CN113947291A - Multi-mode distributed multi-target layered intelligent comprehensive energy system scheduling method - Google Patents

Multi-mode distributed multi-target layered intelligent comprehensive energy system scheduling method Download PDF

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CN113947291A
CN113947291A CN202111140307.8A CN202111140307A CN113947291A CN 113947291 A CN113947291 A CN 113947291A CN 202111140307 A CN202111140307 A CN 202111140307A CN 113947291 A CN113947291 A CN 113947291A
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殷林飞
蔡镇键
孙志响
高放
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Abstract

The invention provides a multi-mode distributed multi-target layered intelligent comprehensive energy system dispatching method, which combines a multi-layer distributed multi-target consistency method and a multi-mode multi-target intelligent method and is used for intelligent dispatching of a comprehensive energy system. Firstly, the multilayer distributed multi-target consistency method in the method is used for solving the problems of calculation speed, robustness and information privacy of the system when the scale of the interconnected comprehensive energy system is increased and the number of intelligent agents is increased. Secondly, the multi-mode multi-target intelligent method is used for solving the multi-mode characteristic problem in multi-mode multi-target scheduling. The method utilizes multi-region layered parallel processing to improve the optimization speed of the comprehensive energy system, solves the problem that one target space pareto front corresponds to two or more optimal pareto solution sets in a decision space in the multi-mode multi-target scheduling problem, and provides various optional solution sets.

Description

Multi-mode distributed multi-target layered intelligent comprehensive energy system scheduling method
Technical Field
The invention belongs to the field of dispatching of power systems, novel power systems and comprehensive energy systems, and particularly relates to a multi-mode distributed multi-target hierarchical intelligent dispatching method.
Background
Aiming at the scheduling problem of multi-mode distributed multi-target layering of the comprehensive energy system, a plurality of local/global optimal pareto solution sets which accord with constraint conditions exist in a multi-target optimization decision space, but only one optimal pareto solution set is obtained after final multi-target optimization, so that the final solution set has no diversity, the requirements of decision makers cannot be met, and the scheduling of a large-scale interconnected power system loses diversity.
Therefore, the method can keep the characteristics of multiple modes, can accelerate the speed of processing problems of the system, ensures the privacy and the robustness of the system and can provide diversified choices for the scheduling of the comprehensive energy system in the scheduling problem of the multiple-mode distributed multiple-target layering of the comprehensive energy system.
Disclosure of Invention
The invention provides a multi-mode distributed multi-target layered intelligent comprehensive energy system dispatching method, which combines a multi-layer distributed multi-target consistency method and a multi-mode multi-target intelligent method and is used for intelligent dispatching of a comprehensive energy system; the method comprises the following steps in the using process:
(1) constructing an optimal power flow model of the comprehensive energy system, and taking the power generation cost and the carbon emission as targets, and following the equality constraint of economic dispatching, the power inequality constraint of economic dispatching and the standby inequality constraint of economic dispatching;
the objective function for minimizing the generation cost and carbon emissions targets is:
Figure BDA0003283495600000011
wherein f is1(x) The cost of electricity generation; f. of2(x) Carbon emissions;
Figure BDA0003283495600000012
at time tCost of the ith conventional generator set;
Figure BDA0003283495600000013
the cost of the jth wind generating set at the moment t is shown;
Figure BDA0003283495600000014
the cost of the z-th solar photovoltaic generator set at the moment t is calculated;
Figure BDA0003283495600000015
cost of the kth hydroelectric generating set at the moment t;
Figure BDA0003283495600000016
the cost of the first geothermal energy generating set at the time t;
Figure BDA0003283495600000017
the carbon emission of the ith conventional generator set at the moment t; n is a radical ofGEThe number of conventional generator sets; n is a radical ofWEThe number of the wind generating sets; n is a radical ofPEThe number of the solar photovoltaic generator sets; n is a radical ofHEThe number of hydroelectric generating sets; n is a radical ofOEThe number of geothermal energy generator sets; t is the statistical time of the objective function; and satisfies the following conditions:
Figure BDA0003283495600000018
wherein,
Figure BDA0003283495600000019
generating capacity of the ith conventional generator set at the moment t;
Figure BDA00032834956000000110
the power generation amount of the jth wind generating set at the moment t;
Figure BDA00032834956000000111
for the power generation of the z-th solar wind generating set at the moment of tAn amount of electricity;
Figure BDA00032834956000000112
generating capacity of the kth hydroelectric generating set at the moment t;
Figure BDA00032834956000000113
