CN112234617A

CN112234617A - Virtual power plant multi-objective optimization scheduling method considering output external characteristics

Info

Publication number: CN112234617A
Application number: CN202011102784.0A
Authority: CN
Inventors: 陈凯玲; 顾闻; 王海群; 费斐; 杨建林; 吕冉; 史松峰; 符杨; 葛晓琳; 王云鹏
Original assignee: Shanghai Electric Power University; State Grid Shanghai Electric Power Co Ltd
Current assignee: Shanghai University of Electric Power; Shanghai Electric Power University; State Grid Shanghai Electric Power Co Ltd; University of Shanghai for Science and Technology
Priority date: 2020-10-15
Filing date: 2020-10-15
Publication date: 2021-01-15

Abstract

The invention relates to a virtual power plant multi-objective optimization scheduling method considering the external force characteristics, which comprises the following steps: 1) defining the characteristics outside the output of the virtual power plant; 2) aggregating various distributed power sources to construct a virtual power plant main body; 3) establishing a virtual power plant multi-objective optimization scheduling model by taking the characteristics of the optimized virtual power plant out of output, the maximized virtual power plant operation income and the minimized virtual power plant carbon emission as scheduling objective functions; 4) converting multiple targets in the virtual power plant multi-target optimization scheduling model into single targets, linearizing nonlinear conditions, solving by adopting a mixed integer linear programming method, and acquiring scheduling statistical information, wherein the scheduling statistical information comprises wind power photovoltaic output conditions, charging and discharging time and power of energy storage equipment, and charging and discharging time and power of an electric vehicle. Compared with the prior art, the method has the advantages of rapidness, reliability, high feasibility, wide application range and the like.

Description

Virtual power plant multi-objective optimization scheduling method considering output external characteristics

Technical Field

The invention relates to the field of virtual power plant optimized scheduling, in particular to a virtual power plant multi-objective optimized scheduling method considering the characteristics of force output.

Background

The virtual power plant is a special power plant composed of different types of distributed energy and also is a comprehensive energy management system, and the virtual power plant aggregates distributed energy such as a distributed power supply, energy storage equipment, an electric automobile and the like through an advanced communication technology and a software system and participates in the operation of an electric power market and an electric power system. The virtual power plant manages and integrates different types of energy power generation within the jurisdiction range, and has schedulability and controllability similar to those of the traditional power plant, so that how to construct the virtual power plant optimization scheduling model with the similar output characteristics of the traditional power plant draws wide attention of people.

The virtual power plant is used as a mode for gathering distributed power supplies, the output external characteristics of the virtual power plant are expressed as the basis of the output characteristics of the traditional power plant, the scheduling mode of internal energy sources is solved, and some existing documents only introduce electric vehicles into the virtual power plant and consider the dual characteristics of the electric vehicles as power supplies and loads; in addition, a virtual power plant optimization scheduling model based on wind-solar energy storage is established in many existing documents, and a management framework, an interaction mechanism and a key technology of a virtual power plant are discussed; however, the existing literature does not consider the external output characteristic of the virtual power plant as a whole, meanwhile, the optimized operation of the virtual power plant needs to consider the operation income of each distributed energy operator and the environmental benefit to the society, and only the external output characteristic is considered to be incomplete and deep.

Therefore, a multi-objective optimization scheduling method for the virtual power plant considering the external output characteristics is urgently needed, so that the virtual power plant can have good external output characteristics while running economically and environmentally.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provide a virtual power plant multi-objective optimization scheduling method considering the characteristics of the external power.

The purpose of the invention can be realized by the following technical scheme:

a virtual power plant multi-objective optimization scheduling method considering the out-of-force characteristics comprises the following steps:

1) defining the characteristics outside the output of the virtual power plant;

2) aggregating various distributed power sources to construct a virtual power plant main body;

3) establishing a virtual power plant multi-objective optimization scheduling model by taking the characteristics of the optimized virtual power plant out of output, the maximized virtual power plant operation income and the minimized virtual power plant carbon emission as scheduling objective functions;

4) converting multiple targets in the virtual power plant multi-target optimization scheduling model into single targets, linearizing nonlinear conditions, solving by adopting a mixed integer linear programming method, and acquiring scheduling statistical information, wherein the scheduling statistical information comprises wind power photovoltaic output conditions, charging and discharging time and power of energy storage equipment, and charging and discharging time and power of an electric vehicle.

