CN110854849A - Distributed energy optimization control method in power grid with uncertain power - Google Patents

Distributed energy optimization control method in power grid with uncertain power Download PDF

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CN110854849A
CN110854849A CN201911186895.1A CN201911186895A CN110854849A CN 110854849 A CN110854849 A CN 110854849A CN 201911186895 A CN201911186895 A CN 201911186895A CN 110854849 A CN110854849 A CN 110854849A
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optimization
distributed
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power grid
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CN110854849B (en
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陈博
张志轩
房俏
李克强
张鹏飞
周宁
李山
麻常辉
王亮
刘文学
马欢
邢鲁华
赵康
安沫霖
蒋哲
李文博
杨冬
张冰
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Shandong Electric Power 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers

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Abstract

The invention discloses a distributed energy optimization control method in a power grid with uncertain power, which comprises the following steps: step 1, optimizing and calculating initial parameters; step 2, collecting information of each distributed power supply access user, and performing optimization calculation by adopting a distributed algorithm according to the requirement of a target function; step 3, adjusting the power generation amount of the local distributed power supply according to the optimization result to obtain an accurate optimization result; and 4, fitting a change curve of the local optimal point according to the accurate optimization result, calculating a predicted value of the optimal point according to the characteristic trend of the curve, and performing local adjustment without scheduling according to the predicted value. The invention analyzes by combining the characteristic rule of the optimization point, and saves the communication and management cost by using the inertia of control and scheduling on the premise of keeping the accuracy and rapidity of control and optimization.

