t), SOC (t-1) are respectively the charging process and discharging process at t moment and the residual electric energy of the storage battery at t-1 moment; epsilon is self-discharge rate and represents the electric quantity of the energy storage battery flowing away by the energy storage battery; e BS is the capacity of the battery; p BS.c and P BS.d respectively represent the charge and discharge power of the storage battery, and take negative values and positive values respectively; η c、ηd represents the charge and discharge efficiency of the stored energy, and is generally 0.6-1 in the charge and 0.8-1 in the discharge.
The thermal power generating unit mainly depends on the output of the micro gas turbine, and a certain amount of polluted gas can be released in the output process, so that the pollution degree is greatly reduced compared with that of the traditional coal-fired power generation, and the output power is adjustable. The output model is as follows:
in the method, in the process of the invention, For the output power of the thermal power generating unit at the time t, eta CN is the conversion efficiency of the controller, eta G is the power generation efficiency of the generator, P T is the output power of the working element, P C is the power consumed by the compressor, P FC is the power consumed by the fuel compressor, and P CF is the power consumed by the cooling fan.
The output constraint of the thermal power generating unit is as follows:
in the method, in the process of the invention, Is the minimum output power of the thermal power generating unit,/>The maximum output power of the thermal power generating unit is obtained.
Further modeling is done for the load aggregator in DRVPP:
Class a load aggregator (translatable uninterruptible load aggregator) aggregate resources including home washing machines and the like;
The B-class load aggregator (interruptible non-translatable load aggregator) aggregates resources including building air conditioners and the like;
Class C load aggregators (interruptible translatable load aggregators), aggregated resources including electric vehicles, and the like;
Thus, the demand response load aggregator response power is formulated as follows:
in the method, in the process of the invention, For the electric quantity of j load aggregators actually participating in demand response at the moment t, delta t is the number of hours of participating in demand response in one day;
The response constraints of the load aggregator are as follows:
in the method, in the process of the invention, And the maximum response power is the j-class load aggregate.
Further, in step 2, an "electricity-carbon-green" joint trading market mechanism is proposed, and SEVPP is modeled to participate in the carbon trading market and the green certificate trading market.
At present, the carbon trade market mostly carries out initial carbon quota release according to historical power generation, if the carbon emission generated by a power generator exceeds the carbon quota, the excess carbon quota needs to be purchased in the carbon trade market, otherwise, the excess carbon quota can be sold. Thus, the model of SEVPP conducting carbon transactions in the carbon trade market is as follows:
λC,sell≤λC,buy (17)
wherein U VPP,C is the benefit obtained by SEVPP participating in selling carbon quota in the carbon trade market, C VPP,C is the cost paid by SEVPP participating in purchasing carbon quota in the carbon trade market, lambda C,sell,λC,buy is the selling price and the purchasing price of carbon quota in the carbon trade market respectively, An initial carbon quota amount is SEVPP, which is issued according to the historical power generation.
The green certificate trade market is an important means for encouraging the improvement of the new energy installation quantity, and is a new idea for additionally improving the benefit of new energy generators after the new energy is complemented with hot tide. The core transaction mechanism is that the government performs green certificate issuance according to the online electric quantity generated by the new energy unit, each green certificate can be sold to the buyer of the green certificate transaction market, and the buyer of the green certificate transaction market is usually a traditional thermal power generator, so that the model of SEVPP participating in the green certificate transaction market is expressed as follows:
Where U VPP,G is SEVPP the benefit obtained by green certificate transaction, lambda G is the price of green certificate transaction, and the price interval is usually {0-800 }/MW.h.
Meanwhile, in order to improve the response enthusiasm of the load aggregator, a step-type excitation mechanism for accounting the demand response subsidy is provided, and different subsidy price discounts are determined according to the proportion of the response electric quantity of the load aggregator to the daily winning electric quantity. The larger the deviation between the user response electric quantity and the daily winning electric quantity is, the lower the step subsidy unit price is.
Wherein: DR j is the demand response subsidy price of the load aggregator j, lambda j is the excitation coefficient of the load aggregator j, and rho clr is the clearing price; p DR,j is the actual response power of the load aggregator j, and P exp,j is the expected response power of the load aggregator j; y i is the demand response excitation coefficient of the i-th step section, delta i-1 is the left boundary of the i-th step section, delta i is the right boundary of the i-th step section; delta 0 is the expected achievement proportion, delta k is the expected capping proportion, where k is the number of capping steps. Delta 0＝0.8,δk =1.2 is generally taken domestic.
