CN112541612B - Multi-network coupling-based virtual power plant economic optimization scheduling method - Google Patents

Multi-network coupling-based virtual power plant economic optimization scheduling method Download PDF

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CN112541612B
CN112541612B CN202011230419.8A CN202011230419A CN112541612B CN 112541612 B CN112541612 B CN 112541612B CN 202011230419 A CN202011230419 A CN 202011230419A CN 112541612 B CN112541612 B CN 112541612B
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CN112541612A (en
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孙乐平
郭小璇
韩帅
吴宛潞
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Electric Power Research Institute of Guangxi Power Grid Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
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    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract

The invention discloses a virtual power plant economic optimization scheduling method based on multi-network coupling, wherein the method comprises the following steps: establishing an energy supply side model comprising a wind turbine generator set, a cogeneration generator set and a small hydroelectric generating set, and designing a generalized demand response benefit model containing resident adjustable electric load, heat load and electric automobile load; forming an electricity-gas-traffic multi-network coupling model based on the multi-coupling effect of the electric network, the natural gas network and the traffic network; establishing an economic optimization scheduling objective function on the basis of describing a revenue function and a line safety penalty function by taking the operation profit of the virtual power plant as a target; and describing a power balance constraint condition based on the uncertainty of the electric load, the thermal load and the wind power output, and solving by adopting a quantum particle swarm algorithm. In the embodiment of the invention, the economic operation of small hydropower stations can be realized by optimizing and scheduling the virtual power plant.

Description

Multi-network coupling-based virtual power plant economic optimization scheduling method
Technical Field
The invention relates to the field of power systems, in particular to a virtual power plant economic optimization scheduling method based on multi-network coupling.
Background
The renewable distributed power supply is vigorously developed and used, the comprehensive energy utilization efficiency is improved, and the topic becomes the current hot research topic, the number of small hydropower stations in a southern power grid area is large, the area distribution is wide, the influence of seasons is obvious, but the operation information is lacked, and the small hydropower stations are far away from a load center, so that the outstanding problem of how to optimize and dispatch the small hydropower stations to improve the power grid operation economy is currently faced.
Aiming at the actual situation that a large amount of distributed energy is connected into a power grid, a virtual power plant technology is introduced, supply side energy (small hydropower station, wind power and cogeneration unit) and generalized demand response side resources (resident adjustable electric load, heat load and electric automobile load) in an area are integrated into a virtual power plant, multi-network coupling among electricity, gas and traffic is considered, a virtual power plant economic operation optimization scheduling model is constructed, and economic operation of the small hydropower station is realized through virtual power plant optimization scheduling.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a virtual power plant economic optimization scheduling method based on multi-network coupling, which is beneficial to realizing the economic operation of small hydropower through the virtual power plant optimization scheduling.
In order to solve the technical problem, an embodiment of the present invention provides a virtual power plant economic optimization scheduling method based on multi-network coupling, including the steps of:
(1) Establishing an energy supply side model comprising a wind turbine generator set, a cogeneration generator set and a small hydroelectric generating set, and designing a generalized demand response benefit model containing resident adjustable electric load, heat load and electric automobile load;
(2) Forming an electricity-gas-traffic multi-network coupling model based on the multi-coupling effect of the electric network, the natural gas network and the traffic network;
(3) Establishing an economic optimization scheduling objective function on the basis of describing a profit function and a line safety punishment function by taking the operation profit of the virtual power plant as an objective;
(4) And describing a power balance constraint condition based on the uncertainties of the electric load, the thermal load and the wind power output, and solving by adopting a quantum particle swarm algorithm.
Optionally, the model of the small hydroelectric generating set in step (1) is:
P HYD (t)＝[k f Q f (t)-k c Q c (t)](h u -h d )；
in the formula: p HYD (t) the output of the small hydroelectric generating set in the time period of t; k is a radical of formula f 、k c The output coefficients of the small hydropower station during power generation and water pumping respectively; q f (t)、Q c (t) flow rates during the small hydroelectric power generation and water pumping operation in the time period t are respectively; h is u 、h d The water level of the upstream reservoir and the downstream reservoir;
operation and maintenance cost S of small hydroelectric generating set in t period HYD (t) can be represented by the following formula:
S HYD (t)＝C H P HYD (t)m hyd +m hyd C U +(1-m hyd )C D ；
in the formula: s HYD (t) is small hydroelectric generating set of time period tThe operation and maintenance cost; c H The unit generated energy cost of the hydropower station is obtained; c U 、C D The starting and stopping costs are low; m is hyd The variable is 0-1, 1 represents starting, and 0 represents stopping;
the influence of seasons on the small hydropower output is obvious, during the dry season, a penalty factor is added into an operation and maintenance cost function to represent the running cost of the dry season, and the corrected running cost is as follows:
S' HYD (t)＝S HYD (t)+λ；
λ＝k(h u,max -h d )；
in the formula: s' HYD (t) the operation and maintenance cost in the dry season at the time period t after correction; s HYD (t) the operation and maintenance cost of the small hydroelectric generating set in the period t before correction; lambda is a penalty factor, and the lower the water level of the water storage is, the larger lambda is; k is a penalty factor coefficient; h is u,max Is the upstream maximum water level.
Optionally, the rated power in the step (1) is P N The generated power P of the wind turbine generator in the time period t WPP (t) satisfies the following relationship with the wind speed v:
Figure GDA0003696992980000031
in the formula: p WPP (t) generating power of the wind turbine generator at t time interval; v is the wind speed; eta is the wind energy utilization coefficient; s w The projected area of the wind in the area where the blades flow; v in 、V N And V out Wind speeds are cut-in, rated and cut-out; p N The rated power of the wind turbine generator is set;
in practical application, the operation and maintenance cost S of the wind turbine generator at t time interval WPP (t) can be reduced to the generated power P WPP (t) a linear model of the expression:
S WPP (t)＝e WPP ·P WPP (t)；
in the formula: s WPP (t) the operation and maintenance cost of the wind turbine generator at the time interval t; e.g. of the type WPP Is a cost coefficient of unit generated power.
Optionally, heat in the step (1)The cogeneration unit consists of a gas turbine and a waste heat boiler, and the operation and maintenance cost S of the cogeneration unit is expressed in the form of a quadratic function CHP (t)：
Figure GDA0003696992980000032
In the formula: s CHP (t) the operation and maintenance cost of the cogeneration unit at the time period t; alpha is alpha q 、β q 、γ q A secondary coefficient, a primary coefficient and a non-cost coefficient of the generating cost of the unit; alpha is alpha h 、β h 、γ h A secondary coefficient, a primary coefficient and a non-cost coefficient of the heat supply cost for the unit; the cost-free coefficient generally takes a positive value; p CHP (t) gas turbine output for a time period t; q GL (t) the heat supply output of the waste heat boiler in the period of t; c S 、C T The starting and stopping costs are low; m is chp Is a variable of 0-1, with 1 representing a startup and 0 representing a shutdown.
