CN112928749B - Virtual power plant day-ahead scheduling method integrating resources at multi-energy demand side - Google Patents

Virtual power plant day-ahead scheduling method integrating resources at multi-energy demand side Download PDF

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CN112928749B
CN112928749B CN202110064910.6A CN202110064910A CN112928749B CN 112928749 B CN112928749 B CN 112928749B CN 202110064910 A CN202110064910 A CN 202110064910A CN 112928749 B CN112928749 B CN 112928749B
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CN112928749A (en
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王建学
杨帆
王建臣
雍维桢
张子龙
齐捷
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Xian Jiaotong University
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H—ELECTRICITY
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
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    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
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Abstract

The invention discloses a virtual power plant day-ahead scheduling method integrating a multi-energy demand side resource, which comprises the steps of obtaining distributed power generation parameters, day-ahead prediction data, parameters of the multi-energy demand side resource, electric vehicle power battery parameters, historical statistical data, an initial running state of the multi-energy demand side resource, and set running demand and electric power market data; constructing a normal operation mode optimization target of the virtual power plant according to the acquired data, and establishing a multi-operation mode constraint condition of the virtual power plant; and carrying out optimal solution on the optimization model to obtain an optimal scheduling result of the virtual power plant, and realizing daily scheduling of the virtual power plant. The invention fully digs the adjustment capability of the resources at the demand side, ensures the running economy of the virtual power plant and provides flexible service for the power grid.

Description

Virtual power plant day-ahead scheduling method integrating resources at multi-energy demand side
Technical Field
The invention belongs to the technical field of virtual power plant optimization, and particularly relates to a virtual power plant day-ahead scheduling method integrating resources of a multi-energy demand side.
Background
With the continuous deepening of energy structure transformation development, the proportion of renewable energy sources such as wind power, photovoltaic and the like in an electric power system is improved year by year, and the uncertainty of the renewable energy sources brings new challenges to the safe and economic operation of the electric power system, so that the structure and the operation mode of the traditional electric power system are changed deeply. The adjustment capability of the power generation side resource is difficult to meet the requirement of the power system on flexibility, so that a large-scale wind and light discarding phenomenon occurs. In this situation, demand side resources with considerable regulatory capabilities are increasingly gaining attention. However, because the resources on the demand side have the characteristics of small scale, large quantity, large characteristic difference and the like, the direct dispatching of the power system is difficult to accept, and meanwhile, the individual will not participate in the dispatching is not strong. The virtual power plant can aggregate a large amount of distributed power generation and multi-energy demand side resources through advanced intelligent measurement, information communication and other technologies, and optimally schedule the resources as a whole, meanwhile, the capacity limit of the distributed resources is overcome, and the virtual power plant is taken as a special main body to participate in electric power market transaction, so that the potential value of the demand side resources is fully exerted.
The existing research and engineering for the virtual power plant has fewer kinds of aggregated resources on the demand side, and the flexibility of the demand side cannot be fully excavated. The demand side resources have considerable regulatory capabilities.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a virtual power plant day-ahead scheduling method integrating the resources of the multi-energy demand side, aiming at the defects in the prior art, so that the flexibility of the resources of the demand side is fully exerted.
The invention adopts the following technical scheme:
a virtual power plant day-ahead scheduling method integrating resources at a multi-energy demand side comprises the following steps:
s1, acquiring distributed power generation parameters, future prediction data, parameters of resources at the side of the multi-energy demand, parameters of power batteries of electric vehicles, historical statistical data, initial running states of the resources at the side of the multi-energy demand, set running demands and electric power market data;
s2, constructing a normal operation mode optimization target of the virtual power plant according to the data acquired in the step S1, and establishing a multi-operation mode constraint condition of the virtual power plant;
and S3, carrying out optimal solution on the optimization model to obtain an optimal scheduling result of the virtual power plant, and realizing daily scheduling of the virtual power plant.
Specifically, in step S1, the distributed power generation parameters and the predicted data before date include: the installed capacity of the distributed photovoltaic power generation and the predicted output before the day; the installed capacity of the distributed wind power generation and the predicted output before the day;
The parameters of the resources at the multi-energy demand side include: the energy storage system comprises distributed energy storage rated capacity, upper and lower limits of charge states, maximum charge and discharge power, charge and discharge efficiency, maintenance cost coefficient, construction cost coefficient and cycle life; rated output and feedback power, output and feedback efficiency of the charging pile; rated power of the air conditioner, upper and lower limits of air supply quantity, heat capacity of a room and heat conductance between the room and outdoor air; the upper and lower limits of the water pump flow, the fitting coefficient of the lift, the running efficiency, the height of the reservoir, the bottom area, the upper and lower limits of the water level and the maximum water supply flow;
the electric automobile power battery parameters and historical statistics data comprise: rated capacity, upper and lower limits of charge state, maximum charge and discharge power and charge and discharge efficiency of a power battery of the electric automobile; determining a distribution rule of the electric vehicles reaching the area based on statistical data of the electric vehicles in the park, wherein the distribution rule comprises reaching time, initial state of charge of the power battery, residence time and expected state of charge of the power battery; charging and discharging prices of electric automobiles;
the initial running state of the resources at the multipotency requirement side and the set running requirement comprise: a distributed energy storage initial state of charge; the air supply temperature is set in each time period of the air conditioner, the indoor temperature requirements in each time period comprise the highest temperature and the lowest temperature, and the predicted outdoor environment temperature in each time period; the initial water level of the reservoir, the water demand of each period;
The power market data includes: electricity rate data, demand response time period, compensation price, reference power, and given operating curve.
Specifically, in step S2, in the normal operation mode, the virtual power plant operation economy is targeted; increasing penalty items deviating from the given curve and the envelope thereof according to the given curve compared with the normal operation mode; adding peak clipping compensation to a peak load clipping mode objective function before the day, and constructing a virtual power plant demand response mode optimization objective; and establishing a constraint condition of multiple operation modes of the virtual power plant.
Further, the running economy of the virtual power plant comprises electricity selling cost, distributed energy storage maintenance cost and depreciation cost of the energy market in the day, and the electric vehicle power supply income is specifically as follows:
Figure BDA0002903762490000031
wherein ,tend Scheduling an end period for a day-ahead; n (N) BES The number of the distributed energy storage in the virtual power plant; n (N) EV The number of the electric automobiles is the number;
Figure BDA0002903762490000032
the electricity purchase price of the virtual power plant in the t period;
Figure BDA0002903762490000033
The power purchasing power is the power purchasing power of the virtual power plant in the t period;
Figure BDA0002903762490000034
the electricity selling price of the virtual power plant in the t period;
Figure BDA0002903762490000035
The electricity selling power is the electricity selling power of the virtual power plant in the t period;
Figure BDA0002903762490000036
N is t time period BES Maintenance cost of the operation of the energy storage unit;
Figure BDA0002903762490000037
N is t time period BES Depreciation cost of operation of the energy storage unit; / >
Figure BDA0002903762490000038
N is t time period EV Charging power of the electric automobile;
Figure BDA0002903762490000039
Charging price for the electric automobile t period;
Figure BDA00029037624900000310
is the nth EV Charging power of the electric automobile in a t period;
Figure BDA00029037624900000311
Discharging price for the electric automobile in the t period;
Figure BDA00029037624900000312
Is the nth EV And discharging power of the electric automobile in a t period.
