CN114493143A - Virtual power plant multi-objective optimization scheduling system and method for grid-connected micro-grid - Google Patents
Virtual power plant multi-objective optimization scheduling system and method for grid-connected micro-grid Download PDFInfo
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Abstract
The invention discloses a virtual power plant multi-objective optimization scheduling system and method facing a grid-connected micro-grid, belonging to the technical field of power grid scheduling, wherein the system comprises the following steps: acquiring a virtual power plant system comprising a demand side distributed main body; performing optimized scheduling on the virtual power plant system based on the carbon emission cost and the power generation cost; and accessing the optimally scheduled virtual power plant system to an external power grid for information and energy interaction. The invention can effectively control regional distributed energy, so that the distributed power supply can obtain good economic benefit and electric energy quality by economic optimization scheduling while being used as a low-carbon peak-shaving frequency modulation resource, and provides technical support for the configuration and the consumption of the distributed power supply under the 'double-carbon' background.
Description
Technical Field
The invention relates to a virtual power plant multi-objective optimization scheduling system and method for a grid-connected micro-grid, and belongs to the technical field of power grid scheduling.
Background
The micro-grid is a system which can be grid-connected and autonomous, namely a distributed energy system, and is formed by a DG, an energy storage unit, a grid-connected interface converter and a load unit, and fluctuation of output power of a distributed power supply unit can be stabilized through the energy storage unit, so that the electric energy quality of the system is remarkably improved, and the grid-connected problem of the distributed power supply is solved to a certain extent. The virtual power plant integrates various distributed power generation units, controllable loads and energy storage units through advanced measurement technology, control technology, communication technology and other means, and the integrated distributed power generation unit, the controllable loads and the energy storage units are used as an entity similar to a real power plant to participate in coordination control and management of the power market and power grid operation, so that the stable reliability of the overall output of the distributed power supply is realized, and safe and efficient power supply is provided for a power grid. Under the background of 'double carbon', the carbon emission quota becomes a key factor for limiting each power generation main body in the power system, and thermal power generating units, distributed energy storage units and other units contained in the virtual power plant are limited by the total carbon emission quota of the virtual power plant.
The existing regional distributed power system is mostly constructed in a form of a grid-connected microgrid, the application and practice of the newly proposed concept of the virtual power plant are still in a preliminary exploration test stage, the problems of high single-machine access cost, difficult management and the like exist in the distributed power which is applied in a large scale under the background of 'double carbon', and a plurality of technical problems such as trend change, line blockage, voltage flicker and the like are brought to the stable operation of the power grid and are limited by geographical regions.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a virtual power plant multi-objective optimization scheduling system and method for a grid-connected microgrid, which can effectively control regional distributed energy, so that a distributed power supply can obtain good economic benefit and electric energy quality through economic optimization scheduling while being used as a low-carbon peak-and-frequency-modulation resource, and provide technical support for the configuration and the consumption of the distributed power supply under the 'double-carbon' background.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the invention provides a virtual power plant multi-objective optimization scheduling method for a grid-connected microgrid, which comprises the following steps:
acquiring a virtual power plant system comprising a demand side distributed main body;
performing optimized scheduling on the virtual power plant system based on the carbon emission cost and the power generation cost;
and accessing the optimally scheduled virtual power plant system to an external power grid for information and energy interaction.
Further, demand side distributed main body includes photovoltaic power generation system, wind power generation system, distributed power generation system and electric energy storage system, wherein:
the photovoltaic power generation output power of the photovoltaic power generation system is as follows:
gst＝gstc(Ir，t/Istc)[1+αθ(θt-θstc)]
in the formula: gstOutput power for photovoltaic power generation, gstcOutput power of the component, alpha, under standard test conditionsθIs the temperature coefficient of the component, θtIs the photovoltaic panel temperature at time t, θstcIs the photovoltaic panel temperature under standard test conditions, Ir，tIs the actual solar radiation intensity at time t, IstcThe solar irradiation intensity under standard test conditions;
the output power of the wind power generation system is as follows:
in the formula:
for the original simulation of output of a wind farm, mt、min、mratedAnd moutThe actual wind speed, the cut-in wind speed, the rated wind speed and the cut-out wind speed of the wind turbine generator set at the moment t are respectively, and R is the rated output power of the wind turbine;
the distributed power generation system comprises a gas turbine power generation system, an internal combustion engine power generation system for peak shaving and a diesel engine power generation system, and the main power generation cost of the distributed power generation system is as follows:
in the formula: cMTFor the main cost of power generation, P, of a distributed power generation systemMT(t) the generated energy of the distributed power generation system at the time t; rhoMT、γMT、
Is a unit operating cost coefficient of the distributed power generation system;
the mathematical models of the electric energy storage system in the energy charging state and the energy discharging state are respectively as follows:
in the formula: sB(t) mathematical model of the electrical energy storage system at time t, B representing the battery energy storage device, δBIn order to increase the consumption rate in the energy storage process,
and
input conversion efficiency and output conversion efficiency, respectively, at is the time step,
and
energy input and energy output, respectively.
