CN111463791A - Optimal power flow optimization method for comprehensive energy system - Google Patents

Optimal power flow optimization method for comprehensive energy system Download PDF

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CN111463791A
CN111463791A CN202010124060.XA CN202010124060A CN111463791A CN 111463791 A CN111463791 A CN 111463791A CN 202010124060 A CN202010124060 A CN 202010124060A CN 111463791 A CN111463791 A CN 111463791A
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stage
value
algorithm
power flow
limits
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周丹
汪蕾
孙可
郑伟民
李颖毅
郑朝明
张全明
刘业伟
童伟
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Zhejiang University of Technology ZJUT
State Grid Zhejiang Electric Power Co Ltd
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State Grid Zhejiang Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • 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
    • H02J3/48—Controlling the sharing of the in-phase component
    • 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
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • 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
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y02A30/60—Planning or developing urban green infrastructure

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Abstract

An optimal power flow optimization method of an integrated energy system comprises the following steps of 1, defining input data of the integrated energy network, 2, initializing population members within a feasible range by using limits specified in the previous step, 3, performing a power flow stage, 4, selecting an optimal individual as a teacher, and operating a teacher stage of an MT L BO algorithm, 5, limiting variables of the optimal individual according to inequality constraints if a certain value exceeds the boundary of the optimal individual, and then operating the power flow stage, 6, replacing the optimal individual, 7, operating a learning stage of the MT L BO algorithm, 8, respectively executing the power flow stage and a replacing stage, 9, operating an improvement stage of the MT L BO algorithm, 10, respectively executing the power flow stage and the replacing stage, 11, turning to the step 4 until the maximum iteration number is reached, effectively improving the convergence characteristic, and enabling the result to have high accuracy.

Description

Optimal power flow optimization method for comprehensive energy system
Technical Field
The invention relates to an optimal power flow optimization method for an integrated energy system, which solves the problem of optimal power flow calculation of the integrated energy system based on a teaching optimization algorithm.
Background
In recent years, in order to deal with the problems of energy crisis, environmental pollution and the like, how to realize efficient utilization, complementary fusion and clean conversion of various energy sources is very important. While the common energy infrastructures (e.g., power, gas and local heating systems) are mostly planned and operated independently, the independent methods employed to process these energy carriers may be disadvantageous for achieving optimal energy operation. Therefore, an Integrated Energy System (IES) is an effective way to realize integrated management and economic dispatch of various energy sources and to improve energy utilization, and is a development trend in the future.
As the coupling degree between each energy system is continuously deepened, the operation of a single system is restricted by the operation state of the system coupled with the single system; meanwhile, the coupling device also has a great influence on the power flow of each system. Optimal power Flow (OEF) is an effective tool for analyzing the safe economic operation of IES. The optimal power flow of the IES system can realize the stable operation state of the comprehensive energy combination system with optimal economy by adjusting the available electric, thermal and pneumatic control means in the system under the condition of meeting the operation and safety constraint conditions of the electric, thermal and pneumatic systems and the like.
In recent years, integration of various energy networks has been discussed in several documents, analyzing the optimal power flow problem of IES, where the concept of energy hubs has been proposed and a unified framework for modeling and supporting integrated energy delivery systems has been discussed. However, if the total number of inputs to the energy hub is greater than the total number of outputs from its energy hub, an irregular set of equations may arise. In order to solve the problem, some documents use some virtual variables, convert the irregular equations into a rule set, and propose a multi-agent genetic algorithm for optimization. Although this approach can successfully solve the mentioned problem, several equalities and inequalities are added to the formula, which increases the complexity of the problem.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an optimal power flow optimization method of an integrated energy system, which effectively improves the convergence characteristic and enables the result to have high accuracy.