Optionally, in the step (1), a generalized demand response benefit model including a resident adjustable electrical load, a thermal load and an electric vehicle load is designed:
for resident adjustable electric load, the electricity utilization behavior of a user is changed based on a power grid time-of-use electricity price policy, and different electricity prices R are set before and after response m0 、R m Relation U of power-off load demand t0 、U t Can be expressed as:
Figure GDA0003696992980000033
in the formula: r is m0 、R m Electricity prices before and after the response; u shape t0 、U t In response to fore and aft electrical load demands; f. of m 、f n Electric price elastic coefficient, generally f, for m and n time periods respectively m Take the negative value, f n Taking a positive value;
for the resident heat load, the demand response of the heat load is realized by changing the temperature value; it is assumed that the indoor and outdoor temperatures of the room are maintained at the temperature T for the period T, respectively in (t)And T out (t), selecting the heating load as a research object, and taking a demand response measure to the research object, wherein the heating power Q (t) can be expressed as:
Figure GDA0003696992980000041
in the formula: q (t) is the heating power in the period of t; t is in (t)、T out (t) indoor and outdoor temperatures of the room for a period of t; l is the thermal resistance of the building and the like; t is in (t + 1) is the indoor temperature at the next time period after the time period t; Δ t is the time step; c is indoor specific heat capacity;
for the electric vehicle load, only the charging behavior is considered, and the corresponding constraint condition is written as:
Figure GDA0003696992980000042
Figure GDA0003696992980000043
Figure GDA0003696992980000044
in the formula:
Figure GDA0003696992980000045
and
Figure GDA0003696992980000046
respectively indicating the electric quantity, the target charging quantity and the maximum battery capacity when the user i accesses the charging station;
Figure GDA0003696992980000047
time to access and leave the charging station for user i; t is the time period when the user i is in the charging state;
Figure GDA0003696992980000048
is a period of tThe load amount and the maximum load amount of the user i;
Figure GDA0003696992980000049
Figure GDA00036969929800000410
charging efficiency and charging power for user i;
the generalized demand response benefit model is as follows:
C DR (t)＝I in (t)-I out (t)；
I in (t)＝L(t)[J E (t)+J H (t)+J V (t)]；
I out (t)＝β E [J E (t)+ε E (t)] 2 +β H [J H (t)+ε H (t)] 2 +β V [J V (t)+ε V (t)] 2 ；
in the formula: c DR (t) represents the total profit from the demand response for the period t; i is in (t)、I out (t) demand response income and incentive cost issued to users in a time period t; l (t) is the marginal price of electricity in the t period; j. the design is a square E (t)、J H (t)、J V (t) is the reduction of the resident adjustable electric load, heat load and electric automobile load in the period t of the user; beta is a E 、β H 、β V Response benefit coefficients of the resident adjustable electric load, the thermal load and the electric automobile load in the period t respectively; epsilon E (t)、ε H (t)、ε V (t) are response deviations of different loads in the t period, respectively.
Optionally, in the step (2), an electric-gas-traffic multi-network coupling model is formed based on a multi-coupling effect of the electric network, the natural gas network and the traffic network:
assuming that the heat value of the natural gas is H, the coupling relation between the power grid and the natural gas grid can pass through the gas consumption D of the gas turbine in the t period CHP (t) is described, namely:
Figure GDA0003696992980000051
in the formula: d CHP (t) is the gas consumption of the gas turbine for a period t; p CHP (t) gas turbine output for a time period t; h is the heat value of natural gas; a is CHP 、b CHP And c CHP Is a gas consumption parameter;
considering the coupling relation between the power grid and the traffic network, the active load predicted value of the node a is assumed to be
Figure GDA0003696992980000052
Will be loaded by conventional power
Figure GDA0003696992980000053
And charging station load
Figure GDA0003696992980000054
Two parts constitute, namely:
Figure GDA0003696992980000055
in the formula:
Figure GDA0003696992980000056
the active load predicted value of the node a is obtained;
Figure GDA0003696992980000057
is the conventional power load prediction value of node a;
Figure GDA0003696992980000058
a charging station load predicted value of the node a; zeta is the charging rate coefficient of the corresponding unit traffic flow;
Figure GDA0003696992980000059
representing a line set which is provided with a charging station for supplying electric energy to the line by the node a in the traffic network; y is b As a line
Figure GDA00036969929800000510
The traffic flow of (2); n is a distribution network node set connected with a charging station。
Optionally, in the step (3), with the operation profit of the virtual power plant as a target, an economic optimization scheduling objective function is established on the basis of describing a profit function and a line safety penalty function, a calculation cycle is taken for 24 hours, and an objective function F is formed by a net profit function C g Line safety penalty function S g The two parts are as follows:
F＝C g -S g ；
in the formula: f is an objective function; c g Is a net gain function; s g Is a line safety penalty function;
the net gain function can be obtained by subtracting the operation and maintenance cost from the virtual power plant gain in the t period; wherein the benefits include selling electricity to the grid benefit C DEAL (t) revenue of heat load of power supply to user C LOAD (t) and generalized demand response revenue C DR (t) composition; the operation and maintenance cost comprises small hydropower S' HYD (t) wind power S WPP (t) Combined Heat and Power generating Unit S CHP (t); when the model is processed, the supplied energy and demand response load are treated as an overall net gain function C g The establishment is as follows:
Figure GDA0003696992980000061
in the formula: c g Is a net gain function; c DEAL (t) trading income of electric power between the virtual power plant and the main network in the period of t; c LOAD (t) the load side income of the virtual power plant in the period t; c DR (t) demand response revenue for time period t; s' HYD (t) the operation and maintenance cost of the small hydroelectric generating set in the time period of t; s WPP (t) the operation and maintenance cost of the wind turbine generator at the time interval t; s CHP (t) the operation and maintenance cost of the cogeneration unit in the time period t;
the safety is described by adding a penalty function to the target function, the line safety penalty function S g Write as:
Figure GDA0003696992980000062
in the formula: s g Is a line safety penalty function; delta U (t) and Delta P (t) are the more limited voltage and active power in the period t, l U 、l P The coefficient is the out-of-limit cost coefficient of voltage and active power.
