CN103258117A - Method for calculating time-of-use electricity price in intelligent micro-grid - Google Patents

Method for calculating time-of-use electricity price in intelligent micro-grid Download PDF

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CN103258117A
CN103258117A CN2013101359322A CN201310135932A CN103258117A CN 103258117 A CN103258117 A CN 103258117A CN 2013101359322 A CN2013101359322 A CN 2013101359322A CN 201310135932 A CN201310135932 A CN 201310135932A CN 103258117 A CN103258117 A CN 103258117A
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electricity
microgrid
price
value
coefficient
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CN103258117B (en
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严玉廷
高云祥
刘友宽
杨洋
李萍
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Yunnan Power Grid Corp Technology Branch
Yunnan Electric Power Experimental Research Institute Group Co Ltd of Electric Power Research Institute
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Yunnan Power Grid Corp Technology Branch
Yunnan Electric Power Experimental Research Institute Group Co Ltd of Electric Power Research Institute
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Abstract

The invention discloses a method for calculating a time-of-use electricity price in an intelligent micro-grid. The method for calculating the time-of-use electricity price in the intelligent micro-grid comprises the following steps that (1) a theoretical electricity price P0 and an actual time-of-use price P are defined, an electricity price coefficient is k, the equation of P=P0*k is established, and an absolute electricity utilization coefficient eta0 and a relative electricity utilization coefficient eta are defined; (2) a predicted load of a current moment is defined to be q, predicted micro-grid generated power is defined to be p, and a predicted resolution ratio is defined to be delta t minutes. The method for calculating the time-of-use electricity price in the intelligent micro-grid is used in the field of time-of-use electricity price calculation in the field of power grids, the objectives that the high electricity price is used in an electricity utilization peak and the low electricity price is used in an electricity utilization trough, users are encouraged to arrange electricity using time reasonably and electricity utilization load is transferred according to adjustment of the electricity price, electricity utilization amount is reduced in the electricity utilization peak and increased in the electricity utilization trough, and utilization efficiency of electric power resources is improved.