In the step 1), the external output characteristic of the virtual power plant needs to be close to that of the traditional power plant, and the expected output requirement of the dispatching on the virtual power plant needs to be met, wherein the expected output requirement specifically comprises a target power curve, a power baseline and an actual output curve.

The target power curve is defined as expected output of scheduling to a virtual power plant, the output characteristics are high in the daytime, low in the night and stable in output and are different from the output of a distributed power supply, the power baseline is defined as an estimated value of the aggregation power of the distributed power supply when the virtual power plant does not adopt a regulation and control means, the output characteristics are mainly related to the characteristics of the distributed power supply and have the characteristics of randomness, volatility, inverse peak regulation and the like, the actual output curve is defined as actual output of the virtual power plant obtained by optimizing scheduling after the virtual power plant aggregates various distributed power supplies, and the output characteristics are related to an actual optimization target.

The output external characteristic of the virtual power plant is represented as the response degree of the scheduling requirement, the closer the actual output is to the target power, the higher the response degree is, the better the output reliability degree is, and otherwise, the lower the output reliability degree is. The difference between the actual output of the virtual power plant and the reference power can be regarded as the product provided by the virtual power plant.

The distributed power supply comprises wind power, photovoltaic, energy storage equipment and an electric automobile.

For wind power and photovoltaic, a virtual power plant is preferentially utilized within the capacity range of the virtual power plant, the output of the virtual power plant is adjustable within a certain range through a pitch angle and an inverter, and the mathematical model expression is as follows:

wherein the content of the first and second substances,

and

the actual output of wind power and photovoltaic power in the period of t respectively,

and

predicted values of wind power and photovoltaic output in the t period, lambda, respectively^WPAnd λ^PVThe regulating coefficients of wind power output and photovoltaic output are respectively, namely the upper limit and the lower limit of a wind power output interval are respectively

The upper limit and the lower limit of the photovoltaic output interval are respectively

The energy storage device serves as an important component of a virtual power plant and plays a role in stabilizing power fluctuation, reliably meeting system load requirements, ensuring efficient and stable operation of the system and improving the electric energy quality of the system in the virtual power plant. The energy storage unit can store redundant energy for standby when the energy in the system is surplus, and release the stored energy to meet the energy requirements of a user side and equipment when the energy or load requirements are in shortage. The storage battery can improve the stability of the system and inhibit the power fluctuation of renewable energy sources, and is also an important means for realizing economic dispatching of a virtual power plant, and for energy storage equipment, the expression of a mathematical model of the storage battery is as follows:

wherein the content of the first and second substances,

the storage capacities of the energy storage device are respectively at the time periods t and t +1,

and

respectively the charging and discharging efficiency of the energy storage device,

and

the charging power and the discharging power of the energy storage device in t time period are respectively, and delta t is a time interval.

In recent years, electric vehicles have been developed rapidly, the load of the electric vehicles is changed to some extent due to electricity utilization, and the V2G (vehicle to grids) technology can realize the discharge of the electric vehicles to the power grid, so that the electric vehicles can be used as energy storage devices of a virtual power plant. In addition, electric automobile's energy storage effect can not only provide auxiliary service for virtual power plant, can also make electric automobile owner obtain partly income, and to electric automobile, its mathematical model expression is:

wherein the content of the first and second substances,

for the actual output of the electric automobile in the period of t, the discharge is represented by more than 0, and the charge is represented by less than 0，

And

a 0-1 variable for respectively judging whether the electric automobile is charged or discharged in the time period t,

and

respectively the charging and discharging power, SOC of the electric automobile in the period of t_t、SOC_t+1The charge states of the electric vehicle in the t and t +1 time periods respectively.

