CN110188950B - Multi-agent technology-based optimal scheduling modeling method for power supply side and demand side of virtual power plant - Google Patents
Multi-agent technology-based optimal scheduling modeling method for power supply side and demand side of virtual power plant Download PDFInfo
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Abstract
A virtual power plant power supply side and demand side optimal scheduling modeling method based on multi-agent technology comprises the following steps: establishing a virtual power plant optimization scheduling model adopting a multi-agent MAS control mode: the virtual power plant optimization scheduling model comprises a power supply side Agent, a demand side Agent and a power grid Agent; the power supply side Agent comprises wind power, photovoltaic power and thermal power; the demand side Agent comprises a flexible load and a chargeable and dischargeable electric automobile; establishing a target function of a virtual power plant; establishing a constraint condition of virtual power plant operation; and establishing a storage battery model and a chargeable and dischargeable electric automobile model. The dispatching model established by the modeling method is composed of a load side containing the electric automobile and the flexible load and a power supply side containing various power types, the demand response method is considered, the output of each unit at the power generation side is optimized, the flexible load at the load side is controlled, the charging and discharging of the electric automobile are coordinated, the capacity of the virtual power plant for absorbing new energy for power generation is improved, and the income of the virtual power plant is increased.
Description
Technical Field
The invention belongs to the technical field of optimal scheduling of virtual power plants of smart power grids, and particularly relates to a modeling method for optimal scheduling of a power supply side and a demand side of a virtual power plant based on a multi-agent technology.
Background
At present, most of distributed power supply grid-connected forms adopt a micro-grid form, the micro-grid mainly aims at consuming distributed energy on the spot by users, is limited by geographical factors, and has certain limitations in the aspects of cross-region and large-scale full utilization of distributed power supplies and large-scale benefits in the power market. The virtual power plant technology can realize the optimal scheduling of the distributed power sources in a large area and a large range, break through the above limitation of the microgrid and effectively integrate a large number of distributed power sources and flexible loads. For this reason, intensive research into virtual power plant technology is necessary. Particularly, with the rapid development of the chargeable and dischargeable electric vehicle, the chargeable and dischargeable electric vehicle is not enough to participate in the optimization scheduling research of the virtual power plant.
In general, a virtual power plant is mainly composed of 3 parts: a power generation system, an energy storage unit and a communication system. The power generation system mainly comprises a distributed power supply and a controllable unit. The energy storage unit is composed of a storage battery and the like, and can compensate the power generation output fluctuation of the renewable energy source. In recent years, with the rapid development of electric automobiles, automobile storage batteries can also become an important component of energy storage units of virtual power plants; the communication system is responsible for energy management and data acquisition and monitoring of the virtual power plant.
At present, much research on the virtual power plant is carried out, but the research on the joint optimization scheduling of the demand side resources and the power supply side resources of the virtual power plant is insufficient.
The prior patent documents related to the optimized scheduling of the virtual power plant include:
patent document 1: chinese patent "a virtual power plant multi-objective optimization scheduling method taking account of load side and power supply side" (application number: 201610587240.5) discloses a virtual power plant multi-objective optimization scheduling method taking account of load side and power supply side, and the virtual power plant multi-objective optimization scheduling method taking account of load side and power supply side includes: the method has the advantages that the multi-objective optimization scheduling model of the virtual power plant and the related solving algorithm are established, the environmental factors and the operation cost are considered, and the utilization efficiency of new energy is improved.
Patent document 2: chinese patent 201710898385.1 provides a virtual power plant internal distributed power supply bidding and benefit distribution method and system considering uncertainty based on a multi-agent mode, and mainly aims to establish an improved Shapley value based on a risk factor to optimize bidding.
Patent document 3: chinese patent 'demand response system in an area scope' (application number: 201810385485.9) discloses a demand response system which integrates users with large, medium and small capacity onto a demand response platform, and enables the users to quickly respond to demand response requirements of power supply companies through the platform.
The patent document 1 considers a joint scheduling and bidding method at two ends of a load side and a power supply side, the patent document 2 establishes a virtual power plant internal distributed power supply bidding and benefit allocation method considering uncertainty based on a multi-agent mode, and the patent document 3 considers that a contract mode is signed by a power grid and a user to respond to a power grid demand response instruction, but does not consider the situation that an electric vehicle at the load side participates in scheduling, and does not consider a communication mechanism of the virtual power plant comprehensively. Further, the above patent documents 1 to 3 do not make a relevant contract for chargeable and dischargeable electric vehicles and flexible load classification when a user participates in scheduling. From the above analysis, the disadvantages of the prior patent documents are specifically as follows:
(1): the situation that the electric automobile participates in scheduling is not considered, and particularly, the research on participation demand response of the chargeable and dischargeable electric automobile is insufficient.
