CN110661277B - Virtual power plant day-ahead scheduling method based on sensitive load access - Google Patents
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Abstract
The invention relates to the field of smart power grids, and discloses a day-ahead scheduling method of a virtual power plant based on sensitive load access, which comprises the following steps of 1) establishing a reliability model of the virtual power plant; 2) Establishing an economical dual-target model for day-ahead optimal scheduling of the virtual power plant according to constraint conditions; 3) And solving the optimal solution under different weights through mixed integer linear programming, particle swarm optimization and pareto optimal selection. Compared with the prior art, the method has the advantages that the scheduling problem is optimized on the basis of considering the high load required by the quality of the electric energy in the virtual power plant, the reliability is guaranteed, the economy of the virtual power plant is guaranteed, the most appropriate weight distribution is found, the balance point of the economy and the reliability of the virtual power plant is found, and the optimal solution of the day-ahead scheduling is achieved.
Description
Technical Field
The invention relates to the field of smart power grids, in particular to a virtual power plant day-ahead scheduling method based on sensitive load access.
Background
The smart grid is applied to safely and efficiently transmitting electric energy to an end user, and with the development, the types of sub-units included in the smart grid are gradually increased, such as an electricity cogeneration unit, a small gas turbine, a photovoltaic system, a wind power system and an energy storage system. However, as these units increase, technical and economic problems become apparent. The intermittency brought by the power generation of the distributed renewable energy sources, small power producers participate in the market, and the maintenance cost of the distributed energy sources is higher. The distributed energy resources are aggregated, and the distributed energy resources integrally participate in the market in a virtual power plant mode to become a good way for solving the problems. The virtual power plant is a multi-site heterogeneous entity integrating technologies such as communication, data processing and the like. Inside the virtual power plant, the operator can schedule each internal unit, and can ensure that the action of each internal unit is the best operation, thereby improving the overall profit of the virtual power plant, stabilizing the electric energy output and increasing the value of the non-schedulable power generation unit.
With the development of economic technology, various fine manufacturing and service industries and the like are gradually increased. The requirements of industries such as metallurgy, measurement, hospitals and the like on the quality of electric energy are often high, and once power failure or fluctuation of the quality of electric energy occurs, great loss is caused. To improve system stability, the virtual power plant must increase its backup capacity. Therefore, how to realize reasonable optimal scheduling of the virtual power plant on the basis of considering the sensitive loads is an important problem to be solved at present, most of the traditional day-ahead scheduling methods of the virtual power plant mainly consider the economy of the virtual power plant, but the traditional day-ahead scheduling methods have less consideration on the sensitive loads and the reliability, and the economy and the reliability are difficult to coordinate, so that an optimal scheduling method combining the economy and the reliability is difficult to obtain, and the day-ahead scheduling method of the virtual power plant based on sensitive load access, which can balance the economy and the reliability, is urgently needed for solving the problems in the prior art.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention provides a virtual power plant day-ahead scheduling method based on sensitive load access, which optimizes the scheduling problem on the basis of considering the load with higher power quality requirement in the virtual power plant, and ensures the economy of the virtual power plant while ensuring the sufficient reliability.
The technical scheme is as follows: the invention provides a virtual power plant day-ahead scheduling method based on sensitive load access, which comprises the following steps:
step 1: establishing a reliability model of the virtual power plant according to the sensitive load in the virtual power plant;
and 2, step: establishing an economical dual-target model for day-ahead optimized scheduling of the virtual power plant according to constraint conditions in the virtual power plant;
and 3, step 3: and solving the established dual-target model for multiple times under different weight coefficients, and screening the solving results under different weights by using a pareto optimal method to obtain a scheduling result under the optimal weight.
Further, the reliability model of the virtual power plant in step 1 is embodied as a minimum spare capacity of the virtual power plant, where the spare resources that can provide the spare capacity are:
1.1 the distributed power supply can provide the spare capacity of a virtual power plant, and the spare capacity is provided as follows:
MR DG,i,t ＝X DG.i,t P DGmax,i,t
in the formula: x DG,i,t The standby coefficient of the distributed power supply i at the moment t is taken as a decision variable; p DGmax,i,t The maximum output power of the ith distributed power supply at time t;
1.2 the virtual power plant purchases electricity from the outside as a standby resource, and the standby capacity is provided as follows:
MR B,s,t ＝X B,s,t P Bmax,s,t
in the formula: x B,s,t The standby coefficient of the power purchased from the external power supplier s for t time is changed into a decisionAn amount; p Bmax,s,t Purchasing maximum active power from a power supplier s for the t time system;
1.3 store energy as a backup resource, providing backup capacity as follows:
MR E,e,t ＝min(E e,t ,P disEmax.e,t )X E,e,t
in the formula: x E,e,t Spare coefficients for t-time energy storage, as decision variables, E e,t Rated discharge power, P, for stored energy e disEmax.e,t And storing the maximum discharge power of the energy e for the time t.
