CN108683211B - Virtual power plant combination optimization method and model considering distributed power supply volatility - Google Patents

Virtual power plant combination optimization method and model considering distributed power supply volatility Download PDF

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CN108683211B
CN108683211B CN201810353496.9A CN201810353496A CN108683211B CN 108683211 B CN108683211 B CN 108683211B CN 201810353496 A CN201810353496 A CN 201810353496A CN 108683211 B CN108683211 B CN 108683211B
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distributed power
power supply
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power plant
distributed
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CN108683211A (en
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喻洁
王斯妤
滕贤亮
丁恰
涂孟夫
吴继平
谢丽荣
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Southeast University
Nari Technology Co Ltd
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Nari Technology Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Abstract

The invention discloses a virtual power plant combination optimization method and a model considering the volatility of distributed power supplies, which comprises the steps of reasonably selecting the distributed power supplies according to the geographical positions, the environmental conditions, the resource distribution and other comprehensive consideration factors of different regions, and predicting the output of each distributed power supply; calculating the matching degree of the predicted output of each distributed power supply and the power generation and utilization plan of the virtual power plant according to a matching degree calculation formula; bringing each distributed power supply with the minimum matching degree into a virtual power plant; judging whether a virtual power plant formed by distributed power supplies meets the scheduling requirement of the system; forming a virtual power plant. The invention adopts a multi-stage nonlinear integer programming method to establish an uncertain-situation virtual power plant combination optimization model, the model takes the running state of distributed resources as a decision variable and the minimum matching degree as a target, and the virtual power plant combination decision can be regulated and controlled in real time according to a system power generation and utilization plan.

Description

Virtual power plant combination optimization method and model considering distributed power supply volatility
Technical Field
The invention relates to an optimization method of a power system, in particular to a virtual power plant combination optimization method and a model considering distributed power supply volatility.
Background
In recent years, a new form of development of a virtual power plant as a distributed power source has been increasingly emphasized by academia. The virtual power plant under the background of the smart power grid is expanded and extended, and the method can be understood as that a distributed power supply, a controllable load and an energy storage system in a power distribution network are combined to be used as a special power plant to participate in the operation of the power grid through a distributed power management system, so that the contradiction between the smart power grid and the distributed power supply is well coordinated, and the value and benefit brought to the power grid and users by distributed energy are fully exploited; different from a micro-grid, a virtual power plant cannot operate in an isolated island mode, and a distributed power supply in the virtual power plant can purchase an actual operation network only through an external power grid. The virtual power plant concept makes it possible to put the distributed power into operation in a wide range of the power grid, and also provides services for the management of the transmission system.
With the rapid development of a user side power supply, the virtual power plant technology effectively utilized as a user side distributed power supply is necessarily widely applied, the research on the user side virtual power plant in China is just started at present, and the mechanism research on the composition, the characteristic form, the access and the operation characteristics of the virtual power plant is lacked, so that the method has very important practical significance.
The existing combination method of the virtual power plants is divided according to regions, all distributed resources in the regions are subjected to aggregation expression, an obtained single virtual power plant model representing the regions does not consider the problem of active optimization, and the requirement of economic operation of a power system cannot be met in some scenes.
Disclosure of Invention
The purpose of the invention is as follows: in order to optimize the combination mode of the distributed power supplies in the virtual power plant, the invention considers the volatility of the distributed power supplies, establishes the optimal combination model of the virtual power plant and provides the optimal combination method and the optimal combination model of the virtual power plant, which consider the volatility of the distributed power supplies.
The technical scheme is as follows: the invention provides a virtual power plant combination optimization method considering distributed power supply volatility, which comprises the following steps of:
(1) reasonably selecting distributed power supplies according to the geographical positions, environmental conditions, resource distribution and the like of different regions by comprehensively considering various factors, and predicting the output of each distributed power supply;
(2) calculating the matching degree of the predicted output of each distributed power supply and the power generation and utilization plan of the virtual power plant according to a matching degree calculation formula;
(3) bringing each distributed power supply with the minimum matching degree into a virtual power plant;
(4) judging whether a virtual power plant formed by distributed power supplies meets the scheduling requirement of the system;
(5) forming a virtual power plant.