(2): the method is not sufficient for research on different classification conditions of the chargeable and dischargeable electric automobile.
(3): the joint optimization scheduling of different power sources on the power supply side and flexible loads on the demand side and electric vehicles based on a multi-agent mode is not considered, and for a virtual power plant, the communication mode of the virtual power plant is an integral part of the virtual power plant.
(4): not for flexible load users: including interruptible load users and electric vehicle users, are not separately contracted.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a multi-agent technology-based optimal scheduling modeling method for a power supply side and a demand side of a virtual power plant, wherein the demand side comprises flexible loads and electric vehicles, and different types of flexible loads and modes of the electric vehicles respectively participating in scheduling are established according to the mode that customers use the electric vehicles, so that the interaction between a power grid and the users is increased, and the customer experience is enhanced. And the income of the virtual power plant is optimized by establishing an objective function, so that the income is maximized.
The technical scheme adopted by the invention is as follows:
a virtual power plant power supply side and demand side optimal scheduling modeling method based on multi-agent technology comprises the following steps:
step 1: establishing a virtual power plant optimization scheduling model adopting a multi-agent MAS control mode:
the virtual power plant optimization scheduling model comprises a power supply side Agent, a demand side Agent and a power grid Agent;
the power supply side Agent comprises wind power, photovoltaic power and thermal power; the power supply side Agent respectively predicts and adjusts the output of each power supply on the next day according to the wind speed, the illumination intensity and duration, the overhaul condition and the standby condition;
the demand side Agent comprises a flexible load and a chargeable and dischargeable electric automobile.
Step 2: establishing an objective function of the virtual power plant:
the method comprises the following steps of establishing a multi-objective optimization function according to the goals that the total power generation cost of a virtual power plant and the incentive-based demand response cost are minimum, and the electricity selling income and the environmental income of the virtual power plant are maximum:
wherein F is the net profit from the operation of the virtual power plant, F 1 For virtual power plant sales revenue, F 2 For virtual plant environmental benefits, F 3 For cost of electricity generation, F 4 For incentive-based demand response cost, T is the number of one scheduling cycle period.
The virtual power plant electricity selling income comprises the following steps:
the income generated by the payment of the user and the income generated by the transaction of the virtual power plant and the large power grid are as follows:
F 1 (t)＝c grid (t)·P grid (t)+c sell (t)·[P load (t)-P L (t)+P I (t)-P car (t)] (2)
in the formula, P grid (t) representing the interaction power of the virtual power plant with the power grid; c. C grid (t) representing the electric energy trading price of the virtual power plant and the power grid in the period t; c. C sell (t) selling electricity prices of the virtual power plant to the power grid and the users; c. C buy (t) purchasing electricity price from the virtual power plant to the power grid; when P is present grid (t) when t is more than or equal to 0, the time-sharing electricity purchasing price is taken, and when P is greater than or equal to 0 grid (t)<0, taking the time-sharing electricity selling price; p is load (t) the original load of the virtual power plant in the t period; p is L (t)、P I (t) respectively a load cut and a load transferred after the demand response method is applied; p car And (t) is the total charging and discharging power of the electric automobile.
Virtual power plant environmental benefits:
F 2 (t)＝C WT P WT (t)+C PV P PV (t)+C peak (P I (t)+P L (t)+|P car (t)|) (4)
in the formula, c WT 、c PV 、c peak Unit prices of wind power, photovoltaic, flexible load and electric vehicle peak regulation power are respectively: yuan/kWh; p WT (t)、P PV And (t) represents the generated power of wind power and photovoltaic power respectively.
The power generation cost of the virtual power plant is as follows:
the virtual power plant generation costs include fuel costs and operational maintenance costs for each power source, as follows:
in the formula, C r,i 、C m,i Respectively representing the unit fuel cost and the unit operation and maintenance cost of the power supply i. C r,i P i (t) Fuel cost of Power i, C m,i P i And (t) is the operation and maintenance cost of the power supply i, and n is the number of the power supply types.
The cost of demand response:
to encourage users to reduce part of the flex load power during peak periods, subsidies are given to users responding to the reduced load, the part cost only considering part P of the reduced flex load L (t), subsidy cost is:
k 1 and k 2 Respectively, a compensation amount coefficient, k 3 And (4) compensation amount coefficients for the electric automobile to participate in scheduling. P car And (t) is the total charging and discharging power of the electric automobile.
