CN116432863A - Integral peak-shifting scheduling method for secondary water supply based on mathematical programming - Google Patents

Integral peak-shifting scheduling method for secondary water supply based on mathematical programming Download PDF

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CN116432863A
CN116432863A CN202310562463.6A CN202310562463A CN116432863A CN 116432863 A CN116432863 A CN 116432863A CN 202310562463 A CN202310562463 A CN 202310562463A CN 116432863 A CN116432863 A CN 116432863A
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邓帮武
姜帅
邓捷
邓卓志
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Anhui Shunyu Water Affairs Co Ltd
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Abstract

The invention discloses a secondary water supply integral peak-shifting scheduling method based on mathematical programming, which relates to the technical field of intelligent water affair to secondary water supply scheduling methods, and comprises the steps of acquiring data from a database and inputting the acquired data into a secondary water supply integral peak-shifting scheduling mathematical programming model; outputting target flow scheduling instructions of all the cells through a secondary water supply integral peak-shifting scheduling mathematical programming model, and adjusting the adjusting valves of the water tanks of all the cells; the target flow scheduling instruction uses the water tank inflow water flow red character and a larger value in the optimal solution 1 st moment as the target flow scheduling instruction; the scheduling method solves the problem of water supply conflict of the original single-cell water tank peak-shifting water supply method, realizes peak-shifting water supply, and relieves water shortage and water shortage in peak periods.

Description

Integral peak-shifting scheduling method for secondary water supply based on mathematical programming
Technical Field
The invention belongs to the technical field of intelligent water affair secondary water supply scheduling methods, and particularly relates to a secondary water supply integral peak-shifting scheduling method based on mathematical programming.
Background
Off-peak water supply is one of the recent research hotspots in the secondary water supply industry. The core purpose of peak-staggering water supply is to relieve the problems of water shortage and under-pressure in peak periods, effectively utilize the surplus water supply capacity in low peak periods of water plants, stabilize the fluctuation of a pipe network and reduce the risk of pipe network leakage. The water storage device mainly utilizes a terminal water tank with water storage capacity, part of water is stored in the water tank during the low-peak period of water supply, and water in the water tank is preferentially used for supplying to high-level users during the high-peak period of water supply. According to the principle, by using an automatic control mode, peak-shifting water supply taking the self water demand of a single water tank as an adjusting target can be realized. For example, a water peak period is set in the morning and evening in one day, the valve opening is reduced at the beginning of the peak period, and the valve opening is increased after the end of the peak period, and the increasing and decreasing amplitude can be dynamically adjusted according to the water tank liquid level.
However, in practical application, the water tank of a single cell is supplied with water in a staggered mode, linkage cannot be formed between the water tank of the single cell and the water tanks of other cells, a large amount of water supplementing conflict is caused due to uncertainty of a water supplementing period, and the purpose of supplying water in a staggered mode is not achieved fundamentally. The idea for solving the peak-staggering water supply problem of the water tank of the single cell is to use a regional peak-staggering water supply method from a global view to comprehensively schedule the pump room water tanks of all cells in the area. On this decision problem, the prior art has not formed an effective solution.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a secondary water supply integral peak-staggering scheduling method based on mathematical programming, which solves the problem of water supplement conflict of the original single-cell water tank peak-staggering water supply method, takes the water tank inflow of each cell at each moment as a decision variable, takes the standard deviation of the total water supply at each moment as a target equation, takes the upper limit of the water tank inflow and the like as optimization constraint, establishes a regional peak-staggering water supply mathematical programming model of global view, realizes peak-staggering water supply, and relieves the problems of water shortage and water shortage in peak periods.
In order to achieve the above purpose, the invention provides a secondary water supply integral peak-shifting scheduling method based on mathematical programming, which comprises the following steps:
acquiring data from a database, and inputting the acquired data into a secondary water supply integral peak-shifting scheduling mathematical programming model;
outputting target flow scheduling instructions of all the cells through the secondary water supply integral peak-shifting scheduling mathematical programming model, and adjusting the adjusting valves of the water tanks of all the cells;
and the target flow scheduling instruction uses the water tank inflow water flow red character and a larger value in the optimal solution 1 st moment as the target flow scheduling instruction.
Optionally, the step of constructing the secondary water supply integral peak-shifting scheduling mathematical programming model comprises the following steps:
setting decision variables;
setting a target liquid level value;
setting optimization constraints;
calculating the water inflow rate of the water tank;
setting a target equation;
generating an initial solution;
iterative solution is carried out to obtain an optimal solution which enables the target equation value to be minimum;
and determining the target flow scheduling instruction.
Optionally, the set decision variable is the water tank inflow at each moment of each cell.
Optionally, the set target liquid level value is a liquid level value of the last moment of the water tank after the instruction sequence is executed by each cell.
Optionally, the set optimization constraint includes that the inflow of water at each time of each water tank is smaller than or equal to the historical upper limit value, the liquid level value at each time of each water tank is between the preset upper limit value and the preset lower limit value of the liquid level of the water tank, and the liquid level value of the water tank at the last time is equal to the target liquid level value.
Optionally, the calculated water tank inflow is in a red character, and is the minimum water tank inflow required for reaching the lowest water tank level at the next moment.
Optionally, the set target equation predicts standard deviations of the sum of the water consumption and the water tank water inflow at each moment for all the district direct supply areas.
Optionally, the generating an initial solution is optimizing an initial value of a decision variable of the iteration.
Optionally, the iterative solution is an optimal solution that minimizes the target equation value, i.e. solving a mathematical programming problem:
Figure SMS_1
Figure SMS_2
Figure SMS_3
Figure SMS_4
Figure SMS_5
in the formula:
Figure SMS_7
for decision variables +.>
Figure SMS_9
For the number of moments>
Figure SMS_10
For the number of cells, +.>
Figure SMS_12
Predicting the water consumption for the direct supply area at each moment of each cell, < >>
Figure SMS_14
As a standard deviation function>
Figure SMS_16
Is->
Figure SMS_18
Personal district->
Figure SMS_19
Inlet flow of water tank at moment>
Figure SMS_21
Is->
Figure SMS_22
The upper limit value of the water inflow history of the water tanks of the cells, < + >>
Figure SMS_24
Is->
Figure SMS_25
The water tank liquid level of each district is preset with a lower limit value, < + >>
Figure SMS_27
Is->
Figure SMS_28
Water tank of each district->
Figure SMS_29
Time level value->
Figure SMS_6
Is->
Figure SMS_8
The water tank liquid level of each district is preset with an upper limit value, < + >>
Figure SMS_11
Is->
Figure SMS_13
Individual cellsWater tank
Figure SMS_15
Time level value->
Figure SMS_17
Is->
Figure SMS_20
Setting target liquid level value of each cell, < >>
Figure SMS_23
Output +.>
Figure SMS_26
The solution can be performed by using the signal domain method, the sequence least squares method, or the particle swarm method.