Description

Distributed energy optimization control method in power grid with uncertain power
Technical Field
The invention relates to a distributed energy optimization control method in a power grid containing uncertain power, and belongs to the technical field of power grid control.
Background
With the massive access of new energy, especially distributed photovoltaic power, in the power grid, the power flow characteristics of the power grid are changed, and the distributed power accessed in the power grid must be effectively managed and scheduled. Furthermore, the operating costs of the power grid are also taken into account, so that the costs for the users are kept as low as possible. Therefore, under the large background of the continuous development of the current power grid and the massive access of the distributed power supplies, the consideration of the cost of the power grid users becomes an important problem to be considered in operation and scheduling.
Chinese patent No. 201810024408.0: the patent discloses a genetic algorithm optimization method for a power distribution network with distributed power supplies, and the method comprises the steps of constructing a multi-objective optimization function by combining an active optimization objective function and a reactive optimization objective function, optimizing and calculating the multi-objective optimization function by adopting a genetic algorithm, designing an optimization algorithm for the power distribution network with the distributed power supplies based on voltage deviation and active network loss, wherein the optimization of the multi-objective function can be directly realized, and the optimization effect is better when the system scale is larger. However, the patent can only realize the scheduling management of the power quality, and does not consider the economic problems of the cost of the user and the operator. Chinese patent No. 201611026729.1: the utility model discloses a distributed power energy efficiency optimization method and system, which carries out scheduling control from two time scales before and in the day and respectively carries out modeling and optimization on the two stages. The load is divided into an uncontrollable load, a translatable load and a reducible load, the energy storage cost in a dispatching period, the interaction cost with a superior power grid and the sum of the load cost are the minimum as the target of an optimization model, the characteristics of the distributed power supply, the energy storage and the multi-element load are comprehensively considered, the interaction relation among the distributed power supply, the energy storage and the multi-element load is enhanced, the benign interaction of a power generation side and a load side is realized, and the comprehensive utilization efficiency of energy is improved while the renewable energy is fully utilized. Although the patent considers the cost problem, only the energy storage cost, the interaction cost and the load cost are considered, and the communication cost and the management cost are not considered in the scheduling aspect.
The distributed energy including photovoltaic power generation has strong fluctuation and uncertainty in power generation, an optimization method and a scheduling strategy need to track network fluctuation as quickly and accurately as possible, but are limited to network communication cost and management cost of the whole power grid, and the scheduling and management cannot be too frequent.
Disclosure of Invention
Aiming at the defects of the method, the invention provides a distributed energy optimization control method in a power grid with uncertain power, which is used for analyzing by combining with the characteristic rule of an optimization point and utilizing the inertia of control and scheduling, and can save the communication and management cost on the premise of keeping the accuracy and rapidity of control and optimization.
The technical scheme adopted for solving the technical problems is as follows:
the embodiment of the invention provides a distributed energy optimization control method in a power grid with uncertain power, which comprises the following steps:
step 1, optimizing and calculating initial parameters;
step 2, collecting information of each distributed power supply access user, and performing optimization calculation by adopting a distributed algorithm according to the requirement of a target function;
step 3, adjusting the power generation amount of the local distributed power supply according to the optimization result to obtain an accurate optimization result;
and 4, fitting a change curve of the local optimal point according to the accurate optimization result, calculating a predicted value of the optimal point according to the characteristic trend of the curve, and performing local adjustment without scheduling according to the predicted value.
As a possible implementation manner of this embodiment, in step 1, the initial parameters include a line parameter of a power grid, a distributed energy power adjustment range of each distributed power access node, a voltage desired control range of each distributed power access node, a parameter of a cost function for distributed power access, and a parameter of a voltage control function.
