CN111815477B - User energy management scheduling method and device - Google Patents

User energy management scheduling method and device Download PDF

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CN111815477B
CN111815477B CN202010645071.2A CN202010645071A CN111815477B CN 111815477 B CN111815477 B CN 111815477B CN 202010645071 A CN202010645071 A CN 202010645071A CN 111815477 B CN111815477 B CN 111815477B
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徐沛
黄海峰
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Electric Housekeeper Group Co ltd
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Abstract

The invention discloses a user energy management scheduling method and a user energy management scheduling device. Secondly, after users of different types receive the real-time electricity prices every 15 minutes, the real-time electricity prices at the future moment are predicted through a seasonal ARIMA prediction model, and then under various constraints of pre-classified and modeled electric equipment used by the users, electricity utilization decisions are made according to the target of minimizing electricity utilization cost, namely demand response is carried out. The method and the system are based on real-time electricity price, and can uniformly consider interruptible equipment, uninterruptable equipment and graded gear electric equipment which can be dispatched in the user, provide reference for user demand response through a seasonal ARIMA prediction model aiming at the user side, and improve the income obtained by the user through the demand response.

Description

User energy management scheduling method and device
Technical Field
The invention relates to a user energy management scheduling method and device, and belongs to the technical field of intelligent power utilization.
Background
User (household or commercial user) energy management is an extension of smart grid demand response project on the user side, and the demand response is increasingly emphasized in promoting renewable energy consumption, realizing peak clipping and valley filling of a power grid, improving user income and improving energy utilization efficiency. The operation scheduling of household and commercial electric equipment is a core problem in user energy management. Real Time Pricing (RTP) refers to that electricity prices continuously fluctuate with Time, can reflect marginal cost of power generation, is linked with power generation cost, and is an ideal demand response mechanism. The update period of the real-time electricity price can reach one hour or less, which is more beneficial to reflecting the dynamic supply and demand balance condition, and the real-time electricity price is hooked with the actual power generation cost, so the real-time electricity price is considered as an effective demand response means in the power market. Based on real-time electricity price, the household commercial electric equipment is classified and modeled to realize optimized operation scheduling of household appliances, so that the purposes of avoiding a power grid demand peak value, minimizing user electricity consumption cost and improving energy utilization efficiency are achieved, and the method has very important significance.
Disclosure of Invention
The invention aims to provide a user energy management and scheduling method and device, which can avoid the peak value of power grid demand, minimize the power consumption expense of a user, improve the energy utilization efficiency and improve the self income of the user.
The purpose of the invention is realized by the following technical scheme:
a user energy management scheduling method comprises the following steps:
step 1: classification and modeling of electrical devices: the classification of the electrical equipment is based on the difference of the operation characteristics of the electrical equipment in time and space, and the modeling of the electrical equipment comprises a daily power utilization plan of the electrical equipment;
the modeling method of the interruptible electric equipment comprises the following steps:
defining a set of interruptible electrical devices of an individual user as
Figure BDA0002572726110000011
Certain electric equipment a epsilon A in time period [ t [ ]1，t2]Internal consumption of electric energy EaWherein
Figure BDA0002572726110000012
Namely, the method comprises the following steps:
Figure BDA0002572726110000013
in the formula (1)
Figure BDA0002572726110000014
Representation pair arbitrarily belonging to a set
Figure BDA0002572726110000017
The electric equipment a of (1) is established;
Figure BDA0002572726110000015
is the amount of power consumed by device a during period h;
removal of [ t ]1，t2]Outside the time period, the electric equipmentNot in operation, namely:
Figure BDA0002572726110000016
h96 in formula (2)
Figure BDA0002572726110000021
96 time period sets representing a day
Figure BDA0002572726110000022
Removal of [ t ]1，t2]The remaining part of the time period outside the time period,
meanwhile, the electric equipment a is specified to be in an arbitrary time interval h e [ t ∈1，t2]Energy consumption of
Figure BDA0002572726110000023
Also lower limit
Figure BDA0002572726110000024
And upper limit of
Figure BDA0002572726110000025
Namely, the method comprises the following steps:
Figure BDA0002572726110000026
in the formula (3)
Figure BDA0002572726110000027
Indicates that the pair belongs to the interval [ t1，t2]In any time period h, the formula (3) is established;
and the total electricity consumption of all the devices of the user in any period also has an upper limit, namely:
Figure BDA0002572726110000028
in the formula of loadhThe total electricity consumption of the user, namely the total load is large for h periodSmall, EmaxIs a maximum load limit;
defining the electricity utilization decision vector of the electricity utilization equipment as
Figure BDA0002572726110000029