Optionally, the step of adjusting the adjusting valve of the water tank of each cell according to the target flow scheduling instruction of each cell includes:
reading the real-time flow of the inlet flowmeter of the water tank;
calculating a proportional error, an integral error and a differential error of the target flow and the real-time flow according to a PID method, multiplying the proportional coefficient, the integral coefficient and the differential coefficient which are calibrated in advance respectively, and summing the proportional coefficient, the integral coefficient and the differential coefficient to obtain a flow increment value;
multiplying the flow increment value by a conversion coefficient which is regulated in advance, and adding the conversion coefficient with the current valve opening value to obtain a valve opening set value;
the PLC is used for issuing a valve opening set value, and an electric regulating valve at the inlet of the water tank is controlled to regulate the valve opening set value;
repeating the steps more than once at fixed time intervals until the absolute value of the proportional error of the target flow and the real-time flow is smaller than or equal to the preset error range.
The invention provides a secondary water supply integral peak-shifting scheduling method based on mathematical programming, which has the beneficial effects that: when secondary water supply scheduling is carried out, unlike a single water tank peak-shifting scheduling method, the method is different from a single water tank peak-shifting scheduling method, and overall planning of all the district water tanks in a scheduling area is carried out from a global view, a mathematical programming model is established, a target flow scheduling instruction output by the model is used as an execution instruction of each district pump house water tank, and a PID method is used for continuously adjusting an electric regulating valve at the inlet of the water tank, so that the water inflow of the water tank is controlled; in the established mathematical programming model, the standard deviation of the total regional water quantity (including the direct supply regional predicted water consumption and the water tank water inflow) at each moment is used as a target equation, and the water consumption at each moment in the region is as even as possible by minimizing the target equation, so that the excessively high or excessively low water consumption is avoided, and the peak staggering water supply of the whole region is realized; the method disclosed by the invention has expandability, can increase and decrease optimization constraint items according to actual application requirements, and adjust the time interval of instruction generation so as to solve the problem of secondary water supply scheduling in different scenes and different requirements.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
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The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the invention.
Fig. 1 shows a flow diagram of a method for overall peak-shifting scheduling of secondary water supply based on mathematical programming according to an embodiment of the present invention.
FIG. 2 shows a comparison of pump house water tank level changes for a cell before and after conditioning according to one embodiment of the invention.
Fig. 3 shows a comparison of total inflow variables of all cells before and after regulation according to an embodiment of the present invention.
FIG. 4 shows a graph of data change versus pressure measurement of a pipe network in the vicinity of a cell before and after regulation according to one embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below. While the preferred embodiments of the present invention are described below, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The invention provides a secondary water supply integral peak-shifting scheduling method based on mathematical programming, which comprises the steps of acquiring data from a database, inputting the acquired data into a secondary water supply integral peak-shifting scheduling mathematical programming model, outputting target flow scheduling instructions of each cell through the secondary water supply integral peak-shifting scheduling mathematical programming model, and adjusting a regulating valve of a water tank of each cell, wherein the step of constructing the secondary water supply integral peak-shifting scheduling mathematical programming model comprises the following steps:
setting decision variables;
setting a target liquid level value;
setting optimization constraints;
calculating the water inflow rate of the water tank;
setting a target equation;
generating an initial solution;
iterative solution is carried out to obtain an optimal solution which enables the target equation value to be minimum;
and determining a target flow scheduling instruction.
Specifically, in the integral peak-shifting scheduling method for secondary water supply, the data obtained from the database comprises the number of cells participating in scheduling, the number of data per hour and the number of scheduling instructions per hour, and the water consumption prediction data of a secondary water supply area, the water consumption prediction data of a direct water supply area, the water tank liquid level data, the preset upper limit value and the preset lower limit value of the water tank liquid level, the water tank bottom area and the maximum water inflow of the water tank of each cell.
The construction specific steps of the secondary water supply integral peak-shifting scheduling mathematical programming model comprise:
setting decision variables for water inflow of water tanks at all moments of all cells by
Figure SMS_30
Representation of->
Figure SMS_31
For the number of moments>
Figure SMS_32
The number of the cells;
setting a target liquid level value, namely setting a liquid level value at the last moment of the water tank after the instruction sequence is executed for each cell, wherein the set value is between a preset upper limit value and a preset lower limit value of the liquid level of the water tank:
Figure SMS_33
in the formula:
Figure SMS_34
is->
Figure SMS_35
Setting target liquid level value of each cell, < >>
Figure SMS_36
Is->
Figure SMS_37
The water tank liquid level of each cell is preset with an upper limit value,
Figure SMS_38
is->
Figure SMS_39
Presetting a lower limit value for the water tank liquid level of each cell;
setting optimization constraint, wherein the optimization constraint comprises that the inflow of water at each moment of each water tank is smaller than or equal to the historical upper limit value of the inflow:
Figure SMS_40
in the formula:
Figure SMS_41
is->
Figure SMS_42
Water tank of each district->
Figure SMS_43
Time water inflow, ->
Figure SMS_44
Is->
Figure SMS_45
The historical upper limit value of the inflow water flow of the water tank of each district;
the liquid level value of each time of each water tank is between the preset upper limit value and the preset lower limit value of the liquid level of the water tank:
Figure SMS_46
in the formula:
Figure SMS_47
is->
Figure SMS_48
Water tank of each district->
Figure SMS_49
A time liquid level value;
the water tank liquid level value at the last moment is equal to the target liquid level value:
Figure SMS_50
in the formula:
Figure SMS_51
is->
Figure SMS_52
Water tank of each district->
Figure SMS_53
A time liquid level value;
further, the above optimization constraints comprising liquid level values are converted into optimization constraints comprising decision variables by the following formula:
Figure SMS_54
in the formula:
Figure SMS_56
is->
Figure SMS_57
Secondary water supply area of individual district->
Figure SMS_58
Predicting water consumption at moment, < >>
Figure SMS_59
Is->
Figure SMS_60
Water tank bottom area of each cell->
Figure SMS_61
For the number of data per hour +.>
Figure SMS_62
Is->
Figure SMS_55
The current liquid level value of the water tank of each cell;
calculating the water inflow of the water tank in a red shape, and obtaining the minimum water inflow of the water tank required by reaching the minimum liquid level of the water tank until the next moment:
Figure SMS_63
in the formula:
Figure SMS_64
is->
Figure SMS_65
Water inflow of water tanks of each district is red, and +.>
Figure SMS_66
Is->
Figure SMS_67
Second water supply area of individual district +.>
Figure SMS_68
Predicting water consumption at moment;
setting a target equation, and predicting standard deviations of water consumption and water inflow of a water tank at all moments for all direct-supply areas of the cells:
Figure SMS_69
in the formula:
Figure SMS_70
predicting the water consumption for the direct supply area at each moment of each cell, < >>
Figure SMS_71
Is a standard deviation function;
generating an initial solution, and generating the decision variable initial value meeting the optimization constraint by adopting a random method for optimizing iterative decision variable initial value
Figure SMS_72
An initial value;
iterative solution the optimal solution with the minimum target equation value, namely solving the mathematical programming problem:
Figure SMS_73
Figure SMS_74
Figure SMS_75
Figure SMS_76
Figure SMS_77
in the formula:
Figure SMS_78
output +.>
Figure SMS_79
Is recorded as the optimal solution->
Figure SMS_80
The solution can use a signal domain method, a sequence least square method, a particle swarm method and the like;
determining a target flow scheduling instruction, and using the water tank inflow water flow red characters and the optimal solution
Figure SMS_81
The larger value in the moment is taken as a target flow scheduling instruction:
Figure SMS_82
in the formula:
Figure SMS_83
is->
Figure SMS_84
Target traffic scheduling instruction for individual cells,/for each cell>
Figure SMS_85
Is the best solution->
Figure SMS_86
Cell no->
Figure SMS_87
Value of time of day->
Figure SMS_88
To take the larger of the two.