The method has the advantages that the method gives consideration to the characteristics of the system outside output, the operation income and the carbon emission, realizes the optimization of the virtual power plant in the aspects of reliable output, economic benefit and social benefit, and three scheduling objective functions in the virtual power plant multi-objective optimization scheduling model are as follows:

1. optimizing the external characteristics of the output of the virtual power plant:

in order to enable the regulated virtual power plant actual output to be close to the expected output of the dispatching end, the first objective function is set to minimize the difference between the virtual power plant target power and the actual output, and the expression is as follows:

wherein the content of the first and second substances,

for a target power of the virtual power plant for a period t,

is at t timeActual output of the virtual power plant;

2. maximizing the operation income of the virtual power plant:

the virtual power plant needs to provide benefits for distributed users participating in aggregation, and considering the operability, a second objective function is set to maximize the operating benefits of the virtual power plant, and the expression is as follows:

wherein the content of the first and second substances,

for the benefit of the virtual power plant actual output during the period t,

for t period of time the electric automobile gains interaction with the main network,

in order to simulate the electricity purchasing cost of the power plant in the period t,

for the maintenance cost of the period t,

for the purchased electric power of the time period t,

and

the price of electricity purchase and sale which are interacted between the virtual power plant and the main network in the period t respectively,

and

charging and discharging electricity prices, C, of the electric vehicle and the main network in interaction at the time of t^PV、C^WPAnd C^EESRespectively unit maintenance costs of wind power, photovoltaic and energy storage equipment;

3. minimizing the carbon emissions of the virtual power plant:

the environmental benefit of the virtual power plant is embodied by its carbon emission, and then the third objective function is set to minimize the carbon dioxide emission of the virtual power plant, and its expression is as follows:

wherein σ_GWPGWP coefficient of the contaminant, ε_eIs the emission coefficient of the pollutants,

total power purchased from the grid, η, for a virtual power plant_genEta, for the efficiency of power generation in the plant_gridThe transmission line loss rate.

In the step 3), the constraint conditions of the virtual power plant multi-objective optimization scheduling model include:

A. and power balance constraint:

in the formula:

selling power for a period of t;

B. energy storage equipment restraint:

in the formula:

respectively an upper limit and a lower limit of the capacity of the energy storage equipment,

respectively the upper and lower limits of the charging power of the energy storage device,

respectively an upper limit and a lower limit of the discharge power of the energy storage device,

respectively are 0-1 variables for judging whether the energy storage equipment is charged or discharged;

C. electric vehicle restraint:

SOC_min≤SOC_t≤SOC_max

in the formula: SOC_max、SOC_minRespectively representing the upper limit and the lower limit of the capacity of the electric automobile;

D. interaction power limit with the main network:

in the formula:

the maximum power for buying and selling electricity interacted with the main network respectively.

In the step 4), aiming at the multi-objective optimization problem, converting multiple objectives into a single objective through a normalization method and linear weighting.

Compared with the prior art, the invention has the following advantages:

firstly, the method is quick and reliable: compared with the prior art, the method disclosed by the invention can quickly and reliably calculate each decision variable of the virtual power plant scheduling model to obtain reliable and accurate scheduling information.

Secondly, the feasibility is high: the characteristics of output, economy and environmental protection are considered in the virtual power plant optimization scheduling model, scheduling personnel can arrange scheduling according to different scheduling targets, actual operation conditions are met better, and a more feasible scheduling scheme can be obtained.

Thirdly, the application range is wide: the optimal charging and discharging distribution of each energy storage device is calculated while the characteristics outside the output are taken into account, and for a system with various variables, the method can also keep rapidity and accuracy and has great potential in solving other random optimization problems in a power system.

Drawings

FIG. 1 is a typical winter solar-wind power forecast.

FIG. 2 shows the power purchase and CO in each case of example 1₂And (5) comparing the discharge amount.