As a possible implementation of this embodiment, steps 2 and 3 are performed every T seconds.
As a possible implementation manner of this embodiment, in step 4, at the current tnowAt the moment, fitting t according to the accurate optimization results of the previous m timesnow-m × T to TnowThe variation curve of the local optimal point in the time period is calculated according to the characteristic trend of the curvenowTo tnowPredicting the optimal point in the + T time period according to the predicted value at TnowTo tnowLocal adjustment without scheduling is performed within the + T period.
As a possible implementation manner of this embodiment, the specific process of step 2 is:
an electrical network having N +1 nodes, denoted by the set NU {0}, where N: {1, …, N }, node 0 denotes a common coupling node or substation, and the network lines are denoted by the set epsilon; vi tE C represents the line-to-ground voltage of the node i at the time t and defines
Figure BDA0002289229220000031
The unit is kV; definition of
Figure BDA0002289229220000032
And
Figure BDA0002289229220000033
respectively injecting active power and reactive power into one distributed power supply positioned at a node i, wherein the unit is kW and kVar;
definition of
Figure BDA0002289229220000034
The feasible set of active power and reactive power injected by the distributed power supply at the node i at the moment t; definition of
Figure BDA0002289229220000035
η is defined for the maximum active power of a distributed energy system at time t in kWiThe apparent rated capacity of the distributed energy system is represented by kVA;
volumetric aggregation for distributed energy systems
Figure BDA0002289229220000036
Comprises the following steps:
Figure BDA0002289229220000037
considering a cost function
Figure BDA0002289229220000038
The unit is an element, and the unit is,
Figure BDA0002289229220000039
and
Figure BDA00022892292200000310
respectively representing feedback signals of active power and reactive power generated by a power grid operator at the time t, wherein the units are yuan/kW and yuan/kVar;
the objective function is as follows:
Figure BDA00022892292200000312
defining:
consider another cost function
Figure BDA00022892292200000314
The unit is element, and the function represents the cost corresponding to the voltage offset at the time t;
the following objective functions can pursue the optimal objectives of both the user side and the power grid operator:
Figure BDA00022892292200000316
Figure BDA0002289229220000041
Figure BDA0002289229220000042
wherein, γt∈R+The parameter is a parameter for balancing between a user target and an operator target, and is dimensionless; v. oftAnd
Figure BDA0002289229220000043
is a vector representing the upper and lower voltage limits in kV; r and X are active and reactive power coefficient matrixes in a linear power flow algorithm of the power grid, the unit is kV/kW and kV/kVar, a is a constant, and the unit is kV;
set of shorthand feedback signals si＝[αi,βi]TUser load of each node zi＝[pi,qi]TIn kW, KVar.
As a possible implementation manner of this embodiment, the specific process of step 3 is:
the calculation process that each node user needs to adjust according to the feedback signal in the precise optimization step is as follows:
Figure BDA0002289229220000044
the calculation process required by the dispatching center of the power grid operator in the precise optimization step is as follows:
Figure BDA0002289229220000045
Figure BDA0002289229220000046
Figure BDA0002289229220000047
Figure BDA0002289229220000048
wherein phi>0 is a predefined constant parameter, ε1And ε2The step size is represented as a function of time,μand
Figure BDA0002289229220000049
are intermediate variables that facilitate calculation and formulation; the voltage and the user load value are obtained through measurement and communication, and k is the iteration number of the single optimization problem solving process; the above algorithm iterates until the iteration result converges to a certain predefined range, or the iteration number k is set in advance.
As a possible implementation manner of this embodiment, the specific process of step 4 is:
firstly, using the accurate optimization result m times before the current moment to perform polynomial curve fitting, setting a fitting curve equation as an n-order polynomial, wherein n can be adjusted according to the selection of m;
polynomial of order n is
y＝anxn+an-1xn-1+...+a1x+a0(14)
The abscissa of the curve is time, and the vector is written
X＝[1,2,...,m](15)
The ordinate of the curve is the accurate optimization result of the previous m times, and the curve is written as a vector
Y＝[y1,y2,...,ym](16)
Expressed in matrix form as
Y＝X0A (17)
Wherein the content of the first and second substances,
Figure BDA0002289229220000051
A＝[an,an-1,...,a0]T(19)
formula (17) is equivalent to:
Figure BDA0002289229220000052
namely:
Figure BDA0002289229220000053
obtaining a coefficient A to obtain a fitted curve, then arbitrarily taking x epsilon (m, m +1) to carry in (14), and calculating the time period tnowTo tnowAnd + T, analyzing and predicting the result based on the characteristic rule of the preorder optimization result.