The closer the ratio of the actual response electric quantity to the expected response electric quantity of the user at the demand side is to 100%, the larger the ladder excitation coefficient is, but in order to prevent the user from randomly participating in the response of the demand side, the capping ladder number is set, so that the non-intelligent behavior of the user is avoided. In order to improve the mechanism settlement efficiency, the step-type demand response excitation coefficient is designed according to 100% as central symmetry, and the actual response quantity of the user is the same as the discount coefficient of the positive deviation and the negative deviation of the expected power generation quantity, so that the step-type excitation coefficient meets the following constraint:
(yi-yi+1)(δi-100％)≥0 (21)
Where y i+1 is the demand response excitation coefficient for the i+1st step interval.
And finally, a master-slave game model is established, and compared with a classical game model, master-slave game is a dynamic process. That is, each participant in the classical game is uniformly located in the game, while the participant in the master-slave game is non-uniformly located, and the policy selection of the follower depends on the policy selection of the leader.
The master-slave gaming method comprises three elements of participants, a strategy set and utility functions:
The participants comprise a leader (SEVPP) and a follower (DRVPP), wherein the leader selects one price strategy from the virtual power plant strategy set to be released to the follower according to the daily preload predicted quantity released by the power market and the output information of each unit collected in the virtual power plant management platform.
The follower adjusts corresponding response electric quantity and electricity price according to the price strategy issued by the leader, and selects one response strategy from the response strategy set at the demand side to feed back to the leader.
The leader adjusts the price strategy again according to the response strategy fed back by the response load aggregator at the demand side and issues the price strategy to the follower, and the steps are repeated until Nash equilibrium is achieved.
Wherein SEVPP policy sets are represented as follows:
wherein: omega st,VPP is a policy set of virtual power plants in master-slave gaming, Bid price for virtual Power plant,/>Bid price minimum for virtual Power plant,/>The bidding price of the virtual power plant is the maximum value.
DRVPP policy sets are expressed as follows:
Wherein: omega st,DR is a set of demand side response strategies in the master-slave game, P DR,t is response electric quantity at time t DRVPP, At a minimum of DRVPP response power,/>Is DRVPP, the maximum value of the response electric quantity.
SEVPP benefits are obtained mainly by participation in the electricity market, the carbon trade market and the green certificate market, which, in order to maximize their objective functions, are expressed as follows:
Wherein: u VPP is a benefit of SEVPP days, The electricity price is the response electricity price at the moment t, P VPP,t is the bidding electricity quantity of the virtual power plant at the moment t, and the cost comprises the cost C VPP,C of purchasing carbon quota of the virtual power plant, the daily initial investment cost C VPP,inv and the daily operation and maintenance cost C VPP,mat.
The utility function for DR benefit maximization is as follows:
Wherein: u DR is DRVPP day benefit, and C j,loss is the subsidy cost of the implicit penalty portion of the different types of load syndicators participating in demand response.
According to the above formula, the Nash equilibrium solution is:
in the method, in the process of the invention, And P DR,t * is the optimal response electric quantity at the moment t for the optimal response electric price at the moment t.
When both sides can not improve the benefit by changing the response price and the response electric quantity, nash equilibrium is achieved. The SEVPP optimal bid electric quantity and DRVPP optimal response electric quantity can be obtained.
The master-slave game model adopts a distributed algorithm, and comprises the following specific steps:
(1) Raw data including, but not limited to, annual wind speed, light intensity, air temperature, load, and various equipment parameters are entered.
(2) The leader issues an initial price policyAnd feeding back a corresponding electric quantity response strategy P DR,t to the leader by the follower according to the price strategy issued by the leader.
(3) And carrying out iterative solution and dynamic game to realize independent optimization of each participant strategy.
(4) Judging whether the current solution meets the Nash equilibrium condition, if not, returning to the step 3 to continue iteration solution, and if so, considering the solutionIs Nash equilibrium solution.
After SEVPP optimal bid electric quantity and DRVPP optimal response electric quantity are obtained, entering capacity configuration of the next stage, and providing a load aggregator capacity distribution method based on non-cooperative game.
Because the demand response subsidy prices of the load aggregators are commonly determined by three types of load aggregators, the benefits of a single load aggregator are not only dependent on the bid amounts of the load aggregators participating in demand responses, but also can be influenced by strategies of other load aggregators, and based on the fact, the non-cooperative game model of the load aggregators is as follows:
(1) Participants: load aggregator of class A, B and C
(2) Policy set:
Wherein: omega nc,DR,j is the policy set of the non-cooperative game demand response load aggregator j, Bid amount of demand response load aggregator j for time t,/>Bid amount minimum for demand response load aggregator j,/>The bid amount for demand response load aggregator j is maximum.
