CN115545501A - Multi-mode multi-target layered distributed comprehensive energy system economic dispatching method - Google Patents
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Abstract
The invention provides an economic dispatching method of a multi-mode multi-target layered distributed comprehensive energy system. Firstly, an analytic multi-mode multi-target method in the method is used for obtaining a multi-mode economic dispatching multi-target scheme with diversity. Secondly, the hierarchical distributed consistency method in the method utilizes hierarchical operation to quickly obtain an accurate economic dispatching decision scheme. The multi-mode multi-target layered distributed economic dispatching method for the comprehensive energy system can solve the problem of multi-mode economic dispatching of the large-scale comprehensive energy system, diversifies the decision scheme of economic dispatching, optimizes the stability of economic dispatching and improves the calculation speed and accuracy.
Description
Technical Field
The invention belongs to the field of power systems and comprehensive energy, and relates to an analytic multi-mode multi-target layered distributed method which is suitable for multi-mode multi-target layered distributed economic dispatching of a comprehensive energy system.
Background
The continuous expansion of the power market and the gradual expansion of the scale of the comprehensive energy system can cause the calculation speed of the economic dispatch of the comprehensive energy system to be slow and the information privacy to be poor. The economic dispatching of the comprehensive energy system is mostly aimed at a single target, and a multi-target method for the economic dispatching of the comprehensive energy is few. And moreover, multi-mode problems are not considered in economic dispatch in the research of the multi-target economic dispatch problem of the comprehensive energy system, so that the multi-target economic dispatch scheme lacks diversity and comprehensiveness.
Therefore, the method can keep the characteristics of multiple modes, can accelerate the speed of system processing problems and ensure the privacy and the robustness of the system in the multi-mode multi-target layered distributed economic dispatching problem of the comprehensive energy system, can also provide multi-mode selection for the dispatching of the comprehensive energy system, and ensures the stability and the safety of the economic dispatching.
Disclosure of Invention
The invention provides an economic dispatching method of a multi-mode multi-target layered distributed comprehensive energy system, which combines an analytic multi-mode multi-target method and a layered distributed consistency method for economic dispatching of the comprehensive energy system, increases the diversity and stability of economic dispatching, improves the calculation speed and precision, and provides rich decision schemes which can be replaced for decision makers; the steps in the using process are as follows:
step (1): the construction of an economic dispatching model of the comprehensive energy system comprises fire energy, water energy, wind energy, light energy and geothermal energy, and conforms to equality constraint and inequality constraint of economic dispatching by taking cost expense and carbon emission as multiple targets;
the objective function for the total power generation cost is:
wherein, the first and the second end of the pipe are connected with each other,
total cost of power generation;
representing the cost of the ith generating set at time t;
representing the cost of the jth thermal power generating unit at the moment t;
representing the cost of the kth hydroelectric generating set at time t;
representing the cost of the first wind generating set at the time t;
representing the cost of the r-th photovoltaic generator set at the time t;
representing the cost of the s th geothermal energy generating set at the time t;
representing the cost of the uth clean energy generator set at the time t; n is the number of the total generator sets; n is a radical of hydrogen TG The number of thermal power generator units; n is a radical of hydrogen HG The number of hydroelectric generator sets; n is a radical of hydrogen WG The number of wind driven generator sets; n is a radical of PG The number of photovoltaic generator sets; n is a radical of OG The number of geothermal energy generator sets; n is a radical of CleanG The number of generator sets is the clean energy; t is the statistical time of the objective function;
for the ith generating set at the moment tAn amount of electricity;
generating capacity of the jth thermal power generating set at the moment t;
the generated energy of the kth hydroelectric generating set at the moment t;
the generated energy of the first wind generating set at the moment t;
generating capacity of the r-th photovoltaic generator set at the moment t;
generating capacity of the s th geothermal energy generating set at the time t;
generating capacity of the u clean energy generator set at the moment t; a is a j 、b j And c j The cost coefficient is the jth thermal power generating set; d k The cost coefficient of the kth hydroelectric generating set; e.g. of the type l The cost coefficient of the first wind generating set; g r The cost coefficient of the r photovoltaic generator set is set; m is s The cost coefficient of the s th geothermal energy generating set; kappa type u The cost coefficient of the u clean energy generator set is; cost of unit using clean energy
Replacement of hydroelectric costs
Cost of wind power
Cost of light energy
And cost of geothermal energy
The objective function for total carbon emissions is:
wherein the content of the first and second substances,
total carbon emissions;
