CN107609690B - Method for optimizing load active management decision - Google Patents

Method for optimizing load active management decision Download PDF

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CN107609690B
CN107609690B CN201710754645.8A CN201710754645A CN107609690B CN 107609690 B CN107609690 B CN 107609690B CN 201710754645 A CN201710754645 A CN 201710754645A CN 107609690 B CN107609690 B CN 107609690B
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future
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CN107609690A (en
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王建春
贺杰
戴晖
徐晓春
刘�东
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State Grid Jiangsu Electric Power Co Jinhu County Power Supply Co
Shanghai Jiaotong University
State Grid Corp of China SGCC
HuaiAn Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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State Grid Jiangsu Electric Power Co Jinhu County Power Supply Co
Shanghai Jiaotong University
State Grid Corp of China SGCC
HuaiAn Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention discloses a method for optimizing load active management decision, which comprises the following steps: step 1, establishing a model according to daily load historical data to predict loads; step 2, respectively obtaining power sets of the N families at m moments in twenty-four hours in the future through load prediction, and obtaining the adjustable range of the flexible power at each moment through flexible power calculation; step 3, on the premise of keeping the total power of each family unchanged within twenty-four hours in the future, adjusting the power of each N families at m moments within twenty-four hours in the future within an adjustable range, so that the difference between the peak value and the valley value of the total power of the N families at m moments within twenty-four hours in the future is minimum; or the predicted electricity prices of m moments in twenty-four hours in the future are combined, so that the electricity consumption cost of each household in twenty-four hours in the future is minimum.

Description

Method for optimizing load active management decision
Technical Field
The invention relates to the field of active power distribution networks, in particular to a method for optimizing load active management decision.
Background
With the development of social economy, the demand of power load is continuously increased, and meanwhile, the traditional energy mainly based on coal-fired thermal power faces increasingly serious problems of resource exhaustion and environmental pollution. Under the background, renewable clean energy represented by wind power and solar energy is widely regarded and rapidly developed, and is often accessed to a power grid as distributed energy, but the wide access of the distributed energy can generate adverse effects on the power distribution grid, such as changing the voltage level, improving the short-circuit capacity, increasing the relay protection complexity, influencing the power supply reliability and the like; in view of the current situation, active power distribution network technology is developed. The active power distribution network has the capabilities of controlling various distributed energy sources in a combined mode, controlling load, storing energy, managing demand side and the like, can increase the receiving capacity of the power distribution network for renewable energy sources, improve the asset utilization rate of the power distribution network, improve the power quality and the power supply reliability, and is a development mode of a future intelligent power grid.
By the active power distribution network management technology, large-scale distributed energy sources can be flexibly accessed, operation modes can be flexibly arranged, load power utilization can be flexibly arranged, and the like; the interactivity is one of important characteristics of the active power distribution network, the load side demand response has the characteristics of flexible scheduling mode and huge potential for participating in system peak shaving, and therefore the realization of the bidirectional interaction between the load and the power grid has very remarkable practical significance. The traditional power distribution network load control is limited by equipment and measurement data and is difficult to popularize all the time; in recent years, with the rapid development of new sensor technologies and communication technologies, the demand-side high-level measurement system technology applied to the active power distribution network provides more possibilities for the development of direct load control technology.
Disclosure of Invention
The invention discloses a method for optimizing load active management decision, which can actively manage and adjust various loads and realize optimization of loads or electricity charges.
The invention is realized by the following technical scheme:
a method for optimizing load active management decision comprises the following steps:
step 1, establishing a model according to daily load historical data to predict loads;
step 2, respectively obtaining power sets of the N families at m moments in twenty-four hours in the future through load prediction, and obtaining the adjustable range of the flexible power at each moment through flexible power calculation;
step 3, on the premise of keeping the total power of each family unchanged within twenty-four hours in the future, adjusting the power of each N families at m moments within twenty-four hours in the future within an adjustable range, so that the difference between the peak value and the valley value of the total power of the N families at m moments within twenty-four hours in the future is minimum;
or on the premise of keeping the total power of each household unchanged within twenty-four hours in the future, the power of each household within m moments within twenty-four hours in the future is adjusted within an adjustable range by combining the predicted power rates of the households within m moments within twenty-four hours in the future, so that the power consumption cost of each household within twenty-four hours in the future is minimum.