the generated energy of the first geothermal energy generating set at the moment t; a isiThe cost coefficient secondary term of the ith conventional generator set is obtained; biThe cost coefficient of the ith conventional generator set is a primary term; c. CiThe cost coefficient constant term is the cost coefficient constant term of the ith conventional generator set; djThe unit generating economic cost of the jth wind generating set; e.g. of the typezThe economic cost of the unit power generation of the z-th solar photovoltaic generator set is the unit power generation economic cost; gkThe unit generating economic cost of the kth hydroelectric generating set; m islThe unit electricity generation economic cost of the first geothermal energy generating set; alpha is alphaiThe second order term of the carbon emission coefficient of the ith conventional generator set; beta is aiIs a primary term of the carbon emission coefficient of the ith conventional generator set; gamma rayiThe constant term is the carbon emission coefficient of the ith conventional generator set;
the equality constraint for economic dispatch is:
Figure BDA0003283495600000021
the power inequality constraint of economic dispatch is:
Figure BDA0003283495600000022
wherein,
Figure BDA0003283495600000023
is the predicted load value at time t;
Figure BDA0003283495600000024
the lower limit of the generating capacity of the ith conventional generator set;
Figure BDA0003283495600000025
the upper limit of the generating capacity of the ith conventional generator set is set;
Figure BDA0003283495600000026
the lower limit of the power generation amount of the jth wind generating set;
Figure BDA0003283495600000027
the upper limit of the power generation amount of the jth wind generating set is set;
Figure BDA0003283495600000028
the lower limit of the generating capacity of the z-th solar photovoltaic generator set;
Figure BDA0003283495600000029
the upper limit of the generating capacity of the z-th solar photovoltaic generator set is set;
Figure BDA00032834956000000210
the lower limit of the generating capacity of the kth hydroelectric generating set;
Figure BDA00032834956000000211
the upper limit of the generating capacity of the kth hydroelectric generating set;
Figure BDA00032834956000000212
the lower limit of the generating capacity of the first geothermal energy generating set;
Figure BDA00032834956000000213
the upper limit of the generating capacity of the first geothermal energy generating set;
Figure BDA00032834956000000214
the power generation amount of the conventional generator set at the t-1 moment;
Figure BDA00032834956000000215
the generated energy of the conventional generator set at the t moment;
Figure BDA00032834956000000216
the value is the downward climbing value of the conventional generator set;
Figure BDA00032834956000000217
the value is the upward climbing value of the conventional generator set; t is6060 minutes;
the standby inequality constraint for economic dispatch is:
Figure BDA00032834956000000218
wherein,
Figure BDA00032834956000000219
the value is the positive rotation standby value of the wind driven generator set at the moment t;
Figure BDA00032834956000000220
the positive rotation standby value of the jth wind driven generator set at the moment t;
Figure BDA00032834956000000221
the value is a negative rotation standby value of the wind turbine generator at the moment t;
Figure BDA00032834956000000222
the negative rotation standby value of the jth wind driven generator set at the moment t;
Figure BDA0003283495600000031
the value is a positive rotation standby value of the solar photovoltaic generator set at the moment t;
Figure BDA0003283495600000032
the positive rotation standby value of the z-th solar photovoltaic generator set at the moment t;
Figure BDA0003283495600000033
the value is a negative rotation standby value of the solar photovoltaic generator set at the moment t;
Figure BDA0003283495600000034
the negative rotation standby value of the z-th solar photovoltaic generator set at the moment t;
Figure BDA0003283495600000035
is the positive rotation standby value of the hydroelectric generator set at the moment t;
Figure BDA0003283495600000036
is the positive rotation standby value of the kth hydroelectric generator set at the moment t;
Figure BDA0003283495600000037
is a negative rotation standby value of the hydroelectric generating set at the moment t;
Figure BDA0003283495600000038
the negative rotation standby value of the kth hydroelectric generator set at the moment t;
Figure BDA0003283495600000039
is the positive rotation standby value of the geothermal energy generator set at the moment t;
Figure BDA00032834956000000310
the value is the positive rotation standby value of the first geothermal energy generator set at the moment t;
Figure BDA00032834956000000311
the value is a negative rotation standby value of the geothermal energy generator set at the moment t;
Figure BDA00032834956000000312
