CN109886446B - Dynamic economic dispatching method of electric power system based on improved chaotic particle swarm algorithm - Google Patents
Dynamic economic dispatching method of electric power system based on improved chaotic particle swarm algorithm Download PDFInfo
- Publication number
- CN109886446B CN109886446B CN201811535876.0A CN201811535876A CN109886446B CN 109886446 B CN109886446 B CN 109886446B CN 201811535876 A CN201811535876 A CN 201811535876A CN 109886446 B CN109886446 B CN 109886446B
- Authority
- CN
- China
- Prior art keywords
- output
- scene
- particle
- representing
- algorithm
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
- Feedback Control In General (AREA)
Abstract
The invention relates to a dynamic economic dispatching method of an electric power system based on an improved chaotic particle swarm algorithm, and belongs to the field of electric power grids. According to the method, a dynamic economic scheduling model based on a scene method is converted into an unconstrained optimization problem by utilizing a penalty function method, and the method is used for searching a population optimal solution obtained in each iteration process by adopting a chaotic optimization algorithm aiming at the defect that a traditional particle swarm algorithm is easy to be trapped in local extremum points and the like. In addition, in order to improve the convergence efficiency of the algorithm, feasibility adjustment is performed according to the deviation degree of the equation constraint after the particle position is updated, so that the particle meets the requirement of the equation constraint, and the algorithm is quickly converged. Compared with the traditional particle swarm optimization algorithm, the method has the advantages that the solving efficiency is greatly improved, and a certain practical value is realized.
Description
Technical Field
The invention belongs to the field of power grids, and relates to a dynamic economic dispatching method of a power system based on an improved chaotic particle swarm algorithm.
Background
With the increasing exhaustion of fossil energy, popularization and application of new energy power generation technology are the problems to be solved urgently. As the most advantageous renewable new energy, wind power generation technology is rapidly developed, however, the output is influenced by external environments such as weather, geographical position and the like, and has random characteristics which are quite uncertain, and when large-scale wind power is connected, the randomness of the output can seriously influence the safe and stable operation of a power grid. Therefore, it is necessary to solve the influence of wind power randomness on grid dispatching.
Aiming at the random characteristics of wind power output, the scenario method is used as a random planning method, the randomness of wind power output is simulated through the predicted output of wind power and a plurality of random error scenarios obtained according to the probability distribution of wind power output, and the uncertainty of wind power is converted into a deterministic model which can be described by a plurality of random error scenarios. In the dispatching optimization model, the scene transfer constraint between the prediction scene and the random error scene is reasonably set, so that a conventional unit can accurately arrange a dispatching plan to effectively cope with the random fluctuation characteristic of wind power output, and the safe and stable operation of a power grid is realized.
At present, the solving algorithm for the problems mainly comprises a mathematical optimization algorithm and an artificial intelligence algorithm. The mathematical optimization algorithm mainly starts from a certain initial set point, and changes the running track continuously according to a certain rule so as to perform optimization, and the calculation is finished until the optimal solution is finally converged, wherein the common solution methods include a nonlinear interior point method, a Benders decomposition method and the like. Although mathematical optimization algorithms have theoretically strict convergence evidence and strong robustness, the use of such optimization algorithms is limited when the above-mentioned non-convex problem and optimization model are discontinuously conductive. In this regard, intelligent optimization algorithms that are not subject to optimization model non-convexity and discontinuous conductivity have evolved. The artificial intelligent algorithm searches the optimal solution through a certain specific updating rule, is not limited to the optimization problem, and common intelligent algorithms include particle swarm algorithm, genetic algorithm and the like.