Further, the carbon emission quota calculation formula of the virtual power plant system is as follows:
in the formula: ptIs the total generated energy P of the controllable distributed generator set of the virtual power plant at the moment tMT，i，tThe output of the controllable distributed generator set i at the moment t, M is the number of the controllable distributed generator sets, Pbat，j，tFor the electric power of the energy storage unit j at time t, PWTFor the power generation of the wind power system at time t, PPVThe generated energy of the photovoltaic power generation system at the moment t, N is the number of the energy storage units, ELThe total carbon emission quota of the system is epsilon, and the unit electric quantity emission quota is epsilon;
the carbon emission calculation formula of the virtual power plant system is as follows:
in the formula: epIs the total carbon emission, delta, of the system in one scheduling periodMT，iIntensity of carbon emission, delta, for a controllable distributed generator set ibat，jThe carbon emission intensity of the energy storage unit j;
the relationship between the carbon emission cost and the carbon emission amount of the virtual power plant system is as follows:
in the formula: cgMu is the trading unit price of the system participating in the carbon market trading each time, d is the coefficient of the carbon emission step range, and k is the increase amplitude of the carbon trading price of the carbon emission rising by 1 step.
Further, the virtual power plant system performs optimal scheduling by taking the minimum total power generation cost and the minimum carbon emission cost of the system as optimization targets, and the specific objective function is as follows:
minC＝CPV+CWT+CGT+CIC+Cg
in the formula: c is the total cost of the virtual power plant, CPVFor photovoltaic power generation system costs, CWTFor the cost of the wind power system, CGTCost of power generation for gas turbine systems, CICFor peak shaving internal combustion engine units, CgAnd is the system carbon emission cost.
Further, the optimized scheduling process satisfies the device output power constraint, the energy storage device operation constraint, the ramp rate constraint, the electric power balance constraint, the tie line capacity constraint and the system off-load rate constraint, wherein:
the device output power constraint is:
in the formula (I), the compound is shown in the specification,
active power output P of the microgrid energy supply unit i at the moment ti，maxAnd Pi，minRespectively setting the upper limit and the lower limit of active power output of the microgrid energy supply unit i;
the energy storage device operation constraints are as follows:
in the formula:
(t) is the charging power at time t,
is the maximum power of the energy to be charged,
(t) is the discharge power at time t,
is the maximum discharge power, X and Y are state variables, T is the charge-discharge period,
the consumed power of the energy storage device in the energy storage process;
the climbing rate constraint is as follows:
in the formula: gi，tActive power output, delta g, of the microgrid energy supply unit i in the time period ti +And Δ gi -Respectively the maximum climbing power and the maximum climbing power;
the electric power balance constraint is:
Pload(t)＝PPV(t)+PWT(t)+PGT(t)+PIC(t)+PST(t)-Pcut(t)
wherein, Pload(t)、PPV(t)、PWT(t)、PGT(t)、PIC(t)、PST(t) are respectively the user sideElectrical load power, photovoltaic power generation system power, wind power generation system power, gas turbine power generation system power, internal combustion engine power generation system power and electrical energy storage system power, Pcut(t) is the load shedding amount of the system in the t period;
the tie line capacity constraint is:
Pgrid(t)≤Pgrid，max
in the formula, Pgrid(t) is the transmission power of the interconnection line between the microgrid and the large power grid at time t, Pgrid，maxThe maximum transmission power allowed by a connecting line between the microgrid and the large power grid;
the system off-load rate constraint is:
in the formula: lambda [ alpha ]LPSPThe load loss rate of the grid-connected microgrid system is represented,
for its maximum allowable loss rate value, TschIs one scheduling period, WLPSPIs the total amount of lost load in one scheduling period.
