CN116228461A - P2P transaction-based multi-virtual power plant random optimization scheduling method - Google Patents
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Abstract
The invention discloses a P2P transaction-based multi-virtual power plant random optimization scheduling method, which belongs to the field of power system energy optimization scheduling and comprises the steps of establishing a virtual power plant aggregation model comprising photovoltaics, energy storage, a central air conditioning system, a gas turbine and user loads; establishing a random optimization scheduling model considering photovoltaic uncertainty; adopting a Shapley value method to distribute the cooperation surplus of the multiple virtual power plants; and finally solving the optimal scheduling model by adopting GAMS software and outputting a result. The invention is beneficial to the active participation of the P2P transaction by the multiple virtual power plants, and can promote the overall operation income more than the traditional centralized transaction, thereby reasonably carrying out the P2P transaction and promoting the economic benefit and the environmental protection benefit of the arrangement.
Description
Technical Field
The invention belongs to the technical field of power, relates to the aspect of power system power supply optimal scheduling, and discloses a multi-virtual power plant random optimal scheduling method based on P2P transaction.
Background
With the problems of large-scale access of distributed power sources, lack of timely information interaction among the distributed power sources and difficulty in scheduling among the distributed power sources, virtual power plants (virtual power plant, VPP) aggregate units such as Photovoltaic (PV), energy Storage (ES), central air conditioner (central air-conditioning system) and flexible load through advanced control, metering, communication and other technologies, and realize coordinated and optimized operation of a plurality of distributed energy sources through a higher level.
At present, the forms of electric energy sharing and distributed transaction between virtual power plants can be basically divided into the following two types: (1) Energy sharing and transaction mechanisms based on cooperative game mode; (2) Energy sharing and transaction mechanisms based on non-cooperative gaming. In the former, the main form is that each virtual power plant integrally participates in market trade in a alliance form, and the internal surplus electric energy is preferentially consumed in an energy sharing form. Domestic scholars put forward a cooperative game model based on single-to-many and multiple-to-multiple transaction conditions, and adopt a nucleolus method to realize cooperative surplus redistribution, and improve the nucleolus method by methods of clustering, calculating excess and the like, so that the method is still applicable to huge virtual power plant groups. Still scholars propose a day-ahead optimal scheduling strategy for building groups based on energy sharing, and redistribute the overall benefits after scheduling is completed by adopting a shape method. Unlike cooperative gaming, non-cooperative gaming-based power trading schemes focus on balancing under various principal interest conflicts, and can be categorized into platform-dominant and participant-dominant based trading schemes. At present, a market transaction model based on platform leading exists in China, wherein a Stackelberg game relationship is formed between a leader market operator and a follower, electric energy sharing is guided through electricity price, the near consumption of clean energy is promoted, and the transmission loss in the energy transaction process is reduced. The domestic scholars research a non-cooperative game model based on participant leading, establish a non-cooperative competition relationship among a plurality of sellers, and obtain an optimal bidding strategy of each electricity seller based on Nash equilibrium solution, wherein the game relationship comprises a master-slave game relationship between an agent and a virtual power plant and an evolution game relationship among a plurality of virtual power plants.
Disclosure of Invention
The invention provides a multi-virtual power plant random optimization scheduling method based on P2P transaction, which can better provide an optimal strategy scheme for a decision maker.
The invention particularly relates to a multi-virtual power plant random optimization scheduling method based on P2P transaction, which comprises the following steps:
and step 4, solving the optimal scheduling model by adopting GAMS software and outputting a result.
The step 1 specifically comprises the following steps:
user load model:
the user consumes certain electric energy to bring certain profit, so that the profit generated by consuming energy by the user is:
U i (d i,t )＝k i,t ln(1+d i,t ) (1)
wherein k is i,t Representing a combination of a weight coefficient and a preference parameter, reflecting the weight of the electricity utilization effect of the user and the energy consumption preference of the user, d i,t Representing the energy consumed by the user.
When there is a shortage between the load required by the user and the load that can be provided, the user will tend to be dissatisfied, so the dissatisfaction of the user is described by the cost of dissatisfaction of the user, as shown in the following formula:
wherein beta is i Representing the priority coefficient of each virtual power plant, and beta i > 0. With a larger beta i Meaning that it is more concerned about comfort than economy. Omega t Is a weight factor for t-period dissatisfaction costs.
