CN116388172A - Low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection - Google Patents

Low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection Download PDF

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CN116388172A
CN116388172A CN202310372730.3A CN202310372730A CN116388172A CN 116388172 A CN116388172 A CN 116388172A CN 202310372730 A CN202310372730 A CN 202310372730A CN 116388172 A CN116388172 A CN 116388172A
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杨晓辉
邓运伟
胡泽成
邓福伟
熊梦兰
胡誉尹
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Abstract

The invention discloses a low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection. And determining addresses of the micro gas turbine (MT), the new energy, the energy storage and the Capacitor (CB) according to the network loss sensitivity, and adopting a double-layer model to realize the planning of the low-carbon power distribution network. The upper planning layer aims at the minimum comprehensive cost, and considers the investment and operation cost of the micro gas turbine, new energy, energy storage (ESS) and the capacitor; the lower operating layer aims at minimizing the operating cost and the voltage offset, and takes capacitor switching, new energy uncertainty, micro gas turbines and energy storage scheduling into consideration. The method provided by the invention can realize low-carbon economic operation of the power distribution network, improve tide distribution, improve voltage quality and reduce network loss.

Description

Low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection
Technical Field
The invention belongs to the technical field of power systems, and particularly relates to a low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection.
Background
The traditional power supply mode of the power distribution network can not meet the requirements of users and social development, and the system is enabled to run economically with low carbon while the reliable electricity utilization of the users is ensured, so that the utilization efficiency of energy is imperative. The active power distribution network with the new energy access can actively regulate and control the local distributed power supply, and becomes an important measure for realizing low-carbon operation of the system by the power grid. However, the randomness of new energy power generation brings uncertainty to the system, so that a scientific planning scheme is formulated, the low-carbon economic operation of the system is ensured, and the important significance is achieved for meeting the power demand of users.
Disclosure of Invention
Aiming at the problems, the invention provides a low-carbon power distribution network double-layer planning method based on grid loss sensitivity site selection, wherein an upper planning layer aims at the minimum comprehensive cost, considers the investment and operation costs of a miniature gas turbine, new energy, energy storage and a capacitor, and adopts an improved weed algorithm to solve the problems; the lower operation layer takes the minimum operation cost and voltage offset as targets, takes capacitor switching, new energy uncertainty, a micro gas turbine and energy storage scheduling into consideration, adopts a multi-target whale algorithm to obtain a uniform pareto front edge, and utilizes a fuzzy decision method to select an optimal solution.
The invention provides a low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection, which comprises the following specific design scheme:
establishing a network loss sensitivity-based site selection model;
step (2) establishing a low-carbon power distribution network planning layer model;
step (3), establishing a low-carbon power distribution network operation layer model;
step (4) establishing a carbon transaction mechanism of the low-carbon power distribution network;
and (5) solving the double-layer model by using an improved weed algorithm and a multi-target whale algorithm.
Further, the selecting the site model based on the loss sensitivity in the step (1) includes:
Figure SMS_1
wherein: LP and LQ are active and reactive power loss sensitivity; p (P) loss Is an active network loss; r and x are branch resistances and reactances; p is p j 、q j Injecting active power and reactive power for the branch end node j; u (U) j Is the voltage at node j.
The larger the sensitivity of the network loss is, the larger the influence of the load power of the node on the network loss is, and the system loss can be reduced by configuring active or reactive compensation equipment at the node with the larger sensitivity of the network loss.
Furthermore, the low-carbon power distribution network planning layer model in the step (2) aims at the minimum comprehensive cost, considers the investment and operation cost of the micro gas turbine MT, new energy, energy storage ESS and capacitor CB, and comprises the following steps:
wherein the new energy source comprises photovoltaic PV and wind power WT;
Figure SMS_2
wherein: f (F) up Is the comprehensive cost; c (C) I Is investment cost; c (C) O The annual running cost;
Figure SMS_4
investment cost for MT; />
Figure SMS_9
Investment cost for PV; />
Figure SMS_12
Investment cost for WT; />
Figure SMS_5
Investment costs for ESS; />
Figure SMS_8
Is CB investment cost; />
Figure SMS_11
The MT running cost; />
Figure SMS_14
The PV operating cost; />
Figure SMS_3
Cost for WT operation; />
Figure SMS_7
The running cost is ESS; />
Figure SMS_10
The electricity purchasing cost is the electricity purchasing cost of the upper-level power grid; />
Figure SMS_13
Carbon emission cost of the power distribution network; />
Figure SMS_6
And selling electricity to the upper power grid.
