CN114841448A - Hierarchical and partitioned load optimization regulation and control method based on multi-agent system - Google Patents

Hierarchical and partitioned load optimization regulation and control method based on multi-agent system Download PDF

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CN114841448A
CN114841448A CN202210529222.7A CN202210529222A CN114841448A CN 114841448 A CN114841448 A CN 114841448A CN 202210529222 A CN202210529222 A CN 202210529222A CN 114841448 A CN114841448 A CN 114841448A
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陈淑娇
祁兵
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North China Electric Power University
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Abstract

The invention discloses a layered and partitioned load optimization regulation and control method based on a multi-agent system. On the basis, a multi-objective optimization strategy considering economy and applicability, small environmental pollution and high safety performance is provided, so that the layered and partitioned optimization regulation of the demand-side resources is realized.

Description

Hierarchical and partitioned load optimization regulation and control method based on multi-agent system
Technical Field
The invention belongs to the technical field of demand side management of a power system, and particularly relates to a hierarchical partitioned load optimization regulation and control method based on a multi-agent system.
Background
With the deep development of the power internet of things, massive distributed resources such as combined cooling heating and power supply, flexible load, distributed energy storage and electric vehicles are connected into a novel power system. And new energy sources are receiving more and more attention driven by carbon peak, carbon neutralization targets. However, the new energy power generation has the characteristics of randomness, intermittence and volatility, and after a large-scale new energy is connected into a power system, the supply and demand balance and the frequency stability of a power grid are greatly tested. In response to these problems, conventional power grids lack the ability to handle flexible resource adjustments. In addition, a large amount of distributed resources on a demand side are considered, and the demand side has flexible and adjustable characteristics, but due to the characteristics of zero dispersion, diversity, small capacity, different parameters and the like, the demand side is difficult to directly participate in the operation of the power system. With the deepening and development of the smart power grid, intelligent technologies such as an internet of things technology, artificial intelligence and a modern communication technology are gradually deepened into the power grid, and powerful power is brought to the intelligent development of the smart power grid. The distributed flexible resources with multiple points, wide areas and small capacity are gathered, flexible interaction between users and a large power grid is effectively realized, auxiliary services such as power grid peak regulation, frequency modulation and voltage regulation and electric energy trading are participated, and optimized operation of a novel power load management system is promoted.
The invention provides a hierarchical partitioned load optimization regulation and control method based on a multi-agent system. The physical characteristics of renewable energy, energy storage and controllable load are respectively considered, high-efficiency energy interaction is realized by combining a distributed management and control mode of a multi-agent system, a layered and partitioned dynamic regulation and control framework which is economical and applicable, small in environmental pollution and high in safety performance is designed, and multi-objective optimization control of resources on the demand side is realized through flexible control of different levels.
Disclosure of Invention
The invention discloses a layered and partitioned load optimization regulation and control method based on a multi-agent system. On the basis, a multi-objective optimization strategy considering economy and applicability, small environmental pollution and high safety performance is provided, so that the layered and partitioned optimization regulation of the demand-side resources is realized.
The invention comprises the following steps:
s1: constructing a layered partition dynamic regulation and control framework based on a multi-agent system;
s2: carrying out load characteristic modeling by a terminal equipment layer agent;
s3: the regional control layer agent calculates the load minimization target;
s4: performing multi-target optimization by the cloud control layer agent;
further, the S1 includes the following steps:
s101: construction of hierarchical partition dynamic regulation and control framework based on multi-agent system
A large number of distributed power supplies and controllable loads are gathered through a layered partition architecture, and the loads are various in types and are geographically dispersed. The hierarchical partition dynamic regulation and control framework is partitioned according to a geographical area, and is divided into a cloud management and control layer, an area coordination layer and a terminal equipment layer according to levels, different agents are used as management and control main bodies among the levels, and optimized scheduling among multiple members is cooperatively performed while the benefits of the agents are guaranteed. The terminal equipment layer mainly comprises wind and light energy storage, an electric automobile and various controllable loads. The edge control agent can acquire, store and upload the information of the terminal load nearby and can receive the instruction issued by the superior agent. The regional coordination layer has the optimization coordination function of multiple regions, a regional agent is arranged for each region according to geographical regions, each agent can extract, calculate and aggregate information of load resources, upload the aggregated information to the cloud control layer, receive instruction information issued by the agent on the upper layer, and execute corresponding tasks through task control instructions. The cloud management and control layer carries out cloud management and control on the whole hierarchical partition framework through a cloud control agent, receives a scheduling instruction of a power grid through the butt joint with the power grid, calculates an optimal task allocation scheme by combining a multi-objective optimization control algorithm, and distributes tasks to lower-layer agents, so that global optimization scheduling is realized, and the safe and reliable operation of the whole network is supported.