Further, the process of optimizing the scheduling further includes model linearization processing, including:
the product of the binary variable b and the continuous variable x can be expressed as a continuous variable y:
y＝b*x
then, the following constraints are added to the model:
at this time, xmin、xmaxRespectively, the minimum value and the maximum value of the continuous variable.
Further, the process of optimizing the scheduling includes: after two evaluation indexes are defined to respectively evaluate the benefits of the virtual power plant in the aspects of economic operation and carbon emission, a multi-target particle swarm algorithm is adopted to optimize a virtual power plant system to obtain a pareto solution set comprising a series of feasible pareto solutions, and the optimal solution is obtained by comparing, calculating and evaluating the system scheduling cost and the operation benefit under a plurality of solutions in the pareto solution set, wherein the multi-target particle swarm algorithm comprises a multi-target evolutionary algorithm, a second-generation non-dominated sorting genetic algorithm and a multi-target particle swarm algorithm.
In a second aspect, the invention provides a virtual power plant multi-objective optimization scheduling system for a grid-connected microgrid, comprising:
a receiving module: the method comprises the steps of obtaining a virtual power plant system comprising a demand side distributed main body;
the optimizing and scheduling module: the system is used for carrying out optimized scheduling on the virtual power plant system based on the carbon emission cost and the power generation cost;
an interaction module: and the virtual power plant system after optimized scheduling is accessed to an external power grid for information and energy interaction.
In a third aspect, the invention provides a virtual power plant multi-objective optimization scheduling device for a grid-connected microgrid, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any of the above.
In a fourth aspect, the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of the methods described above.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a virtual power plant multi-objective optimization scheduling system and method facing a grid-connected microgrid under a 'double-carbon' background, so that a virtual power plant multi-objective optimization scheduling model considering carbon emission cost is constructed on the basis of comprehensively considering influence factors such as carbon emission cost, carbon transaction price and system output, regional distributed energy can be effectively controlled, a distributed power supply is used as a low-carbon peak-regulating frequency-modulating resource, meanwhile, good economic benefit and electric energy quality are obtained through economic optimization scheduling, and technical support is provided for configuration and consumption of the distributed power supply under the 'double-carbon' background.
Drawings
FIG. 1 is a diagram of a virtual power plant scheduling architecture in consideration of carbon emission costs according to an embodiment of the present invention;
FIG. 2 is a flow chart of a multi-objective particle swarm optimization method provided in an embodiment of the present invention;
FIG. 3 is a graph illustrating electrical load, wind power generation power, and solar irradiance predictions provided by an embodiment of the present invention;
FIG. 4 is a distribution diagram of output power of a distributed power supply according to an embodiment of the present invention;
fig. 5 is a line chart of cost of carbon emissions of the system before and after scheduling according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The first embodiment is as follows:
a virtual power plant multi-objective optimization scheduling method for a grid-connected microgrid is characterized in that distributed power supplies such as photovoltaic power generation, wind power generation and a gas turbine, distributed energy storage and internal loads are integrated into a virtual power plant, and during operation of the virtual power plant, user side regulation and control (Coordination control) are coordinated with the distributed power supplies and an energy storage system, so that a virtual power plant operation framework considering carbon emission cost is established. As shown in fig. 1, in a typical virtual power plant system, a demand-side distributed agent includes photovoltaic power generation, wind power generation, a gas turbine, energy storage, and the like, and the operation safety is guaranteed by a uniform scheduling center (VPP scheduling center) of a virtual power plant. The virtual Power plant is connected into an External Power Grid (External Power Grid) through a connecting line, can perform information and energy interaction with other connected virtual Power plants, and can also perform energy interaction with a main network.
Energy supply unit model
(1) Photovoltaic power generation system
The output power of a Photovoltaic array (PV) system can be calculated according to the output power under Standard Test Condition (STC) and the conditions of actual illumination intensity, ambient temperature, and the like, as shown in the following formula:
gst＝gstc(Ir，t/Istc)[1+αθ(θt-θstc)]
in the formula: gstOutput power for photovoltaic power generation, gstcThe output power of the module under standard test conditions (solar radiation intensity I ═ 1.0 kW/m)2Temperature thetastc＝25℃)；αθIs the temperature coefficient of the component; thetatThe photovoltaic panel temperature at time t; thetastcIs the photovoltaic panel temperature under standard test conditions; i isr，tThe actual solar irradiation intensity at the time t; i isstcThe solar radiation intensity under standard test conditions. In actual operation, the marginal cost of the photovoltaic may be approximately 0.