Further, the matching degree between the predicted output of each distributed power source and the power generation and utilization plan of the virtual power plant in the step (2) is calculated by adopting the following formula:
the specific calculation formula of the matching degree in the t period is as follows:
Figure BDA0001634016720000021
in the formula, StRepresenting the matching degree of the t time period, N is the type of the distributed power supply, i is the number of the distributed power supply, and Pi tPredicting power out, P, for a distributed power source i during a time period tePlanning the electric quantity for generating and using electricity for the virtual power plant;
the matching degree of the renewable resource based distributed power sources in the whole scheduling period (1,2, … T, … T) is:
Figure BDA0001634016720000022
in the formula, S represents the matching degree of the whole scheduling cycle, T is the time period taken by the scheduling cycle, T is the time period serial number, N is the type of the distributed power supply, i is the distributed power supply serial number, P is the number of the distributed power supplyi tPredicting power output for distributed power source i in t period，PeAnd planning the electric quantity for generating and using the electricity for the virtual power plant.
Further, the objective function of the combined optimization model of each distributed power source at the time of the minimum matching degree in step (3) is as follows:
Figure BDA0001634016720000023
wherein T is a time period taken by a scheduling cycle; t is a time period number; n is the distributed power type; i is a distributed power supply type number; pi tThe predicted output of the distributed power supply i at the moment t; pePlanning the electric quantity for generating and using electricity for the virtual power plant; u. ofi0-1 variable, u, representing distributed power statei1 denotes a distributed power source i as a power generation unit of a virtual power plant, ui0 means that the distributed power source i does not act as a power generation unit of the virtual power plant.
Furthermore, according to requirements, day-ahead, month-month, quarter or year scheduling is selected as a planning scheduling period of the virtual power plant, and corresponding S is day matching degree, month matching degree, season matching degree or year matching degree.
Further, the scheduling in step (4) needs to be a constraint condition, including:
(41) and (3) power generation plan constraint:
Figure BDA0001634016720000031
Figure BDA0001634016720000032
wherein the content of the first and second substances,
Figure BDA0001634016720000033
respectively representing the charging and discharging power of the storage battery at the time t;
Figure BDA0001634016720000034
respectively the charging efficiency and the discharging efficiency of the storage battery;
Figure BDA0001634016720000035
for the virtual power plant load demand at time t,
Figure BDA0001634016720000036
a power generation plan submitted to the power grid for the virtual power plant; u. ofi0-1 variable, u, representing distributed power statei1 denotes a distributed power source i as a power generation unit of a virtual power plant, ui0 means that the distributed power source i does not act as a power generation unit of the virtual power plant; pi tThe predicted output of the distributed power supply i at the moment t;
(42) and (3) restraining the upper and lower output limits of the controllable distributed power supply:
Figure BDA0001634016720000037
wherein the content of the first and second substances,
Figure BDA0001634016720000038
indicating a controllable distributed power supply minimum capacity,
Figure BDA0001634016720000039
representing the maximum output of the controllable distributed power supply;
(43) the upper limit and the lower limit of the charging and discharging power of the storage battery are restricted:
Figure BDA00016340167200000310
Figure BDA00016340167200000311
SOCmin≤SOCt≤SOCmax(9)；
wherein the content of the first and second substances,
Figure BDA00016340167200000312
respectively charging the storage battery at the time t,Discharge power;
Figure BDA00016340167200000313
respectively the minimum charging and discharging power of the storage battery;
Figure BDA00016340167200000314
respectively the maximum charging and discharging power of the storage battery; SOCtIs the storage capacity, SOC, of the battery at time tminFor minimum storage capacity, SOC, of the batterymaxThe storage capacity maximum value of the storage battery is obtained;
if the dispatching requirement is met, selecting the distributed power supply, and executing the step (5) to form a virtual power plant; and (4) if the scheduling requirement is not met, returning to the step (3) to adjust the selected distributed power supply until the scheduling requirement is met.
