CN112928749A - Virtual power plant day-ahead scheduling method integrating multi-energy demand side resources - Google Patents

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

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CN112928749A
CN112928749A CN202110064910.6A CN202110064910A CN112928749A CN 112928749 A CN112928749 A CN 112928749A CN 202110064910 A CN202110064910 A CN 202110064910A CN 112928749 A CN112928749 A CN 112928749A
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CN112928749B (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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    • H—ELECTRICITY
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • 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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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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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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    • 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
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    • 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 fusing multi-energy demand side resources, which comprises the steps of obtaining distributed generation parameters, day-ahead prediction data, parameters of the multi-energy demand side resources, parameters of an electric vehicle power battery, historical statistical data, initial running states of the multi-energy demand side resources, set running demands and electric power market data; establishing a virtual power plant normal operation mode optimization target according to the acquired data, and establishing a virtual power plant multi-operation mode constraint condition; and performing optimal solution on the optimization model to obtain an optimal scheduling result of the virtual power plant, so as to realize the day-ahead scheduling of the virtual power plant. The invention fully excavates the adjusting capability of the resource on the demand side, and provides flexible service for the power grid while ensuring the operation economy of the virtual power plant.

Description

Virtual power plant day-ahead scheduling method integrating multi-energy demand side resources
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 multi-energy demand side resources.
Background
With the continuous deepening of the transformation and development of energy structures, the occupation ratio of renewable energy sources such as wind power and photovoltaic in a power system is increased year by year, and the uncertainty of the renewable energy sources provides new challenges for the safe and economic operation of the power system, so that the structure and the operation mode of the traditional power system are changed profoundly. The adjusting capability of the power generation side resource is difficult to meet the requirement of the power system on flexibility, and the phenomenon of large-scale wind and light abandonment is caused. Under such circumstances, demand-side resources with considerable regulatory capacity are increasingly being valued. However, because the demand-side resources have the characteristics of small scale, large quantity, large characteristic difference and the like, the direct scheduling of the power system is difficult to accept, and meanwhile, the willingness of the individual to participate in the scheduling is not strong. The virtual power plant can aggregate a large amount of distributed power generation and multi-energy demand side resources through advanced technologies such as intelligent measurement and information communication, optimal scheduling is carried out as a whole, meanwhile, the capacity limit of the distributed resources is overcome, the virtual power plant is used as a special main body to participate in electric power market transaction, and the potential value of the demand side resources is fully exerted.
The existing research and engineering aiming at the virtual power plant have less resource types of the aggregated demand side and can not fully exploit the flexibility of the demand side. Demand side resources have considerable turndown capability.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a virtual power plant day-ahead scheduling method integrating multiple energy demand side resources aiming at the defects in the prior art, so that the flexibility of the demand side resources is fully exerted.
The invention adopts the following technical scheme:
a virtual power plant day-ahead scheduling method fusing multi-energy demand side resources comprises the following steps:
s1, acquiring distributed power generation parameters, day-ahead prediction data, parameters of multi-energy demand side resources, parameters and historical statistical data of power batteries of electric vehicles, initial operation states of the multi-energy demand side resources, set operation 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;
and S3, carrying out optimal solution on the optimization model to obtain an optimal scheduling result of the virtual power plant, and realizing day-ahead scheduling of the virtual power plant.
Specifically, in step S1, the distributed power generation parameters and the day-ahead prediction data include: the installed capacity and the predicted output of the distributed photovoltaic power generation in the day ahead; the installed capacity and the predicted output of the distributed wind power generation in the day ahead;
the parameters of the multipotent demand-side resource include: the distributed energy storage system comprises distributed energy storage rated capacity, upper and lower charge state limits, maximum charge and discharge power, charge and discharge efficiency, a maintenance cost coefficient, a construction cost coefficient and a cycle life; rated output and feedback power and output and feedback efficiency of the charging pile; the rated power of the air conditioner, the upper limit and the lower limit of the air output, the heat capacity of the room and the heat conduction between the room and the outdoor air; the system comprises a water pump, a water level sensor;
the parameters and historical statistical data of the power battery of the electric automobile comprise: the method comprises the following steps of (1) setting capacity, upper and lower limits of a 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 arriving at the region based on the statistical data of the electric vehicles in the park, wherein the distribution rule comprises arrival time, initial charge state of the power battery, residence time and expected charge state of the power battery; the charge and discharge price of the electric automobile;
the initial operation state and the set operation requirement of the multi-energy demand side resource comprise: distributed energy storage initial state of charge; setting the air supply temperature in each time interval of the air conditioner, the temperature requirements in each indoor time interval, including the highest temperature and the lowest temperature, and the predicted outdoor environment temperature in each time interval; the initial water level of the reservoir meets the water demand in each time period;
the power market data includes: electricity price data, demand response time period, compensation price, reference power and given operation curve.
Specifically, in step S2, in the normal operation mode, the virtual plant operation economy is targeted; adding a penalty term deviating from the given curve and the envelope curve of the given curve compared with the normal operation mode according to the given curve; increasing peak clipping compensation by using a peak load clipping mode objective function before the day, and constructing a virtual power plant demand response mode optimization objective; and establishing a virtual power plant multi-operation mode constraint condition.
Further, the operation economy of the virtual power plant comprises the electricity purchase and sale cost, the distributed energy storage maintenance cost and depreciation cost and the electric automobile power supply income of the energy market in the day ahead, and specifically comprises the following steps:
Figure BDA0002903762490000031
wherein ,tendScheduling an end period for the day ahead; n is a radical ofBESThe number of distributed energy storages in the virtual power plant; n is a radical ofEVNumber of electric vehicles;
Figure BDA0002903762490000032
the electricity purchase price of the virtual power plant is t time period;
Figure BDA0002903762490000033
the power purchasing power of the virtual power plant in the t period is obtained;
Figure BDA0002903762490000034
the electricity selling price of the virtual power plant is the t period;
Figure BDA0002903762490000035
the power is the power sold in t period of the virtual power plant;
Figure BDA0002903762490000036
is the n th time period of tBESMaintenance cost for operating the energy storage unit;
Figure BDA0002903762490000037
is the n th time period of tBESDepreciation cost of the operation of the platform energy storage unit;
Figure BDA0002903762490000038
is the n th time period of tEVCharging power of the electric vehicle;
Figure BDA0002903762490000039
the charging price of the electric automobile in the t time period;
Figure BDA00029037624900000310
is n thEVCharging power of the electric vehicle in a t time period;
Figure BDA00029037624900000311
discharging price for the electric automobile in the t time period;
Figure BDA00029037624900000312
is n thEVAnd discharging power of the vehicle electric vehicle in the t period.