Further, constraint conditions of the low-carbon power distribution network planning layer model in the step (2) are as follows:
1) Number of device installations constraint:
Figure SMS_15
wherein: n is n i The number of installations for the i-th type of equipment;
Figure SMS_16
for the maximum purchase amount of the device.
2) Device installation capacity constraints:
Figure SMS_17
wherein: e (E) i The installation capacity of the i-th type equipment;
Figure SMS_18
for maximum installation capacity of the device.
3) New energy permeability constraint:
Figure SMS_19
wherein: n is n node The number of nodes of the power distribution network is counted; e (E) i ' configure capacity for node i new energy; θ is the new energy permeability;
Figure SMS_20
is the maximum load in one year.
Further, in the step (3), the low-carbon power distribution network operation layer model takes the minimum operation cost and voltage offset as targets, takes into account capacitor switching, new energy uncertainty, micro gas turbines and energy storage scheduling, obtains a uniform pareto front by adopting a multi-target whale algorithm, and selects an optimal solution by using a fuzzy decision method:
Figure SMS_21
in which F is down Multi-objective optimization for the operation layer; deltaU all Is the voltage offset;
Figure SMS_22
the voltage per unit value of the inode in the typical day t period of s seasons; d (D) s Is the number of days in season.
Further, the constraint condition of the low-carbon power distribution network operation layer model in the step (3) is specifically as follows:
1) Branch tide constraint of low-carbon power distribution network:
Figure SMS_23
in U i,s,t 、U j,s,t The voltage of the first-end i node and the voltage of the terminal j node of the branch ij in the period t of s seasons are calculated; r is (r) ij 、x ij The resistance and reactance of branch ij; p (P) ij,s,t 、Q ij,s,t Active and reactive power of the head end of the branch ij in the period t of s seasons; i ij,s,t The current of the branch ij in the period t of s seasons is equal to the current of the branch ij in the period t of s seasons; p is p j,s,t 、q j,s,t Active power and reactive power are injected into the j node of the branch ij at the end of the t period of s seasons; w (j) is a branch end node set taking j as a head end node; p (P) jk,s,t 、Q jk,s,t Active and reactive power at the head end of the branch jk in the period t of s seasons.
2) System security constraints:
Figure SMS_24
in U max 、U min The upper limit and the lower limit of the node voltage of the low-carbon power distribution network are adopted; i ij,max The maximum value of the passing current is allowed for the branch ij.
3) CB operation constraints:
Figure SMS_25
in the formula,
Figure SMS_26
reactive power output is carried out for the z-th CB in the s-season t period; />
Figure SMS_27
The CB group number is input; />
Figure SMS_28
Reactive compensation capacity for each group of CBs; />
Figure SMS_29
The maximum number of input groups is CB.
4) ESS operation constraints:
Figure SMS_30
wherein:
Figure SMS_31
the electric quantity stored for the z-th ESS in the s-season t period; η (eta) ch 、η d Charging and discharging efficiency of the ESS;
Figure SMS_32
is a boolean variable; />
Figure SMS_33
Maximum charge and discharge power for the z-th ESS; />
Figure SMS_34
The state of charge of the ESS at the time t of s seasons is z-th; />
Figure SMS_35
Figure SMS_36
Charging and discharging power for the z-th ESS in the period t of s seasons;
5) MT operation constraint:
Figure SMS_37
wherein:
Figure SMS_38
active power and maximum power for the z-table MT at time t;
6) New energy operation constraint:
Figure SMS_39
wherein: alpha 1 、α 2 A probability value for the constraint condition to be established;
Figure SMS_40
predicting a mean value of deviation for the z-th stage PV and the WT in s-season t period; />
Figure SMS_41
A power factor angle for the z-th stage PV and WT during s-season t-period; />
Figure SMS_42
And->
Figure SMS_43
Reactive power injected for the z-th stage PV and WT during the s-season t period; />
Figure SMS_44
Active power injected for the z-th stage PV and WT during the s-season t period; />
Figure SMS_45
Maximum active power injected for the z-th stage PV and WT during the s-season t period;
further, in the step (4), a carbon transaction mechanism of the low-carbon power distribution network is established, which specifically includes the following steps:
Figure SMS_46
wherein:
Figure SMS_47
carbon emission per unit electricity consumption; />
Figure SMS_48
Carbon emission quota for unit electricity consumption;
Figure SMS_49
a trade price for carbon; />
Figure SMS_50
Is the electricity purchasing quantity.