representing the carbon emission of the jth thermal power generating unit at the time t; alpha (alpha) ("alpha") j 、β j And gamma j The carbon emission coefficient of the jth thermal power generating unit is obtained; the equality constraint for power balancing is:
wherein, the first and the second end of the pipe are connected with each other,
the total load demand value at time t;
the upper and lower limits of power are constrained as follows:
wherein the content of the first and second substances,
the lower limit of the power generation amount of the jth thermal power generator set is set;
the upper limit of the power generation amount of the jth thermal power generating set is set;
the lower limit of the generating capacity of the kth hydroelectric generating set;
the upper limit of the generating capacity of the kth hydroelectric generating set;
the lower limit of the generating capacity of the first wind generating set;
the upper limit of the generating capacity of the first wind generating set;
the lower limit of the generating capacity of the r-th photovoltaic generator set;
the upper limit of the generating capacity of the r-th photovoltaic generator set is set;
the lower limit of the generating capacity of the s th geothermal energy generating set;
the upper limit of the power generation amount of the s th geothermal energy power generation unit;
the ramp rate constraint of the thermal power generating unit is as follows:
wherein the content of the first and second substances,
the power generation amount of the jth thermal power generator set at the moment t-1;
the downward climbing speed of the jth thermal power generating unit is obtained;
the upward climbing speed of the jth thermal power generating unit is obtained; t is 60 60 minutes;
the time constraints of the photovoltaic generator set are:
step (2): initializing the weight factor eta according to the proportion R, and enabling
Wherein, R is the ratio of the values of the weighting factors; eta is a weight factor, and the numeric area of eta is more than or equal to 0 and less than or equal to 1; n is η Representing the number of segments for segmenting the value range of the weight factor;
and (3): converting the multi-target problem into a single-target problem by a linear weighting method;
wherein f is Total Is the total target value after linear weighting;
a weighted target value is obtained for the jth thermal power unit line at the moment t;
and (4): setting an initial iteration time t =1;
and (5): inputting a predicted load value, and enabling the initial iteration step number k =0;
and (6): the comprehensive energy system is divided into four layers: the first layer is divided into an area A, an area B and an area C; the second layer is divided into a region Aa, a region Ab, a region Ac, a region Ba, a region Bb, a region Bc, a region Ca and a region Cb; the third layer is divided into a region Aa1, a region Aa2, a region Ab1, a region Ab2, a region Ac1, a region Ac2, a region Ba1, a region Ba2, a region Ba3, a region Bb1, a region Bb2, a region Bc1, a region Bc2, a region Ca1, a region Ca2, a region Cb1, a region Cb2, and a region Cb3; the fourth layer is a generator set in each area of the third layer; and (7): selecting leader and follower agents of each layer, wherein an area A of a first layer is a leader, and an area B and an area C are followers; the area Aa, the area Ba and the area Ca of the second layer are taken as leaders, and the other two-layer areas are taken as followers; the third layer area Aa1, the area Ab1, the area Ac1, the area Ba1, the area Bb1, the area Bc1, the area Ca1, and the area Cb1 are leaders, and the other third layer areas are followers; and (8): updating the consistency variable of the first layer of agents by combining the topological structure diagram of the agents and the formula (15) and the formula (16);
directed graph G = (V, E) of distributed network topology and adjacency matrix a = (a) of weight relationships between respective agents ij ) Forming a network topology structure of the multi-agent; laplace matrix L = [ L ] of directed graph G ij ]Comprises the following steps:
wherein l ii And l ij Is an element in the Laplace matrix L; a is ij Is an element in adjacency matrix a;
row random matrix H of agent ij Comprises the following steps:
the consistency variable λ is expressed as:
λ＝[λ T ，λ Clean ] (12)
wherein λ represents a consistency variable of the integrated energy system; lambda [ alpha ] T Representing a consistency variable of the thermal generator set; lambda [ alpha ] Clean Representing a consistency variable of the clean energy generator set;
the consistency variable updating formulas of the follower and the leader are respectively as follows:
wherein N is IB Representing the number of agents; k represents the kth iteration;
a consistency variable representing the nth agent at the kth iteration at the time t;
a consistency variable representing the mth agent at the time t in the (k + 1) th iteration; τ represents a power balance adjustment factor of the distributed consistency method; delta P t (k) Representing the power deviation of the kth iteration at time t; h mn (k) A row random matrix representing an agent;
and (9): calculating the active output of each agent of the first layer according to a formula (17);
the active power formula of each unit is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the active output of the ith agent at the moment t in k +1 iteration is represented;
the active power output of the jth thermal power generating unit at the moment t in k +1 iterations is represented;
the active power output of the u-th clean energy generator set at the moment t in k +1 iterations is represented;
the active upper limit of the mu region of the omega layer is expressed as the sum of the active upper limits of the lower sub-regions or the units divided by the region;