Further, after the mathematical programming model is used for determining the target flow scheduling instruction of each cell, the target flow scheduling instruction of each cell output by the model is issued to each cell pump room participating in regulation and control, and the specific steps of regulating the electric regulating valve at the inlet of the water tank are as follows:
read the first
Figure SMS_89
Cell no->
Figure SMS_90
Real-time flow of inlet flowmeter of time water tank>
Figure SMS_91
；
Calculating the proportional error, the integral error and the differential error of the target flow and the real-time flow according to the PID method, multiplying the proportional coefficient, the integral coefficient and the differential coefficient which are calibrated in advance respectively, and summing the proportional coefficient, the integral coefficient and the differential coefficient to obtain a flow increment value:
Figure SMS_92
Figure SMS_93
in the formula:
Figure SMS_100
is->
Figure SMS_103
Cell no->
Figure SMS_104
Time of day proportional error->
Figure SMS_106
Is->
Figure SMS_108
Cell no->
Figure SMS_109
Time integral error>Is->
Figure SMS_94
Cell no->
Figure SMS_97
Time differential error->
Figure SMS_99
Is->
Figure SMS_101
Cell scaling factor,/-, for>
Figure SMS_102
Is->
Figure SMS_105
Integral coefficient of individual cell,/->
Figure SMS_107
Is->
Figure SMS_110
Differential coefficient of individual cell, ">
Figure SMS_95
Is->
Figure SMS_96
Cell no->
Figure SMS_98
A moment flow increment value;
multiplying the flow increment value by a conversion coefficient which is regulated in advance, and adding the conversion coefficient with the current valve opening value to obtain a valve opening set value:
Figure SMS_112
in the formula:
Figure SMS_114
is->
Figure SMS_115
Cell no->
Figure SMS_116
Setting value of opening of valve at moment->
Figure SMS_117
Is->
Figure SMS_118
Cell no->
Figure SMS_119
Valve opening current value of moment->
Figure SMS_120
Is->
Figure SMS_113
A cell transfer coefficient;
the PLC is used for issuing a valve opening set value, and an electric regulating valve at the inlet of the water tank is controlled to regulate the valve opening set value;
repeating all the steps at intervals of fixed time until the absolute value of the proportional error of the target flow and the real-time flow is smaller than or equal to the preset error range:
Figure SMS_121
in the formula:
Figure SMS_122
to take absolute value, +.>
Figure SMS_123
Is the error range.
Examples
As shown in fig. 1, the invention provides a secondary water supply integral peak-shifting scheduling method based on mathematical programming, which is characterized in that data are acquired from a database, the acquired data are input into a secondary water supply integral peak-shifting scheduling mathematical programming model, and target flow scheduling instructions of each cell are output through the secondary water supply integral peak-shifting scheduling mathematical programming model and are used for adjusting the regulating valve of the water tank of each cell.
Obtaining data from a database, including the number of cells involved in scheduling
Figure SMS_124
Data per hour
Figure SMS_125
I.e. every->
Figure SMS_126
Data are acquired once in minutes, and the number of instructions is scheduled per hour +.>
Figure SMS_127
I.e. every->
Figure SMS_128
The instruction is issued once in a minute.
The construction specific steps of the secondary water supply integral peak-shifting scheduling mathematical programming model comprise:
setting decision variables for water inflow of water tanks at all moments of all cells by
Figure SMS_129
Representation of wherein
Figure SMS_130
For the number of moments>
Figure SMS_131
The number of the cells;
setting a target liquid level value, namely setting a liquid level value at the last moment of the water tank after the instruction sequence is executed for each cell, wherein the set value is between a preset upper limit value and a preset lower limit value of the liquid level of the water tank:
Figure SMS_132
in the formula:is->
Figure SMS_134
Setting target liquid level value of each cell, < >>
Figure SMS_135
Is->
Figure SMS_136
The water tank liquid level of each district is preset with an upper limit value, < + >>
Figure SMS_137
Is->
Figure SMS_138
Presetting a lower limit value for the water tank liquid level of each cell; setting the target liquid level value equal to the preset lower limit value of the liquid level of the water tank, namely +.>
Figure SMS_139
；
Setting optimization constraint, wherein the optimization constraint comprises that the inflow of water at each moment of each water tank is smaller than or equal to the historical upper limit value of the inflow:
Figure SMS_140
in the formula:
Figure SMS_141
is->
Figure SMS_142
Water tank of each district->
Figure SMS_143
Time water inflow, ->
Figure SMS_144
Is->
Figure SMS_145
The historical upper limit value of the inflow water flow of the water tank of each district;
the liquid level value of each water tank at each moment should be between the liquid level setting upper limit value and the liquid level setting lower limit value:
Figure SMS_146
in the formula:
Figure SMS_147
is->
Figure SMS_148
Water tank of each district->
Figure SMS_149
A time liquid level value;
the water tank liquid level value at the last moment is equal to the target liquid level value:
Figure SMS_150
in the formula:
Figure SMS_151
is->
Figure SMS_152
Water tank of each district->
Figure SMS_153
A time liquid level value;
further, the above optimization constraints comprising liquid level values are converted into optimization constraints comprising decision variables by the following formula:
Figure SMS_154
in the formula:
Figure SMS_155
is->
Figure SMS_156
Secondary water supply area of individual district->
Figure SMS_157
Predicting water consumption at moment, < >>
Figure SMS_158
Is->
Figure SMS_159
Water tank bottom area of each cell->
Figure SMS_160
Is->
Figure SMS_161
The current liquid level value of the water tank of each cell;
calculating the water inflow of the water tank in a red shape, and obtaining the minimum water inflow of the water tank required by reaching the minimum liquid level of the water tank until the next moment:
Figure SMS_162
in the formula:
Figure SMS_163
is->
Figure SMS_164
Water inflow of water tanks of each district is red, and +.>
Figure SMS_165
Is->
Figure SMS_166
Second water supply area of individual district +.>
Figure SMS_167
Predicting water consumption at moment; substituting the data into a formula to calculate the water inflow rate red characters of the water tanks of all the cells.