Further, in the step (5), the double-layer model is solved by using an improved weed algorithm and a multi-objective whale algorithm, and the method is specifically as follows:
step 5.1: the initialization run layer cost lb= - ≡, initializing a planning layer the cost UB = +++ is, the number of iterations d=1.
Step 5.2: solving the run layer, updating lb=max { LB, C O (x) Variable x is passed to the planning layer, updating the planning layer constraints.
Step 5.3: solving the planning layer, updating ub=min { UB, C O (y)}。
Step 5.4: and if convergence accuracy I UB-LB I is less than or equal to xi, outputting a result, otherwise, performing step 5.5.
Step 5.5: the variable y is passed to the run layer, the run layer constraint is updated, d=d+1, and go to step 5.2.
The improved weed algorithm is as follows:
first, an age attribute calculation formula is introduced:
a i ＝e cur -e bom
wherein: e, e cur 、e bom Respectively representing the current algebra and birth algebra of the population where the individual i is located; a, a i Age of the ith individual weed.
The senescence mechanism of weeds can limit the number of offspring that can be propagated by individual weeds, specifically:
Figure SMS_51
wherein: a, a max 、a cur The maximum age and the current age of the individual can survive; y is max 、y min Respectively the upper limit and the lower limit of the fitness of the current population; s is S max 、S min The upper limits of the algebra of the propagules respectively; y, S i The current fitness and the resulting sub-algebra, respectively.
By age attributes, the number of neutrons that an individual can reproduce is closely related to fitness, while enabling an individual to attenuate reproductive capacity as they age.
Compared with the prior art, the invention has the advantages and positive effects that:
(1) Based on the network loss sensitivity, the site selection can reduce the network loss of the system and improve the energy utilization efficiency.
(2) Compared with the traditional multi-objective algorithm, the multi-objective whale algorithm provided by the invention has better convergence, distribution and uniformity, and the fuzzy decision can make a balanced decision on multiple objectives to obtain an optimal solution.
(3) The low-carbon power distribution network double-layer planning model provided by the invention considers new energy configuration, can reduce the carbon emission of the traditional power distribution network, and meets the social development requirement of a double-carbon target.
Drawings
FIG. 1 is a double-layer layout diagram of a low-carbon power distribution network based on network loss sensitivity site selection;
FIG. 2 is a diagram of a modified IEEE33 node low-carbon distribution network;
FIG. 3 is a flow chart of a low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection;
FIG. 4 is a comparison of the proposed solution algorithm with the NSGA-II algorithm.
Detailed Description
The invention provides a low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection, wherein a low-carbon power distribution network double-layer planning model is shown in fig. 1, a low-carbon power distribution network double-layer planning result is shown in fig. 2, a low-carbon power distribution network double-layer planning flow chart based on network loss sensitivity site selection is shown in fig. 3, and the implementation steps are as follows:
(1) Establishing a site selection model based on network loss sensitivity
Figure SMS_52
Wherein: LP and LQ are active and reactive power loss sensitivity; p (P) loss Is an active network loss; r and x are branch resistances and reactances; p is p j 、q j Injecting active power and reactive power for the branch end node j; u (U) j Is the voltage at node j.
The larger the sensitivity of the network loss is, the larger the influence of the load power of the node on the network loss is, and the system loss can be reduced by configuring active or reactive compensation equipment at the node with the larger sensitivity of the network loss.