And 3, step 3: establishing a constraint condition of virtual power plant operation:
the constraint conditions of the operation of the virtual power plant comprise system power balance, limitation of output power of each power supply, and constraint conditions of interactive power limitation between the virtual power plant and a main network:
(1) System power balance constraint:
P load (t)＝P WT (t)+P PV (t)+P MT (t)+P HD (t)+P BA (t)+P grid (t)+P L (t)-P I (t)+P car (t) (7)
in the formula, P MT (t)、P HD (t) the power generation powers of the gas turbine and the thermal power generator respectively; p BA (t) represents the total charge and discharge power of the battery; p car (t) represents the charge/discharge power of the electric vehicle.
(2) Tie line constraints interacting with the main network:
due to the capacity limitation of the tie line between the virtual power plant and the main network, the interaction power of the virtual power plant and the main network should satisfy the following constraints:
in the formula (I), the compound is shown in the specification,
the upper limit and the lower limit of the interaction of the virtual power plant and the main network power are respectively set.
(3) And the gas turbine and the thermal power generating unit are constrained:
|P i (t-1)-P i (t)|≤△ i (10)
in the formula, P i (t) is the output power of power supply i;
respectively an upper limit and a lower limit of the output power of the power supply i. Delta i Is the unit climbing rate.
(4) And flexible load:
the flexible load is controlled through demand response, and when the virtual power plant control center issues a load reduction instruction, the flexible load executes response. The response constraints are:
P L min ≤P L (t)≤P L max (11)
T min ≤T L ≤T max (12)
in the formula, P L (t) interrupt load in response to demand response instruction, P L min 、P L max Respectively, an upper and a lower interruptible load limit, T min ，T max Minimum, maximum response time, T, respectively, for load response reduction L Is the response time duration.
And step 3: establishing a mathematical model:
the mathematical model includes a battery model:
in order to maintain the service life of the battery, the charging and discharging power of the battery needs to be limited, and the discharging power P of the battery energy storage unit in the t-th optimization period dc (t) and charging power P of the battery energy storage unit in the t-th optimization period c (t) is expressed as:
P BA (t)＝P dc (t)-P c (t) (14)
in the formula (I), the compound is shown in the specification,
and
maximum charging and discharging power allowed by the battery energy storage unit respectively;
and
respectively, a charging state variable and a discharging state variable of the kth battery in the t period, and n is the number of the batteries. P BA And (t) is the total charging and discharging power of the storage battery at t.
The mathematical model comprises a chargeable and dischargeable electric automobile model:
the chargeable and dischargeable electric automobile is used as an energy storage unit on the demand side of a virtual power plant to participate in peak clipping and valley filling.
Wherein the content of the first and second substances,
the number of the discharged and charged electric vehicles,
the discharging power and the charging power of the electric automobile are respectively calculated,
respectively are the upper limit and the lower limit of the discharge power of the electric automobile,
and respectively charging the upper limit and the lower limit of the power of the electric automobile.
The invention relates to a multi-agent technology-based optimized scheduling modeling method for a power supply side and a demand side of a virtual power plant. Meanwhile, the utilization efficiency of the chargeable and dischargeable electric automobile and the flexible load is improved by adopting a mode of signing a contract with the user, the combination of power grid scheduling and user contract execution behaviors is realized, the utilization rate of resources on the user side can be increased, and the participation degree of the user in power grid scheduling is enhanced.
Drawings
FIG. 1 is a diagram of a virtual power plant optimal scheduling model according to the present invention.
Fig. 2 is a diagram illustrating a manner in which a user participates in scheduling according to the present invention.
FIG. 3 is a graph of the load, wind force output, photovoltaic output predicted according to the present invention.
Fig. 4 is a comparison graph of output of each unit in scenario 1 of the present invention.
Fig. 5 is a comparison graph of the output of each unit in scenario 2 of the present invention.
Fig. 6 is a comparative graph of output of each unit in scenario 3 according to the embodiment of the present invention.
Detailed Description
A virtual power plant power supply side and demand side optimal scheduling modeling method based on multi-agent technology comprises the following steps:
step 1: establishing a virtual power plant optimization scheduling model adopting a multi-agent MAS control mode:
the virtual power plant optimization scheduling model comprises a power supply side Agent, a demand side Agent and a power grid Agent;
the power supply side Agent comprises wind power, photovoltaic power and thermal power; and the power supply side Agent respectively predicts and adjusts the output of each power supply on the next day according to the wind speed, the illumination intensity and duration, the overhaul condition and the standby condition.