The technical scheme adopted by the invention for solving the technical problems is as follows:
an optimal power flow optimization method of an integrated energy system comprises the following steps:
step 1: defining input data for an integrated energy network, the load levels including electrical and thermal loads; limits for specified variables, including active and reactive power limits for the generator, voltage amplitude, pressure and temperature limits; defining the compression ratio limit of a compressor in a natural gas network and the limitation of equipment used in an energy hub;
step 2: initializing population members within the feasible range by using the limit specified in the previous step;
and step 3: and a tide stage: the input power of each energy hub is determined according to its structure. Operating electricity, gas and heat flows, and calculating the fitness value of an objective function of each member of the population;
step 4, selecting the best individual as a teacher, and using formulas (1) to (3) to run a teacher stage of the MT L BO algorithm;
let Mi and Ti be students and teachers of the ith iteration, Ti tries to promote Mi to the level of the students and teachers, and the teacher stage is expressed as:
κi＝round(1+randi) (1)
DMi＝randi×(Ti-κiMi) (2)
Figure BDA0002393877480000021
wherein rand is [0,1 ]]Random number in between, and kiThe teaching factor is used for determining the mean value to be changed, and if the fitness function value of the Xi is better than that of the old, the new value of the Xi can be accepted;
and 5: if a certain value exceeds the boundary, limiting the variable of the certain value according to inequality constraints from (9) to (22), and then operating the power flow stage;
the variable constraints in the integrated energy system are defined by inequality constraints that are used to obtain a feasible operating point for the problem, the following equations being inequality constraints:
Figure BDA0002393877480000031
Figure BDA0002393877480000032
Figure BDA0002393877480000033
Figure BDA0002393877480000034
Figure BDA0002393877480000035
Figure BDA0002393877480000036
Figure BDA0002393877480000037
Figure BDA0002393877480000038
Figure BDA0002393877480000039
Figure BDA00023938774800000310
Figure BDA00023938774800000311
Figure BDA00023938774800000312
Figure BDA00023938774800000313
Figure BDA00023938774800000314
wherein equations (9) and (10) define the margins of active and reactive power produced by the generator; equations (11) - (14) specify voltage amplitude limits in the electrical sub-network, pressure limits in the natural gas sub-network and temperature limits in the district heating sub-network, respectively; equation (15) specifies the compression ratio margin of the compressor; the thermal energy limits of the cogeneration and the boiler are defined by equations (16) and (17), respectively, (18) and (19) specify the temperature margins of the cogeneration and the boiler, respectively, and finally, the flow limits of the transmission lines of all sub-networks are defined by (20) - (22);
step 6: and (3) replacement stage: checking whether the new individual member has a better fitness value than the old population, and replacing the old individual with the new enhanced individual;
step 7, executing MT L BO algorithm learning by using the formula (4)Stage, randomly selecting two learners
Figure BDA00023938774800000315
And
Figure BDA00023938774800000316
where i ≠ j, then:
Figure BDA0002393877480000041
also, if XiIs better than the old, X is acceptableiA new value of (d);
and 8: respectively executing a trend stage and a replacement stage;
step 9, running the improvement stage of the MT L BO algorithm by using the formulas (5) to (8);
defining a mutation probability P for each learnerXWith a value of [0,1]Then generates a random number between 0 and 1 and combines it with PXComparing; if the random number is equal to or less than PXThen, a mutation occurs, and the new position of the learner is calculated by:
Figure BDA0002393877480000042
wherein, in each iteration, WiIs the worst student, and likewise, is on new XiDepending on whether its fitness value is improved, ξ is calculated by the Morlet wavelet function, as follows:
Figure BDA0002393877480000043
wherein ω iscIs the center frequency of the wavelet;
Figure BDA0002393877480000044
random numbers of + -2.5 h, larger values of ξ make larger alterations to the mutation and vice versa, and furthermore, positive values of ξ make the mutationHeterostudents tend toward the teacher, conversely, if ξ is negative, the mutant learner will exit from the worst learner position since 99% of the total energy of the mother wavelet function lies in the interval [2.5, +2.5]Thus in [2.5h, +2.5h]Randomly generating parameters between
Figure BDA0002393877480000045
In which case the inflation parameter h is successively changed to reach an adjusted value, set initially to a smaller value, making the value of | ξ | large enough to create a larger search space, then increased in each iteration to make the value of | ξ | smaller, making the search space smaller, and as a result h is calculated as
Figure BDA0002393877480000046
Wherein k and kmaxCurrent iteration and total number of iterations, respectively, the upper limit and shape format of the increasing function of h may be determined by
Figure BDA0002393877480000047
And sigma definition, the performance of the algorithm is seriously influenced by the value of sigma, and the value of sigma is correctly defined in order to obtain the exploration capability of the algorithm and the accuracy of a final result; for this purpose, it should be set to a small value at the beginning and the number of iterations is increased successively, as follows:
Figure BDA0002393877480000048
wherein the upper limit and the lower limit of the sigma are respectively formed by the sigmamaxAnd σminDefining;
step 10: respectively executing a trend stage and a replacement stage;
step 11: go to step 4 until the maximum number of iterations is reached.