Optionally, in the step (4), power balance and power balance constraint conditions are described based on uncertainties of electrical load, thermal load and wind power output, and a quantum particle swarm algorithm is adopted to solve:
the electric power balance constraint in the virtual power plant is as follows:
P WPP (t)+P CHP (t)+P HYD (t)＝P ELOAD (t)+P DEAL (t)；
in the formula: p WPP (t)、P CHP (t)、P HYD (t) the electric power of the fan, the gas turbine and the small water in the period of t is respectively output; p ELOAD (t) virtualizing the electrical load power in the power plant for a period of t; p DEAL (t) the exchange power of the virtual power plant and the power grid in a period t;
the actual wind power output value P is obtained WPP (t) Using its predicted value P WPP0 (t) and an error value ε WPP (t) is represented by the sum of WPP (t) obedience mean 0, standard deviation σ WPP Normal distribution of (t); similarly, the user load value P ELOAD (t) can also be expressed as its predicted value P ELOAD0 (t) and an error value ε ELOAD (t) a sum of form (i) in which ELOAD (t) obedience mean 0, standard deviation σ ELOAD Normal distribution of (t);
P WPP (t)＝P WPP0 (t)+ε WPP (t)；
P ELOAD (t)＝P ELOAD0 (t)+ε ELOAD (t)；
in the formula: p WPP (t) is an actual output value of the wind turbine generator at the time period t; p is WPP0 (t) the predicted value of the output of the wind turbine generator at the time interval t; epsilon WPP (t) the output prediction error value of the wind turbine generator at t time interval; p ELOAD (t) the actual electric load value of the user in the period of t; p ELOAD0 (t) the predicted value of the user electrical load in the t period; epsilon ELOAD (t) prediction error of user electrical load in t time periodA value;
because the wind power output and the load prediction deviation are subjected to normal distribution, the deterministic power balance constraint can be converted into an opportunity constraint, and the opportunity constraint planning can be expressed as:
Figure GDA0003696992980000071
in the formula: p WPP (t)、P CHP (t)、P HYD (t) the electric power of the fan, the gas turbine and the small water in the period of t is respectively output; p ELOAD (t) virtualizing the electrical load power in the power plant for a period of t; p DEAL (t) the exchange power of the virtual power plant and the power grid in a period t; phi is a probability distribution function; α is the confidence level; sigma WPP (t) standard deviation of fan output prediction error value obeying normal distribution in t time period; sigma ELOAD (t) standard deviation of user electrical load prediction error value obeying normal distribution in t time period;
the thermal power balance within the virtual power plant may be described by:
Q GL (t)＝Q HLOAD (t)；
in the formula: q GL (t) the heat supply output of the waste heat boiler in the period of t; q HLOAD (t) virtualizing the heat load power in the power plant in a period of t;
similar to the electrical load handling method, due to the thermal load Q HLOAD (t) the prediction also has an uncertainty, ∈ HLOAD (t) is again subject to a mean of 0 and a standard deviation of σ HLOAD Normal distribution of (t);
Q HLOAD (t)＝Q HLOAD0 (t)+ε HLOAD (t)；
in the formula: q HLOAD (t) is the actual thermal load value of the user in the period of t; q HLOAD0 (t) predicting the user heat load in a t period; epsilon HLOAD (t) predicting the user thermal load error value in a period of t;
similarly, the formula Q can be GL (t)＝Q HLOAD (t) is converted to an opportunity constraint to express:
Q GL (t)-Q HLOAD (t)≥- 1 (α)σ HLOAD (t)；
in the formula: q GL (t) the heat supply output of the waste heat boiler in the period of t; q HLOAD (t) virtualizing the heat load power in the power plant in a period of t; phi is a probability distribution function; α is the confidence level; sigma HLOAD (t) standard deviation of user thermal load prediction error value obeying normal distribution in t time period;
and solving the optimized scheduling problem by adopting a quantum particle swarm algorithm.
In the embodiment of the invention, the optimized scheduling problem is solved by adopting a quantum particle swarm algorithm, the optimized scheduling problem of the virtual power plant containing small hydropower plants relates to a large number of solving variables, a plurality of constraint conditions need to be considered, and the traditional particle swarm algorithm can possibly suffer from the problems of low convergence speed, low operability and the like. Based on quantum mechanics, quantum particle swarm optimization is proposed in recent research by assuming that particles have quantum behaviors, and the overall search capability of the traditional particle swarm optimization is greatly improved by the quantum particle swarm optimization. In addition, the traditional particle swarm optimization generally describes the state of the particle through speed and position, and after a quantum space is introduced, the state of the particle needs to be described through a wave function, the probability of the particle occurrence is determined through Schrodinger equation, and the position equation of the particle is finally obtained through Monte Carlo simulation. Therefore, compared with the common particle swarm algorithm, the quantum particle swarm algorithm has obvious advantages in convergence rate, and is selected as a solving algorithm.
The technical scheme provided by the invention has the following beneficial effects: by introducing a virtual power plant technology, supply side energy (small hydropower station, wind power and cogeneration units) and generalized demand response side resources (resident adjustable electric load, heat load and electric automobile load) in an area are integrated into a virtual power plant, multi-network coupling among electricity, gas and traffic is considered, a virtual power plant economic operation optimization scheduling model is constructed, and the economic operation of the small hydropower station is realized through virtual power plant optimization scheduling.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a virtual power plant economic optimization scheduling method based on multi-network coupling in an embodiment of the invention;
FIG. 2 is a basic block diagram of a virtual power plant in an embodiment of the invention;
FIG. 3 is a graph of the result of the optimization of the output curve of the virtual power plant unit in the embodiment of the invention;
FIG. 4 is a graph of spare capacity versus confidence in an embodiment 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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The multi-type distributed energy sources are integrated into a virtual power plant, so that the advantages of different types of energy sources can be exerted, and the complementary coordination of the energy sources is realized. According to the actual situation of the Guangxi power grid, the basic architecture of a virtual power plant in the research is determined as shown in FIG. 2, the virtual power plant is composed of a supply side energy source and a demand side load, wherein the supply side comprises small hydropower, wind power and a cogeneration unit (a gas turbine and a waste heat boiler), and the demand side considers not only the adjustable electric load of conventional residents, but also the heat load and the electric vehicle load. In addition, the coupling between the electric power and the natural gas and the coupling between the electric power and the traffic are realized through a gas turbine and an electric automobile respectively.
The invention provides a virtual power plant economic optimization scheduling method based on multi-network coupling, as shown in fig. 1, the implementation process comprises the following detailed steps:
s11: establishing an energy supply side model comprising wind power, cogeneration and small hydropower, and designing a generalized demand response benefit model containing resident adjustable electric load, heat load and electric automobile load;
the reversible small hydroelectric generating set model is as follows:
P HYD (t)＝[k f Q f (t)-k c Q c (t)](h u -h d ) (1)
in the formula: p HYD (t) the output of the small hydroelectric generating set in the time period of t; k is a radical of f 、k c The output coefficients of the small hydropower station during power generation and water pumping respectively; q f (t)、Q c (t) the flow rates of the small hydroelectric power generation and the water pumping operation in the period of t are respectively; h is u 、h d Is the water level of the upstream and downstream reservoirs.
Operation and maintenance cost S of small hydroelectric generating set in t period HYD (t) can be represented by the following formula:
S HYD (t)＝C H P HYD (t)m hyd +m hyd C U +(1-m hyd )C D (2)
in the formula: s HYD (t) the operation and maintenance cost of the small hydroelectric generating set in the time period t; c H The unit generated energy cost of the hydropower station is obtained; c U 、C D The starting and stopping costs are low; m is hyd Is a variable of 0-1, with 1 representing a startup and 0 representing a shutdown.