Disclosure of Invention
In view of the above, the invention aims to provide a dynamic economic dispatching method of an electric power system based on an improved chaotic particle swarm algorithm, which adopts a fast-convergence chaotic particle swarm algorithm (FastChaotic ParticleSwarm Optimization, FCPSO), a core computing framework is a particle swarm intelligent search algorithm, and the algorithm describes the optimizing process by simulating bird predation, and has the advantages of simple updating rule, strong global searching capability and the like, but the algorithm is too early mature and is easy to be trapped in local extremum points and the like due to the influence of initial particle values, parameter setting and the like.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the dynamic economic dispatching method of the electric power system based on the improved chaotic particle swarm algorithm comprises the following steps:
s1: a dynamic economic dispatch model considering a wind power access system is established based on a scene method, and is converted into an unconstrained optimization model by using a penalty function method;
s2: optimizing the population optimal solution obtained by each iteration based on a chaos optimization algorithm, and updating the population optimal solution;
s3: and carrying out feasibility adjustment according to the deviation degree of the equation constraint after the particle position is updated.
Further, the step S1 specifically includes:
in order to reasonably describe the random fluctuation of the wind power plant output, a dynamic economic dispatch model considering a wind power access system is established based on a scene method:
objective function:
constraint conditions:
wherein T is the time period of the scheduling period, taking t=24; n is the number of conventional generator sets; p (P) i,t Generating power of the generator set i at a time t; a, a i,0 、a i,1 、a i,2 Representing the coal consumption cost coefficient of a conventional unit; s is the number of random error scenes; h is a s Probability of being a random error scene s; beta i Rescheduling fuel cost coefficients for unit i; s=0 represents a prediction scene, s+.0 represents a random error scene;
the power generated by the generator set i at the moment t under the scene s; p (P) i,t,s In the scene s, the output (MW) of the thermal power generating unit i in the period t is shown; p (P) w(t,s) Representing the combined output (MW) of wind power in a scene s in a period t; PL (PL) t Load (MW) representing period t; beta represents the rotation reserve rate, and 5 is taken; p (P) i Representing the lower output limit (MW) of the thermal power unit i; />
Representing an upper output limit (MW) of the thermal power unit i; r is (r) ui Representing the upward climbing rate of a conventional unit; r is (r) di Representing the downward climbing rate of a conventional unit; p (P) i,t The output (MW) of the thermal power generating unit i in the t period under the prediction scene is represented; delta i The adjustable power of the thermal power generating unit at the time t is represented; p (P) ij,t,s Representing active power (MW) of a transmission line from a node i to a node j in a t period in a scene s; />
Representing the upper limit (MW) of active power of the transmission line from node i to node j in a period t;
in order to facilitate the solution by using the particle swarm algorithm, the model needs to be converted into an unconstrained optimization problem as shown in the formula (3) by using a penalty function method:
in the middle ofY (x) represents the augmented objective function after adding the penalty function; f represents an original objective function; psi phi type 1 、ψ 2 Penalty factors representing inequality constraints and equality constraints, respectively; g (x), h (x) represent an inequality constraint and an equality constraint, respectively.
Further, the step S2 specifically includes:
the chaos optimization algorithm is utilized to traverse the search space, and chaos search is carried out on the population optimal solution obtained in the iteration process in the neighborhood of the population optimal solution, so that the capability of the algorithm for jumping out of local extreme points is improved;
carrying out iterative optimization on a population optimal solution obtained by each iteration of the particle swarm algorithm according to a formula (4);
wherein x is zbest Representing the current optimal solution of the particle; ζ represents an adjustment coefficient;
the representation is at [ -1,1]Chaos variable of interval; mu represents the neighborhood radius and is taken to be 0.2; />
Calculated by the formula (5);
wherein ζ represents the control parameter, when ζ=4 and
when the system is in a complete chaotic state, the variable can be mapped to the interval [0,1 ] through the formula (5)]And the map is a full map.