The invention also provides a virtual power plant combined optimization model considering the volatility of the distributed power supply, which comprises an objective function and a constraint condition, wherein the objective function is as follows:
Figure BDA0001634016720000041
wherein T is a time period taken by a scheduling cycle; t is a time period number; n is the distributed power type; i is a distributed power supply type number; pi tThe predicted output of the distributed power supply i at the moment t; pePlanning the electric quantity for generating and using electricity for the virtual power plant; u. ofi0-1 variable, u, representing distributed power statei1 denotes a distributed power source i as a power generation unit of a virtual power plant, ui0 means that the distributed power source i does not act as a power generation unit of the virtual power plant;
the constraint conditions include:
(a) and (3) power generation plan constraint:
Figure BDA0001634016720000042
Figure BDA0001634016720000043
wherein the content of the first and second substances,
Figure BDA0001634016720000044
respectively representing the charging and discharging power of the storage battery at the time t;
Figure BDA0001634016720000045
respectively the charging efficiency and the discharging efficiency of the storage battery;
Figure BDA0001634016720000046
for the virtual power plant load demand at time t,
Figure BDA0001634016720000047
a power generation plan submitted to the power grid for the virtual power plant; u. ofi0-1 variable, u, representing distributed power statei1 denotes a distributed power source i as a power generation unit of a virtual power plant, ui0 means that the distributed power source i does not act as a power generation unit of the virtual power plant; pi tThe predicted output of the distributed power supply i at the moment t;
(b) and (3) restraining the upper and lower output limits of the controllable distributed power supply:
Figure BDA0001634016720000048
wherein the content of the first and second substances,
Figure BDA0001634016720000049
indicating a controllable distributed power supply minimum capacity,
Figure BDA00016340167200000410
representing the maximum output of the controllable distributed power supply;
(c) the upper limit and the lower limit of the charging and discharging power of the storage battery are restricted:
Figure BDA00016340167200000411
Figure BDA00016340167200000412
SOCmin≤SOCt≤SOCmax (9)；
wherein the content of the first and second substances,
Figure BDA0001634016720000051
respectively charging and discharging power for the storage battery at the moment t;
Figure BDA0001634016720000052
respectively the minimum charging and discharging power of the storage battery;
Figure BDA0001634016720000053
respectively the maximum charging and discharging power of the storage battery; SOCtIs the storage capacity, SOC, of the battery at time tminFor minimum storage capacity, SOC, of the batterymaxThe storage capacity of the storage battery is the maximum value.
Has the advantages that: compared with the prior art, the method adopts a multi-stage nonlinear integer programming method to establish the virtual power plant combination optimization model under the uncertain conditions, the model takes the running state of distributed resources as a decision variable and the minimum matching degree as a target, and the virtual power plant combination decision can be regulated and controlled in real time according to a system power generation and utilization plan. The distributed power supply is reasonably selected according to the geographical positions, the environmental conditions, the resource distribution and the like of different regions by comprehensively considering various factors, and the advantages of distributed power generation are fully exerted by utilizing local resources. The reasonable selection of the type of the distributed power generation power supply can fully develop and utilize various available scattered energy sources, improve the utilization efficiency of the sources and reduce the investment cost.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The method is based on the concept of the matching degree of the distributed power supplies, and a distributed power supply combination optimization model is established. The model aims at the minimum matching variance of the distributed power supplies in the planning and scheduling period of the virtual power plant, meets a certain constraint condition, and obtains an optimal combination scheme from distributed power supply combinations of different types and different capacities. The matching degree defined by the method is the output matching degree of the distributed power supply, namely the matching degree of the prediction curve and the actual output curve of the distributed power supply.