Further, the mode of operation is set to a given curve:
Figure BDA0002903762490000041
Figure BDA0002903762490000042
Figure BDA0002903762490000043
wherein ,
Figure BDA0002903762490000044
and
Figure BDA0002903762490000045
Penalty coefficients when deviating from the envelope and given operating curve, respectively;
Figure BDA0002903762490000046
Giving curve power for t period;
Figure BDA0002903762490000047
And->
Figure BDA0002903762490000048
Respectively, the power of t time periods deviating from a given curve and an envelope curve thereof;
Figure BDA0002903762490000049
Giving half of the curve envelope power value for the period t;
peak load peak clipping mode before day
Figure BDA00029037624900000410
wherein ,
Figure BDA00029037624900000411
the power purchasing power of the virtual power plant is the power purchasing power of the virtual power plant in the t period under the normal operation mode;
Figure BDA00029037624900000412
Selling electric power for the virtual power plant in the period t under the normal operation mode;
Figure BDA00029037624900000413
Compensating price for peak load clipping before the day. The baseline power is assumed here to be the switching power for the normal mode of operation.
Further, the constraint conditions of the multiple operation modes of the virtual power plant are specifically as follows:
power balance constraint:
Figure BDA0002903762490000051
wherein ,
Figure BDA0002903762490000052
the total output of the wind turbine generator set is t time period;
Figure BDA0002903762490000053
The total output force of the photovoltaic unit is t time periods;
Figure BDA0002903762490000054
The total output of the energy storage unit is t time periods;
Figure BDA0002903762490000055
The total charging power of the electric automobile is t time periods;
Figure BDA0002903762490000056
The total power of the air conditioner is t time periods;
Figure BDA0002903762490000057
The total power of the pump station in the period t is the total power of the pump station in the period t;
distributed genset constraints:
Figure BDA0002903762490000058
Figure BDA0002903762490000059
wherein ,
Figure BDA00029037624900000510
is the nth PV Predicting output before a photovoltaic unit t period of time;
Figure BDA00029037624900000511
Is the nth PV The actual output force of the photovoltaic unit in the t period;
Figure BDA00029037624900000512
Is the nth WT Predicting output before a period t of the wind turbine;
Figure BDA00029037624900000513
Nth (n) WT Actual output of the wind turbine generator set in a t period;
and (3) constraint of charge and discharge power of the distributed energy storage unit:
Figure BDA00029037624900000514
wherein ,
Figure BDA00029037624900000515
and->
Figure BDA00029037624900000516
Respectively the nth BES Maximum charge and discharge power of the energy storage unit;
state of charge constraints for distributed energy storage units:
Figure BDA00029037624900000517
wherein ,
Figure BDA00029037624900000518
is the nth BES The state of charge of the energy storage unit in the t period;
Figure BDA00029037624900000519
And->
Figure BDA00029037624900000520
Respectively the nth BES The upper and lower limits of the charge state of the energy storage unit;
the charge state process of the distributed energy storage unit is as follows:
Figure BDA00029037624900000521
wherein ：
Figure BDA0002903762490000061
and->
Figure BDA0002903762490000062
Is the nth BES Charging and discharging efficiency of the energy storage unit;
Figure BDA0002903762490000063
Is the nth BES Rated capacity of the energy storage unit;
power battery charge and discharge power constraint:
Figure BDA0002903762490000064
wherein ,
Figure BDA0002903762490000065
and->
Figure BDA0002903762490000066
Respectively the nth EV Maximum charge and discharge power of a power battery of the electric vehicle;
Figure BDA0002903762490000067
and->
Figure BDA0002903762490000068
Respectively the nth EV The charging and discharging state of the electric automobile in the t period;
Figure BDA0002903762490000069
Is the nth EV The access state of the electric automobile t period;
Figure BDA00029037624900000610
And->
Figure BDA00029037624900000611
Respectively the nth EV Charging and discharging power of the electric automobile in a t period;
mutual exclusion constraint of charging and discharging states of electric vehicles:
Figure BDA00029037624900000612
when the electric automobile leaves, the charge state is not lower than the expected value as follows:
Figure BDA00029037624900000613
wherein ,
Figure BDA00029037624900000614
is the nth EV Departure time of electric vehicle, < >>
Figure BDA00029037624900000615
Is the nth EV A desired state of charge set by the vehicle electric vehicle; the relation between the pressure and the lift of the water pump is as follows:
Figure BDA00029037624900000616
wherein ,
Figure BDA00029037624900000617
is the nth RWT Nth of the reservoirs WP The pressure of the water pump is equal to the pressure of the water pump in the t period;
Figure BDA00029037624900000618
Is the nth RWT Nth of the reservoirs WP The pump lifts at the t period; ρ is the water density; g is gravity acceleration;
the relationship between the lift and the flow of the water pump is as follows:
Figure BDA00029037624900000619
wherein ,
Figure BDA0002903762490000071
is the nth RWT Nth of the reservoirs WP The flow rate of the water pump t time period;
Figure BDA0002903762490000072
Figure BDA0002903762490000073
And->
Figure BDA0002903762490000074
Fitting coefficients for the power of the water pump;
Figure BDA0002903762490000075
Is the nth RWT Nth of the reservoirs WP The running state of the water pump in the period t;
the upper and lower limits of the flow of the water pump are restricted as follows:
Figure BDA0002903762490000076
wherein ,
Figure BDA0002903762490000077
and->
Figure BDA0002903762490000078
Respectively the nth RWT Nth of the reservoirs WP Upper and lower flow limits for the individual water pumps; the reservoir water level constraint is:
Figure BDA0002903762490000079
wherein ,
Figure BDA00029037624900000710
and->
Figure BDA00029037624900000711
Respectively the nth RWT The upper and lower limits of the water level of each reservoir;
Figure BDA00029037624900000712
Is the nth RWT Water levels of the reservoirs in the period t;
the dynamic process of the water level of the reservoir is as follows:
Figure BDA00029037624900000713
wherein ：NWP Is the nth RWT The number of water inlet pumps of the water reservoirs;
Figure BDA00029037624900000714
is the nth RWT Water supply amount of the water reservoirs in a t period;
Figure BDA00029037624900000715
is the nth RWT The bottom area of each reservoir;
the relation between the air conditioner power and the air supply quantity is as follows:
Figure BDA00029037624900000716
wherein ,
Figure BDA00029037624900000717
is the nth RM Nth of the rooms AC Power of the air conditioner t period;
Figure BDA00029037624900000718
Is the nth RM Nth of the rooms AC The air quantity of each air conditioner in the t period;
Figure BDA00029037624900000719
Is the nth RM Nth of the rooms AC Rated power of each air conditioner;
Figure BDA00029037624900000720
Respectively the nth RM Nth of the rooms AC The upper limit of the air supply quantity of each air conditioner;
upper and lower limit constraint of air delivery volume of air conditioner:
Figure BDA00029037624900000721
wherein ,
Figure BDA0002903762490000081
is the nth RM Nth of the rooms AC The running state of each air conditioner in the period t;
Figure BDA0002903762490000082
Is the nth RM Nth of the rooms AC The lower limit of the air supply quantity of the air conditioner;
the upper and lower limits of the room temperature are:
Figure BDA0002903762490000083
wherein ,
Figure BDA0002903762490000084
and->
Figure BDA0002903762490000085
Is the nth RM Upper and lower limits of indoor temperature of the individual rooms;
Figure BDA0002903762490000086
Is the nth RM T-period temperatures of the individual rooms;
the heating capacity of the air conditioner can be expressed as:
Figure BDA0002903762490000087
wherein ,
Figure BDA0002903762490000088
is the nth RM Nth of the rooms AC Heating capacity of each air conditioner in t time period;
Figure BDA0002903762490000089
Is the nth RM Nth of the rooms AC Air supply temperature of each air conditioner in a t period; c air Is the specific heat capacity of air;
the room temperature dynamic change process comprises the following steps:
Figure BDA00029037624900000810
wherein ：
Figure BDA00029037624900000811
is the nth RM The heat capacity of the individual rooms;
Figure BDA00029037624900000812
Is the nth RM Thermal conductance between the indoor and outdoor of the individual rooms; n (N) AC The number of air conditioners for a single room;
Figure BDA00029037624900000813
Is the ambient temperature for period t.