the value is the negative rotation standby value of the first geothermal energy generator set at the moment t; l isW+% is the load positive rotation standby demand coefficient in the wind driven generator set; l isW-% is the spare demand coefficient of load negative rotation in the wind driven generator set; l isP+% is the load positive rotation standby demand coefficient in the solar photovoltaic generator set; l isP-% is for standby in solar photovoltaic generator set under load negative rotationA demand factor; l isH+% is the load positive rotation standby demand coefficient in the hydroelectric generator set; l isH-% is the spare demand coefficient of load negative rotation in the hydroelectric generator set; l isO+% is the load positive rotation standby demand coefficient in the geothermal energy generator set; l isO-% is the spare demand coefficient of load negative rotation in the geothermal energy generator set; wu% is the positive rotation standby demand coefficient of the wind power generator set; wd% is the negative rotation standby demand coefficient of the wind power generator set; pu% is the positive rotation standby demand coefficient of the solar photovoltaic generator set; pd% is the negative rotation standby demand coefficient of the solar photovoltaic generator set; hu% is the positive rotation standby demand coefficient of the hydroelectric generator set; hd% is the negative rotation standby demand coefficient of the hydroelectric generator set; o isu% is the positive rotation standby demand coefficient of the geothermal energy generator set; o isd% is the negative rotation standby demand coefficient of the geothermal energy generator set; t is10Is 10 minutes;
(2) the method comprises the steps of solving the problems of multi-modal characteristics and multi-target scheduling by using a multi-modal multi-target intelligent method, firstly adopting a niche strategy, and selecting more various and better individuals as parent populations of each generation; secondly, a special selection mechanism is utilized to increase selection pressure, improve diversity and accelerate convergence speed; finally, by using the special crowding distance, simultaneously considering the distance of the candidate solutions in the decision space and the target space, obtaining an optimal pareto set which is not independent between solution sets, solving the multi-modal characteristics in the scheduling problem, providing more various candidate solutions, simultaneously using the characteristics of knee joint points and knee joint areas, and providing the most appropriate optimal solution under the condition of no preference;
f in the formula (1)1(x) And f2(x) Respectively serving as a horizontal axis coordinate and a vertical axis coordinate of a two-dimensional coordinate system, and taking multi-modal characteristics of the two-dimensional coordinate system into consideration, after a series of feasible solutions are obtained in a decision space and a target space, performing multi-modal multi-objective optimization on the feasible solutions to obtain a proper optimal pareto solution set, wherein the method comprises the following steps of:
step 2.1: setting an initial iteration time t as 1;
step 2.2: taking two mutually contradictory target quantities as a horizontal axis and a vertical axis of a two-dimensional coordinate system, and putting the two contradictory target quantities into the same coordinate system for consideration;
step 2.3: initializing parameters, wherein the iteration number n of other multi-mode and multi-target is 0;
step 2.4: generating a population with the size of M in the range of feasible solutions;
step 2.5: embedding a niche strategy in championship selection to select a mating pool of a parent population;
step 2.6: performing simulated binary intersection and polynomial variation on the parent population by using a traditional genetic method to generate a child population, then combining the parent population and the child population, and performing non-dominated sorting operation;
step 2.7: finding knee joint points and knee joint areas on the front edge of the optimal pareto;
step 2.8: according to the non-dominated sorting, knee joint points and knee joint areas are identified, and special crowding distances, environment selection is carried out to obtain M better individuals;
step 2.9: judging the iteration times, if the iteration times are not met, returning n to n +1 to the step 2.5 to continue the iteration until the iteration times are met; if the number of iteration steps is met, performing step 2.10;
step 2.10: obtaining an optimal pareto solution set with multi-modal characteristics, and selecting a proper solution according to the preference of a decision maker;
(3) the problem of distributed scheduling of the comprehensive energy system is solved by utilizing a multilayer distributed multi-target consistency method, the comprehensive energy system is divided into a plurality of areas, each area is regarded as an intelligent agent to carry out independent optimization, after respective optimization problems are independently completed, internal variables of the areas are not exchanged, integral optimization can be realized only by exchanging boundary information among the areas, the privacy of the system is ensured, the problem solving speed of the comprehensive energy system is accelerated, and the method comprises the following steps:
step 3.1: inputting predicted load data, and optimizing the initial iteration step number k of each region to be 0;
step 3.2: exchanging boundary variables and target variables by combining the topological structure of the agent;
step 3.3: calculating the active power output of each unit;
step 3.4: correcting the active power output;
step 3.5: solving an active deviation value;
step 3.6: judging whether the active power deviation meets the requirement, if not, making k equal to k +1, and continuously returning to the step 3.2 for iteration; if the power generation quantity meets the requirements, ending the operation to obtain the optimal power generation quantity of each area;