(2) Wind power generation system
The relation between the output power of a Wind Turbine (WT) and the actual Wind speed of the Wind turbine can be approximated to a piecewise function, namely Weibull distribution, and the scale parameter xi and the shape parameter k of the Wind speed Weibull distribution are fitted according to the historical Wind speed data of the Wind farm. And (3) setting a random variable as v, and setting the wind speed according to Weibull distribution with the scale parameter and the shape parameter as ξ and k respectively:
in the formula: m is the wind speed. Influenced by the parameters of the wind turbine (cut-in wind speed, cut-out wind speed, rated wind speed and rated output power), the output power of the wind farm can be represented by the following formula:
in the formula:
originally simulating output for the wind power plant; m ist，min，mratedAnd moutThe actual wind speed at the time t, the cut-in wind speed of the wind turbine generator, the rated wind speed and the cut-out wind speed are respectively; and R is the rated output power of the wind power. The cost of wind power is mainly composed of construction cost and maintenance cost, and the marginal cost of the wind power can be approximately 0 in actual operation.
(3) Distributed power generation system
The Distributed generation system (Distributed Genset) mainly comprises a gas turbine power generation system, an internal combustion engine power generation system for peak shaving, a diesel engine power generation system and the like. Main cost of electricity generation CMTIncluding fuel cost, maintenance cost, etc., in system optimization, the model can be represented by a quadratic function:
in the formula: pMT(t) the generated energy of the distributed power generation system at the time t; ρ is a unit of a gradientMT、γMT、
Is a unit operating cost coefficient of the distributed power generation system.
(4) Electrical energy storage system
The electric energy Storage system (Distributed Storage) can operate in 3 states, namely a charging state, a discharging state and a shutdown state. Mathematical model S of charging state and discharging stateB(t) are respectively:
in the formula: subscript B represents a battery energy storage device; deltaBIs the consumption rate in the energy storage process;
and
input conversion efficiency and output conversion efficiency, respectively; Δ t is the time step;
and
energy input and energy output, respectively.
(5) Carbon emission model
Carbon neutralization (carbon neutral) is an effective abatement mechanism. At present, the carbon transaction quota allocation in China follows an allocation scheme mainly based on free allocation, and specifically adopts 2 methods of a historical method and a reference line method, wherein the reference line method has better fairness compared with the historical method, and the reference line method is more widely applied in the carbon quota allocation scheme, so that the reference line method is selected as the allocation method of the carbon emission quota. The system carbon emission quota calculation formula is as follows:
in the formula: ptThe total generated energy of the controllable distributed generator set of the virtual power plant at the moment t is obtained; pMT，i，tThe output of the controllable distributed generator set i at the moment t; m is the number of controllable distributed generator sets; pbat，j，tThe electric power of the energy storage unit j at the moment t is obtained; pWTThe power generation amount of the wind power generation system at the moment t; pPVThe power generation amount of the photovoltaic power generation system at the moment t; n is the number of energy storage units; eLTotal carbon emission quota for the system; epsilon is the unit electric quantity emission quota.
In a virtual power plant, wind power generation units and photovoltaic units are distributed clean energy, the carbon emission is approximately 0, and only the carbon emission of the controllable units and the energy storage units is considered. The carbon emission of the system is calculated by the following formula:
in the formula: epThe total carbon emission of the system in a scheduling period; deltaMT，iThe carbon emission intensity of the controllable distributed generator set i is obtained; deltabat，jThe carbon emission intensity of the energy storage unit j.
The relationship between the carbon emission cost and the carbon emission amount of the system is represented by a 3-stage carbon emission cost model of the following formula:
in the formula: cgCost for system carbon emissions; mu is the trading unit price of the system participating in the carbon market trading each time; d is the carbon emission step range coefficient; k is the increase in carbon trading price per 1 step increase in carbon emissions. Notably, the carbon emission cost C is when the carbon emission of the system is less than the carbon emission allowancegWill be less than 0, at which point the system can sell carbon emission credits in the carbon trading market to obtain a corresponding carbon revenue.