Further, the S2 includes the following steps:
s201: modeling industrial loads
In the hierarchical partition architecture, the terminal demand-side resources can be roughly divided into an industrial load, a business load, and a residential load. The method comprises the steps of firstly, collecting nearby load information through a marginal Internet of things agent, establishing a basic model of the single load on the basis of the nearby load information, and modeling the characteristics of the single load. The industrial load is an arc furnace as an example, and the load model is as follows:
Figure BDA0003645896700000031
in the formula, t on The arc striking moment when the electric arc furnace is electrified; Δ t up The time interval for the arc furnace to reach the stable rated power moment from the arc striking moment is generally 5-10 s; Δ t down The time length required for shutting down the electric arc furnace until the power of the electric arc furnace is 0 is not more than 10 s; t is t off The moment when the electric arc furnace is completely powered off; p rated Rated power for the operation of the electric arc furnace; delta t Representing random power fluctuations, delta, of the arc furnace in steady state operation t (-δ max ,δ max ) Wherein δ max Usually set at 5% -20%.
S202: the business load is modeled.
The commercial load is mainly the central air conditioner and the lighting load, and the air conditioner operation load model is as follows:
Figure BDA0003645896700000041
in the formula: t is air Indicating an operating period of the air conditioner; Δ t is the period length; p Q The power of the ventilator when the air conditioner is stopped. Alpha is alpha k Representing the load rate of the kth water chilling unit in the time period; p n,k Indicating the rated power of the kth water chilling unit.
The lighting load characteristic model is as follows:
Figure BDA0003645896700000042
in the formula: e e The illuminance of natural light; e b Supplementary illumination for manual illumination; e min Setting the lower limit of the ambient illumination;
Figure BDA0003645896700000043
the electricity consumption for supplementing illumination to the artificial light source needed indoors in the period of t; p LS Power of a single artificial light source; Δ t is the period length; s all Is the total area that needs to be illuminated; t is light Effective working time for the user; k is a constant and can be calculated through historical data.
The power model for a commercial load can be described simply as:
Figure BDA0003645896700000044
s203: the load of the residents is modeled.
And (3) from the time sequence of the resident load, considering the behavior habit of the user, and providing a load state matrix S, wherein the matrix element is composed of the electricity utilization probability of the resident load, and the actual electricity utilization condition of the residents obeys the secondary distribution of S. Different types of load characteristics are integrated and equivalent to a mathematical model of the total load of the power utilization.
Figure BDA0003645896700000051
Figure BDA0003645896700000052
Figure BDA0003645896700000053
Figure BDA0003645896700000054
In the formula (5), n is the number of load types; s is the power utilization probability of the a-th power load in the t-th time period; in the formula (6), P e Representing the a-th load rated power;
Figure BDA0003645896700000055
represents the power consumption of the a-th type in the t period. In the formula (7), the reaction mixture is,
Figure BDA0003645896700000056
representing the electricity consumption of residents; equation (8) represents the total electrical load of all residents.
Power of adjustable load
Figure BDA0003645896700000057
Can be represented by the formula (9):
Figure BDA0003645896700000058
and S204, photovoltaic output model.