By combining the concept of the mathematical variance, the method provides the concept of the output matching degree of the distributed power supply and provides a specific calculation formula, wherein the formula is a characterization value which is quantized when a proper distributed power supply is selected to form a virtual power plant.
In a distributed system containing a renewable energy distributed power supply, clean distributed power supplies such as wind power, photovoltaic and the like with strong volatility are generally contained; and other distributed power sources with relatively higher output stability, such as micro gas turbines and the like, are necessarily included to obtain better electric energy. Therefore, a certain difference exists between the total output curve and the total expected curve of the distributed power system with strong output fluctuation, and the optimal combination of the virtual power plant is determined by combining the predicted output of the distributed power system with the concept of matching degree.
As shown in fig. 1, a virtual power plant portfolio optimization method considering distributed power source volatility of the present invention includes the following steps:
(1) reasonably selecting distributed power supplies according to the geographic positions, the environmental conditions, the resource distribution and the like of different regions by comprehensively considering various factors, and predicting the output of each distributed power supply by combining the existing power generation prediction data and the power generation probability model;
(2) calculating the matching degree of the predicted output of each distributed power supply and the power generation and utilization plan of the virtual power plant according to a matching degree calculation formula;
the specific calculation formula of the matching degree in the t period is as follows:
Figure BDA0001634016720000061
in the formula, StRepresenting the matching degree of the t time period, N is the type of the distributed power supply, i is the number of the distributed power supply, and Pi tPredicting power out, P, for a distributed power source i during a time period teAnd planning the electric quantity for generating and using the electricity for the virtual power plant.
The closer the power generation-utilization capacity is, i.e. StThe closer to zero, the smaller the value of the degree of match, indicating less uncertainty information contained therein. The smaller the value of the matching degree is, the more matching the power generation-power utilization characteristics is, and the method is suitable for being combined into a partner.
The matching degree of the renewable resource based distributed power sources in the whole scheduling period (1,2, … T, … T) is:
Figure BDA0001634016720000062
in the formula, S represents the matching degree of the whole scheduling cycle, T is the time period taken by the scheduling cycle, T is the time period serial number, N is the type of the distributed power supply, i is the distributed power supply serial number, P is the number of the distributed power supplyi tPredicting power out, P, for a distributed power source i during a time period teAnd planning the electric quantity for generating and using the electricity for the virtual power plant.
(3) Establishing a distributed power source combination optimization model according to the concept of the matching degree of the distributed power sources, and bringing each distributed power source with the minimum matching degree into a virtual power plant;
the objective function of the model is:
Figure BDA0001634016720000063
wherein T is a time period taken by a scheduling cycle; t is a time period number; n is the distributed power type; i is a distributed power supply type number; pi tThe predicted output of the distributed power supply i at the moment t; pePlanning the electric quantity for generating and using electricity for the virtual power plant; u. ofi0-1 variable, u, representing distributed power statei1 denotes distributedPower supply i as a power generating unit of a virtual power plant ui0 means that the distributed power source i does not act as a power generation unit of the virtual power plant. And S is the matching variance of the distributed power supply, and the smaller S is, the more the actual power generation is matched with the expected power generation curve, and the higher the similarity is.
According to the requirement, day-ahead, month-year, quarter-year or annual scheduling can be selected as a planning scheduling period of the virtual power plant, if the scheduling is day-ahead, the scheduling takes hours as a scale, T is taken as 24 hours, corresponding S is defined as day matching degree, namely the matching degree between the sum of the daily generated energy of the distributed power supply and a date expected power generation curve, and other scheduling periods are similar. According to the self characteristics of each distributed power supply, when the distributed power supply type of the virtual power plant is selected, at least one schedulable distributed power supply is ensured, so that the overall stability of the system is improved.