Setting a target equation, and predicting standard deviations of water consumption and water inflow of a water tank at all moments for all direct-supply areas of the cells:
Figure SMS_168
in the formula:
Figure SMS_169
predicting the water consumption for the direct supply area at each moment of each cell, < >>
Figure SMS_170
Is a standard deviation function;
generating an initial solution, and generating the decision variable initial value meeting the optimization constraint by adopting a random method for optimizing iterative decision variable initial value
Figure SMS_171
An initial value;
iterative solution the optimal solution with the minimum target equation value, namely solving the mathematical programming problem:
Figure SMS_172
Figure SMS_173
Figure SMS_174
Figure SMS_175
Figure SMS_176
in the formula:
Figure SMS_177
output +.>
Figure SMS_178
Is recorded as the optimal solution->
Figure SMS_179
The solution can use a signal domain method, a sequence least square method, a particle swarm method and the like; solving is performed using a sequential least squares method.
Determining a target flow scheduling instruction, and using the water tank inflow water flow red character and a larger value in the optimal solution 1 st moment as the target flow scheduling instruction:
Figure SMS_180
in the formula:
Figure SMS_181
is->
Figure SMS_182
Target traffic scheduling instruction for individual cells,/for each cell>
Figure SMS_183
Is the best solution->
Figure SMS_184
Cell no->
Figure SMS_185
Value of time of day->
Figure SMS_186
To take the larger of the two.
Further, after the mathematical programming model is used for determining the target flow scheduling instruction of each cell, the target flow scheduling instruction of each cell output by the model is issued to each cell pump room participating in regulation and control, and the specific steps of regulating the electric regulating valve at the inlet of the water tank are as follows:
read the first
Figure SMS_187
Cell no->
Figure SMS_188
Real-time flow of inlet flowmeter of time water tank>
Figure SMS_189
；
Calculating a proportional error, an integral error and a differential error of the target flow and the real-time flow according to a PID method, multiplying a proportional coefficient, an integral coefficient and a differential coefficient which are calibrated in advance respectively, and summing the three:
Figure SMS_190
Figure SMS_191
in the formula:
Figure SMS_198
is->
Figure SMS_200
Cell no->
Figure SMS_201
Time of day proportional error->
Figure SMS_203
Is->
Figure SMS_205
Cell no->
Figure SMS_207
Time integral error>
Figure SMS_209
Is->
Figure SMS_192
Cell no->
Figure SMS_195
Time differential error->
Figure SMS_197
Is->
Figure SMS_199
Cell scaling factor,/-, for>
Figure SMS_202
Is->
Figure SMS_204
Integral coefficient of individual cell,/->
Figure SMS_206
Is->
Figure SMS_208
Differential coefficient of individual cell, ">
Figure SMS_193
Is->
Figure SMS_194
Cell no->
Figure SMS_196
A moment flow increment value;
multiplying the flow increment value by a conversion coefficient which is regulated in advance, and adding the conversion coefficient with the current valve opening value to obtain a valve opening set value:
Figure SMS_210
in the formula:
Figure SMS_212
is->
Figure SMS_213
Cell no->
Figure SMS_214
Setting value of opening of valve at moment->
Figure SMS_215
Is->
Figure SMS_216
Cell no->
Figure SMS_217
Valve opening current value of moment->
Figure SMS_218
Is->
Figure SMS_211
A cell transfer coefficient;
the PLC is used for issuing a valve opening set value, and an electric regulating valve at the inlet of the water tank is controlled to regulate the valve opening set value;
each interval
Figure SMS_219
Repeating the steps for more than one time until the absolute value of the proportional error of the target flow and the real-time flow is smaller than or equal to the preset error range:
Figure SMS_220
in the formula:
Figure SMS_221
to take absolute value, +.>
Figure SMS_222
Is the error range.
In the present embodiment, each
Figure SMS_223
And the scheduling instruction is issued once in a minute to the pump rooms of all the cells, and the instruction sequence issued in one day is shown in the following table.
