CN117595338A - Micro-grid economic optimization scheduling method based on improved limited time consistency - Google Patents

Micro-grid economic optimization scheduling method based on improved limited time consistency Download PDF

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CN117595338A
CN117595338A CN202311475695.4A CN202311475695A CN117595338A CN 117595338 A CN117595338 A CN 117595338A CN 202311475695 A CN202311475695 A CN 202311475695A CN 117595338 A CN117595338 A CN 117595338A
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consistency
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agent
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李浩宇
张春
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Anhui Polytechnic University
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Anhui Polytechnic University
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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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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]

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a micro-grid economic optimization scheduling method based on improved limited time consistency, relates to the technical field of micro-grid economic optimization scheduling, and provides an improved limited time consistency algorithm based on a leader-follower multi-agent for adapting to the topological structure change of a micro-grid and the requirements of the agent such as plug and play and accelerating the system convergence speed; the algorithm is based on a traditional finite time consistency algorithm of a general cost function gradient algorithm term, introduces a gain coefficient and a power deviation elimination term, and proves the stability of the improved algorithm by applying the Lyapunov stability theorem and the graph theory theorem; and the method is based on a micro-grid economic dispatching strategy for improving limited time consistency, and the strategy utilizes information interaction between each agent and neighbor agents in the system to update own consistency variable and controls the change of global consistency variable by selecting a leading agent.

Description

Micro-grid economic optimization scheduling method based on improved limited time consistency
Technical Field
The invention belongs to the technical field of micro-grid economic optimization scheduling, and particularly relates to a micro-grid economic optimization scheduling method based on improved limited time consistency.
Background
The micro-grid is internally integrated with a large number of distributed power generation units, the power generation characteristics of various power generation units are diversified, and the control characteristics and the power generation cost characteristics are different. And the goal of the micro-grid economic dispatch is to reduce the economic cost of the whole micro-grid while ensuring the whole real-time power balance of the micro-grid and meeting the safety constraint.
The power system dispatching mode is divided into a centralized dispatching mode and a distributed dispatching mode, the traditional power system dispatching mode mostly adopts the centralized dispatching mode, and intelligent algorithms such as a particle swarm algorithm, a whale optimization algorithm and the like are utilized to carry out optimization solving on the system. However, as a large number of distributed power generation units are connected into a power system, the centralized scheduling has poor effect in multi-region large-scale scheduling, so that the multi-region interconnection model is solved for lack of strength, the privacy of users cannot be well protected, the requirement on a control center is high, and once the control center collapses, the whole system is paralyzed. Aiming at the problems of centralized scheduling, a plurality of scholars propose to adopt a distributed scheduling mode, and the mode does not depend on a control center, only needs to communicate between adjacent intelligent agents, and has the advantages of small calculated amount, high reliability, capability of meeting 'plug and play', and the like.
In fact, according to the equal consumption micro-increment rate criterion, the micro-grid distributed economic scheduling problem can be converted into the incremental cost consistency problem in the power distribution process, and the distributed algorithm is mainly adopted to solve the problem, wherein the common multi-agent consistency algorithm is adopted, and compared with the traditional intelligent algorithm, the intelligent algorithm has the advantages of short information acquisition time, simple convergence condition, high convergence speed and the like. Although the traditional multi-agent consistency algorithm can deal with the problem of distributed economic dispatch, the algorithm convergence speed is low, so that the finite time consistency algorithm is concerned, but the finite time consistency algorithm is less applied to the optimal dispatch of the micro-grid at present, so that the research of an economic optimal dispatch strategy of the micro-grid based on improving the finite time consistency is of great significance.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art; therefore, the invention provides a micro-grid economic optimization scheduling method based on improved limited time consistency.