Specifically, in step S3, the optimal scheduling result of the virtual power plant includes: the purchase and sale electric power of the virtual power plant; virtual power plant economic indicators; the virtual power plant arranges the running conditions of the distributed generator set and the multi-energy demand side resource on the next day according to the day scheduling result, and simultaneously reports the exchange power of the virtual power plant and the main network to a scheduling department, and the virtual power plant performs subsequent real-time scheduling according to the day scheduling result.
Compared with the prior art, the invention has at least the following beneficial effects:
according to the day-ahead scheduling method for the virtual power plant, under the background that new energy is used for generating electricity and is accessed into a power system in a large scale, the adjustment capability of the power generation side can not meet the flexibility requirement of the power system gradually, and the adjustment capability of the resource on the excavation requirement side is more important. The virtual power plant coordinates and optimizes a large amount of demand side resources, so that the adjustment capability of the demand side resources can be effectively exerted, and the construction of a new power plant is delayed. The energy storage characteristics of the resources on the demand side such as an air conditioner, a pump station and the like are effectively utilized, the adjusting capacity of the energy storage device is fully exerted, and the adjusting pressure on the power generation side can be effectively reduced. The conventional demand side resource management does not consider the multi-type demand response of the power grid and cannot provide corresponding demand response service. According to the method provided by the invention, from the perspective of a power grid, a large amount of demand side resources are aggregated by utilizing a virtual power plant technology to perform unified management, so that the damage of disordered charging of the electric automobile to a power system can be reduced, and meanwhile, the adjustment capability of the demand side resources can be fully excavated under the condition that the normal use of a user is not influenced, and flexible service is provided for the power grid; from the perspective of the virtual power plant, the virtual power plant may receive additional revenue by responding to the external demand response signal.
Further, step S1 obtains all information required by the virtual power plant scheduling model, and the virtual power plant aggregates various resources including distributed generator sets and multi-energy demand side resources, and meanwhile needs to exchange electric energy with the main network, so that input data includes technical and economic parameters of each device, predicted output of the distributed generator sets, statistical data of electric vehicles, energy market electricity prices and the like, and subsequent calculation is performed by using the data.
Further, in order to fully exert the capacity of adjusting the demand side resources, the virtual power plant can respond to the demand response signal of the dispatching center to adjust the output so as to meet the operation demands of the power grid. The invention considers that the dispatching center has different flexibility demands according to the running condition of the power system. In the peak load period, the dispatching center transmits a peak clipping signal to the virtual power plant; in extreme cases, the dispatch center may directly issue the operating curve. And setting corresponding objective functions according to different requirements, and realizing multiple operation modes of the virtual power plant.
Further, the normal operation mode objective function of the virtual power plant considers the electricity purchasing and selling cost of the virtual power plant in the energy market, the maintenance cost and depreciation cost of distributed energy storage and the benefit of providing charge and discharge service for the electric automobile, and the economical efficiency of the operation of the virtual power plant can be realized under the objective.
Further, in a given curve running mode, the virtual power plant needs to run along the curve issued by the dispatching department, so that compared with a normal running mode, penalty items deviating from the given curve and the envelope curve of the given curve are increased; in the peak load clipping mode before the day, the virtual power plant can acquire compensation by increasing the output force or reducing the load in the peak clipping period, so that the peak clipping compensation is increased by the objective function compared with the normal operation mode.
Further, virtual power plants aggregate a large amount of distributed resources, and various elements have different characteristics and operation requirements, so that analysis of technical and economic characteristics of the elements is required. The virtual power plant scheduling model fully considers the operation constraint of each element, including the output constraint of a distributed generator set, the operation constraint of an electric vehicle power battery, the operation constraint of a pump station and a central air conditioner, and the like. Through these constraints, safe operation of the system may be ensured.
Furthermore, the virtual power plant scheduling model is a mixed integer linear programming, and a branch-and-bound method is utilized for solving to obtain a day-ahead scheduling result. And according to the scheduling result, the running conditions of the next-day distributed generator set and the multi-energy demand side resource are arranged, meanwhile, the exchange power of the virtual power plant and the main network is reported to a scheduling department, and the follow-up real-time scheduling is carried out according to the day-ahead scheduling result.
In summary, the invention fully exploits the regulation capability of the demand side resources, and provides flexible services to the power grid while guaranteeing the operational economy of the virtual power plant.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
FIG. 1 is a graph of virtual power plant scheduling results in a normal operating mode;
FIG. 2 is a graph of virtual power plant scheduling results for a given curve operation;
FIG. 3 is a graph of a virtual power plant scheduling result in a peak load clipping mode before the day;
fig. 4 is a flow chart of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Various structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the accompanying drawings. The figures are not drawn to scale, wherein certain details are exaggerated for clarity of presentation and may have been omitted. The shapes of the various regions, layers and their relative sizes, positional relationships shown in the drawings are merely exemplary, may in practice deviate due to manufacturing tolerances or technical limitations, and one skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions as actually required.