(1) Wind power prediction: although wind speed exhibits randomness in both the short and long term, large statistical data sets have shown that wind speed can be approximately described by a Weibull distribution based on the probability density function in equation (1).
In the formula, the velocity of the wind v,
shape factor, theta scale factor, may be used to obtain mean and variance of wind speeds over respective periods of time to obtain theta and
thereafter, the wind speed was simulated with a MATLAB tool. The relationship between WPP output and real-time wind speed may be described by an equation.
Wherein, P WT Output of the wind turbine at t, v in Cut-in wind speed, v rated Rated wind speed, v out The wind speed is cut off.
(2) Photovoltaic prediction: for each time period, the solar radiation is considered to be a random variable and is assumed to follow a beta distribution, and therefore the output of the photovoltaic power generation also follows a beta distribution. The probability density function gives equation (3).
Where β (ψ, ζ) is the beta distribution function, ψ and ζ can be calculated using the equations.
Wherein P is PV Indicating the output of the photovoltaic at t, phi and xi are shape parameters of beta distribution, mu PV Mean value of photovoltaic output, delta PV Standard values for photovoltaic output.
(3) Thermal power generating unit: the output of the thermal power generating unit is adjusted according to the load prediction data, the overhaul and other conditions, and can be obtained according to the power balance constraint equation (13).
The demand side Agent comprises a flexible load and a chargeable and dischargeable electric automobile.
Step 2: establishing an objective function of a virtual power plant:
the method comprises the following steps of establishing a multi-objective optimization function according to the goals of minimum total power generation cost of a virtual power plant and incentive-based demand response cost and maximum virtual power plant power sale income and environmental income:
wherein F is the net profit from the operation of the virtual power plant, F 1 For virtual power plant sales revenue, F 2 For virtual plant environmental benefits, F 3 For cost of electricity generation, F 4 For incentive-based demand response cost, T is the number of one scheduling cycle period.
The virtual power plant electricity selling income comprises the following steps:
the income generated by the payment of the user and the income generated by the transaction of the virtual power plant and the large power grid are as follows:
F 1 (t)＝c grid (t)·P grid (t)+c sell (t)·[P load (t)-P L (t)+P I (t)-P car (t)] (8)
in the formula, P grid (t) representing the interaction power of the virtual power plant with the power grid; c. C grid (t) representing the electric energy trading price of the virtual power plant and the power grid in the period t; c. C sell (t) selling electricity prices of the virtual power plant to the power grid and the users; c. C buy (t) purchasing electricity price from the virtual power plant to the power grid; when P is present grid (t) when t is more than or equal to 0, the time-sharing electricity purchasing price is taken, and when P is greater than or equal to 0 grid (t)<0, taking the time-sharing electricity selling price; p load (t) is the original load of the virtual power plant in the period t; p L (t)、P I (t) load shedding and load shifting after applying the demand response method, respectively; p car And (t) is the total charging and discharging power of the electric automobile.
Virtual power plant environmental revenue: in order to encourage the use of renewable new energy, wind power and photovoltaic power generation are subsidized. Because the demand response based on excitation and the time-of-use electricity price strategy are adopted, a part of flexible load participates in peak shaving, the load curve changes more stably, the power quality is improved, and certain subsidy is obtained.
F 2 (t)＝C WT P WT (t)+C PV P PV (t)+C peak (P I (t)+P L (t)+|P car (t)|) (10)
In the formula, c WT 、c PV 、c peak Unit prices of wind power, photovoltaic, flexible load and electric vehicle peak regulation power are respectively: yuan/kWh; p WT (t)、P PV And (t) represents the generated power of wind power and photovoltaic power respectively.
The power generation cost of the virtual power plant is as follows:
the virtual power plant generation costs include fuel costs and operational maintenance costs for each power source as follows:
in the formula, C r,i 、C m,i Respectively representing the unit fuel cost and the unit operation and maintenance cost of the power supply i. C r,i P i (t) Fuel cost of Power supply i, C m,i P i And (t) is the operation and maintenance cost of the power supply i, and n is the number of the power supply types.
Cost of demand response:
to encourage users to reduce part of the flex load power usage during peak periods, subsidies are given to users responding to the reduced load, the part cost considering only part P of the reduced flex load L (t), subsidy cost is:
k 1 and k 2 Respectively, a compensation amount coefficient, k 3 And (4) compensation amount coefficients for the electric automobile to participate in scheduling. P car And (t) is the total charging and discharging power of the electric automobile.