A micro-grid economic optimization scheduling method based on improved limited time consistency specifically comprises the following steps:
SS1: adopting an improved finite time consistency algorithm based on a leader-follower multi-agent to determine consistency variables of the agents; the intelligent body comprises a distributed power generation unit, energy storage and controllable load, and corresponding disposable variables are IF, IC and IX in sequence;
SS2: in a multi-agent system, each agent updates its own consistent variable by interacting information with neighboring nodes and controls the change of the global consistent variable by selecting a leader node;
SS3: for the distributed power generation unit and the energy storage, when the total output of the distributed power generation unit and the energy storage is larger than the total load demand power, delta P in the system is smaller than 0, and corresponding consistency variables IF and IC are reduced, so that the output of the distributed power generation unit and the energy storage is reduced, and otherwise, the output of the distributed power generation unit and the energy storage is increased;
when the total load demand power is larger than the total output of the distributed power generation units and the energy storage, delta P in the system is larger than 0, IX of the controllable load is increased, so that the reduction of the controllable load is increased, and the total load of the system is reduced; otherwise, the reduction is reduced, and the total load of the system is increased;
SS4: in the process of step SS3, the system gradually realizes power balance to achieve convergence consistency.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides an improved finite time consistency algorithm based on a leader-follower multi-agent, which is used for adapting to the topological structure change of a micro-grid and the requirements of the agent such as plug and play and the like and accelerating the convergence speed of a system; the algorithm is based on a traditional finite time consistency algorithm of a general cost function gradient algorithm term, introduces a gain coefficient and a power deviation elimination term, and proves the stability of the improved algorithm by applying the Lyapunov stability theorem and the graph theory theorem; and the method is based on a micro-grid economic dispatching strategy for improving limited time consistency, and the strategy utilizes information interaction between each agent and neighbor agents in the system to update own consistency variable and controls the change of global consistency variable by selecting a leading agent.
Drawings
FIG. 1 is a flow chart of an improved finite time consistency algorithm of the present invention;
FIG. 2 is a flow chart of the distributed economic optimization scheduling of the present invention;
FIG. 3 is a diagram of the topology of the system of the present invention;
FIG. 4 is a diagram of a consistent variable convergence scenario in the incremental cost consistency convergence scenario of the present invention;
FIG. 5 is a schematic diagram of total load and total output in the case of incremental cost consistency convergence according to the present invention;
FIG. 6 is a diagram illustrating a consistent variable convergence scenario in the case of incremental cost consistent convergence in multiple dispatch instructions of the present invention;
FIG. 7 is a diagram of total load and total output for incremental cost consistent convergence in multiple dispatch instructions according to the present invention;
FIG. 8 is a schematic diagram of a consistent variable convergence procedure in the case of incremental cost consistent convergence in the "plug and play" case of the present invention;
FIG. 9 is a schematic diagram of total load and total output in the case of incremental cost consistency convergence at the time of "plug and play" according to the present invention;
FIG. 10 shows the incremental cost uniformity convergence r for different gain factors according to the present invention 1 Consistency variable convergence case diagram at=1;
FIG. 11 is a graph of the incremental cost uniformity convergence for different gain factors for the present invention 1 Consistency variable convergence case diagram at=15;
FIG. 12 is a graph of the incremental cost uniformity convergence for different gain factors for the present invention 1 Consistency variable partial magnified graph at=15;
FIG. 13 is a diagram showing the consistency variable comparison between the present invention and a classical master-slave consistency algorithm;
FIG. 14 is a diagram showing the consistency variation of the present invention compared with the conventional finite time consistency algorithm;
FIG. 15 is a diagram showing the consistency variation of the present invention with a finite time consistency algorithm;
FIG. 16 is a graph showing the comparison of algorithm supply and demand power bias in the consistency variable comparison of the present invention and the conventional finite time consistency algorithm.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, 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.