In the present invention, the optimization algorithm T L BO (Teaching-based optimization) is a new method based on selecting an appropriate state variable set for the problem, which can avoid adding any new variable while the optimization problem can still be solved, and the inspiration thereof comes from the influence of the teacher on the learner in the classroom. compared with other evolutionary algorithms, the method has the main advantage that T L BO is a technique without algorithm parameters, and the effectiveness of the method is not influenced by algorithm parameters such as genetic algorithm, particle swarm optimization and ant colony optimization, and the improved T L BO method, namely MT L BO (modified-learning-based optimization) can effectively improve the convergence property by adding an improved stage in the algorithm, so that the result has high accuracy.
The invention has the following beneficial effects: effectively improves the convergence characteristic and ensures that the result has high accuracy.
Drawings
Fig. 1 is a flow chart of an optimal power flow optimization method of an integrated energy system.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, a method for optimizing optimal power flow of an integrated energy system includes the following steps:
step 1: input data defining the integrated energy network, such as electrical wire data, specifications of natural gas and heat pipelines, parameters of hub units (e.g. CHP, boiler and circulation pumps), load levels including electrical and thermal loads; limits for specified variables, such as active and reactive power limits for the generator, voltage amplitude, pressure and temperature limits; defining the compression ratio limit of a compressor in a natural gas network and the limitation of equipment used in an energy hub;
step 2: initializing population members within the feasible range by using the limit specified in the previous step;
and step 3: (tidal flow stage): the input power of each energy hub is determined according to its structure. Operating electricity, gas and heat flows, and calculating the fitness value of an objective function of each member of the population;
step 4, selecting the best individual as a teacher, and using formulas (1) to (3) to run a teacher stage of the MT L BO algorithm;
let Mi and Ti be students and teachers of the ith iteration, Ti tries to promote Mi to the level of the students and teachers, and the teacher stage is expressed as:
κi＝round(1+randi) (1)
DMi＝randi×(Ti-κiMi) (2)
Figure BDA0002393877480000061
wherein rand is [0,1 ]]Random number in between, and kiThe teaching factor is used for determining the mean value to be changed, and if the fitness function value of the Xi is better than that of the old, the new value of the Xi can be accepted;
and 5: if a certain value exceeds the boundary, limiting the variable of the certain value according to inequality constraints from (9) to (22), and then operating the power flow stage;
the variable constraints in the integrated energy system are defined by inequality constraints that are used to obtain a feasible operating point for the problem, the following equations being inequality constraints:
Figure BDA0002393877480000062
Figure BDA0002393877480000063
Figure BDA0002393877480000064
Figure BDA0002393877480000065
Figure BDA0002393877480000066
Figure BDA0002393877480000067
Figure BDA0002393877480000068
Figure BDA0002393877480000069
Figure BDA00023938774800000610
Figure BDA00023938774800000611
Figure BDA0002393877480000071
Figure BDA0002393877480000072
Figure BDA0002393877480000073
Figure BDA0002393877480000074