representing a consistency variable of the ith agent at the moment t in k +1 iterations;
representing a consistency variable of the jth thermal power generating unit at the moment t in k +1 iterations;
representing a consistency variable of the u clean energy unit at the moment t in k +1 iterations;
representing the number of thermal power generating units in the mu region of the omega layer;
representing the number of clean energy generator sets of the mu region of the omega layer; u represents the total number of regions of the layer; w represents the total number of layers;
step (10): correcting the first layer active power output obtained in the step (9) according to a formula (18);
the active power correction formula is as follows:
wherein, the first and the second end of the pipe are connected with each other,
representing the lower limit of the active output of the ith power generation intelligent agent;
the upper limit of the active output of the ith power generation intelligent agent is represented;
step (11): solving an active power deviation value of the first layer according to a formula (19);
active power deviation Δ P t (k + 1) is:
step (12): judging whether the multi-mode active power deviation value of the first layer obtained in the step (11) meets the precision requirement or not
Wherein δ represents a power deviation allowable maximum value;
representing a multi-modal power deviation allowance value;
if it is not
Enabling the iteration step number k = k +1, returning to the step (8), and continuing to perform the iterative computation of the first layer;
if it is not
Storing the obtained active output of the group of the intelligent agents, then performing another iteration step number k = k +1, returning to the step (8), and continuing to perform the iteration calculation of the first layer;
if it is not
The multi-mode active deviation value of the first layer meets the requirement, iteration is finished, and active outputs of a plurality of groups of intelligent bodies are obtained;
step (13): performing multi-mode judgment and decision congestion distance screening on the active power output of the multiple groups of intelligent generator sets obtained in the step (12) to obtain multi-mode active power output meeting the requirements of decision makers; the multi-modal judgment is as follows:
|f(x 1 )-f(x 2 )|<ε (21)
wherein x is 1 And x 2 Two sets of solutions for multimodal; epsilon represents the critical threshold for multiple modes;
the decision space crowding distance screening is as follows:
wherein the content of the first and second substances,
a crowding distance representing a jth decision variable of the ith set of solutions; x is a radical of a fluorine atom i+1,j A jth decision variable representing the i +1 th set of solutions; x is a radical of a fluorine atom i,j A jth decision variable representing the ith set of solutions;
a jth decision variable representing a maximum target value;
a jth decision variable representing a minimum target value; x is the number of i-1,j A jth decision variable representing the i-1 th set of solutions;
indicating the congestion distance of the i-th group solution; d represents the number of decision variables; ξ represents the minimum decision crowding distance allowed by multimodal;
step (14): inputting the multiple groups of multi-modal generated energy of the first layer obtained in the step (13) as load values into a second layer, and enabling the initial iteration step number k =0;
step (15): updating the consistency variable of the agent at the second layer by combining the topological structure diagram of the agent and the formula (15) and the formula (16);
step (16): working out the active output of each intelligent unit of the second layer according to a formula (17);
step (17): correcting the active power output obtained in the step (16) according to a formula (18);
step (18): solving the deviation value of the active power of the second layer according to a formula (19);
step (19): judging whether the multi-mode active power deviation value obtained in the step (18) meets the precision requirement or not;
if it is not
Making the iteration step number k = k +1, returning to the step (15), and continuing to perform the iterative computation of the second layer;
if it is not
Storing the obtained active output of the group of the intelligent agents, then performing another iteration step number k = k +1, returning to the step (15), and continuing to perform iterative computation of the second layer;
if it is not
The multi-mode active deviation value of the second layer meets the requirement, iteration is finished, and active output of a plurality of groups of intelligent generator sets is obtained;
step (20): performing multi-mode judgment and decision congestion distance screening on the active output of the multiple groups of agents on the second layer obtained in the step (19) to obtain multi-mode active output meeting the requirements of a decision maker;
step (21): inputting the multi-group multi-modal power generation amount of the second layer obtained in the step (20) as a load value to obtain three layers, and enabling the initial iteration step number k =0;
step (22): updating the consistency variable of the agent at the third layer by combining the topological structure diagram of the agent and the formula (15) and the formula (16);
step (23): working out the active output of each intelligent machine set of the third layer according to a formula (17);
step (24): correcting the active power output obtained in the step (23) according to a formula (18);
step (25): solving the deviation value of the active power of the third layer according to a formula (19);