Representing the ideal electricity demand of the user in the virtual power plant i during period t. As can be seen from the above, when the actual load is equal to the desired electricity demand, i.e.
When (I)>
The value of (2) is 0; when->
When it is indicated that the user load demand is not met, and as d i,t Is reduced (1)>
Is growing faster and faster; when->
When it is indicated that the load demand has been met, and as the actual electricity load d is used i,t Increase of->
The speed of the decrease of (1) starts to slow down, i.e. the user satisfaction reaches saturation.
And (3) energy storage model:
the battery capacity in the operation of the energy storage system may be expressed as:
in the method, in the process of the invention,
representing the state of charge of the stored energy, u c 、u d Charge and discharge power respectively representing energy storage capacity, +.>
And->
Respectively representing the maximum charge-discharge efficiency of the stored energy.
Photovoltaic model:
the predicted output power of the photovoltaic array, based on the solar radiation intensity, is:
wherein eta is ct For the energy conversion efficiency of the photovoltaic array,
is the total output of the photovoltaic unit at the moment t, S CA Is the photovoltaic array area. G t The predicted solar radiation intensity at a certain point in time t is indicated.
Central air conditioning model:
the invention adopts a modeling method based on cold and hot load calculation to describe a heat storage model of a public building, for the public building, a central air conditioning unit provides refrigerating capacity for the indoor during working, and meanwhile, the indoor and outdoor heat sources of the building have the heat release function and the heat storage function of the inner wall of the building so that the room temperature is continuously increased, and the specific expression is as follows:
in which Q i,s,t Total cooling capacity provided for a central air conditioning system;
the power consumption of the refrigerating unit in the refrigerating process and the power consumption of the cold storage tank in the cold releasing process are respectively shown. />
The indoor temperature is represented, alpha, beta and gamma are respectively characteristic parameters of the intelligent building and an air conditioning system thereof, Δt is the time interval, < >>
Is the output of the central air conditioning system, < >>
To the capacity of the cold accumulation tank at the moment t, eta i,st 、η i,re The cold accumulation/release efficiency of the cold accumulation groove is respectively.
The step 2 specifically comprises the following steps:
the invention aims to establish a random optimization scheduling model considering uncertainty of the photovoltaic output, wherein randomness is expressed as uncertainty of the photovoltaic output, so that a scene s is adopted to represent actual output of the photovoltaic, and the maximum social benefit generated by virtual power plant operation is established. Thus, the objective function is:
wherein u is P2P The objective function of the multiple virtual power plants when participating in the P2P transaction and the objective function of the multiple virtual power plants when not participating in the P2P transaction. Wherein N is i (i=1, 2, 3) represents the number of virtual power plants, s represents the photovoltaic scene, ρ s The probability of representing the scene s is indicated,
represents profit of the user from consuming energy, < +.>
Representing costs incurred by a virtual power plant in trade with a superordinate grid,/->
Representing the cost of energy storage operation>
Representing user dissatisfaction costs, +.>
Representing the costs incurred by the operation of the gas turbine, +.>
Representing the cost of the photovoltaic array generation. The specific calculation formula of each part is as follows:
1) Profit generated by consuming energy by users
U i (d i,t )＝k i,t ln(1+d i,t ) (10)
Wherein k is i,t Representing a combination of a weight coefficient and a preference parameter, reflecting the weight of the electricity utilization effect of the user and the energy consumption preference of the user, d i,t Representing the energy consumed by the user.
2) Cost of electric market trade
In the method, in the process of the invention,
representing the price of electricity purchase->
Representing the price of electricity selling, ->
Representing the electricity purchase amount @ and @ of>
Indicating the sales power.
3) Energy storage charge and discharge costs
In the method, in the process of the invention,
and->
Respectively charging and discharging power of energy storage, +.>
And->
The cost coefficients of the charging and discharging of the energy storage are respectively.
4) User dissatisfaction cost
Wherein beta is i Representing the priority coefficient of each virtual power plant, and beta i And is more than or equal to 0. With a larger beta i Meaning that it is more concerned about comfort than economy. Omega t Is a weight factor for t-period dissatisfaction costs.
Representing the ideal electricity demand of the user in the virtual power plant i during period t. As can be seen from the above, when the actual load is equal to the desired electricity demand, i.e.