Figure BDA0003645896700000059
In the formula:
Figure BDA00036458967000000510
the output power represents the photovoltaic output at the t moment; eta represents the conversion efficiency of the photovoltaic inverter; p s Rated power of the photovoltaic panel under standard conditions; r s Represents the solar irradiance at time t; g a0 Represents irradiance at standard conditions; t is t Means the temperature of the assembly at time t; t is s Representing a temperature of 25 ℃ in the standard state; n is a radical of T And represents a photovoltaic power generation temperature coefficient.
Further, the S3 includes the following steps:
in a hierarchical partition control architecture based on a multi-agent system, a regional control layer can assist a cloud control agent in achieving a target through coordination and cooperation. Here, the calculation is performed with the objective of minimizing economic cost, minimizing environmental pollution, and minimizing network loss, respectively.
S301: economic cost minimization
Q 1 ＝min{C d +C o +C en } (11)
Figure BDA0003645896700000061
Figure BDA0003645896700000062
C en ＝∑c i P i t,buy Δt (14)
The formula (11) represents Q in order to minimize economic cost 1 The depreciation cost C is required d And operation and maintenance cost C o Energy cost C en The sum of (a) and (b) is minimal. In formula (12): c represents the energy purchase price per unit power; j represents a terminal energy device type; i represents the energy type, r represents the annual interest rate, and is taken as 6%; y refers to the estimated service life of the equipment; in formula (13): beta represents the unit operation and maintenance cost, P t,out Refers to the output power of the device at time t; in formula (14): p t,buy Refers to the energy power purchased at time t.
S302: environmental cost minimization
Figure BDA0003645896700000063
In the formula: v. of i,j The emission amount of a pollutant j representing the unit power generation amount of the distributed energy source in the ith; l j Representing the environmental remediation cost for contaminant j.
S303 network loss minimization
Figure BDA0003645896700000071
In the formula: n represents the number of nodes, T ij Represents the line conductance between connecting nodes i and j; u. of i 、u j Represents the voltage between the maters i and j; theta ij Representing the phase angle difference.
Further, the S4 includes the following steps:
s401: different objective functions are uploaded to a cloud control agent according to the regional agents of the regional control layer, the cloud control agent performs comprehensive calculation processing according to different sub-objective functions, and the single objective function can be changed into the single objective function through a weighting method.
The sub-objective functions can be generally divided into functions for calculating the maximum value and the minimum value, and normalization processing is performed on the functions:
Figure BDA0003645896700000072
the comprehensive cost of the objective function solved by the cloud control layer agent can be obtained through the processing:
Figure BDA0003645896700000073
Figure BDA0003645896700000074
in the formula:
Figure BDA0003645896700000075
is a weight value.
Therefore, a multi-objective optimization model for minimizing economic cost, minimizing environmental pollution and minimizing network loss is obtained, an optimal control strategy can be obtained through calculation and solution of the model, and suggestions are provided for optimization regulation and control based on a multi-agent control hierarchical partition framework.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the description of the embodiments or technical solutions will be briefly described below.
FIG. 1 is a flow chart of hierarchical partitioned load optimization regulation based on a multi-agent system according to the present invention;
FIG. 2 is a hierarchical partitioned load optimization regulation and control architecture based on a multi-agent system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 2, in the hierarchical and partitioned load optimization regulation and control method based on the multi-agent system, a hierarchical and partitioned dynamic regulation and control architecture is designed, and a multi-objective optimization strategy considering economic applicability, low environmental pollution and high safety performance is provided, so that hierarchical and partitioned optimization regulation and control of demand-side resources is realized.