(2) Establishing low-carbon power distribution network planning layer model
The low-carbon power distribution network planning layer aims at the minimum comprehensive cost, and considers the investment and operation cost of the miniature gas turbine MT, new energy, the energy storage ESS and the capacitor CB, wherein the new energy comprises photovoltaic PV and wind power WT:
Figure SMS_53
wherein: f (F) up Is the comprehensive cost; c (C) I Is investment cost; c (C) O The annual running cost;
Figure SMS_56
investment cost for MT; />
Figure SMS_59
Investment cost for PV; />
Figure SMS_61
Investment cost for WT; />
Figure SMS_57
Investment costs for ESS; />
Figure SMS_58
Is CB investment cost; />
Figure SMS_62
The MT running cost;
Figure SMS_64
the PV operating cost; />
Figure SMS_54
Cost for WT operation; />
Figure SMS_60
The running cost is ESS; />
Figure SMS_63
The electricity purchasing cost is the electricity purchasing cost of the upper-level power grid; />
Figure SMS_65
Carbon emission cost of the power distribution network; />
Figure SMS_55
And selling electricity to the upper power grid.
Constraint conditions of the low-carbon power distribution network planning layer are as follows:
1) Number of device installations constraint:
Figure SMS_66
wherein: n is n i The number of installations for the i-th type of equipment;
Figure SMS_67
for the maximum purchase amount of the device.
2) Device installation capacity constraints:
Figure SMS_68
wherein: e (E) i The installation capacity of the i-th type equipment;
Figure SMS_69
for maximum installation capacity of the device.
3) New energy permeability constraint:
Figure SMS_70
wherein: n is n node The number of nodes of the power distribution network is counted; e (E) i ' configure capacity for node i new energy; θ is the new energy permeability;
Figure SMS_71
is the maximum load in one year.
(3) Establishing low-carbon power distribution network operation layer model
The low-carbon power distribution network operation layer takes the minimum operation cost and voltage offset as targets, takes capacitor switching, new energy uncertainty, a miniature gas turbine and energy storage scheduling into consideration, adopts a multi-target whale algorithm to obtain uniform pareto fronts, and selects an optimal solution by using a fuzzy decision method:
Figure SMS_72
in which F is down Multi-objective optimization for the operation layer; deltaU all Is the voltage offset;
Figure SMS_73
the voltage per unit value of the inode in the typical day t period of s seasons; d (D) s Is the number of days in season.
The operation layer constraint conditions are as follows:
1) Branch tide constraint of low-carbon power distribution network:
Figure SMS_74
in U i,s,t 、U j,s,t The voltage of the first-end i node and the voltage of the terminal j node of the branch ij in the period t of s seasons are calculated; r is (r) ij 、x ij The resistance and reactance of branch ij; p (P) ij,s,t 、Q ij,s,t Active and reactive power of the head end of the branch ij in the period t of s seasons; i ij,s,t The current of the branch ij in the period t of s seasons is equal to the current of the branch ij in the period t of s seasons; pj, s, t, q j,s,t Active power and reactive power are injected into the j node of the branch ij at the end of the t period of s seasons; w (j) is a branch end node set taking j as a head end node; p (P) jk,s,t 、Q jk,s,t Active and reactive power at the head end of the branch jk in the period t of s seasons.
2) System security constraints:
Figure SMS_75
in U max 、U min The upper limit and the lower limit of the node voltage of the low-carbon power distribution network are adopted; i ij,max The maximum value of the passing current is allowed for the branch ij.
3) CB operation constraints:
Figure SMS_76
in the formula,
Figure SMS_77
reactive power output is carried out for the z-th CB in the s-season t period; />
Figure SMS_78
The CB group number is input; />
Figure SMS_79
Reactive compensation capacity for each group of CBs; />
Figure SMS_80
The maximum number of input groups is CB.