FIG. 3 is a comparison of the external force characteristics of the respective schemes of example 1, wherein FIG. 3a is a comparison of the external force characteristics of scheme 1, FIG. 3b is a comparison of the external force characteristics of scheme 2, FIG. 3c is a comparison of the external force characteristics of scheme 3, and FIG. 3d is a comparison of the external force characteristics of scheme 4.

Fig. 4 is a comparison of the stored energy of the energy storage devices in each embodiment 1.

FIG. 5 is a comparison of the electric energy storage capacity of the electric vehicles in each embodiment of example 1.

FIG. 6 is a flow chart of a method of the present invention.

Detailed Description

The invention is described in detail below with reference to the figures and specific embodiments.

As shown in fig. 6, the invention provides a virtual power plant multi-objective optimization scheduling method considering the characteristics of the external power, which specifically includes the following steps:

firstly, defining the external output characteristics of a virtual power plant, and elaborating the external output characteristics of the virtual power plant through a target power curve, a power baseline and an actual output curve; meanwhile, the current situations of power generation and utilization and the operating characteristics of all distributed energy sources, energy storage elements and electric vehicles in the virtual power plant are researched and analyzed, a modeling method for researching the characteristics of wind-light output, the energy storage elements and the electric vehicles is obtained, and a virtual power plant optimization scheduling model containing wind-light storage and the electric vehicles is jointly constructed.

And then, establishing a virtual power plant multi-objective optimization scheduling model considering the out-of-force characteristics, the economy and the environmental protection, wherein the model takes the optimal out-of-force characteristics, the maximum operation income and the minimum carbon emission of the virtual power plant as objective functions and simultaneously meets the power balance constraint, the energy storage device out-of-force constraint, the electric vehicle constraint and the main network interaction power constraint. The constructed virtual power plant optimization scheduling model is a complex multi-target nonlinear model, after an absolute value item in a target function is linearized through a certain linearization method, a multi-target problem is converted into a single-target problem through a normalization method and linear weighting, and finally the single-target problem is expressed as a Mixed Integer Linear Programming (MILP) model.

Finally, solving is carried out through a mixed integer programming method, and scheduling statistical information is obtained, wherein the scheduling statistical information comprises wind power photovoltaic output conditions, charging and discharging time and power of energy storage equipment, and charging and discharging time and power of the electric vehicle.

The method comprises the steps of firstly, defining the characteristics of the virtual power plant outside the output in detail, accurately modeling each aggregated distributed power supply in the virtual power plant, linearizing nonlinear terms in the distributed power supply so as to solve the distributed power supply quickly, converting a multi-target problem into a single-target problem through a normalization method and linear weighting, and solving the single-target problem, so that the solving precision is guaranteed, and the solving speed is increased. Therefore, the method provided by the invention has the advantages of high calculation progress, high calculation speed and the like. In addition, the out-of-service characteristic, the economy and the environmental protection are considered in the virtual power plant optimization scheduling model, and scheduling personnel can arrange and schedule according to different scheduling targets, so that the method is more in line with the actual operation condition, and can obtain a more feasible scheduling scheme. Finally, in the model, the optimal charging and discharging distribution of each energy storage device is calculated while the characteristics of the power output are taken into account, and for a system with various variables, the method can also keep rapidity and accuracy and has great potential in solving other random optimization problems in a power system.

Example 1:

the operating parameters of the virtual plant are shown in table 1. The electricity prices of the main network and the electric vehicle adopt time-of-use electricity prices, and the time interval division of the electricity prices is shown in tables 2 and 3. The energy storage equipment and the electric vehicle meet a periodic invariant principle, namely, the electric storage quantity can still be restored to an initial value after the electric storage quantity runs for a scheduling period; lambda [ alpha ]^WPAnd λ^PVAll are 5%; the weights of the 3 objective functions are calculated to be 0.327, 0.325, 0.347, respectively.