The invention further relates to a process for preparing the compound of formula 1
Figure DEST_PATH_IMAGE002
Representing a set of powers at m times of day x in the daily load history data, Dx = [ Dx =1，Dx2，……Dxt，……Dxm]T =1, 2 … … m, P representing the predicted set of powers at m time instants in the twenty-four hours future, a1~anRespectively and correspondingly representing the fitting coefficients of the current day to obtain a model:
P=a1D1+a2D2+……+axDx+……+anDn，
x=1，2，……，n，
s．t． a1+a2+……+ax+……+an=1；
a is to1~anAnd recording a value set as alpha, and solving the optimal alpha.
According to a further aspect of the invention, an enumeration method is used to solve the optimal alpha, a1~anHas a value range of 0 to 1, a minimum interval of 0.05, and a maximum valueThe criterion of the goodness α is the degree of closeness between the predicted value and the actual value at the first several times of P.
In a further embodiment of the invention, step 2 is performed with PiRepresents the power set of the ith household at m moments in the twenty-four hours into the future, and is represented by Pi（t）Representing the power at time t within twenty-four hours of the ith household, t =1, 2 … … m, yielding:
Pi=[ Pi（1），Pi（2），……Pi（t），……Pi（m）]，
i=1，2，3，……N；
obtaining the adjustable range of the flexible power at each moment by calculating the flexible power, and calculating the adjustable range of the flexible power at each moment by Pi（t）minRepresents the flexible power adjustable minimum value of the ith household at the t moment in twenty-four hours in the future
Figure DEST_PATH_IMAGE004
The maximum adjustable flexible power of the ith family at the tth moment in the twenty-four hours in the future is represented as follows:
Pi（t）min≤
Figure DEST_PATH_IMAGE006
≤
Figure 421067DEST_PATH_IMAGE004
，
i=1，2，3，……N；
to be provided with
Figure 210031DEST_PATH_IMAGE006
Denotes the adjusted power of the ith household at time t in the next twenty-four hours,
s.t. ∑
Figure 171034DEST_PATH_IMAGE006
*=∑
Figure 943818DEST_PATH_IMAGE006
。
further aspects of the inventionIs, step 3 with
Figure DEST_PATH_IMAGE008
Means the power set of the i-th family at m moments adjusted twenty-four hours in the future to
Figure DEST_PATH_IMAGE010
totalRepresents the total power set of the N households at m moments adjusted twenty-four hours in the future,
Figure 812417DEST_PATH_IMAGE010
total*=Σ
Figure 987046DEST_PATH_IMAGE008
a first step of; with (a)
Figure DEST_PATH_IMAGE012
）maxAnd
Figure DEST_PATH_IMAGE014
minrespectively representing the maximum power value and the minimum power value in the total power set of m moments of N households after twenty-four hours of adjustment in the future, and solving a total power peak-valley difference minimization objective function: min (, (
Figure 712426DEST_PATH_IMAGE012
）max-
Figure 972506DEST_PATH_IMAGE014
min）。
A further aspect of the present invention is to solve the total power peak-to-valley difference minimization objective function by a particle swarm algorithm, comprising:
a. k represents the total number of particles, X representsiThe position of the ith particle is represented by XijA power set representing twenty-four hours into the future of the jth family in the ith particle location yields:
Xi=[Xi1，Xi2，……，Xij，……，XiN]，
i=1，2，3，……K，
j=1，2，3，……N；
with Pij（t）Representing the power at time t of the twenty-four hours into the future of the jth family in the ith particle location, we get:
Xij=[ Pij（1），Pij（2），……Pij（t），……Pij（m）]，
t=1，2，3，……m；