Further, the reliability of the system in step 1 is represented by the minimum spare capacity of the system, and the greater the minimum spare capacity is, the greater the reliability of the system is, so that the reliability model of the virtual power plant is the minimum spare capacity model of the system, which is:
in the formula: I. s, E, L is the number of distributed power supplies, the number of external power suppliers, the number of energy storage units and the number of loads respectively; p DG,i,t Active power for distributed power supply, P L,l,t For loading active power, minMR t Is the minimum spare capacity.
Further, the objective function of the reliability model of the virtual power plant in step 1 is:
wherein, T is the number of the scheduled time segments, and Δ T is the duration of each scheduled time segment.
Further, the constraint conditions of step 2 include:
2.1 active and reactive balance constraint;
2.2 bus voltage constraints;
2.3, grid-connected power flow capacity constraint;
2.4 purchasing power capacity constraint;
2.5 transformer power flow balance limit.
Further, the economic optimization scheduling objective function of the virtual power plant in the step 2 is as follows:
where Δ t is the duration of each scheduling period, MC t The total cost in the virtual power plant time t.
Further, the step 3 comprises the following steps:
3.1 respectively solving the built reliability and economical dual-target model by using a mixed integer linear rule method, wherein only an objective function is solved during solving, and constraint conditions in the model are temporarily not considered;
3.2 taking the solution of the step 3.1 as a group of initial particles of a particle algorithm, and randomly distributing the rest particles in an optimization space to ensure the diversity of the particles;
3.3 when the particle algorithm further processes the optimization problem, the two objective functions are processed by a weighting method, and the two objective functions are multiplied by respective weights to carry out optimization;
3.4 in the process of optimizing the particles, if the constraint conditions are violated, the particles are converted into corresponding penalty values to be embodied in the fitness function without adopting a direct elimination mode.
Has the beneficial effects that:
1. the requirements of the system stability for loads that are sensitive to power supply fluctuations are taken into account and this reliability is quantified as a system backup. And excavating resources which can be used as standby in the system, constructing a reliability model according to the standby capacity which can be provided by the resources, and taking the minimum standby capacity of the system as a maximum objective function of reliability optimization.
2. And considering the economic problem of the virtual power plant, quantifying the economic problem of the virtual power plant into a power generation cost problem, and taking the minimum cost of the virtual power plant as an objective function of economic optimization. In economic optimization, the principle limitation problems such as active and reactive power balance are followed, and the hardware limitation of connecting wires, transformers and the like is also considered.
3. The economic optimization and the reliability optimization are two mutually contradictory problems, the invention reflects the mature and pernicious problem of the two problems by weight, and the dual targets are classified into single target optimization. And solving the single-target problem weighted by the double targets for multiple times by using a particle algorithm, wherein two targets randomly adopt different weights when solving each time. After multiple times of different weight solutions, the most appropriate weight distribution is found by using pareto to find a balance point of two contradictory problems, so that the day-ahead scheduling of the virtual power plant reaches an optimal solution, the economical efficiency is met, and the requirement of the sensitive load on the reliability is guaranteed to a certain extent.
Drawings
FIG. 1 is a flow chart of a virtual power plant day-ahead scheduling method of the present invention.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
The invention belongs to the field of intelligent power grids, and discloses a day-ahead scheduling method of a virtual power plant based on sensitive load access, which solves the problem that the prior scheduling method in the prior art mainly considers the economical efficiency and is difficult to balance the economical efficiency and the reliability.
Step 1: and considering the sensitive load in the virtual power plant, and establishing a reliability model of the virtual power plant.
As the virtual power plant day-ahead scheduling method based on sensitive load access, the step 1 mainly comprises the following contents:
1.1 Standby resource selection
The sensitive load in the invention refers to the load which has higher requirements on power supply reliability in medical treatment, metering and the like and can pay certain cost for improving the reliability.