wherein equations (9) and (10) define the margins of active and reactive power produced by the generator; equations (11) - (14) specify voltage amplitude limits in the electrical sub-network, pressure limits in the natural gas sub-network and temperature limits in the district heating sub-network, respectively; equation (15) specifies the compression ratio margin of the compressor; the thermal energy limits of the cogeneration and the boiler are defined by equations (16) and (17), respectively, (18) and (19) specify the temperature margins of the cogeneration and the boiler, respectively, and finally, the flow limits of the transmission lines of all sub-networks are defined by (20) - (22);
step 6: and (3) replacement stage: checking whether the new individual member has a better fitness value than the old population, and replacing the old individual with the new enhanced individual;
step 7, using the formula (4) to run the learning phase of the MT L BO algorithm, and randomly selecting two learners
Figure BDA0002393877480000075
And
Figure BDA0002393877480000076
where i ≠ j, then:
Figure BDA0002393877480000077
also, if XiIs better than the old, X is acceptableiA new value of (d);
and 8: respectively executing a trend stage and a replacement stage;
step 9, running the improvement stage of the MT L BO algorithm by using the formulas (5) to (8);
defining a mutation probability P for each learnerXWith a value of [0,1]Then generates a random number between 0 and 1 and combines it with PXComparing; if the random number is equal to or less than PXThen, a mutation occurs, and the new position of the learner is calculated by:
Figure BDA0002393877480000078
wherein, in each iteration, WiIs the worst student, and likewise, is on new XiDepending on whether its fitness value is improved, ξ is calculated by the Morlet wavelet function, as follows:
Figure BDA0002393877480000081
wherein ω iscIs the center frequency of the wavelet;
Figure BDA0002393877480000082
a random number between 2.5h, a larger value of ξ greatly modifies the mutation and vice versa, furthermore, a positive value of ξ tends the mutated student towards the teacher, whereas if ξ is negative, the mutated learner will exit from the worst learner position, since 99% of the total energy of the mother wavelet function lies in the interval 2.5, +2.5]Thus in [2.5h, +2.5h]Randomly generating parameters between
Figure BDA0002393877480000083
In which case the inflation parameter h is successively changed to reach an adjusted value, set initially to a smaller value, making the value of | ξ | large enough to create a larger search space, then increased in each iteration to make the value of | ξ | smaller, making the search space smaller, and as a result h is calculated as
Figure BDA0002393877480000084
Wherein k and kmaxCurrent iteration and total number of iterations, respectively, the upper limit and shape format of the increasing function of h may be determined by
Figure BDA0002393877480000085
And sigma definition, the performance of the algorithm is seriously influenced by the value of sigma, and the value of sigma is correctly defined in order to obtain the exploration capability of the algorithm and the accuracy of a final result; for this purpose, it should be set to a small value at the beginning and the number of iterations is increased successively, as follows:
Figure BDA0002393877480000086
wherein the upper limit and the lower limit of the sigma are respectively formed by the sigmamaxAnd σminDefining;
step 10: respectively executing a trend stage and a replacement stage;
step 11: go to step 4 until the maximum number of iterations is reached.