Further, the step S3 specifically includes:
s301, sorting; in order to ensure the economical efficiency in the process of feasibility adjustment, the conventional units are firstly ordered according to the coal consumption cost; calculating the coal consumption cost of each machine under the rated power, and sequencing the machines according to the coal consumption cost from low to high, wherein if the conditions of the same coal consumption cost exist, the sequencing of the machines with the same coal consumption cost are the same;
s302: adjusting; when the equality constraint is not satisfied, the output of each unit is sequentially adjusted according to the ordering sequence until the equality constraint is satisfied; if the units with the same sequencing positions exist, distributing according to the adjustable margin;
the output of the machine set obtained by the kth iteration is as follows
Load size PL, define the deviation of equality constraint +.>
The following is P Δ The case of 0 or more;
P Δ more than or equal to 0 indicates that the output force of the unit is larger than the load, at the moment, according to the unit sequencing obtained in the step S301, the output force of the unit is sequentially reduced from high to low, and when the first unit is reduced to the lower output limit
When the method is used, the output adjustment is continued for the next high coal consumption unit until the equality constraint is satisfied;
if the units with the same sequencing positions exist, the sequencing positions of the m units are the same, and the adjustable margin of the m units is calculated, because of P Δ More than or equal to 0, so that the downward adjustable margin of m units is obtained
If->
The output of the m units is adjusted to the lower limit value; if->
The output of the m units is adjusted as shown in a formula (6);
the specific solving steps are as follows:
s31: a dynamic economic dispatch model considering a wind power access system is established based on a scene method, and is converted into an unconstrained optimization model by using a penalty function method, as shown in a formula (3);
s32: determining a parameter value of a chaotic particle swarm algorithm;
s33: initializing the position and the speed of each particle of the population; the specific formula is as follows:
position of the jth particle in the ith population:
wherein rand represents a random number in the (0, 1) interval; the initial speed is as follows:
wherein K represents the maximum number of iterations;
s34: performing feasibility adjustment on the initial particles according to the feasibility adjustment strategy;
s35: the k+1th iteration of each particle of the population is updated according to the following formula rule:
wherein omega k Representing the inertial factor, omega max 、ω min Respectively taking 0.9 and 0.4; c 1 、c 2 Representing a learning factor equal to 2;
and->
The position information of the individual history optimal particle and the position information of the population optimal particle at the kth iteration are respectively represented;
s36: carrying out feasibility adjustment on the updated particles;
s37: performing chaos optimization search on the population optimal particles obtained in each iteration, iterating according to formulas (4) - (5) in the search process, and performing feasibility adjustment on new particle positions generated in the iteration process;
s38: judging whether the objective function value meets convergence accuracy or whether the iteration number reaches a maximum set value, if so, outputting a result; otherwise, jump to step S35: until convergence.
The invention has the beneficial effects that:
(1) The dynamic economic dispatching unconstrained optimization model of the wind power-containing power system, which is established based on the scene method and the penalty function method, can accurately reflect the random characteristics of wind power, and is convenient to solve by using a particle swarm algorithm.
(2) According to the invention, a chaos optimization algorithm is introduced to optimize the population optimal solution in the iteration of the particle swarm algorithm, so that the capability of the particle swarm algorithm to jump out of local extreme points is effectively improved, and premature maturation of the algorithm is avoided.
(3) The invention provides a feasibility adjustment strategy, which aims at the defect that a particle swarm algorithm is irregular and random in updating, and the feasibility adjustment is carried out by using the equation constraint deviation of the updated particles, so that the convergence rate of the algorithm is effectively improved.
(4) The capability of the particle swarm algorithm for jumping out of local extreme points is improved, and the solving efficiency of the algorithm is effectively improved;
(5) The feasibility adjustment strategy is provided, a large number of invalid solutions generated in the updating process of the particle swarm algorithm are avoided, and the solving speed of the algorithm is further improved.
Drawings
In order to make the objects, technical solutions and advantageous effects of the present invention more clear, the present invention provides the following drawings for description:
FIG. 1 is a flow chart of a dynamic economic dispatch algorithm for solving a wind-powered electricity-containing power system based on FCPSO.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
When a dynamic economic dispatch model of the power system based on a scene method is solved by using a particle swarm algorithm, the algorithm has the defect of easy existence of local extreme points and the like, and cannot meet the actual application. Aiming at the problems, the invention provides a corresponding solving method, as shown in fig. 1, which is characterized in that:
(1) A dynamic economic dispatch model considering a wind power access system is established based on a scene method, and is converted into an unconstrained optimization model by using a penalty function method;
(2) Optimizing the population optimal solution obtained by each iteration based on a chaos optimization algorithm, and updating the population optimal solution;
(3) And carrying out feasibility adjustment according to the deviation degree of the equation constraint after the particle position is updated.