The invention comprises the following steps:
1: constructing a layered partition dynamic regulation and control framework based on a multi-agent system;
2: carrying out load characteristic modeling by a terminal equipment layer agent;
3: performing load minimization target calculation by the regional control layer agent;
4: performing multi-target optimization by the cloud control layer agent;
in this embodiment, the step 1 is implemented by the following preferred scheme:
1-1, constructing a hierarchical and partitioned dynamic regulation and control framework based on a multi-agent system
A large number of distributed power supplies and controllable loads are gathered through a layered partition architecture, and the loads are various in types and are geographically dispersed. The hierarchical partition dynamic regulation and control framework is partitioned according to a geographical area, and is divided into a cloud management and control layer, an area coordination layer and a terminal equipment layer according to levels, different agents are used as management and control main bodies among the levels, and optimized scheduling among multiple members is cooperatively performed while the benefits of the agents are guaranteed. The terminal equipment layer mainly comprises wind and light energy storage, an electric automobile and various controllable loads. The edge control agent can acquire, store and upload the information of the terminal load nearby and can receive the instruction issued by the superior agent. The regional coordination layer has the optimization coordination function of multiple regions, a regional agent is arranged for each region according to geographical regions, each agent can extract, calculate and aggregate information of load resources, upload the aggregated information to the cloud control layer, receive instruction information issued by the agent on the upper layer, and execute corresponding tasks through task control instructions. The cloud management and control layer carries out cloud management and control on the whole layered partition framework through a cloud control agent, receives a scheduling instruction of the power grid through butt joint with the power grid, calculates an optimal task allocation scheme by combining a multi-objective optimization control algorithm, and distributes tasks to lower-layer agents, so that overall optimization scheduling is realized, and the safe and reliable operation of the whole network is supported.
In this embodiment, the step 2 is implemented by the following preferred scheme:
2-1 modeling Industrial loads
In the hierarchical partition architecture, the terminal demand-side resources can be roughly divided into an industrial load, a business load, and a residential load. The method comprises the steps of firstly, collecting nearby load information through a marginal Internet of things agent, establishing a basic model of the single load on the basis of the nearby load information, and modeling the characteristics of the single load. The industrial load is an arc furnace as an example, and the load model is as follows:
Figure BDA0003645896700000101
in the formula, t on The arc striking moment when the electric arc furnace is electrified; Δ t up The time interval for the arc furnace to reach the stable rated power moment from the arc striking moment is generally 5-10 s; Δ t down The time required for shutting down the electric arc furnace until the electric arc furnace power is 0 is not more than 10 s; t is t off The moment when the electric arc furnace is completely powered off; p rated Rated power for the operation of the electric arc furnace; delta t Representing random power fluctuations, delta, of the arc furnace in steady state operation t (-δ max ,δ max ) Wherein δ max Usually set at 5% -20%.
2-2, modeling the business load.
The commercial load is mainly the central air conditioner and the lighting load, and the air conditioner operation load model is as follows:
Figure BDA0003645896700000102
in the formula: t is air Indicating an operating period of the air conditioner; Δ t is the period length; p Q The power of the ventilator when the air conditioner is stopped. Alpha is alpha k Representing the load rate of the kth water chilling unit in the time period; p n,k Indicating the rated power of the kth water chilling unit.
The lighting load characteristic model is as follows:
Figure BDA0003645896700000103
in the formula: e e The illuminance of natural light; e b Supplementary illumination for manual illumination; e min Setting the lower limit of the ambient illumination;
Figure BDA0003645896700000104
the electricity consumption for supplementing illumination to the artificial light source needed indoors in the period of t; p LS Power of a single artificial light source; Δ t is the period length; s all Total area to be illuminated; t is light Effective working time for the user; k is a constantAnd the calculation can be carried out through historical data.
The power model for a commercial load can be described simply as:
Figure BDA0003645896700000111
and 2-3, modeling the load of residents.
And (3) from the time sequence of the resident load, considering the behavior habit of the user, and providing a load state matrix S, wherein the matrix element is composed of the electricity utilization probability of the resident load, and the actual electricity utilization condition of the residents obeys the secondary distribution of S. Different types of load characteristics are integrated and equivalent to a mathematical model of the total load of the power utilization.
Figure BDA0003645896700000112
Figure BDA0003645896700000113
Figure BDA0003645896700000114
Figure BDA0003645896700000115
In the formula (5), n is the number of load types; s is the power utilization probability of the a-th power load in the t-th time period;
in the formula (6), P e Representing the a-th load rated power;
Figure BDA0003645896700000116
indicating the power consumption of the a-th type in the t period.