Claims (2)

1. An optimal power flow optimization method for an integrated energy system is characterized by comprising the following steps:
step 1: defining input data for an integrated energy network, the load levels including electrical and thermal loads; limits for specified variables, including active and reactive power limits for the generator, voltage amplitude, pressure and temperature limits; defining the compression ratio limit of a compressor in a natural gas network and the limitation of equipment used in an energy hub;
step 2: initializing population members within the feasible range by using the limit specified in the previous step;
and step 3: and a tide stage: determining the input power, operating electricity, gas and heat flow of each energy hub according to the structure of the energy hub, and calculating the fitness value of an objective function of each member of the population;
step 4, selecting the best individual as a teacher and operating a teacher stage of the MT L BO algorithm;
let Mi and Ti be students and teachers of the ith iteration, Ti tries to promote Mi to the level of the students and teachers, and the teacher stage is expressed as:
κi＝round(1+randi) (1)
DMi＝randi×(Ti-κiMi) (2)
Figure FDA0002393877470000011
wherein rand is [0,1 ]]Random number in between, and kiThe teaching factor is used for determining the mean value to be changed, and if the fitness function value of the Xi is better than that of the old, the new value of the Xi can be accepted;
and 5: if a certain value exceeds the boundary, limiting the variable according to inequality constraint, and then operating a trend stage;
step 6: and (3) replacement stage: checking whether the new individual member has a better fitness value than the old population, and replacing the old individual with the new enhanced individual;
step 7, using the formula (4) to run the learning phase of the MT L BO algorithm, and randomly selecting two learners
Figure FDA0002393877470000012
And
Figure FDA0002393877470000013
where i ≠ j, then:
Figure FDA0002393877470000014
also, if XiIs better than the old, X is acceptableiA new value of (d);
and 8: respectively executing a trend stage and a replacement stage;
step 9, running the improvement stage of the MT L BO algorithm by using the formulas (5) to (8);
defining a mutation probability P for each learnerXWith a value of [0,1]Then generates a random number between 0 and 1 and combines it with PXComparing; if the random number is equal to or less than PXThen, a mutation occurs, and the new position of the learner is calculated by:
Figure FDA0002393877470000015
wherein, in each iteration, WiIs the worst student, and likewise, is on new XiDepending on whether its fitness value is improved, ξ is calculated by the Morlet wavelet function, as follows:
Figure FDA0002393877470000016
whereinωcIs the center frequency of the wavelet;
Figure FDA0002393877470000017
a random number between 2.5h, a larger value of ξ greatly modifies the mutation and vice versa, furthermore, a positive value of ξ tends the mutated student towards the teacher, whereas if ξ is negative, the mutated learner will exit from the worst learner position, since 99% of the total energy of the mother wavelet function lies in the interval 2.5, +2.5]Thus in [2.5h, +2.5h]Randomly generating parameters between
Figure FDA0002393877470000021
In which case the inflation parameter h is successively changed to reach an adjusted value, set initially to a smaller value, making the value of | ξ | large enough to create a larger search space, then increased in each iteration to make the value of | ξ | smaller, making the search space smaller, and as a result h is calculated as
Figure FDA0002393877470000022
Wherein k and kmaxCurrent iteration and total number of iterations, respectively, the upper limit and shape format of the increasing function of h may be determined by
Figure FDA0002393877470000023
And sigma definition, the performance of the algorithm is seriously influenced by the value of sigma, and the value of sigma is correctly defined in order to obtain the exploration capability of the algorithm and the accuracy of a final result; for this purpose, it should be set to a small value at the beginning and the number of iterations is increased successively, as follows:
Figure FDA0002393877470000024
wherein the upper limit and the lower limit of the sigma are respectively formed by the sigmamaxAnd σminDefining;
step 10: respectively executing a trend stage and a replacement stage;
step 11: go to step 4 until the maximum number of iterations is reached.
2. The optimal power flow optimization method for the integrated energy system according to claim 1, wherein in the step 5, the variables are limited according to inequality constraints from (9) to (22), and the power flow stage is operated again; the variable constraints in the integrated energy system are defined by inequality constraints that are used to obtain a feasible operating point for the problem, the following equations being inequality constraints:
Figure FDA0002393877470000025
Figure FDA0002393877470000026
Figure FDA0002393877470000027
Figure FDA0002393877470000028
Figure FDA0002393877470000029
Figure FDA00023938774700000210
Figure FDA00023938774700000211
Figure FDA00023938774700000212
Figure FDA00023938774700000213
Figure FDA00023938774700000214
Figure FDA00023938774700000215
Figure FDA00023938774700000216
Figure FDA0002393877470000031
Figure FDA0002393877470000032
wherein equations (9) and (10) define the margins of active and reactive power produced by the generator; equations (11) - (14) specify voltage amplitude limits in the electrical sub-network, pressure limits in the natural gas sub-network and temperature limits in the district heating sub-network, respectively; equation (15) specifies the compression ratio margin of the compressor; the thermal energy limits of the cogeneration and the boiler are defined by equations (16) and (17), respectively, (18) and (19) specify the temperature margins of the cogeneration and the boiler, respectively, and finally, the flow limits of the transmission lines of all sub-networks are defined by (20) - (22).
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