CN111369386B - Smart grid demand side management method based on synchronization algorithm - Google Patents

Smart grid demand side management method based on synchronization algorithm Download PDF

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CN111369386B
CN111369386B CN202010138570.2A CN202010138570A CN111369386B CN 111369386 B CN111369386 B CN 111369386B CN 202010138570 A CN202010138570 A CN 202010138570A CN 111369386 B CN111369386 B CN 111369386B
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陈征
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Abstract

The invention relates to a smart grid demand side management method based on a synchronization algorithm, which is characterized by comprising the following steps: establishing a power supply side target electric quantity model and setting a constraint condition I; establishing a demand response aggregation target electric quantity model, and setting a constraint condition II; establishing a user target electric quantity model and setting a constraint condition III; and establishing a demand side management model based on the power supply side target electric quantity model, the demand response aggregation side target electric quantity model and the user target electric quantity model, and carrying out optimization solution under a set constraint condition X to realize the optimal allocation of the resources of three parties. The invention considers the power supply party, the demand response aggregation party and the user party simultaneously, and realizes the optimal allocation of the resources of the three parties through the synchronization algorithm, thereby being more accurate.

Description

Smart grid demand side management method based on synchronization algorithm
Technical Field
The invention relates to a smart grid demand side management method based on a synchronization algorithm, and relates to the technical field of smart grids.
Background
In the smart grid field, demand side management is beneficial to improving electricity consumption and improving reliability of the whole grid, and there are two methods for demand side management: first, incentive-based demand side management; and secondly, time-based demand side management.
At present, research on demand side management is very hot, but some problems of the prior art are still unresolved. For example, how to give an efficient strategy to achieve optimal resource allocation for the power supply side, the demand response aggregator side, and the user side, a common approach is to convert multi-objective optimization into single-objective optimization, although converting the multi-objective optimization problem into single-objective optimization problem facilitates solution. However, when single-objective optimization is performed, the objective of each objective optimization is contradictory to a great extent, and conversion into single-objective optimization is actually a compromise optimization on each objective, so that the optimization result is inaccurate.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a smart grid demand side management method based on a synchronization algorithm, which can realize the optimal resource allocation of a power supply party, a demand response aggregation party and a user party through the technology provided by the synchronization algorithm.
In order to achieve the above purpose, the present invention adopts the following technical scheme: a smart grid demand side management method based on a synchronization algorithm comprises the following steps:
establishing a power supply side target electric quantity model and setting a constraint condition I;
establishing a demand response aggregation target electric quantity model, and setting a constraint condition II;
establishing a user target electric quantity model and setting a constraint condition III;
and establishing a demand side management model based on the power supply side target electric quantity model, the demand response aggregation side target electric quantity model and the user target electric quantity model, and carrying out optimization solution under a set constraint condition X to realize the optimal allocation of the resources of three parties.
Further, the method is characterized in that the power supply side target electric quantity model and the constraint condition I are respectively as follows:
Figure BDA0002398206500000011
constraint I:
Figure BDA0002398206500000021
0≤μ＜1
Figure BDA0002398206500000022
in the formula g c Vector representing the power generation of daily conventional energy, c 1 Represents the electricity generation price of the traditional energy after the management of the application requirement side, c 0 Represents the electricity generation price of the traditional energy before the management of the application demand side, t is a certain moment, mu represents the rewarding coefficient,
Figure BDA0002398206500000023
representation->
Figure BDA0002398206500000024
Is the minimum of (2); />
Figure BDA0002398206500000025
Representation->
Figure BDA0002398206500000026
Maximum value of>
Figure BDA0002398206500000027
Is the generated energy of the traditional energy source at the time t.
Further, the demand response aggregation target electric quantity model and the constraint condition II are as follows:
Figure BDA0002398206500000028
constraint II:
Figure BDA0002398206500000029
Figure BDA00023982065000000210
Figure BDA00023982065000000211
in the formula g c Generating capacity vector x representing daily traditional energy 1 Representing the aggregate consumption vector,
Figure BDA00023982065000000212
representation->
Figure BDA00023982065000000213
Minimum value->
Figure BDA00023982065000000214
Representation->
Figure BDA00023982065000000215
Alpha and beta are compensation coefficients, +.>
Figure BDA00023982065000000216
Representation->
Figure BDA00023982065000000217
Minimum value->
Figure BDA00023982065000000218
Representation->
Figure BDA00023982065000000219
T is the maximum value of a set period of time (e.g. 24 hours),. About.>
Figure BDA00023982065000000220
The power is consumed for aggregation at time t.
Further, the user target electric quantity model and the constraint condition III are specifically:
Figure BDA00023982065000000221
constraint III: alpha > 0
β＞0
ε＞0
Figure BDA00023982065000000222
Figure BDA00023982065000000223
Figure BDA00023982065000000224
Wherein α and β are compensation coefficients;
Figure BDA00023982065000000225
representation->
Figure BDA00023982065000000226
Is the minimum of (2); />
Figure BDA00023982065000000227
Representation->
Figure BDA00023982065000000228
Is the maximum value of (2); epsilon representsThe inelastic coefficient, W, is a reference value for the total power consumption of the user in one day.
Further, the demand side management model and the constraint condition X are:
Min：y＝f(g c ,x 1 )＝(f u (g c ),-f a (g c ,x 1 ),-f c (x 1 ))
constraint X: satisfying constraints I, II, III.
Furthermore, the solving method for realizing the optimal allocation of the resources of the three parties adopts an MO-SOO algorithm.