In the formula (7), the reaction mixture is,
Figure BDA0003645896700000117
to indicate residentsUsing electric power; the formula (8) represents the total electrical load of all residents.
Power of adjustable load
Figure BDA0003645896700000121
Can be represented by the formula (9):
Figure BDA0003645896700000122
2-4, photovoltaic output model.
Figure BDA0003645896700000123
In the formula:
Figure BDA0003645896700000124
the output power represents the photovoltaic output at the t moment; eta represents the conversion efficiency of the photovoltaic inverter; p s Rated power of the photovoltaic panel under standard conditions; r s Represents the solar irradiance at time t; g a0 Represents irradiance at standard conditions; t is t Means the temperature of the assembly at time t; t is s Representing a temperature of 25 ℃ in the standard state; n is a radical of T And represents a photovoltaic power generation temperature coefficient.
In this embodiment, the step 3 is implemented by the following preferred scheme:
in a layered partition architecture based on a multi-agent system, a regional control layer can assist a cloud control agent to achieve a target through coordination and cooperation. Here, the calculation is performed with the objective of minimizing economic cost, minimizing environmental pollution, and minimizing network loss, respectively.
3-1, minimization of economic cost
Q 1 ＝min{C d +C o +C en } (11)
Figure BDA0003645896700000125
Figure BDA0003645896700000126
C en ＝∑c i P i t,buy Δt (14)
The formula (11) represents Q in order to minimize economic cost 1 The depreciation cost C is required d And operation and maintenance cost C o Energy cost C en The sum of (c) is minimal. In formula (12): c represents the energy purchase price per unit power; j represents a terminal energy device type; i represents the energy type, r represents the annual interest rate, and is taken as 6%; y refers to the estimated service life of the equipment; in formula (13): beta represents the unit operation and maintenance cost, P t,out Refers to the output power of the device at time t; in formula (14): p t,buy Refers to the energy power purchased at time t.
3-2, minimization of environmental costs
Figure BDA0003645896700000131
In the formula: v. of i,j The emission amount of a pollutant j representing the unit power generation amount of the distributed energy source in the ith; l j Representing the environmental remediation cost for contaminant j.
3-3, network loss minimization
Figure BDA0003645896700000132
In the formula: n represents the number of nodes, T ij Represents the line conductance between connecting nodes i and j; u. of i 、u j Represents the voltage between the maters i and j; theta ij Representing the phase angle difference.
In this embodiment, the step 4 is implemented by adopting the following preferred scheme:
4-1, uploading different objective functions to a cloud control agent according to a regional agent of a regional control layer, and performing comprehensive calculation processing by the cloud control agent according to different sub-objective functions to change the objective functions into single objective functions by a weighting method.
The sub-objective functions can be generally divided into functions for calculating the maximum value and the minimum value, and normalization processing is performed on the functions:
Figure BDA0003645896700000133
the comprehensive cost of the objective function solved by the cloud control layer agent can be obtained through the processing:
Figure BDA0003645896700000134
Figure BDA0003645896700000135
in the formula:
Figure BDA0003645896700000136
is a weight value.
Therefore, a multi-objective optimization model for minimizing economic cost, minimizing environmental pollution and minimizing network loss is obtained, an optimal control strategy can be obtained through calculation and solution of the model, and suggestions are provided for optimization regulation and control based on a multi-agent control hierarchical partition framework.

Claims (5)

1. A hierarchical and partitioned load optimization regulation and control method based on a multi-agent system is characterized by comprising the following steps:
s1: constructing a layered partition dynamic regulation and control framework based on a multi-agent system;
s2: carrying out load characteristic modeling by a terminal equipment layer agent;
s3: performing load minimization target calculation by the regional control layer agent;
s4: and performing multi-objective optimization by the cloud control layer agent.