The small hydropower output is obviously influenced by seasons, and the scheduling strategies in the rich water period and the dry water period are different. In the rich water period, the small water power should generate as much as possible without limiting the total water consumption; during the dry season, the water quantity of the water storage is required to be ensured, the running cost of the dry season can be represented by adding a penalty factor in an operation and maintenance cost function, and the corrected running cost is as follows:
S' HYD (t)＝S HYD (t)+λ (3)
λ＝k(h u,max -h d ) (4)
in the formula: s' HYD (t) the operation and maintenance cost in the dry season at the time period t after correction; s HYD (t) correcting the operation and maintenance cost of the small hydroelectric generating set at the previous t time period; lambda is a penalty factor, and the lower the water level of the water storage is, the larger lambda is; k is a penalty factor coefficient；h u,max Is the upstream maximum water level.
The wind turbine set in the virtual power plant is an important component on the supply side and has a rated power P N The generated power P of the wind turbine generator in the time period t WPP (t) satisfies the following relationship with the wind speed v:
Figure GDA0003696992980000101
in the formula: p WPP (t) generating power of the wind turbine generator at a time period t; v is the wind speed; eta is the wind energy utilization coefficient; s w The projected area of the wind in the area where the blades flow; v in 、V N And V out Wind speeds are cut-in, rated and cut-out; p N The rated power of the wind turbine generator is obtained.
In practical application, the operation and maintenance cost S of the wind turbine generator at t time interval WPP (t) can be reduced to the generated power P WPP (t) a linear model of the expression:
S WPP (t)＝e WPP ·P WPP (t) (6)
in the formula: s WPP (t) the operation and maintenance cost of the wind turbine generator at the time interval t; e.g. of the type WPP Is a cost coefficient of unit generated power.
The cogeneration unit consists of a gas turbine and a waste heat boiler, the gas turbine is taken as a controllable power supply, and high-temperature gas after acting is collected by the waste heat boiler and then supplied to a heat load. The operating cost S of a cogeneration unit can be generally expressed in the form of a quadratic function CHP (t)：
Figure GDA0003696992980000111
In the formula: alpha is alpha q 、β q 、γ q A secondary coefficient, a primary coefficient and a non-cost coefficient of the generating cost of the unit; alpha is alpha h 、β h 、γ h A secondary coefficient, a primary coefficient and a non-cost coefficient of the heat supply cost for the unit; the cost-free coefficient generally takes a positive value; p CHP When (t) is tA segment gas turbine output; q GL (t) the heat supply output of the waste heat boiler in the period of t; c S 、C T The starting and stopping costs are low; m is a unit of chp The variable is 0-1, 1 represents the starting, and 0 represents the stopping.
The user load demand increases in the peak section and decreases in the valley section, with obvious features. In addition to the resident adjustable electric load, the heat load and the electric vehicle load in the Guangxi electric network area develop rapidly, therefore, the generalized demand response model is established by introducing the concept of generalized demand response and combining the resident adjustable electric load, the heat load and the electric vehicle load for comprehensive regulation.
For the electricity load adjustable by residents, the electricity consumption behavior of users is changed based on the time-of-use electricity price policy of the Guangxi power grid, peak clipping and valley filling are realized, and different electricity prices R are set before and after response m0 、R m Relation U of power-off load demand t0 、U t Can be expressed as:
Figure GDA0003696992980000121
in the formula: r m0 、R m Electricity prices before and after the response; u shape t0 、U t In response to fore and aft electrical load demands; f. of m 、f n Electric price elastic coefficient, generally f, for m and n time periods respectively m Take the negative value, f n Take a positive value.
For the resident heat load, the change of the temperature in a small range does not affect the comfort of the human body, so the research realizes the demand response of the heat load by changing the temperature value. It is assumed that the indoor and outdoor temperatures of the room are maintained at the temperature T for the period T, respectively in (T) and T out (t), selecting the heating load as a research object, and taking a demand response measure to the research object, wherein the heating power Q (t) can be expressed as:
Figure GDA0003696992980000122
in the formula: q (t) is the heating power in the period of t; t is in (t)、T out (t) indoor and outdoor temperatures of the room for a period of t; l is the thermal resistance of the building and the like; t is in (t + 1) is the indoor temperature at the next time period after the time period t; Δ t is the time step; and C is the indoor specific heat capacity.
The load of the electric automobile has extremely strong schedulability. Considering the actual running condition of the electric automobile, only the charging behavior is considered, and the corresponding constraint conditions can be written as follows:
Figure GDA0003696992980000123
Figure GDA0003696992980000124
Figure GDA0003696992980000125
in the formula:
Figure GDA0003696992980000126
and
Figure GDA0003696992980000127
respectively indicating the electric quantity, the target charging quantity and the maximum battery capacity when the user i accesses the charging station;
Figure GDA0003696992980000128
time to access and leave the charging station for user i; t is the time period when the user i is in the charging state;
Figure GDA0003696992980000129
the load capacity and the maximum load capacity of the user i in the t period;
Figure GDA00036969929800001210
Figure GDA00036969929800001211
for user iCharging efficiency and charging power.
In order to better reflect the comprehensive benefits of generalized demand response, the benefits and the payment cost of demand response are combined into a benefit model, and the benefit model can better reflect the demand response to a virtual power plant manager in the form of economic benefits and provide reference for decision making of the virtual power plant manager. The virtual power plant manager reports the load reduction amount and the corresponding price in the energy market in the day ahead, the reduction amount lower than the clearing price is committed after the unified clearing of the power market, and the settlement is carried out uniformly by the clearing price. The maximum demand response income of the virtual power plant is the target, and the target function is as follows:
C DR (t)＝I in (t)-I out (t) (13)
I in (t)＝L(t)[J E (t)+J H (t)+J V (t)] (14)
I out (t)＝β E [J E (t)+ε E (t)] 2 +β H [J H (t)+ε H (t)] 2 +β V [J V (t)+ε V (t)] 2 (15)
in the formula: c DR (t) represents the total benefit over time t; i is in (t)、I out (t) demand response income and incentive cost issued to users in a time period t; l (t) is the marginal price of electricity in the t period; j. the design is a square E (t)、J H (t)、J V (t) is the reduction of the resident adjustable electric load, heat load and electric automobile load in the period t of the user; beta is a E 、β H 、β V Response benefit coefficients of the resident adjustable electric load, the thermal load and the electric automobile load in the period t respectively; epsilon E (t)、ε H (t)、ε V (t) are response deviations of different loads in the t period, respectively.
S12: further considering multiple coupling effects of an electric network, a natural gas network and a traffic network to form an electric traffic multi-network coupling model;
the deep fusion of the power grid and the natural gas network and the deep interaction of the power grid and the traffic network provide great challenges for the safe and economic operation of new energy sources such as water, electricity and wind, therefore, the diversified coupling relationship among the natural gas network, the traffic network and the power distribution network is comprehensively considered, and the gas turbine in the cogeneration unit in the virtual power plant is used as a coupling element of the natural gas network and the power distribution network, so that the conversion of gas and electricity energy is realized; the electric automobile is used as a coupling element of a traffic network and a power distribution network to realize the coupling of electric power traffic flow; and finally, constructing the electric traffic multi-network coupling model.