Further, according to a given curve operation mode:
Figure BDA0002903762490000041
Figure BDA0002903762490000042
Figure BDA0002903762490000043
wherein ,
Figure BDA0002903762490000044
and
Figure BDA0002903762490000045
respectively are penalty coefficients when deviating from the envelope curve and a given operation curve;
Figure BDA0002903762490000046
curve power is given for a t period;
Figure BDA0002903762490000047
and
Figure BDA0002903762490000048
the power deviating from a given curve and its envelope respectively for the period t;
Figure BDA0002903762490000049
half of the power value of the envelope curve is given for the t period;
peak load peak clipping mode before day
Figure BDA00029037624900000410
wherein ,
Figure BDA00029037624900000411
purchasing power for the virtual power plant in the normal operation mode at the t period;
Figure BDA00029037624900000412
the power selling power of the virtual power plant is in a t-period under the normal operation mode;
Figure BDA00029037624900000413
the price is compensated for peak load peak clipping before the day. It is assumed here that the baseline power is the exchange power of the normal operation mode.
Further, the virtual power plant multiple operation mode constraint conditions are as follows:
and power balance constraint:
Figure BDA0002903762490000051
wherein ,
Figure BDA0002903762490000052
the total output of the wind turbine generator is t time period;
Figure BDA0002903762490000053
the total output of the photovoltaic unit is obtained in the time period t;
Figure BDA0002903762490000054
the total output of the energy storage unit is t time period;
Figure BDA0002903762490000055
the total charging power of the electric automobile is t time period;
Figure BDA0002903762490000056
the total power of the air conditioner is t time period;
Figure BDA0002903762490000057
the total power of the pump station in the t period;
and (3) constraint of the distributed generator set:
Figure BDA0002903762490000058
Figure BDA0002903762490000059
wherein ,
Figure BDA00029037624900000510
is n thPVPredicting output of the photovoltaic unit at t time period before day;
Figure BDA00029037624900000511
is n thPVActually outputting power of the photovoltaic unit at a time t;
Figure BDA00029037624900000512
is n thWTPredicting output before the day at the time interval t of the typhoon generator set;
Figure BDA00029037624900000513
n thWTActually outputting force at t time interval of the typhoon generator set;
and (3) charge and discharge power constraint of the distributed energy storage unit:
Figure BDA00029037624900000514
wherein ,
Figure BDA00029037624900000515
and
Figure BDA00029037624900000516
are respectively nBESThe maximum charge and discharge power of the platform energy storage unit;
and (3) carrying out charge state constraint on the distributed energy storage unit:
Figure BDA00029037624900000517
wherein ,
Figure BDA00029037624900000518
is n thBESThe stage energy storage unit is in a state of charge at t time period;
Figure BDA00029037624900000519
and
Figure BDA00029037624900000520
are respectively nBESThe upper and lower limits of the state of charge of the energy storage unit;
the charge dynamic process of the distributed energy storage unit comprises the following steps:
Figure BDA00029037624900000521
wherein ：
Figure BDA0002903762490000061
and
Figure BDA0002903762490000062
is n thBESThe charging and discharging efficiency of the platform energy storage unit;
Figure BDA0002903762490000063
is n thBESRated capacity of the energy storage unit;
and power battery charge and discharge power constraint:
Figure BDA0002903762490000064
wherein ,
Figure BDA0002903762490000065
and
Figure BDA0002903762490000066
are respectively nEVThe maximum charge and discharge power of a power battery of the electric vehicle;
Figure BDA0002903762490000067
and
Figure BDA0002903762490000068
are respectively nEVCharging and discharging states of the electric vehicle at t time period;
Figure BDA0002903762490000069
is n thEVThe access state of the electric vehicle in a time period t;
Figure BDA00029037624900000610
and
Figure BDA00029037624900000611
are respectively nEVCharging and discharging function of electric automobile in t time periodRate;
mutually exclusive constraint of the charging and discharging states of the electric vehicle:
Figure BDA00029037624900000612
when the electric automobile leaves, the state of charge is not lower than the expected value as follows:
Figure BDA00029037624900000613
wherein ,
Figure BDA00029037624900000614
is n thEVThe time of departure of the vehicle electric vehicle,
Figure BDA00029037624900000615
is n thEVA desired state of charge set for the vehicle electric; the relationship between the pressure intensity and the lift of the water pump is as follows:
Figure BDA00029037624900000616
wherein ,
Figure BDA00029037624900000617
is n thRWTN th of water reservoirWPThe pressure intensity of each water pump in the t time period;
Figure BDA00029037624900000618
is n thRWTN th of water reservoirWPThe lift of each water pump in t time period; rho is water density; g is the acceleration of gravity;
the relationship between the pump lift and the flow is as follows:
Figure BDA00029037624900000619
wherein ,
Figure BDA0002903762490000071
is n thRWTN th of water reservoirWPThe flow rate of each water pump in a t period;
Figure BDA0002903762490000072
Figure BDA0002903762490000073
and
Figure BDA0002903762490000074
fitting coefficients for water pump power;
Figure BDA0002903762490000075
is n thRWTN th of water reservoirWPThe running state of each water pump in a t period;
the restriction of the upper and lower limits of the flow of the water pump is as follows:
Figure BDA0002903762490000076
wherein ,
Figure BDA0002903762490000077
and
Figure BDA0002903762490000078
are respectively nRWTN th of water reservoirWPThe upper and lower flow limits of each water pump; the reservoir water level constraint is:
Figure BDA0002903762490000079
wherein ,
Figure BDA00029037624900000710
and
Figure BDA00029037624900000711
are respectively nRWTThe upper and lower limits of the water level of each reservoir;
Figure BDA00029037624900000712
is n thRWTThe water level of each reservoir in a period t;
the dynamic process of the water level of the reservoir is as follows:
Figure BDA00029037624900000713
wherein ：NWPIs n thRWTThe number of water inlet pumps of each reservoir;
Figure BDA00029037624900000714
is n thRWTSupplying water to the water storage tanks at t time intervals;
Figure BDA00029037624900000715
is n thRWTThe bottom area of each water storage tank;
the relationship between the air conditioner power and the air supply quantity is as follows:
Figure BDA00029037624900000716
wherein ,
Figure BDA00029037624900000717
is n thRMN th of a roomACPower of each air conditioner in t time period;
Figure BDA00029037624900000718
is n thRMN th of a roomACThe air supply quantity of each air conditioner in a t period;
Figure BDA00029037624900000719
is n thRMN th of a roomACRated power of each air conditioner;
Figure BDA00029037624900000720
are respectively asN thRMN th of a roomACThe upper limit of the air supply quantity of the air conditioner;
and (3) restricting the upper limit and the lower limit of the air supply volume of the air conditioner:
Figure BDA00029037624900000721
wherein ,
Figure BDA0002903762490000081
is n thRMN th of a roomACThe running state of each air conditioner in a t period;
Figure BDA0002903762490000082
is n thRMN th of a roomACThe lower limit of the air supply quantity of each air conditioner;
the upper and lower limits of the room temperature are constrained as follows:
Figure BDA0002903762490000083
wherein ,
Figure BDA0002903762490000084
and
Figure BDA0002903762490000085
is n thRMUpper and lower limits of indoor temperature of each room;
Figure BDA0002903762490000086
is n thRMTime t-period temperature of each room;
the heating capacity of the air conditioner can be expressed as:
Figure BDA0002903762490000087
wherein ,
Figure BDA0002903762490000088
is n thRMN th of a roomACHeating capacity of each air conditioner in t time period;
Figure BDA0002903762490000089
is n thRMN th of a roomACThe air supply temperature of each air conditioner in a t time period; c. CairIs the air specific heat capacity;
the room temperature dynamic change process is as follows:
Figure BDA00029037624900000810
wherein ：
Figure BDA00029037624900000811
is n thRMHeat capacity of individual rooms;
Figure BDA00029037624900000812
is n thRMThermal conductance inside and outside the individual room; n is a radical ofACNumber of air conditioners for a single room;
Figure BDA00029037624900000813
is the ambient temperature for time t.