Referring to fig. 1-16, the present application provides a micro grid economic optimization scheduling method based on improving limited time consistency, comprising;
s1: selecting a node with the largest algebraic connectivity in the system topological graph as a leading node;
s2: based on agent position state x in kinetic model i (t) and its input u i (t) relationships between and equal micro-increment criteria, and applying them to microgrid optimization scheduling;
s3: position state x i (t) can be equivalently the consistency variable lambda in economic dispatch i (t) its input u i (t) the variation amount equivalent to the consistency variable
S4: in the invention, the formula of the improved algorithm is as follows:
wherein lambda is 0 (t) and lambda i (t) represents the consistency variables of the leading node and the following node in the system, respectively; r is (r) 1 Is the gain coefficient, r 1 > 0; sigma is a convergence coefficient, sigma is more than 0, and the convergence coefficient can influence the iterative convergence speed of an algorithm and the stability of a system; ΔP is the total power required by the load in the system and the total output of the distributed power generation unit and the stored energySupply and demand power deviation between;
s5: the multi-agent system is proved to be capable of realizing convergence in a limited time under the condition of the improved algorithm by utilizing the Lyapunov function stability theory and the graph theory;
s6: in a multi-agent system, selecting an agent with the largest algebraic connectivity as a leading agent, and the rest being following agents; each intelligent agent can only acquire information from the adjacent intelligent agent, so that a dynamic model of a leading-following first-order multi-intelligent agent system is established as follows:
wherein x is i (t) and u i (t) is the position state and input of the following agent i at time t, respectively; x is x h (t) and u h (t) the position state and input of the leading agent h at time t, respectively; v (V) 0 A set of vertices for a leading agent;
s7: let t time follow the tracking error c between node i and the leader node i The method comprises the following steps:
c i (t)＝λ i (t)-λ 0 (t)；
s8: the method is characterized by comprising the steps 4, 6 and 7:
wherein, c j (t) tracking error of the neighbor node of the following node i at the moment t;the variation of tracking error between the node i and the leader node is followed at the moment t; c 0 (t) is the tracking error of the leader node at time t;
s9: let Lyapunov function be
From step 8:
discussion of the inventionPositive and negative of (3);
let F (t) =λ 0 (t)-λ i (t),
Therefore, it is
Wherein C is 1 、C 2 Is a constant;the variable quantities of the consistency variables of the leading node and the following node at the time t are respectively; lambda (lambda) j (t) is a consistency variable of a neighbor node of the following node i at the moment t; n (N) 0 A set of neighbor nodes representing a leader node;
s10: assuming that the state of the agent is the same at the initial time, the discussion is as follows;
when (when)When (I)>So F (t) > F (0) =0, λ 0 (t)＞λ i (t)；
Then
Wherein a is 0j The connection weight between the node j and the leading node is given;
r 1 a i0 sign a (λ 0 (t)-λi(t))＞0，
η 2 a i0 (λ 0 (t)-λ i (t))＞0，
a (t) < b (t),
and because of c i (t)＝λ i (t)-λ 0 (t) < 0, therefore
When (when)When (I)>So F (t) < F (0) =0, λ 0 (t)＜λ i (t)；
Then
r 1 a i0 sign a (λ 0 t)-λ i (t)＜0，
η 2 a i0 (λ 0 (t)-λ i (t))＜0，
A (t) > b (t),
and because of c i (t)＝λ i (t)-λ 0 (t) > 0, thus->
When lambda is 0 (t)-λ i When (t) =0,
s11: as can be derived from the discussion above:
let b=l+diag (a) 10 ，a 20 ，...，a n0 ) Then:
wherein L is B Laplacian matrix of B。θ 2 (L B ) A second eigenvalue of the laplace matrix of B;
s12: the following steps are combined with step 11:
from the above, it is proved that when T is not less than T (x (0)), the system can achieve convergence consistency in a limited time under the effect of the improved algorithm;
s13: improving the finite time consistency algorithm flow;
s14: each node in the system is regarded as an agent;
s15: judging whether the agent is a leading agent;
s16: judging whether the power of the intelligent body is out of limit;
s17: calculating a power bias in the system;
s18: judging whether a termination condition is met;
s19: in the step 6, the calculation formula of the agent consistency variable includes;
s20: if the agent is the leading agent, the formula for calculating the consistency variable is:
z∈(S FZ 、S CZ 、S XZ )；
wherein N is z Representing a set of neighbor nodes of the leader node z; a, a zm Is A 0 Connection between a middle node m and a leader node zA weight; sigma (sigma) 1 For leading node convergence factor, σ 1 ＞0；S FZ 、S CZ 、S XZ Respectively collecting leader nodes in a distributed power generation unit, energy storage and controllable load; lambda (lambda) z (t) is a consistent variable of a leader node z in the distributed generation unit, the energy storage or the controllable load;the variable quantity of the consistency variable of the leading node; lambda (lambda) m (t) is the following node->Or a consistency variable of a neighbor node m of the leading node z;