step (26): judging whether the multi-mode active power deviation value obtained in the step (25) meets the precision requirement or not;
if it is used
Making the iteration step number k = k +1, returning to the step (22), and continuing to perform the iterative computation of the third layer;
if it is not
Storing the obtained active output of the group of the intelligent agents, then performing another iteration step number k = k +1, returning to the step (22), and continuing to perform iterative computation of a third layer;
if it is not
Then the multi-modal active deviation of the third layerThe value meets the requirement, iteration is finished, and the active output of a plurality of groups of intelligent generator sets is obtained;
step (27): performing multi-mode judgment and decision congestion distance screening on the active output of the multiple groups of agents on the third layer obtained in the step (26) to obtain multi-mode active output meeting the requirements of a decision maker;
step (28): inputting the multi-group multi-modal power generation amount of the third layer obtained in the step (27) as a load value to obtain four layers, and enabling the initial iteration step number k =0;
step (29): updating the consistency variable of each generator set on the fourth layer by combining the topological structure diagram of the agent and the formula (15) and the formula (16);
a step (30): working out the active power output of each generator set on the fourth layer according to a formula (17);
step (31): correcting the active power output obtained in the step (30) according to a formula (18); step (32): solving the deviation value of the active power of the fourth layer according to a formula (19);
step (33): judging whether the multi-mode active power deviation value obtained in the step (32) meets the precision requirement or not;
if it is used
Making the iteration step number k = k +1, returning to the step (29), and continuing to perform the iterative computation of the fourth layer;
if it is used
Storing the obtained active power output of the group of generator sets, then performing another iteration step number k = k +1, returning to the step (29), and continuing to perform iterative calculation of the fourth layer;
if it is not
The multi-mode active deviation value of the fourth layer meets the requirement, iteration is finished, and active output of multiple groups of generator sets is obtained;
step (34): performing multi-mode judgment and decision congestion distance screening on the active power output of the multiple groups of generator sets on the fourth layer obtained in the step (33) to obtain a multi-mode active power output decision scheme meeting the requirements of a decision maker;
step (35): judging whether T < T is met, if so, enabling T = T +1, and turning to the step (5); if not, executing the next step;
step (36): judging whether eta <1 is satisfied, if yes, storing a multi-modal solution obtained when eta is taken as value, enabling eta = eta + R, and turning to the step (4); if not, executing the next step;
step (37): and reversely solving all the multi-mode solutions obtained by taking different eta values to obtain multi-mode multi-target values, wherein the multi-mode multi-target values obtained under all the eta values form a pareto frontier in a target space.
Compared with the prior art, the invention has the following advantages and effects:
(1) An analytic framework of the multi-mode multi-target layered distribution method is established for the economic dispatching problem of the comprehensive energy system.
(2) Because the constructed analytic multi-mode multi-target layered distribution method adopts layered scheduling and only exchanges the consistency variables of adjacent areas, the detailed operation information of each unit does not need to be exchanged, so that the calculation speed is accelerated and the information privacy is better guaranteed; and the problem of multi-modal characteristics in the dispatching of the comprehensive energy system is solved, and the diversity and comprehensiveness of an economic dispatching solution are increased.
(3) The obtained economic dispatching scheme of the comprehensive energy system has a plurality of mutually-replaced economic dispatching schemes, and when some sudden accidents or serious wind, light and water abandonment in partial areas are faced, decision makers have more alternative schemes, so that the stability and the safety of economic dispatching are improved.
Drawings
FIG. 1 is a schematic diagram of the hierarchical distribution of the process of the present invention.
FIG. 2 is a flow chart of an analytical multi-modal multi-target distributed consistency method of the present invention.
FIG. 3 is an overall flow chart of the method of the present invention.
Detailed Description
The invention provides an economic dispatching method of a multi-mode multi-target layered distributed comprehensive energy system, which is explained in detail by combining the attached drawings as follows:
FIG. 1 is a schematic diagram of the hierarchical distribution of the process of the present invention. The comprehensive energy system is divided into four layers: the first layer is divided into an area A, an area B and an area C; the second layer is divided into a region Aa, a region Ab, a region Ac, a region Ba, a region Bb, a region Bc, a region Ca and a region Cb; the third layer is divided into a region Aa1, a region Aa2, a region Ab1, a region Ab2, a region Ac1, a region Ac2, a region Ba1, a region Ba2, a region Ba3, a region Bb1, a region Bb2, a region Bc1, a region Bc2, a region Ca1, a region Ca2, a region Cb1, a region Cb2, and a region Cb3; the fourth layer is a generator set in each area of the third layer. Each layer elects a leader and a follower.