Specifically, in step S3, the optimal scheduling result of the virtual power plant includes: the electricity purchasing and selling power of the virtual power plant; virtual power plant economic indicators; the virtual power plant arranges the running conditions of the next day distributed generator set and the multi-energy demand side resource according to the day-ahead scheduling result, reports the exchange power of the virtual power plant and the main network to a scheduling department, and performs subsequent real-time scheduling according to the day-ahead scheduling result.
Compared with the prior art, the invention has at least the following beneficial effects:
according to the day-ahead scheduling method of the virtual power plant, under the background that new energy power generation is connected to a power system in a large scale, the adjusting capacity of a power generation side gradually cannot meet the flexibility requirement of the power system, and the adjusting capacity of mining resources on a demand side is more and more important. A large amount of demand side resources are coordinated and optimized through the virtual power plant, the adjusting capacity of the demand side resources can be effectively exerted, and therefore the construction of a new power plant is delayed. The energy storage characteristic of the resources on demand sides of an air conditioner, a pump station and the like is 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. In the traditional demand side resource management, the multi-type demand response of a power grid is not considered, and corresponding demand response service cannot be provided. 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 using a virtual power plant technology for unified management, so that the damage of the electric automobile disorderly charging to a power system can be reduced, and meanwhile, the regulation capacity 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 obtain additional revenue by responding to the external demand response signal.
Further, step S1 obtains all information required by the virtual power plant scheduling model, the virtual power plant aggregates various resources including distributed generator sets, multi-energy demand side resources, and the like, and needs to exchange electric energy with the main network, so that the input data includes technical and economic parameters of each device, predicted output of the distributed generator sets, electric vehicle statistical data, energy market electricity price, and the like, and the data is used for subsequent calculation.
Furthermore, in order to fully exert the adjusting capacity of the resources on the demand side, the virtual power plant can respond to the demand response signal of the dispatching center and adjust the output to meet the operation demand of the power grid. The invention considers that the dispatching center has different flexibility requirements according to the operation condition of the power system. In the peak load period, the dispatching center sends a peak clipping signal to the virtual power plant; in extreme cases, the dispatching center can directly issue the operation curve. And setting corresponding objective functions according to different requirements, and realizing multiple operation modes of the virtual power plant.
Furthermore, the electricity purchasing and selling cost, the maintenance cost and the depreciation cost of distributed energy storage and the benefit of providing charging and discharging service for the electric automobile of the virtual power plant are considered by the target function of the normal operation mode of the virtual power plant, and the economical efficiency of the operation of the virtual power plant can be realized under the target.
Furthermore, in a given curve operation mode, the virtual power plant needs to operate along a curve given by a dispatching department, so that compared with a normal operation mode, a punishment item deviating from the given curve and an envelope line of the given curve is added; in the peak load clipping mode before the day, the virtual power plant can obtain compensation by increasing output or decreasing load in the peak clipping period, so that compared with the normal operation mode, the target function increases the peak clipping compensation.
Furthermore, the virtual power plant aggregates a large amount of distributed resources, and various elements have different characteristics and operation requirements, so that the technical and economic characteristics of the elements need to be analyzed. The virtual power plant scheduling model fully considers the operation constraints of all elements, 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 can be ensured.
Furthermore, the virtual power plant scheduling model is a mixed integer linear programming, and a branch-and-bound method is utilized to solve to obtain a day-ahead scheduling result. And arranging the running conditions of the next day distributed generator set and the multi-energy demand side resources according to the scheduling result, reporting the exchange power of the virtual power plant and the main network to a scheduling department, and performing subsequent real-time scheduling according to the day-ahead scheduling result.
In conclusion, the method fully excavates the adjusting capability of the resource on the demand side, and provides flexible service for the power grid while ensuring the operation economy of the virtual power plant.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a diagram of a virtual power plant scheduling result in a normal operation mode;
FIG. 2 is a diagram of a virtual power plant scheduling result operating according to a given curve;
FIG. 3 is a diagram 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 technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "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 herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention 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 this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Various structural schematics according to the disclosed embodiments of the invention are shown in the drawings. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, according to actual needs.