TABLE 1 operating parameters of virtual Power plants

TABLE 2 Main network time-of-use electricity price

TABLE 3 electric vehicle on-line time-of-use electricity price

The predicted value of the wind power photovoltaic output in a typical day is shown in fig. 1, no light is emitted at night, and the radiation intensity at noon is high. The wind speed is high at night and low in daytime, and the wind speed has reverse peak regulation.

In order to compare the operation characteristics of the virtual power plant under different optimization targets, the invention sets 4 schemes as follows:

scheme 1: the set objective function is to minimize the difference between the virtual plant target power and the actual output.

Scheme 2: the set objective function is to maximize the virtual plant operating yield.

Scheme 3: the set objective function is to minimize the virtual plant carbon dioxide emissions.

Scheme 4: meanwhile, the external characteristics, economy and environmental protection of the output of the virtual power plant are considered, and multiple targets are taken as optimization targets.

The benefits and costs of the 4 schemes in the daily scheduling period are shown in table 4, and as can be seen from table 4, the scheme 1 takes the near-target output curve as the objective function, so that the economy is sacrificed, and the benefits are mainly reflected in the actual output benefits of the virtual power plant, the interactive benefits of the electric automobile and the electricity purchasing cost; although the electricity purchasing cost is low in the scheme 2, as can be seen from fig. 2, actually, the total electricity purchasing quantity is the largest, and meanwhile, the electric vehicle interaction profit of the scheme 2 is the largest, which shows that the scheme 2 is more prone to maximizing the profit, and interacts with the main network as much as possible according to a proper time-of-use electricity price; scheme 3 aims at minimizing pollutant discharge, and the total purchased electricity quantity is minimum, but the running economy is the worst; and the fourth scheme comprehensively considers a plurality of targets of the characteristics of force output, economy and environmental protection, the total income of the fourth scheme is respectively increased by 10.01 percent and 10.45 percent compared with the first scheme 1 and the second scheme 3, and is only lower than the second scheme 2, and the operation economy of the fourth scheme is better.

TABLE 4 comparison of earnings and costs for each case

Further comparing the external output characteristics of the schemes, as is apparent from fig. 3, the overall output curves of the

schemes

1, 3, 4 are smoother compared to the scheme 2, and as can be seen from table 5, the average value of the output differences among the batches and the difference between the response degrees to scheduling demands are smaller, which is far better than the scheme 2 in terms of operational reliability; in addition, because the virtual power plant constructed by the invention does not aggregate conventional units, the main output of the virtual power plant is derived from various renewable energy sources, the output is small in the transition period of the day and the night, and the target output curve in the period of time is large, so that even the scheme 1 cannot be completely optimized to the target output curve, the output requirement of the virtual power plant on the output curve can be met as far as possible, and the effect of peak clipping and valley filling of the virtual power plant is realized.

TABLE 5 comparison of operational reliability of each protocol

The adjustment of the output external characteristics of the virtual power plant is mainly realized by energy storage equipment, and as can be seen from fig. 4 and 5,

schemes

1, 3 and 4 tend to store energy when the output of renewable energy is large and release energy when the output of renewable energy is small, and scheme 2 tends to release energy when the electricity price is high and store energy when the electricity price is low; therefore, the

schemes

1, 3 and 4 play a role in peak clipping and valley filling of the energy storage device, and the scheme 2 plays an economic role in the energy storage device.

In summary, the performance of the solution 4 considering the multi-objective optimization in both the operation reliability and the environmental protection is very close to the performance of the

solutions

1 and 3, but the economic performance is better than the

solutions

1 and 3, and is second only to the solution 2, while the reliability and the environmental protection performance of the solution 2 are poorer; therefore, the optimal operation of the virtual power plant can be realized by the optimal scheduling method comprehensively considering multiple targets.

Claims

1. A virtual power plant multi-objective optimization scheduling method considering the external force characteristics is characterized by comprising the following steps:

1) defining the characteristics outside the output of the virtual power plant;

2. The method for multi-objective optimization scheduling of a virtual power plant taking account of the external force characteristics as claimed in claim 1, wherein in the step 1), the external force characteristics of the virtual power plant need to be close to those of the conventional power plant, and the expected force requirements of the virtual power plant for scheduling need to be met, specifically comprising a target power curve, a power baseline and an actual force curve.