FIG. 2 is a flow chart of an analytical multi-modal multi-target distributed consistency method of the present invention. The method comprises the following specific steps:
step 1: converting the multi-target problem into a single-target problem according to a formula (9);
step 2: inputting a predicted load value, and enabling the initial iteration step number k =0;
and step 3: determining a leader and a follower of the agent, and updating a consistency variable of the agent by combining a topological structure diagram of the agent and a formula (15) and a formula (16);
and 4, step 4: working out the active output of each intelligent unit according to a formula (17);
and 5: correcting the active power output obtained in the step 4 according to a formula (18);
and 6: solving an active power deviation value according to a formula (19);
and 7: judging whether the multi-mode active power deviation value obtained in the step 6 meets the precision requirement, if so, judging whether the multi-mode active power deviation value meets the precision requirement
Enabling the iteration step number k = k +1, and returning to the step 3; if it is used
Storing the obtained active output of the group of the intelligent agents, then iterating the step number k = k +1, and returning to the step 3; if it is not
The multi-mode active deviation value meets the requirement, iteration is finished, and active output of a plurality of groups of intelligent generator sets is obtained;
and step 8: and (4) performing multi-mode judgment and decision congestion distance screening on the active output of the plurality of groups of intelligent generator sets obtained in the step (7) to obtain a multi-mode active output scheduling scheme meeting the requirements of a decision maker.
Fig. 3 is an overall flow chart of the method of the present invention. The method comprises the following specific steps:
step 1: initializing the weighting factor, let eta =1/b η ；
Step 2: setting an initial iteration time t =1; and step 3: inputting a predicted load value, and enabling the initial iteration step number k =0;
and 4, step 4: dividing the system into four layers, wherein the first layer is divided into an area A, an area B and an area C; the second layer is divided into a region Aa, a region Ab, a region Ac, a region Ba, a region Bb, a region BC, a region Ca, and a region Cb; the third layer is divided into a region Aa1, a region Aa2, a region Ab1, a region Ab2, a region Ac1, a region Ac2, a region Ba1, a region Ba2, a region Ba3, a region Bb1, a region Bb2, a region Bc1, a region Bc2, a region Ca1, a region Ca2, a region Cb1, a region Cb2, and a region Cb3; the fourth layer is a generator set in each area of the third layer;
and 5: selecting leader and follower agents of each layer, wherein an area A of a first layer is a leader, and an area B and an area C are followers; the area Aa, the area Ba and the area Ca of the second layer are taken as leaders, and the other two-layer areas are taken as followers; the third layer area Aa1, the area Ab1, the area Ac1, the area Ba1, the area Bb1, the area Bc1, the area Ca1, and the area Cb1 are leaders, and the other third layer areas are followers;
step 6: carrying out optimization solution on the first layer of the system by using an analytic multi-mode multi-target distributed consistency method in FIG. 2;
and 7: inputting the multi-group multi-modal power generation quantities of the 3 areas of the first layer obtained in the step 6 into the second layer as load values respectively;
and 8: performing optimization solution on a second layer of the system by using the analytic multi-mode multi-target distributed consistency method in FIG. 2;
and step 9: inputting the multi-group multi-modal power generation quantities of the 8 areas of the second layer obtained in the step 8 into the third layer as load values respectively;
step 10: carrying out optimization solution on the third layer of the system by using the analytic multi-mode multi-target distributed consistency method in the figure 2;
step 11: inputting the multi-group multi-modal power generation quantities of the 18 areas in the third layer obtained in the step 10 into the fourth layer as load values, regarding each power generation unit in the fourth layer as an agent, and selecting a leader agent and a follower agent;
step 12: performing optimization solution on the fourth layer of the system by using the analytic multi-mode multi-target distributed consistency method in the figure 2;
step 13: judging whether T < T is met, if so, enabling T = T +1, and turning to the step 2; if not, executing the next step;
step 14: judging whether eta <1 is satisfied, if yes, storing a multi-modal solution obtained when eta is taken as value, enabling eta = eta + R, and turning to the step 2; if not, executing the next step; step 15: obtaining multi-modal multi-target values by reversely solving all multi-modal solutions obtained by taking different eta values; step 16: and forming pareto frontier in the target space by the multi-modal multi-target values obtained under all the eta values.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (1)
1. The economic dispatching method of the multi-mode multi-target layered distributed comprehensive energy system is characterized in that an analytic multi-mode multi-target method and a layered distributed consistency method are combined and used for economic dispatching of the comprehensive energy system, diversity and stability of economic dispatching are improved, computing speed and precision are improved, and a rich decision scheme capable of being replaced is provided for a decision maker; the steps in the using process are as follows:
step (1): the construction of an economic dispatching model of the comprehensive energy system comprises fire energy, water energy, wind energy, light energy and geothermal energy, and conforms to equality constraint and inequality constraint of economic dispatching by taking cost expense and carbon emission as multiple targets;
the objective function for the total power generation cost is:
wherein the content of the first and second substances,
total cost of power generation;
representing the cost of the ith generating set at time t;
representing the cost of the jth thermal power generating unit at the moment t;
representing the cost of the kth hydroelectric generating set at time t;
indicates the first wind power generation at the time tThe cost of the unit;
representing the cost of the r-th photovoltaic generator set at the time t;
representing the cost of the s th geothermal energy generating set at the time t;
representing the cost of the uth clean energy generator set at the time t; n is the number of the total generator sets; n is a radical of TG The number of thermal power generator units; n is a radical of HG The number of hydroelectric generator sets; n is a radical of WG The number of wind turbine generator sets; n is a radical of hydrogen PG The number of photovoltaic generator sets; n is a radical of hydrogen OG The number of geothermal energy generator sets; n is a radical of hydrogen CleanG The number of generator sets for clean energy; t is the statistical time of the objective function;
generating capacity of an ith generating set at the moment t;
generating capacity of the jth thermal power generating set at the moment t;
the generating capacity of the kth hydroelectric generating set at the moment t;
the generated energy of the first wind generating set at the moment t;
generating capacity of the r-th photovoltaic generator set at the time t;
generating capacity of the s th geothermal energy generating set at the time t;
generating capacity of the u clean energy generator set at the moment t; a is j 、b j And c j The cost coefficient is the jth thermal power generating set; d is a radical of k The cost coefficient of the kth hydroelectric generating set; e.g. of a cylinder l The cost coefficient of the first wind generating set; g r The cost coefficient of the r-th photovoltaic generator set is obtained; m is s The cost coefficient of the s th geothermal energy generating set; k is a radical of u The cost coefficient of the u clean energy generator set is; cost of unit using clean energy
Replacement of hydroelectric costs
Cost of wind power
Cost of light energy
And cost of geothermal energy
The objective function for total carbon emissions is:
wherein the content of the first and second substances,
total carbon emissions;
representing the carbon emission of the jth thermal power generating unit at the t moment; alpha (alpha) ("alpha") j 、β j And gamma j The carbon emission coefficient of the jth thermal power generating unit is obtained;
the equality constraint for power balancing is:
wherein, the first and the second end of the pipe are connected with each other,
the total load demand value at the moment t;
the upper and lower limits of power are constrained as follows:
wherein, the first and the second end of the pipe are connected with each other,
the lower limit of the power generation amount of the jth thermal power generator set is set;
the upper limit of the generating capacity of the jth thermal power generating unit is set;
the lower limit of the generating capacity of the kth hydroelectric generating set;
the upper limit of the generating capacity of the kth hydroelectric generating set;
for the first wind-driven generator setA lower limit of the electric quantity;
the upper limit of the generating capacity of the first wind generating set;
the lower limit of the generating capacity of the r-th photovoltaic generator set;
the upper limit of the generating capacity of the r-th photovoltaic generator set is set;
the lower limit of the generating capacity of the s th geothermal energy generating set;
the upper limit of the power generation amount of the s th geothermal energy generating set;
the ramp rate constraint of the thermal power generating unit is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the power generation amount of the jth thermal power generator set at the moment t-1;
the downward climbing speed of the jth thermal power generating unit is obtained;
the upward climbing speed of the jth thermal power generating unit is obtained; t is a unit of 60 60 minutes;
the time constraints of the photovoltaic generator set are:
step (2): initializing the weight factor eta according to the proportion R to ensure that
Wherein, R is the ratio of the values of the weighting factors; eta is a weight factor, and the numeric area of eta is more than or equal to 0 and less than or equal to 1; n is η The number of the segments for segmenting the value range of the weight factor is represented;
and (3): converting the multi-target problem into a single-target problem by a linear weighting method;
wherein, f Total Is the total target value after linear weighting;
a weighted target value for the jth thermal power unit line at the time t;
and (4): setting an initial iteration time t =1;
and (5): inputting a predicted load value, and enabling the initial iteration step number k =0;
and (6): the comprehensive energy system is divided into four layers: the first layer is divided into an area A, an area B and an area C; the second layer is divided into a region Aa, a region Ab, a region Ac, a region Ba, a region Bb, a region Bc, a region Ca and a region Cb; the third layer is divided into a region Aa1, a region Aa2, a region Ab1, a region Ab2, a region Ac1, a region Ac2, a region Ba1, a region Ba2, a region Ba3, a region Bb1, a region Bb2, a region Bc1, a region Bc2, a region Ca1, a region Ca2, a region Cb1, a region Cb2, and a region Cb3; the fourth layer is a generator set in each area of the third layer; and (7): selecting leader and follower agents of each layer, wherein an area A of a first layer is a leader, and an area B and an area C are followers; the area Aa, the area Ba and the area Ca of the second layer are taken as leaders, and the other two-layer areas are taken as followers; a third layer region Aa1, a region Ab1, a region Ac1, a region Ba1, a region Bb1, a region Bc1, a region Ca1, and a region Cb1 are leaders, and the other third layer regions are followers; and (8): updating the consistency variable of the first layer of agents by combining the topological structure diagram of the agents and the formula (15) and the formula (16);