The invention provides a virtual power plant day-ahead scheduling method fusing multi-energy demand side resources, which is based on a park-level virtual power plant and fully considers the demand side resources which can be scheduled in a park, and comprises distributed energy storage, electric vehicles, pump stations and air conditioners. In order to fully exert the adjusting capacity of the resources on the demand side, the virtual power plant can respond to the demand response signal of the dispatching center and adjust the output to meet the operation demand of the power grid. The invention considers that the dispatching center has different flexibility requirements according to the operation condition of the power system. In the peak load period, the dispatching center sends a peak clipping signal to the virtual power plant; in extreme cases, the dispatching center can directly issue the operation curve. Based on the above, the invention provides 3 virtual power plant operation modes, which are a normal operation mode, an operation mode according to a given curve and a peak load clipping mode before the day. According to a given curve, the priority of the operation mode is highest, and if a dispatching center issues a command, the virtual power plant is immediately switched to the mode; if the command does not exist, the virtual power plant determines whether to respond to the peak load clipping signal or not by taking economy as a target; the flexibility of the resources on the demand side can be effectively utilized while the operation economy of the virtual power plant is realized, the demand response signal of the dispatching center is responded, and a basis is provided for the flexible management of the resources on the multi-energy demand side.
Referring to fig. 4, the method for day-ahead scheduling of a virtual power plant integrating multi-energy demand side resources according to the present invention includes the following steps:
s1, acquiring distributed power generation parameters and day-ahead prediction data, parameters of multi-energy demand side resources, parameters of electric vehicle power batteries, historical statistical data, initial operation states of the multi-energy demand side resources, set operation demands and electric power market data from related departments;
distributed generation parameters and day-ahead prediction data: the installed capacity and the predicted output of the distributed photovoltaic power generation in the day ahead; the installed capacity and the predicted output of the distributed wind power generation in the day ahead.
Parameters of the multi-energy demand side resources: the distributed energy storage system comprises distributed energy storage rated capacity, upper and lower charge state limits, maximum charge and discharge power, charge and discharge efficiency, a maintenance cost coefficient, a construction cost coefficient and a cycle life; rated output and feedback power and output and feedback efficiency of the charging pile; the rated power of the air conditioner, the upper limit and the lower limit of the air output, the heat capacity of the room and the heat conduction between the room and the outdoor air; the water pump comprises upper and lower flow limits of a water pump, a lift fitting coefficient, operation efficiency, height of a reservoir, bottom area, upper and lower water level limits and maximum water supply flow.
The parameters and historical statistical data of the power battery of the electric automobile are as follows: the method comprises the following steps of (1) setting capacity, upper and lower limits of a 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 arriving at the region based on the statistical data of the electric vehicles in the park, wherein the distribution rule comprises arrival time, initial charge state of the power battery, residence time and expected charge state of the power battery; and the charge and discharge price of the electric automobile.
The initial running state and the set running requirement of the multi-energy demand side resource are as follows: distributed energy storage initial state of charge; setting the air supply temperature in each time interval of the air conditioner, the temperature requirements in each indoor time interval, including the highest temperature and the lowest temperature, and the predicted outdoor environment temperature in each time interval; initial water level of the reservoir and water demand in each time period.
Electric power market data: electricity price data, demand response time period, compensation price, reference power and given operation curve.
S2, constructing a virtual power plant normal operation mode optimization target;
s201, in the normal operation mode, the operation economy of the virtual power plant is taken as a target, and the normal operation mode comprises the electricity purchasing cost, the distributed energy storage maintenance cost and depreciation cost and the electric automobile power supply income in the energy market in the day.
Figure BDA0002903762490000131
wherein ,tendScheduling an end period for the day ahead; n is a radical ofBESThe number of distributed energy storages in the virtual power plant; n is a radical ofEVNumber of electric vehicles;
Figure BDA0002903762490000132
the electricity purchase price of the virtual power plant is t time period;
Figure BDA0002903762490000133
the power purchasing power of the virtual power plant in the t period is obtained;
Figure BDA0002903762490000134
the electricity selling price of the virtual power plant is the t period;
Figure BDA0002903762490000135
the power is the power sold in t period of the virtual power plant;
Figure BDA0002903762490000136
is the n th time period of tBESMaintenance cost for operating the energy storage unit;
Figure BDA0002903762490000137
is the n th time period of tBESDepreciation cost of the operation of the platform energy storage unit;
Figure BDA0002903762490000138
is the n th time period of tEVCharging power of the electric vehicle;
Figure BDA0002903762490000139
the charging price of the electric automobile in the t time period;
Figure BDA00029037624900001310
is n thEVCharging power of the electric vehicle in a t time period;
Figure BDA00029037624900001311
discharging price for the electric automobile in the t time period;
Figure BDA00029037624900001312
is n thEVAnd discharging power of the vehicle electric vehicle in the 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 n thBESThe operation and maintenance cost coefficient of the platform energy storage unit;
Figure BDA0002903762490000145
is n thBESThe construction cost coefficient of the platform energy storage unit;
Figure BDA0002903762490000146
is n thBESCycle life of the energy storage unit;
Figure BDA0002903762490000147
is n thBESThe power of the energy storage unit at t time interval;
Figure BDA0002903762490000148
and
Figure BDA0002903762490000149
are respectively nBESCharging and discharging power of the energy storage unit at t time period;
Figure BDA00029037624900001410
and
Figure BDA00029037624900001411
are respectively nBESAnd the upper and lower limits of the state of charge of the energy storage unit.
S202, establishing a virtual power plant demand response mode optimization target
Adding a penalty term deviating from the given curve and the envelope curve of the given curve compared with the normal operation mode according to the given curve; the peak load peak clipping mode objective function before the day increases the peak clipping compensation.
1) Operating mode according to given curve
Figure BDA00029037624900001412
Figure BDA00029037624900001413
Figure BDA00029037624900001414
wherein ,
Figure BDA0002903762490000151
and
Figure BDA0002903762490000152
respectively are penalty coefficients when deviating from the envelope curve and a given operation curve;
Figure BDA0002903762490000153
curve power is given for a t period;
Figure BDA0002903762490000154
and
Figure BDA0002903762490000155
the power deviating from a given curve and its envelope respectively for the period t;
Figure BDA0002903762490000156
half the power value of the curve envelope is given for the t period.
2) Peak load peak clipping mode before day
Figure BDA0002903762490000157
wherein ,
Figure BDA0002903762490000158
purchasing power for the virtual power plant in the normal operation mode at the t period;
Figure BDA0002903762490000159
the power selling power of the virtual power plant is in a t-period under the normal operation mode;
Figure BDA00029037624900001510
the price is compensated for peak load peak clipping before the day. It is assumed here that the baseline power is the exchange power of the normal operation mode.