(3) The utility function is as follows:
the load aggregators of various types can maximize their benefits by changing the scalar quantity, until any load is aggregated
The self benefit is not increased by changing the bidding strategy by the quotient, and then Nash equilibrium solution is obtained as follows:
in the method, in the process of the invention, The optimal scalar is calculated for the demand response load aggregator j at time t.
The non-cooperative game model is solved by utilizing a heuristic intelligent algorithm, and the method comprises the following steps:
(1): input system related parameters including but not limited to annual wind speed, illumination intensity, air temperature, load, various equipment parameters and the like, and simultaneously take the optimal response quantity of demand response in master-slave games as one of input conditions.
(2): And initializing a policy set of each load aggregator, and randomly initializing capacity configuration within a constraint range.
(3): And each load aggregator independently optimizes the response electric quantity, and each load aggregator optimizes and obtains the optimal capacity configuration scheme of the load aggregator according to the policy set of the previous round of other load aggregators and with the aim of maximizing the benefit per se.
(4): Judging whether a Nash equilibrium solution is obtained, if so, outputting the optimal response capacity of each load aggregator, and if not, returning to (3) to continue iterative optimization until the optimal capacity configuration scheme of each load aggregator is obtained.
Further, after the optimal bidding power is obtained SEVPP, the capacity configuration in the next stage is carried out, and a virtual power plant internal capacity distribution method based on a cooperative game improved SHAPLEY value method is provided.
The distribution scheme based on the Shapley value method is proved to meet the individual rationality and the overall rationality, and is a more reasonable method for solving the problem of multi-person cooperation benefit distribution; the Shapley method is based on the contribution of the participants in the federation, and the benefit allocated by the participant i from the federation ψ is denoted as x i (ψ):
Wherein: x i (ψ) is the benefit obtained after allocation of the federation member I, I (s\i) is the benefit after removal of member I by federation S, Is the probability of occurrence of the federation S, also known as the weighting factor.
The following drawbacks exist due to the classical SHAPLEY value method:
(1) The contribution rate is the only basis for members to participate in benefit distribution.
(2) Absolute benefit is the only criterion for measuring contribution.
(3) Shapley does not consider the risk factors and actual contributions of the participants.
Based on the method, the classical SHAPLEY value method is improved by introducing the risk coefficient and the contribution coefficient, and the method is further refined by combining the analytic hierarchy process and the entropy weight method.
(1) Risk coefficient index
Because the aggregated resources of the virtual power plants have great variability, the risk degrees faced by different investors in actual operation are different, and all participants default in SHAPLEY value method share risks, the SHAPLEY value method is improved by taking the introduced risk coefficient into consideration.
In order to accurately determine the risks born by each investor in the actual running of the virtual power plant, a comprehensive index system for comprehensively evaluating the actual bearing risk rate of different investors is mainly constructed from the aspects of dominant risks and implicit risks.
TABLE 1 investor risk assessment index
Based on the fact that the actual bearing risk factors of different investors are different, the benefit distribution model based on the improvement of the risk factors is obtained as follows:
wherein: r k is the risk actually taken by the kth investor, n is the total of n different investors, To comprehensively consider the risk coefficient after dominant and recessive risks,/>For the benefit of the kth investor before improvement based on risk assessment,/>Benefits of the kth investor obtained for the modified correction model based on risk assessment;
(2) Contribution coefficient
Because the aggregated resources of the virtual power plants have great variability and the actual contribution degrees made by different investors in actual operation are different, the SHAPLEY value method is improved by taking the introduced contribution coefficient into consideration.
In order to accurately make contributions of each investor in actual operation of the virtual power plant, the invention mainly constructs a comprehensive index system for comprehensively evaluating actual contribution rates of different investors from the two aspects of dominant contribution and recessive contribution, as shown in table 2:
Table 2 investor contribution assessment index
Based on the fact that the actual bearing contribution coefficients of different investors are different, the benefit distribution model improved based on the contribution coefficients is obtained as follows:
Wherein: d k is the contribution actually made by the kth investor, To comprehensively consider the contribution coefficient after explicit contribution and implicit contribution,/>To evaluate the benefit of the kth investor before improvement based on contribution; /(I)To evaluate the benefit of the kth investor based on the contribution to the improved correction model.
The improved model of the comprehensive risk coefficient and the contribution coefficient is as follows:
wherein: the weight coefficient values of eta and mu are combined with the actual situation, and the weight is assigned by adopting an analytic hierarchy process and an entropy weight process.