And 3, step 3: establishing a constraint condition of virtual power plant operation:
the constraint conditions of the operation of the virtual power plant comprise system power balance, limitation of output power of each power supply, and constraint conditions of interactive power limitation between the virtual power plant and a main network:
(1) System power balance constraint:
the power balance constraint is important for the operation of the virtual power plant, and the sum of the output of each power supply is equal to the load demand at any time.
P load (t)＝P WT (t)+P PV (t)+P MT (t)+P HD (t)+P BA (t)+P grid (t)+P L (t)-P I (t)+P car (t) (13)
In the formula, P MT (t)、P HD (t) the power generation powers of the gas turbine and the thermal power generator respectively; p BA (t) represents the total charge and discharge power of the battery; p is car (t) represents the charge/discharge power of the electric vehicle.
(2) Tie line constraints interacting with the main network:
due to the capacity limitation of the tie line between the virtual power plant and the main network, the interaction power of the virtual power plant and the main network should satisfy the following constraints:
in the formula (I), the compound is shown in the specification,
the upper limit and the lower limit of the interaction of the virtual power plant and the main network power are respectively set.
(3) And the gas turbine and the thermal power generating unit are constrained:
|P i (t-1)-P i (t)|≤△ i (16)
in the formula, P i (t) is the output power of power supply i;
respectively an upper limit and a lower limit of the output power of the power supply i. Delta i The unit climbing rate.
(4) And flexible load:
the flexible load is controlled through demand response, and when the virtual power plant control center issues a load reduction instruction, the flexible load executes response. The response constraints are:
P L min ≤P L (t)≤P L max (17)
T min ≤T L ≤T max (18)
in the formula, P L (t) interrupt load in response to demand response instruction, P L min 、P L max Respectively an upper and a lower interruptible load limit, T min ，T max Minimum, maximum response time, T, respectively, for load response reduction L Is the response time duration.
And 3, step 3: establishing a mathematical model:
the mathematical model includes a battery model:
in order to maintain the service life of the battery, the charging and discharging power of the battery needs to be limited, and the discharging power P of the battery energy storage unit in the t-th optimization period dc (t) and charging power P of the battery energy storage unit in the t-th optimization period c (t) is expressed as:
P BA (t)＝P dc (t)-P c (t) (20)
in the formula (I), the compound is shown in the specification,
and
maximum charging and discharging power allowed by the battery energy storage unit respectively;
and
respectively, a charging state variable and a discharging state variable of the kth battery in the t period, and n is the number of the batteries. P BA And (t) is the total charging and discharging power of the storage battery at t.
The mathematical model comprises a chargeable and dischargeable electric automobile model:
when the electric automobile is connected to the power distribution network, the aggregation characteristic shown by the electric automobile can greatly influence the operation of the power distribution network, and if a demand response strategy is adopted, the problem that the impact of large-scale charging on the power distribution network is caused can be solved by taking the chargeable and dischargeable electric automobile as a scheduling unit. The chargeable and dischargeable electric automobile is used as an energy storage unit on the demand side of a virtual power plant to participate in peak clipping and valley filling. The daily load curve has peak load and also has valley load, and the peak clipping and valley filling means that the peak load is reduced, the valley load is filled, the load peak-valley difference of the power grid is reduced, and the power generation and the power utilization tend to be balanced.
In the invention, the chargeable and dischargeable electric automobile is used as the energy storage unit on the load side, and if the energy storage unit discharges in the peak time and charges in the valley time, the effects of peak clipping and valley filling can be achieved.
Wherein the content of the first and second substances,
the number of the discharged and charged electric vehicles,
the discharging power and the charging power of the electric automobile are respectively calculated,
respectively are the upper limit and the lower limit of the discharge power of the electric automobile,
and respectively charging the upper limit and the lower limit of the power of the electric automobile.
In the step 1, the scheduling scheme of the present invention has user participation, and the user participation modes have diversity, and different scheduling modes influence the power grid to make the scheduling scheme, for example, when a flexible load user participates in scheduling, the user can adopt two modes of reporting that the flexible load can be interrupted next day or the flexible load can be interrupted conventionally, and the two modes have influence on the power grid to make the scheduling scheme, so the following classification is made according to the diversity of the user participation:
the user of the chargeable and dischargeable electric automobile comprises two scheduling participation modes: (1) the client declares the next day available charge-discharge time period; (2) and the users which are not declared but agree to participate in the power grid dispatching according to a conventional charging and discharging mode, namely, when the power grid sends a demand response, the users respond to the charging and discharging time period according to the demand.