The technical scheme of the embodiment of the invention has the following beneficial effects:
according to the technical scheme of the embodiment of the invention, the distributed energy optimization control method in the power grid containing uncertain power performs optimization control on distributed energy, can ensure higher optimization precision on the premise of higher calculation and control speed, can relieve the problem of power distribution network overvoltage caused by introduction of a distributed power supply, and can ensure that the power grid operates in the most economic state.
The technical scheme of the embodiment of the invention is based on a rapid distributed algorithm, takes the quality of electric energy and the cost of users as targets, rapidly and accurately schedules and controls each node in the power grid, particularly distributed energy, considers the characteristic rule of an optimization point on a time line, and combines a prediction algorithm to save the communication and management cost of a scheduling party of the power grid, thereby finally achieving the win-win purpose.
Description of the drawings:
FIG. 1 is a flow diagram illustrating a method for distributed energy optimization control in an uncertainty-containing power grid in accordance with an exemplary embodiment;
fig. 2 is a flow chart of the distributed energy optimization control method in the power grid with uncertainty according to the present invention.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
in order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
Fig. 1 is a flow chart illustrating a method for distributed energy optimization control in an uncertainty-containing power grid in accordance with an exemplary embodiment. As shown in fig. 1, an embodiment of the present invention provides a method for optimally controlling distributed energy in a power grid with uncertainty, including the following steps:
step 1, optimizing and calculating initial parameters;
step 2, collecting information of each distributed power supply access user, and performing optimization calculation by adopting a distributed algorithm according to the requirement of a target function;
step 3, adjusting the power generation amount of the local distributed power supply according to the optimization result to obtain an accurate optimization result;
and 4, fitting a change curve of the local optimal point according to the accurate optimization result, calculating a predicted value of the optimal point according to the characteristic trend of the curve, and performing local adjustment without scheduling according to the predicted value.
As shown in fig. 2, the distributed energy optimization control process using the distributed energy optimization control method in the power grid with uncertainty of the present invention is as follows.
(1) Optimizing and calculating initial parameters including line parameters of a power grid, such as resistance, reactance, connection relation of nodes and the like; the power regulation range of each node distributed energy source; the expected control range of the voltage of each node; parameters of a user cost function; parameters of the voltage control function;
(2) and the operator collects the user information of each point, performs optimization calculation by adopting a distributed algorithm according to the requirement of the objective function, and sends the optimization result to the user through a feedback signal.
(3) And the user receives the feedback signal and adjusts the power generation amount of the local distributed power supply to meet the optimization requirement at the current moment. And (3) performing the steps (2) and (3) every T seconds, and called as precise optimization.
(4) User is at current tnowAt the moment, fitting t according to the accurate optimization results of the previous m timesnow-m × T to TnowThe variation curve of the local optimal point in the time period is calculated according to the characteristic trend of the curvenowTo tnowPredicting the optimal point in the + T time period according to the predicted value at TnowTo tnowLocal adjustment without scheduling is performed within the + T period.
(5) At tnowAt + T, steps (2) and (3) are continued, i.e. a fine optimization is performed. And so on, the circulation is repeated.
In the steps (2) and (3), namely the precise optimization step, the modeling of the optimization problem and the calculation process of the distributed algorithm are specifically as follows:
an electrical network having N +1 nodes is denoted by the set NU 0, where N: {1, …, N }, node 0 denotes a common coupling node or substation, and the network lines are denoted by the set epsilon. Vi tE C represents the line-to-ground voltage of the node i at the time t and definesThe unit is kV. Definition of
Figure BDA0002289229220000072
And
Figure BDA0002289229220000073
the active power and the reactive power injected by one distributed power supply at the node i are respectively in kW and kVar. Definition ofAnd injecting a feasible set of active power and reactive power for the distributed power supply at the node i at the moment t.
Capacity of distributed energy system: definition of