Cell time 1 2 3 4 5
0:00:00 4.46 5.76 0.13 10.02 2.06
0:05:00 2.54 5.29 0.29 9.27 2.07
0:10:00 3.14 4.62 0.63 8.91 3.62
0:15:00 2.92 3.98 1.17 8.35 5.09
0:20:00 3.21 4.14 1.46 7.45 6.43
0:25:00 3.32 4.46 1.75 6.64 6.94
0:30:00 3.37 4.66 1.89 6.43 7.07
0:35:00 3.28 4.72 1.93 6.55 7.02
0:40:00 2.97 4.94 2.11 6.84 6.94
0:45:00 2.66 5.19 2.29 7.08 6.83
0:50:00 2.69 5.32 2.4 7.21 6.68
0:55:00 2.71 5.49 2.48 7.39 6.7
1:00:00 2.51 4.97 2.27 7.5 3.81
1:05:00 2.6 5.05 2.25 7.68 3.73
1:10:00 2.62 5.14 2.29 7.76 3.78
1:15:00 2.7 5.15 2.33 7.99 3.79
1:20:00 2.81 5.21 2.41 8.17 3.82
1:25:00 2.91 5.29 2.41 8.38 3.88
1:30:00 2.94 5.37 2.48 8.51 3.78
1:35:00 2.97 5.41 2.51 8.7 3.98
1:40:00 3.07 5.47 2.55 8.9 3.95
1:45:00 3.1 5.57 2.58 9.19 3.91
1:50:00 3.2 5.64 2.67 9.36 3.92
1:55:00 3.25 5.66 2.71 9.59 3.98
2:00:00 3.19 5.73 2.69 9.81 4.06
2:05:00 3.21 5.7 2.58 10.08 3.93
2:10:00 3.26 5.68 2.57 10.2 3.96
2:15:00 3.25 5.59 2.53 10.33 4.05
2:20:00 3.23 5.6 2.46 10.55 4.1
2:25:00 3.25 5.64 2.54 10.94 4.05
2:30:00 3.27 5.65 2.58 11.07 4.14
2:35:00 3.28 5.62 2.65 11.22 4.15
2:40:00 3.33 5.54 2.69 11.41 4.18
2:45:00 3.34 5.53 2.64 11.61 4.12
2:50:00 3.33 5.53 2.57 11.64 4.19
2:55:00 3.31 5.57 2.6 11.68 4.14
3:00:00 3.42 5.6 2.64 11.8 4.22
3:05:00 3.55 5.6 2.71 11.79 4.34
3:10:00 3.7 5.65 2.81 11.87 4.27
3:15:00 3.7 5.7 2.79 11.95 4.28
3:20:00 3.65 5.69 2.75 12.06 4.31
3:25:00 3.77 5.63 2.76 12.02 4.35
3:30:00 3.94 5.66 2.7 12.04 4.37
3:35:00 4 5.72 2.67 12.1 4.41
3:40:00 3.93 5.81 2.69 12.18 4.44
3:45:00 3.86 5.77 2.77 12.13 4.53
3:50:00 4 5.74 2.67 12.16 4.56
3:55:00 4.02 5.72 2.72 12.08 4.58
4:00:00 3.88 5.81 2.67 12.26 4.69
4:05:00 3.9 5.67 2.7 12.28 4.63
4:10:00 4.06 5.74 2.81 12.21 4.71
4:15:00 4.17 5.71 2.73 12.22 4.7
4:20:00 4.14 6.23 2.77 12.95 4.96
4:25:00 4.14 6.13 2.72 12.89 4.95
4:30:00 4.23 6.14 3.05 12.75 5.08
4:35:00 4.42 6.23 3.01 12.55 4.93
4:40:00 4.43 6.25 2.89 12.51 5.04
4:45:00 4.39 6.19 2.58 12.45 4.64
4:50:00 4.22 6.17 2.89 12.3 4.68
4:55:00 4.3 6.22 2.63 12.36 4.59
5:00:00 4.33 6.4 2.46 12.48 4.72
5:05:00 4.35 6.44 2.27 12.16 4.02
5:10:00 4.21 6.53 2.63 11.59 4.71
5:15:00 3.98 6.44 2.77 11.21 4.49
5:20:00 4.02 6.24 2.59 10.75 4.42
5:25:00 3.82 5.77 2.71 10.86 4.48
5:30:00 3.85 6.24 2.35 11.36 4.57
5:35:00 3.88 5.49 2.66 11.64 4.59
5:40:00 3.7 4.82 2.72 13.06 4.77
5:45:00 3.49 4.45 2.6 13.29 4.27
5:50:00 3.23 4.03 2.85 14.13 2.74
5:55:00 2.96 4.46 3.12 15.09 3.29
6:00:00 3.23 4.98 3.41 15.6 3.43
6:05:00 3.21 5.49 3.73 16.83 4.27
6:10:00 3.9 6.01 4.01 12.91 4.71
6:15:00 4.23 6.51 4.25 10.58 5.16
6:20:00 4.24 6.9 4.4 8.18 5.48
6:25:00 4.92 5.8 4.64 6.8 5.69
6:30:00 5.03 5.15 4.91 6.14 5.99
6:35:00 4.62 4.29 4.62 5.29 5.57
6:40:00 4.52 4.1 4.48 5.12 5.62
6:45:00 4.27 3.78 4.23 4.84 5.43
6:50:00 4.12 3.77 4 4.83 5.35
6:55:00 3.95 3.67 3.83 4.71 4.92
7:00:00 3.87 3.51 3.74 4.51 4.89
7:05:00 3.75 3.41 3.67 4.39 4.91
7:10:00 3.58 3.41 3.45 4.39 4.62
7:15:00 3.48 3.24 3.32 4.25 4.65
7:20:00 3.61 3.43 3.42 4.3 6.82
7:25:00 3.64 3.43 3.39 4.26 6.66
7:30:00 3.55 3.36 3.3 4.33 6.51
7:35:00 3.62 3.44 3.39 4.44 6.37
7:40:00 3.66 3.45 3.41 4.37 6.37
7:45:00 3.58 3.36 3.34 4.26 6.29
7:50:00 3.43 3.33 3.17 4.29 6.32
7:55:00 3.4 3.28 3.13 4.25 6.37
8:00:00 3.37 3.24 3.03 4.19 6.4
8:05:00 3.44 3.33 3.14 4.18 6.38
8:10:00 3.33 3.32 3.03 4.29 6.25
8:15:00 3.27 3.31 2.97 4.3 6.14
8:20:00 3.4 3.29 3.01 4.29 5.95
8:25:00 3.37 3.25 3.06 4.24 5.7
8:30:00 3.36 3.45 3.03 4.45 5.53
8:35:00 3.51 3.54 3.17 4.57 5.32
8:40:00 3.65 3.55 3.14 4.55 5.3
8:45:00 3.71 3.59 3.18 4.58 4.67
8:50:00 3.61 3.67 3.08 4.7 3.82
8:55:00 3.66 3.7 3.12 4.75 2.95
9:00:00 3.9 3.88 3.23 4.86 2.39
9:05:00 4.06 4.05 3.39 4.95 1.91
9:10:00 3.98 3.98 3.37 5.06 1.26
9:15:00 3.41 3.43 2.83 7.3 0.99
9:20:00 3.31 3.28 2.42 8.61 1.2
9:25:00 2.85 2.79 1.89 11.02 1.35
9:30:00 2.43 2.46 1.65 12.64 1.51
9:35:00 2.43 2.46 1.62 13.03 1.78
9:40:00 2.57 2.56 1.76 13.06 1.91
9:45:00 2.76 2.8 1.95 13.25 1.89
9:50:00 2.73 2.73 1.89 13.81 2.02
9:55:00 2.82 2.82 1.86 14.47 2.16
10:00:00 2.89 2.97 1.99 14.31 2.19
10:05:00 2.83 2.79 1.93 15.01 2.24
10:10:00 2.91 2.91 2.08 14.98 2.32
10:15:00 3.46 2.87 1.97 14.69 2.35
10:20:00 4.44 2.44 1.7 14.76 2.54
10:25:00 5.05 2.5 1.58 14.55 2.69
10:30:00 4.94 3.7 0.91 14.28 2.82
10:35:00 4.16 5.18 0.68 13.99 3.04