S203, establishing constraint conditions of multiple operation modes of the virtual power plant
1) And power balance constraint:
Figure BDA00029037624900001511
wherein ,
Figure BDA00029037624900001512
the total output of the wind turbine generator is t time period;
Figure BDA00029037624900001513
the total output of the photovoltaic unit is obtained in the time period t;
Figure BDA00029037624900001514
the total output of the energy storage unit is t time period;
Figure BDA00029037624900001515
the total charging power of the electric automobile is t time period;
Figure BDA00029037624900001516
the total power of the air conditioner is t time period;
Figure BDA00029037624900001517
the total power of the pump station in the period t.
2) And (3) constraint of the distributed generator set:
Figure BDA00029037624900001518
Figure BDA00029037624900001519
wherein ,
Figure BDA0002903762490000161
is n thPVPredicting output of the photovoltaic unit at t time period before day;
Figure BDA0002903762490000162
is n thPVActually outputting power of the photovoltaic unit at a time t;
Figure BDA0002903762490000163
is n thWTPredicting output before the day at the time interval t of the typhoon generator set;
Figure BDA0002903762490000164
n thWTAnd (5) actually outputting force at the time of the typhoon generator set t.
3) And (3) constraint of the distributed energy storage unit:
the charging and discharging power of the energy storage unit is limited by the converter, and the charging and discharging power of the energy storage unit cannot exceed the rated value of the converter, namely:
Figure BDA0002903762490000165
wherein ,
Figure BDA0002903762490000166
and
Figure BDA0002903762490000167
are respectively nBESThe maximum charge and discharge power of the platform energy storage unit.
And (3) restraining the charge state of the energy storage unit:
Figure BDA0002903762490000168
wherein ,
Figure BDA0002903762490000169
is n thBESAnd (5) the stage energy storage unit is in a state of charge at t time.
The dynamic process of the state of charge of the energy storage unit is as follows:
Figure BDA00029037624900001610
wherein ,
Figure BDA00029037624900001611
and
Figure BDA00029037624900001612
is n thBESThe charging and discharging efficiency of the energy storage unit is improved;
Figure BDA00029037624900001613
is n thBESRated capacity of the energy storage unit.
In order to ensure the normal operation of the energy storage unit, the consistency of the initial and final charge states of a scheduling period needs to be ensured, namely:
Figure BDA00029037624900001614
4) electric vehicle restraint:
and power battery charge and discharge power constraint:
Figure BDA0002903762490000171
wherein ,
Figure BDA0002903762490000172
and
Figure BDA0002903762490000173
are respectively nEVThe maximum charge and discharge power of a power battery of the electric vehicle;
Figure BDA0002903762490000174
and
Figure BDA0002903762490000175
are respectively nEVCharging and discharging states of the electric vehicle at t time period;
Figure BDA0002903762490000176
is n thEVThe access state of the electric vehicle in a time period t;
Figure BDA0002903762490000177
and
Figure BDA0002903762490000178
are respectively nEVAnd (4) charging and discharging power of the vehicle electric vehicle in a t period.
Mutually exclusive constraint of the charging and discharging states of the electric vehicle:
Figure BDA0002903762490000179
when the electric vehicle leaves, the state of charge is not lower than the expected value, i.e.
Figure BDA00029037624900001710
wherein ,
Figure BDA00029037624900001711
is n thEVThe time of departure of the vehicle electric vehicle,
Figure BDA00029037624900001712
is n thEVDesired state of charge set for an 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 (4) pump station restraint:
the relationship between the pressure intensity and the lift of the water pump is as follows:
Figure BDA00029037624900001713
wherein ,
Figure BDA00029037624900001714
is n thRWTN th of water reservoirWPThe pressure intensity of each water pump in the t time period;
Figure BDA00029037624900001715
is n thRWTN th of water reservoirWPThe lift of each water pump in t time period; rho is water density; g is the acceleration of gravity.
The relationship between the pump lift and the flow is as follows:
Figure BDA00029037624900001716
wherein ,
Figure BDA0002903762490000181
is n thRWTN th of water reservoirWPThe flow rate of each water pump in a t period;
Figure BDA0002903762490000182
Figure BDA0002903762490000183
and
Figure BDA0002903762490000184
fitting coefficients for water pump power;
Figure BDA0002903762490000185
is n thRWTN th of water reservoirWPThe running state of each water pump in a t period;
the water pump power obtained from the above relationship is:
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 divide the power function into mWPSegment, namely:
Figure BDA0002903762490000187
wherein ,
Figure BDA0002903762490000188
the slope of each section of the power function after the section linearization is carried out;
Figure BDA0002903762490000189
for starting up water pumps with minimum flow
Figure BDA00029037624900001810
Power consumed by the operation;
Figure BDA00029037624900001811
is segmented flow; and s is the segment number.
Order to
Figure BDA00029037624900001812
The parameters of the above equation can be expressed as:
Figure BDA00029037624900001813
Figure BDA00029037624900001814
in the formula, s is a segment number. The variables of the above equation need to satisfy the following constraints:
Figure BDA00029037624900001815
Figure BDA00029037624900001816
Figure BDA0002903762490000191
the restriction of the upper and lower limits of the flow of the water pump is as follows:
Figure BDA0002903762490000192
wherein ,
Figure BDA0002903762490000193
and
Figure BDA0002903762490000194
are respectively nRWTN th of water reservoirWPThe 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 ,NWPIs n thRWTThe number of water inlet pumps of each reservoir;
Figure BDA0002903762490000196
is n thRWTWater supply requirement of a reservoir in a period t.
The reservoir water level constraint is:
Figure BDA0002903762490000197
wherein ,
Figure BDA0002903762490000198
and
Figure BDA0002903762490000199
are respectively nRWTThe upper and lower limits of the water level of each reservoir;
Figure BDA00029037624900001910
is n thRWTWater level of each reservoir at t time interval
6) Air-conditioning restraint:
the relationship between the air conditioner power and the air supply quantity is as follows:
Figure BDA00029037624900001911
wherein ,
Figure BDA00029037624900001912
is n thRMN th of a roomACPower of each air conditioner in t time period;
Figure BDA00029037624900001913
is n thRMN th of a roomACThe air supply quantity of each air conditioner in a t period;
Figure BDA00029037624900001914
is n thRMN th of a roomACRated power of each air conditioner;
Figure BDA00029037624900001915
are respectively nRMN th of a roomACThe upper limit of the air supply amount of the air conditioner.
And the air-conditioning power function is also linearized in sections by adopting an incremental method, and the process is similar to the linearization of the water pump power function.