(4) the problem of layered scheduling of the comprehensive energy system is solved by utilizing a multilayer distributed multi-target consistency method, the region divided in the first step is regarded as a first layer, the region is divided again in the region divided in the first layer, the region divided again is subjected to multi-mode multi-target distributed optimal scheduling, and the problem solving speed of the problem which is reduced due to the fact that the comprehensive energy system is continuously increased is increased because the problem solving speed of the second layer region divided in each region of the first layer is parallel when the problem is solved; and can carry out layering many times to the region, reduce the degree of difficulty of huge complicated comprehensive energy system solution problem, the step is:
step 4.1: respectively inputting the optimal power generation amounts calculated in the area 1, the area 2 and the area 3 in the first floor into the second floor as load values;
step 4.2: for the second layer of region 1, using the methods of step 2.1 to step 2.10 and step 3.1 to step 3.6: region 11, region 12, region 13; for the second layer of region 2: region 21, region 22, region 23; for the third layer of region 3: processing is performed in the area 31, the area 32 and the area 33;
step 4.3: the optimal power generation amounts calculated in the region 11, the region 12, the region 13, the region 21, the region 22, the region 23, the region 31, the region 32, and the region 33 are input to the third layer as load values, respectively;
step 4.4: for the third layer of region 11, using the methods of step 2.1 to step 2.10 and step 3.1 to step 3.6: region 111, region 112, region 113; for the third layer of region 12: region 121, region 122, region 123; for the third layer of region 13: region 131, region 132, region 133; for the third layer of region 21: region 211, region 212, region 213; for the third layer of region 22: region 221, region 222, region 223; for the third layer of region 23: region 231, region 232, region 233; for the third layer of region 31: region 311, region 312, region 313; for the area 321, the area 322, and the area 323 of the area 32; for the third layer of region 33: the area 331, the area 332, and the area 333 perform parallel processing;
step 4.5: inputting the optimal power generation amount obtained by parallel calculation of each area as a load to the next layer, and solving the problem by using the methods from step 2.1 to step 2.10 and from step 3.1 to step 3.6;
step 4.6: judging whether the conditions are met, if the conditions are not met, turning t to t +1 into step 2.2 to continue iteration; and if the conditions are met, finishing iteration and outputting the optimal power generation amount of each region.
Compared with the prior art, the invention has the following advantages and effects:
(1) the clean energy can be effectively utilized, and the problems of serious wind abandonment, light abandonment and water abandonment in partial areas are solved; aiming at the problem that the clean energy is greatly merged into the power grid so as to cause difficulty in consumption, an effective method is provided;
(2) a framework of a multi-mode distributed multi-target layered intelligent method is established for the economic dispatching problem of the comprehensive energy system;
(3) the multi-modal concept is introduced, and the conventional dispatching of the power system has not only the problem of multiple targets but also the problem of multiple modes, so that a plurality of schemes meeting constraint conditions exist when the problems are processed, but the multi-modal characteristics of the schemes are not considered in the prior dispatching;
(4) the constructed multi-mode distributed multi-target layered intelligent method solves the multi-mode characteristic problem in the dispatching of the comprehensive energy system on the basis of improving the speed and guaranteeing the privacy.
Drawings
FIG. 1 is a schematic diagram of a distributed hierarchy of the method of the present invention.
FIG. 2 is a schematic diagram of distributed multi-modal multi-objective of the method of the present invention.
FIG. 3 is a flow chart of a distributed multi-modal multi-objective method of the present invention method.
FIG. 4 is an overall flow chart of the method of the present invention.
Detailed Description
The invention provides a multi-mode distributed multi-target layered intelligent comprehensive energy system scheduling method, which is explained in detail by combining the accompanying drawings as follows:
FIG. 1 is a schematic diagram of a distributed hierarchy of the method of the present invention. Firstly, dividing a comprehensive energy system into three areas, namely an area 1, an area 2 and an area 3, which are first layers; secondly, dividing the area 1 into an area 11, an area 12 and an area 13, dividing the area 2 into an area 21, an area 22 and an area 23, and dividing the area 3 into an area 31, an area 32 and an area 33, which are second layers; then, the region of the second layer is divided into regions 111, 112, 113, 121, 122, 123, 131, 132, 133, 211, 212, 213, 221, 222, 223, 231, 232, 233, 311, 312, 313, 321, 322, 323, 331, 332, and 333. Each intelligent agent of each layer solves the problem independently, does not exchange internal information, only exchanges external information, and ensures the privacy of the system. Meanwhile, all the agents in each layer are parallel when solving problems, and the speed of solving the problems by the comprehensive energy system is improved. And distributed hierarchical scheduling of the comprehensive energy system is realized.