The flexible load comprises two scheduling modes: (1) the user declares the interruptible load power and time period of the next day; (2) users who do not report but agree to participate in the power grid dispatching can execute a conventional load interruption mode to participate in the power grid dispatching, namely, when the power grid sends a demand response, loads are reduced according to requirements.
The execution mode and model are shown in fig. 2, and the specific contents are as follows:
(1) and the flexible load of the user participates in the power grid dispatching, executes demand response and interrupts part of the flexible load according to the demand. Which can interrupt power P L And the time period is divided into two modes: one is that the next day is declared the interruptible load P L1 And time-of-day mode, the other being conventional interrupt mode, with daily fixed interrupt load P L2 And a time period. The sum of the interruptible loads of both modes is the interruptible power, i.e. P L ＝P L1 +P L2 ；
(2) And the electric automobile of the user participates in power grid dispatching, executes demand response and needs to be charged and discharged according to the demand. The execution modes are divided into two types: one is that the time period in which the next day can participate in power grid dispatching is declared on the same day, and the quantity N of the electric vehicles summarized by the power grid in the mode chr1 And N dis1 And the second mode is a conventional charge-discharge control mode, the charge-discharge time period is fixed every day, and the electric network collects the number N of the electric automobiles in the mode chr2 And N dis2 And a time period. The quantity of the two charging electric vehicles in different time periods is
The number of the discharged electric vehicles is
(3) And aiming at the user executing the demand response, the power supply company needs to give the user certain economic subsidy, and the subsidy measures are shown according to a formula (12).
The virtual power plant scheduling mode is shown in fig. 1, and the dotted line in fig. 1 represents information communication of a multi-agent method.
Example (b):
the method comprises the following steps: according to a power grid dispatching contract participated by a user, for the convenience of example analysis, and according to a vehicle using rule of an electric vehicle user, the electric vehicle dispatching needs to meet the following requirements: the user accesses the power supply before 6 o 'clock in the evening, and cuts off the power supply after 6 o' clock in the morning, and the owner agrees that the storage battery of the electric automobile participates in peak shaving scheduling during the period. If the number of chargeable and dischargeable electric vehicles in a certain area is 2000, since part of the electric vehicles do not need to be charged every day, the number of vehicles participating in scheduling every day should be random between 0 and 2000. Because of the strong uncertainty of responding to the flexible load based on the excitation strategy, the flexible load is set to be 30MW at the maximum, and the participation scheduling time is not more than 12 hours.
Step two: and setting the power supply capacity of the power supply side of the virtual power plant. The power supply side power supply of the virtual power plant consists of a 220MW wind power plant, a 100MW photovoltaic power plant, 2 100MW gas turbines, 2 100MW thermal power generators and 3 50MW energy storage batteries.
Step three: and (3) obtaining a virtual power plant total load power curve and the output of the wind driven generator and the photovoltaic power generation according to the prediction, wherein the curve is shown in figure 3, and the power generation fuel cost, the operation maintenance cost and the local time-of-use electricity price of each power supply are respectively shown in tables 1-2.
TABLE 1 coefficient of generating cost for each power supply
TABLE 2 price list for purchasing and selling electricity in virtual power plant
Step four: according to the virtual power plant structure shown in fig. 1 and the model constructed in the scheme, the objective function is optimized, better unit output is obtained, better economic benefit is achieved, and three scenes are set for better comparing the effects of the electric vehicle and the flexible load under the demand response:
scene 1: the unit participating in scheduling comprises a new power supply side energy source unit which comprises wind power, a photovoltaic power generator, a gas turbine, a thermal power generator and an energy storage battery;
scene 2: on the basis of the scenario 1, flexible loads are made to participate in scheduling by utilizing incentive-based demand-side response and a time-of-use electricity price strategy;
scene 3: and adding an electric vehicle scheduling process on the basis of the scene 2.
Step five: wind power and photovoltaic are fully consumed in 3 scenes, and on the basis, the output of the wind power and photovoltaic is optimal in economy by scheduling different units.
And obtaining a relevant result through an optimization algorithm. The following are the results obtained according to the above method to verify the validity of the method. The output of each unit under three scenes is shown in fig. 4-6, and table 3 is an economic comparison of the three scenes.
TABLE 3 comparison of economics for three scenarios
Compared with the data shown in fig. 4 and the data shown in fig. 5 and 6, respectively, it can be seen that the output of the conventional unit can be reduced when the flexible load and the electric vehicle participate in scheduling, so that the effect of peak clipping and valley filling is achieved, and the economic benefit of the power grid is improved. It can be seen from table 3 that the profit of scenario 3 is the highest, that is, the benefit is greater than that of other scenarios in the scenario of jointly participating in scheduling of the electric vehicle and the flexible load, and the fuel and operation and maintenance costs are less than that of other scenarios.