Description

A kind of tou power price computing method for intelligent microgrid
Technical field
The present invention relates to intelligent grid tou power price field.
Background technology
What present tou power price adopted is the pattern of TOU, and its period divides and rate all is pre-determined, and the update cycle is long, can not effectively carry out reasonable standard to the user.And RTP Dynamic Pricing mechanism does not have a kind of effective means to be implemented in tou power price computing method in the microgrid.
Summary of the invention
The objective of the invention is, realize dynamic tou power price according to user's load variations situation, encourage the user rationally to arrange the electricity consumption time, shift power load, peak load shifting improves the utilization ratio of electric power resource.
To achieve these goals, the invention provides following technical scheme:
(1) the theoretical electricity price P of definition 0, actual tou power price P, electricity price coefficient k, then P=P 0* k.Definition is absolute to be η with electrostrictive coefficient 0, use electrostrictive coefficient η relatively, it is defined as follows:
The η value [1.5 ,+∞) electricity consumption is outstanding, sharp k=1.5;
η value (1.2,1.5) peak of power consumption, peak k=1.2;
The η value (0.8,1.2] conventional electricity consumption, flat k=1.0;
η value [0,0.8] low power consumption, paddy k=0.8;
K is the electricity price coefficient, and concrete electricity price constantly is:
P=P 0*k；
(2) the prediction load of definition current time is q, and the prediction microgrid generated output of current time is p, and prediction resolution is Δ t minute.Electrostrictive coefficient is definitely used in definition
Figure BDA00003070492500011
Use electrostrictive coefficient relatively
Figure BDA00003070492500012
N=24*60/ Δ t wherein;
(3) the microgrid function situation is divided three classes: lonely network operation, be incorporated into the power networks, the whole network operation; Computation schema with electrostrictive coefficient also divides three classes relatively:
1) the whole network operational mode: namely microgrid is not powered, and the user uses mains supply fully
Definitely use electrostrictive coefficient
Figure BDA00003070492500013
This moment p=0, then η=η 0
2) be incorporated into the power networks that pattern (insert network system, satisfied from the time spent, can be to mains supply by the microgrid generating; The microgrid generating can not be satisfied from the time spent, and network system is to the power supply of microgrid system balance)
Current, electricity consumption ratio: δ=p/q;
The relative electrostrictive coefficient η that uses under the pattern that is incorporated into the power networks:
As δ 〉=1 the time, η value 0, this moment, the microgrid generating can be satisfied need for electricity fully, can also additionally think mains supply;
When δ＜1, η = q / [ Σ i = 1 i = n ( p i - q i ) / n ] ;
3) lonely network operation pattern: used for internal load by microgrid generating, energy storage etc., do not accept mains supply
Definitely use electrostrictive coefficient η 0 = q / [ ( Σ i = 1 i = n q i ) / n ] ;
Current, electricity consumption ratio: δ=p/q;
The relative electrostrictive coefficient η that uses under the lonely net pattern:
As δ 〉=1 the time, this moment, the microgrid generating can be satisfied need for electricity fully, can also be extraly to the energy-storage units charging, if energy-storage battery has been then η value 0 of the state that is full of, otherwise η value η 0
When δ＜1, this moment, the microgrid generating can not be satisfied the needs of self, needed energy storage to replenish, if energy storage can satisfy fully today with can supplementary information then η value η 0, otherwise η value 1.5.
The present invention is applied in the tou power price calculating field in electrical network field, adopt low electricity price when adopting high electricity price, low power consumption when realizing peak of power consumption, encourage the user rationally to arrange the electricity consumption time, shift power load, peak load shifting, the utilization ratio of raising electric power resource by adjusting electricity price.
Below in conjunction with accompanying drawing the present invention is done further explanation.
Description of drawings
Fig. 1 is embodiment of the invention generating, electricity consumption, electricity price curve map.
Embodiment
Method of the present invention is as follows:
(1) the theoretical electricity price P of definition 0, actual tou power price P, electricity price coefficient k, then P=P 0* k.Definition is absolute to be η with electrostrictive coefficient 0, use electrostrictive coefficient η relatively, it is defined as follows:
The η value [1.5 ,+∞) electricity consumption is outstanding, sharp k=1.5;