2. The multi-agent system-based hierarchical partition load optimization regulation and control method according to claim 1, wherein the S1 comprises the following steps:
s101: construction of hierarchical partition dynamic regulation and control framework based on multi-agent system
A large number of distributed power supplies and controllable loads are gathered through a layered partition architecture, and the loads are various in types and are geographically dispersed. The hierarchical partition dynamic regulation and control framework is partitioned according to a geographical area, and is divided into a cloud management and control layer, an area coordination layer and a terminal equipment layer according to levels, different agents are used as management and control main bodies among the levels, and optimized scheduling among multiple members is cooperatively performed while the benefits of the agents are guaranteed. The terminal equipment layer mainly comprises wind and light energy storage, an electric automobile and various controllable loads. The edge control agent can acquire, store and upload the information of the terminal load nearby and can receive the instruction issued by the superior agent. The regional coordination layer has the optimization coordination function of multiple regions, a regional agent is arranged for each region according to geographical regions, each agent can extract, calculate and aggregate information of load resources, upload the aggregated information to the cloud control layer, receive instruction information issued by the agent on the upper layer, and execute corresponding tasks through task control instructions. The cloud management and control layer carries out cloud management and control on the whole layered partition framework through a cloud control agent, receives a scheduling instruction of the power grid through butt joint with the power grid, calculates an optimal task allocation scheme by combining a multi-objective optimization control algorithm, and distributes tasks to lower-layer agents, so that overall optimization scheduling is realized, and the safe and reliable operation of the whole network is supported.
3. The multi-agent system-based hierarchical partition load optimization regulation and control method according to claim 1, wherein the S2 comprises the following steps:
s201: modeling industrial loads
In the hierarchical partition architecture, the terminal demand-side resources can be roughly divided into an industrial load, a business load, and a residential load. The method comprises the steps of firstly, collecting nearby load information through an edge control agent, establishing a basic model of the single load on the basis of the nearby load information, and modeling the characteristics of the single load. The industrial load is an arc furnace as an example, and the load model is as follows:
Figure FDA0003645896690000021
in the formula, t on The arc striking moment when the electric arc furnace is electrified; Δ t up The time interval for the arc furnace to reach the stable rated power moment from the arc striking moment is generally 5-10 s; Δ t down The time length required for shutting down the electric arc furnace until the power of the electric arc furnace is 0 is not more than 10 s; t is t off The moment when the electric arc furnace is completely powered off; p rated Rated power for the operation of the electric arc furnace; delta t Representing random power fluctuations, delta, of the arc furnace in steady state operation t (-δ max ,δ max ) Wherein δ max Usually set at 5% -20%.
S202: the business load is modeled.
The commercial load is mainly the central air conditioner and the lighting load, and the air conditioner operation load model is as follows:
Figure FDA0003645896690000022
in the formula: t is air Indicating an operating period of the air conditioner; Δ t is the period length; p Q The power of the ventilator when the air conditioner is stopped. Alpha is alpha k Representing the load rate of the kth water chilling unit in the time period; p n,k Indicating the rated power of the kth water chilling unit.
The lighting load characteristic model is as follows:
Figure FDA0003645896690000031
in the formula: e e The illuminance of natural light; e b Supplementary illumination for manual illumination; e min Setting the lower limit of the ambient illumination;
Figure FDA0003645896690000032
the electricity consumption for supplementing illumination to the artificial light source needed indoors in the period of t; p LS Power of a single artificial light source; Δ t is the period length; s. the all Total area to be illuminated; t is light Effective working time for the user; k is a constant and can be calculated through historical data.
The power model for a commercial load can be described simply as:
Figure FDA0003645896690000033
s203: the load of the residents is modeled.
And (3) from the time sequence of the resident load, considering the behavior habit of the user, and providing a load state matrix S, wherein the matrix element is composed of the electricity utilization probability of the resident load, and the actual electricity utilization condition of the residents obeys the secondary distribution of S. Different types of load characteristics are integrated and equivalent to a mathematical model of the total load of the power utilization.
Figure FDA0003645896690000034
Figure FDA0003645896690000041
Figure FDA0003645896690000042
Figure FDA0003645896690000043
In the formula (5), n is the number of load types; s is the power utilization probability of the a-th power load in the t-th time period; in the formula (6), P e Representing the a-th load rated power;
Figure FDA0003645896690000044
representing the electric power used in the t period of the a-th seed; in the formula (7), the reaction mixture is,
Figure FDA0003645896690000045
representing the electricity consumption of residents; the formula (8) represents the total electrical load of all residents.