b. x obtained by load predictionijRandomly adjusting the power within the adjustable range of the flexible power to obtain an adjusted power set XijX isijAnd XijRespectively summing to obtain respective total power Xi（total）=ΣXijAnd Xi（total）*=ΣXijX, if Xi（total）Less than Xi（total）Total power of (2), then randomly decimating XijThe power at a certain moment is adjusted upwards, and X cannot be balanced if the power is adjusted to the maximum value of the flexible power adjustable rangei（total）And Xi（total）Then, other time points except the time point are randomly extracted for adjustment until X is satisfiedi（total）And Xi（total）The same constraints; if Xi（total）Greater than Xi（total）Then, X is randomly extractedijThe power at a certain moment is adjusted downwards, and X cannot be balanced if the power is adjusted to the minimum value of the flexible power adjustable rangei（total）And Xi（total）Then, other time points except the time point are randomly extracted for adjustment until X is satisfiedi（total）And Xi（total）The same constraints;
c. with ViIndicates the velocity corresponding to the ith particle, in VijRepresenting the component velocity of the ith particle in the jth dimension, we get:
Vi=[Vi1，Vi2，……，Vij，……，ViN]，
i=1，2，3，……K，
j=1，2，3，……N；
with Vij（t）And the speed at the time t of the ith particle in the j dimension is represented, and the following results are obtained:
Vij=[Vi1（t），Vi2（t），……，Vij（t），……，ViN（t）]，
t=1，2，3，……m；
initializing particle speeds, namely initializing the component speeds in N dimensions and the speeds at m moments corresponding to the component speeds to be 0;
d. the position X of the ith particleiSubstituting the obtained result into an objective function to obtain an adaptive value Fit (i) of the ith particle, which is expressed as:
Fit(i)=（Xi（total）*）max-
Figure DEST_PATH_IMAGE016
min；
the smaller the adaptive value Fit (i), the more optimal the position of the ith particle is, so as to calculate the historical optimal position X searched by the particle itselfpiAnd the optimal position X searched by the whole particle swarmpg；
e. The update formula of the particle velocity and position after the (k + 1) th iteration is expressed as:
Vi k+1=ω·Vi k+ c1·（Xpi k -Xi k）+ c2η·（Xpg k -Xi k），
Xi k+1= Xi k+r·Vi k+1；
where ω is the inertial weight, c1、 c2The coefficients are respectively the historical optimal value searched by the particle tracking itself and the optimal value searched by all the particles; and η is a random number with a value between 0 and 1, and r is a position updating coefficient;
f. and performing load optimization calculation with the minimum difference between the peak value and the valley value by using a standard particle swarm algorithm to obtain the power of the load optimization at m moments in twenty-four hours in the future.
In a further aspect of the present invention, step 3 represents a set of prices at m time points of twenty-four hours in the future in price, and p represents a set of prices at m time points of twenty-four hours in the future in price（t）Representing the electricity price at time t, twenty-four hours into the future, yields:
price=[ P（1），P（2），……P（t），……P（m）]，
t=1，2，3，……m；
solving an electric charge minimization objective function: min (
Figure 113637DEST_PATH_IMAGE006
*·price）。
In a further aspect of the present invention, solving the electric charge minimization objective function includes:
a. the power P of the ith household at the t moment within twenty-four hours in the futurei（t）Arranging the electricity prices in ascending order at the t time to form a new set Pin=[Pin1，Pin2，……Pinz，……Pinm]，z=1，2，……m；
b. The power P of each timeinzAll are adjusted to the minimum value of the flexible power adjustable range, and the adjusted power set Pin*=[ Pin1（min），Pin2（min），……Pinz（min），……Pinm（min）]Then from Pin1Starting to regulate the power to the maximum value P of the flexible power adjustable rangein1（max）Up to Pin zAdjusted power Pin zIs less than Pinz（max）And then, the conditions are satisfied:
Pin1+Pin2+……+Pinm= Pin1（max）+Pin2（max）+……Pin z*+……+Pinm（min）；
c. will Pin1（max）、Pin2（max）、……Pin z*、……、Pinm（min）And rearranging according to the time sequence to obtain the power of twenty-four hours m time points in the future of the optimization of the electric charge.
The method further comprises a step 4 of obtaining the total power Q of twenty-four hours in the future of the ith family through load prediction, and calculating the carbon emission E according to the total power Q.