The reliability is embodied in the minimum spare capacity of the virtual power plant in the invention, and mainly comprises the following spare resources:
(1) The distributed power source, as one of the power sources of the virtual power plant, can provide spare capacity as follows:
MR DG,i,t ＝X DG.i,t P DGmax,i,t
in the formula: MR DG,i,t Spare capacity, X, provided for distributed power DG,i,t The standby coefficient of the distributed power supply i at the moment t is taken as a decision variable; p DGmax,i,t Maximum output power at time t of the ith distributed power supply.
(2) The virtual power plant purchases electricity from the outside as a standby resource, and the standby capacity is provided as follows:
MR B,s,t ＝X B,s,t P Bmax,s,t
in the formula: MR B,s,t Spare capacity, X, for external purchase of electricity from a virtual power plant B,s,t A standby coefficient for purchasing power from an external power supplier s at the time t is a decision variable; p is Bmax,s,t And the system purchases the maximum active power from the power supplier s for the time t.
(3) The stored energy may serve as one of the backup sources, providing backup capacity as follows:
MR E,e,t ＝min(E e,t ,P disEmax.e,t )X E,e,t
in the formula: MR E,e,t Reserve capacity, X, provided for energy storage E,e,t And the standby coefficient provided for the energy storage at the time t is a decision variable. E e,t Rated discharge power, P, for stored energy e disEmax.e,t And (5) storing the maximum discharge power of the energy e for the time t, and taking the minimum of the maximum discharge power and the maximum discharge power.
Electric vehicles are not considered as a backup resource in the present invention due to uncertainty in mileage requirements and low capacity of electric vehicles.
1.2 sensitive load to virtual power plant reliability requirement model
The system minimum spare capacity model is as follows:
in the formula: minMR t For the minimum spare capacity, I, S, E, L is the number of distributed power supplies, the number of external power suppliers, the number of energy storage units and the number of loads respectively; p is DG,i,t Active power for distributed power supply, P L,l,t Is the load active power.
In the invention, the size of the system reliability is represented by the size of the minimum spare capacity of the system, and the larger the minimum spare capacity is, the larger the system reliability is, the objective function form of the reliability model is as follows:
wherein, T is the number of the scheduled time segments, and Δ T is the duration of each scheduled time segment.
There is a minimum constraint on the minimum reserve capacity of the system to meet the most basic reserve of the virtual power plant, and the constraint is as follows:
minMR t ≥MR min
in the formula: MR min The minimum spare capacity required by the system.
Step 2: and (4) considering constraints such as active power, reactive power, network loss, bus voltage and the like in the virtual power plant, and establishing an economic dual-objective model for day-ahead optimal scheduling of the virtual power plant.
As the virtual power plant day-ahead scheduling method based on sensitive load access, the step 2 mainly comprises the following contents:
2.1 the economic optimization scheduling constraint conditions of the virtual power plant comprise:
(1) Active and reactive power balance constraint
The constraint relation of active power balance is as follows:
in the formula: p DG,i,t The active power output of the ith distributed power supply at t time; p CDG,i,t The active output of the ith station DG is reduced at t time; p B,s,t Purchasing power from an s-th external supplier for a time t; p NL,l,t The ith unsatisfied active load value at the time t; p LDR,l,t Responding to the active load reduction value for the demand of the ith load at the time t; p L,l,t Load value of the first load at time t, P disV,b,t 、P chaV,b,t Charging and discharging power P of the b-th electric vehicle at t time disE,e,t 、P chaE.e,t The discharge and charge power of the e-th energy storage unit at t time; v p,t Summarizing the voltage amplitude, V, of the node p at time t for the virtual power plant k,t For the virtual power plant grid-connected point k point voltage amplitude, G pk Is the susceptance value between nodes p, k, B pk Is the value of the conductance between the nodes pk, θ p,t -θ k,t Is the phase difference of the voltage between the nodes pk; i is the total number of the distributed power supplies, S is the total number of external suppliers, L is the total number of loads, V is the total number of electric vehicles, E is the total number of energy storage units, and K is the number of nodes on a bus b.
The constraint relation of reactive balance is as follows:
in the formula: q DG.i,t Reactive power output of the ith distributed power supply at t time; q Buy,s,t An amount of idle power provided to the s-th external provider for time t;
is a reactive load in a virtual power plant.
(2) Bus voltage constraints:
in the formula (I), the compound is shown in the specification,
is the bus voltage lower limit;
is the bus voltage upper limit;
the lower limit of the phase value of the bus voltage;
is the upper phase limit of the bus voltage.
(3) And (3) constraint of grid-connected power flow capacity:
in the formula: y is pk Is the admittance between node k and node p, y Shunt_p A parallel admittance of a line that is a p-node;
the maximum sustainable capacity for the line to which pk is connected.