4) ESS operation constraints:
Figure SMS_81
wherein:
Figure SMS_82
the electric quantity stored for the z-th ESS in the s-season t period; η (eta) ch 、η d Charging and discharging efficiency of the ESS;
Figure SMS_83
is a boolean variable; />
Figure SMS_84
Maximum charge and discharge power for the z-th ESS; />
Figure SMS_85
The state of charge of the ESS at the time t of s seasons is z-th; />
Figure SMS_86
Figure SMS_87
Charging the z-th ESS in the s-season t periodDischarge power;
5) MT operation constraint:
Figure SMS_88
wherein:
Figure SMS_89
active power and maximum power for the z-stage MTt period;
6) New energy operation constraint:
Figure SMS_90
wherein: alpha 1 、α 2 A probability value for the constraint condition to be established;
Figure SMS_91
predicting a mean value of deviation for the z-th stage PV and the WT in s-season t period; />
Figure SMS_92
A power factor angle for the z-th stage PV and WT during s-season t-period; />
Figure SMS_93
And->
Figure SMS_94
Reactive power injected for the z-th stage PV and WT during the s-season t period; />
Figure SMS_95
Active power injected for the z-th stage PV and WT during the s-season t period; />
Figure SMS_96
Maximum active power injected for the z-th stage PV and WT during the s-season t period;
(4) Establishing carbon transaction mechanism of low-carbon power distribution network
Figure SMS_97
Wherein:
Figure SMS_98
carbon emission per unit electricity consumption; />
Figure SMS_99
Carbon emission quota for unit electricity consumption; />
Figure SMS_100
A trade price for carbon; />
Figure SMS_101
To purchase electric power.
(5) And solving the double-layer model by using an improved weed algorithm and a multi-target whale algorithm. The method comprises the following specific steps:
step 5.1: the initialization run layer LB = - ≡, initializing a plan layer UB = +++ is provided, the number of iterations d=1 is set.
Step 5.2: solving the run layer, updating lb=max { LB, C O (x) Variable x is passed to the planning layer, updating the planning layer constraints.
Step 5.3: solving the planning layer, updating ub=min { UB, C O (y)}。
Step 5.4: and if convergence accuracy I UB-LB I is less than or equal to xi, outputting a result, otherwise, performing step 5.5.
Step 5.5: the variable y is passed to the run layer, the run layer constraint is updated, d=d+1, and go to step 5.2.
The improved weed algorithm is as follows:
first, an age attribute calculation formula is introduced:
a i ＝e cur -e bom
wherein: e, e cur 、e bom Respectively representing the current algebra and birth algebra of the population where the individual i is located; a, a i Age of the ith individual weed.
The senescence mechanism of weeds can limit the number of offspring that can be propagated by individual weeds, specifically:
Figure SMS_102
wherein: a, a max 、a cur The maximum age and the current age of the individual can survive; y is max 、y min Respectively the upper limit and the lower limit of the fitness of the current population; s is S max 、S min The upper limits of the algebra of the propagules respectively; y, S i The current fitness and the resulting sub-algebra, respectively.
By age attributes, the number of neutrons that an individual can reproduce is closely related to fitness, while enabling an individual to attenuate reproductive capacity as they age.
To verify the effectiveness of the method mentioned, consider the following three operating strategy results when the spring is considered.
Strategy 1: the stored energy configuration is not considered.
Strategy 2: on the basis of strategy 1, the same-capacity energy storage is configured at the wind-light node.
Strategy 3: based on strategy 2, the loss sensitivity is considered.
Table 1 shows the system operation results under three strategies in spring, and from the table, the method can effectively reduce the system operation cost and improve the voltage quality.
Table 1 three strategy results
Figure SMS_103
Finally, only specific embodiments of the present invention have been described in detail above. The invention is not limited to the specific embodiments described above. Equivalent modifications and substitutions of the invention will occur to those skilled in the art, and are intended to be within the scope of the present invention. Accordingly, equivalent changes and modifications are intended to be included within the scope of the present invention without departing from the spirit and scope thereof.

Claims (8)

1. A low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection is characterized by comprising the following steps of: the method comprises the following steps:
step 1, establishing a network loss sensitivity-based site selection model;
step 2, establishing a low-carbon power distribution network planning layer model;
step 3, establishing a low-carbon power distribution network operation layer model;
step 4, establishing a carbon transaction mechanism of the low-carbon power distribution network;
and 5, solving the double-layer model by using an improved weed algorithm and a multi-target whale algorithm.
2. The low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection according to claim 1, wherein the method is characterized by comprising the following steps of: in the step 1, the site selection model based on the network loss sensitivity is specifically as follows:
Figure QLYQS_1
wherein: LP and LQ are active and reactive power loss sensitivity; p (P) loss Is an active network loss; r and x are branch resistances and reactances; p is p j 、q j Injecting active power and reactive power for the branch end node j; u (U) j Is the voltage at node j.