The invention adopts the technical proposal and has the following characteristics: for a power supply party, the invention provides an optimal strategy, and can give an optimal daily conventional energy generating capacity vector, thereby realizing the optimization target of enterprises; for a demand response aggregator, the invention provides a strategy for arranging the power generation quantity vector and the aggregate consumption vector of daily traditional energy sources, so as to realize the target requirement of the demand response aggregator and greatly reduce the operation time and the space complexity; for the user side, how to give an optimal strategy of generating capacity vector and gathering consumption vector of daily traditional energy, thereby realizing benefit maximization of the user. Therefore, the power supply party, the demand response aggregation party and the user party are considered at the same time, and the optimal allocation of the resources of the three parties is realized through the synchronization algorithm, so that the method is more accurate.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
For convenience of description, the present invention first defines parameters:
Figure BDA0002398206500000031
a power generation amount vector representing daily conventional energy; />
Figure BDA0002398206500000032
c 1 Representing the electricity generation price of the conventional energy source after the application of DSM (demand side management); c 0 Representing the price of electricity generated using the traditional energy source prior to the use of the DSM; mu represents a reward coefficient; />
Figure BDA0002398206500000033
Representation->
Figure BDA0002398206500000034
Is the minimum of (2);
Figure BDA0002398206500000035
representation->
Figure BDA0002398206500000036
Is the maximum value of (2); />
Figure BDA0002398206500000037
Representing aggregate consumption vectors; alpha and beta are compensation coefficients; />
Figure BDA0002398206500000038
Representation->
Figure BDA0002398206500000039
Is the minimum of (2); />
Figure BDA00023982065000000310
Representation->
Figure BDA00023982065000000311
Is the maximum value of (2); epsilon represents the inelastic coefficient, +.>
Figure BDA00023982065000000312
Representing a desired power generation amount vector, < >>
Figure BDA00023982065000000313
Representing the amount of electricity generated by a renewable energy source, < >>
Figure BDA00023982065000000314
Indicating the reference aggregate demand at time interval t,/->
Figure BDA00023982065000000315
And (5) the power consumption is aggregated at the time interval t, and W is a reference value of the total power consumption of the user in one day.
Based on the set parameters, the intelligent power grid demand side management method based on the synchronization algorithm provided by the embodiment comprises the following specific processes:
1. establishing a power supply side target electric quantity model
Specifically, the power supply target electric quantity model is an optimal compensation scheme for giving the optimal power generation amount of the traditional energy source in each time period t and the demand response gathering party in each time period t, and the established power supply target electric quantity model is optimized under constraint conditions, wherein the optimization process is as follows:
Figure BDA00023982065000000316
constraint I:
Figure BDA00023982065000000317
0≤μ＜1
Figure BDA0002398206500000041
2. establishing a target electric quantity model of a demand response aggregator
Specifically, the electric quantity model of the demand response aggregator is to give the compensation of the power supply side and the compensation of the user side in each time period t, and optimize the established target electric quantity model under the constraint condition, wherein the specific optimization process is as follows:
Figure BDA0002398206500000042
constraint II:
Figure BDA0002398206500000049
Figure BDA0002398206500000043
Figure BDA0002398206500000044
3. establishing a target electric quantity model of a user
Specifically, the target electric quantity model of the user gives the optimal satisfaction degree of the user and the optimal demand response aggregator compensation in each time period t, and optimizes the established target model under the constraint condition, wherein the optimization process is as follows:
Figure BDA0002398206500000045
constraint III: alpha > 0
β＞0
ε＞0
Figure BDA0002398206500000046
Figure BDA0002398206500000047
Figure BDA0002398206500000048
4. And establishing a demand side management model based on the power supply side target electric quantity model, the target electric quantity model of the demand response aggregator and the target electric quantity model of the user, and carrying out optimization solution under constraint conditions to realize optimal allocation of the resources of the three parties.
Specifically, the demand side management model is:
Min：y＝f(g c ,x 1 )＝(f u (g c ),-f a (g c ,x 1 ),-f c (x 1 ))
constraint conditions: set X satisfying constraints I, II, III
The specific optimization of the three-party resource optimal configuration technology is realized by adopting the existing MO-SOO algorithm to solve the problem that the specific process of the MO-SOO algorithm is as follows:
input: the function f=f (g c ,x 1 )＝f(X)
Search space X, x= (g c ,x 1 )
Dividing a factor K into specified constants;
evaluating a budget v;
maximum depth function h max (t)；
Depth function depth (τ) t )；
Initializing: τ 1 ←{(0,0)}
t←1
And (3) outputting: min x∈X f, approximating the set;
the optimization process is as follows:
when the evaluation budget v is not exhausted, operation continues,
setting V++phi;
for h+.0 to min (h) max (t),depth(τ t ) A) performing the following operation;
P t ζ leaf node at depth h
V←ND(p t ∪V)
Where ND represents a non-dominant operator;
Q t ←p t ∩V
unfolding Q t All nodes in (1), evaluate and compare their K·|Q t Child add to τ t
Figure BDA0002398206500000052
t←t+1
Wherein i is the number of layers;
return to
Figure BDA0002398206500000051
In summary, on the premise of the demand side management model, an optimal power generation amount of the traditional energy source in each time period t and an optimal compensation scheme for the demand response aggregator in each time period t are given, and compensation by the power supply party and compensation by the user party in each time period t, and user optimal satisfaction degree and optimal demand response aggregator compensation in each time period t are given.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the scope of protection thereof, although the present application is described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: various changes, modifications, or equivalents may be made to the particular embodiments of the application by those skilled in the art after reading the present application, but such changes, modifications, or equivalents are within the scope of the claims appended hereto.