And (3) restricting the upper limit and the lower limit of the air supply volume of the air conditioner:
Figure BDA0002903762490000201
wherein ,
Figure BDA0002903762490000202
is n thRMN th of a roomACThe running state of each air conditioner in a t period;
Figure BDA0002903762490000203
is n thRMN th of a roomACThe lower limit of the air supply amount of the air conditioner.
The heating capacity of the air conditioner can be expressed as:
Figure BDA0002903762490000204
wherein ,
Figure BDA0002903762490000205
is n thRMN th of a roomACHeating capacity of each air conditioner in t time period;
Figure BDA0002903762490000206
is n thRMTime t-period temperature of each room;
Figure BDA0002903762490000207
is n thRMN th of a roomACThe air supply temperature of each air conditioner in a t time period; c. CairThe specific heat capacity of air.
Figure BDA0002903762490000208
For bilinear terms, the method adopts a Boolean expansion method for linearization. Discretizing the indoor temperature, namely:
Figure BDA0002903762490000209
Figure BDA00029037624900002010
wherein ,M＝2K，λk(t) is an introduced binary variable, and the selection of each segment can be realized, then
Figure BDA00029037624900002011
Conversion to 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 time period t;
Figure BDA0002903762490000213
is n thRMIndividual room heat capacity;
Figure BDA0002903762490000214
is n thRMHeat conduction indoor and outdoor of each room; n is a radical ofACThe number of air conditioners for the room.
In order to ensure the comfort of the user, the upper and lower limits of the room temperature are constrained as follows:
Figure BDA0002903762490000215
wherein ,
Figure BDA0002903762490000216
and
Figure BDA0002903762490000217
is n thRMThe upper and lower temperature limits in the room.
And S3, solving and optimizing the optimization model formed in the previous step to obtain an optimized scheduling result of the virtual power plant.
The optimized scheduling result comprises the following steps:
the electricity purchasing and selling power of the virtual power plant; virtual power plant economic indicators; distributed generation output conditions; and the virtual power plant arranges the operation conditions of the next day distributed generator set and the multi-energy demand side resources according to the day-ahead scheduling result, reports the exchange power of the virtual power plant and the main network to a scheduling department, and performs subsequent real-time scheduling according to the day-ahead scheduling result.
The invention provides a virtual power plant day-ahead scheduling system fusing multi-energy demand side resources, which can be used for realizing the virtual power plant day-ahead scheduling method.
The acquisition module acquires distributed power generation parameters, day-ahead prediction data, parameters of multi-energy demand side resources, parameters of electric vehicle power batteries, historical statistical data, initial operation states of the multi-energy demand side resources, set operation demands and electric power market data;
the optimization module is used for constructing a virtual power plant normal operation mode optimization target and establishing a virtual power plant multi-operation mode constraint condition;
and the scheduling module is used for carrying out optimal solution on the optimization model to obtain an optimal scheduling result of the virtual power plant and realizing the day-ahead scheduling of the virtual power plant.
The present invention provides, in one embodiment, a terminal device comprising a processor and a memory, the memory storing a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor provided by the embodiment of the invention can be used for the operation of the virtual power plant day-ahead scheduling method fusing the multi-energy demand side resources, and comprises the following steps: acquiring distributed power generation parameters, day-ahead prediction data, parameters of multi-energy demand side resources, parameters and historical statistical data of electric vehicle power batteries, initial operation states of the multi-energy demand side resources, set operation demands and electric power market data; establishing a virtual power plant normal operation mode optimization target according to the acquired data, and establishing a virtual power plant multi-operation mode constraint condition; and performing optimal solution on the optimization model to obtain an optimal scheduling result of the virtual power plant, so as to realize the day-ahead scheduling of the virtual power plant.
The present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a terminal device and is used for storing programs and data. It is understood that the computer readable storage medium herein may include a built-in storage medium in the terminal device, and may also include 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, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor can load and execute one or more instructions stored in the computer readable storage medium to realize the corresponding steps of the virtual power plant day-ahead scheduling method for fusing the resources on the multi-energy demand side in the embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and perform the steps of: acquiring distributed power generation parameters, day-ahead prediction data, parameters of multi-energy demand side resources, parameters and historical statistical data of electric vehicle power batteries, initial operation states of the multi-energy demand side resources, set operation demands and electric power market data; establishing a virtual power plant normal operation mode optimization target according to the acquired data, and establishing a virtual power plant multi-operation mode constraint condition; and performing optimal solution on the optimization model to obtain an optimal scheduling result of the virtual power plant, so as to realize the day-ahead 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 connection of the electric automobile into a charging pile; because of the existence of thermal inertia, the indoor temperature can not be changed violently in a short time, so that the power of the air conditioner can be adjusted while the room temperature is ensured to be proper; as a supporting facility of a pump station, a large reservoir is usually built on the top building of a building, and under the buffer action of the reservoir, the power of a water pump can be properly adjusted while domestic water is not influenced.
In order to fully exert the adjusting capacity of the resources on the demand side, the virtual power plant can respond to the demand response signal of the dispatching center and adjust the output to meet the operation demand of the power grid. The invention considers that the dispatching center has different flexibility requirements according to the operation condition of the power system. In the peak load period, the dispatching center sends a peak clipping signal to the virtual power plant; in extreme cases, the dispatching center can directly issue the operation curve. Based on the above, the invention provides 3 virtual power plant operation modes, which are a normal operation mode, an operation mode according to a given curve and a peak load clipping mode before the day. According to a given curve, the priority of the operation mode is highest, and if a dispatching center issues a command, the virtual power plant is immediately switched to the mode; if the command is not available, the virtual power plant determines whether to respond to the peak load clipping signal with the economy as a target.
Referring to fig. 1, it can be seen that, as a result of scheduling the virtual power plant in the normal operation mode, the virtual power plant reasonably arranges the operation conditions of each element in the system according to the change of the external electricity price, so as to obtain better economy.
Referring to fig. 2, as a result of scheduling the virtual power plant operating according to the given curve, the given curve is set as 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 virtual power plant can strictly follow the given curve.
Referring to fig. 3, it can be seen that, in two peak clipping periods, in order to obtain more peak clipping compensation, the output of the virtual power plant is obviously increased.