1 establishing a dynamic economic dispatch model considering a wind power access system based on a scene method
In order to reasonably describe the random fluctuation of the wind power plant output, a dynamic economic dispatch model considering a wind power access system is established based on a scene method:
objective function:
constraint conditions:
wherein, T is the time period of the scheduling period, and the invention takes t=24; n is the number of conventional generator sets; p (P) i,t Generating work at time t for generator set iA rate; a, a i,0 、a i,1 、a i,2 Representing the coal consumption cost coefficient of a conventional unit; s is the number of random error scenes; h is a s Probability of being a random error scene s; beta i Rescheduling fuel cost coefficients for unit i; s=0 represents a prediction scene, s+.0 represents a random error scene;
the power generated by the generator set i at the moment t under the scene s; p (P) i,t,s In the scene s, the output (MW) of the thermal power generating unit i in the period t is shown; p (P) w(t,s) Representing the combined output (MW) of wind power in a scene s in a period t; PL (PL) t Load (MW) representing period t; beta represents a rotation reserve rate, generally 5; i Prepresenting the lower output limit (MW) of the thermal power unit i; />
Representing an upper output limit (MW) of the thermal power unit i; r is (r) ui Representing the upward climbing rate of a conventional unit; r is (r) di Representing the downward climbing rate of a conventional unit; p (P) i,t The output (MW) of the thermal power generating unit i in the t period under the prediction scene is represented; delta i The adjustable power of the thermal power generating unit at the time t is represented; p (P) ij,t,s Representing active power (MW) of a transmission line from a node i to a node j in a t period in a scene s; />
Representing the upper limit (MW) of the active power of the transmission line from node i to node j during period t.
In order to facilitate the solution by using the particle swarm algorithm, the model needs to be converted into an unconstrained optimization problem as shown in the formula (3) by using a penalty function method:
wherein Y (x) represents an augmented objective function after adding a penalty function; f represents an original objective function; psi phi type 1 、ψ 2 Respectively indicate notEquality constraints and penalty factors for equality constraints; g (x), h (x) represent an inequality constraint and an equality constraint, respectively.
2 basic steps of chaos optimization algorithm
The chaos optimization algorithm has the characteristic of traversing the search space, and chaos search is carried out on the population optimal solution obtained in the iteration process in the neighborhood of the population optimal solution, so that the capability of the algorithm for jumping out of local extreme points is improved.
And (3) carrying out iterative optimization on the population optimal solution obtained by each iteration of the particle swarm algorithm according to the formula (4).
Wherein x is zbest Representing the current optimal solution of the particle; ζ represents an adjustment coefficient;
the representation is at [ -1,1]Chaos variable of interval; mu represents the neighborhood radius, typically taken as 0.2./>
Calculated from equation (5).
Wherein ζ represents the control parameter, when ζ=4 and
when the system is in a complete chaotic state, the variable can be mapped to the interval [0,1 ] through the method (5)]And the map is a full map.
3 feasibility adjustment strategy
The feasibility adjustment can help the particles updated in each iteration to meet the constraint of the equation, so that a large number of invalid solutions generated in the iterative process by the algorithm are effectively avoided, and the calculation speed is further improved. The specific steps are as follows:
(1) And (5) sequencing. To ensure the economy in the process of feasibility adjustment, the conventional units are firstly ordered according to the coal consumption cost. Calculating the coal consumption cost of each machine under the rated power, and sequencing the machines according to the coal consumption cost from low to high, wherein if the conditions of the same coal consumption cost exist, the sequencing of the machines with the same coal consumption cost are the same;
(2) And (5) adjusting. When the equality constraint is not satisfied, the output of each unit is sequentially adjusted according to the ordering sequence until the equality constraint is satisfied; and if the units with the same sequencing positions exist, distributing according to the adjustable margin.