When (I)>
The value of (2) is 0; when->
When it is indicated that the user load demand is not met, and as d i,t Is reduced (1)>
And grow faster and faster. When->
When it is indicated that the load demand has been met, and as the actual electricity load d is used i,t Increase of->
The speed of the decrease of (1) starts to slow down, i.e. the user satisfaction reaches saturation.
5) Cost of operation of gas turbine
Wherein a is MT 、b MT 、c MT A correlation coefficient representing the power generation cost of the gas turbine,
indicating the gas turbine output.
6) Cost of photovoltaic output
The PV aggregated inside the virtual power plant is mainly a PV panel or roof PV, and its basic operation maintenance cost is:
in the method, in the process of the invention,
the power generated by the PV in the producer i at the moment t; />
Maintenance costs for PV unit power generation in producer i.
7) P2P transaction cost
Wherein, c ij Representing the trading coefficient between virtual power plant i and virtual power plant j.
The constraint conditions related to the invention are as follows:
1) Dissatisfaction function constraint
From the dissatisfaction function, the dissatisfaction function
Should be relative to d i,t Is a monotonically decreasing function of (a). But->
At this time, the dissatisfaction function begins to increment during this period. Thus (S)>
Should be less than or equal to 2. Furthermore, in order to guarantee the basic and comfort requirements of the electrical load, the actual electrical load d i,t Should not be less than the ideal load +.>
Half of (a) is provided. Thus (S)>
The values of (2) must satisfy the following relationship:
wherein d i,t Representing the load of the user and,
indicating the desired power demand by the user.
2) Energy storage constraint
In the method, in the process of the invention,
indicating the state of charge of the stored energy->
Respectively representing upper and lower limits of the energy storage capacity;
respectively representing the maximum charge and discharge power of the stored energy, < + >>
Respectively representing the charge and discharge power of the stored energy at the time t.
3) Gas turbine constraints
Where ramp represents the rate of climb of the gas turbine,
representing maximum and minimum output of the gas turbine, respectively,/->
The output of the gas turbine at time t is shown.
4) Central air conditioning load constraint
In the method, in the process of the invention,
respectively represents the power consumption of the refrigerating machine in the refrigerating process and the power consumption of the cold accumulation tank in the cold accumulation and release process, Q i ch,max Represents the maximum refrigerating capacity of the refrigerating unit, Q i st,max 、Q i,remax The maximum cold accumulation amount and the cold release amount of the cold accumulation groove are respectively +.>
The capacity at the time t of the cold accumulation tank and the upper limit thereof are respectively set.
5) Power balance constraint
In the method, in the process of the invention,
representing photovoltaic output, ">
Indicating gas turbine output,/->
Representing the purchase power, < >>
Represents the energy storage discharge power, P i,j,t Power representing P2P transactions, +.>
Indicating the power of electricity selling, < >>
Representing the stored charge power>
Indicating central air conditioning power,/->
Representing the load power.
The step 3 specifically comprises the following steps:
in fact, for the stability of cooperation among multiple virtual power plants, it is necessary to reasonably distribute the cooperation residuals of the multiple virtual power plants by using a certain profit sharing means. The invention adopts a Shapley value method as a residual distribution method for cooperation of multiple virtual power plants. The Shapley method has two main characteristics: 1. emphasis on fairness, i.e., the more virtual power plants that cooperate to produce utility contributions should distribute more revenue; 2. the importance of certain virtual power plants to the collaborative formation may be reflected. Without a particular virtual power plant, it is difficult to form a collaboration. Thus, it can help build up a collaborative virtual power plant that is allocated more revenue.
The benefit of the centralized transaction among the virtual power plants is shown as a formula (30), under the condition of sharing the energy of multiple virtual power plants, the overall operation benefit is increased, the excessive benefit becomes the emerging benefit, the actual benefit after the i-th virtual power plant is cooperated is shown as a formula (32),
f 0 ＝f 1 0 +f 2 0 +···+f i 0 (30)
f＝f 1 +f 2 +···+f i (31)
wherein f 0 Representing a virtual power plant total prior to participation in a P2P transactionIncome (E); f represents the total income of the virtual power plant after participating in P2P transaction; f (f) i 0 Representing the revenue of the virtual power plant i before participating in the P2P transaction; f (f) i Representing the benefits of the virtual power plant i for Shapley profit allocation after participation in the P2P transaction; s represents the number of virtual power plants in the set S; n represents the number of total virtual power plants; f (f) S\{i} Represents federated profits without virtual power plant i, (|S| -1) |! (n- |S|) is-! Representing the weighting factors.