FIG. 2 is a schematic diagram of distributed multi-modal multi-objective of the method of the present invention. Taking the third layer of area inside the area 11 as an example, each of the areas 111, 112 and 113 is regarded as an intelligent agent to solve the problem independently, the optimal pareto frontier of the target space of each area corresponds to a plurality of optimal pareto solution sets of the decision space, so that the multi-modal characteristic is embodied, the multi-modal scheduling problem of the comprehensive energy scheduling system is solved by using a multi-objective multi-modal intelligent method, and the scheduling diversity is embodied.
FIG. 3 is a flow chart of a distributed multi-modal multi-objective method of the present invention method. The basic idea of the distributed multi-mode multi-target method is to reserve the multi-mode characteristics of a multi-target problem, provide a diversified optimal pareto solution set for a decision maker to select, and carry out overall scheduling by exchanging boundary areas of all intelligent agent areas, and the method comprises the following specific steps:
step 1: initializing parameters, and setting the multi-modal multi-objective optimization iteration number n as 0;
step 2: taking two mutually contradictory target quantities as a horizontal axis and a vertical axis of a two-dimensional coordinate system, and putting the two contradictory target quantities into the same coordinate system for consideration;
and step 3: generating a population with the size of M in the range of feasible solutions;
and 4, step 4: embedding a niche strategy in championship selection to select a mating pool of a parent population;
and 5: performing simulated binary intersection and polynomial variation on the parent population by using a traditional genetic method to generate a child population, then combining the parent population and the child population, and performing non-dominated sorting operation;
step 6: finding knee joint points and knee joint areas on the front edge of the optimal pareto;
and 7: according to the non-dominated sorting, knee joint points and knee joint areas are identified, and special crowding distances, environment selection is carried out to obtain M better individuals;
and 8: judging the iteration times, if the iteration times are not met, making n equal to n +1, and returning to the step 4 to continue the iteration until the iteration times are met; if the iteration times are met, the next step is carried out;
and step 9: obtaining an optimal pareto solution set with multi-modal characteristics, and selecting a proper solution according to the preference of a decision maker;
step 10: inputting predicted load data, and setting the initial iteration step number k to be 0;
step 11: exchanging boundary variables and target variables by combining the topological structure of the agent;
step 12: calculating the active power output of each unit;
step 13: correcting the active power output;
step 14: solving an active deviation value;
step 15: judging whether the active power deviation meets the requirement, if not, making k equal to k +1 and returning to the step 11 for iteration; and if the power generation quantity meets the requirement, ending the process to obtain the optimal power generation quantity of each area.
FIG. 4 is an overall flow chart of the method of the present invention. The method comprises the following specific steps:
step 1: setting an initial iteration time t as 1;
step 2: inputting a predicted load value, and setting the initial iteration step number k to be 0;
and step 3: dividing the comprehensive energy system into a region 1, a region 2 and a region 3, and regarding each region as an intelligent agent;
and 4, step 4: processing the region 1, the region 2 and the region 3 of the first layer by using a distributed multi-mode multi-target method in the figure 3;
and 5: respectively inputting the optimal power generation amounts calculated in the region 1, the region 2 and the region 3 into a second layer as load values;
step 6: for the second layer of region 1 using the distributed multi-modal multi-target approach in FIG. 3: region 11, region 12, region 13; for the second layer of region 2: region 21, region 22, region 23; for the third layer of region 3: processing is performed in the area 31, the area 32 and the area 33;
and 7: the optimal power generation amounts calculated in the region 11, the region 12, the region 13, the region 21, the region 22, the region 23, the region 31, the region 32, and the region 33 are input to the third layer as load values, respectively;
and 8: for the third layer of region 11 using the distributed multi-modal multi-target method of FIG. 3: region 111, region 112, region 113; for the third layer of region 12: region 121, region 122, region 123; for the third layer of region 13: region 131, region 132, region 133; for the third layer of region 21: region 211, region 212, region 213; for the third layer of region 22: region 221, region 222, region 223; for the third layer of region 23: region 231, region 232, region 233; for the third layer of region 31: region 311, region 312, region 313; for the area 321, the area 322, and the area 323 of the area 32; for the third layer of region 33: the area 331, the area 332, and the area 333 perform parallel processing;
and step 9: inputting the optimal power generation amount obtained by parallel calculation of each region as a load to the next layer, and solving by using the distributed multi-mode multi-target method in the figure 3;
step 10: judging whether T is less than or equal to T, if the condition is not met, switching T to T +1 to step 2 for continuing iteration; and if the conditions are met, finishing iteration and outputting the optimal power generation amount of each region.