(4) Judging whether a virtual power plant formed by the distributed power supply meets the scheduling requirement of the system, namely a constraint condition;
wherein the constraint condition comprises:
(41) and (3) power generation plan constraint:
Figure BDA0001634016720000071
Figure BDA0001634016720000072
wherein the content of the first and second substances,
Figure BDA0001634016720000073
respectively representing the charging and discharging power of the storage battery at the time t;
Figure BDA0001634016720000074
respectively the charging efficiency and the discharging efficiency of the storage battery;
Figure BDA0001634016720000075
for the virtual power plant load demand at time t,
Figure BDA0001634016720000076
a power generation plan submitted to the power grid for the virtual power plant; u. ofi0-1 variable, u, representing distributed power statei1 denotes a distributed power source i as a power generation unit of a virtual power plant, ui0 means that the distributed power source i does not act as a power generation unit of the virtual power plant; pi tAnd (4) the predicted output of the distributed power supply i at the moment t.
(42) And (3) restraining the upper and lower output limits of the controllable distributed power supply:
Figure BDA0001634016720000077
wherein the content of the first and second substances,
Figure BDA0001634016720000078
indicating a controllable distributed power supply minimum capacity,
Figure BDA0001634016720000079
representing the maximum output of the controllable distributed power supply.
(43) The upper limit and the lower limit of the charging and discharging power of the storage battery are restricted:
Figure BDA0001634016720000081
Figure BDA0001634016720000082
SOCmin≤SOCt≤SOCmax(9)；
wherein the content of the first and second substances,
Figure BDA0001634016720000083
respectively charging and discharging power for the storage battery at the moment t;
Figure BDA0001634016720000084
respectively the minimum charging and discharging power of the storage battery;
Figure BDA0001634016720000085
respectively the maximum charging and discharging power of the storage battery; SOCtIs the storage capacity, SOC, of the battery at time tminFor minimum storage capacity, SOC, of the batterymaxThe storage capacity of the storage battery is the maximum value.
If the dispatching requirement is met, selecting the distributed power supply, and executing the step (5) to form a virtual power plant; and (4) if the scheduling requirement is not met, returning to the step (3) for adjustment, and selecting the distributed power supplies according to the increasing sequence of the matching degrees from small to large until the scheduling requirement is met.
(5) Forming a virtual power plant.
A virtual power plant combination that can achieve optimal matching of distributed power output to predictions. And establishing an optimization model by taking the minimum matching degree of the distributed power supplies as a target in a planning day-ahead scheduling period of the virtual power plant, satisfying power generation plan constraints, controllable distributed power supply output upper and lower limit constraints and storage battery charging and discharging power upper and lower limit constraints, and obtaining an optimal combination scheme from distributed power supply combinations of different types and different capacities.
And on the basis of the fluctuation analysis of the distributed power sources, carrying out matching degree calculation analysis on the prediction curve and the actual output curve of each distributed power source in the virtual power plant to obtain the matching degree of the prediction curve and the actual output curve.
Selecting day-ahead scheduling as a planning and scheduling period of a virtual power plant, and defining day matching variance, namely the matching degree between the total daily generated energy of the distributed power supply and a date expected power generation curve by taking hours as a scale. And obtaining an optimal combination scheme from distributed power supply combinations of different types and different capacities. According to the self characteristics of each distributed power supply, when the distributed power supply type of the virtual power plant is selected, at least one schedulable distributed power supply is ensured, so that the overall stability of the system is improved.
And (3) power generation plan constraint: the power generation plan submitted by the virtual power plant to the power grid is equal to the charging and discharging output of the distributed power supply and the storage battery except for the internal load requirement of the virtual power plant.
And (3) restraining the upper and lower output limits of the controllable distributed power supply: the output of the controllable distributed power supply is between the upper and lower output limits.