directed graph G = (V, E) of distributed network topologies and adjacency matrix a = (a) of weight relationships between respective agents ij ) Forming a network topology structure of the multi-agent; laplace matrix L = [ L ] of directed graph G ij ]Comprises the following steps:
wherein l ii And l ij Is an element in the Laplace matrix L; a is a ij Is an element in the adjacency matrix a;
row random matrix H of agent ij Comprises the following steps:
the consistency variable λ is expressed as:
λ＝[λ T ，λ Clean ] (12)
wherein λ represents a consistency variable of the integrated energy system; lambda [ alpha ] T Representing a consistency variable of the thermal generator set; lambda Clean Representing a consistency variable of the clean energy generator set;
the consistency variable updating formulas of the follower and the leader are respectively as follows:
wherein N is IB Representing the number of agents; k represents the kth iteration;
a consistency variable representing the nth agent at the kth iteration at the time t;
representing the consistency variable of the mth agent at the time t in the (k + 1) th iteration; τ represents a power balance adjustment factor for the distributed consistency approach; delta P t (k) Representing the power deviation of the kth iteration at time t; h mn (k) A row random matrix representing an agent;
and (9): calculating the active output of each agent of the first layer according to a formula (17);
the active power formula of each unit is as follows:
wherein, the first and the second end of the pipe are connected with each other,
denotes the ith agent at time t is at k +1 timesAn iterative active power output;
the active power output of the jth thermal power generating unit at the moment t in k +1 iterations is represented;
the active power output of the u clean energy generator set at the moment t in k +1 iterations is represented;
the active upper limit of the mu region of the omega layer is expressed as the sum of the active upper limits of the lower sub-regions or the units divided by the region;
representing a consistency variable of the ith agent at the moment t in k +1 iterations;
representing a consistency variable of the jth thermal power generating unit at the moment t in k +1 iterations;
representing a consistency variable of the u clean energy unit at the moment t in k +1 iterations;
representing the number of thermal power generating units in the mu region of the omega layer;
representing the number of clean energy generator sets of the mu region of the omega layer; u represents the total number of regions of the layer; w represents the total number of layers;
step (10): correcting the first layer active power output obtained in the step (9) according to a formula (18);
the active power modification formula is as follows:
wherein the content of the first and second substances,
representing the lower limit of the active output of the ith power generation intelligent agent;
representing the upper limit of the active output of the ith power generation agent;
step (11): solving an active power deviation value of the first layer according to a formula (19);
active power deviation Δ P t (k + 1) is:
step (12): judging whether the multi-mode active power deviation value of the first layer obtained in the step (11) meets the precision requirement or not
Wherein δ represents a power deviation allowable maximum value;
representing a multi-modal power deviation allowance value;
if it is not
Enabling the iteration step number k = k +1, returning to the step (8), and continuing to perform the iterative computation of the first layer;
if it is not
Storing the obtained active output of the group of the intelligent agents, then performing another iteration step number k = k +1, returning to the step (8), and continuing to perform the iteration calculation of the first layer;
if it is used
The multi-mode active deviation value of the first layer meets the requirement, iteration is finished, and active outputs of multiple groups of intelligent agents are obtained;
step (13): performing multi-mode judgment and decision congestion distance screening on the active power output of the multiple groups of intelligent generator sets obtained in the step (12) to obtain multi-mode active power output meeting the requirements of decision makers;
the multi-modal judgment is as follows:
|f(x 1 )-f(x 2 )|<ε (21) wherein x 1 And x 2 Two sets of solutions for multi-modal; epsilon represents the critical threshold for multiple modes;
the selection of the space crowding distance is as follows:
wherein the content of the first and second substances,
a crowding distance representing a jth decision variable of the ith set of solutions; x is the number of i+1,j A jth decision variable representing the i +1 th set of solutions; x is a radical of a fluorine atom i,j A jth decision variable representing the ith set of solutions;
a j-th decision variable representing a maximum target value;
a j-th decision variable representing a minimum target value; x is a radical of a fluorine atom i-1,j A jth decision variable representing the i-1 th set of solutions;
indicating the congestion distance of the i-th group solution; d represents the number of decision variables; ξ represents the minimum decision crowding distance allowed by multimodal;