Claims (1)

1. A multi-mode distributed multi-target layered intelligent comprehensive energy system dispatching method is characterized in that the method combines a multi-layer distributed multi-target consistency method and a multi-mode multi-target intelligent method and is used for intelligent dispatching of a comprehensive energy system; the method comprises the following steps in the using process:
(1) constructing an optimal power flow model of the comprehensive energy system, and taking the power generation cost and the carbon emission as targets, and following the equality constraint of economic dispatching, the power inequality constraint of economic dispatching and the standby inequality constraint of economic dispatching;
the objective function for minimizing the generation cost and carbon emissions targets is:
Figure FDA0003283495590000011
wherein f is1(x) The cost of electricity generation; f. of2(x) Carbon emissions;
Figure FDA0003283495590000012
the cost of the ith conventional generator set at the moment t;
Figure FDA0003283495590000013
the cost of the jth wind generating set at the moment t is shown;
Figure FDA0003283495590000014
the cost of the z-th solar photovoltaic generator set at the moment t is calculated;
Figure FDA0003283495590000015
cost of the kth hydroelectric generating set at the moment t;
Figure FDA0003283495590000016
the cost of the first geothermal energy generating set at the time t;
Figure FDA0003283495590000017
the carbon emission of the ith conventional generator set at the moment t; n is a radical ofGEThe number of conventional generator sets; n is a radical ofWEThe number of the wind generating sets; n is a radical ofPEThe number of the solar photovoltaic generator sets; n is a radical ofHEThe number of hydroelectric generating sets; n is a radical ofOEThe number of geothermal energy generator sets; t is the statistical time of the objective function; and satisfies the following conditions:
Figure FDA0003283495590000018
wherein,
Figure FDA0003283495590000019
generating capacity of the ith conventional generator set at the moment t;
Figure FDA00032834955900000110
the power generation amount of the jth wind generating set at the moment t;
Figure FDA00032834955900000111
the power generation amount of the z th solar wind generating set at the moment t;
Figure FDA00032834955900000112
generating capacity of the kth hydroelectric generating set at the moment t;
Figure FDA00032834955900000113
the generated energy of the first geothermal energy generating set at the moment t; a isiThe cost coefficient secondary term of the ith conventional generator set is obtained; biThe cost coefficient of the ith conventional generator set is a primary term; c. CiThe cost coefficient constant term is the cost coefficient constant term of the ith conventional generator set; djThe unit generating economic cost of the jth wind generating set; e.g. of the typezThe economic cost of the unit power generation of the z-th solar photovoltaic generator set is the unit power generation economic cost; gkThe unit generating economic cost of the kth hydroelectric generating set; m islThe unit electricity generation economic cost of the first geothermal energy generating set; alpha is alphaiThe second order term of the carbon emission coefficient of the ith conventional generator set; beta is aiIs a primary term of the carbon emission coefficient of the ith conventional generator set; gamma rayiThe constant term is the carbon emission coefficient of the ith conventional generator set;
the equality constraint for economic dispatch is:
Figure FDA00032834955900000114
the power inequality constraint of economic dispatch is:
Figure FDA0003283495590000021
wherein,
Figure FDA0003283495590000022
is the predicted load value at time t;
Figure FDA0003283495590000023
the lower limit of the generating capacity of the ith conventional generator set;
Figure FDA0003283495590000024
the upper limit of the generating capacity of the ith conventional generator set is set;
Figure FDA0003283495590000025
the lower limit of the power generation amount of the jth wind generating set;
Figure FDA0003283495590000026
the upper limit of the power generation amount of the jth wind generating set is set;
Figure FDA0003283495590000027
the lower limit of the generating capacity of the z-th solar photovoltaic generator set;
Figure FDA0003283495590000028
the upper limit of the generating capacity of the z-th solar photovoltaic generator set is set;
Figure FDA0003283495590000029
the lower limit of the generating capacity of the kth hydroelectric generating set;
Figure FDA00032834955900000210
the upper limit of the generating capacity of the kth hydroelectric generating set;
Figure FDA00032834955900000211
the lower limit of the generating capacity of the first geothermal energy generating set;
Figure FDA00032834955900000212
the upper limit of the generating capacity of the first geothermal energy generating set;
Figure FDA00032834955900000213
the power generation amount of the conventional generator set at the t-1 moment;
Figure FDA00032834955900000214
the generated energy of the conventional generator set at the t moment;
Figure FDA00032834955900000215
the value is the downward climbing value of the conventional generator set;
Figure FDA00032834955900000216
the value is the upward climbing value of the conventional generator set; t is6060 minutes;
the standby inequality constraint for economic dispatch is:
Figure FDA00032834955900000217
wherein,
Figure FDA00032834955900000218
the value is the positive rotation standby value of the wind driven generator set at the moment t;
Figure FDA00032834955900000219