The model of the cooperative game in the virtual power plant is as follows:
participants: photovoltaic investors, wind power investors, energy storage investors and thermal power investors
Policy set:
Investment quotient benefit function:
in the method, in the process of the invention, Benefit of class j investors,/>And the bidding power of the j-class investors at the t moment is obtained.
Nash equalization solution:
in the method, in the process of the invention, For the optimal electric quantity of the participation response of the photovoltaic unit,/>For the optimal electric quantity of the wind turbine participating in response,/>For the optimal electric quantity of the energy storage unit participating in response,/>And the optimal electric quantity for the response of the thermal power generating unit is obtained.
The combined game model is solved by utilizing a particle swarm algorithm, and the method comprises the following specific steps:
(1): raw data including but not limited to annual wind speed, illumination intensity, air temperature, load, various equipment parameters and the like are input, and the optimal scalar of the virtual power plant in the master-slave game is also taken as one of input conditions.
(2): And setting a configuration capacity initial value, and randomly assigning values among various investors in the virtual power plant.
(3): And carrying out iterative solution, and adopting a strategy of eliminating the inferior solution to continuously and iteratively eliminate the strict disfigurement strategy.
(4): Judging whether the current solution meets Nash equilibrium conditions, if not, returning to the step (3) to continue iteration solution, and if so, considering the solution as Nash equilibrium solution, and outputting the optimal capacity configuration scheme bidding by various investors in the virtual power plant.
The beneficial effects of the invention are as follows: the method effectively increases the benefits of virtual power plant investors and load aggregators by using a master-slave game mode, considers an 'electricity-carbon-green' combined market trading mechanism, further promotes the consumption of new energy power generation, reduces the wind-discarding light-discarding rate, ensures the power generation task, reduces the carbon dioxide emission, and lays a solid foundation for realizing a double-carbon target; meanwhile, the method of the invention utilizes SHAPLEY method in improved cooperative game to make the benefit distribution mode of different investors in the virtual power plant more fair and reasonable, and utilizes non-cooperative game to make the response of different types of load aggregation Shang Duijia grid signals more accurate.
Drawings
FIG. 1 is a block diagram of an "electricity-carbon-green" joint transaction mechanism;
FIG. 2 is a schematic diagram of a typical daily load profile;
FIG. 3 is a virtual power plant and load aggregator iterative settlement process;
FIG. 4 is a schematic diagram showing the effect of carbon trade prices on virtual power plant capacity allocation and waste-to-wind and waste-to-light rates;
FIG. 5 is a schematic flow chart of the method of the present invention.
Detailed Description
In order that the invention may be more readily understood, a more particular description of the invention will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
As shown in fig. 5, the method for optimizing the capacity configuration of the virtual power plant considering the multi-transaction market according to the invention comprises the following steps:
S1, as shown in FIG. 1, dividing the virtual power plant into an electricity selling type virtual power plant SEVPP and a demand response type virtual power plant DRVPP according to the functionality of the virtual power plant, and sequentially modeling output power of a wind power investor, a photovoltaic investor, a thermal power investor, an energy storage investor and a load aggregator contained in DRVPP; wherein, the load aggregator in DRVPP is divided into a class A load aggregator, a class B load aggregator and a class C load aggregator, wherein the class A load aggregator can translate the non-interruptible load aggregator, the class B load aggregator can interrupt the non-translatable load aggregator, and the class C load aggregator can interrupt the translatable load aggregator according to different response time and response depth;
S2, according to the established output model, a joint transaction mechanism considering electric market transaction, carbon transaction and green certificate transaction and a step-type excitation type demand response mechanism are provided, so that a game double party can game by taking response price and response electric quantity as decision variables;
S3, SEVPP and DRVPP are taken as two market main bodies to participate in bidding of daily preload demand, game modeling is carried out on the two market main bodies, a master-slave game method in a game theory is utilized, SEVPP is taken as a leader, DRVPP is taken as a follower, a master-slave game model is established by taking response price and response electric quantity as game quantities, and the model is solved by a distributed algorithm until an optimal casting quantity of Nash equilibrium solutions, namely SEVPP and DRVPP, is obtained;
S4, three types of load aggregators aggregated in DRVPP take the maximization of the benefits of the load aggregators as targets, the total demand side load response electric quantity as constraint, a non-cooperative game model is built by using a non-cooperative game method, and the model is solved by using a heuristic intelligent algorithm until a Nash equilibrium solution is obtained, namely the optimal response electric quantity of each type of load aggregators;
S5, SEVPP, internally polymerizing resources including photovoltaic battery units, wind power generation units, thermal power units and energy storage battery units, establishing a cooperative alliance for benefit distribution to establish a cooperative game model for internal investors of the virtual power plant by utilizing an improved SHAPLEY value method in the cooperative game, and solving the model by utilizing a particle swarm algorithm until a Nash equilibrium solution is obtained, so that the optimal capacity allocation scheme required by each unit can be determined.