Power of adjustable load
Figure FDA0003645896690000046
Can be represented by the formula (9):
Figure FDA0003645896690000047
and S204, photovoltaic output model.
Figure FDA0003645896690000048
In the formula:
Figure FDA0003645896690000049
the output power represents the photovoltaic output at the t moment; eta represents the conversion efficiency of the photovoltaic inverter; p s Rated power of the photovoltaic panel under standard conditions; r s Represents the solar irradiance at time t; g a0 Represents irradiance at standard conditions; t is a unit of t Means the temperature of the assembly at time t; t is s Representing a temperature of 25 ℃ in the standard state; n is a radical of hydrogen T And represents a photovoltaic power generation temperature coefficient.
4. The multi-agent system-based hierarchical partition load optimization regulation and control method according to claim 1, wherein the S3 comprises the following steps:
in a layered partition architecture based on a multi-agent system, a regional control layer can assist a cloud control agent to achieve a target through coordination and cooperation. Here, the calculation is performed with the objective of minimizing economic cost, minimizing environmental pollution, and minimizing network loss, respectively.
S301: economic cost minimization
Q 1 ＝min{C d +C o +C en } (11)
Figure FDA0003645896690000051
Figure FDA0003645896690000052
C en ＝∑c i P i t,buy Δt (14)
The formula (11) represents Q in order to minimize economic cost 1 The depreciation cost C is required d And operation and maintenance cost C o Energy cost C en The sum of (a) and (b) is minimal. In formula (12): c represents the energy purchase price per unit power; j represents the terminal energy equipment type; i represents the energy type, r represents the annual interest rate, and is taken as 6%; y refers to the estimated service life of the equipment; in formula (13): beta represents the unit operation and maintenance cost, P t ,out Refers to the output power of the device at time t; in formula (14): p t,buy Refers to the energy power purchased at time t.
S302: environmental cost minimization
Figure FDA0003645896690000053
In the formula: v. of i,j The emission amount of a pollutant j representing the unit power generation amount of the distributed energy source in the ith; l j Representing the environmental remediation cost for contaminant j.
S303, minimizing network loss
Figure FDA0003645896690000054
In the formula: n represents the number of nodes, T ij Represents the line conductance between connecting nodes i and j; u. of i 、u j Represents the voltage between the maters i and j; theta ij Representing the phase angle difference.
5. The multi-agent system-based hierarchical partition load optimization regulation and control method according to claim 1, wherein the S4 comprises the following steps:
s401: different objective functions are uploaded to a cloud control agent according to the regional agents of the regional control layer, the cloud control agent performs comprehensive calculation processing according to different sub-objective functions, and the single objective function can be changed into the single objective function through a weighting method.
The sub-objective functions can be generally divided into functions for calculating the maximum value and the minimum value, and normalization processing is performed on the functions:
Figure FDA0003645896690000061
the comprehensive cost of the objective function solved by the cloud control layer agent can be obtained through the processing:
Figure FDA0003645896690000062
Figure FDA0003645896690000063
in the formula:
Figure FDA0003645896690000064
is a weight value.
Therefore, a multi-objective optimization model for minimizing economic cost, minimizing environmental pollution and minimizing network loss is obtained, an optimal control strategy can be obtained through calculation and solution of the model, and suggestions are provided for optimization regulation and control based on a multi-agent control hierarchical partition framework.
CN202210529222.7A 2022-05-16 2022-05-16 Hierarchical and partitioned load optimization regulation and control method based on multi-agent system Pending CN114841448A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115857364A (en) * 2022-10-17 2023-03-28 东南大学溧阳研究院 Load comfort optimal regulation and control method based on multi-agent mechanism

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115857364A (en) * 2022-10-17 2023-03-28 东南大学溧阳研究院 Load comfort optimal regulation and control method based on multi-agent mechanism

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