η value (1.2,1.5) peak of power consumption, peak k=1.2;
The η value (0.8,1.2] conventional electricity consumption, flat k=1.0;
η value [0,0.8] low power consumption, paddy k=0.8;
K is the electricity price coefficient, and concrete electricity price constantly is:
P=P 0*k；
(2) the prediction load of definition current time is q, and the prediction microgrid generated output of current time is p, and prediction resolution is Δ t minute.Electrostrictive coefficient is definitely used in definition
Figure BDA00003070492500031
Use electrostrictive coefficient relatively
Figure BDA00003070492500032
N=24*60/ Δ t wherein;
(3) the microgrid function situation is divided three classes: lonely network operation, be incorporated into the power networks, the whole network operation; Computation schema with electrostrictive coefficient also divides three classes relatively:
1) the whole network operational mode: namely microgrid is not powered, and the user uses mains supply definitely to use electrostrictive coefficient fully
Figure BDA00003070492500033
This moment p=0, then η=η 0
2) be incorporated into the power networks that pattern (insert network system, satisfied from the time spent, can be to mains supply by the microgrid generating; The microgrid generating can not be satisfied from the time spent, and network system is to the power supply of microgrid system balance)
Current, electricity consumption ratio: δ=p/q;
The relative electrostrictive coefficient η that uses under the pattern that is incorporated into the power networks:
As δ 〉=1 the time, η value 0, this moment, the microgrid generating can be satisfied need for electricity fully, can also additionally think mains supply;
When δ＜1, η = q / [ Σ i = 1 i = n ( p i - q i ) / n ] ;
3) lonely network operation pattern: used for internal load by microgrid generating, energy storage etc., do not accept mains supply
Definitely use electrostrictive coefficient η 0 = q / [ ( Σ i = 1 i = n q i ) / n ] ;
Current, electricity consumption ratio: δ=p/q;
The relative electrostrictive coefficient η that uses under the lonely net pattern:
As δ 〉=1 the time, this moment, the microgrid generating can be satisfied need for electricity fully, can also be extraly to the energy-storage units charging, if energy-storage battery has been then η value 0 of the state that is full of, otherwise η value η 0
When δ＜1, this moment, the microgrid generating can not be satisfied the needs of self, needed energy storage to replenish, if energy storage can satisfy fully today with can supplementary information then η value η 0, otherwise η value 1.5.
Example:
Intelligent microgrid garden based on photovoltaic generation, theoretical electricity price P 0=0.4 yuan, the predicted data of its one day following (prediction resolution is 15min):
Its predicted data is as follows:
Sequence number (t+n Δ t) Period Prediction generated output (kW) Prediction garden load (kW)
1 0:00 0.00 3.00
2 0:15 0.00 2.00
3 0:30 0.00 1.3
4 0:45 0.00 0.5
5 1:00 0.00 0.5
6 1:15 0.00 0.5
7 1:30 0.00 0.5
8 1:45 0.00 0.5
9 2:00 0.00 0.5
10 2:15 0.00 0.5
11 2:30 0.00 0.5
12 2:45 0.00 0.5
13 3:00 0.00 0.5
14 3:15 0.00 0.5
15 3:30 0.00 0.5
16 3:45 0.00 0.5
17 4:00 0.00 0.5
18 4:15 0.00 0.5
19 4:30 0.00 0.5
20 4:45 0.00 0.5
21 5:00 0.00 0.5
22 5:15 0.00 0.5
23 5:30 0.00 0.5
24 5:45 0.00 0.5
25 6:00 0.00 40
26 6:15 0.00 60
27 6:30 0.00 60
28 6:45 0.00 90
29 7:00 8.00 90
30 7:15 10.00 120
31 7:30 15.00 120
32 7:45 18.00 90
33 8:00 21.50 120
34 8:15 25.00 145
35 8:30 28.50 120
36 8:45 32.00 60
37 9:00 35.50 60
38 9:15 39.00 60
39 9:30 42.50 80
40 9:45 46.00 80
41 10:00 49.50 120
42 10:15 53.00 120
43 10:30 56.50 145
44 10:45 60.00 180
45 11:00 63.50 180
46 11:15 67.00 180
47 11:30 70.50 180
48 11:45 74.00 180
49 12:00 77.50 120
50 12:15 81.00 120
51 12:30 84.50 80
52 12:45 88.00 80
53 13:00 91.50 15
54 13:15 95.00 15
55 13:30 98.50 15
56 13:45 102.00 35
57 14:00 105.50 35
58 14:15 109.00 35
59 14:30 112.50 35
60 14:45 116.00 35
61 15:00 119.50 35
62 15:15 102.00 35
63 15:30 105.50 35
64 15:45 109.00 35
65 16:00 112.50 35
66 16:15 116.00 60
67 16:30 119.50 60
68 16:45 116.32 90
69 17:00 117.17 120
70 17:15 70.00 125
71 17:30 63.00 140
72 17:45 45.00 180
73 18:00 45.00 180
74 18:15 23.00 180
75 18:30 23.00 180
76 18:45 15.00 180
77 19:00 0.00 180
78 19:15 0.00 135
79 19:30 0.00 135
80 19:45 0.00 135
81 20:00 0.00 135
82 20:15 0.00 135
83 20:30 0.00 135
84 20:45 0.00 135
85 21:00 0.00 90
86 21:15 0.00 90
87 21:30 0.00 90
88 21:45 0.00 90
89 22:00 0.00 90
90 22:15 0.00 90
91 22:30 0.00 120
92 22:45 0.00 120
93 23:00 0.00 30
94 23:15 0.00 30
95 23:30 0.00 12
96 23:45 0.00 5
The on time of microgrid generator unit is 7:00 as can be seen from data, and the unused time is 19:00, the pattern that is incorporated into the power networks that then between 7:00 to 19:00, adopts, and other periods are the whole network pattern (only using mains supply).