In summary, the invention provides a virtual power plant day-ahead scheduling method fusing multi-energy demand side resources, and from the perspective of a power grid, a large amount of demand side resources are aggregated by using a virtual power plant technology for unified management, so that the damage of the disordered charging of an electric vehicle to a power system can be reduced, and meanwhile, the regulation capacity of the demand side resources can be fully excavated without affecting the normal use of users, and flexible service is provided for the power grid; from the perspective of the virtual power plant, the virtual power plant may obtain additional revenue by responding to the external demand response signal.
As will be appreciated by one skilled in the art, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (7)

1. A virtual power plant day-ahead scheduling method fusing multi-energy demand side resources is characterized by comprising the following steps:
s1, acquiring distributed power generation parameters, day-ahead prediction data, parameters of multi-energy demand side resources, parameters and historical statistical data of power batteries of electric vehicles, initial operation states of the multi-energy demand side resources, set operation 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;
and S3, carrying out optimal solution on the optimization model to obtain an optimal scheduling result of the virtual power plant, and realizing day-ahead scheduling of the virtual power plant.
2. The method according to claim 1, wherein in step S1, the distributed generation parameters and the forecast data include: the installed capacity and the predicted output of the distributed photovoltaic power generation in the day ahead; the installed capacity and the predicted output of the distributed wind power generation in the day ahead;
the parameters of the multipotent demand-side resource include: the distributed energy storage system comprises distributed energy storage rated capacity, upper and lower charge state limits, maximum charge and discharge power, charge and discharge efficiency, a maintenance cost coefficient, a construction cost coefficient and a cycle life; rated output and feedback power and output and feedback efficiency of the charging pile; the rated power of the air conditioner, the upper limit and the lower limit of the air output, the heat capacity of the room and the heat conduction between the room and the outdoor air; the system comprises a water pump, a water level sensor;
the parameters and historical statistical data of the power battery of the electric automobile comprise: the method comprises the following steps of (1) setting capacity, upper and lower limits of a 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 arriving at the region based on the statistical data of the electric vehicles in the park, wherein the distribution rule comprises arrival time, initial charge state of the power battery, residence time and expected charge state of the power battery; the charge and discharge price of the electric automobile;
the initial operation state and the set operation requirement of the multi-energy demand side resource comprise: distributed energy storage initial state of charge; setting the air supply temperature in each time interval of the air conditioner, the temperature requirements in each indoor time interval, including the highest temperature and the lowest temperature, and the predicted outdoor environment temperature in each time interval; the initial water level of the reservoir meets the water demand in each time period;
the power market data includes: electricity price data, demand response time period, compensation price, reference power and given operation curve.
3. The method of claim 1, wherein in step S2, in a normal operation mode, virtual plant operating economics are targeted; adding a penalty term deviating from the given curve and the envelope curve of the given curve compared with the normal operation mode according to the given curve; increasing peak clipping compensation by using a peak load clipping mode objective function before the day, and constructing a virtual power plant demand response mode optimization objective; and establishing a virtual power plant multi-operation mode constraint condition.
4. The method of claim 3, wherein the virtual plant operating economics include electricity purchase and sale costs, distributed energy storage maintenance costs and depreciation costs, and electric vehicle power supply revenue at the energy market by day, specifically:
Figure FDA0002903762480000021
wherein ,tendScheduling an end period for the day ahead; n is a radical ofBESThe number of distributed energy storages in the virtual power plant; n is a radical ofEVIs powered electricallyThe number of cars;
Figure FDA0002903762480000022
the electricity purchase price of the virtual power plant is t time period;
Figure FDA0002903762480000023
the power purchasing power of the virtual power plant in the t period is obtained;
Figure FDA0002903762480000024
the electricity selling price of the virtual power plant is the t period;
Figure FDA0002903762480000025
the power is the power sold in t period of the virtual power plant;
Figure FDA0002903762480000026
is the n th time period of tBESMaintenance cost for operating the energy storage unit;
Figure FDA0002903762480000027
is the n th time period of tBESDepreciation cost of the operation of the platform energy storage unit;
Figure FDA0002903762480000028
is the n th time period of tEVCharging power of the electric vehicle;
Figure FDA0002903762480000029
the charging price of the electric automobile in the t time period;
Figure FDA00029037624800000210
is n thEVCharging power of the electric vehicle in a t time period;
Figure FDA00029037624800000211
discharging price for the electric automobile in the t time period;
Figure FDA00029037624800000212
is n thEVAnd discharging power of the vehicle electric vehicle in the t period.
5. A method according to claim 3, characterized in that in a given curve operating mode:
Figure FDA0002903762480000031
Figure FDA0002903762480000032
Figure FDA0002903762480000033
wherein ,
Figure FDA0002903762480000034
and
Figure FDA0002903762480000035
respectively are penalty coefficients when deviating from the envelope curve and a given operation curve;
Figure FDA0002903762480000036
curve power is given for a t period;
Figure FDA0002903762480000037
and
Figure FDA0002903762480000038
the power deviating from a given curve and its envelope respectively for the period t;
Figure FDA0002903762480000039
half of the power value of the envelope curve is given for the t period;
peak load peak clipping mode before day
Figure FDA00029037624800000310
wherein ,
Figure FDA00029037624800000311
purchasing power for the virtual power plant in the normal operation mode at the t period;
Figure FDA00029037624800000312
the power selling power of the virtual power plant is in a t-period under the normal operation mode;
Figure FDA00029037624800000313
the price is compensated for peak load peak clipping before the day.