Comprehensively considering the above situations, the following electricity utilization decision sets are defined:
Figure BDA00025727261100000210
the modeling method of the electric equipment with the graded gears comprises the following steps: defining a hierarchical set of electric devices for a certain independent user as
Figure BDA00025727261100000217
Certain electric equipment B epsilon B in time period [ t [ ]1，t2]Internal consumption of electric energy EbNamely, the following steps are provided:
Figure BDA00025727261100000211
in the formula
Figure BDA00025727261100000212
Is the electric quantity consumed by the electric equipment b in the h time period;
removal of [ t ]1，t2]Outside the time period, the electric equipment does not work, namely:
Figure BDA00025727261100000213
at the same time
Figure BDA00025727261100000214
Is a discrete value, i.e. having:
Figure BDA00025727261100000215
in the formula 0, L1、L2、...、LnRespectively the power consumption of the device b in different working gears in a single time period, wherein n represents the number of the working gears of the device b except for the power-off state;
and the total electricity consumption of the user in any period also has an upper limit, namely:
Figure BDA00025727261100000216
in the formula of loadhFor a period of h, the total electricity consumption, i.e. the total load, EmaxIs a maximum load limit;
defining the electricity utilization decision vector of the grading gear electricity utilization equipment as
Figure BDA0002572726110000031
Comprehensively considering the above situations, the following electricity utilization decision sets are defined:
Figure BDA0002572726110000032
the uninterruptible power consumer is modeled as: defining a set of uninterruptible power consumers of an individual user as
Figure BDA0002572726110000033
A certain electric equipment C epsilon C has a definite electric utilization curve, so that only the time period k for starting the equipment is selected, and k epsilon [ t ]1，t2]Defining an auxiliary binary variable
Figure BDA0002572726110000034
To indicate the start-up period of the device when
Figure BDA0002572726110000035
The electric equipment c is started in the h period, namely:
Figure BDA0002572726110000036
removal of [ t ]1，t2]Outside the time period, the electric equipment is not started, namely:
Figure BDA0002572726110000037
meanwhile, the electric equipment c is provided with 1 to 3 working stages, and the number of the required time stages for completing each working stage is n1、n2、 n3Wherein n is2、n3May be 0; and the electric equipment c uses the electricity quantity in each period in the same working phase
Figure BDA0002572726110000038
Are identical, i.e.
Figure BDA0002572726110000039
Wherein P is1、P2、P3Respectively represent the power consumption of a single time interval when the electric equipment c is in different working phases, namely:
Figure BDA00025727261100000310
Figure BDA00025727261100000311
in formula (14)
Figure BDA00025727261100000312
Whether the electric equipment c is started in the h-th time period or not is judged, and if yes, a subsequent expression in brackets is established;
the total electricity consumption of the user in any period has an upper limit, namely:
Figure BDA00025727261100000313
in the formula of loadhTotal electricity consumption, i.e. total load, for users in h time period, EmaxIs a maximum load limit;
defining a power usage decision vector for an uninterruptible power consumer as
Figure BDA00025727261100000314
Comprehensively considering the above situations, the following electricity utilization decision sets are defined:
Figure BDA0002572726110000041
step 2, setting 96 time intervals in one day as a time window for the demand response of each independent user, namely, taking 24 hours in one day as a planned optimization period, and taking 15 minutes as a time unit, namely, any time interval
Figure BDA0002572726110000045
Figure BDA0002572726110000046
During the h-th period, for an independent user:
(1) in the past h-1 time periods, the real-time electricity price and electricity utilization decision of each time period are determined;
(2) the real-time electricity price of the current h period is known;
(3) the real-time electricity prices for the next 96-h periods are unknown, and are predicted by an seasonal ARIMA prediction model (differential Integrated Moving Average Autoregressive model, also called Integrated Moving Average Autoregressive model);
(4) the electricity utilization decisions of the current h time period and the future 96-h time periods are variables needing to be determined, after a real-time electricity price predicted value is obtained through a seasonal ARIMA prediction model, under the constraint condition of used electrical equipment which is classified and modeled in advance in the step 1, the electricity utilization decisions are made according to the target of minimizing the electricity utilization cost, namely, demand response is carried out; the electricity usage decision process for an individual user at the current ith time of day can be modeled as the following objective function:
Figure BDA0002572726110000042
where Minimize represents the value of the minimization objective function,
Figure BDA0002572726110000043
is a predicted value of the real-time electricity price for the future time period h, and lhThe electric quantity, p, required by the grid for a certain h period of timehReal-time electricity price, p, for h time periodiFor the real-time electricity rate, l, of the current time period, i.e. the i time periodiThe electric quantity provided by the power grid is needed by the user in the current time period, namely the i time period
Figure BDA0002572726110000044
piEquivalent to a known quantity, solving equation (19) under the constraint conditions of all electrical equipment modeled in advance can obtain the current i time period and the future 96-i time periods of user optimized power utilization decisions, wherein the current i time period power utilization decision is actually executed.