The step 4 specifically comprises the following steps:
and solving an optimal scheduling model by adopting GAMS software, outputting a result, analyzing the advantage of P2P transaction relative to centralized transaction, and distributing profits by adopting a Shapley value method according to the contribution degree of different virtual power plants to P2P.
Drawings
FIG. 1 is a basic flow chart of the method of the present invention;
FIG. 2 is a sales of electricity to an upper grid by the virtual power plant 1;
FIG. 3 is a diagram illustrating the power trade of a virtual power plant with a superior grid under a centralized trade and a P2P trade;
FIG. 4 is a diagram illustrating power interactions between virtual power plants;
FIG. 5 is a diagram of the stored energy power situation in a virtual power plant;
FIG. 6 is a graph showing the energy of each unit in the virtual power plant 1 under a centralized transaction;
FIG. 7 is a graph showing the energy of each unit in the virtual power plant 1 under P2P transactions;
FIG. 8 is a diagram of profit sharing for each virtual power plant.
Detailed Description
Embodiments of the present invention will be described below with reference to the drawings.
As shown in fig. 1, the invention relates to a multi-virtual power plant random optimization scheduling method based on P2P transaction, which specifically comprises the following steps:
and step 4, solving the optimal scheduling model by adopting GAMS software and outputting a result.
In order to verify that the energy sharing performed in the P2P transaction process can improve the social benefit, the effectiveness of the method provided by the simulation example verification model formed by three virtual power plants is designed. The virtual power plant 1 comprises a micro gas turbine, a central air conditioning system, photovoltaics, energy storage and a load, and the virtual power plants 2 and 3 comprise the central air conditioning system, the photovoltaics, the energy storage and the load. The electricity purchase price of the virtual power plant is according to the time-sharing electricity price issued by Jiangsu 2021 in 1 month, and at the time of peak (11:00-17:00, 22:00-24:00): 1.89 yuan/kWh, peak time (8:00-11:00, 17:00-22:00): 2.97 yuan/kWh, gu Shi (0:00-8:00): 0.85 yuan/kWh, and according to the current distributed power residual point internet surfing policy, the electricity purchasing price is kept unchanged to 0.85 yuan/kWh throughout the day. For better analysis of P2P trade conditions between virtual power plants, only 9:00-19:00 time periods are considered herein. In order to compare the overall operation modes and operation costs of multiple virtual power plants under different transaction mechanisms, the following comparative calculation examples are set: case1, P2P energy transaction is not carried out among the virtual power plants, and each virtual power plant only carries out centralized energy transaction with an upper power grid; case2, P2P energy sharing is carried out among the virtual power plants. The profit and cost of trading with three virtual power plants are shown in table 1, and it can be seen that the cost of trading with the higher grid for virtual power plants in case2 is reduced by 846.978 yuan compared to case1, however, because the trade between virtual power plants requires additional payment of service fees, the cost of dispersion increases by 24.327 yuan. Overall, the P2P transaction mechanism may reduce the operating cost by about 4.99% compared to the centralized transaction set.
Table 1 virtual Power plant operating amount under two examples
The electric energy trade situation between the virtual power plant 1 and the upper power grid under the two calculation examples is shown in fig. 2, and it can be seen from the figure that under the condition of centralized trade, the electric energy selling quantity of the virtual power plant 1 to the upper power grid is higher than that of the P2P trade situation. It can be seen that when P2P transactions are performed between virtual power plants, the multi-electric virtual power plant can flexibly select the transaction object, and enable clean energy and residual electric energy to be consumed as much as possible inside the alliance.