6. The method of claim 3, wherein the virtual plant multiple operating mode constraints are specified as follows:
and power balance constraint:
Figure FDA0002903762480000041
wherein ,
Figure FDA0002903762480000042
the total output of the wind turbine generator is t time period;
Figure FDA0002903762480000043
the total output of the photovoltaic unit is obtained in the time period t;
Figure FDA0002903762480000044
the total output of the energy storage unit is t time period;
Figure FDA0002903762480000045
the total charging power of the electric automobile is t time period;
Figure FDA0002903762480000046
the total power of the air conditioner is t time period;
Figure FDA0002903762480000047
the total power of the pump station in the t period;
and (3) constraint of the distributed generator set:
Figure FDA0002903762480000048
Figure FDA0002903762480000049
wherein ,
Figure FDA00029037624800000410
is n thPVPredicting output of the photovoltaic unit at t time period before day;
Figure FDA00029037624800000411
is n thPVActually outputting power of the photovoltaic unit at a time t;
Figure FDA00029037624800000412
is n thWTPredicting output before the day at the time interval t of the typhoon generator set;
Figure FDA00029037624800000413
n thWTActually outputting force at t time interval of the typhoon generator set;
and (3) charge and discharge power constraint of the distributed energy storage unit:
Figure FDA00029037624800000414
wherein ,
Figure FDA00029037624800000415
and
Figure FDA00029037624800000416
are respectively nBESThe maximum charge and discharge power of the platform energy storage unit;
and (3) carrying out charge state constraint on the distributed energy storage unit:
Figure FDA00029037624800000417
wherein ,
Figure FDA00029037624800000418
is n thBESThe stage energy storage unit is in a state of charge at t time period;
Figure FDA00029037624800000419
and
Figure FDA00029037624800000420
are respectively nBESThe upper and lower limits of the state of charge of the energy storage unit;
the charge dynamic process of the distributed energy storage unit comprises the following steps:
Figure FDA0002903762480000051
wherein ：
Figure FDA0002903762480000052
and
Figure FDA0002903762480000053
is n thBESThe charging and discharging efficiency of the platform energy storage unit;
Figure FDA0002903762480000054
is n thBESRated capacity of the energy storage unit;
and power battery charge and discharge power constraint:
Figure FDA0002903762480000055
wherein ,
Figure FDA0002903762480000056
and
Figure FDA0002903762480000057
are respectively nEVThe maximum charge and discharge power of a power battery of the electric vehicle;
Figure FDA0002903762480000058
and
Figure FDA0002903762480000059
are respectively nEVCharging and discharging states of the electric vehicle at t time period;
Figure FDA00029037624800000510
is n thEVThe access state of the electric vehicle in a time period t;
Figure FDA00029037624800000511
and
Figure FDA00029037624800000512
are respectively nEVCharging and discharging power of the electric vehicle in a t period;
mutually exclusive constraint of the charging and discharging states of the electric vehicle:
Figure FDA00029037624800000513
when the electric automobile leaves, the state of charge is not lower than the expected value, as follows:
Figure FDA00029037624800000514
wherein ,
Figure FDA00029037624800000515
is n thEVThe time of departure of the vehicle electric vehicle,
Figure FDA00029037624800000516
is n thEVA desired state of charge set for the vehicle electric; the relationship between the pressure intensity and the lift of the water pump is as follows:
Figure FDA00029037624800000517
wherein ,
Figure FDA00029037624800000518
is n thRWTN th of water reservoirWPThe pressure intensity of each water pump in the t time period;
Figure FDA00029037624800000519
is n thRWTN th of water reservoirWPThe lift of each water pump in t time period; rho is water density; g is the acceleration of gravity;
the relationship between the pump lift and the flow is as follows:
Figure FDA00029037624800000520
wherein ,
Figure FDA0002903762480000061
is n thRWTN th of water reservoirWPThe flow rate of each water pump in a t period;
Figure FDA0002903762480000062
Figure FDA0002903762480000063
and
Figure FDA0002903762480000064
fitting coefficients for water pump power;
Figure FDA0002903762480000065
is n thRWTN th of water reservoirWPThe running state of each water pump in a t period;
the restriction of the upper and lower limits of the flow of the water pump is as follows:
Figure FDA0002903762480000066
wherein ,
Figure FDA0002903762480000067
and
Figure FDA0002903762480000068
are respectively nRWTN th of water reservoirWPThe upper and lower flow limits of each water pump;
the reservoir water level constraint is:
Figure FDA0002903762480000069
wherein ,
Figure FDA00029037624800000610
and
Figure FDA00029037624800000611
are respectively nRWTWater level of water reservoirUpper and lower limits;
Figure FDA00029037624800000612
is n thRWTThe water level of each reservoir in a period t;
the dynamic process of the water level of the reservoir is as follows:
Figure FDA00029037624800000613
wherein ：NWPIs n thRWTThe number of water inlet pumps of each reservoir;
Figure FDA00029037624800000614
is n thRWTSupplying water to the water storage tanks at t time intervals;
Figure FDA00029037624800000615
is n thRWTThe bottom area of each water storage tank;
the relationship between the air conditioner power and the air supply quantity is as follows:
Figure FDA00029037624800000616
wherein ,
Figure FDA00029037624800000617
is n thRMN th of a roomACPower of each air conditioner in t time period;
Figure FDA00029037624800000618
is n thRMN th of a roomACThe air supply quantity of each air conditioner in a t period;
Figure FDA00029037624800000619
is n thRMN th of a roomACRated power of each air conditioner;
Figure FDA00029037624800000620
are respectively nRMN th of a roomACThe upper limit of the air supply quantity of the air conditioner;
and (3) restricting the upper limit and the lower limit of the air supply volume of the air conditioner:
Figure FDA00029037624800000621
wherein ,
Figure FDA0002903762480000071
is n thRMN th of a roomACThe running state of each air conditioner in a t period;
Figure FDA0002903762480000072
is n thRMN th of a roomACThe lower limit of the air supply quantity of each air conditioner;
the upper and lower limits of the room temperature are constrained as follows:
Figure FDA0002903762480000073
wherein ,
Figure FDA0002903762480000074
and
Figure FDA0002903762480000075
is n thRMUpper and lower limits of indoor temperature of each room;
Figure FDA0002903762480000076
is n thRMTime t-period temperature of each room;
the heating capacity of the air conditioner can be expressed as:
Figure FDA0002903762480000077
wherein ,
Figure FDA0002903762480000078
is n thRMN th of a roomACHeating capacity of each air conditioner in t time period;
Figure FDA0002903762480000079
is n thRMN th of a roomACThe air supply temperature of each air conditioner in a t time period; c. CairIs the air specific heat capacity;
the room temperature dynamic change process is as follows:
Figure FDA00029037624800000710
wherein ：
Figure FDA00029037624800000711
is n thRMHeat capacity of individual rooms;
Figure FDA00029037624800000712
is n thRMThermal conductance inside and outside the individual room; n is a radical ofACNumber of air conditioners for a single room;
Figure FDA00029037624800000713
is the ambient temperature for time t.
7. The method of claim 1, wherein in step S3, the optimized scheduling result of the virtual power plant comprises: the electricity purchasing and selling power of the virtual power plant; virtual power plant economic indicators; the virtual power plant arranges the running conditions of the next day distributed generator set and the multi-energy demand side resource according to the day-ahead scheduling result, reports the exchange power of the virtual power plant and the main network to a scheduling department, and performs subsequent real-time scheduling according to the day-ahead scheduling result.
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