step (14): inputting the multiple groups of multi-modal generated energy of the first layer obtained in the step (13) as load values into a second layer, and enabling the initial iteration step number k =0;
step (15): updating the consistency variable of the agent at the second layer by combining the topological structure diagram of the agent and the formula (15) and the formula (16);
step (16): working out the active output of each intelligent machine set of the second layer according to a formula (17);
step (17): correcting the active power output obtained in the step (16) according to a formula (18);
step (18): solving the deviation value of the active power of the second layer according to a formula (19);
step (19): judging whether the multi-mode active power deviation value obtained in the step (18) meets the precision requirement or not;
if it is used
Enabling the iteration step number k = k +1, returning to the step (15), and continuing to perform the iterative computation of the second layer;
if it is used
Storing the obtained active output of the group of the intelligent agents, then performing another iteration step number k = k +1, returning to the step (15), and continuing to perform iterative computation of the second layer;
if it is used
The multi-mode active deviation value of the second layer meets the requirement, iteration is finished, and active output of multiple groups of intelligent generator sets is obtained;
step (20): performing multi-mode judgment and decision congestion distance screening on the active output of the multiple groups of agents on the second layer obtained in the step (19) to obtain multi-mode active output meeting the requirements of a decision maker;
step (21): inputting the multi-group multi-modal generating capacity of the second layer obtained in the step (20) as a load value to obtain three layers, and enabling the initial iteration step number k =0;
step (22): updating the consistency variable of the third layer of agents by combining the topological structure diagram of the agents and the formula (15) and the formula (16);
step (23): working out the active output of each intelligent unit of the third layer according to a formula (17);
step (24): correcting the active power output obtained in the step (23) according to a formula (18);
step (25): solving the deviation value of the active power of the third layer according to a formula (19);
step (26): judging whether the multi-mode active power deviation value obtained in the step (25) meets the precision requirement or not;
if it is not
Making the iteration step number k = k +1, returning to the step (22), and continuing to perform the iterative computation of the third layer;
if it is not
Storing the obtained active output of the group of the intelligent agents, then performing another iteration step number k = k +1, returning to the step (22), and continuing to perform the iteration calculation of the third layer;
if it is not
The multi-mode active deviation value of the third layer meets the requirement, iteration is finished, and active output of a plurality of groups of intelligent generator sets is obtained;
step (27): performing multi-mode judgment and decision congestion distance screening on the active output of the multiple groups of intelligent agents on the third layer obtained in the step (26) to obtain multi-mode active output meeting the requirements of a decision maker;
step (28): inputting the multi-group multi-modal power generation amount of the third layer obtained in the step (27) as a load value to obtain four layers, and enabling the initial iteration step number k =0;
step (29): updating the consistency variable of each generator set on the fourth layer by combining the topological structure diagram of the agent and the formula (15) and the formula (16); a step (30): working out the active power output of each generator set on the fourth layer according to a formula (17);
step (31): correcting the active power output obtained in the step (30) according to a formula (18);
step (32): solving the deviation value of the active power of the fourth layer according to a formula (19);
step (33): judging whether the multi-mode active power deviation value obtained in the step (32) meets the precision requirement or not;
if it is not
Making the iteration step number k = k +1, returning to the step (29), and continuing to perform the iterative computation of the fourth layer;
if it is used
Storing the obtained active power output of the group of generator sets, then performing another iteration step number k = k +1, returning to the step (29), and continuing to perform iterative calculation of the fourth layer;
if it is not
The multi-mode active deviation value of the fourth layer meets the requirement, iteration is finished, and multiple groups of power generation are obtainedThe active power output of the unit;
step (34): performing multi-mode judgment and decision congestion distance screening on the active output of the multiple groups of generator sets on the fourth layer obtained in the step (33) to obtain a multi-mode active output decision scheme meeting the requirements of a decision maker;
step (35): judging whether T < T is met, if so, enabling T = T +1, and turning to the step (5); if not, executing the next step;
step (36): judging whether eta <1 is met, if yes, storing a multi-modal solution obtained when the value is eta, enabling eta = eta + R, and turning to the step (4); if not, executing the next step;
a step (37): and reversely solving all multi-modal solutions obtained by taking different eta values to obtain multi-modal multi-target values, wherein the multi-modal multi-target values obtained under all the eta values form a pareto frontier in a target space.
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