The invention provides a virtual power plant day-ahead scheduling method integrating multi-energy demand side resources, which is based on a park-level virtual power plant and fully considers the schedulable demand side resources of a park, and comprises distributed energy storage, an electric automobile, a pump station and an air conditioner. In order to fully exert the regulation capability of the resource at the demand side, the virtual power plant can respond to the demand response signal of the dispatching center to regulate the output so as to meet the operation requirement of the power grid. The invention considers that the dispatching center has different flexibility demands according to the running condition of the power system. In the peak load period, the dispatching center transmits a peak clipping signal to the virtual power plant; in extreme cases, the dispatch center may directly issue the operating curve. Based on the above, the invention provides 3 virtual power plant operation modes, namely a normal operation mode, an operation mode according to a given curve and a peak load peak clipping mode before the day. The priority of the running mode is highest according to a given curve, and if a command is issued by a dispatching center, the virtual power plant immediately switches to the mode; if the command is not available, the virtual power plant determines whether to respond to the peak load peak clipping signal with the aim of economy; the virtual power plant operation economy can be realized, meanwhile, the flexibility of the resources at the demand side is effectively utilized, the demand response signal of the dispatching center is responded, and a basis is provided for flexible management of the resources at the multi-energy demand side.
Referring to fig. 4, the day-ahead scheduling method of the virtual power plant integrating the resources of the multi-energy demand side of the invention comprises the following steps:
s1, acquiring distributed power generation parameters, future prediction data, parameters of the resources at the multi-energy demand side, electric vehicle power battery parameters, historical statistical data, initial running states of the resources at the multi-energy demand side, set running demands and electric power market data from related departments;
distributed power generation parameters and day-ahead prediction data: the installed capacity of the distributed photovoltaic power generation and the predicted output before the day; the installed capacity of the distributed wind power generation and the predicted output before the day.
Parameters of the resources on the multi-energy demand side: the energy storage system comprises distributed energy storage rated capacity, upper and lower limits of charge states, maximum charge and discharge power, charge and discharge efficiency, maintenance cost coefficient, construction cost coefficient and cycle life; rated output and feedback power, output and feedback efficiency of the charging pile; rated power of the air conditioner, upper and lower limits of air supply quantity, heat capacity of a room and heat conductance between the room and outdoor air; the water pump flow upper and lower limits, the lift fitting coefficient, the running efficiency, the reservoir height, the bottom area, the water level upper and lower limits and the maximum water supply flow.
Electric vehicle power battery parameters and historical statistics: rated capacity, upper and lower limits of charge state, maximum charge and discharge power and charge and discharge efficiency of a power battery of the electric automobile; determining a distribution rule of the electric vehicles reaching the area based on statistical data of the electric vehicles in the park, wherein the distribution rule comprises reaching time, initial state of charge of the power battery, residence time and expected state of charge of the power battery; charging and discharging prices of electric automobiles.
Initial running state of resources at multipotency requirement side and set running requirement: a distributed energy storage initial state of charge; the air supply temperature is set in each time period of the air conditioner, the indoor temperature requirements in each time period comprise the highest temperature and the lowest temperature, and the predicted outdoor environment temperature in each time period; the initial water level of the reservoir and the water demand in each period.
Electric market data: electricity rate data, demand response time period, compensation price, reference power, and given operating curve.
S2, constructing a virtual power plant normal operation mode optimization target;
s201, the normal operation mode aims at the operation economy of the virtual power plant and comprises the electricity selling cost, the distributed energy storage maintenance cost and the depreciation cost of the energy market before the day, and the power supply income of the electric automobile.
Figure BDA0002903762490000131
wherein ,tend Scheduling an end period for a day-ahead; n (N) BES The number of the distributed energy storage in the virtual power plant; n (N) EV The number of the electric automobiles is the number;
Figure BDA0002903762490000132
the electricity purchase price of the virtual power plant in the t period;
Figure BDA0002903762490000133
The power purchasing power is the power purchasing power of the virtual power plant in the t period;
Figure BDA0002903762490000134
the electricity selling price of the virtual power plant in the t period;
Figure BDA0002903762490000135
The electricity selling power is the electricity selling power of the virtual power plant in the t period;
Figure BDA0002903762490000136
N is t time period BES Maintenance of operation of energy storage unitCost;
Figure BDA0002903762490000137
N is t time period BES Depreciation cost of operation of the energy storage unit; / >
Figure BDA0002903762490000138
N is t time period EV Charging power of the electric automobile;
Figure BDA0002903762490000139
Charging price for the electric automobile t period;
Figure BDA00029037624900001310
is the nth EV Charging power of the electric automobile in a t period;
Figure BDA00029037624900001311
Discharging price for the electric automobile in the t period;
Figure BDA00029037624900001312
Is the nth EV And discharging power of the electric automobile in a t period.
The maintenance cost and depreciation cost of the operation of the energy storage unit are as follows:
Figure BDA0002903762490000141
Figure BDA0002903762490000142
Figure BDA0002903762490000143
wherein ,
Figure BDA0002903762490000144
is the nth BES Operation and maintenance cost coefficient of energy storage unit；
Figure BDA0002903762490000145
Is the nth BES The construction cost coefficient of the energy storage unit;
Figure BDA0002903762490000146
Is the nth BES The cycle life of the energy storage unit;
Figure BDA0002903762490000147
Is the nth BES The power of the energy storage unit t period;
Figure BDA0002903762490000148
And->
Figure BDA0002903762490000149
Respectively the nth BES Charging and discharging power of the energy storage unit in a t period;
Figure BDA00029037624900001410
And (3) with
Figure BDA00029037624900001411
Respectively the nth BES And the upper and lower limits of the charge state of the energy storage unit are set.
S202, constructing a virtual power plant demand response mode optimization target
Increasing penalty items deviating from the given curve and the envelope thereof according to the given curve compared with the normal operation mode; and adding peak clipping compensation to the peak load clipping mode objective function before the day.
1) Operating in a given curvilinear mode
Figure BDA00029037624900001412
Figure BDA00029037624900001413
Figure BDA00029037624900001414
wherein ,
Figure BDA0002903762490000151
and
Figure BDA0002903762490000152
Penalty coefficients when deviating from the envelope and given operating curve, respectively;
Figure BDA0002903762490000153
Giving curve power for t period;
Figure BDA0002903762490000154
And->
Figure BDA0002903762490000155
Respectively, the power of t time periods deviating from a given curve and an envelope curve thereof;
Figure BDA0002903762490000156
Half of the curve envelope power value is given for period t.
2) Peak load peak clipping mode before day
Figure BDA0002903762490000157
wherein ,
Figure BDA0002903762490000158
the power purchasing power of the virtual power plant is the power purchasing power of the virtual power plant in the t period under the normal operation mode;
Figure BDA0002903762490000159
Selling electric power for the virtual power plant in the period t under the normal operation mode;
Figure BDA00029037624900001510
Is the day beforePeak load peak clipping compensation price. The baseline power is assumed here to be the switching power for the normal mode of operation.