And (3) restricting the upper and lower limits of the charge and discharge power of the storage battery: the storage battery is characterized in that the charge and discharge power of the storage battery is limited between the upper limit and the lower limit of the charge and discharge power, and the storage capacity of the storage battery is limited between the upper limit and the lower limit of the storage capacity.

Claims (5)

1. A virtual power plant portfolio optimization method considering distributed power supply volatility, comprising the steps of:
(1) reasonably selecting distributed power supplies according to the geographic positions, the environmental conditions and the resource distribution of different regions by comprehensively considering various factors, and predicting the output of each distributed power supply;
(2) calculating the matching degree of the predicted output of each distributed power supply and the power generation and utilization plan of the virtual power plant according to a matching degree calculation formula;
the matching degree of the predicted output of each distributed power supply and the power generation and utilization plan of the virtual power plant is calculated by adopting the following formula:
the specific calculation formula of the matching degree in the t period is as follows:
Figure FDA0002932977770000011
in the formula, StRepresenting the matching degree of the t time period, N is the type of the distributed power supply, i is the number of the distributed power supply, and Pi tPredicting power out, P, for a distributed power source i during a time period tePlanning the electric quantity for generating and using electricity for the virtual power plant;
the matching degree of the renewable resource based distributed power sources in the whole scheduling period (1,2, … T, … T) is:
Figure FDA0002932977770000012
wherein S represents the matching degree of the whole scheduling cycle, T is the time period taken by the scheduling cycle, T is the time period serial number, N is the type of the distributed power supply,uia variable 0-1 representing the state of the distributed power source, i being the distributed power source number, Pi tPredicting power out, P, for a distributed power source i during a time period tePlanning the electric quantity for generating and using electricity for the virtual power plant;
(3) bringing each distributed power supply with the minimum matching degree into a virtual power plant;
(4) judging whether a virtual power plant formed by distributed power supplies meets the scheduling requirement of the system;
(5) forming a virtual power plant.
2. The virtual power plant combined optimization method considering the distributed power supply volatility as claimed in claim 1, wherein the objective function of the combined optimization model of each distributed power supply at the minimum matching degree in the step (3) is as follows:
Figure FDA0002932977770000013
wherein T is a time period taken by a scheduling cycle; stRepresenting the matching degree of a t time period, wherein t is a time period number; n is the distributed power type; i is a distributed power supply type number; pi tThe predicted output of the distributed power supply i at the moment t; pePlanning the electric quantity for generating and using electricity for the virtual power plant; u. ofi0-1 variable, u, representing distributed power statei1 denotes a distributed power source i as a power generation unit of a virtual power plant, ui0 means that the distributed power source i does not act as a power generation unit of the virtual power plant.
3. The virtual power plant portfolio optimization method of claim 2, wherein the consideration of distributed power volatility is as follows: according to requirements, day-ahead, month-month, quarter or year scheduling is selected as a planning scheduling period of the virtual power plant, and corresponding S is day matching degree, month matching degree, quarter matching degree or year matching degree.