Figure BDA0002289229220000075
η is defined for the maximum active power of a distributed energy system at time t in kWiIs the apparent rated capacity of the distributed energy system in kVA. Volumetric aggregation for distributed energy systems
Figure BDA0002289229220000076
Comprises the following steps:
Figure BDA0002289229220000077
considering a cost function
Figure BDA0002289229220000078
The unit is an element, and the unit is,
Figure BDA0002289229220000079
and
Figure BDA00022892292200000710
respectively representing the feedback signals of active power and reactive power generated by a power grid operator at the time t, wherein the units are yuan/kW and yuan/kVar.
The optimization problem is as follows:
Figure BDA0002289229220000081
Figure BDA0002289229220000082
defining:
Figure BDA0002289229220000083
second, consider another cost function
Figure BDA0002289229220000084
In units of elements, the function represents the cost corresponding to the voltage offset at time t. The following optimization problems can pursue both user side and grid operator objectives to be optimal:
Figure BDA0002289229220000085
Figure BDA0002289229220000087
Figure BDA0002289229220000088
wherein, γt∈R+The method is a parameter for carrying out balance between a user target and an operator target, and is dimensionless. v. oftAnd
Figure BDA0002289229220000089
is a vector representing the upper and lower voltage limits in kV. R and X are active and reactive power coefficient matrixes in a linear power flow algorithm of the power grid, the unit is kV/kW and kV/kVar, and a is a constant and the unit is kV.
Set of shorthand feedback signals si＝[αi,βi]TUser load of each node zi＝[pi,qi]TIn kW, KVar.
The calculation process that each node user needs to adjust according to the feedback signal in the precise optimization step is as follows:
Figure BDA00022892292200000810
the calculation process required by the dispatching center of the power grid operator in the precise optimization step is as follows:
Figure BDA00022892292200000811
Figure BDA00022892292200000812
Figure BDA00022892292200000813
Figure BDA00022892292200000814
wherein phi>0 is a predefined constant parameter, ε1And ε2The step size is represented as a function of time,μandare intermediate variables that facilitate calculation and formulation. The voltage and the user load value are obtained through measurement and communication, and k is the iteration number of the single optimization problem solving process. The above algorithm iterates until the iteration result converges to a certain predefined range, or the iteration number k is set in advance.
The principle of the curve fitting and prediction process involved in the step (4) is as follows:
firstly, using the accurate optimization result m times before the current moment to perform polynomial curve fitting, setting a fitting curve equation as an n-order polynomial, wherein n can be adjusted according to the selection of m. Polynomial of order n is
y＝anxn+an-1xn-1+...+a1x+a0(14)
The abscissa of the curve is time, and the vector is written
X＝[1,2,...,m](15)
The ordinate of the curve is the accurate optimization result of the previous m times, and the curve is written as a vector
Y＝[y1,y2,...,ym](16)
Expressed in matrix form as
Y＝X0A (17)
Wherein
Figure BDA0002289229220000091
A＝[an,an-1,...,a0]T(19)
Formula (17) is equivalent to:
namely:
Figure BDA0002289229220000093
obtaining a coefficient A to obtain a fitted curve, then arbitrarily taking x epsilon (m, m +1) to carry in (14), and calculating the time period tnowTo tnowAnd + T, analyzing and predicting the result based on the characteristic rule of the preorder optimization result.
The invention analyzes by combining the characteristic rule of the optimization point, and saves the communication and management cost by using the inertia of control and scheduling on the premise of keeping the accuracy and rapidity of control and optimization.
Compared with the prior art, the invention has the following characteristics:
1. the characteristic rule analysis technology for the uncertain power and distributed energy optimization control extracts the numerical rule between the uncertain power such as renewable energy, load and the like and the distributed energy optimization control result based on the input of the optimization problem and the sequence optimization result, and is convenient for analyzing the distributed energy optimization and control behavior in the power grid.
2. And deducing a local optimization control strategy of the distributed energy based on the uncertainty power and the characteristic rule analysis result of the distributed energy optimization control, and saving communication and management cost of a scheduling party of the power grid without centralized optimization and scheduling.
3. And finally, analyzing the characteristic rule of the uncertain power and distributed energy optimization control to improve the operation efficiency of the power grid and ensure the maximum benefit of a distributed energy owner.
The foregoing is only a preferred embodiment of the present invention, and it will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements are also considered to be within the scope of the present invention.