Analysis is performed by taking the configuration of the construction capacity of the virtual power plant in a certain area as an example, and specific system information parameters are shown in the following tables 3-5:
TABLE 3 New energy Unit System parameters
TABLE 4 energy storage unit system parameters
TABLE 5 thermal power generating unit system parameters
A typical daily load curve for this region is shown in fig. 2.
In order to verify the feasibility of the technical scheme, the following four scene cases are described:
Scene 1: virtual power plant power supply benefit distribution and volume fixing of carbon market transaction, green certificate transaction and demand response are not considered;
scene 2: considering carbon market transaction and green certificate transaction, and not considering virtual power plant power supply benefit distribution and volume fixing of demand response;
Scene 3: considering demand response, and not considering virtual power plant power supply benefit distribution and volume fixing of carbon market transaction and green certificate transaction;
Scene 4: and meanwhile, virtual power plant power supply benefit distribution and volume fixing of carbon market transaction, green certificate transaction and demand response are considered.
Table 6: comparison of planning results of various scenes
As can be seen from table 6, the virtual power plant capacity plan after considering the carbon trade, green license trade and demand response mechanisms varies mainly in the following ways.
(1) The thermal power generating unit and the energy storage unit are reduced in capacity, because the thermal power generating unit is the only controllable power generation resource in the virtual power plant before carbon transaction, green card transaction and demand response mechanisms are not considered, the thermal power generating unit occupies an important position in the virtual power plant due to the characteristic of controllable stability, and the thermal power generating unit can alleviate the situation to a certain extent due to frequency fluctuation caused by the uncertainty of new energy power generation. However, after the carbon transaction, the green card transaction and the demand response mechanism are considered, a large number of thermal power units can cause additional cost to be paid by a virtual power plant to purchase a sufficient amount of carbon quota, and the demand response mechanism further optimizes a load curve, so that peak clipping and valley filling on a demand side are realized, and a large number of thermal power units and energy storage units are not needed for standby.
(2) On one hand, more new energy units can bring more carbon quota and sell more green certificates for the virtual power plant, and the benefit of the virtual power plant is greatly improved due to the extra income. On the other hand, the characteristic of wind-solar power generation complementation can be further improved under the help of a demand response mechanism, the wind-discarding light-discarding rate is further reduced, and the new energy power generation cost is far lower than that of the traditional thermal power generation, so that more new energy units are favored to be configured in the virtual power plant after the factors are considered.
Comparing scene 1 with scene 4, it can be seen that, after considering carbon transaction, green card transaction and demand response mechanism, the total installed capacity of the virtual power plant is reduced by 40MW under the condition that the load demand can be completed, the benefit is increased by 1552.4 ten thousand yuan, the actual carbon emission is reduced by 68.8 tons, and the potential of low-carbon emission reduction and new energy consumption of the virtual power plant is further explored.
The scenario 4 is selected for simulation analysis, and as can be seen from fig. 3, the virtual power plant benefit curve is iterated to 13 times to obtain an optimal value, and the load aggregator is iterated to 73 times to obtain the optimal value. And the master-slave game ends, and the virtual power plant and the load aggregator cannot change the benefits of the virtual power plant and the load aggregator by changing the price quotation and the electric quantity of the virtual power plant and the load aggregator, so that the Nash equilibrium solution is obtained.
The thermal power generating unit is used as the schedulable unit with the largest power generation side occupation ratio and is influenced by carbon transaction price. And setting the carbon trade price to be 0-300 yuan/t, solving the power supply planning model provided herein, and the planning result is shown in figure 4.
It can be seen that when the carbon trade price is 0 yuan/t, the planning capacity of the thermal power unit occupies a relatively high proportion, and the virtual power plant still takes the relatively stable and controllable thermal power output as a main part due to the fact that the carbon trade price is too low. And as the price of carbon trade increases, the planning capacity of the thermal power generating unit gradually decreases, and the planning capacity of the wind power generating unit and the energy storage unit and the demand of leading a load aggregator to participate in DR gradually increase. When the carbon trade price rises to 300 yuan/t, the capacity of the new energy generator set is greatly improved by the virtual power plant in order to improve the overall benefit, so that the wind and light discarding rate is improved, and more energy storage is required to be configured to meet the uncertainty of new energy power generation.