Assuming that the heat value of the natural gas is H, the coupling relation between the power grid and the natural gas grid can pass through the gas consumption D of the gas turbine in the t period CHP (t) is described, namely:
Figure GDA00036969929800001411
in the formula: d CHP (t) is the gas consumption of the gas turbine for a period t; p is CHP (t) gas turbine output for a time period t; h is the heat value of natural gas; a is CHP 、b CHP And c CHP Is a gas consumption parameter.
Considering the coupling relation between the power grid and the traffic network, the active load predicted value of the node a is assumed to be
Figure GDA0003696992980000141
Will be loaded by conventional power
Figure GDA0003696992980000142
And charging station load
Figure GDA0003696992980000143
Two parts constitute, namely:
Figure GDA0003696992980000144
in the formula:
Figure GDA0003696992980000145
the active load predicted value of the node a is obtained;
Figure GDA0003696992980000146
a traditional power load prediction value for node a;
Figure GDA0003696992980000147
a charging station load predicted value of the node a; zeta is the charging rate coefficient of the corresponding unit traffic flow;
Figure GDA0003696992980000148
representing a line set which is provided with a charging station for supplying electric energy to the line by the node a in the traffic network; y is b As a line
Figure GDA0003696992980000149
The traffic flow of (2); and N is a distribution network node set connected with a charging station.
S13: establishing an economic optimization scheduling objective function on the basis of describing a revenue function and a line safety penalty function by taking the operation profit of the maximized virtual power plant as a target;
the economic dispatching model of the virtual power plant aims at realizing the maximum profit, the calculation period is 24 hours, and the objective function F is the net profit function C g Line safety penalty function S g The two parts are as follows:
F＝C g -S g (18)
in the formula: f is an objective function; c g Is a net gain function; s g Is a line security penalty function.
Wherein the net revenue function may be obtained by subtracting the operation and maintenance cost from the virtual plant revenue at time t. Wherein the benefits include selling electricity to the grid and benefits C DEAL (t) revenue of heat load of electricity supply to user LOAD (t) and generalized demand response revenue C DR (t) composition; the operation and maintenance cost comprises small hydropower S' HYD (t) wind power S WPP (t) Combined Heat and Power Generation System S CHP (t) of (d). In model processing, the supply energy and demand response load are treated as an overall net gain function C g The establishment is as follows:
Figure GDA00036969929800001410
in the formula: c g Is a net gain function; c DEAL (t) trading income of electric power between the virtual power plant and the main network in the period of t; c LOAD (t) the load side income of the virtual power plant in the period t; c DR (t) demand response revenue for time period t; s' HYD (t) the operation and maintenance cost of the small hydroelectric generating set in the time period of t; s. the WPP (t) the operation and maintenance cost of the wind turbine generator at the time interval t; s CHP And (t) is the operation and maintenance cost of the cogeneration unit in the period of t.
If only considering the economic profit maximization, the power flow constraint may not be met, and the problems of power out-of-limit, voltage out-of-limit and the like occur. Therefore, the economic benefit maximization is considered in the virtual power plant economic dispatching model, and the operation safety of the virtual power plant is also considered. The safety is described by adding a penalty function to the target function, the line safety penalty function S g Write as:
Figure GDA0003696992980000151
in the formula: delta U (t) and Delta P (t) are the more limited voltage and active power in the period t, l U 、l P The coefficient is the out-of-limit cost coefficient of voltage and active power. An effective punishment function depends on a reasonable cost coefficient, and the line safety problem is integrated into the economic dispatch so as to form a unified solution of the economic problem and be beneficial to the safe and stable operation of the virtual power plant.
S14: considering the uncertainty of electric load, heat load and wind power output, describing constraint conditions such as power balance and the like, and solving by adopting a quantum particle swarm algorithm:
the electric power balance constraint in the virtual power plant is as follows:
P WPP (t)+P CHP (t)+P HYD (t)＝P ELOAD (t)+P DEAL (t) (21)
in the formula: p is WPP (t)、P CHP (t)、P HYD (t) the electric power of the fan, the gas turbine and the small water in the period of t is respectively output; p ELOAD (t) virtualizing the electrical load power in the power plant for a period of t; p DEAL And (t) the exchange power of the virtual power plant and the power grid in the period t.
The construction of the prediction model depends on factors such as geographic environment, historical data and the like, and errors exist in the predicted values of the wind turbine generator output and the electric load due to uncertainty of the wind power output, the electric automobile and other electric loads in the virtual power plant. So as to adjust the fan output value P WPP (t) Using its predicted value P WPP0 (t) and an error value ε WPP (t) is represented by the sum of WPP (t) obedience mean 0, standard deviation σ WPP Normal distribution of (t); similarly, the user load value P ELOAD (t) can also be expressed as its predicted value P ELOAD0 (t) and an error value ε ELOAD (t) a sum of form (i) in which ELOAD (t) obedience mean 0, standard deviation σ ELOAD Normal distribution of (t).
P WPP (t)＝P WPP0 (t)+ε WPP (t) (22)
P ELOAD (t)＝P ELOAD0 (t)+ε ELOAD (t) (23)
In the formula: p WPP (t) is an actual output value of the wind turbine generator at the time period t; p WPP0 (t) the predicted value of the output of the wind turbine generator at the time interval t; epsilon WPP (t) the output prediction error value of the wind turbine generator in the time period t; p is ELOAD (t) the actual electric load value of the user in the period of t; p ELOAD0 (t) the predicted value of the user electrical load is t time period; epsilon ELOAD And (t) the user electrical load prediction error value in the t period.
Because the fan output and the load forecast deviation are both in normal distribution, the deterministic power balance constraint can be converted into an opportunity constraint, and the opportunity constraint planning can be expressed as:
Figure GDA0003696992980000161
in the formula: p is WPP (t)、P CHP (t)、P HYD (t) the electric power of the fan, the gas turbine and the small water in the period of t is respectively output; p is ELOAD (t) virtualizing the electrical load power in the power plant for a period of t; p DEAL (t) intersection of virtual power plant and power grid in t periodPower is exchanged; phi is a probability distribution function; α is the confidence level; sigma WPP (t) standard deviation of fan output prediction error value obeying normal distribution in t time period; sigma ELOAD And (t) is the standard deviation of the user electrical load prediction error value obeying normal distribution in the period of t. The right side of equation (24) represents the reserve capacity of the virtual plant system during time t.
The thermal power balance within the virtual power plant may be described by:
Q GL (t)＝Q HLOAD (t) (25)
in the formula: q GL (t) the heat supply output of the waste heat boiler in the period of t; q HLOAD And (t) the heat load power in the virtual power plant in the period of t.