Second, objective function and constraint condition
When the optimal scheduling of the virtual power plant is analyzed, the minimum total power generation cost and the minimum carbon emission cost of the system are taken as optimization targets, and specific objective functions are as follows:
minC＝CPV+CWT+CGT+CIC+Cg
in the formula: c is the total cost of the virtual power plant; cPVFor photovoltaic power generation system costs, CWTFor the cost of the wind power system, CGTCost of power generation for gas turbine systems, CICFor peak shaving internal combustion engine units, CgAnd is the system carbon emission cost.
Meanwhile, in the optimization process, certain constraints are required to be met, and the constraints mainly include equipment constraints and system operation constraints of a virtual power plant, which are specifically as follows.
(1) Device output power constraints
Pi，min≤Pi t≤Pi，max
In the formula (I), the compound is shown in the specification,
active power output P of the microgrid energy supply unit i at the moment ti，maxAnd Pi，minThe active output upper limit and the active output lower limit of the microgrid energy supply unit i are respectively set.
(2) Energy storage device operational constraints
The charging-discharging power of the energy storage device is continuously adjustable within a certain range, but the charging-discharging processes cannot be performed simultaneously, so that the following constraints exist:
in the formula:
(t) is the charging power at time t;
is the maximum charging power;
(t) the discharge power at time t;
is the maximum discharge power; x and Y are state variables.
The energy storage device should set a proper charging-discharging period, that is, the energy stored in the energy storage device should be discharged in a certain period, so as to avoid energy loss caused by long-term storage and cost increase caused by excessive storage capacity, and therefore, the following constraints are provided:
in the formula: t is the charge-discharge period;
is the power consumption of the energy storage device during energy storage.
(3) Slope rate constraint
In the formula: gi，tActive power output delta g of the microgrid energy supply unit i in the period ti +And Δ gi -Respectively the maximum climbing power and the climbing power.
(4) Electrical power balance
In the whole interaction process, the balance constraint of the electric energy supply and the load needs to be satisfied, as shown in the following formula:
Pload(t)＝PPV(t)+PWT(t)+PGT(t)+PIC(t)+PST(t)-Pcut(t)
wherein, Pload(t)、PPV(t)、PWT(t)、PGT(t)、PIC(t)、PST(t) respectively the user side electrical load power, the photovoltaic power generation system power, the wind power generation system power, the gas turbine power generation system power, the internal combustion engine power generation system power and the electrical energy storage system power, Pcut(t) is the load shedding amount (load loss amount) of the system in the period t.
(5) Junctor capacity constraint
Pgrid(t)≤Pgrid，max
In the formula, Pgrid(t) is the transmission power of the interconnection line between the microgrid and the large power grid at time t, Pgrid，maxThe maximum transmission power allowed by the connecting line between the microgrid and the large power grid.
(6) System off-load rate constraints
In the formula: lambda [ alpha ]LPSPThe Loss rate (LPSP) of the grid-connected microgrid system is expressed and used for representing the Power Supply reliability of the system.
For its maximum allowable loss rate value, TschIs a scheduling period, and WLPSPThen is the total loss of load in a scheduling cycle, and when the system load is completely satisfied in the period of t, λ isLPSPIs 0, otherwise is λLPSP＝Pcut(t)Δt。
Third, solving method and result analysis
(1) Model linearization
According to the modeling of each element, the final result is a mixed integer nonlinear optimization model. Considering that the nonlinear model requires a long calculation time, the optimization speed is improved by converting the nonlinear model into a mixed integer linear model through model linearization. The linearization method is specifically as follows.
The product of the binary variable b and the continuous variable x can be represented by a continuous variable y as:
y＝b*x
then, the following constraints are added to the model:
at this time, xmin、xmaxWhen b is 0, y is 0; when b is 1, y is x. By the linearization method, the original mixed integer nonlinear model can be converted into the mixed integer linear model without any approximation.
(2) Object optimization method
Referring to fig. 2, the overall framework of the optimal scheduling of the virtual power plant includes a multi-objective optimization method and a corresponding decision method. First, 2 evaluation indexes are defined to evaluate the benefits of the virtual power plant in terms of economic operation and carbon emission, respectively. Secondly, a multi-objective particle swarm optimization (MOPSO) is adopted to optimize the system, and a palitological solution set comprising a series of feasible palitological solutions is obtained. And finally, carrying out comparison calculation and evaluation on the system scheduling cost and the operation benefit under a plurality of solutions in the pareto solution set to obtain the optimal solution.