S203, establishing a constraint condition of multiple operation modes of the virtual power plant
1) Power balance constraint:
Figure BDA00029037624900001511
wherein ,
Figure BDA00029037624900001512
the total output of the wind turbine generator set is t time period;
Figure BDA00029037624900001513
The total output force of the photovoltaic unit is t time periods;
Figure BDA00029037624900001514
The total output of the energy storage unit is t time periods;
Figure BDA00029037624900001515
The total charging power of the electric automobile is t time periods;
Figure BDA00029037624900001516
The total power of the air conditioner is t time periods;
Figure BDA00029037624900001517
and (5) the total power of the pump station in the period t.
2) Distributed genset constraints:
Figure BDA00029037624900001518
Figure BDA00029037624900001519
wherein ,
Figure BDA0002903762490000161
is the nth PV Predicting output before a photovoltaic unit t period of time;
Figure BDA0002903762490000162
Is the nth PV The actual output force of the photovoltaic unit in the t period;
Figure BDA0002903762490000163
Is the nth WT Predicting output before a period t of the wind turbine;
Figure BDA0002903762490000164
Nth (n) WT And the actual output of the wind turbine generator is output in the t period.
3) Constraint of a distributed energy storage unit:
the charging and discharging power of the energy storage unit can be limited by the converter, and the charging and discharging power cannot exceed the rated value of the converter, namely:
Figure BDA0002903762490000165
wherein ,
Figure BDA0002903762490000166
and->
Figure BDA0002903762490000167
Respectively the nth BES And the maximum charge and discharge power of the energy storage unit is achieved.
State of charge constraints for energy storage units:
Figure BDA0002903762490000168
wherein ,
Figure BDA0002903762490000169
is the nth BES And the state of charge of the energy storage unit in the period t.
The dynamic process of the state of charge of the energy storage unit comprises the following steps:
Figure BDA00029037624900001610
wherein ,
Figure BDA00029037624900001611
and->
Figure BDA00029037624900001612
Is the nth BES Charging and discharging efficiency of the energy storage unit;
Figure BDA00029037624900001613
Is the nth BES Rated capacity of the energy storage unit.
In order to ensure the normal operation of the energy storage unit, the consistent charge state at the beginning and the end of a scheduling period is required to be ensured, namely:
Figure BDA00029037624900001614
4) Electric automobile restraint:
power battery charge and discharge power constraint:
Figure BDA0002903762490000171
wherein ,
Figure BDA0002903762490000172
and->
Figure BDA0002903762490000173
Respectively the nth EV Maximum charge and discharge power of a power battery of the electric vehicle;
Figure BDA0002903762490000174
And->
Figure BDA0002903762490000175
Respectively the nth EV The charging and discharging state of the electric automobile in the t period;
Figure BDA0002903762490000176
Is the nth EV The access state of the electric automobile t period;
Figure BDA0002903762490000177
And->
Figure BDA0002903762490000178
Respectively the nth EV And charging and discharging power of the electric automobile t period.
Mutual exclusion constraint of charging and discharging states of electric vehicles:
Figure BDA0002903762490000179
when the electric automobile leaves, the charge state is not lower than the expected value, namely
Figure BDA00029037624900001710
wherein ,
Figure BDA00029037624900001711
is the nth EV Departure time of electric vehicle, < >>
Figure BDA00029037624900001712
Is the nth EV The desired state of charge set by the electric vehicle.
In addition, the dynamic process and the constraint of the state of charge of the power battery of the electric automobile are similar to those of an energy storage unit.
5) And (3) constraint of a pump station:
the relation between the pressure and the lift of the water pump is as follows:
Figure BDA00029037624900001713
wherein ,
Figure BDA00029037624900001714
Is the nth RWT Nth of the reservoirs WP The pressure of the water pump is equal to the pressure of the water pump in the t period;
Figure BDA00029037624900001715
Is the nth RWT Nth of the reservoirs WP The pump lifts at the t period; ρ is the water density; g is gravitational acceleration.
The relationship between the lift and the flow of the water pump is as follows:
Figure BDA00029037624900001716
wherein ,
Figure BDA0002903762490000181
is the nth RWT Nth of the reservoirs WP The flow rate of the water pump t time period;
Figure BDA0002903762490000182
Figure BDA0002903762490000183
And (3) with
Figure BDA0002903762490000184
Fitting coefficients for the power of the water pump;
Figure BDA0002903762490000185
Is the nth RWT Nth of the reservoirs WP The running state of the water pump in the period t;
the water pump power can be obtained from the above relation:
Figure BDA0002903762490000186
because the power curve of the water pump is not convex, the incremental method is adopted to carry out piecewise linearization on the power function of the water pump, and the power function of the water pump is divided into m WP Segment, namely:
Figure BDA0002903762490000187
wherein ,
Figure BDA0002903762490000188
slope of each segment of the power function after piecewise linearization;
Figure BDA0002903762490000189
The water pump is started and the minimum flow is +.>
Figure BDA00029037624900001810
Running the consumed power;
Figure BDA00029037624900001811
Is a segmented flow; s is the segment number. />
Order the
Figure BDA00029037624900001812
The parameters of the above formula can be expressed as:
Figure BDA00029037624900001813
Figure BDA00029037624900001814
where s is a segment number. The variables of the above formula satisfy the following constraints:
Figure BDA00029037624900001815
Figure BDA00029037624900001816
Figure BDA0002903762490000191
the upper and lower limits of the flow of the water pump are restricted as follows:
Figure BDA0002903762490000192
wherein ,
Figure BDA0002903762490000193
and->
Figure BDA0002903762490000194
Respectively the nth RWT Nth of the reservoirs WP The upper and lower flow limits of the individual water pumps.
The dynamic process of the water level of the reservoir is as follows:
Figure BDA0002903762490000195
wherein ,NWP Is the nth RWT The number of water inlet pumps of the water reservoirs;
Figure BDA0002903762490000196
is the nth RWT Water supply requirements for each reservoir t period.
The reservoir water level constraint is:
Figure BDA0002903762490000197
wherein ,
Figure BDA0002903762490000198
and->
Figure BDA0002903762490000199
Respectively the nth RWT The upper and lower limits of the water level of each reservoir;
Figure BDA00029037624900001910
Is the nth RWT Water level of each reservoir t period
6) Air conditioning constraints:
the relation between the air conditioner power and the air supply quantity is as follows:
Figure BDA00029037624900001911
wherein ,
Figure BDA00029037624900001912
is the nth RM Nth of the rooms AC Power of the air conditioner t period;
Figure BDA00029037624900001913
Is the nth RM Nth of the rooms AC The air quantity of each air conditioner in the t period;
Figure BDA00029037624900001914
Is the nth RM Nth of the rooms AC Rated power of each air conditioner;
Figure BDA00029037624900001915
Respectively the nth RM Nth of the rooms AC The upper limit of the air supply quantity of the air conditioner.
The incremental method is also adopted to carry out piecewise linearization on the air conditioner power function, and the process is similar to linearization of the water pump power function.