Similar to the electrical load handling method, due to the thermal load Q HLOAD (t) prediction is also uncertain, and so is also written as determining the predicted value Q HLOAD0 (t) and uncertainty error ε HLOAD Form (25) of the sum of (t) ∈ HLOAD (t) is again subject to a mean of 0 and a standard deviation of σ HLOAD Normal distribution of (t).
Q HLOAD (t)＝Q HLOAD0 (t)+ε HLOAD (t) (26)
In the formula: q HLOAD (t) is the actual thermal load value of the user in the period of t; q HLOAD0 (t) the predicted value of the user heat load in the t period; epsilon HLOAD And (t) the user heat load prediction error value in a period of t.
Similarly, equation (25) can be expressed in terms of an opportunity constraint:
Q GL (t)-Q HLOAD (t)≥φ -1 (α)σ HLOAD (t) (27)
in the formula: q GL (t) the heat supply output of the waste heat boiler in the period of t; q HLOAD (t) virtualizing the heat load power in the power plant in a period of t; phi is a probability distribution function; α is the confidence level; sigma HLOAD And (t) the standard deviation of the user heat load prediction error value obeying normal distribution in the period of t.
The optimal scheduling problem is solved by adopting a quantum particle swarm algorithm, the optimal scheduling problem of the virtual power plant with small hydropower plants relates to a large number of solving variables, a plurality of constraint conditions need to be considered, and the traditional particle swarm algorithm can possibly suffer from the problems of low convergence speed, low operability and the like. Based on quantum mechanics, by assuming that particles have quantum behaviors, a quantum particle swarm algorithm is recently researched and proposed, and the algorithm greatly improves the global search capability of the traditional particle swarm algorithm. In addition, the conventional particle swarm optimization generally describes the state of the particle through speed and position, and after a quantum space is introduced, the state of the particle needs to be described through a wave function, the probability of the particle occurrence is determined through Schrodinger equation, and the position equation of the particle is finally obtained through Monte Carlo simulation. Therefore, compared with the common particle swarm algorithm, the quantum particle swarm algorithm has obvious advantages in convergence rate, and is selected as a solving algorithm.
For further understanding of the present invention, a simple multi-energy center system in a certain development area of Guangxi is taken as an example to explain the practical application of the present invention.
The system comprises 1 adjustable small hydropower station with 8MW water storage, 1 wind power station with 5MW, 1 cogeneration unit with 20MW, and adjustable resident electrical load, thermal load and electric vehicle load which can be used as demand response, wherein the controllable load comprising the electric vehicle is 8MW, and the predicted power fluctuation variance of the fan is 0.1MW 2 The variance of load prediction fluctuation is 0.01MW 2 。
To better discuss the role of small hydropower and generalized demand response in virtual plant operation, the following 4 scenarios are set up herein for example analysis:
scenario 1: generalized demand response measures and the regulating effect of small hydropower stations are not considered;
scenario 2: only the regulating effect of small hydropower is considered;
scenario 3: only generalized demand responses are considered;
scenario 4: and meanwhile, generalized demand response measures and the regulating effect of small hydropower are considered.
The peak clipping and valley filling effects of small hydropower and generalized demand response are researched through simulation of the virtual power plant power generation output, and fig. 3 depicts the optimization result of the virtual power plant power generation output curve under four scenes.
For scenario 1, because the regulation effect and the generalized demand response measure of the small hydropower station are not considered, the peak-valley difference of the power generation output curve of the virtual power plant is large, and adverse effects are brought to the scheduling and the income of the virtual power plant.
For scenario 2, when the small hydropower station participates in regulation, the small hydropower station generates electricity at the peak of load and stores energy by pumping water at the valley of load due to the function of pumping water, so that the output curve of the unit in the virtual power plant is lifted at the valley part and reduced at the peak part. However, considering factors such as the small water electric capacity and the operation cost of pumping and storing energy, the optimization degree of the curve is limited.
For scenario 3, the output curve is also optimized considering only excitation of the generalized demand response compensation mechanism. However, due to the cost constraint in the compensation process, the optimization result is not very ideal.
For scenario 4, under the dual regulation influence of the generalized demand response measure and the small hydropower regulation effect, the curve becomes relatively gentle, and the optimization result reaches a relatively ideal state.
Through the 4 scenes, the active effects of peak clipping and valley filling can be fully exerted by the cooperation of the small hydropower stations in the virtual power plant to respond to the demand, so that the small hydropower stations are orderly operated, and economic optimized operation is realized.
And calculating the income data of the virtual power plant according to the economic benefit model of the virtual power plant as shown in the table 1. From the result, when the small hydropower station regulation effect and the generalized demand response are considered under the scene 4, the electric power transaction amount between the virtual power plant and the main network is small, the transaction charge is low, the dependence on the cogeneration unit is reduced, the unit start-stop times are reduced, and the operation and maintenance cost of the unit is reduced. Thus, the network out-of-limit penalty for scenario 4 is greatly reduced compared to other scenarios, maximizing its overall revenue. Therefore, for the embodiment, the economic benefit of the virtual power plant is obviously improved by adding small hydropower and generalized demand response measures at the same time.
TABLE 1 virtual Power plant yield under different scenarios (Wanyuan)
Tab.1 Revenue of virtual power plant in differential sciences
Figure GDA0003696992980000191
Different confidence levels have a greater impact on spare capacity. Fig. 4 depicts the virtual power plant system reserve capacity corresponding to different confidence degrees α in scenario 4, and it can be seen that the larger the reserve capacity is, the more reliable the system is in operation, but the higher the corresponding virtual power plant operation cost is. As can be seen from fig. 3, the power generation reserve capacity becomes faster and faster as α increases, and α should not be set too large from the viewpoint of virtual plant revenue. Therefore, virtual plant deciders need to strike a balance between economy and reliability.
The comparison of multi-scene calculation examples shows that the output curve of the virtual power plant can be optimized by comprehensively considering the small hydropower station regulation effect and the generalized demand response, and the optimal economic benefit is obtained, so that the feasibility and the effectiveness of the model are verified.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: read Only Memory (ROM), random Access Memory (RAM), magnetic or optical disks, and the like.