According to a further aspect of the present invention, the formula for calculating the carbon emission E is:
E=Q·（ηre·ere+ηfossil·efossil）；
wherein
Figure DEST_PATH_IMAGE018
Representing the proportion of new energy in the total power Q of the user,
Figure DEST_PATH_IMAGE020
the proportion of fossil energy is shown,
Figure DEST_PATH_IMAGE022
is the unit carbon emission of new energy,
Figure DEST_PATH_IMAGE024
the carbon emission of fossil energy unit; unit carbon emission of new energy
Figure 267407DEST_PATH_IMAGE022
The percentage of electric quantity occupied by various new energy sources in the total new energy sources is determined as follows:
Figure 570212DEST_PATH_IMAGE022
=Σei·ai；i=1，2，……，M；
wherein a isiRepresenting the percentage of the electricity quantity of the ith kind in the M kinds of new energy resources to the total electricity quantity of the new energy resources, eiRepresents the unit carbon emission of the i-th new energy.
Compared with the prior art, the invention has the advantages that:
load optimization is carried out aiming at two aspects of peak clipping and valley filling of total load in a region and household electricity economy of each household; firstly, model abstraction is carried out on load prediction, an enumeration method is used for realizing the model abstraction, then a relevant mathematical model is established for load optimization based on minimum peak-valley load difference, a particle swarm algorithm is applied to solve the nonlinear optimization problem of an objective function, a corresponding mathematical model is also established for load optimization based on electricity economy, a certain optimization strategy is adopted for solving, and the method has strong adaptability;
secondly, the carbon emission is defined and quantitatively analyzed, and the method has great significance for the development of a green power grid;
and thirdly, the application is wide, and the social benefit and the economic benefit are remarkable.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a flow chart of step 1 of the present invention.
Fig. 3 is a graph fitted with a twenty-four hour future power set of the user obtained by load prediction in the embodiment.
Fig. 4 is a flow chart of total power peak-to-valley difference minimization.
FIG. 5 is a flexible power adjustable range curve diagram of a curve fitted to a twenty-four hour future power set of a user in an embodiment.
FIG. 6 is a flow chart for solving a total power peak-to-valley difference minimization objective function with a particle swarm optimization.
FIG. 7 is a graph comparing twenty-four hours of total power of all users in the future before and after load optimization in the example.
FIG. 8 is a graph of the electricity prices fitted to the set of electricity prices at the time of twenty-four hours m in the future in the example.
FIG. 9 is a comparison graph before and after the minimization of the electricity charges in the examples.
Fig. 10 is a graph obtained by fitting the power sets before and after minimizing the electric charge in the embodiment.
Detailed Description
A method for optimizing load active management decision as shown in fig. 1 includes the following steps:
step 1, as shown in fig. 2, establishing a model according to daily load historical data to predict load;
to be provided with
Figure 786430DEST_PATH_IMAGE002
Representing a set of powers at 24 times of day x in the daily load history data, Dx = [ Dx =1，Dx2，……Dxt，……Dx24]T =1, 2 … … 24, P denotes the predicted set of powers at 24 instants in the twenty-four hours future, a1~anRespectively and correspondingly representing the fitting coefficients of the current day to obtain a model:
P=a1D1+a2D2+……+axDx+……+anDn，
x=1，2，……，n，
s．t． a1+a2+……+ax+……+an=1；
a is to1~anThe value set is marked as alpha, and the optimal alpha, a is solved by adopting an enumeration method1~anThe value range of (a) is 0 to 1, the minimum interval is 0.05, the judgment standard of the optimal alpha is the degree of closeness between the predicted value and the actual value of the P at the first two moments, and the optimal alpha solution is obtained and then substituted into the model, so that the power set of twenty-four hours in the future shown in fig. 3 is obtained through prediction.