s21: if the agent is a following agent, the consistency variable calculation formula is:
in the method, in the process of the invention,representing following node +.>A set of neighbor nodes; />Is node m and following node +.>Connection betweenA weight; sigma (sigma) 2 To follow the node convergence coefficient, 0 < sigma 2 ＜σ 1 ；/>Follow node in distributed generation unit, energy storage or controllable load>Consistency variable of->The variable quantity is the variable quantity of the consistency variable of the following node; s is S F Is a distributed power generation unit set; s is S C Is an energy storage set; s is S X Is a controllable load set;
s22: in the step 7, the calculation formula of the power output of the intelligent agent is as follows:
wherein beta is i 、γ i 、w j 、θ j 、q g 、s g Constant terms, primary terms and secondary term coefficients of the distributed power generation unit, the energy storage and the controllable load cost model are respectively represented; p (P) Fi Power output by the distributed generation unit i; p (P) Cj The discharge power of the energy storage j; p (P) Xg The power curtailed for the controllable load g; p (P) Fi,min And P Fi,max Respectively the minimum and maximum output power of the distributed power generation unit i; p (P) Cj,min And P Cj,max Respectively representing the minimum and maximum discharge power of the energy storage j; PX (PX) g,min And P Xg,max Respectively representing the minimum and maximum cut-down powers of the controllable load g;
s23: distributed economic dispatch;
s24: when the objective function of the economic optimization scheduling of the micro-grid is that the agent meets a certain constraint condition, the optimization problem of the minimum running cost of the whole system is solved;
s25: the cost models of the distributed power generation unit, the energy storage and the controllable load all adopt quadratic function models; the method comprises the following steps:
the power generation cost model of the distributed power generation unit is as follows:
the discharge cost model of the stored energy is as follows:
the controllable load reduction cost model is as follows:
the objective function is:
the constraint conditions are as follows:
P Fi，min ≤P Fi ≤P Fi，max i∈S F
P Cj，min ≤P Cj ≤P Cj，max j∈S C
P Xg，min ≤P Xg ≤P Xg，max g∈S X
wherein P is Fi,min And P Fi,max Respectively the minimum and maximum output power of the distributed power generation unit i; p (P) Cj,min And P Cj,max Respectively representing the minimum and maximum discharge power of the energy storage j; p (P) Xg,min And P Xg,max Respectively representing the minimum and maximum cut-down powers of the controllable load g; p (P) L Is a fixed load; p (P) LL Initial load amount for controllable load;
s26: for the objective function, the classical Lagrangian multiplier method is utilized to solve, and under the condition that inequality constraint is not considered, the proposed constraint optimization problem can be converted into:
where ζ is the Lagrangian multiplier corresponding to the equality constraint;
s27: since the power supply and demand balance in the power system needs to be satisfied, let Δp be:
s28: within the power constraint range, it is assumed that all distributed generation units, energy storage and controllable loads are operating normally. In the improved finite time consistency algorithm herein, IF, IC, IX can be defined as:
wherein lambda is i 、λ j 、λ g The consistency variables are distributed power generation units, energy storage and controllable loads respectively;
s29: scheduling strategies;
s30: the microgrid economic optimization goal is to minimize the microgrid operating economic cost. The invention adopts an improved finite time consistency algorithm based on a leader-follower multi-agent, and takes IF, IC and IX as consistency variables of distributed power generation units, energy storage, controllable loads and other agents respectively;
s31: in a multi-agent system, each agent updates its own consistent variable by interacting information with neighboring nodes and controls the change of the global consistent variable by selecting a leader node;
s32: for the distributed power generation units and the energy storage, when the total output of the distributed power generation units and the energy storage is larger than the total load demand power, delta P in the system is smaller than 0, corresponding consistency variables IF and IC are reduced, so that the output of the distributed power generation units and the energy storage is reduced, and otherwise, the output of the distributed power generation units and the energy storage is increased;
s33: when the total load demand power is larger than the total output of the distributed power generation units and the energy storage, delta P in the system is larger than 0, IX of the controllable load is increased, so that the reduction of the controllable load is increased, and the total load of the system is reduced; otherwise, the reduction is reduced, and the total load of the system is increased;
s34: in the process, the system gradually realizes power balance to achieve convergence consistency.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.