the positive rotation standby value of the jth wind driven generator set at the moment t;
Figure FDA00032834955900000220
the value is a negative rotation standby value of the wind turbine generator at the moment t;
Figure FDA00032834955900000221
the negative rotation standby value of the jth wind driven generator set at the moment t;
Figure FDA00032834955900000222
the value is a positive rotation standby value of the solar photovoltaic generator set at the moment t;
Figure FDA00032834955900000223
the positive rotation standby value of the z-th solar photovoltaic generator set at the moment t;
Figure FDA00032834955900000224
the value is a negative rotation standby value of the solar photovoltaic generator set at the moment t;
Figure FDA00032834955900000225
the negative rotation standby value of the z-th solar photovoltaic generator set at the moment t;
Figure FDA00032834955900000226
is the positive rotation standby value of the hydroelectric generator set at the moment t;
Figure FDA00032834955900000227
is the positive rotation standby value of the kth hydroelectric generator set at the moment t;
Figure FDA00032834955900000228
is a negative rotation standby value of the hydroelectric generating set at the moment t;
Figure FDA00032834955900000229
the negative rotation standby value of the kth hydroelectric generator set at the moment t;
Figure FDA00032834955900000230
is the positive rotation standby value of the geothermal energy generator set at the moment t;
Figure FDA00032834955900000231
the value is the positive rotation standby value of the first geothermal energy generator set at the moment t;
Figure FDA00032834955900000232
the value is a negative rotation standby value of the geothermal energy generator set at the moment t;
Figure FDA00032834955900000233
for generating the first geothermal energy at the time tNegative rotation standby value of the machine set; l isW+% is the load positive rotation standby demand coefficient in the wind driven generator set; l isW-% is the spare demand coefficient of load negative rotation in the wind driven generator set; l isP+% is the load positive rotation standby demand coefficient in the solar photovoltaic generator set; l isP-% is a spare demand coefficient for load negative rotation in the solar photovoltaic generator set; l isH+% is the load positive rotation standby demand coefficient in the hydroelectric generator set; l isH-% is the spare demand coefficient of load negative rotation in the hydroelectric generator set; l isO+% is the load positive rotation standby demand coefficient in the geothermal energy generator set; l isO-% is the spare demand coefficient of load negative rotation in the geothermal energy generator set; wu% is the positive rotation standby demand coefficient of the wind power generator set; wd% is the negative rotation standby demand coefficient of the wind power generator set; pu% is the positive rotation standby demand coefficient of the solar photovoltaic generator set; pd% is the negative rotation standby demand coefficient of the solar photovoltaic generator set; hu% is the positive rotation standby demand coefficient of the hydroelectric generator set; hd% is the negative rotation standby demand coefficient of the hydroelectric generator set; o isu% is the positive rotation standby demand coefficient of the geothermal energy generator set; o isd% is the negative rotation standby demand coefficient of the geothermal energy generator set; t is10Is 10 minutes;
(2) the method comprises the steps of solving the problems of multi-modal characteristics and multi-target scheduling by using a multi-modal multi-target intelligent method, firstly adopting a niche strategy, and selecting more various and better individuals as parent populations of each generation; secondly, a special selection mechanism is utilized to increase selection pressure, improve diversity and accelerate convergence speed; finally, by using the special crowding distance, simultaneously considering the distance of the candidate solutions in the decision space and the target space, obtaining an optimal pareto set which is not independent between solution sets, solving the multi-modal characteristics in the scheduling problem, providing more various candidate solutions, simultaneously using the characteristics of knee joint points and knee joint areas, and providing the most appropriate optimal solution under the condition of no preference;
f in the formula (1)1(x) And f2(x) Are respectively provided withThe method is characterized in that the method is used as a horizontal axis coordinate and a vertical axis coordinate of a two-dimensional coordinate system, in consideration of multi-modal characteristics of the coordinate system, after a series of feasible solutions are obtained in a decision space and a target space, multi-modal multi-objective optimization is carried out on the feasible solutions to obtain a proper optimal pareto solution set, and the method comprises the following steps:
step 2.1: setting an initial iteration time t as 1;
step 2.2: taking two mutually contradictory target quantities as a horizontal axis and a vertical axis of a two-dimensional coordinate system, and putting the two contradictory target quantities into the same coordinate system for consideration;
step 2.3: initializing parameters, wherein the iteration number n of other multi-mode and multi-target is 0;
step 2.4: generating a population with the size of M in the range of feasible solutions;
step 2.5: embedding a niche strategy in championship selection to select a mating pool of a parent population;