Step 2 as shown in fig. 4, through load prediction, power sets of 10 households at 24 moments in the twenty-four hours in the future are respectively obtained, and the power sets are calculated according to PiRepresents the power set of the ith household at 24 moments in the twenty-four hours into the future, in Pi（t）Representing the power at time t within twenty-four hours of the ith household, t =1, 2 … … 24, yielding:
Pi=[ Pi（1），Pi（2），……Pi（t），……Pi（24）]，
i=1，2，3，……N；
the intelligent user terminal obtains the adjustable range of the flexible power at each moment as shown in fig. 5 through the flexible power calculation, and the adjustable range is represented by Pi（t）minRepresents the flexible power adjustable minimum value of the ith household at the t moment in twenty-four hours in the future
Figure 200094DEST_PATH_IMAGE004
The maximum adjustable flexible power of the ith family at the tth moment in the twenty-four hours in the future is represented as follows:
Pi（t）min≤
Figure 880474DEST_PATH_IMAGE006
≤
Figure 354180DEST_PATH_IMAGE004
，
i=1，2，3，……N；
step 3, the power of each time on the power curve of each household in the twenty-four hours in the future can be adjusted up and down within the adjustable range of the flexible power, but in consideration of the power consumption demand of the user, on the premise of keeping the total power of each household in the twenty-four hours in the future unchanged, the power of each household in 10 times within the twenty-four hours in the future is adjusted within the adjustable range, namely: to be provided with
Figure 323273DEST_PATH_IMAGE006
Denotes the adjusted power of the ith household at time t in the next twenty-four hours,
s.t. ∑
Figure 275049DEST_PATH_IMAGE006
*=∑
Figure 278777DEST_PATH_IMAGE006
。
the difference between the peak and valley of the total power of 10 households over the next twenty-four hours at 24 times is minimized, namely: to be provided with
Figure 923385DEST_PATH_IMAGE008
Represents the power set of the i-th family at 24 moments adjusted twenty-four hours in the future
Figure 379774DEST_PATH_IMAGE010
totalRepresents the aggregate of the total power of 10 households over the next 24 hours after twenty-four hours of adjustment,
Figure 135241DEST_PATH_IMAGE010
total*=Σ
Figure 462317DEST_PATH_IMAGE008
a first step of; with (a)
Figure 277826DEST_PATH_IMAGE012
）maxAnd
Figure 221511DEST_PATH_IMAGE014
minrespectively representing the maximum power value and the minimum power value in a total power set of 24 moments of 10 families after twenty-four hours of adjustment in the future, and solving a total power peak-valley difference minimization objective function by a particle swarm algorithm: min (, (
Figure 780668DEST_PATH_IMAGE012
）max-
Figure 493410DEST_PATH_IMAGE014
min) As shown in fig. 6, includes:
a. k represents the total number of particles, X representsiThe position of the ith particle is represented by XijA power set representing twenty-four hours into the future of the jth family in the ith particle location yields:
Xi=[Xi1，Xi2，……，Xij，……，XiN]，
i=1，2，3，……K，
j=1，2，3，……N；
with Pij（t）Representing the power at time t of the twenty-four hours into the future of the jth family in the ith particle location, we get:
Xij=[ Pij（1），Pij（2），……Pij（t），……Pij（24）]，
t=1，2，3，……24；
b. x obtained by load predictionijRandomly adjusting the power within the adjustable range of the flexible power to obtain an adjusted power set XijX isijAnd XijRespectively summing to obtain respective total power Xi（total）=ΣXijAnd Xi（total）*=ΣXijX, if Xi（total）Less than Xi（total）Total power of (2), then randomly decimating XijThe power at a certain moment is adjusted upwards, and X cannot be balanced if the power is adjusted to the maximum value of the flexible power adjustable rangei（total）And Xi（total）Then, other time points except the time point are randomly extracted for adjustment until X is satisfiedi（total）And Xi（total）The same constraints; if Xi（total）Greater than Xi（total）Then, X is randomly extractedijThe power at a certain moment is adjusted downwards, and X cannot be balanced if the power is adjusted to the minimum value of the flexible power adjustable rangei（total）And Xi（total）Then, other time points except the time point are randomly extracted for adjustment until X is satisfiedi（total）And Xi（total）The same constraints;