Upper and lower limit constraint of air delivery volume of air conditioner:
Figure BDA0002903762490000201
wherein ,
Figure BDA0002903762490000202
is the nth RM Nth of the rooms AC The running state of each air conditioner in the period t;
Figure BDA0002903762490000203
Is the nth RM Nth of the rooms AC The lower limit of the air supply quantity of the air conditioner.
The heating capacity of the air conditioner can be expressed as:
Figure BDA0002903762490000204
wherein ,
Figure BDA0002903762490000205
is the nth RM Nth of the rooms AC Heating capacity of each air conditioner in t time period;
Figure BDA0002903762490000206
Is the nth RM T-period temperatures of the individual rooms;
Figure BDA0002903762490000207
Is the nth RM Nth of the rooms AC Air supply temperature of each air conditioner in a t period; c air Is the specific heat capacity of air.
Figure BDA0002903762490000208
For bilinear terms, linearization is performed using the boolean expansion method. Discretizing the indoor temperature, namely:
Figure BDA0002903762490000209
Figure BDA00029037624900002010
wherein ,M＝2K ，λ k (t) is an introduced binary variable, the selection of each segment can be realized, Then
Figure BDA00029037624900002011
The conversion into the following form:
Figure BDA00029037624900002012
wherein
Figure BDA00029037624900002013
Linearization is carried out by adopting a Big-M method, namely:
Figure BDA00029037624900002014
Figure BDA00029037624900002015
the indoor temperature dynamics can be expressed as:
Figure BDA0002903762490000211
wherein ,
Figure BDA0002903762490000212
outdoor ambient temperature for a period t;
Figure BDA0002903762490000213
Is the nth RM Heat capacity of each room;
Figure BDA0002903762490000214
Is the nth RM Indoor and outdoor thermal conductance of the individual rooms; n (N) AC Is the number of room air conditioners.
To ensure user comfort, the upper and lower room temperature limits are constrained as:
Figure BDA0002903762490000215
wherein ,
Figure BDA0002903762490000216
and->
Figure BDA0002903762490000217
Is the nth RM Upper and lower limits of indoor temperature in individual rooms.
And S3, solving and optimizing the optimization model formed in the previous step to obtain an optimized dispatching result of the virtual power plant.
The optimal scheduling result comprises the following steps:
the purchase and sale electric power of the virtual power plant; virtual power plant economic indicators; a distributed power generation output condition; and the virtual power plant arranges the running conditions of the next-day distributed generator set and the multi-energy demand side resources according to the day-ahead scheduling result, and simultaneously reports the exchange power of the virtual power plant and the main network to a scheduling department, and the virtual power plant performs subsequent real-time scheduling according to the day-ahead scheduling result.
In one embodiment, the invention provides a virtual power plant day-ahead dispatching system integrating multi-energy demand side resources, which can be used for realizing the virtual power plant day-ahead dispatching method, and specifically comprises an acquisition module, an optimization module and a dispatching module.
The acquisition module acquires distributed power generation parameters, future prediction data, parameters of the resources at the multi-energy demand side, electric vehicle power battery parameters, historical statistical data, initial running states of the resources at the multi-energy demand side, set running demands and electric power market data;
the optimizing module is used for constructing a normal operation mode optimizing target of the virtual power plant and establishing a multi-operation mode constraint condition of the virtual power plant;
and the scheduling module is used for optimally solving the optimization model to obtain an optimal scheduling result of the virtual power plant, and realizing daily scheduling of the virtual power plant.
In one embodiment, the invention provides a terminal device comprising a processor and a memory, the memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor of the embodiment of the invention can be used for fusing the operation of the day-ahead scheduling method of the virtual power plant of the resources of the multi-energy demand side, and comprises the following steps: acquiring distributed power generation parameters, daily forecast data, parameters of the resources at the multi-energy demand side, parameters of the power battery of the electric vehicle, historical statistical data, initial running states of the resources at the multi-energy demand side, set running demands and electric power market data; constructing a normal operation mode optimization target of the virtual power plant according to the acquired data, and establishing a multi-operation mode constraint condition of the virtual power plant; and carrying out optimal solution on the optimization model to obtain an optimal scheduling result of the virtual power plant, and realizing daily scheduling of the virtual power plant.
The present invention also provides, in one embodiment, a storage medium, in particular, a computer-readable storage medium (Memory), which is a Memory device in a terminal device, for storing programs and data. It will be appreciated that the computer readable storage medium herein may include both a built-in storage medium in the terminal device and an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the respective steps of the virtual power plant day-ahead scheduling method with respect to fusing multi-energy demand-side resources in the above-described embodiments; one or more instructions in a computer-readable storage medium are loaded by a processor and perform the steps of: acquiring distributed power generation parameters, daily forecast data, parameters of the resources at the multi-energy demand side, parameters of the power battery of the electric vehicle, historical statistical data, initial running states of the resources at the multi-energy demand side, set running demands and electric power market data; constructing a normal operation mode optimization target of the virtual power plant according to the acquired data, and establishing a multi-operation mode constraint condition of the virtual power plant; and carrying out optimal solution on the optimization model to obtain an optimal scheduling result of the virtual power plant, and realizing daily scheduling of the virtual power plant.
The invention is based on a park-level virtual power plant, fully considers the schedulable demand side resources of the park, and comprises distributed energy storage, an electric automobile, a water pump station and an air conditioner. Under the support of the V2G technology, the power battery of the electric automobile has the characteristic of electrochemical energy storage, and the charging and discharging power of the electric automobile can be managed during the process of connecting the electric automobile into the charging pile; because of the existence of thermal inertia, the indoor temperature cannot be changed drastically in a short time, so that the air conditioner power can be adjusted while ensuring the room temperature to be proper; as the supporting facility of pump station, building attic is built with large-scale cistern generally, under the cistern cushioning effect, can guarantee not influenced at domestic water, carries out suitable adjustment to the power of water pump.
In order to fully exert the regulation capability of the resource at the demand side, the virtual power plant can respond to the demand response signal of the dispatching center to regulate the output so as to meet the operation requirement of the power grid. The invention considers that the dispatching center has different flexibility demands according to the running condition of the power system. In the peak load period, the dispatching center transmits a peak clipping signal to the virtual power plant; in extreme cases, the dispatch center may directly issue the operating curve. Based on the above, the invention provides 3 virtual power plant operation modes, namely a normal operation mode, an operation mode according to a given curve and a peak load peak clipping mode before the day. The priority of the running mode is highest according to a given curve, and if a command is issued by a dispatching center, the virtual power plant immediately switches to the mode; if not, the virtual power plant determines whether to respond to the peak load clipping signal with an economic goal.
Referring to fig. 1, as a result of scheduling a virtual power plant in a normal operation mode, it can be seen from the figure that the virtual power plant reasonably schedules the operation conditions of elements in the system according to the change of the external electricity price, so as to obtain better economy.