3. The method according to claim 2, wherein the target power curve is defined as an expected output of the virtual power plant in the scheduling process, the power baseline is defined as an estimated value of the aggregated power of the distributed power sources when the virtual power plant does not adopt a regulation and control measure, and the actual output curve is defined as an actual output of the virtual power plant obtained by optimizing and scheduling after the virtual power plant aggregates various distributed power sources.

4. The method for multi-objective optimal scheduling of a virtual power plant taking account of the external force characteristics of the virtual power plant as claimed in claim 1, wherein in the step 2), the distributed power sources comprise wind power, photovoltaic power, energy storage equipment and electric vehicles.

5. The method for multi-objective optimization scheduling of the virtual power plant considering the characteristics outside the force as claimed in claim 4, wherein for wind power and photovoltaic, the virtual power plant is preferentially utilized within the capability range and the output of the virtual power plant is adjustable within a certain range through a pitch angle and an inverter, and the mathematical model expression is as follows:

wherein, P_t ^WPAnd P_t ^PVThe actual output of wind power and photovoltaic power in the period of t respectively,

and

6. The virtual power plant multi-objective optimization scheduling method considering the external force characteristics is characterized in that for the energy storage device, the mathematical model expression is as follows:

wherein the content of the first and second substances,

and

respectively charge and discharge efficiency, P, of the energy storage device_t ^storeAnd P_t ^releaseThe charging power and the discharging power of the energy storage device in t time period are respectively, and delta t is a time interval.

7. The virtual power plant multi-objective optimization scheduling method considering the external force characteristics is characterized in that for an electric automobile, the mathematical model expression is as follows:

SOC_t+1＝SOC_t-P_t ^PEV

wherein, P_t ^PEVThe actual output of the electric automobile in the period of t is greater than 0, which represents discharging, less than 0 represents charging,

and

0-1 variable P for respectively judging whether the electric automobile is charged or discharged in the t period_t ^cAnd P_t ^dRespectively the charging and discharging power, SOC of the electric automobile in the period of t_t、SOC_t+1The charge states of the electric vehicle in the t and t +1 time periods respectively.

8. The method for virtual power plant multi-objective optimization scheduling considering the external force characteristics as claimed in claim 7, wherein in the step 3), three scheduling objective functions in the virtual power plant multi-objective optimization scheduling model are specifically:

min F₁＝|P_t ^obj-P_t ^gen|

P_t ^gen＝P_t ^PV+P_t ^WP+P_t ^PEV-P_t ^store+P_t ^release

wherein, P_t ^objTarget power, P, for a virtual plant during a period t_t ^genThe actual output of the virtual power plant is obtained in the period t;

2. maximizing the operation income of the virtual power plant:

wherein the content of the first and second substances,

for the benefit of the virtual power plant actual output during the period t,

for maintenance cost of period t, P_t ^buyFor the purchased electric power of the time period t,

and

and

3. minimizing the carbon emissions of the virtual power plant:

9. The virtual power plant multi-objective optimization scheduling method considering the external force characteristics as claimed in claim 8, wherein in the step 3), the constraint conditions of the virtual power plant multi-objective optimization scheduling model include:

A. and power balance constraint:

P_t ^gen+P_t ^buy-P_t ^sell＝P_t ^obj

in the formula: p_t ^sellSelling power for a period of t;

B. energy storage equipment restraint:

in the formula:

C. electric vehicle restraint:

SOC_min≤SOC_t≤SOC_max

D. interaction power limit with the main network:

in the formula:

10. The method for scheduling multi-objective optimization of a virtual power plant taking external force characteristics into consideration as claimed in claim 1, wherein in the step 4), aiming at the multi-objective optimization problem, multi-objectives are converted into single objectives through a normalization method and linear weighting.