Common multi-objective optimization algorithms include a multi-objective evolutionary algorithm (MOEA), a second generation non-dominated sorting genetic algorithm (NSGA-II), a multi-objective particle swarm algorithm (MOPSO) and the like, wherein the MOPSO has the advantages of high convergence rate, suitability for optimization of continuous variables, short calculation time and the like, so the method is adopted as the optimization algorithm.
In the optimization process, the carbon trading reference price is set to be 50 yuan/t, the carbon displacement step range coefficient d is 40t, the amplification coefficient k of each incremental carbon trading price is 25%, the system reference line emission factor is 0.75, and the wind power operation and maintenance cost is 150 yuan/MW.
The multi-objective optimization scheduling model established by the invention can be regarded as a mixed integer linear programming problem from the classification of optimization problems, and a commercial solver CPLEX can be adopted to solve the problem.
(3) Analysis of results
The load (electric) and wind power (WT) and solar irradiance (solar) prediction curves of the examples involved in this example are shown in fig. 3. In the optimal scheduling of the virtual power plant, on one hand, the output of the gas turbine and the stored energy needs to be distributed to reduce the operation cost and the carbon emission cost of the system, and on the other hand, the balance of the system needs to be ensured through the adjustment of the distributed stored energy. The optimization results for each energy supply system and distributed energy storage capacity are shown in fig. 4. In order to better show the balance effect of the energy storage system on the load fluctuation and the clean energy output fluctuation, the sum of the electric load and the energy storage system output (the charging is positive, and the discharging is negative) is regarded as the conversion load.
As can be seen from fig. 4, the optimization result of the algorithm provided in this embodiment can achieve better tracking of the user load. However, the composition of the output force varies from time to time, which is determined by the unit cost and the carbon emission cost. Because the output cost of different gas turbines is different from the carbon neutralization cost, the output of the gas turbine with lower carbon neutralization cost is preferentially used in the optimized scheduling process, before 19:00, the power in the virtual power plant is borne by different gas turbines due to the constraint of the upper and lower limits of the output of the gas turbine, and the peak regulation task is mainly borne by the internal combustion engine generator set with more flexible power regulation. Meanwhile, the marginal cost of the distributed photovoltaic power and the wind power is 0, the distributed photovoltaic power and the wind power are preferentially used by the system all the time, and in the evening period, the distributed power supply has higher output and more adjustable flexible loads, so all the power requirements of the system are mainly borne by the clean energy power supply and the power supply with lower carbon emission cost.
As shown in fig. 5, after the optimized scheduling, the carbon emission cost of the system is significantly increased under the condition that the output cost is basically unchanged, and the original positive value is converted into a negative value, that is, the system can benefit by selling carbon emission quota, thereby significantly improving the operating benefit of the system. Meanwhile, the energy storage equipment is introduced, so that the running cost of a virtual power plant can be reduced, the utilization rate of energy is improved, and the randomness and the fluctuation of clean energy and load are stabilized.
Example two:
the virtual power plant multi-objective optimization scheduling system for the grid-connected microgrid can realize the virtual power plant multi-objective optimization scheduling method for the grid-connected microgrid, which comprises the following steps of:
a receiving module: the method comprises the steps of obtaining a virtual power plant system comprising a demand side distributed main body;
the optimizing and scheduling module: the system is used for carrying out optimized scheduling on the virtual power plant system based on the carbon emission cost and the power generation cost;
an interaction module: and the virtual power plant system after optimized scheduling is accessed to an external power grid for information and energy interaction.
Example three:
the embodiment of the invention also provides a virtual power plant multi-objective optimization scheduling device for the grid-connected micro-grid, which can realize the virtual power plant multi-objective optimization scheduling method for the grid-connected micro-grid in the embodiment, and comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method of:
acquiring a virtual power plant system comprising a demand side distributed main body;
performing optimized scheduling on the virtual power plant system based on the carbon emission cost and the power generation cost;
and accessing the optimally scheduled virtual power plant system to an external power grid for information and energy interaction.
Example four:
the embodiment of the present invention further provides a computer-readable storage medium, which can implement the method for multi-objective optimal scheduling of virtual power plants for grid-connected micro-grid in the first embodiment, where a computer program is stored on the computer program, and when the computer program is executed by a processor, the method includes the following steps:
acquiring a virtual power plant system comprising a demand side distributed main body;
performing optimized scheduling on the virtual power plant system based on the carbon emission cost and the power generation cost;
and accessing the optimally scheduled virtual power plant system to an external power grid for information and energy interaction.