10:40:00 4.1 5.99 0.28 13.62 3.18
10:45:00 3.77 7.27 0.22 12.83 3.27
10:50:00 3.76 7.72 0.06 12.58 3.5
10:55:00 3.71 8.43 0.06 12.72 3.74
11:00:00 4.4 10.96 0 13.4 4.21
11:05:00 6.71 8.41 0 15.2 4.74
11:10:00 9.31 7.67 0 15.16 5
11:15:00 7.28 9.34 0 17.98 5.31
11:20:00 4.42 9.78 0.1 15.14 5.21
11:25:00 4.38 8.19 0.41 12.69 5.69
11:30:00 4.94 7.49 0.54 12.85 5.86
11:35:00 6.01 6.86 1.56 12.6 5.73
11:40:00 5.26 6.51 2.24 12.54 5.96
11:45:00 5.26 6.45 2.96 14.97 6.35
11:50:00 7.23 6.96 3.22 14.74 6.49
11:55:00 9.21 8.03 4.29 11.99 6.31
12:00:00 7.22 7.95 4.29 11.86 6.08
12:05:00 4.27 7.71 5.22 12.12 6.01
12:10:00 3.49 7.67 3.98 12.92 6.09
12:15:00 3.16 8.25 5.1 13.36 5.71
12:20:00 3.71 8.16 5.29 13.72 5.54
12:25:00 6.54 8.36 5.75 14.03 5.68
12:30:00 6.44 8.62 5.69 14.38 5.44
12:35:00 8.31 8.12 6.49 15.25 5.84
12:40:00 8.17 8.65 4.61 15.01 5.85
12:45:00 7.44 8.97 3.02 15.2 6.01
12:50:00 5.94 9.33 3.55 14.23 6.33
12:55:00 3.93 9.78 5.37 13.4 6.27
13:00:00 3.96 10.21 7.09 13.06 6.43
13:05:00 4.16 10.34 5.16 12.65 6.82
13:10:00 4.01 10.28 5.13 11.85 7.21
13:15:00 4.89 9.72 5.34 11.7 7.29
13:20:00 5.19 9.53 5.54 11.31 7.26
13:25:00 5.72 9.39 5.24 11.3 7.48
13:30:00 6.01 9.2 5.13 11.35 7.31
13:35:00 6 8.6 5.29 11.34 7.23
13:40:00 6 8.76 5.4 11.53 7
13:45:00 6.36 8.84 5.46 11.85 7.09
13:50:00 6.04 9.34 5.93 11.52 7.12
13:55:00 5.97 9.6 6.04 11.78 7.2
14:00:00 6.13 9.83 6.19 12.1 6.91
14:05:00 6.23 10.01 6.32 12.37 6.66
14:10:00 6.28 10.11 6.4 12.47 6.22
14:15:00 6.35 10.31 6.41 12.67 6.01
14:20:00 6.42 10.42 6.48 12.88 5.64
14:25:00 6.38 10.5 6.46 13.07 5.4
14:30:00 6.34 10.58 6.5 13.15 5.24
14:35:00 6.38 10.73 6.46 13.31 4.87
14:40:00 6.31 10.83 6.41 13.48 4.71
14:45:00 6.38 10.93 6.46 13.72 4.62
14:50:00 6.32 11.01 6.42 13.84 4.63
14:55:00 6.31 11.14 6.3 13.98 4.39
15:00:00 6.35 11.36 6.14 14.25 4.3
15:05:00 6.34 11.43 6.12 14.46 4.07
15:10:00 6.4 11.55 5.8 14.7 4.41
15:15:00 6.34 11.58 5.58 14.73 4.21
15:20:00 6.48 11.79 5.56 14.99 4.34
15:25:00 6.52 11.76 5.64 15.23 4.2
15:30:00 6.57 11.69 5.53 15.61 3.99
15:35:00 6.57 11.55 5.33 15.88 4.07
15:40:00 6.53 11.27 5.28 16.3 4.16
15:45:00 6.57 11.08 5.08 16.62 4.02
15:50:00 6.31 11.05 5.07 17.19 4.14
15:55:00 6.33 11.01 5.03 17.69 4.29
16:00:00 6.26 10.68 4.89 18.21 4.37
16:05:00 6.14 10.36 4.67 18.9 4.33
16:10:00 6.15 10.13 4.77 19.2 4.28
16:15:00 6.03 9.92 4.59 19.71 4.31
16:20:00 5.99 9.96 4.68 19.8 3.96
16:25:00 6.04 9.93 4.7 19.88 3.92
16:30:00 6.11 9.95 4.78 19.84 4.24
16:35:00 6.08 9.92 4.83 20.02 4.5
16:40:00 6.12 9.97 4.96 19.88 4.29
16:45:00 5.94 10.09 5.02 20.03 4.44
16:50:00 5.98 10.21 4.87 20.07 4.69
16:55:00 6.12 9.97 5.08 20.16 4.76
17:00:00 6.13 10.1 5.37 19.91 4.95
17:05:00 6.14 10.15 5.5 19.77 5.1
17:10:00 6.14 10.2 5.67 19.8 5.34
17:15:00 6.14 10.43 5.57 20.04 5.43
17:20:00 6.28 10.47 5.55 20.05 5.83
17:25:00 6.56 10.29 5.74 20.46 5.8
17:30:00 6.7 10.11 6.03 20.34 6.08
17:35:00 6.87 10.26 6.03 20.7 6.35
17:40:00 7.09 10.5 5.71 20.59 6.4
17:45:00 7.08 10.78 5.7 20.73 6.44
17:50:00 7.03 11.34 5.94 20.99 6.76
17:55:00 6.98 11.27 6.18 21.42 6.89
18:00:00 6.84 11.38 6.12 21.26 8.13
18:05:00 6.63 11.74 6.2 21.63 8.78
18:10:00 7.01 11.41 6.1 22.09 8.66
18:15:00 7.09 11.42 6 22.06 8.53
18:20:00 7.38 11.45 5.55 22.84 8.67
18:25:00 7.56 11.18 5.36 22.99 9.14
18:30:00 7.21 11.08 5.7 22.66 9.51
18:35:00 6.17 11.2 5.96 22.8 9.62
18:40:00 6.56 11.27 5.13 23.3 9.74
18:45:00 5.62 10.67 5.7 24.96 10.11
18:50:00 6.56 11.26 6.29 25.02 10.07
18:55:00 7.24 11.89 6.7 25.04 10.1
19:00:00 7.54 12.1 6.64 24.67 10.18
19:05:00 7.14 13.41 6.35 23.62 10.34
19:10:00 7.06 14.55 6.1 22.36 10.24
19:15:00 8.19 15.02 6.77 20.02 9.39
19:20:00 9.24 14.95 6.64 18.11 10.12
19:25:00 10.14 14.26 7.3 16.87 10.67
19:30:00 9.76 15.41 6.86 15.51 11.06
19:35:00 8.65 15.29 7.56 15.39 11.26
19:40:00 8.56 14.15 9.06 14.45 10.51
19:45:00 8.13 13.9 8.97 14.51 11.17
19:50:00 9.13 13.06 9.03 13.78 11.35
19:55:00 10.08 12.6 8.33 13.32 11.94
20:00:00 10.07 12.15 8.02 12.67 11.26
20:05:00 9.84 11.67 8.48 11.89 11.51
20:10:00 9.93 10.93 8.71 10.93 13.03
20:15:00 10.04 10.04 9.12 10.04 11.42
20:20:00 9.31 9.31 9.21 9.31 9.69
20:25:00 8.87 8.87 8.57 9.09 8.24
20:30:00 8.65 8.65 8.3 8.88 7.1
20:35:00 8.37 8.37 7.97 8.57 5.85
20:40:00 8.18 8.18 7.8 8.44 4.67
20:45:00 7.77 7.77 7.42 8.03 3.77