c. with ViIndicates the velocity corresponding to the ith particle, in VijRepresenting the component velocity of the ith particle in the jth dimension, we get:
Vi=[Vi1，Vi2，……，Vij，……，ViN]，
i=1，2，3，……K，
j=1，2，3，……N；
with Vij（t）And the speed at the time t of the ith particle in the j dimension is represented, and the following results are obtained:
Vij=[Vi1（t），Vi2（t），……，Vij（t），……，ViN（t）]，
t=1，2，3，……24；
initializing particle speeds, namely initializing the component speeds in N dimensions and the speeds at 24 moments corresponding to the component speeds to be 0;
d. the position X of the ith particleiSubstituting the obtained result into an objective function to obtain an adaptive value Fit (i) of the ith particle, which is expressed as:
Fit(i)=（Xi（total）*）max-
Figure 10979DEST_PATH_IMAGE016
min；
the smaller the adaptive value Fit (i), the more optimal the position of the ith particle is, so as to calculate the historical optimal position X searched by the particle itselfpiAnd the optimal position X searched by the whole particle swarmpg；
e. The update formula of the particle velocity and position after the (k + 1) th iteration is expressed as:
Vi k+1=ω·Vi k+ c1·（Xpi k -Xi k）+ c2η·（Xpg k -Xi k），
Xi k+1= Xi k+r·Vi k+1；
where ω is the inertial weight, c1、 c2The coefficients are respectively the historical optimal value searched by the particle tracking itself and the optimal value searched by all the particles; and η is a random number with a value between 0 and 1, and r is a position updating coefficient;
f. and (3) carrying out load optimization calculation with the minimum difference between the peak value and the valley value by using a standard particle swarm algorithm to obtain the power of 24 moments in the twenty-four hours in the future of load optimization shown in fig. 7, wherein the peak-valley difference of the total load of the 10 families before optimization is 14.26kW, and the peak-valley difference after optimization is 8.63kW, so that the reduction is 39.5%.
The power distribution network main station can obtain the power rates of 24 moments in twenty-four hours in the future through power rate prediction calculation and sends the power rates to the upper-layer system for load active management. On the premise of keeping the total power of each household unchanged within twenty-four hours in the future, the method combines the pre-power of 24 moments within twenty-four hours in the futureAnd measuring the electricity price, and adjusting the power of each of 10 families in an adjustable range at 24 moments in twenty-four hours in the future to minimize the electricity cost of each family in the twenty-four hours in the future, namely: as shown in FIG. 8, the set of electricity prices at 24 moments twenty-four hours in the future is represented by price, and p is used（t）Representing the electricity price at time t, twenty-four hours into the future, yields:
price=[ P（1），P（2），……P（t），……P（24）]，
t=1，2，3，……24；
solving an electric charge minimization objective function: min (
Figure 441960DEST_PATH_IMAGE006
Price) comprising:
a. the power P of the ith household at the t moment within twenty-four hours in the futurei（t）Arranging the electricity prices in ascending order at the t time to form a new set Pin=[Pin1，Pin2，……Pinz，……Pin24]，z=1，2，……24；
b. The power P of each timeinzAll are adjusted to the minimum value of the flexible power adjustable range, and the adjusted power set Pin*=[ Pin1（min），Pin2（min），……Pinz（min），……Pin24（min）]Then from Pin1Starting to regulate the power to the maximum value P of the flexible power adjustable rangein1（max）Up to Pin zAdjusted power Pin zIs less than Pinz（max）Then, as shown in fig. 9, the condition:
Pin1+Pin2+……+Pin24= Pin1（max）+Pin2（max）+……Pin z*+……+Pin24（min）；
c. will Pin1（max）、Pin2（max）、……Pin z*、……、Pin24（min）Rearrangement in time sequence yields the electric charge optimized future twenty-four as shown in FIG. 10The power of 24 hours, before optimization, the electricity consumption cost of the A user in the next twenty-four hours is 20.38 yuan, and after optimization, the electricity consumption cost is 18.48, which is reduced by 9.3%.