3. The low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection according to claim 1, wherein the method is characterized by comprising the following steps of: the low-carbon power distribution network planning layer model in the step 2 aims at the minimum comprehensive cost, considers investment and operation costs of MT, new energy, ESS and CB, and specifically comprises the following steps:
new energy sources include PV and WT;
Figure QLYQS_2
wherein: f (F) up Is the comprehensive cost; c (C) I Is investment cost; c (C) O The annual running cost;
Figure QLYQS_6
investment cost for MT; />
Figure QLYQS_11
Investment cost for PV; />
Figure QLYQS_12
Investment cost for WT; />
Figure QLYQS_5
Investment costs for ESS; />
Figure QLYQS_9
Is CB investment cost; />
Figure QLYQS_13
The MT running cost; />
Figure QLYQS_14
The PV operating cost; />
Figure QLYQS_3
Cost for WT operation; />
Figure QLYQS_7
The running cost is ESS; />
Figure QLYQS_8
The electricity purchasing cost is the electricity purchasing cost of the upper-level power grid; />
Figure QLYQS_10
Carbon emission cost of the power distribution network; />
Figure QLYQS_4
And selling electricity to the upper power grid.
4. The low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection according to claim 1, wherein the method is characterized by comprising the following steps of: the constraint conditions of the low-carbon power distribution network planning layer model in the step 2 are specifically as follows:
1) Number of device installations constraint:
Figure QLYQS_15
wherein: n is n i The number of installations for the i-th type of equipment;
Figure QLYQS_16
maximum purchase amount for the device;
2) Device installation capacity constraints:
Figure QLYQS_17
wherein: e (E) i The installation capacity of the i-th type equipment;
Figure QLYQS_18
maximum installation capacity for the device;
3) New energy permeability constraint:
Figure QLYQS_19
wherein: n is n node The number of nodes of the power distribution network is counted; e (E) i ' configure capacity for node i new energy; θ is the new energy permeability;
Figure QLYQS_20
is the maximum load in one year.
5. The low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection according to claim 3, wherein the method is characterized by comprising the following steps of: in the step 3, the low-carbon power distribution network operation layer takes the minimum operation cost and voltage offset as targets, takes into account capacitor switching, new energy uncertainty, micro gas turbines and energy storage scheduling, adopts a multi-target whale algorithm to obtain a uniform pareto front edge, and utilizes a fuzzy decision method to select an optimal solution, and specifically comprises the following steps:
Figure QLYQS_21
in which F is down The multi-objective optimization is carried out on the running layer of the low-carbon power distribution network; deltaU all Is the voltage offset;
Figure QLYQS_22
the voltage per unit value of the inode in the typical day t period of s seasons; d (D) s Is the number of days in season.
6. The low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection according to claim 1, wherein the method is characterized by comprising the following steps of: the constraint conditions of the low-carbon power distribution network operation layer model in the step 3 are specifically as follows:
1) Branch tide constraint of low-carbon power distribution network:
Figure QLYQS_23
in U i,s,t 、U j,s,t The voltage of the first-end i node and the voltage of the terminal j node of the branch ij in the period t of s seasons are calculated; r is (r) ij 、x ij The resistance and reactance of branch ij; p (P) ij,s,t 、Q ij,s,t Active and reactive power of the head end of the branch ij in the period t of s seasons; i ij,s,t The current of the branch ij in the period t of s seasons is equal to the current of the branch ij in the period t of s seasons; p is p j,s,t 、q j,s,t Active power and reactive power are injected into the j node of the branch ij at the end of the t period of s seasons; w (j) is a branch end node set taking j as a head end node; p (P) jk,s,t 、Q jk,s,t Active and reactive power of the head end of the branch jk in the period t of s seasons;
2) System security constraints:
Figure QLYQS_24
in U max 、U min Is a low-carbon power distribution networkUpper and lower limits of node voltage; i ij,max The maximum value of the allowed through current for branch ij;
3) CB operation constraints:
Figure QLYQS_25
in the formula,
Figure QLYQS_26
reactive power output is carried out for the z-th CB in the s-season t period; />
Figure QLYQS_27
The CB group number is input; />
Figure QLYQS_28