(5) And (4) the demand response of the user in 96 time periods in one day is solved in a rolling manner, so that the optimized electricity utilization decision of the user in one day can be obtained.
Further, a method for predicting the real-time electricity price at a future time by using an seasonal ARIMA prediction model (differential Integrated moving average Autoregressive model, also called Integrated moving average Autoregressive model) includes:
(1) firstly, reading a historical data sequence of the real-time electricity price, wherein the length of the sequence is required to be more than 120;
(2) then, stability inspection is carried out on the historical data sequence through ADF inspection (unit root inspection), if the historical data sequence is not stable, differential operation can be carried out on the historical data sequence until the data sequence after differential operation is stable;
(3) then, utilizing AIC to carry out order determination, wherein the AIC refers to an Akaike Information Criterion to determine the order of the seasonal ARIMA prediction model;
(4) and finally, establishing a seasonal ARIMA prediction model according to the determined order, predicting, and reducing the difference to obtain a required prediction result.
Further, a method for predicting the real-time electricity price at a future time by using an seasonal ARIMA prediction model (differential Integrated moving average Autoregressive model, also called Integrated moving average Autoregressive model) includes:
(1) firstly, reading a historical data sequence of the real-time electricity price, wherein the length of the sequence is required to be more than 120;
(2) then, stability inspection is carried out on the historical data sequence through ADF inspection (unit root inspection), if the historical data sequence is not stable, differential operation can be carried out on the historical data sequence until the data sequence after differential operation is stable;
(3) secondly, utilizing a BIC Criterion to carry out order determination, wherein the BIC is a Bayesian Information Criterion (Bayesian Information Criterion) so as to determine the order of the seasonal ARIMA prediction model;
(4) and finally, establishing a seasonal ARIMA prediction model according to the determined order, predicting, and reducing the difference to obtain a required prediction result.
A user energy management scheduling electronics, comprising: a processor and a memory, the memory is used for storing executable instructions of the processor, and the processor is configured to execute the user energy management scheduling method according to any one of the first to third technical aspects by executing the executable instructions.
Compared with the prior art, the invention has the following advantages: the model provided by the invention considers interruptible equipment, uninterruptable equipment and graded gear power utilization equipment which can be dispatched in a family or commercial user in a unified way based on real-time electricity price, provides reference for the demand response of the user through a seasonal ARIMA prediction model aiming at a demand side, namely a user side, and improves the benefit which can be obtained by the user through the demand response.
Drawings
Fig. 1 is an internal structure diagram of a user energy management scheduling electronic device according to the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Considering 96 periods of a day as the time window for each individual user's demand response, for convenience of explanation, 24 hours is taken as a planned optimization cycle and 15 minutes is taken as a time unit (simulation step size), that is, there is an arbitrary period
Figure BDA0002572726110000061
Step 1: the user firstly classifies and models the electric equipment, the user can classify and model the electric equipment used by the user according to a certain specific standard according to different operation characteristics of the electric equipment on time and space, and a power utilization plan of the electric equipment in one day is added into the model, and the power utilization plan comprises work tasks which must be completed by the electric equipment in one day and the time period for operation.