(4) And (3) power purchase capacity constraint:
in the formula (I), the compound is shown in the specification,
is the maximum purchased electric capacity.
(5) And limiting the power flow balance of the transformer:
active power constraint:
in the formula, P Tra,t Active power of a transformer connected to the bus at time t。
Reactive power constraint:
in the formula, Q Tra,t Is the reactive power of the transformer connected to the bus at time t.
In the formula:
is the maximum capacity of the transformer on the bus.
2.2 virtual plant economic optimization scheduling objective function
In the invention, the optimized scheduling of the virtual power plant is divided into two aspects of reliability scheduling and economic scheduling.
The reliability scheduling is that the minimum reserve capacity is maximum:
the economic dispatching is the minimum operation cost, and the operation cost comprises the following aspects:
(1) Distributed power generation incurs costs, as well as corresponding costs when it cuts power generation for economic dispatch purposes, and the cost at time t can be expressed as:
MC DG,i,t ＝c DG,i P DG,i,t
MC CDG,i,t ＝c CDG.i P CDG.i,t
in the formula: c. C DG,i Is a cost factor; c. C CDG.i And the power generation cost coefficient is reduced for the distributed power supply.
(2) The demand response of the load in the virtual power plant or the load is not satisfied generates a cost, and the cost of t time can be expressed as:
MC LDR,l ＝c LDR.l P LDR.l,t
MC NL,l,t ＝c NL.l P NL,l,t
in the formula: c. C LDR.l As a demand response cost factor, c NL.l Is the cost factor of the unsatisfied load.
(3) The virtual power plant needs to spend a certain cost for purchasing electricity from an external power supplier, and the cost of t time can be expressed as:
MC B,s,t ＝c B.s P B.s,t
in the formula: c. C B.s The cost coefficient of electricity purchase.
(4) When the energy storage and the electric automobile as the load are discharged, certain loss is generated, and the loss cost at t time can be expressed as:
MC V,b,t ＝c disV,b P disV,b,t
MC E,e,t ＝c disE,e P disE,e,t
in the formula: c. C disV,b Is the discharge loss cost coefficient of the electric vehicle, c disE,e Is the energy storage discharge loss cost coefficient.
To sum up, the total cost MC in the virtual plant t time t Namely:
therefore, the economic dispatch objective function of the virtual power plant is as follows:
and step 3: firstly, preliminary solution is carried out through mixed integer linear programming, then particle swarm optimization is carried out to solve an objective function in two steps, and finally, the pareto optimal is used for selecting optimal solutions under different weights.
3.1 respectively solving the established economic dual-objective model and the reliability model by using a mixed integer linear programming method, wherein only the objective function is solved during solving, and the constraint condition in the model is not considered temporarily.
3.2 using the solution obtained by mixed integer linear programming as a group of initial particles of the particle algorithm, and randomly distributing the rest particles in the optimizing space to ensure the diversity of the particles.
3.3 when the particle algorithm further processes the optimization problem, the two targets are processed by a weighting method, and the optimization is carried out by multiplying the two target functions by respective weights.
3.4 in the process of optimizing, if the particle violates the constraint condition, the particle does not adopt a direct elimination mode but is converted into a corresponding penalty value to be embodied in the fitness function.
The fitness function in the particle algorithm is as follows:
in the formula: alpha is alpha 1 、α 2 The weights of the economic and reliability objective functions are respectively, and the value ranges of the weights are all between 0 and 1; the pendites refers to corresponding punishment brought by constraint violation, and different values can be taken according to the type of constraint violation and the degree of constraint exceeding; s 1 、s 2 Are normalized coefficients.
When the particle algorithm is optimized for the first time, the weights of the two objective functions are random, but the following constraint conditions need to be satisfied:
α 1 +α 2 ＝1
in the optimization step of the particle algorithm, a speed clamping factor C is introduced f To limit the particle velocity, which ranges from 0 to 1, the maximum and minimum particle velocities are as follows:
V max ＝C f (x max -x min )/2
V min ＝-V max
in the formula x max 、x min The upper and lower particle boundaries.
First order particle algorithmAfter optimizing to obtain the result, the weight alpha is calculated 1 、α 2 Random variation is carried out, and optimization is carried out again.
Weight α 1 、α 2 And after the set change times are reached, namely the set optimization times of the particle algorithm are finished, screening all solutions with different weight coefficients for optimization by using the pareto optima, and selecting a non-dominated solution as a final optimization result.