step 2.6: performing simulated binary intersection and polynomial variation on the parent population by using a traditional genetic method to generate a child population, then combining the parent population and the child population, and performing non-dominated sorting operation;
step 2.7: finding knee joint points and knee joint areas on the front edge of the optimal pareto;
step 2.8: according to the non-dominated sorting, knee joint points and knee joint areas are identified, and special crowding distances, environment selection is carried out to obtain M better individuals;
step 2.9: judging the iteration times, if the iteration times are not met, returning n to n +1 to the step 2.5 to continue the iteration until the iteration times are met; if the number of iteration steps is met, performing step 2.10;
step 2.10: obtaining an optimal pareto solution set with multi-modal characteristics, and selecting a proper solution according to the preference of a decision maker;
(3) the problem of distributed scheduling of the comprehensive energy system is solved by utilizing a multilayer distributed multi-target consistency method, the comprehensive energy system is divided into a plurality of areas, each area is regarded as an intelligent agent to carry out independent optimization, after respective optimization problems are independently completed, internal variables of the areas are not exchanged, integral optimization can be realized only by exchanging boundary information among the areas, the privacy of the system is ensured, the problem solving speed of the comprehensive energy system is accelerated, and the method comprises the following steps:
step 3.1: inputting predicted load data, and optimizing the initial iteration step number k of each region to be 0;
step 3.2: exchanging boundary variables and target variables by combining the topological structure of the agent;
step 3.3: calculating the active power output of each unit;
step 3.4: correcting the active power output;
step 3.5: solving an active deviation value;
step 3.6: judging whether the active power deviation meets the requirement, if not, making k equal to k +1, and continuously returning to the step 3.2 for iteration; if the power generation quantity meets the requirements, ending the operation to obtain the optimal power generation quantity of each area;
(4) the problem of layered scheduling of the comprehensive energy system is solved by utilizing a multilayer distributed multi-target consistency method, the region divided in the first step is regarded as a first layer, the region is divided again in the region divided in the first layer, the region divided again is subjected to multi-mode multi-target distributed optimal scheduling, and the problem solving speed of the problem which is reduced due to the fact that the comprehensive energy system is continuously increased is increased because the problem solving speed of the second layer region divided in each region of the first layer is parallel when the problem is solved; and can carry out layering many times to the region, reduce the degree of difficulty of huge complicated comprehensive energy system solution problem, the step is:
step 4.1: respectively inputting the optimal power generation amounts calculated in the area 1, the area 2 and the area 3 in the first floor into the second floor as load values;
step 4.2: for the second layer of region 1, using the methods of step 2.1 to step 2.10 and step 3.1 to step 3.6: region 11, region 12, region 13; for the second layer of region 2: region 21, region 22, region 23; for the third layer of region 3: processing is performed in the area 31, the area 32 and the area 33;
step 4.3: the optimal power generation amounts calculated in the region 11, the region 12, the region 13, the region 21, the region 22, the region 23, the region 31, the region 32, and the region 33 are input to the third layer as load values, respectively;
step 4.4: for the third layer of region 11, using the methods of step 2.1 to step 2.10 and step 3.1 to step 3.6: region 111, region 112, region 113; for the third layer of region 12: region 121, region 122, region 123; for the third layer of region 13: region 131, region 132, region 133; for the third layer of region 21: region 211, region 212, region 213; for the third layer of region 22: region 221, region 222, region 223; for the third layer of region 23: region 231, region 232, region 233; for the third layer of region 31: region 311, region 312, region 313; for the area 321, the area 322, and the area 323 of the area 32; for the third layer of region 33: the area 331, the area 332, and the area 333 perform parallel processing;
step 4.5: inputting the optimal power generation amount obtained by parallel calculation of each area as a load to the next layer, and solving the problem by using the methods from step 2.1 to step 2.10 and from step 3.1 to step 3.6;
step 4.6: judging whether the conditions are met, if the conditions are not met, turning t to t +1 into step 2.2 to continue iteration; and if the conditions are met, finishing iteration and outputting the optimal power generation amount of each region.
CN202111140307.8A 2021-09-28 2021-09-28 Multi-mode distributed multi-target layered intelligent comprehensive energy system scheduling method Pending CN113947291A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114565239A (en) * 2022-02-15 2022-05-31 石河子大学 Comprehensive low-carbon energy scheduling method and system for industrial park

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114565239A (en) * 2022-02-15 2022-05-31 石河子大学 Comprehensive low-carbon energy scheduling method and system for industrial park

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