(1) Classification and modeling of electric devices: considering one of the simplest types of electric devices, namely, the electric device can be interrupted, due to the operating characteristics of the electric device, the electric device can be stopped at any time, and the power consumption level of the electric device is a continuous value during working. It should be noted that, the shortest time window in the present invention is 15 minutes, and the one-time continuous operation time with the shortest load is also 15 minutes.
The following models are made for this type of load and consumer: defining a set of such consumers of an individual user as
Figure BDA0002572726110000062
A certain electric device a ∈ A within a certain time period [ t [ ]1，t2]Must consume a certain amount of electric energy EaWherein
Figure BDA0002572726110000063
Comprises the following steps:
Figure BDA0002572726110000064
in the formula
Figure BDA0002572726110000065
Refers to the amount of power consumed by device a during period h.
To remove [ t ]1，t2]Outside the time period, the electric equipment does not work, namely:
Figure BDA0002572726110000066
meanwhile, the electric equipment a is specified to be in an arbitrary time interval h e [ t ∈1，t2]Energy consumption of
Figure BDA0002572726110000067
There are also lower and upper limits, namely:
Figure BDA0002572726110000068
and the total electricity consumption of the user in any period also has an upper limit, namely:
Figure BDA0002572726110000069
in the formula of loadhFor a period of h, the total electricity consumption, i.e. the total load, EmaxIs the maximum load limit.
Defining the electricity utilization decision vector of the electricity utilization equipment as
Figure BDA00025727261100000610
Taking the above into account, the following electricity usage decision sets may be defined:
Figure BDA0002572726110000071
on the basis of this, other types of electric devices can be extended: for example, the electric equipment with graded gears uses the electricity in each period
Figure BDA0002572726110000072
Is a discrete rather than continuous quantity, such as some electric devices with only two states of switch or with graded gears, typically greenhouse lamps and water heaters, etc.
The following models are made for this type of load and consumer: defining a set of such consumers of an individual user as
Figure BDA0002572726110000073
A certain electric device B e B in a certain time period t1，t2]Must consume a certain amount of electric energy EbNamely, the following steps are provided:
Figure BDA0002572726110000074
in the formula
Figure BDA0002572726110000075
Refers to the amount of power consumed by device b during the h period.
To remove [ t ]1，t2]Outside the time period, the electric equipment does not work, namely:
Figure BDA0002572726110000076
at the same time
Figure BDA0002572726110000077
Is a discrete value, i.e. having:
Figure BDA0002572726110000078
in the formula 0, L1、L2、...、LnThese values refer to the power consumption of device b in different operating ranges for a single period of time, respectively, and n represents the total number of operating ranges of device b except for the off state.
And the total electricity consumption of the user in any period also has an upper limit, namely:
Figure BDA0002572726110000079
in the formula of loadhTotal electricity consumption, i.e. total load, for users in h time period, EmaxIs the maximum load limit.
Defining the electricity utilization decision vector of the electricity utilization equipment as
Figure BDA00025727261100000710
Taking the above into account, the following electricity usage decision sets may be defined:
Figure BDA00025727261100000711
in addition, there are uninterruptible consumers, which means, constrained by the operating characteristics, that they must complete a certain sequence of operations once they start to operate, i.e. they have an uninterruptible operating characteristic, corresponding to a translatable load, typically a washing machine or a factory production machine, whose power curve can be translated only for a certain period of time.
The following models are made for this type of load and consumer: defining a set of such consumers of an individual user as
Figure BDA00025727261100000813
A certain consumer C e C has a determined power utilization curve, so that only the time period k for starting the operation of the device needs to be selected, and k e t1，t2]Defining an auxiliary binary variableMeasurement of
Figure BDA0002572726110000081
To indicate the start-up period of the device when
Figure BDA0002572726110000082
Indicating that device c is active during the h-th period, namely:
Figure BDA0002572726110000083
to remove [ t ]1，t2]Outside the time period, the electric equipment is not started, namely:
Figure BDA0002572726110000084
meanwhile, assuming that the equipment c has 1 to 3 working stages, the number of the required time periods for completing each working stage is n1、n2、n3Wherein n is2、n3May be 0. And the equipment c uses the electricity quantity in each period in the same working phase
Figure BDA0002572726110000085
Are identical, i.e.
Figure BDA0002572726110000086
Figure BDA0002572726110000087
Wherein P is1、P2、P3Respectively, the power consumption of a single time interval when the device c is in different operating phases, namely:
Figure BDA0002572726110000088
Figure BDA0002572726110000089
in formula (14)
Figure BDA00025727261100000810
And the judgment of whether the equipment c is started in the h-th time interval is shown, and if so, the corresponding subsequent equation is established.
And the total electricity consumption of the user in any period also has an upper limit, namely:
Figure BDA00025727261100000811
in the formula of loadhTotal electricity consumption, i.e. total load, for users in h time period, EmaxIs the maximum load limit.
Defining the electricity utilization decision vector of the electricity utilization equipment as
Figure BDA00025727261100000812
Taking the above into account, the following electricity usage decision sets may be defined:
Figure BDA0002572726110000091
in addition, there are some unadjustable electric appliances, such as a refrigerator or a heating system, a lighting system, a security system, a fire-fighting system, etc., which must be used all day long, and the electric appliances need not to be considered in the present invention.
Step 2: after users of different types receive the real-time electricity price once every 15 minutes, the real-time electricity price at the future moment and the self renewable energy output are predicted through a seasonal ARIMA prediction model:
(1) firstly, reading a historical data sequence of the real-time electricity price, wherein the length of the sequence is required to be more than 120;
(2) then, stability inspection is carried out on the historical data sequence through ADF inspection (unit root inspection), if the historical data sequence is not stable, differential operation can be carried out on the historical data sequence until the data sequence after differential operation is stable;
(3) and then, utilizing an AIC or BIC Criterion to carry out order determination, wherein the AIC refers to an Akaike Information Criterion and the BIC refers to a Bayesian Information Criterion so as to determine the order of the seasonal ARIMA prediction model.
(4) And finally, establishing a seasonal ARIMA prediction model according to the determined order, predicting, and reducing the difference to obtain a required prediction result.
After a required predicted value is obtained through a seasonal ARIMA prediction model, under various constraints of electrical equipment used by the device, which is classified and modeled in advance in step 1, a power utilization decision is made according to a target of minimizing power utilization cost, namely, demand response is carried out. The electricity usage decision process for an individual user at the current ith time of day can be modeled as the following objective function:
Figure BDA0002572726110000092
where Minimize represents the value of the minimization objective function,
Figure BDA0002572726110000093
is a predicted value of the real-time electricity price for the future time period h, and lhThe electric quantity, p, required by the grid for a certain h period of timehReal-time electricity price, p, for h time periodiFor the real-time electricity rate, l, of the current time period, i.e. the i time periodiThe electric quantity provided by the power grid is needed by the user in the current time period, namely the i time period
Figure BDA0002572726110000094
piEquivalent to a known quantity, solving equation (19) under the constraint of all electrical devices modeled in advance can obtain the current i time period and the future 96-i time periods of user optimized power utilization decisions, wherein the current i time period power utilization decision is actually executed. And (4) the demand response of the user in 96 time periods in one day is solved in a rolling mode, and the optimal electricity utilization decision of the user in one day is obtained.
As shown in fig. 1, the electronic device for user energy management scheduling of the present invention includes: a processor 1 and a memory 2, the memory 2 is used for storing executable instructions of the processor 1, and the processor 1 is configured to execute the user energy management scheduling method according to any one of the first to third technical aspects by executing the executable instructions.
In addition to the above embodiments, the present invention may have other embodiments, and any technical solutions formed by equivalent substitutions or equivalent transformations fall within the scope of the claims of the present invention.

Claims (4)

1. A method for managing and scheduling energy of a user, the method comprising the steps of:
step 1: classification and modeling of electrical devices: the classification of the electrical equipment is based on the difference of the operation characteristics of the electrical equipment in time and space, and the modeling of the electrical equipment comprises a daily power utilization plan of the electrical equipment;
the modeling method of the interruptible electric equipment comprises the following steps:
defining a set of interruptible electrical devices of an individual user as
Figure FDA0002572726100000011
Certain electric equipment
Figure FDA0002572726100000012
In a time period t1，t2]Internal consumption of electric energy EaWherein
Figure FDA0002572726100000013
Namely, the method comprises the following steps:
Figure FDA0002572726100000014
in the formula (1)
Figure FDA0002572726100000015
Representation pair arbitrarily belonging to a set
Figure FDA0002572726100000016
The electric equipment a of (1) is established;
Figure FDA0002572726100000017
is the amount of power consumed by device a during period h;
removal of [ t ]1，t2]Outside the time period, the electric equipment does not work, namely:
Figure FDA0002572726100000018
h96 in formula (2)
Figure FDA0002572726100000019
96 time period sets representing a day
Figure FDA00025727261000000110
Removal of [ t ]1，t2]The remaining part of the time period outside the time period,
meanwhile, the electric equipment a is specified to be in an arbitrary time interval h e [ t ∈1，t2]Energy consumption of
Figure FDA00025727261000000111
Also lower limit
Figure FDA00025727261000000112
And upper limit of
Figure FDA00025727261000000113
Namely, the method comprises the following steps:
Figure FDA00025727261000000114
in the formula (3)
Figure FDA00025727261000000115
Indicates that the pair belongs to the interval [ t1，t2]In any time period h, the formula (3) is established;
and the total electricity consumption of all the devices of the user in any period also has an upper limit, namely:
Figure FDA00025727261000000116
in the formula of loadhFor a period of h, the total electricity consumption, i.e. the total load, EmaxIs a maximum load limit;
defining the electricity utilization decision vector of the electricity utilization equipment as
Figure FDA00025727261000000117
Comprehensively considering the above situations, the following electricity utilization decision sets are defined:
Figure FDA0002572726100000021
the modeling method of the electric equipment with the graded gears comprises the following steps: defining a hierarchical set of electric devices for a certain independent user as
Figure FDA0002572726100000022
Certain electric equipment
Figure FDA0002572726100000023
In a time period t1，t2]Internal consumption of electric energy EbNamely, the following steps are provided:
Figure FDA0002572726100000024
in the formula
Figure FDA0002572726100000025
Is the electric quantity consumed by the electric equipment b in the h time period;
removal of [ t ]1，t2]Outside the time period, the electric equipment does not work, namely:
Figure FDA0002572726100000026
at the same time
Figure FDA0002572726100000027
Is a discrete value, i.e. having:
Figure FDA0002572726100000028
in the formula 0, L1、L2、...、LnRespectively the power consumption of the device b in different working gears in a single time period, wherein n represents the number of the working gears of the device b except for the power-off state;
and the total electricity consumption of the user in any period also has an upper limit, namely:
Figure FDA0002572726100000029
in the formula of loadhFor a period of h, the total electricity consumption, i.e. the total load, EmaxIs a maximum load limit;
defining the electricity utilization decision vector of the grading gear electricity utilization equipment as
Figure FDA00025727261000000210
Comprehensively considering the above situations, the following electricity utilization decision sets are defined:
Figure FDA00025727261000000211
the uninterruptible power consumer is modeled as: defining uninterruptible usage of a certain isolated userThe electrical equipment set is
Figure FDA00025727261000000212
Certain electric equipment
Figure FDA0002572726100000031
With a defined power usage curve, it is therefore only necessary to select the time period k during which the installation starts to operate, k ∈ [ t ]1，t2]Defining an auxiliary binary variable
Figure FDA0002572726100000032
To indicate the start-up period of the device when
Figure FDA0002572726100000033
The electric equipment c is started in the h period, namely:
Figure FDA0002572726100000034
removal of [ t ]1，t2]Outside the time period, the electric equipment is not started, namely:
Figure FDA0002572726100000035
meanwhile, the electric equipment c is provided with 1 to 3 working stages, and the number of the required time stages for completing each working stage is n1、n2、n3Wherein n is2、n3May be 0; and the electric equipment c uses the electricity quantity in each period in the same working phase
Figure FDA0002572726100000036
Are identical, i.e.
Figure FDA0002572726100000037
Wherein P is1、P2、P3Respectively represent the power consumption of a single time interval when the electric equipment c is in different working phases, namely:
Figure FDA0002572726100000038
Figure FDA0002572726100000039
in formula (14)
Figure FDA00025727261000000310
Whether the electric equipment c is started in the h-th time period or not is judged, and if yes, a subsequent expression in brackets is established;
the total electricity consumption of the user in any period has an upper limit, namely:
Figure FDA00025727261000000311
in the formula of loadhTotal electricity consumption, i.e. total load, for users in h time period, EmaxIs a maximum load limit;
defining a power usage decision vector for an uninterruptible power consumer as
Figure FDA00025727261000000312
Comprehensively considering the above situations, the following electricity utilization decision sets are defined:
Figure FDA0002572726100000041
step 2, setting 96 time intervals in one day as a time window for the demand response of each independent user, namely, taking 24 hours in one day as a planned optimization period, and taking 15 minutes as a time unit, namely, any time interval
Figure FDA0002572726100000042
During the h-th period, for an independent user:
(1) in the past h-1 time periods, the real-time electricity price and electricity utilization decision of each time period are determined;
(2) the real-time electricity price of the current h period is known;
(3) the real-time electricity prices of the next 96-h periods are unknown, and are predicted through a seasonal ARIMA prediction model;
(4) the electricity utilization decisions of the current h time period and the future 96-h time periods are variables needing to be determined, after a real-time electricity price predicted value is obtained through a seasonal ARIMA prediction model, under the constraint condition of used electrical equipment which is classified and modeled in advance in the step 1, the electricity utilization decisions are made according to the target of minimizing the electricity utilization cost, namely, demand response is carried out; the electricity usage decision process for an individual user at the current ith time of day can be modeled as the following objective function:
Figure FDA0002572726100000043
where Minimize represents the value of the minimization objective function,
Figure FDA0002572726100000044
is a predicted value of the real-time electricity price for the future time period h, and lhThe electric quantity, p, required by the grid for a certain h period of timehReal-time electricity price, p, for h time periodiFor the real-time electricity rate, l, of the current time period, i.e. the i time periodiThe electric quantity provided by the power grid is needed by the user in the current time period, namely the i time period
Figure FDA0002572726100000045
piEquivalent to a known quantity, solving the formula (19) under the constraint conditions of all electrical equipment modeled in advance can obtain the current i time period and the future 96-i time periods of user optimized power utilization decisions, wherein the current i time period power utilization decision is actually executed;
(5) and (4) the demand response of the user in 96 time periods in one day is solved in a rolling manner, so that the optimized electricity utilization decision of the user in one day can be obtained.
2. The user energy management scheduling method of claim 1 wherein the method of predicting the real-time electricity prices at a future time using a seasonal ARIMA prediction model is:
(1) firstly, reading a historical data sequence of the real-time electricity price, wherein the length of the sequence is required to be more than 120;
(2) then, stability inspection is carried out on the historical data sequence through ADF inspection, and if the historical data sequence is not stable, differential operation can be carried out on the historical data sequence until the data sequence after differential operation is stable;
(3) secondly, utilizing AIC to carry out order determination, wherein the AIC refers to a Chichi pool information criterion so as to determine the order of the seasonal ARIMA prediction model;
(4) and finally, establishing a seasonal ARIMA prediction model according to the determined order, predicting, and reducing the difference to obtain a required prediction result.
3. The user energy management scheduling method of claim 1 wherein the method of predicting the real-time electricity prices at a future time using a seasonal ARIMA prediction model is:
(1) firstly, reading a historical data sequence of the real-time electricity price, wherein the length of the sequence is required to be more than 120;
(2) then, stability inspection is carried out on the historical data sequence through ADF inspection, and if the historical data sequence is not stable, differential operation can be carried out on the historical data sequence until the data sequence after differential operation is stable;
(3) then, utilizing a BIC criterion to carry out order determination, wherein the BIC refers to a Bayesian information criterion to determine the order of the seasonal ARIMA prediction model;
(4) and finally, establishing a seasonal ARIMA prediction model according to the determined order, predicting, and reducing the difference to obtain a required prediction result.
4. A user energy management scheduling electronic device, comprising: a processor and a memory for storing executable instructions of the processor, the processor configured to perform the user energy management scheduling method of any of claims 1-3 via execution of the executable instructions.
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CN103400199A (en) * 2013-07-09 2013-11-20 国家电网公司 Power demand side optimization method combining market demand response with physical demand response
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