The output of the machine set obtained by the kth iteration is as follows
Load size PL, define the deviation of equality constraint +.>
Hereinafter referred to as P Δ And 0. Gtoreq.0 is taken as an example for detailed description.
P Δ More than or equal to 0 indicates that the output force of the unit is larger than the load, at the moment, according to the unit sequencing obtained in the step (1), the output force of the unit is sequentially reduced from high to low, and when the first unit is reduced to the lower output limit
And when the method is used, the output adjustment is continued for the next high-coal consumption unit until the equality constraint is satisfied.
If the units with the same sequencing positions exist, the sequencing positions of the m units are the same, and the adjustable margin of the m units is calculated, because of P Δ More than or equal to 0, so that the downward adjustable margin of m units is obtained
If->
The output of the m units is adjusted to the lower limit value; if->
The m unit outputs are adjusted as shown in equation (6).
4 specific solving step
(1) A dynamic economic dispatch model considering a wind power access system is established based on a scene method, and is converted into an unconstrained optimization model by using a penalty function method, as shown in a formula (3);
(2) Determining a parameter value of a chaotic particle swarm algorithm;
(3) The position and speed initialization of each particle of the population is performed. The specific formula is as follows:
position of the jth particle in the ith population:
where rand represents a random number in the (0, 1) interval. The initial speed is as follows:
where K represents the maximum number of iterations.
(4) Feasibility modification of the initial particles according to the procedure described in 2.2.3;
(5) The k+1th iteration of each particle of the population is updated according to the following formula rule:
wherein omega k Representing the inertial factor, omega max 、ω min Respectively taking 0.9 and 0.4; c 1 、c 2 Represent learning factors, allEqual to 2;
and->
The position information of the individual history optimum and population optimum particles at the kth iteration is represented respectively.
(6) Carrying out feasibility adjustment on the updated particles;
(7) Performing chaos optimization search on the population optimal particles obtained in each iteration, iterating according to formulas (4) - (5) in the search process, and performing feasibility adjustment on new particle positions generated in the iteration process;
(8) Judging whether the objective function value meets convergence accuracy or whether the iteration number reaches a maximum set value, if so, outputting a result; otherwise, jumping to the step (5) until convergence.
Finally, it is noted that the above-mentioned preferred embodiments are only intended to illustrate rather than limit the invention, and that, although the invention has been described in detail by means of the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (3)
1. The dynamic economic dispatching method of the electric power system based on the improved chaotic particle swarm algorithm is characterized by comprising the following steps of: the method comprises the following steps:
s1: a dynamic economic dispatch model considering a wind power access system is established based on a scene method, and is converted into an unconstrained optimization model by using a penalty function method; the step S1 specifically comprises the following steps:
in order to reasonably describe the random fluctuation of the wind power plant output, a dynamic economic dispatch model considering a wind power access system is established based on a scene method:
objective function:
constraint conditions:
wherein T is the time period of the scheduling period, taking t=24; n (N) g The number of the conventional generator sets; p (P) i,t Generating power of the generator set i at a time t; a, a i,0 、a i,1 、a i,2 Representing the coal consumption cost coefficient of a conventional unit; s is the number of random error scenes; h is a s Probability of being a random error scene s; beta i Rescheduling the fuel cost factor for genset i; s=0 represents a prediction scene, s+.0 represents a random error scene;
the power generated by the generator set i at the moment t under the scene s; p (P) i,t,s In the scene s, the output of the generator set i at the moment t is shown; p (P) w(t,s) In the scene s, the combined output of wind power at the moment t is shown; PL (PL) t The load at time t is represented; beta represents the rotation reserve rate, and 5 is taken; i Prepresenting the lower output limit of the generator set i; />
Representing the upper output limit of the generator set i; r is (r) ui Representing the upward climbing rate of a conventional unit; r is (r) di Representing the downward climbing rate of a conventional unit; p (P) i,t The output of the generator set i at the moment t under the prediction scene is shown; delta i Representing the adjustable power of the generator set i at time t; p (P) ij,t,s In a scene s, the active power of a transmission line from node i to node j at the time t is represented; />
Representing the upper limit of active power of the transmission line from node i to node j at time t;
in order to facilitate the solution by using the particle swarm algorithm, the model needs to be converted into an unconstrained optimization problem as shown in the formula (3) by using a penalty function method:
wherein Y (x) represents an augmented objective function after adding a penalty function; f represents an original objective function; psi phi type 1 、ψ 2 Penalty factors representing inequality constraints and equality constraints, respectively; g (x) and h (x) respectively represent an inequality constraint and an equality constraint;
s2: optimizing the population optimal solution obtained by each iteration based on a chaos optimization algorithm, and updating the population optimal solution;
s3: and carrying out feasibility adjustment according to the deviation degree of the equation constraint after the particle position is updated.