The above embodiments are merely illustrative of the technical concepts and features of the present invention, and the purpose of the embodiments is to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the protection scope of the present invention. All equivalent changes and modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.
Claims (5)
1. A day-ahead scheduling method of a virtual power plant based on sensitive load access is characterized by comprising the following steps:
step 1: establishing a reliability model of the virtual power plant according to the sensitive load in the virtual power plant;
the system reliability in the step 1 is represented by the minimum spare capacity of the system, and the greater the minimum spare capacity is, the greater the system reliability is, so the reliability model of the virtual power plant is the minimum spare capacity model of the system, which is:
in the formula: MR (magnetic resonance) DG,i,t Spare capacity, MR, for distributed power supply B,s,t Spare capacity, MR, for a virtual power plant to purchase power from the outside E,e,t The number of the distributed power supplies, the number of external power suppliers, the number of the energy storage units and the number of loads are I, S, E, L respectively; p DG,i,t Active power for distributed power supply, P L,l,t For loading active power, minMR t Is the minimum spare capacity;
the objective function of the reliability model of the virtual power plant is:
wherein T is the number of the scheduled time segments, and delta T is the duration of each scheduled time segment;
there is a minimum constraint on the minimum reserve capacity of the system to meet the most basic reserve of the virtual power plant, and the constraint is as follows:
min MR t ≥MR min
in the formula: MR min The minimum spare capacity required for the system;
and 2, step: establishing an economic dual-objective model for day-ahead optimal scheduling of the virtual power plant according to constraint conditions in the virtual power plant;
and step 3: and solving the established dual-target model for multiple times under different weight coefficients, and screening the solving results under different weights by using a pareto optimal method to obtain a scheduling result under the optimal weight.
2. The method for day-ahead scheduling of a virtual power plant based on sensitive load access according to claim 1, wherein the reliability model of the virtual power plant in step 1 is embodied as a minimum spare capacity of the virtual power plant, and spare resources capable of providing the spare capacity are:
1.1 the distributed power supply can provide the spare capacity of a virtual power plant, and the spare capacity is provided as follows:
MR DG,i,t ＝X DG.i,t P DGmax,i,t
in the formula: x DG,i,t The standby coefficient of the distributed power supply i at the moment t is a decision variable; p DGmax,i,t The maximum output power of the ith distributed power supply at time t;
1.2 the virtual power plant purchases electricity from the outside as a standby resource, and the standby capacity is provided as follows:
MR B,s,t ＝X B,s,t P Bmax,s,t
in the formula: x B,s,t Is t time fromThe standby coefficient of the electricity purchased by the external power supplier s is a decision variable; p is Bmax,s,t The maximum electricity purchasing active power is obtained from a power supplier s for the t time system;
1.3 store energy as a backup resource, providing backup capacity as follows:
MR E,e,t ＝min(E e,t ,P disEmax.e,t )X E,e,t
in the formula: x E,e,t Spare coefficients for t-time energy storage, as decision variables, E e,t Rated discharge power, P, for stored energy e disEmax.e,t The maximum discharge power of the energy e is stored for the time t.
3. The method for day-ahead scheduling of a virtual power plant based on sensitive load access according to claim 1, wherein the constraint conditions of step 2 include:
2.1 active and reactive balance constraint;
2.2 bus voltage constraints;
2.3, grid-connected power flow capacity constraint;
2.4 purchasing power capacity constraint;
2.5 transformer power flow balance limitation.
4. The day-ahead virtual power plant scheduling method based on sensitive load access as claimed in claim 3, wherein the virtual power plant economic optimization scheduling objective function in step 2 is:
where Δ t is the duration of each scheduling period, MC t The total cost in the virtual power plant time t.
5. The method for day-ahead scheduling of a virtual power plant based on sensitive load access according to claim 1, wherein the step 3 comprises the steps of:
3.1 respectively solving the built reliability and economical dual-target model by using a mixed integer linear rule method, wherein only an objective function is solved during solving, and constraint conditions in the model are temporarily not considered;
3.2 taking the solution of the step 3.1 as a group of initial particles of a particle algorithm, and randomly distributing the rest particles in an optimization space to ensure the diversity of the particles;
3.3 when the particle algorithm further processes the optimization problem, the two objective functions are processed by a weighting method, and the two objective functions are multiplied by respective weights to carry out optimization;
3.4 in the process of optimizing, if the particle violates the constraint condition, the particle does not adopt a direct elimination mode but is converted into a corresponding penalty value to be embodied in the fitness function.
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