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 description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. The virtual power plant multi-objective optimization scheduling method facing the grid-connected microgrid is characterized by comprising the following steps:
acquiring a virtual power plant system comprising a demand side distributed main body;
performing optimized scheduling on the virtual power plant system based on the carbon emission cost and the power generation cost;
and accessing the optimally scheduled virtual power plant system to an external power grid for information and energy interaction.
2. The multi-objective optimization scheduling method for the virtual power plant of the grid-connected microgrid according to claim 1, wherein the demand-side distributed main body comprises a photovoltaic power generation system, a wind power generation system, a distributed power generation system and an electric energy storage system, wherein:
the photovoltaic power generation output power of the photovoltaic power generation system is as follows:
gst＝gstc(Ir，t/Istc)[1+αθ(θt-θstc)]
in the formula: gstOutput power for photovoltaic power generation, gstcIs the output power of the component under standard test conditions, alphaθIs the temperature coefficient of the component, θtIs the photovoltaic panel temperature at time t, θstcIs the photovoltaic panel temperature under standard test conditions, Ir,tIs the actual solar radiation intensity at time t, IstcThe solar irradiation intensity under standard test conditions;
the output power of the wind power generation system is as follows:
in the formula:
for the original simulation of output of a wind farm, mt、min、mratedAnd moutThe actual wind speed, the cut-in wind speed, the rated wind speed and the cut-out wind speed of the wind turbine generator set at the moment t are respectively, and R is the rated output power of the wind turbine;
the distributed power generation system comprises a gas turbine power generation system, an internal combustion engine power generation system for peak shaving and a diesel engine power generation system, and the main power generation cost of the distributed power generation system is as follows:
in the formula: cMTFor the main cost of power generation, P, of a distributed power generation systemMT(t) the generated energy of the distributed power generation system at the time t; rhoMT、γMT、
Is a unit operating cost coefficient of the distributed power generation system;
the mathematical models of the electric energy storage system in the charging state and the discharging state are respectively as follows:
in the formula: sB(t) mathematical model of the electrical energy storage system at time t, B representing the battery energy storage device, δBIn order to increase the consumption rate in the energy storage process,
and
input conversion efficiency and output conversion efficiency, respectively, at is the time step,
and
respectively energy inputVolume and energy output.
3. The virtual power plant multi-objective optimization scheduling method for the grid-connected microgrid as claimed in claim 2, characterized in that the carbon emission quota calculation formula of the virtual power plant system is as follows:
in the formula: ptIs the total generated energy P of the controllable distributed generator set of the virtual power plant at the moment tMT,i,tThe output of the controllable distributed generator set i at the moment t, M is the number of the controllable distributed generator sets, Pbat,j,tFor the electric power of the energy storage unit j at time t, PWTFor the power generation of the wind power system at time t, PPVThe generated energy of the photovoltaic power generation system at the moment t, N is the number of the energy storage units, ELThe total carbon emission quota of the system is epsilon, and the unit electric quantity emission quota is epsilon;
the carbon emission calculation formula of the virtual power plant system is as follows:
in the formula: epIs the total carbon emission, delta, of the system in one scheduling periodMT,iIntensity of carbon emission, delta, for a controllable distributed generator set ibat,jThe carbon emission intensity of the energy storage unit j;
the relationship between the carbon emission cost and the carbon emission amount of the virtual power plant system is as follows:
in the formula: cgMu is the trade unit price of each time the system participates in the carbon market transaction, d is the coefficient of the carbon emission step range, and k is the carbon emission per timeThe increase in carbon trading price was 1 step up.
4. The multi-objective optimization scheduling method for the virtual power plant facing the grid-connected micro-grid as claimed in claim 3, wherein the virtual power plant system performs optimization scheduling with the minimum total power generation cost and the minimum carbon emission cost of the system as optimization objectives, and the specific objective functions are as follows:
minC＝CPV+CWT+CGT+CIC+Cg
in the formula: c is the total cost of the virtual power plant, CPVFor photovoltaic power generation system costs, CWTFor the cost of the wind power system, CGTCost of power generation for gas turbine systems, CICFor peak shaving internal combustion engine units, CgAnd is the system carbon emission cost.