4. The virtual power plant portfolio optimization method considering distributed power supply volatility according to claim 1, wherein the scheduling requirement in step (4) is a constraint condition, comprising:
(41) and (3) power generation plan constraint:
Figure FDA0002932977770000021
Figure FDA0002932977770000022
wherein N is a distributed power type,
Figure FDA0002932977770000023
respectively representing the charging and discharging power of the storage battery at the time t;
Figure FDA0002932977770000024
respectively the charging efficiency and the discharging efficiency of the storage battery;
Figure FDA0002932977770000025
for the virtual power plant load demand at time t,
Figure FDA0002932977770000026
a power generation plan submitted to the power grid for the virtual power plant; u. ofi0-1 variable, u, representing distributed power statei1 denotes a distributed power source i as a power generation unit of a virtual power plant, ui0 means that the distributed power source i does not act as a power generation unit of the virtual power plant; pi tThe predicted output of the distributed power supply i at the moment t;
(42) and (3) restraining the upper and lower output limits of the controllable distributed power supply:
Figure FDA0002932977770000027
wherein the content of the first and second substances,
Figure FDA0002932977770000028
indicating a controllable distributed power supply minimum capacity,
Figure FDA0002932977770000029
representing the maximum output of the controllable distributed power supply;
(43) the upper limit and the lower limit of the charging and discharging power of the storage battery are restricted:
Figure FDA00029329777700000210
Figure FDA0002932977770000031
SOCmin≤SOCt≤SOCmax (9)；
wherein the content of the first and second substances,
Figure FDA0002932977770000032
respectively charging and discharging power for the storage battery at the moment t;
Figure FDA0002932977770000033
respectively the minimum charging and discharging power of the storage battery;
Figure FDA0002932977770000034
respectively the maximum charging and discharging power of the storage battery; SOCtIs the storage capacity, SOC, of the battery at time tminFor minimum storage capacity, SOC, of the batterymaxThe storage capacity maximum value of the storage battery is obtained;
if the dispatching requirement is met, selecting the distributed power supply, and executing the step (5) to form a virtual power plant; and (4) if the scheduling requirement is not met, returning to the step (3) to adjust the selected distributed power supply until the scheduling requirement is met.
5. A virtual power plant combined optimization system considering distributed power supply volatility is characterized by comprising a model, wherein the model comprises an objective function and a constraint condition, and the objective function is as follows:
Figure FDA0002932977770000035
s represents the matching degree of the whole scheduling cycle, and T is the time period taken by the scheduling cycle; stRepresenting the matching degree of a t time period, wherein t is a time period number; n is the distributed power type; i is a distributed power supply type number; pi tThe predicted output of the distributed power supply i at the moment t; pePlanning the electric quantity for generating and using electricity for the virtual power plant; u. ofi0-1 variable, u, representing distributed power statei1 denotes a distributed power source i as a power generation unit of a virtual power plant, ui0 means that the distributed power source i does not act as a power generation unit of the virtual power plant;
the constraint conditions include:
(a) and (3) power generation plan constraint:
Figure FDA0002932977770000036
Figure FDA0002932977770000037
wherein the content of the first and second substances,
Figure FDA0002932977770000038
respectively representing the charging and discharging power of the storage battery at the time t;
Figure FDA0002932977770000039
respectively the charging efficiency and the discharging efficiency of the storage battery;
Figure FDA00029329777700000310
for the virtual power plant load demand at time t,
Figure FDA00029329777700000311
a power generation plan submitted to the power grid for the virtual power plant; u. ofi0-1 variable, u, representing distributed power statei1 denotes a distributed power source i as a power generation unit of a virtual power plant, ui0 means that the distributed power source i does not act as a power generation unit of the virtual power plant; pi tThe predicted output of the distributed power supply i at the moment t;
(b) and (3) restraining the upper and lower output limits of the controllable distributed power supply:
Figure FDA0002932977770000041
wherein the content of the first and second substances,
Figure FDA0002932977770000042
indicating a controllable distributed power supply minimum capacity,
Figure FDA0002932977770000043
representing the maximum output of the controllable distributed power supply;
(c) the upper limit and the lower limit of the charging and discharging power of the storage battery are restricted:
Figure FDA0002932977770000044
Figure FDA0002932977770000045
SOCmin≤SOCt≤SOCmax (9)；
wherein the content of the first and second substances,
Figure FDA0002932977770000046
respectively charging and discharging power for the storage battery at the moment t;
Figure FDA0002932977770000047
respectively the minimum charging and discharging power of the storage battery;
Figure FDA0002932977770000048
respectively the maximum charging and discharging power of the storage battery; SOCtIs the storage capacity, SOC, of the battery at time tminFor minimum storage capacity, SOC, of the batterymaxThe storage capacity of the storage battery is the maximum value.
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