In addition, the method for economic optimization scheduling of a virtual power plant based on multi-network coupling provided by the embodiment of the invention is described in detail, a specific embodiment is adopted to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (7)

1. A virtual power plant economic optimization scheduling method based on multi-network coupling is characterized by comprising the following steps:
(1) Establishing an energy supply side model comprising a wind turbine generator set, a cogeneration generator set and a small hydroelectric generating set, and designing a generalized demand response benefit model containing resident adjustable electric load, heat load and electric automobile load;
(2) Forming an electricity-gas-traffic multi-network coupling model based on the multi-coupling effect of the electric network, the natural gas network and the traffic network;
(3) Establishing an economic optimization scheduling objective function on the basis of describing a revenue function and a line safety penalty function by taking the operation profit of the virtual power plant as a target;
(4) Describing a power balance constraint condition based on the uncertainty of the electric load, the thermal load and the wind power output, and solving by adopting a quantum particle swarm algorithm;
designing a generalized demand response benefit model containing resident adjustable electric load, heat load and electric vehicle load in the step (1):
for resident adjustable electric load, the electricity utilization behavior of a user is changed based on a power grid time-of-use electricity price policy, and different electricity prices R are set before and after response m0 、R m Relation U of power-off load demand t0 、U t Can be expressed as:
Figure FDA0003696992970000011
in the formula: r m0 、R m Electricity prices before and after the response; u shape t0 、U t In response to fore and aft electrical load demands; f. of m 、f n Electric power price elastic coefficient, f, in m and n time periods respectively m Take the negative value, f n Taking a positive value;
for the resident heat load, the demand response of the heat load is realized by changing the temperature value; assuming that the indoor and outdoor temperatures of the room are maintained at the temperature T for the period T, respectively in (T) and T out (t), selecting the heating load as a research object, and taking a demand response measure to the research object, wherein the heating power Q (t) can be expressed as:
Figure FDA0003696992970000021
in the formula: q (t) is the heating power in the t period; t is in (t)、T out (t) indoor and outdoor temperatures of the room for a period of t; l is the thermal resistance of the building; t is a unit of in (t + 1) is the indoor temperature at the next time period after the time period t; Δ t is the time step; c is indoor specific heat capacity;
for the electric vehicle load, only the charging behavior is considered, and the corresponding constraint condition is written as:
Figure FDA0003696992970000022
Figure FDA0003696992970000023
Figure FDA0003696992970000024
in the formula:
Figure FDA0003696992970000025
and
Figure FDA0003696992970000026
respectively indicating the electric quantity, the target charging quantity and the maximum battery capacity when the user i accesses the charging station;
Figure FDA0003696992970000027
time to access and leave the charging station for user i; t is the time period when the user i is in the charging state;
Figure FDA0003696992970000028
the load capacity and the maximum load capacity of the user i in the t period;
Figure FDA0003696992970000029
P i l charging efficiency and charging power for user i;
the generalized demand response benefit model is:
C DR (t)＝I in (t)-I out (t)；
I in (t)＝L(t)[J E (t)+J H (t)+J V (t)]；
I out (t)＝β E [J E (t)+ε E (t)] 2 +β H [J H (t)+ε H (t)] 2 +β V [J V (t)+ε V (t)] 2 ；
in the formula: c DR (t) represents the total revenue of demand response for the period t; i is in (t)、I out (t) the demand response income of the time period t and the incentive cost issued to the user; l (t) is the marginal price of electricity in the t period; j. the design is a square E (t)、J H (t)、J V (t) is the reduction of the resident adjustable electric load, heat load and electric automobile load in the period t of the user; beta is a E 、β H 、β V Response benefit coefficients of the resident adjustable electric load, the thermal load and the electric automobile load in the period t are respectively; epsilon E (t)、ε H (t)、ε V (t) are response deviations of different loads in the t period, respectively.
2. The economic optimization scheduling method of the virtual power plant according to claim 1, characterized in that the model of the small hydroelectric generating set in the step (1) is as follows:
P HYD (t)＝[k f Q f (t)-k c Q c (t)](h u -h d )；
in the formula: p HYD (t) the output of the small hydroelectric generating set in the time period of t; k is a radical of f 、k c The output coefficients of the small hydropower station during power generation and water pumping respectively; q f (t)、Q c (t) flow rates during the small hydroelectric power generation and water pumping operation in the time period t are respectively; h is u 、h d The water level of the upstream reservoir and the downstream reservoir;
operation and maintenance cost S of small hydroelectric generating set in t period HYD (t) can be represented by the following formula:
S HYD (t)＝C H P HYD (t)m hyd +m hyd C U +(1-m hyd )C D ；
in the formula: s HYD (t) the operation and maintenance cost of the small hydroelectric generating set in the time period of t; c H The unit generated energy cost of the hydropower station is obtained; c U 、C D The machine-starting and shutdown costs are high; m is hyd The variable is 0-1, 1 represents starting, and 0 represents stopping;
the influence of seasons on the small hydropower output is obvious, during the dry season, a penalty factor is added into an operation and maintenance cost function to represent the running cost of the dry season, and the corrected running cost is as follows:
S' HYD (t)＝S HYD (t)+λ；
λ＝k(h u,max -h d )；
in the formula: s' HYD (t) the operation and maintenance cost in the dry season at the time period t after correction; s HYD (t) the operation and maintenance cost of the small hydroelectric generating set in the period t before correction; lambda is a penalty factor, and the lower the water level of the water storage is, the larger lambda is; k is a penalty factor coefficient; h is u,max Is the upstream maximum water level.
3. The virtual power plant economic optimized scheduling method of claim 1, wherein the rated power in step (1) is P N The generated power P of the wind turbine generator in the time period t WPP (t) satisfies the following relationship with the wind speed v:
Figure FDA0003696992970000041
in the formula: p WPP (t) generating power of the wind turbine generator at a time period t; v is the wind speed; eta is the wind energy utilization coefficient; s w The projected area of the wind in the area where the blades flow; v in 、V N And V out Wind speeds are cut-in, rated and cut-out; p N The rated power of the wind turbine generator is set;
in practical application, the operation and maintenance cost S of the wind turbine generator at t time interval WPP (t) can be reduced to the generated power P WPP (t) a linear model of the expression:
S WPP (t)＝e WPP ·P WPP (t)；
in the formula: s. the WPP (t) the operation and maintenance cost of the wind turbine generator at the time interval t; e.g. of the type WPP Is a cost coefficient of unit generated power.
4. The economic optimization scheduling method of the virtual power plant according to claim 1, wherein the cogeneration unit in the step (1) is composed of a gas turbine and a waste heat boiler, and the operation and maintenance cost S of the cogeneration unit is expressed in the form of a quadratic function CHP (t)：
Figure FDA0003696992970000042
In the formula: s CHP (t) the operation and maintenance cost of the cogeneration unit at the time period t; alpha is alpha q 、β q 、γ q A secondary coefficient, a primary coefficient and a non-cost coefficient of the generating cost of the unit; alpha (alpha) ("alpha") h 、β h 、γ h A secondary coefficient, a primary coefficient and a non-cost coefficient of the heat supply cost for the unit; taking a positive value without a cost coefficient; p CHP (t) gas turbine output for a time period t; q GL (t) the heat supply output of the waste heat boiler in the period of t; c S 、C T The machine-starting and shutdown costs are high; m is chp Is a variable of 0-1, with 1 representing a startup and 0 representing a shutdown.