5. The grid-connected microgrid-oriented virtual power plant multi-objective optimization scheduling method of claim 2, characterized in that in the process of optimization scheduling, equipment output power constraint, energy storage equipment operation constraint, ramp rate constraint, electric power balance constraint, tie line capacity constraint and system off-load rate constraint are satisfied, wherein:
the device output power constraint is:
Pi，min≤Pi t≤Pi，max
in the formula (I), the compound is shown in the specification,
active power output P of the microgrid energy supply unit i at the moment ti,maxAnd Pi,minRespectively setting the upper limit and the lower limit of active power output of the microgrid energy supply unit i;
the operating constraints of the energy storage equipment are as follows:
in the formula:
for the charging power at the time t,
is the maximum power of the energy to be charged,
for the discharge power at the time t,
is the maximum discharge power, X and Y are state variables, T is the charge-discharge period,
the consumed power of the energy storage device in the energy storage process;
the climbing rate constraint is as follows:
in the formula: gi,tActive power output, delta g, of the microgrid energy supply unit i in the time period ti +And Δ gi -Respectively the maximum climbing power and the maximum climbing power;
the electric power balance constraint is:
Pload(t)＝PPV(t)+PWT(t)+PGT(t)+PIC(t)+PST(t)-Pcut(t)
wherein, Pload(t)、PPV(t)、PWT(t)、PGT(t)、PIC(t)、PST(t) the power of the user side electric load and the photovoltaic power generation system respectivelySystem power, wind power generation system power, gas turbine power generation system power, internal combustion engine power generation system power and electrical energy storage system power, Pcut(t) is the load shedding amount of the system in the t period;
the tie line capacity constraint is:
Pgrid(t)≤Rrid，max
in the formula, Pgrid(t) is the transmission power of the interconnection line between the microgrid and the large power grid at time t, Pgrid,maxThe maximum transmission power allowed by a connecting line between the microgrid and the large power grid;
the system off-load rate constraint is:
in the formula: lambda [ alpha ]LPSPThe load loss rate of the grid-connected microgrid system is represented,
for its maximum allowable loss rate value, TschIs one scheduling period, WLPSPIs the total amount of lost load in one scheduling period.
6. The virtual power plant multi-objective optimization scheduling method for the grid-connected micro-grid according to claim 1, wherein the optimization scheduling process further comprises model linearization processing, and the model linearization processing comprises:
the product of the binary variable b and the continuous variable x can be expressed as a continuous variable y:
y＝b*x
then, the following constraints are added to the model:
at this time, xmin、xmaxRespectively, the minimum value and the maximum value of the continuous variable.
7. The virtual power plant multi-objective optimization scheduling method for the grid-connected micro-grid according to claim 1, wherein the optimization scheduling process comprises the following steps: after two evaluation indexes are defined to respectively evaluate the benefits of the virtual power plant in the aspects of economic operation and carbon emission, optimizing a virtual power plant system by adopting a multi-target particle swarm algorithm to obtain a pareto solution set comprising a series of feasible pareto solutions, and comparing, calculating and evaluating system scheduling cost and operation benefits under a plurality of solutions in the pareto solution set to obtain an optimal solution, wherein the multi-target particle swarm algorithm comprises a multi-target evolutionary algorithm, a second-generation non-dominated sorting genetic algorithm and a multi-target particle swarm algorithm.
8. Virtual power plant multi-objective optimization scheduling system for grid-connected microgrid is characterized by comprising:
a receiving module: the method comprises the steps of obtaining a virtual power plant system comprising a demand side distributed main body;
the optimizing and scheduling module: the system is used for carrying out optimized scheduling on the virtual power plant system based on the carbon emission cost and the power generation cost;
an interaction module: and the virtual power plant system after optimized scheduling is accessed to an external power grid for information and energy interaction.
9. The virtual power plant multi-objective optimization scheduling device facing the grid-connected microgrid is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 7.
10. Computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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| CN115049323B (en) * | 2022-08-16 | 2022-11-15 | 东方电子股份有限公司 | Virtual power plant monitoring system based on distributed resource collaboration |
| CN118229454A (en) * | 2024-03-18 | 2024-06-21 | 兰州交通大学 | Expressway micro-grid virtual power plant optimal scheduling method considering carbon emission |
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