2. The power system dynamic economic dispatch method based on improved chaotic particle swarm algorithm according to claim 1, wherein the power system dynamic economic dispatch method is characterized in that: the step S2 specifically comprises the following steps:
the chaos optimization algorithm is utilized to traverse the search space, and chaos search is carried out on the population optimal solution obtained in the iteration process in the neighborhood of the population optimal solution, so that the capability of the algorithm for jumping out of local extreme points is improved;
carrying out iterative optimization on a population optimal solution obtained by each iteration of the particle swarm algorithm according to a formula (4);
wherein x is zbest Representing the current optimal solution of the particle; ζ represents an adjustment coefficient;
the representation is at [ -1,1]Chaos variable of interval; mu represents the neighborhood radius and is taken to be 0.2; />
Calculated by the formula (5);
wherein ζ represents the control parameter, when ζ=4 and
when the system is in a complete chaotic state, the variable can be mapped to the interval [0,1 ] through the formula (5)]And the map is a full map.
3. The power system dynamic economic dispatch method based on improved chaotic particle swarm algorithm according to claim 2, wherein the power system dynamic economic dispatch method is characterized in that: the step S3 specifically comprises the following steps:
s301: sequencing; in order to ensure the economical efficiency in the process of feasibility adjustment, the conventional units are firstly ordered according to the coal consumption cost; calculating the coal consumption cost of each machine under the rated power, and sequencing the machines according to the coal consumption cost from low to high, wherein if the conditions of the same coal consumption cost exist, the sequencing of the machines with the same coal consumption cost are the same;
s302: adjusting; when the equality constraint is not satisfied, the output of each unit is sequentially adjusted according to the ordering sequence until the equality constraint is satisfied; if the units with the same sequencing positions exist, distributing according to the adjustable margin;
the output of the machine set obtained by the kth iteration is as follows
Load size PL, define the deviation of equality constraint +.>
The following is P Δ The case of 0 or more;
P Δ more than or equal to 0 indicates that the unit output force is greater than the load, and the unit output force is obtained according to the step S301Sequencing the units, sequentially reducing the output sizes of the units from high to low, and reducing the output lower limit of the first unit
When the method is used, the output adjustment is continued for the next high coal consumption unit until the equality constraint is satisfied;
if the units with the same sequencing positions exist, the sequencing positions of the m units are the same, and the adjustable margin of the m units is calculated, because of P Δ More than or equal to 0, so that the downward adjustable margin of m units is obtained
If->
The output of the m units is adjusted to the lower limit value; if->
The output of the m units is adjusted as shown in a formula (6); />
The specific solving steps are as follows:
s31: a dynamic economic dispatch model considering a wind power access system is established based on a scene method, and is converted into an unconstrained optimization model by using a penalty function method, as shown in a formula (3);
s32: determining a parameter value of a chaotic particle swarm algorithm;
s33: initializing the position and the speed of each particle of the population; the specific formula is as follows:
position of the jth particle in the ith population:
wherein rand represents a random number in the (0, 1) interval; the initial speed is as follows:
wherein K represents the maximum number of iterations;
s34: performing feasibility adjustment on the initial particles according to the feasibility adjustment strategy;
s35: the k+1th iteration of each particle of the population is updated according to the following formula rule:
wherein omega k Representing the inertial factor, omega max 、ω min Respectively taking 0.9 and 0.4; c 1 、c 2 Representing a learning factor equal to 2;
and->
The position information of the individual history optimal particle and the position information of the population optimal particle at the kth iteration are respectively represented;
s36: carrying out feasibility adjustment on the updated particles;
s37: performing chaos optimization search on the population optimal particles obtained in each iteration, iterating according to formulas (4) - (5) in the search process, and performing feasibility adjustment on new particle positions generated in the iteration process;
s38: judging whether the objective function value meets convergence accuracy or whether the iteration number reaches a maximum set value, if so, outputting a result; otherwise, jump to step S35: until convergence.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811535876.0A CN109886446B (en) | 2018-12-14 | 2018-12-14 | Dynamic economic dispatching method of electric power system based on improved chaotic particle swarm algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811535876.0A CN109886446B (en) | 2018-12-14 | 2018-12-14 | Dynamic economic dispatching method of electric power system based on improved chaotic particle swarm algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109886446A CN109886446A (en) | 2019-06-14 |
| CN109886446B true CN109886446B (en) | 2023-04-21 |
Family
ID=66925171
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811535876.0A Active CN109886446B (en) | 2018-12-14 | 2018-12-14 | Dynamic economic dispatching method of electric power system based on improved chaotic particle swarm algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109886446B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110363343B (en) * | 2019-07-11 | 2020-09-11 | 水利部交通运输部国家能源局南京水利科学研究院 | Hybrid self-adaptive hydropower station group intelligent optimization scheduling method and system |
| CN110401210A (en) * | 2019-07-22 | 2019-11-01 | 国网天津市电力公司 | Demand response participates in lower wind-powered electricity generation energy-storage system dispatching method |
| CN112926185B (en) * | 2021-01-26 | 2022-11-22 | 吉林化工学院 | Power economy emission scheduling method based on improved kernel search optimization |
| CN113131464A (en) * | 2021-03-31 | 2021-07-16 | 国电南瑞南京控制系统有限公司 | Multi-energy collaborative optimization method based on chaotic frog-leaping algorithm and storage medium |
| CN114066282A (en) * | 2021-11-23 | 2022-02-18 | 吉林化工学院 | Power economic emission scheduling method based on hybrid nuclear search and slime bacteria optimization |
| CN114498654B (en) * | 2022-04-18 | 2022-08-16 | 国网江西省电力有限公司电力科学研究院 | Voltage optimization method for small hydropower-contained access power distribution network based on variable-scale chaotic algorithm |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103337861A (en) * | 2013-05-31 | 2013-10-02 | 上海交通大学 | Power distribution network reactive power optimization method based on gold chaotic ecological niche particle swarm algorithm |
| CN104036331A (en) * | 2014-05-30 | 2014-09-10 | 国家电网公司 | Dynamic and economical dispatching method of power system based on improved particle swarm optimization |
| CN104850908A (en) * | 2015-05-22 | 2015-08-19 | 上海电力学院 | Chaos particle swarm based thermal power unit load optimal distribution method |
| CN106169109A (en) * | 2016-08-17 | 2016-11-30 | 国网江西省电力公司柘林水电厂 | A kind of Optimized Scheduling of Hydroelectric Power method based on chaos difference particle cluster algorithm |
| CN106712035A (en) * | 2017-03-29 | 2017-05-24 | 南方电网科学研究院有限责任公司 | Electric system economical dispatching method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106549394B (en) * | 2016-12-07 | 2018-11-27 | 东北大学 | Electric power idle work optimization system and method based on double fish-swarm algorithms |
-
2018
- 2018-12-14 CN CN201811535876.0A patent/CN109886446B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103337861A (en) * | 2013-05-31 | 2013-10-02 | 上海交通大学 | Power distribution network reactive power optimization method based on gold chaotic ecological niche particle swarm algorithm |