20:50:00 7.48 7.48 7.23 7.63 2.96
20:55:00 7.35 7.35 7.24 7.35 2.41
21:00:00 7.1 7.1 5.64 8.68 2.03
21:05:00 5.59 5.67 4.12 12.77 1.63
21:10:00 3.82 3.97 3.82 15.34 1.19
21:15:00 2.39 3.11 2.39 17.5 1.1
21:20:00 1.25 2.02 1.25 19.6 1.01
21:25:00 0.64 1.53 0.64 19.87 0.9
21:30:00 0 1.59 0.09 20.22 1.13
21:35:00 0 1.57 0 19.85 0
21:40:00 0 1.72 0 18.25 0
21:45:00 0 1.9 0.01 17.52 0.6
21:50:00 0 1.36 1.09 16.46 0.82
21:55:00 0 1.29 0.87 15.48 1.24
22:00:00 0 1.25 0.68 15.88 0.52
22:05:00 0 1.57 0 15.64 0
22:10:00 0 1.85 0 14.72 0
22:15:00 0 1.76 0 14.69 0
22:20:00 0 2.33 0 14.01 0
22:25:00 0 2.65 0 12.95 0
22:30:00 0 3.51 0.2 11.44 0
22:35:00 0 4.33 0.8 9.07 0
22:40:00 0 4.16 1.03 7.64 0
22:45:00 0 4.03 0.87 7.28 0
22:50:00 0 4.19 0.88 6.87 0
22:55:00 0 4.11 0.64 6.97 0
23:00:00 0 3.96 0.63 6.29 0
23:05:00 0 4 0.45 6.01 0
23:10:00 0 4.07 0.27 5.19 0
23:15:00 0.08 3.65 0.16 4.67 0
23:20:00 0 3.71 0.31 4.83 0
23:25:00 0 3.74 0.5 4.75 0
23:30:00 0.23 3.97 1.24 4.62 0
23:35:00 0.51 4.08 1.97 4.87 0
23:40:00 0 3.31 1.93 4.44 0
23:45:00 0 3.73 1.93 4.14 0
23:50:00 0.38 3.49 2.93 4.32 0
23:55:00 0.8 4.77 1.52 4.73 0
When the water tank of the district pump house is not regulated, the electric regulating valve at the inlet of the water tank is always in a full-open state, namely the opening of the electric regulating valve is kept
Figure SMS_224
At this time, the liquid level of the water tank is small in change, and the water tank utilization rate is low. After the method is used for regulation, the liquid level of the water tank is changed greatly, and the water tank utilization rate is high. Fig. 2 shows the comparison of tank levels before and after regulation.
The regional peak clipping and valley filling effect realized by the method can be obviously seen through the statistical regulation of the change of the total water inflow of the cells before and after the regulation as shown in figure 3; by counting the changes of the pipe network pressure measuring points before and after regulation, as shown in fig. 4, it can be obviously seen that the method can stabilize the pipe network pressure fluctuation, and especially can promote the pipe network pressure in the early and late water use peak period.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described.

Claims (10)

1. The utility model provides a secondary water supply overall peak-shifting scheduling method based on mathematical programming which is characterized in that the method comprises the following steps:
acquiring data from a database, and inputting the acquired data into a secondary water supply integral peak-shifting scheduling mathematical programming model;
outputting target flow scheduling instructions of all the cells through the secondary water supply integral peak-shifting scheduling mathematical programming model, and adjusting the adjusting valves of the water tanks of all the cells;
and the target flow scheduling instruction uses the water tank inflow water flow red character and a larger value in the optimal solution 1 st moment as the target flow scheduling instruction.
2. The secondary water supply integral peak-shifting scheduling method based on mathematical programming as set forth in claim 1, wherein the constructing step of the secondary water supply integral peak-shifting scheduling mathematical programming model includes:
setting decision variables;
setting a target liquid level value;
setting optimization constraints;
calculating the water inflow rate of the water tank;
setting a target equation;
generating an initial solution;
iterative solution is carried out to obtain an optimal solution which enables the target equation value to be minimum;
and determining the target flow scheduling instruction.
3. The method for scheduling integral peak shifting of secondary water supply based on mathematical programming according to claim 2, wherein the set decision variables are water tank inflow rates at each moment of each cell.
4. The method for scheduling integral peak shifting of secondary water supply based on mathematical programming according to claim 2, wherein the set target liquid level value is the liquid level value at the last moment of the water tank after the instruction sequence is executed for each cell.