5. The virtual power plant economic optimization scheduling method of claim 1, wherein in the step (2), based on multiple coupling effect of the electric network, the natural gas network and the traffic network, an electric-gas-traffic multiple network coupling model is formed:
assuming that the natural gas has a calorific value H, the coupling relationship between the power grid and the natural gas grid can be determined by the gas consumption D of the gas turbine in the t period CHP (t) is described, namely:
Figure FDA0003696992970000051
in the formula: d CHP (t) is the gas consumption of the gas turbine for a period t; p is CHP (t) gas turbine output for a time period t; h is the heat value of natural gas; a is CHP 、b CHP And c CHP Is a gas consumption parameter;
considering the coupling relation between the power grid and the traffic network, the active load predicted value of the node a is assumed to be
Figure FDA0003696992970000052
Will be loaded by conventional power
Figure FDA0003696992970000053
And charging station load
Figure FDA0003696992970000054
Two parts constitute, namely:
Figure FDA0003696992970000055
in the formula:
Figure FDA0003696992970000056
the active load predicted value of the node a is obtained;
Figure FDA0003696992970000057
a traditional power load prediction value for node a;
Figure FDA0003696992970000058
a charging station load predicted value of the node a; zeta is the charging rate coefficient of the corresponding unit traffic flow;
Figure FDA0003696992970000059
representing a route set for supplying electric energy to a charging station arranged on the route by a node a in the traffic network; y is b As a line
Figure FDA00036969929700000510
The traffic flow of (2); and N is a distribution network node set connected with a charging station.
6. The economic optimization scheduling method of the virtual power plant as claimed in claim 1, wherein in the step (3), the economic optimization scheduling objective function is established based on the income function and the line safety penalty function with the objective of the virtual power plant operation profit as the target, the calculation period is 24 hours, and the objective function F is the net income function C g Line safety penalty function S g Two parts are formed, namely:
F＝C g -S g ；
in the formula: f is an objective function; c g Is a net gain function; s. the g Is a line safety penalty function;
the net gain function can be obtained by subtracting the operation and maintenance cost from the virtual power plant gain in the t period; wherein the benefits include selling electricity to the grid and benefits C DEAL (t) revenue of heat load of power supply to user C LOAD (t) and generalized demand response revenue C DR (t) composition; the operation and maintenance cost comprises small hydropower S' HYD (t) wind power S WPP (t) Combined Heat and Power generating Unit S CHP (t); when the model is processed, the supplied energy and demand response load are treated as an overall net gain function C g The establishment is as follows:
Figure FDA0003696992970000061
in the formula: c g Is a net gain function; c DEAL (t) power trading income between the virtual power plant and the main network in the period of t; c LOAD (t) the load side income of the virtual power plant in the period of t; c DR (t) demand response revenue for time period t; s' HYD (t) the operation and maintenance cost of the small hydroelectric generating set in the time period of t; s WPP (t) wind of period tThe operation and maintenance cost of the motor set; s CHP (t) the operation and maintenance cost of the cogeneration unit in the time period t;
the safety is described by adding a penalty function to the target function, the line safety penalty function S g Write as:
Figure FDA0003696992970000062
in the formula: s. the g Is a line safety penalty function; delta U (t) and Delta P (t) are the more limited voltage and active power in the period t, l U 、l P The coefficient is the out-of-limit cost coefficient of voltage and active power.
7. The economic optimization scheduling method of the virtual power plant according to claim 1, wherein the step (4) describes power balance and power balance constraint conditions based on uncertainty of electric load, heat load and wind power output, and adopts quantum particle swarm optimization to solve:
the electric power balance constraint in the virtual power plant is as follows:
P WPP (t)+P CHP (t)+P HYD (t)＝P ELOAD (t)+P DEAL (t)；
in the formula: p WPP (t)、P CHP (t)、P HYD (t) the electric power of the fan, the gas turbine and the small water in the period of t is respectively output; p ELOAD (t) virtualizing the electrical load power in the power plant for a period of t; p is DEAL (t) the exchange power of the virtual power plant and the power grid in a period t;
the actual wind power output value P is obtained WPP (t) Using the predicted value P WPP0 (t) and an error value ε WPP (t) is represented by the sum of WPP (t) obedience mean 0, standard deviation σ WPP Normal distribution of (t); similarly, the user load value P ELOAD (t) is expressed as its predicted value P ELOAD0 (t) and an error value ε ELOAD (t) a sum of form (i) in which ELOAD (t) obedience mean 0, standard deviation σ ELOAD Normal distribution of (t);
P WPP (t)＝P WPP0 (t)ε WPP (t)；
P ELOAD (t)＝P ELOAD0 (t)+ε ELOAD (t)；
in the formula: p is WPP (t) is an actual output value of the wind turbine generator at the time period t; p WPP0 (t) the predicted value of the output of the wind turbine generator at the time interval t; epsilon WPP (t) the output prediction error value of the wind turbine generator in the time period t; p is ELOAD (t) the actual electric load value of the user in the period of t; p ELOAD0 (t) the predicted value of the user electrical load is t time period; epsilon ELOAD (t) predicting error values of the user electrical loads in a period of t;
because the wind power output and the load prediction deviation are subjected to normal distribution, the deterministic power balance constraint can be converted into an opportunity constraint, and the opportunity constraint planning can be expressed as:
Figure FDA0003696992970000071
in the formula: p WPP (t)、P CHP (t)、P HYD (t) the electric power of the fan, the gas turbine and the small water in the period of t is respectively output; p is ELOAD (t) virtualizing the electrical load power in the power plant for a period of t; p DEAL (t) the exchange power of the virtual power plant and the power grid in a period t; phi is a probability distribution function; α is the confidence level; sigma WPP (t) the standard deviation of the fan output prediction error value obeying normal distribution in the period t; sigma ELOAD (t) standard deviation of the user electrical load prediction error value obeying normal distribution in t time period;
the thermal power balance within the virtual power plant may be described by:
Q GL (t)＝Q HLOAD (t)；
in the formula: q GL (t) the heat supply output of the waste heat boiler in the period of t; q HLOAD (t) virtualizing the heat load power in the power plant in a period of t;
due to thermal load Q HLOAD (t) the prediction also has an uncertainty, ε HLOAD (t) is again subject to a mean of 0 and a standard deviation of σ HLOAD Normal distribution of (t);
Q HLOAD (t)＝Q HLOAD0 (t)+ε HLOAD (t)；
in the formula: q HLOAD (t) is the actual thermal load value of the user in the period of t; q HLOAD0 (t) the predicted value of the user heat load in the t period; epsilon HLOAD (t) predicting the user thermal load error value in a period of t;
general formula Q GL (t)＝Q HLOAD (t) is converted to an opportunity constraint to express:
Q GL (t)-Q HLOAD (t)≥φ -1 (α)σ HLOAD (t)；
in the formula: q GL (t) the heat supply output of the waste heat boiler in the period of t; q HLOAD (t) virtualizing the heat load power in the power plant in a period of t; phi is a probability distribution function; α is the confidence level; sigma HLOAD (t) standard deviation of user thermal load prediction error value obeying normal distribution in t time period;
and solving the optimized scheduling problem by adopting a quantum particle swarm algorithm.
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