| CN104036331A (en) * | 2014-05-30 | 2014-09-10 | 国家电网公司 | Dynamic and economical dispatching method of power system based on improved particle swarm optimization |
| CN104850908A (en) * | 2015-05-22 | 2015-08-19 | 上海电力学院 | Chaos particle swarm based thermal power unit load optimal distribution method |
| CN106169109A (en) * | 2016-08-17 | 2016-11-30 | 国网江西省电力公司柘林水电厂 | A kind of Optimized Scheduling of Hydroelectric Power method based on chaos difference particle cluster algorithm |
| CN106712035A (en) * | 2017-03-29 | 2017-05-24 | 南方电网科学研究院有限责任公司 | Electric system economical dispatching method |
Non-Patent Citations (3)
| Title |
|---|
| "Online unit load economic dispatch based on chaotic-particle swarm optimization algorithm";Si Fengqi 等;《Proceedings of the CSEE》;20120621;第31卷(第26期);第103-109页 * |
| "基于粒子群优化算法的机组组合问题的研究";李整;《中国优秀博硕士学位论文全文数据库(博士)工程科技Ⅱ辑》;20170215(第02期);第C042-118页 * |
| "粒子群算法改进及其在电力系统的应用";黄平;《中国优秀博硕士学位论文全文数据库(博士)工程科技Ⅱ辑》;20130115(第01期);第C042-11页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109886446A (en) | 2019-06-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109886446B (en) | Dynamic economic dispatching method of electric power system based on improved chaotic particle swarm algorithm | |
| CN107887903B (en) | Micro-grid robust optimization scheduling method considering element frequency characteristics | |
| CN109980685B (en) | Uncertainty-considered active power distribution network distributed optimization operation method | |
| CN105846461B (en) | Control method and system for large-scale energy storage power station self-adaptive dynamic planning | |
| CN112186766B (en) | Multistage decomposition method based on Benders decomposition | |
| CN110266038B (en) | Distributed coordination regulation and control method for multiple virtual power plants | |
| CN110929964B (en) | Energy-storage-containing power distribution network optimal scheduling method based on approximate dynamic programming algorithm | |
| CN108471143A (en) | Micro-grid multi-energy method for optimizing scheduling based on positive and negative feedback particle cluster algorithm | |
| CN110070292B (en) | Micro-grid economic dispatching method based on cross variation whale optimization algorithm | |
| CN106712035B (en) | A kind of Economic Dispatch method | |
| CN105896575B (en) | Hundred megawatt energy storage power control method and system based on self-adaptive dynamic programming | |
| CN113054688B (en) | Renewable energy cluster output distributed coordination control method considering uncertainty | |
| CN112994022A (en) | Source-storage-load distributed cooperative voltage control method and system thereof | |
| CN107968430A (en) | Consider the defeated collaboration stochastic programming method of storage of wind-storage association system probabilistic model | |
| CN113285490B (en) | Power system scheduling method, device, computer equipment and storage medium | |
| CN106684913B (en) | Multi-agent-based energy storage power station tracking power generation plan control system and method | |
| CN111276968A (en) | Singular perturbation-based distributed convergence control method and system for comprehensive energy system | |
| CN110867907B (en) | Power system scheduling method based on multi-type power generation resource homogenization | |
| CN110445186B (en) | Self-synchronizing microgrid control system and secondary frequency modulation control method | |
| CN116667325A (en) | Micro-grid-connected operation optimization scheduling method based on improved cuckoo algorithm | |
| US20240006891A1 (en) | Two-stage self-organizing optimized aggregation method and system for distributed resources of virtual power plant (vpp) | |
| CN116979611A (en) | Hierarchical optimization scheduling method for source network load storage | |
| CN116191421A (en) | Novel power system multi-objective optimized scheduling method based on improved NSGA-II algorithm | |
| CN107104429B (en) | power system load scheduling method considering distributed energy storage system | |
| CN114398777A (en) | Power system flexibility resource allocation method based on Bashi game theory |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |