CN116136978B - Method and system for evaluating load aggregation demand response potential of massive small residents - Google Patents

Method and system for evaluating load aggregation demand response potential of massive small residents Download PDF

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CN116136978B
CN116136978B CN202310399677.6A CN202310399677A CN116136978B CN 116136978 B CN116136978 B CN 116136978B CN 202310399677 A CN202310399677 A CN 202310399677A CN 116136978 B CN116136978 B CN 116136978B
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demand response
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CN116136978A (en
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胡新雨
林林
胡楠
殷俊
吴晓楠
罗勇
林亚阳
郁海彭
王嘉楠
苏伟伟
曹鑫楠
周进飞
孙川
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Nantong Power Supply Co Of State Grid Jiangsu Electric Power Co
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
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Abstract

A method and a system for evaluating the load aggregation demand response potential of a massive small living people comprise the following steps: respectively constructing power models of a plurality of resident flexible loads; constructing an optimized scheduling model according to the power model; respectively constructing a demand response potential model of each resident flexible load according to the power model; and obtaining a load aggregation demand response potential evaluation model based on the demand response potential model and the optimization model solving result, so as to evaluate the aggregation potential of the load. The invention can construct the load model which reflects the characteristics of large flexibility load difference and large uncertainty of resident electricity behavior.

Description

Method and system for evaluating load aggregation demand response potential of massive small residents
Technical Field
The invention belongs to the field of power data analysis, and particularly relates to a method and a system for evaluating load aggregation demand response potential of a mass small living people based on daily load optimization scheduling.
Background
At present, jiangsu province electricity generation mainly uses fossil energy, which accounts for 76% of the total installed energy, but the proportion is reduced year by year, and the utilization hours of a thermal power unit are reduced by about 300 hours each year; new energy of Jiangsu develops rapidly, the new energy loader 2606 kilowatts, the first place of Huadong, and the 3 rd place of national network system. Especially, the total capacity of the offshore wind power is 463 kilowatts, which accounts for more than 75% of the whole country, and the future planning will reach 1313 kilowatts. The capacity of a rotary standby unit of the Jiangsu power grid is gradually reduced due to the rapid increase of the ratio of the new energy installation to the power generation, and the frequency modulation and peak regulation capacity of the whole power grid is reduced due to the randomness, the fluctuation and the uncontrollable property of the new energy power generation. In addition, the power system morphology is undergoing a deep change under the new situation, and the power balance is gradually changed from the mode mainly comprising the power saving network to the cross-regional full-network balance mode in the early stage. The transmitting and receiving end, the alternating current-direct current network and the high-low voltage network are highly coupled, the cascading failure mode is more complex, the influence range is wide, and the impact is large. The operation of the power grid faces the problems of high characteristic cognition difficulty, complex regulation control, difficult fault defense and the like. The direct current scale of the cross-region is rapidly increased, under the huge impact of direct current faults, the problems of power angle, voltage and frequency stability of an alternating current system, the problems of power flow blockage, cascading reaction after faults and the like are increasingly outstanding, the power grid has great safety risks, and research on the aspects of source network load coordinated operation mechanism, demand side response and the like is urgently needed.
With the development and progress of intelligent measurement equipment and various intelligent household appliances, the dispatching of residential loads as demand response resources has wider prospects and possibilities. However, the uncertainty of the resident's electricity usage behavior and the heterogeneity of the large number of small micro-loads present a significant challenge to the implementation of demand response. The dispatching department needs to know the size of the adjustable measurement of the demand response resource so as to facilitate the implementation of a response plan, and if the issued demand response plan is not matched with the schedulable amount of the demand response, the unbalance of supply and demand is aggravated, and the safe and stable operation of the power system is influenced. Therefore, the research on the resident flexibility load aggregation potential evaluation method has important significance.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a method and a system for evaluating the load aggregation demand response potential of a massive small resident based on daily load optimization scheduling, which can be used for carrying out model construction on resident flexible loads and evaluating the load aggregation demand response potential under the background of large resident electricity behavior variability and various loads, and the evaluation result can be used for a scheduling department to formulate a demand response plan, thereby having an important academic significance and engineering practical value.
The invention adopts the following technical scheme.
The invention provides a method for evaluating the load aggregation demand response potential of a mass small-scale resident, which comprises the following steps:
step 1, respectively constructing power models of a plurality of resident flexible loads;
step 2, constructing an optimized scheduling model according to the power model;
step 3: respectively constructing a demand response potential model of each resident flexible load according to the power model;
step 4: and obtaining a load aggregation demand response potential evaluation model based on the demand response potential model and the optimization model solving result, so as to evaluate the aggregation potential of the load.
Further, the method comprises the steps of,
the air conditioner changes the electricity consumption by adjusting the temperature, and the power model of the air conditioner in the step 1 is a temperature-power model in the air conditioner refrigeration mode:
Figure SMS_1
Figure SMS_2
Figure SMS_3
Figure SMS_4
wherein,,
Figure SMS_16
and->
Figure SMS_10
User +.>
Figure SMS_17
At time->
Figure SMS_9
Indoor temperature and outdoor temperature;
Figure SMS_15
Is the energy efficiency coefficient of the air conditioner, < >>
Figure SMS_21
Is user->
Figure SMS_22
At time->
Figure SMS_7
Operating power of the air conditioner, < >>
Figure SMS_19
Is user->
Figure SMS_5
Air conditioner rated power, < >>
Figure SMS_20
Is a temperature change delay parameter, +.>
Figure SMS_8
And->
Figure SMS_13
The heat capacity and the heat resistance of the air conditioner are respectively +.>
Figure SMS_11
Is user->
Figure SMS_14
Is time->
Figure SMS_12
Constant temperature control parameters of>
Figure SMS_18
Is the comfort temperature set point for the user,
Figure SMS_6
Is the dead zone set value of the air conditioner.
Further, the method comprises the steps of,
the electric automobile changes the electricity consumption through adjusting the size of charging discharge volume, changes the charge-discharge time and adjusts the electricity consumption period, and the power model of electric automobile in step 1 is:
Figure SMS_23
Figure SMS_24
Figure SMS_25
Figure SMS_26
Figure SMS_27
Figure SMS_28
wherein the method comprises the steps of
Figure SMS_30
,
Figure SMS_32
,
Figure SMS_43
,
Figure SMS_34
And->
Figure SMS_44
User +.>
Figure SMS_33
At time->
Figure SMS_42
The energy, the charging power, the discharging power, the charging efficiency and the discharging efficiency of the electric automobile;
Figure SMS_45
And->
Figure SMS_48
Is user->
Figure SMS_29
Maximum energy and minimum energy of electric vehicle, +.>
Figure SMS_37
Is user->
Figure SMS_35
Maximum charge-discharge power of the electric automobile;
Figure SMS_38
Is user->
Figure SMS_41
At the moment of time
Figure SMS_47
Electric automobile charging indicating variable of +.>
Figure SMS_36
Is user->
Figure SMS_40
At time->
Figure SMS_46
A schedulable indication variable of (a);
Figure SMS_49
For user->
Figure SMS_31
Electric vehicle time of (2)>
Figure SMS_39
Is a function of the energy required for travel.
Further, the method comprises the steps of,
the operation of the cleaning electric appliance is circulated in different periods to form an operation sequence, and the power model of the cleaning electric appliance in the step 1 is as follows:
Figure SMS_50
Figure SMS_51
Figure SMS_58
and->
Figure SMS_54
Is user->
Figure SMS_66
Designated start time and end time of operation of the cleaning appliance, < ->
Figure SMS_55
And->
Figure SMS_64
Is the current running cycle and total cycle number of the cleaning electric appliance, < >>
Figure SMS_59
Is circulation->
Figure SMS_65
Rated power of +.>
Figure SMS_56
Is user->
Figure SMS_62
At time->
Figure SMS_52
Current operating power of the cleaning appliance, +.>
Figure SMS_61
Is user->
Figure SMS_53
Indicating the cleaning appliance at the moment->
Figure SMS_63
Opening an indicating variable +_>
Figure SMS_60
Is user->
Figure SMS_67
At time->
Figure SMS_57
A schedulable indicator variable of a cleaning appliance.
Further, the method comprises the steps of,
the power model of other base loads in the step 1 is as follows:
Figure SMS_68
Figure SMS_69
wherein the method comprises the steps of
Figure SMS_70
For the type of electricity consumption with maximum inflexible load, +.>
Figure SMS_71
Maximum power consumption for the class II inflexible load, < >>
Figure SMS_72
For television load->
Figure SMS_73
The notebook is charged with the load.
Further, the method comprises the steps of,
the optimal scheduling model in the step 2 is as follows:
Figure SMS_74
Figure SMS_75
wherein,,
Figure SMS_78
is->
Figure SMS_82
Electric price at time->
Figure SMS_85
Is user->
Figure SMS_79
At time->
Figure SMS_83
Is a sum of (2)Electric power, comprising: user' s
Figure SMS_84
At time->
Figure SMS_88
Operating power of an air conditioner>
Figure SMS_76
The current operating power of the cleaning appliance>
Figure SMS_80
Inflexible load power->
Figure SMS_87
Charging power of electric automobile>
Figure SMS_89
And discharge power->
Figure SMS_77
；
Figure SMS_81
Is the total number of users->
Figure SMS_86
Is the number of sampling instants.
Further, the method comprises the steps of,
in step 3, the demand response potential model includes an air conditioner demand response potential model, where the air conditioner demand response potential model is represented by the following formula:
Figure SMS_90
wherein,,
Figure SMS_91
is user->
Figure SMS_95
At time->
Figure SMS_97
Is reduced in power, +.>
Figure SMS_92
For user->
Figure SMS_96
At the moment of time
Figure SMS_98
Operating power of the air conditioner, < >>
Figure SMS_101
And->
Figure SMS_93
Is user->
Figure SMS_94
Is acceptable minimum or maximum room temperature, < ->
Figure SMS_99
Means user +.>
Figure SMS_100
Is provided.
Further, the method comprises the steps of,
in the step 3, the demand response potential model includes an electric vehicle demand response potential model, and the electric vehicle demand response potential model includes: the electric vehicle demand response potential model is charged and the electric vehicle demand response potential model is discharged;
the electric automobile demand response potential model being charged is:
Figure SMS_102
Figure SMS_103
Figure SMS_104
Figure SMS_105
Figure SMS_106
Figure SMS_107
Figure SMS_108
Figure SMS_109
Figure SMS_110
Figure SMS_111
wherein,,
Figure SMS_117
and->
Figure SMS_122
User +.>
Figure SMS_128
At time->
Figure SMS_115
The electric vehicle without considering the energy limit value and the energy limit value required by travel can increase the reducible power, < + >>
Figure SMS_120
Is user->
Figure SMS_130
At time->
Figure SMS_138
The true chargeable power of the electric car being charged, +.>
Figure SMS_114
Is user->
Figure SMS_124
At time->
Figure SMS_116
The real curtailable power of the electric car being charged, +.>
Figure SMS_123
Is user->
Figure SMS_119
Maximum energy of electric vehicle->
Figure SMS_127
Is user->
Figure SMS_126
Is used for controlling the minimum energy of the electric automobile,
Figure SMS_134
is user->
Figure SMS_133
At time->
Figure SMS_141
Electric vehicle energy of->
Figure SMS_135
Is user->
Figure SMS_142
At time->
Figure SMS_112
Charging power of electric automobile, < >>
Figure SMS_121
Is user->
Figure SMS_129
Charging efficiency of electric vehicle>
Figure SMS_137
Is user->
Figure SMS_132
Electric vehicle discharge efficiency of>
Figure SMS_140
Is user->
Figure SMS_113
Maximum chargeable energy in remaining chargeable time, +.>
Figure SMS_125
Is user->
Figure SMS_131
Energy required for travel on day d of electric car,/-for>
Figure SMS_139
Is user->
Figure SMS_136
In the time of leaving the charging station of the electric vehicle +.>
Figure SMS_143
Is user \ ->
Figure SMS_118
Maximum charge-discharge power of the electric automobile;
the electric automobile demand response potential model being discharged is:
Figure SMS_144
Figure SMS_145
Figure SMS_146
Figure SMS_147
Figure SMS_148
wherein,,
Figure SMS_150
and->
Figure SMS_153
Is user->
Figure SMS_160
At time->
Figure SMS_152
Real curtailable power and real increasable power of the discharging electric car of +.>
Figure SMS_154
Is user->
Figure SMS_158
Maximum energy of electric vehicle->
Figure SMS_162
Is user->
Figure SMS_149
Electric vehicle minimum energy of +.>
Figure SMS_155
Is user->
Figure SMS_159
At time->
Figure SMS_163
Discharge power of electric car>
Figure SMS_151
Is user->
Figure SMS_156
Charging efficiency of electric vehicle>
Figure SMS_157
Is user->
Figure SMS_161
Is provided.
Further, the method comprises the steps of,
the cleaning electrical appliance demand response potential model is as follows:
Figure SMS_164
Figure SMS_165
wherein,,
Figure SMS_168
and->
Figure SMS_173
User +.>
Figure SMS_177
At time->
Figure SMS_169
The power of the cleaning electric appliance can be reduced, and the power can be increased;
Figure SMS_172
Is user->
Figure SMS_174
At time = =>
Figure SMS_178
The current running power of the cleaning electric appliance;
Figure SMS_166
Is user->
Figure SMS_171
At time->
Figure SMS_176
Opening time of cleaning electric appliance, +.>
Figure SMS_180
And->
Figure SMS_167
Is user->
Figure SMS_170
Designated start time and end time of operation of the cleaning appliance, < ->
Figure SMS_175
Is user->
Figure SMS_179
The total operating time of the cleaning appliance.
Further, the method comprises the steps of,
in the step 4, the load aggregation demand response potential evaluation model is as follows:
Figure SMS_181
Figure SMS_182
Figure SMS_192
refers to the increased power of the flexible load, +.>
Figure SMS_186
Refers to lingThe power of the active load can be cut down,
Figure SMS_195
is user->
Figure SMS_185
At time->
Figure SMS_196
The power of the cleaning appliance can be increased, +.>
Figure SMS_201
And->
Figure SMS_202
User +.>
Figure SMS_200
At time->
Figure SMS_204
The true increasable power of the electric vehicle being charged and discharged, < >>
Figure SMS_183
Is user->
Figure SMS_191
At time->
Figure SMS_189
An electric vehicle charge indicator variable;
Figure SMS_197
Is user->
Figure SMS_199
At time->
Figure SMS_203
Can reduce the power of the cleaning electric appliance,
Figure SMS_187
is user->
Figure SMS_194
At time->
Figure SMS_190
Is reduced in power, +.>
Figure SMS_198
And->
Figure SMS_188
User +.>
Figure SMS_193
At time->
Figure SMS_184
The actual curtailable power of the electric car being charged and being discharged.
The second aspect of the present invention provides a system for evaluating the load aggregate demand response potential of a mass small-scale micro-living people, which is used for executing a method for evaluating the load aggregate demand response potential of the mass small-scale micro-living people, and is characterized in that:
the system comprises: the system comprises a load power module, a scheduling module, a demand response potential model module and an evaluation module;
the load power module is used for respectively constructing power models of a plurality of resident flexible loads;
the scheduling module is used for constructing an optimized scheduling model according to the power model
The demand response potential model module is used for respectively constructing a demand response potential model of each resident flexible load according to the power model;
the evaluation module is used for obtaining a load aggregation demand response potential evaluation model based on the demand response potential model and the optimization model solving result, so that the load is subjected to aggregation potential evaluation.
A third aspect of the present invention proposes a terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is used for storing instructions;
the processor is used for operating according to the instruction to execute the step of the method for evaluating the load aggregation demand response potential of the mass micro residents.
A fourth aspect of the present invention proposes a computer-readable storage medium having stored thereon a computer program, characterized in that:
the program is executed by a processor to obtain the step of the method for evaluating the response potential of the load aggregation requirement of the mass small residents.
Compared with the prior art, the invention has the beneficial effects that:
(1) The load model which reflects the characteristics of large flexibility load difference and large uncertainty of resident electricity consumption behavior can be constructed.
(2) The proposed load aggregation response potential evaluation model can reflect the schedulable potential of the load day before, so that a scheduling department can issue a demand response plan.
Drawings
FIG. 1 is a flow chart of a method for evaluating the load aggregation demand response potential of a mass small-scale micro-citizen.
Fig. 2 is a diagram of a demand response model of an electric vehicle during charging.
Fig. 3 is a diagram of a demand response model of an electric vehicle when discharging.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are merely some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art without making any inventive effort, are within the scope of the present invention.
A method for evaluating the load aggregation demand response potential of a massive small living people, as shown in figure 1, comprises the following steps:
step 1: respectively constructing power models of a plurality of resident flexible loads; wherein, resident flexibility load includes: air conditioning, electric vehicles, cleaning appliances, and other base loads;
step 2: constructing an optimized scheduling model according to the power model;
step 3: according to the power model, respectively constructing a demand response potential model of the resident flexible load, which comprises the following steps: an air conditioner demand response potential model, an electric automobile demand response potential model and a cleaning electric appliance demand response potential model; constructing a load aggregation demand response potential model;
step 4: and carrying out potential evaluation on the aggregate demand response of the flexible load based on the demand response potential model and the optimal scheduling model.
The specific steps in the step 1 include:
building a model of resident flexible load;
the resident's flexible loads include air conditioning, electric vehicles, cleaning appliances, and other base loads. The flexibility of the load is defined as the amount of electricity consumption or the period of electricity consumption that can be adjusted by changing the operating parameters or the period of operation of the load.
The electric automobile can change the electricity consumption by adjusting the charge and discharge quantity, and can adjust the electricity consumption period by changing the charge and discharge time, and the cleaning electric appliance can change the electricity consumption period by changing the load operation period, so as to model and construct the loads.
(1) Air conditioner
The temperature-power model in air conditioning cooling mode is as follows:
Figure SMS_205
（1）
Figure SMS_206
（2）
Figure SMS_207
（3）
Figure SMS_208
（4）
wherein,,
Figure SMS_226
is->
Figure SMS_212
Increment symbol of->
Figure SMS_223
And->
Figure SMS_211
User +.>
Figure SMS_224
At time->
Figure SMS_216
Indoor temperature and outdoor temperature.
Figure SMS_222
Is the energy efficiency coefficient of the air conditioner, < >>
Figure SMS_215
Is user->
Figure SMS_220
At time->
Figure SMS_209
Air-conditioning operation power of>
Figure SMS_218
Is user->
Figure SMS_214
Air conditioner rated power, < >>
Figure SMS_221
Is a temperature change delay parameter, +.>
Figure SMS_219
And->
Figure SMS_227
Air conditioner heat capacity and heat resistance, respectively, +.>
Figure SMS_213
Is user->
Figure SMS_217
Is time->
Figure SMS_225
Is controlled by the control parameters of the constant temperature of the air conditioner, the operation of the air conditioner uses hysteresis-dead zone control,/and/or->
Figure SMS_228
Is the comfort temperature set point for the user,
Figure SMS_210
Is the dead zone set value of the air conditioner.
(2) Electric automobile
The model of the electric automobile is as follows:
Figure SMS_229
（5）
Figure SMS_230
（6）
Figure SMS_231
（7）
Figure SMS_232
（8）
Figure SMS_233
（9）
Figure SMS_234
（10）
wherein the method comprises the steps of
Figure SMS_257
,
Figure SMS_266
,
Figure SMS_269
,
Figure SMS_237
And->
Figure SMS_248
User +.>
Figure SMS_256
At time->
Figure SMS_264
Electric automobile energy, charging power, discharging power, charging efficiency and discharging efficiency.
Figure SMS_253
And->
Figure SMS_261
Is user->
Figure SMS_240
Maximum energy and minimum energy of electric vehicle, +.>
Figure SMS_249
Is user->
Figure SMS_242
Maximum charge-discharge power of the electric automobile.
Figure SMS_245
Is user->
Figure SMS_252
At time->
Figure SMS_260
The value of the electric vehicle charge instruction variable is 1 when charging and 0 when discharging.
Figure SMS_238
Is user->
Figure SMS_243
At time->
Figure SMS_251
If the electric vehicle is in schedulable period +.>
Figure SMS_259
Inner->
Figure SMS_235
And->
Figure SMS_244
User +.>
Figure SMS_236
Electric car arrival charging station time and departure charging station time), then +.>
Figure SMS_250
1, otherwise 0.
Figure SMS_255
Must be greater than the first
Figure SMS_262
Energy required for travel>
Figure SMS_239
。
Figure SMS_247
For user->
Figure SMS_267
Electric vehicle time of (2)>
Figure SMS_270
Energy required for travel of (2), which can only be used in non-schedulable periods +.>
Figure SMS_268
(i.e., the external period of time after the electric vehicle leaves the charging station and before it reaches the charging station) is positive.
Figure SMS_271
For user->
Figure SMS_254
A moment of departure from the charging station;
Figure SMS_263
Is->
Figure SMS_241
Energy required for travel on a day;
Figure SMS_246
For user->
Figure SMS_258
Electric vehicle time of (2)>
Figure SMS_265
Is a function of the energy required for travel.
(3) Cleaning electric appliance
The operation of the cleaning appliance forms an operation sequence (e.g. rinsing, dewatering, etc.) with different cycle cycles, wherein the cleaning appliance may be a washing machine or a dishwasher. The model is as follows:
Figure SMS_272
（11 ）
Figure SMS_273
（12）
Figure SMS_277
and->
Figure SMS_284
Is user->
Figure SMS_291
The start time and end time of operation of the cleaning appliance are specified (note that not the actual operation time of the device, but a user-specified period of time during which the device can be operated).
Figure SMS_279
And->
Figure SMS_285
Is the current running cycle and total cycle number of the cleaning electric appliance.
Figure SMS_293
Is circulation->
Figure SMS_298
Rated power of +.>
Figure SMS_276
Is user->
Figure SMS_283
At time->
Figure SMS_289
The current operating power of the cleaning appliance.
Figure SMS_296
Is user->
Figure SMS_280
Indicating the cleaning appliance at the moment->
Figure SMS_281
Opening indicating variable (opening time is 1), +.>
Figure SMS_288
Is user->
Figure SMS_295
At time->
Figure SMS_274
A schedulable indicating variable of the cleaning appliance in a schedulable period +.>
Figure SMS_286
And its value is 1. At->
Figure SMS_292
Time->
Figure SMS_297
The turn-on indication variable is used to calculate the time +.>
Figure SMS_275
Is->
Figure SMS_287
Because of->
Figure SMS_294
Refers to user +.>
Figure SMS_299
At time->
Figure SMS_278
Only the time at which it is turned on is 1, the remaining times are all 0. Therefore, after opening, run +.>
Figure SMS_282
The starting time is needed in the period>
Figure SMS_290
A variable value of 1.
Inflexible loads for residents include other base loads including lighting, cell phone charging, television, notebook charging, and the like. It is modeled as parameters subject to uniform distribution, taking into account the variability of residential electricity, two types of load distribution
Figure SMS_300
And->
Figure SMS_301
To simulate the electricity consumption situation, wherein +.>
Figure SMS_302
For television load->
Figure SMS_303
The notebook is charged with the load.
Figure SMS_304
(13)
Figure SMS_305
(14)
Figure SMS_306
(15)
Wherein the method comprises the steps of
Figure SMS_307
Maximum power consumption for television load, +.>
Figure SMS_308
And the maximum electricity consumption is used for charging the notebook. Inflexible load power->
Figure SMS_309
Including the total charge of televisions and notebooks.
The specific steps in the step 2 include:
constructing an optimized scheduling model by considering heterogeneity of domestic and civil electrical behaviors and loads;
according to the difference of real-time electricity prices, residents can reduce electricity fees by changing the electricity consumption and the electricity consumption time period.
Figure SMS_310
Figure SMS_311
Figure SMS_312
（15）
Wherein the method comprises the steps of
Figure SMS_314
Is->
Figure SMS_317
Electric price at time->
Figure SMS_321
Is user->
Figure SMS_315
At time->
Figure SMS_318
Is used for the total power consumption of the electric power system. User->
Figure SMS_322
At time->
Figure SMS_325
The decision variables of (a) include the operating power of the air conditioner +.>
Figure SMS_313
Current operating power of the cleaning appliance ∈>
Figure SMS_320
Inflexible load power->
Figure SMS_324
Charging power of electric automobile>
Figure SMS_326
And discharge power->
Figure SMS_316
；
Figure SMS_319
Is the total number of users and,
Figure SMS_323
is the total number of sampling instants.
In the model, the variability of the resident user electricity usage behavior is simulated as different schedulable periods (cleaning appliances
Figure SMS_329
With electric automobile->
Figure SMS_328
) Temperature set point of air conditioner->
Figure SMS_337
Is +_with dead zone set point>
Figure SMS_333
. The heterogeneity of the load is simulated as different load parameters including +.>
Figure SMS_339
、
Figure SMS_332
、
Figure SMS_341
、
Figure SMS_340
、
Figure SMS_344
、
Figure SMS_327
Air conditioner->
Figure SMS_336
、
Figure SMS_334
、
Figure SMS_338
、
Figure SMS_335
、
Figure SMS_343
、
Figure SMS_331
Cleaning appliance->
Figure SMS_342
、
Figure SMS_330
. These parameters can be modeled using a uniform distribution or a truncated normal distribution.
The specific steps in the step 3 include:
constructing a demand response potential model of resident flexible load; wherein the flexible load aggregate demand response potential is defined as the power usage of the flexible load that can be increased or decreased at a certain time by adjusting the load parameters.
(1) Air conditioner demand response potential model
Figure SMS_345
Wherein the method comprises the steps of
Figure SMS_346
Is user->
Figure SMS_351
At time->
Figure SMS_355
Is reduced in power, +.>
Figure SMS_349
For user->
Figure SMS_350
At the moment of time
Figure SMS_353
Load power of air conditioner, < >>
Figure SMS_356
And->
Figure SMS_347
Is user->
Figure SMS_352
Is acceptable minimum or maximum room temperature, < ->
Figure SMS_354
Means user +.>
Figure SMS_357
Is only at the temperature +.>
Figure SMS_348
And has the potential to cut power when in a downward trend.
(2) Electric automobile demand response potential model
The demand response potential model of the electric automobile is complex, and is divided into two scenes, namely aiming at the electric automobile which is being charged and the electric automobile which is being discharged.
1) Electric automobile demand response potential model being charged
The method is divided into four cases, and a demand response model is as follows:
Figure SMS_358
Figure SMS_359
Figure SMS_360
Figure SMS_361
Figure SMS_362
Figure SMS_363
Figure SMS_364
Figure SMS_365
Figure SMS_366
Figure SMS_367
wherein,,
Figure SMS_375
and->
Figure SMS_374
User +.>
Figure SMS_382
At time->
Figure SMS_369
The electric vehicle without considering the energy limit value and the energy limit value required by travel can increase the reducible power, < + >>
Figure SMS_378
Is user->
Figure SMS_384
At time->
Figure SMS_392
The true chargeable power of the electric car being charged, +.>
Figure SMS_381
Is user->
Figure SMS_388
At time->
Figure SMS_372
The real curtailable power of the electric car being charged, +.>
Figure SMS_379
Is user->
Figure SMS_370
Maximum energy of electric vehicle->
Figure SMS_377
Is user->
Figure SMS_385
Electric vehicle minimum energy of +.>
Figure SMS_393
Is user->
Figure SMS_386
At time->
Figure SMS_394
Electric vehicle energy of->
Figure SMS_399
Is user->
Figure SMS_401
At time->
Figure SMS_368
Charging power of electric automobile, < >>
Figure SMS_376
Is user->
Figure SMS_389
Charging efficiency of electric vehicle>
Figure SMS_396
Is user->
Figure SMS_387
Electric vehicle discharge efficiency of>
Figure SMS_397
Is user->
Figure SMS_371
Maximum chargeable energy in remaining chargeable time, +.>
Figure SMS_380
Is user->
Figure SMS_391
Energy required for travel on day d of electric car,/-for>
Figure SMS_398
Is user->
Figure SMS_395
In the time of leaving the charging station of the electric vehicle +.>
Figure SMS_400
Is user->
Figure SMS_383
Maximum charge-discharge power of the electric automobile. Wherein->
Figure SMS_390
Corresponding to case1, formula +.>
Figure SMS_373
The (a), (b) and (c) in (b) correspond to case2, case3 and case4 in fig. 2, respectively.
2) Discharging electric automobile
The electric vehicle demand response potential model being discharged is as follows:
Figure SMS_402
Figure SMS_403
Figure SMS_404
Figure SMS_405
Figure SMS_406
wherein,,
Figure SMS_411
and->
Figure SMS_409
Is user->
Figure SMS_420
At time->
Figure SMS_410
Real curtailable power and real increasable power of the discharging electric car of +.>
Figure SMS_417
Is user->
Figure SMS_412
Maximum energy of electric vehicle->
Figure SMS_418
Is user->
Figure SMS_413
Electric vehicle minimum energy of +.>
Figure SMS_416
Is user->
Figure SMS_407
At time->
Figure SMS_415
Discharge power of electric car>
Figure SMS_408
Is user->
Figure SMS_422
Charging efficiency of electric vehicle>
Figure SMS_421
Is user->
Figure SMS_423
Is provided. Wherein power can be cut down->
Figure SMS_414
Corresponding to case5 in FIG. 3, formula +.>
Figure SMS_419
The cases (e) and (f) in (b) correspond to case6 and case7 in fig. 3, respectively.
(3) Cleaning electrical appliance demand response potential model
Figure SMS_424
Figure SMS_425
Wherein the method comprises the steps of
Figure SMS_433
And->
Figure SMS_430
User +.>
Figure SMS_441
At time->
Figure SMS_431
The power of the cleaning appliance can be reduced and the power can be increased.
Figure SMS_438
Is user->
Figure SMS_432
At time->
Figure SMS_440
Using the moment of opening of the cleaning appliance, i.e. opening indicating variable +.>
Figure SMS_427
Time 1, < >>
Figure SMS_434
And->
Figure SMS_426
Is user->
Figure SMS_435
Designated start time and end time of operation of the cleaning appliance, < ->
Figure SMS_428
Is user->
Figure SMS_439
Total operating time of the cleaning appliance, +.>
Figure SMS_437
Is the running time of the total period of the cleaning appliance,
Figure SMS_442
means that the cleaning appliance is at the user +.>
Figure SMS_429
Designated runnability time period->
Figure SMS_436
An on time within.
The specific steps in the step 4 include:
and obtaining a load aggregation demand response potential evaluation model based on the demand response potential model and the optimization model solving result, so as to evaluate the aggregation potential of the load, namely, evaluate the increasable power and the reducible power of the (flexible) load.
Firstly, collecting future electricity prices and air temperature forecast of an optimization period, and solving the residential load day-ahead optimization scheduling model constructed in the step (2). The constructed optimal scheduling model is an MILP mixed integer linear programming problem. The predicted temperature and power of the air conditioner at each moment, the running time of the cleaning electric appliance, the charging and discharging power of the electric vehicle and the charging and discharging time are obtained. Based on the flexible load demand response potential models in the step (3), the demand response potential values of the flexible loads are solved according to the calculation results of the resident load optimization scheduling model, then the flexible load demand response potentials are aggregated, then the demand response potentials after load aggregation are evaluated, and the resident flexible load at each moment can be increased or the total power can be reduced. The load aggregate demand response potential assessment model is as follows:
Figure SMS_443
Figure SMS_444
Figure SMS_464
refers to the increased power of the flexible load, +.>
Figure SMS_449
Refers to lingThe power of the active load can be cut down,
Figure SMS_456
is user->
Figure SMS_452
At time->
Figure SMS_459
The power of the cleaning appliance can be increased, +.>
Figure SMS_462
And->
Figure SMS_465
User +.>
Figure SMS_450
At time->
Figure SMS_460
The true increasable power of the electric vehicle being charged and discharged, < >>
Figure SMS_445
Is user->
Figure SMS_454
At time->
Figure SMS_451
The value of the charging indicating variable of the electric automobile is 1 when charging and 0 when discharging;
Figure SMS_458
Is user->
Figure SMS_446
At the moment of time
Figure SMS_453
The power consumption of the cleaning appliance, +.>
Figure SMS_448
Is user->
Figure SMS_457
At time->
Figure SMS_463
Can reduce the power of the air conditioner,
Figure SMS_466
and->
Figure SMS_447
User +.>
Figure SMS_455
At time->
Figure SMS_461
The actual curtailable power of the electric car being charged and being discharged.
Correspondingly, the invention also discloses a system for evaluating the load aggregation demand response potential of the mass small-size residents, which comprises the following steps: the system comprises a load power module, a scheduling module, a demand response potential model module and an evaluation module;
the load power module is used for respectively constructing power models of a plurality of resident flexible loads;
the scheduling module is used for constructing an optimized scheduling model according to the power model
The demand response potential model module is used for respectively constructing a demand response potential model of each resident flexible load according to the power model;
the evaluation module is used for obtaining a load aggregation demand response potential evaluation model based on the demand response potential model and the optimization model solving result, so that the load is subjected to aggregation potential evaluation.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.

Claims (8)

1. The method for evaluating the load aggregation demand response potential of the massive small residents is characterized by comprising the following steps of:
step 1, respectively constructing power models of a plurality of resident flexible loads;
step 2, constructing an optimized scheduling model according to the power model; expressed in terms of the following formula,
Figure QLYQS_1
Figure QLYQS_2
wherein,,
Figure QLYQS_5
is->
Figure QLYQS_10
Electric price at time->
Figure QLYQS_14
Is user->
Figure QLYQS_6
At time->
Figure QLYQS_9
Comprises: user->
Figure QLYQS_13
At time->
Figure QLYQS_16
Operating power of an air conditioner>
Figure QLYQS_3
The current operating power of the cleaning appliance>
Figure QLYQS_7
Inflexible loadPower of
Figure QLYQS_11
Charging power of electric automobile>
Figure QLYQS_15
And discharge power->
Figure QLYQS_4
；
Figure QLYQS_8
Is the total number of users->
Figure QLYQS_12
The number of sampling moments;
step 3: respectively constructing a demand response potential model of each resident flexible load according to the power model; the air conditioner demand response potential model is:
Figure QLYQS_17
wherein,,
Figure QLYQS_19
is user->
Figure QLYQS_23
At time->
Figure QLYQS_25
Is reduced in power, +.>
Figure QLYQS_20
For user->
Figure QLYQS_21
At time->
Figure QLYQS_24
Is of the air conditionerOperating power of>
Figure QLYQS_27
And->
Figure QLYQS_18
Is user->
Figure QLYQS_22
Is acceptable minimum or maximum room temperature, < ->
Figure QLYQS_26
Means user +.>
Figure QLYQS_28
Is a room temperature of (2);
the electric automobile demand response potential model comprises: the electric vehicle demand response potential model is charged and the electric vehicle demand response potential model is discharged; the electric automobile demand response potential model being charged is:
Figure QLYQS_29
Figure QLYQS_30
Figure QLYQS_31
Figure QLYQS_32
Figure QLYQS_33
Figure QLYQS_34
Figure QLYQS_35
Figure QLYQS_36
Figure QLYQS_37
Figure QLYQS_38
wherein,,
Figure QLYQS_56
and->
Figure QLYQS_62
User +.>
Figure QLYQS_69
At time->
Figure QLYQS_41
The electric vehicle without considering the energy limit value and the energy limit value required by travel can increase the reducible power, < + >>
Figure QLYQS_53
Is user->
Figure QLYQS_57
At time->
Figure QLYQS_64
The true chargeable power of the electric car being charged, +.>
Figure QLYQS_59
Is user->
Figure QLYQS_68
At time->
Figure QLYQS_55
The real curtailable power of the electric car being charged, +.>
Figure QLYQS_63
Is user->
Figure QLYQS_58
Maximum energy of electric vehicle->
Figure QLYQS_65
Is user->
Figure QLYQS_66
Electric vehicle minimum energy of +.>
Figure QLYQS_70
Is user->
Figure QLYQS_43
At time->
Figure QLYQS_48
Electric vehicle energy of->
Figure QLYQS_45
Is user->
Figure QLYQS_52
At time->
Figure QLYQS_40
Charging power of electric automobile, < >>
Figure QLYQS_50
Is user->
Figure QLYQS_44
Charging efficiency of electric vehicle>
Figure QLYQS_54
Is user->
Figure QLYQS_61
Electric vehicle discharge efficiency of>
Figure QLYQS_67
Is user->
Figure QLYQS_46
Maximum chargeable energy in remaining chargeable time, +.>
Figure QLYQS_51
Is user->
Figure QLYQS_39
Energy required for travel on day d of electric car,/-for>
Figure QLYQS_47
Is user->
Figure QLYQS_49
In the time of leaving the charging station of the electric vehicle +.>
Figure QLYQS_60
Is user->
Figure QLYQS_42
Maximum charge-discharge power of the electric automobile;
the electric automobile demand response potential model being discharged is:
Figure QLYQS_71
Figure QLYQS_72
Figure QLYQS_73
Figure QLYQS_74
Figure QLYQS_75
wherein,,
Figure QLYQS_77
and->
Figure QLYQS_81
Is user->
Figure QLYQS_84
At time->
Figure QLYQS_78
Real curtailable power and real increasable power of the discharging electric car of +.>
Figure QLYQS_82
Is user->
Figure QLYQS_85
Maximum energy of electric vehicle->
Figure QLYQS_88
Is user->
Figure QLYQS_76
Electric vehicle minimum energy of +.>
Figure QLYQS_80
Is user->
Figure QLYQS_87
At time->
Figure QLYQS_90
Discharge power of electric car>
Figure QLYQS_79
Is user->
Figure QLYQS_83
Charging efficiency of electric vehicle>
Figure QLYQS_86
Is user->
Figure QLYQS_89
The discharge efficiency of the electric automobile;
the cleaning electrical appliance demand response potential model is as follows:
Figure QLYQS_91
Figure QLYQS_92
wherein,,
Figure QLYQS_94
and->
Figure QLYQS_99
User +.>
Figure QLYQS_103
At time->
Figure QLYQS_96
Is used for cleaningThe power of the device can be reduced, and the power can be increased;
Figure QLYQS_97
Is user->
Figure QLYQS_101
At time->
Figure QLYQS_105
The current running power of the cleaning electric appliance;
Figure QLYQS_93
Is user->
Figure QLYQS_98
At time->
Figure QLYQS_102
Opening time of cleaning electric appliance, +.>
Figure QLYQS_106
And->
Figure QLYQS_95
Is user->
Figure QLYQS_100
The designated start time and end time of the operation of the cleaning appliance,
Figure QLYQS_104
is user->
Figure QLYQS_107
The total operation time of the cleaning electric appliance;
step 4: obtaining a load aggregation demand response potential evaluation model based on the demand response potential model and the optimization model solving result, so as to evaluate the aggregation potential of the load;
the load aggregation demand response potential evaluation model is as follows:
Figure QLYQS_108
Figure QLYQS_109
Figure QLYQS_111
refers to the increased power of the flexible load, +.>
Figure QLYQS_117
Refers to a reducible power for a flexible load,
Figure QLYQS_120
is user->
Figure QLYQS_112
At time->
Figure QLYQS_118
The power of the cleaning appliance can be increased, +.>
Figure QLYQS_113
And->
Figure QLYQS_121
Respectively the users
Figure QLYQS_116
At time->
Figure QLYQS_122
The true increasable power of the electric vehicle being charged and discharged, < >>
Figure QLYQS_110
Is user->
Figure QLYQS_119
At the moment of time
Figure QLYQS_115
An electric vehicle charge indicator variable;
Figure QLYQS_123
Is user->
Figure QLYQS_125
At time->
Figure QLYQS_129
Can reduce the power of the cleaning electric appliance,
Figure QLYQS_126
is user->
Figure QLYQS_130
At time->
Figure QLYQS_127
Is reduced in power, +.>
Figure QLYQS_131
And->
Figure QLYQS_114
User +.>
Figure QLYQS_124
At time->
Figure QLYQS_128
The actual curtailable power of the electric car being charged and being discharged.
2. The method for evaluating the load aggregation demand response potential of mass micro-residents according to claim 1, which is characterized by comprising the following steps of:
the air conditioner changes the electricity consumption by adjusting the temperature, and the power model of the air conditioner in the step 1 is a temperature-power model in the air conditioner refrigeration mode:
Figure QLYQS_132
Figure QLYQS_133
Figure QLYQS_134
Figure QLYQS_135
wherein,,
Figure QLYQS_140
is->
Figure QLYQS_139
Increment symbol of->
Figure QLYQS_146
And->
Figure QLYQS_137
User +.>
Figure QLYQS_149
At time->
Figure QLYQS_143
Indoor temperature and outdoor temperature;
Figure QLYQS_150
Is the energy efficiency coefficient of the air conditioner, < >>
Figure QLYQS_152
Is user->
Figure QLYQS_155
At time->
Figure QLYQS_138
Operating power of the air conditioner, < >>
Figure QLYQS_148
Is the user
Figure QLYQS_141
Air conditioner rated power, < >>
Figure QLYQS_144
Is a temperature change delay parameter, +.>
Figure QLYQS_142
And->
Figure QLYQS_151
The heat capacity and the heat resistance of the air conditioner are respectively +.>
Figure QLYQS_145
Is user->
Figure QLYQS_153
Is time->
Figure QLYQS_147
Constant temperature control parameters of>
Figure QLYQS_154
Is the comfort temperature set point for the user,
Figure QLYQS_136
Is the dead zone set value of the air conditioner.
3. The method for evaluating the load aggregation demand response potential of mass micro-residents according to claim 1, which is characterized by comprising the following steps of:
the electric automobile changes the electricity consumption through adjusting the size of charging discharge volume, changes the charge-discharge time and adjusts the electricity consumption period, and the power model of electric automobile in step 1 is:
Figure QLYQS_156
Figure QLYQS_157
Figure QLYQS_158
Figure QLYQS_159
Figure QLYQS_160
Figure QLYQS_161
wherein the method comprises the steps of
Figure QLYQS_166
,
Figure QLYQS_170
,
Figure QLYQS_177
,
Figure QLYQS_164
And->
Figure QLYQS_176
User +.>
Figure QLYQS_183
At time->
Figure QLYQS_188
The energy, the charging power, the discharging power, the charging efficiency and the discharging efficiency of the electric automobile;
Figure QLYQS_165
And->
Figure QLYQS_173
Is user->
Figure QLYQS_182
Maximum energy and minimum energy of electric vehicle, +.>
Figure QLYQS_189
Is user->
Figure QLYQS_167
Maximum charge-discharge power of the electric automobile;
Figure QLYQS_175
Is user->
Figure QLYQS_180
At time->
Figure QLYQS_186
Electric automobile charging indicating variable of +.>
Figure QLYQS_163
Is user->
Figure QLYQS_171
At time->
Figure QLYQS_178
A schedulable indication variable of (a);
Figure QLYQS_184
For user->
Figure QLYQS_162
Electric vehicle time of (2)>
Figure QLYQS_174
Is the energy required for travel;
Figure QLYQS_179
For user->
Figure QLYQS_187
A moment of departure from the charging station;
Figure QLYQS_169
Is->
Figure QLYQS_172
Energy required for travel on a day;
Figure QLYQS_181
For user->
Figure QLYQS_185
Electric vehicle time of (2)>
Figure QLYQS_168
Is a function of the energy required for travel.
4. The method for evaluating the load aggregation demand response potential of mass micro-residents according to claim 1, which is characterized by comprising the following steps of:
the operation of the cleaning electric appliance is circulated in different periods to form an operation sequence, and the power model of the cleaning electric appliance in the step 1 is as follows:
Figure QLYQS_190
Figure QLYQS_191
in the method, in the process of the invention,
Figure QLYQS_199
and->
Figure QLYQS_196
Is user->
Figure QLYQS_200
Designated start time and end time of operation of the cleaning appliance, < ->
Figure QLYQS_195
And->
Figure QLYQS_202
Is the current running cycle and total cycle number of the cleaning electric appliance, < >>
Figure QLYQS_198
Is circulation->
Figure QLYQS_205
Rated power of +.>
Figure QLYQS_201
Is user->
Figure QLYQS_207
At time->
Figure QLYQS_192
Current operating power of the cleaning appliance, +.>
Figure QLYQS_206
Is user->
Figure QLYQS_194
Indicating the time of the cleaning appliance
Figure QLYQS_204
Opening an indicating variable +_>
Figure QLYQS_193
Is user->
Figure QLYQS_203
At time->
Figure QLYQS_197
A schedulable indicator variable of a cleaning appliance.
5. The method for evaluating the load aggregation demand response potential of mass micro-residents according to claim 1, which is characterized by comprising the following steps of:
the power model of other base loads in the step 1 is as follows:
Figure QLYQS_208
Figure QLYQS_209
wherein the method comprises the steps of
Figure QLYQS_210
For the type of electricity consumption with maximum inflexible load, +.>
Figure QLYQS_211
Maximum for class II inflexible loadIs (are) the electricity consumption of the car>
Figure QLYQS_212
For television load->
Figure QLYQS_213
The notebook is charged with the load.
6. A massive small dwelling load aggregate demand response potential evaluation system for performing the method of any of claims 1-5, characterized by:
the system comprises: the system comprises a load power module, a scheduling module, a demand response potential model module and an evaluation module;
the load power module is used for respectively constructing power models of a plurality of resident flexible loads;
the scheduling module is used for constructing an optimized scheduling model according to the power model
The demand response potential model module is used for respectively constructing a demand response potential model of each resident flexible load according to the power model;
the evaluation module is used for obtaining a load aggregation demand response potential evaluation model based on the demand response potential model and the optimization model solving result, so that the load is subjected to aggregation potential evaluation.
7. A terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1-5.
8. A computer readable storage medium having stored thereon a computer program characterized by:
which program, when being executed by a processor, carries out the steps of the method according to any one of claims 1-5.
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