According to η ( t ) = q ( t ) / [ Σ i = 1 i = n ( p i - q i ) / n ] , Calculate by the predicted data in the form [ Σ i = 1 i = n ( p i - q i ) / n ] = 50.30 , Concrete electricity price is as follows:
Sequence number Period The prediction generated output Prediction garden load Send out, the electricity consumption ratio Use electrostrictive coefficient relatively The electricity price coefficient Tou power price
1 0:00 0.00 3.00 0 0.073802497 0.8 0.32
2 0:15 0.00 2.00 0 0.049201665 0.8 0.32
3 0:30 0.00 1.3 0 0.031981082 0.8 0.32
4 0:45 0.00 0.5 0 0.012300416 0.8 0.32
5 1:00 0.00 0.5 0 0.012300416 0.8 0.32
6 1:15 0.00 0.5 0 0.012300416 0.8 0.32
7 1:30 0.00 0.5 0 0.012300416 0.8 0.32
8 1:45 0.00 0.5 0 0.012300416 0.8 0.32
9 2:00 0.00 0.5 0 0.012300416 0.8 0.32
10 2:15 0.00 0.5 0 0.012300416 0.8 0.32
11 2:30 0.00 0.5 0 0.012300416 0.8 0.32
12 2:45 0.00 0.5 0 0.012300416 0.8 0.32
13 3:00 0.00 0.5 0 0.012300416 0.8 0.32
14 3:15 0.00 0.5 0 0.012300416 0.8 0.32
15 3:30 0.00 0.5 0 0.012300416 0.8 0.32
16 3:45 0.00 0.5 0 0.012300416 0.8 0.32
17 4:00 0.00 0.5 0 0.012300416 0.8 0.32
18 4:15 0.00 0.5 0 0.012300416 0.8 0.32
19 4:30 0.00 0.5 0 0.012300416 0.8 0.32
20 4:45 0.00 0.5 0 0.012300416 0.8 0.32
21 5:00 0.00 0.5 0 0.012300416 0.8 0.32
22 5:15 0.00 0.5 0 0.012300416 0.8 0.32
23 5:30 0.00 0.5 0 0.012300416 0.8 0.32
24 5:45 0.00 0.5 0 0.012300416 0.8 0.32
25 6:00 0.00 40 0 0.984033293 1 0.4
26 6:15 0.00 60 0 1.476049939 1.2 0.48
27 6:30 0.00 60 0 1.476049939 1.2 0.48
28 6:45 0.00 90 0 2.214074909 1.5 0.6
29 7:00 8.00 90 0.088888889 2.01726825 1.5 0.6
30 7:15 10.00 120 0.083333333 2.706091555 1.5 0.6
31 7:30 15.00 120 0.125 2.583087394 1.5 0.6
32 7:45 18.00 90 0.2 1.771259927 1.5 0.6
33 8:00 21.50 120 0.179166667 2.423181984 1.5 0.6
34 8:15 25.00 145 0.172413793 2.952099879 1.5 0.6
35 8:30 28.50 120 0.2375 2.250976157 1.5 0.6
36 8:45 32.00 60 0.533333333 0.688823305 0.8 0.32
37 9:00 35.50 60 0.591666667 0.602720392 0.8 0.32
38 9:15 39.00 60 0.65 0.516617479 0.8 0.32
39 9:30 42.50 80 0.53125 0.922531212 1 0.4
40 9:45 46.00 80 0.575 0.836428299 1 0.4
41 10:00 49.50 120 0.4125 1.734358679 1.5 0.6
42 10:15 53.00 120 0.441666667 1.648255766 1.5 0.6
43 10:30 56.50 145 0.389655172 2.17717366 1.5 0.6
44 10:45 60.00 180 0.333333333 2.952099879 1.5 0.6
45 11:00 63.50 180 0.352777778 2.865996965 1.5 0.6
46 11:15 67.00 180 0.372222222 2.779894052 1.5 0.6
47 11:30 70.50 180 0.391666667 2.693791139 1.5 0.6
48 11:45 74.00 180 0.411111111 2.607688226 1.5 0.6
49 12:00 77.50 120 0.645833333 1.045535374 1 0.4
50 12:15 81.00 120 0.675 0.959432461 1 0.4
51 12:30 84.50 80 1.05625 0 0.8 0.32
52 12:45 88.00 80 1.1 0 0.8 0.32
53 13:00 91.50 15 6.1 0 0.8 0.32
54 13:15 95.00 15 6.333333333 0 0.8 0.32
55 13:30 98.50 15 6.566666667 0 0.8 0.32
56 13:45 102.00 35 2.914285714 0 0.8 0.32
57 14:00 105.50 75 1.406666667 0 0.8 0.32
58 14:15 109.00 78 1.397435897 0 0.8 0.32
59 14:30 112.50 78 1.442307692 0 0.8 0.32
60 14:45 116.00 78 1.487179487 0 0.8 0.32
61 15:00 119.50 35 3.414285714 0 0.8 0.32
62 15:15 102.00 77 1.324675325 0 0.8 0.32
63 15:30 105.50 35 3.014285714 0 0.8 0.32
64 15:45 109.00 35 3.114285714 0 0.8 0.32
65 16:00 112.50 35 3.214285714 0 0.8 0.32
66 16:15 116.00 60 1.933333333 0 0.8 0.32
67 16:30 119.50 60 1.991666667 0 0.8 0.32
68 16:45 116.32 90 1.292424242 0 0.8 0.32
69 17:00 117.17 120 0.976456876 0.069501652 0.8 0.32
70 17:15 70.00 125 0.56 1.353045778 1.2 0.48
71 17:30 63.00 140 0.45 1.894264089 1.5 0.6
72 17:45 45.00 180 0.25 3.321112363 1.5 0.6
73 18:00 45.00 180 0.25 3.321112363 1.5 0.6
74 18:15 23.00 180 0.127777778 3.862330674 1.5 0.6
75 18:30 23.00 180 0.127777778 3.862330674 1.5 0.6
76 18:45 15.00 180 0.083333333 4.059137333 1.5 0.6
77 19:00 0.00 180 0 4.428149818 1.5 0.6
78 19:15 0.00 135 0 3.321112363 1.5 0.6
79 19:30 0.00 135 0 3.321112363 1.5 0.6
80 19:45 0.00 135 0 3.321112363 1.5 0.6
81 20:00 0.00 135 0 3.321112363 1.5 0.6
82 20:15 0.00 135 0 3.321112363 1.5 0.6
83 20:30 0.00 135 0 3.321112363 1.5 0.6
84 20:45 0.00 135 0 3.321112363 1.5 0.6
85 21:00 0.00 90 0 2.214074909 1.5 0.6
86 21:15 0.00 90 0 2.214074909 1.5 0.6
87 21:30 0.00 90 0 2.214074909 1.5 0.6
88 21:45 0.00 90 0 2.214074909 1.5 0.6
89 22:00 0.00 90 0 2.214074909 1.5 0.6
90 22:15 0.00 90 0 2.214074909 1.5 0.6
91 22:30 0.00 120 0 2.952099879 1.5 0.6
92 22:45 0.00 120 0 2.952099879 1.5 0.6
93 23:00 0.00 30 0 0.73802497 0.8 0.32
94 23:15 0.00 30 0 0.73802497 0.8 0.32
95 23:30 0.00 12 0 0.295209988 0.8 0.32
96 23:45 0.00 5 0 0.123004162 0.8 0.32
Draw generating, electricity consumption and electricity price curve by data:
As seen, the low electricity price of employing when adopting high electricity price, low power consumption when realizing peak of power consumption is encouraged the user rationally to arrange the electricity consumption time, is shifted power load, peak load shifting, the utilization ratio of raising electric power resource by adjusting electricity price among Fig. 1.