Referring to fig. 2, in order to set the given curve as the exchange power between the virtual power plant and the main network in the normal operation mode, the virtual power plant can be operated strictly following the given curve, as can be seen from the figure.
Referring to fig. 3, for the virtual power plant scheduling result in the peak load clipping mode in the day-ahead, the reference power is set to be the exchange power between the virtual power plant and the main network in the normal operation mode, and it can be seen from the figure that the output of the virtual power plant is obviously increased in order to obtain more peak clipping compensation in two peak clipping periods.
In summary, the invention provides a day-ahead scheduling method of a virtual power plant integrating multi-energy demand side resources, which uses virtual power plant technology to aggregate a large amount of demand side resources for unified management from the perspective of a power grid, can reduce the damage of disordered charging of an electric automobile to a power system, and can fully mine the adjustment capability of the demand side resources and provide flexible service for the power grid under the condition of not affecting normal use of users; from the perspective of the virtual power plant, the virtual power plant may receive additional revenue by responding to the external demand response signal.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (3)

1. A virtual power plant day-ahead scheduling method integrating multipotency demand side resources is characterized by comprising the following steps:
S1, acquiring distributed power generation parameters, future prediction data, parameters of resources at the side of the multi-energy demand, parameters of power batteries of electric vehicles, historical statistical data, initial running states of the resources at the side of the multi-energy demand, set running demands and electric power market data;
s2, constructing a virtual power plant normal operation mode optimization target according to the data acquired in the step S1, and establishing a virtual power plant multi-operation mode constraint condition, wherein in the normal operation mode, the virtual power plant operation economy is targeted; increasing penalty items deviating from the given curve and the envelope thereof according to the given curve compared with the normal operation mode; adding peak clipping compensation to a peak load clipping mode objective function before the day, and constructing a virtual power plant demand response mode optimization objective; establishing constraint conditions of multiple operation modes of a virtual power plant, wherein the operation economy of the virtual power plant comprises electricity purchasing expense, distributed energy storage maintenance expense and depreciation expense of the energy market in the day, and the power supply income of an electric vehicle is as follows:
Figure FDA0004042219110000011
wherein ,tend Scheduling an end period for a day-ahead; n (N) BES The number of the distributed energy storage in the virtual power plant; n (N) EV The number of the electric automobiles is the number;
Figure FDA0004042219110000012
the electricity purchase price of the virtual power plant in the t period;
Figure FDA0004042219110000013
The power purchasing power is the power purchasing power of the virtual power plant in the t period; / >
Figure FDA0004042219110000014
The electricity selling price of the virtual power plant in the t period;
Figure FDA0004042219110000015
The electricity selling power is the electricity selling power of the virtual power plant in the t period;
Figure FDA0004042219110000016
N is t time period BES Maintenance cost of the operation of the energy storage unit;
Figure FDA0004042219110000017
N is t time period BES Depreciation cost of operation of the energy storage unit;
Figure FDA0004042219110000018
n is t time period EV Charging power of the electric automobile;
Figure FDA0004042219110000019
Charging price for the electric automobile t period;
Figure FDA00040422191100000110
is the nth EV Charging power of the electric automobile in a t period;
Figure FDA00040422191100000111
Discharging price for the electric automobile in the t period;
Figure FDA00040422191100000112
Is the nth EV Discharging power of the electric automobile in a t period;
operating mode in a given curve:
Figure FDA0004042219110000021
Figure FDA0004042219110000022
Figure FDA0004042219110000023
wherein ,
Figure FDA0004042219110000024
and
Figure FDA0004042219110000025
Penalty coefficients when deviating from the envelope and given operating curve, respectively;
Figure FDA0004042219110000026
Giving curve power for t period;
Figure FDA0004042219110000027
And->
Figure FDA0004042219110000028
Respectively, the power of t time periods deviating from a given curve and an envelope curve thereof;
Figure FDA0004042219110000029
giving half of the curve envelope power value for the period t;
peak load peak clipping mode before day
Figure FDA00040422191100000210
wherein ,
Figure FDA00040422191100000211
the power purchasing power of the virtual power plant is the power purchasing power of the virtual power plant in the t period under the normal operation mode;
Figure FDA00040422191100000212
Selling electric power for the virtual power plant in the period t under the normal operation mode;
Figure FDA00040422191100000213
Compensating the price for peak load clipping before the day;
the constraint conditions of the multiple operation modes of the virtual power plant are specifically as follows:
power balance constraint:
Figure FDA0004042219110000031
wherein ,
Figure FDA0004042219110000032
the total output of the wind turbine generator set is t time period;
Figure FDA0004042219110000033
The total output force of the photovoltaic unit is t time periods; / >
Figure FDA0004042219110000034
The total output of the energy storage unit is t time periods;
Figure FDA0004042219110000035
The total charging power of the electric automobile is t time periods;
Figure FDA0004042219110000036
The total power of the air conditioner is t time periods;
Figure FDA0004042219110000037
the total power of the pump station in the period t is the total power of the pump station in the period t;
distributed genset constraints:
Figure FDA0004042219110000038
Figure FDA0004042219110000039
wherein ,
Figure FDA00040422191100000310
is the nth PV Predicting output before a photovoltaic unit t period of time;
Figure FDA00040422191100000311
Is the nth PV The actual output force of the photovoltaic unit in the t period;
Figure FDA00040422191100000312
Is the nth WT Predicting output before a period t of the wind turbine;
Figure FDA00040422191100000313
Nth (n) WT Actual output of the wind turbine generator set in a t period;
and (3) constraint of charge and discharge power of the distributed energy storage unit:
Figure FDA00040422191100000314
wherein ,
Figure FDA00040422191100000315
and->
Figure FDA00040422191100000316
Respectively the nth BES Maximum charge and discharge power of the energy storage unit;
state of charge constraints for distributed energy storage units:
Figure FDA00040422191100000317
wherein ,
Figure FDA00040422191100000318
is the nth BES The state of charge of the energy storage unit in the t period;
Figure FDA00040422191100000319
And->
Figure FDA00040422191100000320
Respectively the nth BES The upper and lower limits of the charge state of the energy storage unit;
the charge state process of the distributed energy storage unit is as follows:
Figure FDA00040422191100000321
wherein ：
Figure FDA00040422191100000322
and->
Figure FDA00040422191100000323
Is the nth BES Charging and discharging efficiency of the energy storage unit;
Figure FDA00040422191100000324
Is the nth BES Rated capacity of the energy storage unit;
power battery charge and discharge power constraint:
Figure FDA0004042219110000041
wherein ,
Figure FDA0004042219110000042
and->
Figure FDA0004042219110000043
Respectively the nth EV Maximum charge and discharge power of a power battery of the electric vehicle;
Figure FDA0004042219110000044
And (3) with
Figure FDA0004042219110000045