Claims (7)

1. A distributed energy optimization control method in a power grid with uncertain power is characterized by comprising the following steps:
step 1, optimizing and calculating initial parameters;
step 2, collecting information of each distributed power supply access user, and performing optimization calculation by adopting a distributed algorithm according to the requirement of a target function;
step 3, adjusting the power generation amount of the local distributed power supply according to the optimization result to obtain an accurate optimization result;
and 4, fitting a change curve of the local optimal point according to the accurate optimization result, calculating a predicted value of the optimal point according to the characteristic trend of the curve, and performing local adjustment without scheduling according to the predicted value.
2. The method according to claim 1, wherein in step 1, the initial parameters include line parameters of the power grid, the distributed energy power regulation range of each distributed power access node, the voltage desired control range of each distributed power access node, parameters of the cost function for distributed power access, and parameters of the voltage control function.
3. The method as claimed in claim 2, wherein the step 2 and the step 3 are performed every T seconds.
4. The method according to claim 3, wherein in step 4, at the current t, the distributed energy resource optimization control method is performednowAt the moment, fitting t according to the accurate optimization results of the previous m timesnow-m × T to TnowThe variation curve of the local optimal point in the time period is calculated according to the characteristic trend of the curvenowTo tnowPredicting the optimal point in the + T time period according to the predicted value at TnowTo tnowLocal adjustment without scheduling is performed within the + T period.
5. The method for optimizing and controlling the distributed energy resources in the power grid with uncertainty according to any one of claims 1-4, characterized in that the specific process of the step 2 is as follows:
an electrical network having N +1 nodes, denoted by the set NU {0}, where N: {1, …, N }, node 0 denotes a common coupling node or substation, and the network lines are denoted by the set epsilon; vi tE C represents the line-to-ground voltage of the node i at the time t and defines
Figure FDA0002289229210000011
Definition of
Figure FDA0002289229210000012
And
Figure FDA0002289229210000013
are respectively a positionActive power and reactive power injected by one distributed power supply at a node i;
definition ofThe feasible set of active power and reactive power injected by the distributed power supply at the node i at the moment t; definition of
Figure FDA0002289229210000022
η is defined for the maximum active power of a distributed energy system at time tiApparent rated capacity for the distributed energy system;
volumetric aggregation for distributed energy systems
Figure FDA0002289229210000023
Comprises the following steps:
Figure FDA0002289229210000024
considering a cost function
Figure FDA0002289229210000025
And
Figure FDA0002289229210000026
respectively representing feedback signals of active power and reactive power generated by a power grid operator at time t;
the objective function is as follows:
Figure FDA0002289229210000027
Figure FDA0002289229210000028
defining:
Figure FDA0002289229210000029
consider another cost function
Figure FDA00022892292100000215
The function represents the cost corresponding to the voltage offset at time t;
the following objective functions can pursue the optimal objectives of both the user side and the power grid operator:
Figure FDA00022892292100000210
Figure FDA00022892292100000211
Figure FDA00022892292100000212
Figure FDA00022892292100000213
wherein, γt∈R+The parameter is a parameter for balancing between a user target and an operator target, and is dimensionless; v. oftAnd
Figure FDA00022892292100000214
is a vector representing the upper and lower voltage limits; r and X are active and reactive power coefficient matrixes in a linear power flow algorithm of the power grid, and a is a constant;
set of shorthand feedback signals si＝[αi,βi]TUser load of each node zi＝[pi,qi]T。
6. The method for optimizing and controlling the distributed energy resources in the power grid with uncertainty according to any one of claims 1-4, characterized in that the specific process of the step 3 is as follows:
the calculation process that each node user needs to adjust according to the feedback signal in the precise optimization step is as follows:
Figure FDA0002289229210000031
the calculation process required by the dispatching center of the power grid operator in the precise optimization step is as follows:
Figure FDA0002289229210000032
Figure FDA0002289229210000033
Figure FDA0002289229210000034
Figure FDA0002289229210000035
wherein phi>0 is a predefined constant parameter, ε1And ε2The step size is represented as a function of time,μand
Figure FDA0002289229210000036
are intermediate variables that facilitate calculation and formulation; the voltage and the user load value are obtained through measurement and communication, and k is the iteration number of the single optimization problem solving process.
7. The method for optimizing and controlling the distributed energy in the power grid with uncertainty according to any one of claims 1-4, characterized in that the specific process of the step 4 is as follows:
firstly, using the accurate optimization result m times before the current moment to perform polynomial curve fitting, setting a fitting curve equation as an n-order polynomial, and adjusting n according to the selection of m;
the n-th order polynomial is:
y＝anxn+an-1xn-1+...+a1x+a0(14)
the abscissa of the curve is time, and the vector is written as:
X＝[1,2,...,m](15)
the ordinate of the curve is the accurate optimization result of the previous m times, and the writing vector is as follows:
Y＝[y1,y2,...,ym](16)
expressed in matrix form as:
Y＝X0A (17)
wherein the content of the first and second substances,
A＝[an,an-1,...,a0]T(19)
formula (17) is equivalent to:
Figure FDA0002289229210000042
namely:
obtaining a coefficient A to obtain a fitted curve, then arbitrarily taking x epsilon (m, m +1) to carry in (14), and calculating the time period tnowTo tnowAnd + T, analyzing and predicting the result based on the characteristic rule of the preorder optimization result.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150311713A1 (en) * 2014-04-28 2015-10-29 Nec Laboratories America, Inc. Service-based Approach Toward Management of Grid-Tied Microgrids
CN105515006A (en) * 2016-01-20 2016-04-20 上海电力学院 Micro-grid multi-master-slave mixed control method based on improved droop control
US20170214244A1 (en) * 2016-01-27 2017-07-27 Alliance For Sustainable Energy, Llc Real time voltage regulation through gather and broadcast techniques
CN107453407A (en) * 2017-07-03 2017-12-08 东华大学 A kind of intelligent micro-grid distributed energy dispatching method
CN108875247A (en) * 2018-06-29 2018-11-23 国网山东省电力公司电力科学研究院 Distributed photovoltaic power power fast Optimization in a kind of power distribution network
CN109361237A (en) * 2018-11-30 2019-02-19 国家电网公司西南分部 Based on the micro-capacitance sensor capacity configuration optimizing method for improving Hybrid Particle Swarm

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150311713A1 (en) * 2014-04-28 2015-10-29 Nec Laboratories America, Inc. Service-based Approach Toward Management of Grid-Tied Microgrids
CN105515006A (en) * 2016-01-20 2016-04-20 上海电力学院 Micro-grid multi-master-slave mixed control method based on improved droop control
US20170214244A1 (en) * 2016-01-27 2017-07-27 Alliance For Sustainable Energy, Llc Real time voltage regulation through gather and broadcast techniques
CN107453407A (en) * 2017-07-03 2017-12-08 东华大学 A kind of intelligent micro-grid distributed energy dispatching method
CN108875247A (en) * 2018-06-29 2018-11-23 国网山东省电力公司电力科学研究院 Distributed photovoltaic power power fast Optimization in a kind of power distribution network
CN109361237A (en) * 2018-11-30 2019-02-19 国家电网公司西南分部 Based on the micro-capacitance sensor capacity configuration optimizing method for improving Hybrid Particle Swarm

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
WENBO LI等: "Fast Optimal Dispatch of Renewable Energy", 《IOP CONFERENCE SERIES: EARTH AND ENVIRONMENTAL SCIENCE》 *
赵中原: "基于事件驱动的二次凸优化问题分布式优化算法", 《控制与决策》 *

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