Claims (1)

1. tou power price computing method that are used for intelligent microgrid are the performance matchings for the tou power price of realizing the microgrid user, from the better standard user's in back consumption habit, realize the effect of the peak load shifting in the electrical network, it is characterized in that, may further comprise the steps:
(1) the theoretical electricity price P of definition 0, actual tou power price P, electricity price coefficient k, then P=P 0* k, definition is absolute to be η with electrostrictive coefficient 0, use electrostrictive coefficient η relatively, it is defined as follows:
The η value [1.5 ,+∞) electricity consumption is outstanding, sharp k=1.5;
η value (1.2,1.5) peak of power consumption, peak k=1.2;
The η value (0.8,1.2] conventional electricity consumption, flat k=1.0;
η value [0,0.8] low power consumption, paddy k=0.8;
K is the electricity price coefficient, and concrete electricity price constantly is:
P=P 0*k；
(2) the prediction load of definition current time is q, and the prediction microgrid generated output of current time is p, and prediction resolution is Δ t minute; Electrostrictive coefficient is definitely used in definition
Figure FDA00003070492400011
Use electrostrictive coefficient relatively
Figure FDA00003070492400012
N=24*60/ Δ t wherein;
(3) the microgrid function situation is divided three classes: lonely network operation, be incorporated into the power networks, the whole network operation; Computation schema with electrostrictive coefficient also divides three classes relatively:
1) the whole network operational mode: namely microgrid is not powered, and the user uses mains supply fully
Definitely use electrostrictive coefficient
Figure FDA00003070492400013
This moment p=0, then η=η 0
2) be incorporated into the power networks that pattern (insert network system, satisfied from the time spent, can be to mains supply by the microgrid generating; The microgrid generating can not be satisfied from the time spent, and network system is to the power supply of microgrid system balance)
Current, electricity consumption ratio: δ=p/q;
The relative electrostrictive coefficient η that uses under the pattern that is incorporated into the power networks:
As δ 〉=1 the time, η value 0, this moment, the microgrid generating can be satisfied need for electricity fully, can also additionally think mains supply;
When δ＜1, η = q / [ Σ i = 1 i = n ( p i - q i ) / n ] ;
3) lonely network operation pattern: used for internal load by microgrid generating, energy storage etc., do not accept mains supply
Definitely use electrostrictive coefficient η 0 = q / [ ( Σ i = 1 i = n q i ) / n ] ;
Current, electricity consumption ratio: δ=p/q;
The relative electrostrictive coefficient η that uses under the lonely net pattern:
As δ 〉=1 the time, this moment, the microgrid generating can be satisfied need for electricity fully, can also be extraly to the energy-storage units charging, if energy-storage battery has been then η value 0 of the state that is full of, otherwise η value η 0
When δ＜1, this moment, the microgrid generating can not be satisfied the needs of self, needed energy storage to replenish, if energy storage can satisfy fully today with can supplementary information then η value η 0, otherwise η value 1.5.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103679555A (en) * 2013-12-16 2014-03-26 成都安健发科技有限公司 Time-of-use electricity price determining method based on load characteristic classification
CN104156581A (en) * 2014-08-01 2014-11-19 重庆大学 Peak-valley time-of-use power price determining method taking both system reliability and power purchase risk into consideration
CN112713588A (en) * 2020-12-16 2021-04-27 西安交通大学 Electricity price counting method capable of reducing abandoned light and abandoned wind
CN113743978A (en) * 2021-07-23 2021-12-03 国网河北省电力有限公司电力科学研究院 Time-of-use electricity price making method and device of source network charge storage system and terminal equipment