Claims (8)

1. The utility model provides a micro-grid economic optimization scheduling method based on improved limited time consistency, which is characterized by comprising the following steps:
SS1: adopting an improved finite time consistency algorithm based on a leader-follower multi-agent to determine consistency variables of the agents; the intelligent body comprises a distributed power generation unit, energy storage and controllable load, and corresponding disposable variables are IF, IC and IX in sequence;
SS2: in a multi-agent system, each agent updates its own consistent variable by interacting information with neighboring nodes and controls the change of the global consistent variable by selecting a leader node;
SS3: for the distributed power generation unit and the energy storage, when the total output of the distributed power generation unit and the energy storage is larger than the total load demand power, delta P in the system is smaller than 0, and corresponding consistency variables IF and IC are reduced, so that the output of the distributed power generation unit and the energy storage is reduced, and otherwise, the output of the distributed power generation unit and the energy storage is increased;
when the total load demand power is larger than the total output of the distributed power generation units and the energy storage, delta P in the system is larger than 0, IX of the controllable load is increased, so that the reduction of the controllable load is increased, and the total load of the system is reduced; otherwise, the reduction is reduced, and the total load of the system is increased;
SS4: in the process of step SS3, the system gradually realizes power balance to achieve convergence consistency.
2. The method for optimizing and scheduling micro-grid economy based on improving finite time consistency according to claim 1, wherein the leader node in step SS2 is selected in such a way that the node with the largest algebraic connectivity in the system topology is selected as the leader node.
3. The micro grid economic optimization scheduling method based on the improved limited time consistency according to claim 2, wherein the improved limited time consistency algorithm in step SS1 is specifically:
wherein lambda is 0 (t) and lambda i (t) represents the consistency variables of the leading node and the following node in the system, respectively; r is (r) 1 Is gain ofCoefficient r i > 0; sigma is a convergence coefficient, sigma is more than 0, and the convergence coefficient can influence the iterative convergence speed of an algorithm and the stability of a system; Δp is the supply-demand power deviation between the total load demand power in the system and the total output of the distributed power generation unit and the stored energy;
s2: based on agent position state x in kinetic model i (t) and its input u i (t) relationships between and equal micro-increment criteria, and applying them to microgrid optimization scheduling;
position state x i (t) can be equivalently the consistency variable lambda in economic dispatch i (t) its input u i (t) the variation lambda which can be equivalently a consistent variable i (t)。
4. A micro grid economic optimization scheduling method based on improved limited time consistency according to claim 3, wherein,
in step SS2, each agent updates its own consistent variable by performing information interaction with the neighboring node, and controls the change of the global consistent variable by selecting the leader node in the following specific ways:
s1: each node in the system is regarded as an agent; judging whether the intelligent agent is a leading intelligent agent, and calculating consistent variables according to whether the intelligent agent is the leading intelligent agent, wherein the steps are as follows:
if the agent is the leading agent, the formula for calculating the consistency variable is:
wherein N is z Representing a set of leading node Z neighbor nodes; a, a zm Is A 0 The connection weight between the middle node m and the leading node Z; sigma (sigma) 1 For leading node convergence factor, σ 1 ＞0；S FZ 、S CZ 、S XZ Respectively collecting leader nodes in a distributed power generation unit, energy storage and controllable load; lambda (lambda) z (t) is a consistent variable of a leader node z in the distributed generation unit, the energy storage or the controllable load; lambda (lambda) z (t) is the variation of the leader node consistency variable; lambda (lambda) m (t) is a following nodeOr a consistency variable of a neighbor node m of the leading node z;