The market transaction electricity of the virtual power plant and the upper power grid under two examples is shown in fig. 3. The power interactions between the various virtual power plants are shown in FIG. 4, and each of the energy storage conditions in the virtual power plants, as well as the overall energy storage conditions, are shown in FIG. 5. By comprehensively analyzing the several graphs, it can be known that at t=10 to 11 hours, the electric quantity of the three virtual power plants is expressed as follows: the virtual power plant 1 is surplus in electric quantity, and the virtual power plants 2 and 3 are insufficient in electric quantity, and at this time, the virtual power plant 1 transmits electric energy to the virtual power plants 2 and 3 and stores the remaining electric energy in the energy storage. At this time, as shown in the figure, the energy storage in the virtual power plant 1 increases. At t=12 h, the three virtual power plant capacities appear as: the power of the virtual power plant 1 and the virtual power plant 3 are surplus and the power of the virtual power plant 2 is insufficient, at this time, the virtual power plant 1 and the virtual power plant 3 transmit electric power to the virtual power plant 2, and store the remaining energy in the energy storage in the units of the virtual power plant 1 and the virtual power plant 3, at this time, the energy storage capacities in the virtual power plant 1 and the virtual power plant 3 are increased to some extent as shown in fig. 5. At t=13 h, the power of the three virtual power plants appears as: the virtual power plant 1 and the virtual power plant 2 are full of power and the virtual power plant 3 is insufficient of power. At this time, the virtual power plant 1 and the virtual power plant 2 transmit surplus energy to the virtual power plant 3, but at this time, the electric energy shortage of the virtual power plant 3 cannot be completely balanced, and thus, the energy storage unit of the virtual power plant 3 releases part of the energy to ensure energy balance. At t=15-16 h, the power of the three virtual power plants appears as: the virtual power plant 1 is surplus and the virtual power plants 2 and 3 are deficient. At this time, the virtual power plant 1 and the virtual power plant 2 transmit electric power into the virtual power plant 3 and the electric power shortage of the virtual power plant 2 and the virtual power plant 3 cannot be completely balanced, and at this time, the electric power shortage is replenished by the energy storage released by the virtual power plant 2 and the virtual power plant 3. At t=17 to 18 hours, the electric quantity of the three virtual power plants represents surplus electric energy of the virtual power plant 1 and the virtual power plant 3, the electric energy of the virtual power plant 2 is insufficient, and the electric energy deficiency of the virtual power plant 2 is supplemented by the virtual power plant 3. At this time, the energy is released by the influence of the market price, and surplus electric energy of the virtual power plant 1 and the virtual power plant 3 is sold to the market to obtain the maximum profit.
As can be seen from fig. 6 and fig. 7, the energy management policies of the virtual power plant aggregation units in the two scenarios are basically consistent, but the energy storage scheduling policies of the virtual power plants in different transaction modes are slightly different, for example, the energy storage of the virtual power plant 1 is larger in the case of case2 in charge-discharge power, and smaller in the case of case1, which is mainly because in the case of case2, power shortage is invoked for balancing the power balance of the P2P transaction to perform energy balance management.
The operational benefits of each virtual power plant in case of case1 and case2 are shown in FIG. 8. Compared with a centralized transaction mode, under the condition that the virtual power plants participate in the P2P transaction, the respective operation profits can be improved to a certain extent under the condition that the Shapley value is carried out for profit allocation, profit allocation can be carried out according to the contribution value of each virtual power plant, fairness is guaranteed, and therefore each virtual power plant can actively participate in the P2P transaction.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. A multi-virtual power plant random optimization scheduling method based on P2P transaction is characterized by comprising the following steps:
step 1, constructing a virtual power plant aggregation model comprising photovoltaic, energy storage, a central air conditioning system, a gas turbine and user loads;
step 2, establishing a random optimization scheduling model considering photovoltaic uncertainty;
step 3, constructing a multi-virtual power plant cooperation residual distribution model based on a Shapley value method;
and step 4, solving the optimal scheduling model by adopting GAMS software and outputting a result.
2. The P2P transaction-based multi-virtual power plant random optimization scheduling method according to claim 1, wherein the step 1 specifically includes:
constructing a user load model:
the user consumes certain electric energy to bring certain profit, so that the profit generated by consuming energy by the user is:
U i (d i,t )＝k i,t ln(1+d i,t ) (1)
wherein k is i,t Representing a combination of a weight coefficient and a preference parameter, reflecting the weight of the electricity utilization effect of the user and the energy consumption preference of the user, d i,t Representing energy consumed by a user;
when there is a shortage between the load required by the user and the load that can be provided, the user will tend to be dissatisfied, so the dissatisfaction of the user is described by the cost of dissatisfaction of the user, as shown in the following formula:
wherein beta is i Representing the priority coefficient of each virtual power plant, and beta i > 0, with larger beta i Meaning that it is more concerned about comfort than economy; omega t Is a weight factor for t period dissatisfaction costs;
representing the ideal electricity demand of the user in the virtual power plant i in the period t; as can be seen from the above, when the actual load is equal to the desired electricity demand, i.e.