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TW378299B (en) * 1996-12-23 2000-01-01 Scientific Atlanta Method and apparatus using distributed intelligence for applying real time pricing and time of use rates in wide area network including a headend and subscriber
CN101996281A (en) * 2010-11-11 2011-03-30 中华电信股份有限公司 Electric charge diagnosis system and method
CN102521398A (en) * 2011-12-23 2012-06-27 清华大学 Modeling method for substation-dispatching center two-level distributed type power grid

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TW378299B (en) * 1996-12-23 2000-01-01 Scientific Atlanta Method and apparatus using distributed intelligence for applying real time pricing and time of use rates in wide area network including a headend and subscriber
CN101996281A (en) * 2010-11-11 2011-03-30 中华电信股份有限公司 Electric charge diagnosis system and method
CN102521398A (en) * 2011-12-23 2012-06-27 清华大学 Modeling method for substation-dispatching center two-level distributed type power grid

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
武云甫: "住宅配电系统需要系数KX的合理选择", 《电气技术》, no. 3, 15 March 2006 (2006-03-15), pages 59 - 62 *
牛铭等: "微网并网时的经济运行研究", 《电网技术》, vol. 34, no. 11, 5 November 2010 (2010-11-05), pages 38 - 42 *

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CN103679555A (en) * 2013-12-16 2014-03-26 成都安健发科技有限公司 Time-of-use electricity price determining method based on load characteristic classification
CN104156581A (en) * 2014-08-01 2014-11-19 重庆大学 Peak-valley time-of-use power price determining method taking both system reliability and power purchase risk into consideration
CN104156581B (en) * 2014-08-01 2017-01-25 重庆大学 Peak-valley time-of-use power price determining method taking both system reliability and power purchase risk into consideration
CN112713588A (en) * 2020-12-16 2021-04-27 西安交通大学 Electricity price counting method capable of reducing abandoned light and abandoned wind
CN113743978A (en) * 2021-07-23 2021-12-03 国网河北省电力有限公司电力科学研究院 Time-of-use electricity price making method and device of source network charge storage system and terminal equipment
CN113743978B (en) * 2021-07-23 2024-01-23 国网河北省电力有限公司电力科学研究院 Time-of-use electricity price making method and device of source network charge storage system and terminal equipment

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