Claims (2)

1. A smart grid demand side management method based on a synchronization algorithm is characterized by comprising the following steps:
establishing a power supply side target electric quantity model and setting a constraint condition I;
establishing a demand response aggregation target electric quantity model, and setting a constraint condition II;
establishing a user target electric quantity model and setting a constraint condition III;
establishing a demand side management model based on the power supply side target electric quantity model, the demand response aggregation side target electric quantity model and the user target electric quantity model, and carrying out optimization solution under a set constraint condition X to realize the optimal allocation of the resources of three parties; wherein:
the power supply side target electric quantity model and the constraint condition I are respectively as follows:
Figure FDA0004185338440000011
constraint I:
Figure FDA0004185338440000012
0≤μ＜1
Figure FDA0004185338440000013
in the formula g c A power generation amount vector representing a daily conventional energy source,
Figure FDA0004185338440000014
c 0 represents the price of electricity generation using the traditional energy source before DSM, c 1 Represents the electricity generation price of the traditional energy after the management of the application requirement side, t is the time interval, mu represents the rewarding coefficient,/>
Figure FDA0004185338440000015
Representation->
Figure FDA0004185338440000016
Is the minimum of (2); />
Figure FDA0004185338440000017
Representation->
Figure FDA0004185338440000018
Maximum value of>
Figure FDA0004185338440000019
The generated energy of the traditional energy source at the moment t;
the demand response aggregation target electric quantity model and the constraint condition II are as follows:
Figure FDA00041853384400000110
constraint II:
Figure FDA00041853384400000111
Figure FDA00041853384400000112
Figure FDA00041853384400000113
in the formula g c Generating capacity vector x representing daily traditional energy 1 Representing the aggregate consumption vector,
Figure FDA00041853384400000114
representation->
Figure FDA00041853384400000115
Minimum value->
Figure FDA00041853384400000116
Representation->
Figure FDA00041853384400000117
Alpha and beta are compensation coefficients, +.>
Figure FDA00041853384400000118
Representation->
Figure FDA00041853384400000119
Minimum value->
Figure FDA00041853384400000120
Representation->
Figure FDA00041853384400000121
T is the set time, +.>
Figure FDA00041853384400000122
Consumption of electric energy for aggregation of time interval t g t Representing a desired power generation amount vector;
the user target electric quantity model and the constraint condition III are specifically as follows:
Figure FDA00041853384400000123
constraint III: alpha > 0
β＞0
ε＞0
Figure FDA0004185338440000021
Figure FDA0004185338440000022
Figure FDA0004185338440000023
/>
Wherein α and β are compensation coefficients; x is x 1 Representing aggregate consumption vectors;
Figure FDA0004185338440000024
representation->
Figure FDA0004185338440000025
Is the minimum of (2); />
Figure FDA0004185338440000026
Representation->
Figure FDA0004185338440000027
Is the maximum value of (2); epsilon represents an inelastic coefficient, and W is a reference value of total power consumption of a user in one day; />
Figure FDA0004185338440000028
Representing a reference aggregate demand at time interval t;
the demand side management model and constraint condition X are:
Min：y＝f(g c ,x 1 )＝(f u (g c ),-f a (g c ,x 1 ),-f c (x 1 ))
constraint X: satisfying constraints I, II, III.
2. The intelligent power grid demand side management method based on the synchronization algorithm according to claim 1, wherein the solution method for achieving the optimal allocation of the resources of three parties adopts an MO-SOO algorithm.
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