When (I)>
The value of (2) is 0; when->
When it is indicated that the user load demand is not met, and as d i,t Is reduced (1)>
Is growing faster and faster; when->
When it is indicated that the load demand has been met, and as the actual electricity load d is used i,t Increase of->
The speed of the decrease of (1) starts to slow down, i.e. the user satisfaction reaches saturation;
constructing an energy storage model:
the battery capacity in the operation of the energy storage system may be expressed as:
in the method, in the process of the invention,
representing the state of charge of the stored energy, u c 、u d Charge and discharge power respectively representing energy storage capacity, +.>
And->
Respectively representing the maximum charge and discharge efficiency of the stored energy;
building a photovoltaic model:
the predicted output power of the photovoltaic array, based on the solar radiation intensity, is:
P t pre ＝η ct ·S CA ·G t (4)
wherein eta is ct For photovoltaic array energy conversion efficiency, P t pre Is the total output of the photovoltaic unit at the moment t, S CA Is the photovoltaic array area; g t The predicted solar radiation intensity at a certain point in time t is indicated.
Constructing a central air conditioner model:
the regulation and control of the central air conditioning system should meet the indoor temperature requirement of building users, a modeling method based on cold and hot load calculation is adopted to describe a heat storage model of the public building, for the public building, the central air conditioning unit provides refrigerating capacity for the indoor space during the working period, meanwhile, the indoor and outdoor heat sources of the building have the heat release effect and the heat storage effect of the building inner wall so that the indoor temperature is continuously increased, and the specific expression is as follows:
in which Q i,s,t Total cooling capacity provided for a central air conditioning system;
respectively representing the power consumption of the refrigerating unit in the refrigerating process and the power consumption of the cold storage tank in the cold releasing process; />
The indoor temperature is represented, alpha, beta and gamma are respectively characteristic parameters of the intelligent building and an air conditioning system thereof, Δt is the time interval, < >>
Is the output of the central air conditioning system, < >>
To the capacity of the cold accumulation tank at the moment t, eta i,st 、η i,re The cold accumulation/release efficiency of the cold accumulation groove is respectively.
3. The P2P transaction-based multi-virtual power plant random optimization scheduling method according to claim 1, wherein the step 2 specifically comprises:
a random optimization scheduling model considering the uncertainty of the photovoltaic output is established, the randomness is expressed as the uncertainty of the photovoltaic output, so that the scene S is adopted to represent the actual output of the photovoltaic, the maximization of the social benefit generated by the operation of the virtual power plant is established, and the objective function is as follows:
wherein u is P2P An objective function when the multiple virtual power plants participate in the P2P transaction and an objective function when the multiple virtual power plants do not participate in the P2P transaction; wherein N is i (i=1, 2, 3) represents the number of virtual power plants, s represents the photovoltaic scene, ρ s Representing the probability of scene s, U i (d i t ) Represents the profit of the user from consuming energy,
representing the costs incurred by the virtual power plant in trade with the upper grid,
representing the cost of energy storage operation>
Representing user dissatisfaction costs, +.>
Representing the costs incurred by the operation of the gas turbine, +.>
Representing the cost of the photovoltaic array generation; the specific calculation formula of each part is as follows:
1) Profit generated by consuming energy by users
U i (d i,t )＝k i,t ln(1+d i,t ) (10)
Wherein k is i,t Representing a combination of a weight coefficient and a preference parameter, reflecting the weight of the electricity utilization effect of the user and the energy consumption preference of the user, d i,t Representing energy consumed by a user;
2) Cost of electric market trade
In the method, in the process of the invention,
representing the price of electricity purchase->
Representing the price of electricity selling, ->
Representing the electricity purchase amount @ and @ of>
Representing the sales power;
3) Energy storage charge and discharge costs
In the method, in the process of the invention,
and->
Respectively charging and discharging power of energy storage, +.>
And->
Respectively the cost coefficients of energy storage charge and discharge; />
4) User dissatisfaction cost
Wherein beta is i Representing the priority coefficient of each virtual power plant, and beta i Not less than 0, has larger beta i Virtual plant user intent of (3)Meaning that it is more concerned with comfort than economy; omega t Is a weight factor for t period dissatisfaction costs;
representing the ideal electricity demand of the user in the virtual power plant i in the period t; as can be seen from the above, when the actual load is equal to the desired electricity demand, i.e.