Reactive compensation capacity for each group of CBs; />
Figure QLYQS_29
The maximum investment group number is CB;
4) ESS operation constraints:
Figure QLYQS_30
wherein:
Figure QLYQS_31
the electric quantity stored for the z-th ESS in the s-season t period; η (eta) ch 、η d Charging and discharging efficiency of the ESS; />
Figure QLYQS_32
Is a boolean variable; />
Figure QLYQS_33
Maximum charge and discharge power for the z-th ESS; />
Figure QLYQS_34
In s-season for z-th ESSThe charge state of the section t period; />
Figure QLYQS_35
Figure QLYQS_36
Charging and discharging power for the z-th ESS in the period t of s seasons;
5) MT operation constraint:
Figure QLYQS_37
wherein:
Figure QLYQS_38
active power and maximum power for the z-table MT at time t;
6) New energy constraint:
Figure QLYQS_39
wherein: alpha 1 、α 2 A probability value for the constraint condition to be established;
Figure QLYQS_40
predicting a mean value of deviation for the z-th stage PV and the WT in s-season t period; />
Figure QLYQS_41
A power factor angle for the z-th stage PV and WT during s-season t-period; />
Figure QLYQS_42
And->
Figure QLYQS_43
Reactive power injected for the z-th stage PV and WT during the s-season t period; />
Figure QLYQS_44
Injection for z-th stage PV and WT during s-season t-periodActive power of (2);
Figure QLYQS_45
maximum active power injected for the z-th stage PV and WT during the s-season t-period.
7. The low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection according to claim 5, wherein the method is characterized by comprising the following steps of: step 4 is to establish a carbon transaction mechanism of the low-carbon power distribution network, which specifically comprises the following steps:
Figure QLYQS_46
wherein:
Figure QLYQS_47
carbon emission per unit electricity consumption; />
Figure QLYQS_48
Carbon emission quota for unit electricity consumption; />
Figure QLYQS_49
A trade price for carbon; />
Figure QLYQS_50
To purchase electric power.
8. The low-carbon power distribution network double-layer planning method based on network loss sensitivity site selection according to claim 7, wherein the method is characterized by comprising the following steps of: the step 5 is to solve a double-layer model by using an improved weed algorithm and a multi-target whale algorithm, and specifically comprises the following steps:
step 5.1: the initialization run layer cost lb= - ≡, initializing a planning layer the cost UB = +++ is, the number of iterations d=1;
step 5.2: solving low-carbon power distribution network operation layer, and updating LB=max { LB, C O (x) The variable x is transmitted to a low-carbon power distribution network planning layer, and the constraint of the low-carbon power distribution network planning layer is updated;
step 5.3: solving for low-carbon power distributionNetwork planning layer, update ub=min { UB, C O (y)}；
Step 5.4: if convergence condition |UB-LB| is less than or equal to ζ, outputting a result, otherwise, performing step 5.5;
step 5.5: transmitting the variable y to a low-carbon power distribution network operation layer, updating the constraint of the low-carbon power distribution network operation layer, and turning to step 5.2, wherein d=d+1;
the improved weed algorithm is as follows:
first, an age attribute calculation formula is introduced:
a i ＝e cur -e bom
wherein: e, e cur 、e bom Respectively representing the current algebra and birth algebra of the population where the individual i is located; a, a i Age of the ith individual weed;
the senescence mechanism of weeds can limit the number of offspring that can be propagated by individual weeds, specifically:
Figure QLYQS_51
wherein: a, a max 、a cur The maximum age and the current age of the individual can survive; y is max 、y min Respectively the upper limit and the lower limit of the fitness of the current population; s is S max 、S min The upper limits of the algebra of the propagules respectively; y, S i Respectively the current fitness and the generated sub algebra;
by age attributes, the number of neutrons that an individual can reproduce is closely related to fitness, while enabling an individual to attenuate reproductive capacity as they age.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117767369A (en) * 2023-12-25 2024-03-26 中国长江电力股份有限公司 Energy storage site selection and hierarchical configuration method considering medium-long term planning

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117767369A (en) * 2023-12-25 2024-03-26 中国长江电力股份有限公司 Energy storage site selection and hierarchical configuration method considering medium-long term planning

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