Respectively the nth EV The charging and discharging state of the electric automobile in the t period;
Figure FDA0004042219110000046
Is the nth EV The access state of the electric automobile t period;
Figure FDA0004042219110000047
And->
Figure FDA0004042219110000048
Respectively the nth EV Charging and discharging power of the electric automobile in a t period;
mutual exclusion constraint of charging and discharging states of electric vehicles:
Figure FDA0004042219110000049
when the electric automobile leaves, the charge state is not lower than the expected value, and the charge state is as follows:
Figure FDA00040422191100000410
wherein ,
Figure FDA00040422191100000411
is the nth EV Departure time of electric vehicle, < >>
Figure FDA00040422191100000412
Is the nth EV A desired state of charge set by the vehicle electric vehicle; the relation between the pressure and the lift of the water pump is as follows:
Figure FDA00040422191100000413
wherein ,
Figure FDA00040422191100000414
is the nth RWT Nth of the reservoirs WP The pressure of the water pump is equal to the pressure of the water pump in the t period;
Figure FDA00040422191100000415
Is the nth RWT Nth of the reservoirs WP The pump lifts at the t period; ρ is the water density; g is gravity acceleration;
the relationship between the lift and the flow of the water pump is as follows:
Figure FDA00040422191100000416
wherein ,
Figure FDA00040422191100000417
is the nth RWT Nth of the reservoirs WP The flow rate of the water pump t time period;
Figure FDA00040422191100000418
And (3) with
Figure FDA00040422191100000419
Fitting coefficients for the power of the water pump;
Figure FDA00040422191100000420
Is the nth RWT Nth of the reservoirs WP The running state of the water pump in the period t;
the upper and lower limits of the flow of the water pump are restricted as follows:
Figure FDA00040422191100000421
wherein ,
Figure FDA00040422191100000422
and->
Figure FDA00040422191100000423
Respectively the nth RWT Nth of the reservoirs WP Upper and lower flow limits for the individual water pumps;
the reservoir water level constraint is:
Figure FDA0004042219110000051
wherein ,
Figure FDA0004042219110000052
and->
Figure FDA0004042219110000053
Respectively the nth RWT The upper and lower limits of the water level of each reservoir;
Figure FDA0004042219110000054
Is the nth RWT Water levels of the reservoirs in the period t;
the dynamic process of the water level of the reservoir is as follows:
Figure FDA0004042219110000055
wherein ：NWP Is the nth RWT Water inlet pump of water reservoirsNumber of;
Figure FDA0004042219110000056
is the nth RWT Water supply amount of the water reservoirs in a t period; / >
Figure FDA0004042219110000057
Is the nth RWT The bottom area of each reservoir;
the relation between the air conditioner power and the air supply quantity is as follows:
Figure FDA0004042219110000058
wherein ,
Figure FDA0004042219110000059
is the nth RM Nth of the rooms AC Power of the air conditioner t period;
Figure FDA00040422191100000510
Is the nth RM Nth of the rooms AC The air quantity of each air conditioner in the t period;
Figure FDA00040422191100000511
Is the nth RM Nth of the rooms AC Rated power of each air conditioner;
Figure FDA00040422191100000512
respectively the nth RM Nth of the rooms AC The upper limit of the air supply quantity of each air conditioner;
upper and lower limit constraint of air delivery volume of air conditioner:
Figure FDA00040422191100000513
wherein ,
Figure FDA00040422191100000514
is the nth RM Nth of the rooms AC The running state of each air conditioner in the period t;
Figure FDA00040422191100000515
Is the nth RM Nth of the rooms AC The lower limit of the air supply quantity of the air conditioner;
the upper and lower limits of the room temperature are:
Figure FDA00040422191100000516
wherein ,
Figure FDA00040422191100000517
and->
Figure FDA00040422191100000518
Is the nth RM Upper and lower limits of indoor temperature of the individual rooms;
Figure FDA00040422191100000519
Is the nth RM T-period temperatures of the individual rooms;
the air-conditioning heat is expressed as:
Figure FDA0004042219110000061
wherein ,
Figure FDA0004042219110000062
is the nth RM Nth of the rooms AC Heating capacity of each air conditioner in t time period;
Figure FDA0004042219110000063
Is the nth RM Nth of the rooms AC Air supply temperature of each air conditioner in a t period; c air Is the specific heat capacity of air;
the room temperature dynamic change process comprises the following steps:
Figure FDA0004042219110000064
wherein ：
Figure FDA0004042219110000065
is the nth RM The heat capacity of the individual rooms;
Figure FDA0004042219110000066
Is the nth RM Thermal conductance between the indoor and outdoor of the individual rooms; n (N) AC The number of air conditioners for a single room;
Figure FDA0004042219110000067
Ambient temperature for period t;
and S3, carrying out optimal solution on the optimization model to obtain an optimal scheduling result of the virtual power plant, and realizing daily scheduling of the virtual power plant.
2. The method according to claim 1, wherein in step S1, the distributed power generation parameters and the day-ahead prediction data include: the installed capacity of the distributed photovoltaic power generation and the predicted output before the day; the installed capacity of the distributed wind power generation and the predicted output before the day;
the parameters of the resources at the multi-energy demand side include: the energy storage system comprises distributed energy storage rated capacity, upper and lower limits of charge states, maximum charge and discharge power, charge and discharge efficiency, maintenance cost coefficient, construction cost coefficient and cycle life; rated output and feedback power, output and feedback efficiency of the charging pile; rated power of the air conditioner, upper and lower limits of air supply quantity, heat capacity of a room and heat conductance between the room and outdoor air; the upper and lower limits of the water pump flow, the fitting coefficient of the lift, the running efficiency, the height of the reservoir, the bottom area, the upper and lower limits of the water level and the maximum water supply flow;
the electric automobile power battery parameters and historical statistics data comprise: rated capacity, upper and lower limits of charge state, maximum charge and discharge power and charge and discharge efficiency of a power battery of the electric automobile; determining a distribution rule of the electric vehicles reaching the area based on statistical data of the electric vehicles in the park, wherein the distribution rule comprises reaching time, initial state of charge of the power battery, residence time and expected state of charge of the power battery; charging and discharging prices of electric automobiles;
The initial running state of the resources at the multipotency requirement side and the set running requirement comprise: a distributed energy storage initial state of charge; the air supply temperature is set in each time period of the air conditioner, the indoor temperature requirements in each time period comprise the highest temperature and the lowest temperature, and the predicted outdoor environment temperature in each time period; the initial water level of the reservoir, the water demand of each period;
the power market data includes: electricity rate data, demand response time period, compensation price, reference power, and given operating curve.
3. The method according to claim 1, wherein in step S3, the optimized scheduling result of the virtual power plant comprises: the purchase and sale electric power of the virtual power plant; virtual power plant economic indicators; the virtual power plant arranges the running conditions of the distributed generator set and the multi-energy demand side resource on the next day according to the day scheduling result, and simultaneously reports the exchange power of the virtual power plant and the main network to a scheduling department, and the virtual power plant performs subsequent real-time scheduling according to the day scheduling result.
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