The foregoing is merely a preferred embodiment of the present invention, and is not intended to limit the present invention, and all equivalent variations using the description and drawings of the present invention are within the scope of the present invention.
Claims (8)
1. The virtual power plant capacity configuration optimization method considering the multi-transaction market is characterized by comprising the following steps of:
step 1, dividing the virtual power plant into an electricity selling type virtual power plant SEVPP and a demand response type virtual power plant DRVPP according to the functionality of the virtual power plant, and sequentially modeling output power of a wind power investor, a photovoltaic investor, a thermal power investor, an energy storage investor and a load aggregator contained in DRVPP; wherein, the load aggregators in DRVPP are divided into a class A load aggregator, a class B load aggregator and a class C load aggregator, wherein the class A load aggregator can translate the load aggregators without interruption, the class B load aggregator can interrupt the load aggregators without translation, and the class C load aggregator can interrupt the load aggregators with translation according to different response time and response depth;
Step 2, according to the established output model, a combined transaction mechanism considering electric market transaction, carbon transaction and green certificate transaction and a step-type excitation type demand response mechanism are provided, so that two game parties play games by taking response price and response electric quantity as decision variables;
step 3, SEVPP and DRVPP are taken as two market main bodies to participate in bidding of daily preload demand, game modeling is carried out on the two market main bodies, a master-slave game method in a game theory is utilized, SEVPP is taken as a leader, DRVPP is taken as a follower, a master-slave game model is established by taking response price and response electric quantity as game quantities, and the model is solved by a distributed algorithm until an optimal casting quantity of Nash equilibrium solutions, namely SEVPP and DRVPP, is obtained;
Step 4, three kinds of load aggregators aggregated in DRVPP take the maximum benefit of the load aggregators as a target, take the total demand side load response electric quantity as a constraint, construct a non-cooperative game model by using a non-cooperative game method, and solve the model by using a heuristic intelligent algorithm until a Nash equilibrium solution is obtained, namely the optimal response electric quantity of each kind of load aggregators;
And 5, SEVPP, internally polymerizing resources including a photovoltaic cell unit, a wind power generation unit, a thermal power unit and an energy storage battery unit, establishing a cooperation alliance for benefit distribution to establish a cooperation game model for internal investors of the virtual power plant by utilizing an improved SHAPLEY value method in cooperation game, and solving the model by utilizing a particle swarm algorithm until a Nash equilibrium solution is obtained, and determining an optimal capacity allocation scheme required to bear various units.
2. The method for optimizing capacity configuration of a virtual power plant in consideration of a multi-market according to claim 1, wherein in step 1, the daily load demand is determined to be modeled as follows:
The predicted daily load predicted electric quantity needs of the electric power market release are balanced with the virtual power plant projected quantity and the demand response load aggregate response quantity:
PForecasting＝PVPP+PDR (1)，
Wherein: p Forecasting is the predicted power of daily load, P VPP is the bidding power of SEVPP, P DR is the participation response power of DRVPP, For wind turbine generator to participate in response to electric quantity,/>For the photovoltaic unit to participate in responding to the electric quantity,/>For the energy storage unit to participate in responding to the electric quantity,/>For the thermal power generating unit to participate in responding to the electric quantity,/>Responding to power for class A load aggregators,/>Responding to power for class B load aggregators,/>Responding to the power for class C load aggregators.