Through the analysis of the example, the rationality of the optimal scheduling modeling method for the power supply side and the demand side of the virtual power plant based on the multi-agent technology is verified. By means of contract signing with the user, interaction between the power grid and the user is increased, and user experience is improved. By applying the demand response method, the output of each unit at the power supply side, the flexible load at the load side and the charging and discharging of the electric automobile are coordinated, the capacity of the virtual power plant for consuming new energy for power generation is improved, and the income of the virtual power plant is increased.
Claims (2)
1. A virtual power plant power supply side and demand side optimal scheduling modeling method based on multi-agent technology is characterized by comprising the following steps:
step 1: establishing a virtual power plant optimization scheduling model adopting a multi-agent MAS control mode:
the virtual power plant optimization scheduling model comprises a power supply side Agent, a demand side Agent and a power grid Agent;
the power supply side Agent comprises wind power, photovoltaic power and thermal power; the power supply side Agent respectively predicts and adjusts the output of each power supply the next day according to the wind speed, the illumination intensity and duration, the overhaul and the standby conditions;
the demand side Agent comprises a flexible load and a chargeable and dischargeable electric automobile;
step 2: establishing an objective function of a virtual power plant:
the method comprises the following steps of establishing a multi-objective optimization function according to the goals that the total power generation cost of a virtual power plant and the incentive-based demand response cost are minimum, and the electricity selling income and the environmental income of the virtual power plant are maximum:
wherein F is the net profit from the virtual power plant operation, F 1 For virtual power plant sales revenue, F 2 For virtual plant environmental benefits, F 3 For the cost of electricity generation, F 4 For incentive-based demand response cost, T is the number of time segments of a scheduling period;
the virtual power plant electricity selling income comprises the following steps:
the income generated by the payment of the user and the income generated by the transaction of the virtual power plant and the large power grid are as follows:
F 1 (t)＝c grid (t)·P grid (t)+c sell (t)·[P l o ad (t)-P L (t)+P I (t)-P car (t)] (2)
in the formula, P grid (t) representing the interaction power of the virtual power plant with the power grid; c. C grid (t) representing the electric energy trading price of the virtual power plant and the power grid in the period t; c. C sell (t) selling electricity prices of the virtual power plant to the power grid and the users; c. C buy (t) purchasing electricity price from the virtual power plant to the power grid; when P is present grid (t) when t is more than or equal to 0, the time-sharing electricity purchasing price is taken, and when P is greater than or equal to 0 grid (t)<0, taking the time-sharing electricity selling price; p load (t) is the original load of the virtual power plant in the period t; p L (t)、P I (t) respectively a load cut and a load transferred after the demand response method is applied; p car (t) is the total charging and discharging power of the electric automobile;
virtual power plant environmental revenue:
F 2 (t)＝C WT P WT (t)+C PV P PV (t)+C peak (P I (t)+P L (t)+|P car (t)|) (4)
in the formula, c WT 、c PV 、c peak Unit prices of wind power, photovoltaic, flexible load and electric vehicle peak regulation power are respectively as follows: yuan/kWh; p WT (t)、P PV (t) and respectively representing the power generation power of wind power and photovoltaic;
the power generation cost of the virtual power plant is as follows:
the virtual power plant generation costs include fuel costs and operational maintenance costs for each power source as follows:
in the formula, C r,i 、C m,i Respectively representing the unit fuel cost and the unit operation and maintenance cost of the power supply i; c r,i P i (t) Fuel cost of Power supply i, C m,i P i (t) the operation and maintenance cost of the power supply i, and n is the number of the power supply types;
the cost of demand response:
to encourage user reduction during peak periodsThe portion of the flexible load is powered up and subsidized for the user responding to the load reduction, the cost of which only considers the portion P of the flexible load reduction L (t), subsidy cost is:
k 1 and k 2 Respectively, a compensation amount coefficient, k 3 A compensation amount coefficient for the electric vehicle to participate in scheduling;
and step 3: establishing a constraint condition of virtual power plant operation:
the constraint conditions of the operation of the virtual power plant comprise system power balance, limitation of output power of each power supply, and constraint conditions of interactive power limitation between the virtual power plant and a main network:
(1) System power balance constraint:
P load (t)＝P WT (t)+P PV (t)+P MT (t)+P HD (t)+P BA (t)+P grid (t)+P L (t)-P I (t)+P car (t) (7)
in the formula, P MT (t)、P HD (t) the power generation powers of the gas turbine and the thermal power generator respectively; p BA (t) represents the total charge and discharge power of the battery; p car (t) represents the charge/discharge power of the electric vehicle;
(2) Tie line constraints interacting with the main network:
due to the capacity limitation of the tie line between the virtual power plant and the main network, the interaction power of the virtual power plant and the main network should satisfy the following constraint:
in the formula (I), the compound is shown in the specification,
respectively representing the upper limit and the lower limit of power interaction between the virtual power plant and the main network;
(3) And constraining a gas turbine and a thermal power generating unit:
P i min ≤P i (t)≤P i max (9)
|P i (t-1)-P i (t)|≤△ i (10)
in the formula, P i (t) is the output power of power supply i; p i max 、P i min The upper limit and the lower limit of the output power of the power supply i are respectively set; delta i The climbing speed of the unit;
(4) And flexible load:
the flexible load is controlled through demand response, and when the virtual power plant control center issues a load reduction instruction, the flexible load executes response; the response constraints are:
P Lmin ≤P L (t)≤P Lmax (11)
T min ≤T L ≤T max (12)
in the formula, P L (t) interrupt load in response to demand response instruction, P Lmin 、P Lmax Respectively an upper and a lower interruptible load limit, T min ，T max Minimum, maximum response time, T, respectively, for load response reduction L Is the response time length;
and step 3: establishing a mathematical model:
the mathematical model includes a battery model:
in order to maintain the service life of the battery, the charging and discharging power of the battery needs to be limited, and the discharging power P of the battery energy storage unit in the t-th optimization period dc (t) and charging power P of the battery energy storage unit in the t-th optimization period c (t) is expressed as:
P BA (t)＝P dc (t)-P c (t) (14)
in the formula (I), the compound is shown in the specification,
and
maximum charging and discharging power allowed by the battery energy storage unit respectively;
and
respectively a charging state variable and a discharging state variable of a kth battery in a t-th time period, wherein n is the number of the batteries; p BA (t) is the total charge-discharge power of the storage battery at t;
the mathematical model comprises a chargeable and dischargeable electric automobile model:
the chargeable and dischargeable electric automobile is used as an energy storage unit on the demand side of a virtual power plant to participate in peak clipping and valley filling;
wherein the content of the first and second substances,
the number of the discharged and charged electric vehicles,
the discharging power and the charging power of the electric automobile are respectively calculated,
are respectively electric steamThe upper and lower limits of the vehicle discharge power,
and respectively charging the upper limit and the lower limit of the power of the electric automobile.
2. The multi-agent technology-based optimal scheduling modeling method for the power supply side and the demand side of the virtual power plant according to claim 1, characterized in that: in the step 1, the following classifications are made for the diversity of user participation:
the user of the chargeable and dischargeable electric automobile comprises two scheduling participation modes: (1) the client declares the next day available charge-discharge time period; (2) users which are not declared but agree to participate in the power grid dispatching according to a conventional charging and discharging mode, namely when the power grid sends a demand response, the users respond to a charging and discharging period according to the demand;
the flexible load comprises two scheduling modes: (1) the user declares the interruptible load power and time period of the next day; (2) users who are not declared but agree to participate in the power grid dispatching can execute a conventional load interruption mode to participate in the power grid dispatching, namely when the power grid sends a demand response, the load is reduced according to the demand;
the specific contents of the execution mode and the model are as follows:
(1) the flexible load of the user participates in power grid dispatching, demand response is executed, and partial flexible load is interrupted according to the demand; which can interrupt power P L And the time period is divided into two modes: one is that the next day is declared the interruptible load P L1 And time-of-day mode, the other being conventional interrupt mode, with daily fixed interrupt load P L2 And time period; the sum of the interruptible loads of both modes is the interruptible power, i.e. P L ＝P L1 +P L2 ；
(2) The electric automobile of the user participates in power grid dispatching, executes demand response and needs to be charged and discharged according to the demand; the execution modes are divided into two types: one is that the time period in which the next day can participate in power grid dispatching is declared on the same day, and the quantity N of the electric vehicles summarized by the power grid in the mode chr1 And N dis1 And time interval, the second is a conventional charge-discharge control mode, and the charge is fixedly charged every dayThe period of discharge, the number of electric vehicles N summarized by the power grid in this way chr2 And N dis2 And time period; the quantity of the two charging electric vehicles in different time periods is
The number of the discharged electric vehicles is
(3) And aiming at the user who executes the demand response, the power supply company needs to give a certain economic subsidy to the user, and the subsidy measure is shown according to the formula (6).
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