5. The method for scheduling integral peak shifting of secondary water supply based on mathematical programming according to claim 2, wherein the set optimization constraint comprises that the inflow rate of each time of each water tank is smaller than or equal to the historical upper limit value, the liquid level value of each time of each water tank is between the preset upper limit value and the preset lower limit value of the liquid level of the water tank, and the liquid level value of the water tank at the last time is equal to the target liquid level value.
6. The method for scheduling integral peak shifting of secondary water supply based on mathematical programming according to claim 2, wherein the calculated water tank inflow is a red letter, which is the minimum water tank inflow required to reach the lowest water tank level until the next moment.
7. The method for scheduling integral peak shifting of secondary water supply based on mathematical programming according to claim 2, wherein the set target equation predicts standard deviations of sum of water consumption and water tank water inflow for all the district direct supply areas at each moment.
8. The method for scheduling integral peak staggering of secondary water supply based on mathematical programming according to claim 2, wherein the initial solution is generated as an initial value of decision variable for optimization iteration.
9. The mathematical programming-based overall peak-staggering scheduling method for secondary water supplies of claim 2, wherein the iterative solution is an optimal solution for minimizing the target equation value, i.e. solving a mathematical programming problem:
Figure QLYQS_1
in the formula:
Figure QLYQS_4
for decision variables +.>
Figure QLYQS_5
For the number of moments>
Figure QLYQS_7
For the number of cells, +.>
Figure QLYQS_9
Predicting the water consumption for the direct supply area at each moment of each cell, < >>
Figure QLYQS_12
As a standard deviation function>
Figure QLYQS_13
Is->
Figure QLYQS_15
Personal district->
Figure QLYQS_16
Inlet flow of water tank at moment>
Figure QLYQS_18
Is->
Figure QLYQS_20
The upper limit value of the water inflow history of the water tanks of the cells, < + >>
Figure QLYQS_22
Is->
Figure QLYQS_23
The water tank liquid level of each district is preset with a lower limit value, < + >>
Figure QLYQS_24
Is->
Figure QLYQS_25
Water tank of each district->
Figure QLYQS_2
Time level value->
Figure QLYQS_3
Is->
Figure QLYQS_6
The water tank liquid level of each district is preset with an upper limit value, < + >>
Figure QLYQS_8
Is->
Figure QLYQS_10
Water tank of each district->
Figure QLYQS_11
Time level value->
Figure QLYQS_14
Is->
Figure QLYQS_17
Setting target liquid level value of each cell, < >>
Figure QLYQS_19
Output +.>
Figure QLYQS_21
The solution can be performed by using the signal domain method, the sequence least squares method, or the particle swarm method.
10. The mathematical programming-based integral peak-shifting scheduling method of secondary water supply according to claim 1, wherein the target flow scheduling command of each cell, the step of adjusting the adjusting valve of the water tank of each cell, comprises:
reading the real-time flow of the inlet flowmeter of the water tank;
calculating a proportional error, an integral error and a differential error of the target flow and the real-time flow according to a PID method, multiplying the proportional coefficient, the integral coefficient and the differential coefficient which are calibrated in advance respectively, and summing the proportional coefficient, the integral coefficient and the differential coefficient to obtain a flow increment value;
multiplying the flow increment value by a conversion coefficient which is regulated in advance, and adding the conversion coefficient with the current valve opening value to obtain a valve opening set value;
the PLC is used for issuing a valve opening set value, and an electric regulating valve at the inlet of the water tank is controlled to regulate the valve opening set value;
repeating the steps more than once at fixed time intervals until the absolute value of the proportional error of the target flow and the real-time flow is smaller than or equal to the preset error range.
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Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170139065A1 (en) * 2015-11-18 2017-05-18 Cgg Services Sas Adaptive ensemble-based method and device for highly-nonlinear problems
CN110084537A (en) * 2019-05-22 2019-08-02 芜湖华衍水务有限公司 A kind of the secondary water-supply control method and system of demand orientation
CN110569248A (en) * 2019-07-31 2019-12-13 杭州电子科技大学 Improved SPC (selective pressure control) residential water supply leakage monitoring and early warning method
CN111206650A (en) * 2020-01-13 2020-05-29 上海威派格智慧水务股份有限公司 Water tank peak load shifting regulation management system
CN112580969A (en) * 2020-12-15 2021-03-30 重庆昕晟环保科技有限公司 Method for calculating theoretical inflow water quantity of secondary water supply tank
CN113240314A (en) * 2021-05-28 2021-08-10 浙江机电职业技术学院 Secondary water supply peak shifting scheduling system
CN115062904A (en) * 2022-05-12 2022-09-16 广东鹤山北控水务有限公司 Digital water supply pipe network scheduling method and system
CN115423224A (en) * 2022-11-04 2022-12-02 佛山市电子政务科技有限公司 Secondary water supply amount prediction method and device based on big data and storage medium
CN116025026A (en) * 2023-02-08 2023-04-28 浙江嘉源和达水务有限公司 Urban water tank linkage control method

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170139065A1 (en) * 2015-11-18 2017-05-18 Cgg Services Sas Adaptive ensemble-based method and device for highly-nonlinear problems
CN110084537A (en) * 2019-05-22 2019-08-02 芜湖华衍水务有限公司 A kind of the secondary water-supply control method and system of demand orientation
CN110569248A (en) * 2019-07-31 2019-12-13 杭州电子科技大学 Improved SPC (selective pressure control) residential water supply leakage monitoring and early warning method
CN111206650A (en) * 2020-01-13 2020-05-29 上海威派格智慧水务股份有限公司 Water tank peak load shifting regulation management system
CN112580969A (en) * 2020-12-15 2021-03-30 重庆昕晟环保科技有限公司 Method for calculating theoretical inflow water quantity of secondary water supply tank
CN113240314A (en) * 2021-05-28 2021-08-10 浙江机电职业技术学院 Secondary water supply peak shifting scheduling system
CN115062904A (en) * 2022-05-12 2022-09-16 广东鹤山北控水务有限公司 Digital water supply pipe network scheduling method and system
CN115423224A (en) * 2022-11-04 2022-12-02 佛山市电子政务科技有限公司 Secondary water supply amount prediction method and device based on big data and storage medium
CN116025026A (en) * 2023-02-08 2023-04-28 浙江嘉源和达水务有限公司 Urban water tank linkage control method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
刘国华, 程伟平, 郑冠军: "序列二次规划法在多水源管网优化调度中的应用研究", 水利学报, no. 02 *
曾令侯, 麦继平: "一种采用模糊控制的小区换热机组", 天津工业大学学报, no. 03 *

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