When (I)>
The value of (2) is 0; when->
When it is indicated that the user load demand is not met, and as d i,t Is reduced (1)>
Is growing faster and faster; when->
When it is indicated that the load demand has been met, and as the actual electricity load d is used i,t Increase of->
The speed of the decrease of (1) starts to slow down, i.e. the user satisfaction reaches saturation;
5) Cost of operation of gas turbine
Wherein a is MT 、b MT 、c MT A correlation coefficient representing the power generation cost of the gas turbine,
representing the output of the gas turbine;
6) Cost of photovoltaic output
The PV aggregated inside the virtual power plant is mainly a PV panel or roof PV, and its basic operation maintenance cost is:
in the method, in the process of the invention,
the power generated by the PV in the producer i at the moment t; />
Maintenance cost for the PV unit power generation in producer i;
7) P2P transaction cost
Wherein, c ij Representing a trading coefficient between virtual power plant i and virtual power plant j;
the constraints involved are as follows:
1) Dissatisfaction function constraint
From the dissatisfaction function, the dissatisfaction function
Is relative to d i,t But is monotonically decreasing in function of
At this time, the dissatisfaction function begins to increment during this period; thus (S)>
Should be less than or equal to 2; furthermore, in order to guarantee the basic and comfort requirements of the electrical load, the actual electrical load d i,t Should not be less than the ideal load +.>
Half of (2); thus (S)>
The values of (2) must satisfy the following relationship:
wherein d i,t Representing the load of the user and,
representing the ideal required power of the user;
2) Energy storage constraint
In the method, in the process of the invention,
indicating the state of charge of the stored energy->
Respectively representing upper and lower limits of the energy storage capacity; />
Respectively representing the maximum charge and discharge power of the stored energy, < + >>
Respectively representing the charge and discharge power of the stored energy at the time t;
3) Gas turbine constraints
Where ramp represents the rate of climb of the gas turbine,
representing the maximum and minimum output of the gas turbine respectively,
the output of the gas turbine at the time t is shown;
4) Central air conditioning load constraint
In the method, in the process of the invention,
respectively represents the power consumption of the refrigerating machine in the refrigerating process and the power consumption of the cold accumulation tank in the cold accumulation and release process, Q ich,max Represents the maximum refrigerating capacity of the refrigerating unit, Q ist,max 、Q i,remax The maximum cold accumulation amount and the cold release amount of the cold accumulation groove are respectively +.>
The capacity at the moment t of the cold accumulation tank and the upper limit thereof are respectively;
5) Power balance constraint
In the method, in the process of the invention,
representing photovoltaic output, ">
Indicating gas turbine output,/->
Representing the purchase power, < >>
Represents the energy storage discharge power, P i,j,t Power representing P2P transactions, +.>
Indicating the power of electricity selling, < >>
Representing the stored charge power>
Indicating central air conditioning power,/->
Representing the load power.
4. The P2P transaction-based multi-virtual power plant random optimization scheduling method according to claim 1, wherein the step 3 specifically comprises:
adopting a Shapley value method as a multi-virtual power plant cooperation residual distribution method; the benefit of the centralized transaction among the virtual power plants is shown as a formula (30), under the condition of sharing the energy of multiple virtual power plants, the overall operation benefit is increased, the excessive benefit becomes the emerging benefit, the actual benefit after the i-th virtual power plant is cooperated is shown as a formula (32),
f＝f 1 +f 2 +···+f i (31)
wherein f 0 Representing the total revenue of the virtual power plant before participating in the P2P transaction; f represents the total income of the virtual power plant after participating in P2P transaction; f (f) i 0 Representing the revenue of the virtual power plant i before participating in the P2P transaction; f (f) i Representing the benefits of the virtual power plant i for Shapley profit allocation after participation in the P2P transaction; s represents the number of virtual power plants in the set S; n represents the number of total virtual power plants; f (f) S\{i} Represents federated profits without virtual power plant i, (|S| -1) |! (n- |S|) is-! Representing the weighting factors.
5. The P2P transaction-based multi-virtual power plant random optimization scheduling method according to claim 1, wherein the step 4 specifically comprises: and solving an optimal scheduling model by adopting GAMS software, outputting a result, and analyzing the advantage of the P2P transaction relative to the centralized transaction.
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