if the agent is a following agent, the consistency variable calculation formula is:
in the method, in the process of the invention,representing following node +.>A set of neighbor nodes; />Is node m and following node +.>The connection weight between the two; sigma (sigma) 2 To follow the node convergence coefficient, 0 < sigma 2 ＜σ 1 ；/>For distributed power generation units, energy storage or controllable negativesFollowing node in load->Consistency variable of->The variable quantity is the variable quantity of the consistency variable of the following node; s is S F Is a distributed power generation unit set; s is S C Is an energy storage set; s is S X Is a controllable load set;
s2: judging whether the power of the intelligent body is out of limit, wherein the calculating formula of the power output of the intelligent body is as follows:
wherein beta is i 、γ i 、w j 、θ j 、q g 、s g Constant terms, primary terms and secondary term coefficients of the distributed power generation unit, the energy storage and the controllable load cost model are respectively represented; p (P) Fi Power output by the distributed generation unit i; p (P) Cj The discharge power of the energy storage j; p (P) Xg The power curtailed for the controllable load g; p (P) Fi，min And P Fi，max Respectively the minimum and maximum output power of the distributed power generation unit i; p (P) Cj，min And P Cj，max Respectively representing the minimum and maximum discharge power of the energy storage j; p (P) Xg，min And P Xg，max The minimum and maximum cut-down powers of the controllable load g are respectively indicated.
5. The method for economically optimizing a dispatch of a micro-grid based on improved finite time consistency of claim 1,
in the step SS3, a quadratic function model is adopted as a cost model of the distributed power generation unit, the energy storage and the controllable load; the method comprises the following steps:
the power generation cost model of the distributed power generation unit is as follows:
the discharge cost model of the stored energy is as follows:
the controllable load reduction cost model is as follows:
the objective function is:
the constraint conditions are as follows:
P Fi，min ≤P Fi ≤P Fi，max i∈S F
P Cj，min ≤P Cj ≤P Cj，max j∈S C
P Xg，min ≤P Xg ≤P Xg，max g∈S X
wherein P is Fi，min And P Fi，max Respectively divide intoMinimum and maximum output power of the cloth type power generation unit i; p (P) Cj，min And P Cj，max Respectively representing the minimum and maximum discharge power of the energy storage j; p (P) Xg，min And P Xg，max Respectively representing the minimum and maximum cut-down powers of the controllable load g; p (P) L Is a fixed load; p (P) LL The initial load amount is the controllable load.
6. The micro grid economic optimization scheduling method based on improved limited time consistency according to claim 5, wherein for objective functions, the classical lagrangian multiplier method is used for solving, and the proposed constraint optimization problem can be converted into that without considering inequality constraint:
where ζ is the Lagrangian multiplier corresponding to the equality constraint.
7. The method for optimized dispatching of micro-grid economics based on improved finite time consistency of claim 6, wherein in step SS3: since the power supply and demand balance in the power system needs to be satisfied, let Δp be:
8. the method for economically optimizing a dispatch of a micro-grid based on improved limited time consistency of claim 7, wherein all distributed generation units, stored energy and controllable loads are assumed to be operating normally within a power constraint range; in improving the finite time consistency algorithm, IF, IC, IX can be defined as:
wherein lambda is i 、λ j 、λ g And the consistency variables are distributed power generation units, energy storage and controllable loads respectively.
CN202311475695.4A 2023-11-07 2023-11-07 Micro-grid economic optimization scheduling method based on improved limited time consistency Pending CN117595338A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118174343A (en) * 2024-05-11 2024-06-11 北京智芯微电子科技有限公司 Micro-grid coordinated control method, device, system, storage medium and electronic equipment

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