3. The method for optimizing capacity allocation of a virtual power plant in consideration of multiple markets according to claim 2, wherein since various distributed resources belong to different investors, the modeling of internal resources in SEVPP is as follows:
the photovoltaic cells are connected in series and parallel to form a photovoltaic unit, and the output power equation is as follows:
in the method, in the process of the invention, For the output power of the photovoltaic unit at the time T, N PV is the number of integrated photovoltaic cells, S (T) is the actual solar radiation intensity at the time T, S ref is the reference solar radiation intensity, P st is the output power of the photovoltaic array in the rated state, T q (T) is the actual working point temperature at the time T, and T st is the temperature in the rated state;
The output constraint of the photovoltaic unit is as follows:
in the method, in the process of the invention, The maximum installed capacity of the photovoltaic unit;
The mathematical model of the output power of the wind turbine is as follows:
in the method, in the process of the invention, The output power of the wind turbine generator set at the moment t is v ci、vco、vrated, the cut-in wind speed and the cut-out wind speed are respectively the rated wind speed, v (t) is the wind speed at the moment t, P rated is the rated output power, and k 1、k2、k3 is the corresponding system parameter;
the output constraint of the wind turbine is as follows:
in the method, in the process of the invention, The maximum installed capacity of the wind turbine generator is set;
The state of charge (SOC) represents the residual capacity of the energy of the storage battery, and the value of the SOC is continuously increased in the charging process; the discharge SOC value is continuously reduced; the charging process relationship is shown as follows:
SOCSC(t)＝(1-ε)SOC(t-1)-PBS.cΔtηc/EBS (8)，
The discharge process is as follows:
SOCSD(t)＝(1-ε)SOC(t-1)-PBS.dΔt/(EBSηd) (9)，
In the formula, SOC SC(t),SOCSD (t), SOC (t-1) is the charging process at time t, the discharging process and the residual electric energy of the storage battery at time t-1 respectively; epsilon is self-discharge rate and represents the electric quantity of the energy storage battery flowing away by the energy storage battery; e BS is the capacity of the battery; p BS.c and P BS.d respectively represent the charge and discharge power of the storage battery, and take negative values and positive values respectively; η c、ηd represents the charge and discharge efficiency of the stored energy;
The thermal power generating unit output model is as follows:
in the method, in the process of the invention, For the output power of the thermal power generating unit at the time t, eta CN is the conversion efficiency of the controller, eta G is the power generation efficiency of the generator, P T is the output power of the working element, P C is the power consumed by the compressor, P FC is the power consumed by the fuel compressor, and P CF is the power consumed by the cooling fan;
The output constraint of the thermal power generating unit is as follows:
in the method, in the process of the invention, Is the minimum output power of the thermal power generating unit,/>The maximum output power of the thermal power generating unit is obtained.
4. A virtual power plant capacity allocation optimization method considering multi-trade markets according to claim 3, wherein the load aggregator in DRVPP is modeled:
the response power of the demand response load aggregator is as follows:
in the method, in the process of the invention, For the electric quantity of j load aggregators actually participating in demand response at the moment t, delta t is the number of hours of participating in demand response in one day;
The response constraints of the load aggregator are as follows:
in the method, in the process of the invention, And the maximum response power is the j-class load aggregate.
5. The method for optimizing virtual power plant capacity configuration in consideration of multiple trading markets according to claim 4, wherein a joint trading mechanism in consideration of electric power market trading, carbon trading and green certificate trading is provided, and SEVPP participating in the carbon trading market and the green certificate trading market are modeled;
SEVPP a model of participation in the carbon trade market is shown below:
λC,sell≤λC,buy (17)，
Wherein U VPP,C is the benefit of SEVPP participating in the carbon trade market to sell carbon quota, C VPP,C is the cost of SEVPP participating in the carbon trade market to buy carbon quota, lambda C,sell,λC,buy is the selling price and the purchasing price of carbon quota in the carbon trade market respectively, An initial carbon quota amount issued by SEVPP according to the historical power generation amount;
SEVPP a model of participation in the green certificate trade market is represented as follows:
Wherein U VPP,G is the benefit of SEVPP to participate in the green certificate trade market, and lambda G is the clearing price of the green certificate trade;
providing a stepped incentive mechanism for accounting the demand response subsidy, and determining different subsidy price discounts according to the proportion of the response electric quantity of the load aggregator to the daily bid electric quantity;
DRj＝λjρclrPDR,j (19)，
Wherein: DR j is the demand response subsidy price of the load aggregator j, lambda j is the excitation coefficient of the load aggregator j, and rho clr is the clearing price; p DR,j is the actual response power of the load aggregator j, and P exp,j is the expected response power of the load aggregator j; y i is the demand response excitation coefficient of the i-th step section, delta i-1 is the left boundary of the i-th step section, delta i is the right boundary of the i-th step section; delta 0 is the expected up-to-standard ratio, delta k is the expected capping ratio, where k is the capping step number;
The step excitation coefficient satisfies the following constraint:
(yi-yi+1)(δi-100％)≥0 (21)，
Where y i+1 is the demand response excitation coefficient for the i+1st step interval.
6. The method for optimizing capacity configuration of a virtual power plant taking into account multi-transaction markets according to claim 5, wherein in step 3, a master-slave gaming model is established; the master-slave gaming method comprises three elements of participants, a strategy set and utility functions:
The participants comprise a leader SEVPP and a follower DRVPP, wherein the leader selects one price strategy from the virtual power plant strategy set to be released to the follower according to the daily preload pre-measurement released by the power market and the output information of each unit collected in the virtual power plant management platform; the follower adjusts corresponding response electric quantity and electricity price according to the price strategy issued by the leader, and selects one response strategy from the response strategy set at the demand side to feed back to the leader; the leader adjusts the price strategy again according to the response strategy fed back by the demand side response load aggregator and issues the price strategy to the follower, and the steps are repeated until Nash equilibrium is achieved;
Wherein SEVPP policy sets are represented as follows:
wherein: omega st,VPP is a policy set of virtual power plants in master-slave gaming, Bid price for virtual Power plant,/>Bid price minimum for virtual Power plant,/>The bidding price of the virtual power plant is the maximum;
DRVPP policy sets are expressed as follows:
Wherein: omega st,DR is a set of demand side response strategies in the master-slave game, P DR,t is response electric quantity at time t DRVPP, At a minimum of DRVPP response power,/>A response power maximum of DRVPP;
SEVPP utility functions are expressed as follows:
Wherein: u VPP is a benefit of SEVPP days, The electricity price is the response electricity price at the moment t, P VPP,t is the bidding electricity quantity of the virtual power plant at the moment t, and the cost comprises the cost C VPP,C of purchasing carbon quota of the virtual power plant, the daily initial investment cost C VPP,inv and the daily operation and maintenance cost C VPP,mat;
DRVPP utility functions are expressed as follows:
Wherein: u DR is DRVPP days benefit, C j,loss is subsidy cost of the hidden loss part caused by participation of different types of load aggregators in demand response;
according to the above formula, the Nash equilibrium solution is:
in the method, in the process of the invention, The optimal response electricity price at the moment t is obtained, and P DR,t * is the optimal response electric quantity at the moment t;
When both sides can not improve the self utility function by changing the response price and the response electric quantity, nash equilibrium is achieved, and then SEVPP optimal bidding electric quantity and DRVPP optimal response electric quantity are obtained.
7. The method for optimizing capacity configuration of a virtual power plant in consideration of multiple markets according to claim 6, wherein in step 4, the non-cooperative game model of the load aggregator is as follows:
(1) Participants: load aggregator of class A, B and C
(2) Policy set:
Wherein: omega nc,DR,j is the policy set of the non-cooperative game demand response load aggregator j, Bid amount of demand response load aggregator j for time t,/>Bid amount minimum for demand response load aggregator j,/>Bid amount maximum for demand response load aggregator j;
(3) The utility function is as follows:
by changing the bid amount, each load aggregator maximizes the benefit of the load aggregator until any load aggregator changes the bid strategy without increasing the utility function of the load aggregator, and then a Nash equilibrium solution is obtained, which is:
in the method, in the process of the invention, The optimal scalar is calculated for the demand response load aggregator j at time t.
8. The method for optimizing capacity configuration of virtual power plants in consideration of multi-trading market according to claim 7, wherein in step 5, the allocation is performed based on the contribution of the participants in the federation, and the benefit allocated by the participant i from the federation ψ is denoted as x i (ψ):
Wherein: x i (ψ) is the benefit obtained after allocation of the federation member I, I (s\i) is the benefit after removal of member I by federation S, Is the probability of occurrence of a federation S, also known as a weighting factor;
(1) Based on the fact that the actual bearing risk factors of different investors are different, the benefit distribution model based on the improvement of the risk factors is obtained as follows:
wherein: r k is the risk actually taken by the kth investor, n is the total of n different investors, To comprehensively consider the risk coefficient after dominant and recessive risks,/>For the benefit of the kth investor before improvement based on risk assessment,/>Benefits of the kth investor obtained for the modified correction model based on risk assessment;
(2) Based on the fact that the actual bearing contribution coefficients of different investors are different, the benefit distribution model improved based on the contribution coefficients is obtained as follows:
Wherein: d k is the contribution actually made by the kth investor, To comprehensively consider the contribution coefficient after explicit contribution and implicit contribution,/>To evaluate the benefit of the kth investor before improvement based on contribution; Benefits of the kth investor obtained for evaluating the modified correction model based on contribution;
(3) The improved model of the comprehensive risk coefficient and the contribution coefficient is as follows:
Wherein: the weight coefficient values of eta and mu are combined with the actual situation, and the weight is assigned by adopting a hierarchical analysis method and an entropy weight method;
the model of the cooperative game in the virtual power plant is as follows:
participants: photovoltaic investors, wind power investors, energy storage investors and thermal power investors
Policy set:
investment quotient utility function:
in the method, in the process of the invention, Benefit of class j investors,/>The bidding electric quantity of the j-class investors at the t moment is calculated;
Nash equalization solution:
in the method, in the process of the invention, For the optimal electric quantity of the participation response of the photovoltaic unit,/>The optimal electric quantity for the wind turbine to participate in the response is obtained,For the optimal electric quantity of the energy storage unit participating in response,/>And the optimal electric quantity for the response of the thermal power generating unit is obtained.
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