Step 4, obtaining the total power Q of twenty-four hours in the future of the ith family through load prediction, and calculating the carbon emission E according to the total power Q, wherein the calculation formula is as follows:
E=Q·（ηre·ere+ηfossil·efossil）；
wherein
Figure 539229DEST_PATH_IMAGE018
Representing the proportion of new energy in the total power Q of the user,
Figure 575318DEST_PATH_IMAGE020
the proportion of fossil energy is shown,
Figure 287226DEST_PATH_IMAGE022
is the unit carbon emission of new energy,
Figure 205503DEST_PATH_IMAGE024
the carbon emission of fossil energy unit; unit carbon emission of new energy
Figure 106463DEST_PATH_IMAGE022
The percentage of electric quantity occupied by various new energy sources in the total new energy sources is determined as follows:
Figure 528217DEST_PATH_IMAGE022
=Σei·ai；i=1，2，……，24；
wherein a isiRepresenting the percentage of the electricity quantity of the ith kind in the 24 kinds of new energy resources to the total electricity quantity of the new energy resources, eiRepresents the unit carbon emission of the i-th new energy.

Claims (6)

1. A method for optimizing load active management decision is characterized by comprising the following steps:
step 1, establishing a model according to daily load historical data to predict loads;
step 2, respectively obtaining power sets of the N families at m moments in twenty-four hours in the future through load prediction, and obtaining the adjustable range of the flexible power at each moment through flexible power calculation;
step 3, on the premise of keeping the total power of each family unchanged within twenty-four hours in the future, adjusting the power of each N families at m moments within twenty-four hours in the future within an adjustable range, so that the difference between the peak value and the valley value of the total power of the N families at m moments within twenty-four hours in the future is minimum;
or on the premise of keeping the total power of each family unchanged within twenty-four hours in the future, the power of each N families at m moments within twenty-four hours in the future is adjusted within an adjustable range by combining the predicted power rates at m moments within twenty-four hours in the future, so that the power consumption cost of each family within twenty-four hours in the future is minimum;
step 3 with PiRepresents the power set of the i-th family at m moments adjusted twenty-four hours in the future, and is expressed by PtotalRepresents the total power set of the N households at m moments adjusted twenty-four hours in the future, Ptotal*=ΣPiA first step of; with (P)total*）maxAnd (P)total*）minRespectively representing the maximum power value and the minimum power value in the total power set of m moments of N households after twenty-four hours of adjustment in the future, and solving a total power peak-valley difference minimization objective function: min ((P)total*）max-（Ptotal*）min）；
Solving a total power peak-valley difference minimization objective function by a particle swarm algorithm, wherein the total power peak-valley difference minimization objective function comprises the following steps:
a. k represents the total number of particles, X representsiThe position of the ith particle is represented by XijA power set representing twenty-four hours into the future of the jth family in the ith particle location yields:
Xi=[Xi1，Xi2，……，Xij，……，XiN]，
i=1，2，3，……K，
j=1，2，3，……N；
with Pij（t）Representing the power at time t of the twenty-four hours into the future of the jth family in the ith particle location, we get:
Xij=[ Pij（1），Pij（2），……Pij（t），……Pij（m）]，
t=1，2，3，……m；
b. x obtained by load predictionijRandomly adjusting the power within the adjustable range of the flexible power to obtain an adjusted power set XijX isijAnd XijRespectively summing to obtain respective total power Xi（total）=ΣXijAnd Xi（total）*=ΣXijX, if Xi（total）Less than Xi（total）Total power of (2), then randomly decimating XijThe power at a certain moment is adjusted upwards, and X cannot be balanced if the power is adjusted to the maximum value of the flexible power adjustable rangei（total）And Xi（total）Then, other time points except the time point are randomly extracted for adjustment until X is satisfiedi（total）And Xi（total）The same constraints; if Xi（total）Greater than Xi（total）Then, X is randomly extractedijThe power at a certain moment is adjusted downwards, and X cannot be balanced if the power is adjusted to the minimum value of the flexible power adjustable rangei（total）And Xi（total）Then, other time points except the time point are randomly extracted for adjustment until X is satisfiedi（total）And Xi（total）The same constraints;
c. with ViIndicates the velocity corresponding to the ith particle, in VijRepresenting the component velocity of the ith particle in the jth dimension, we get:
Vi=[Vi1，Vi2，……，Vij，……，ViN]，
i=1，2，3，……K，
j=1，2，3，……N；
with Vij（t）And the speed at the time t of the ith particle in the j dimension is represented, and the following results are obtained:
Vij=[Vi1（t），Vi2（t），……，Vij（t），……，ViN（t）]，
t=1，2，3，……m；
initializing particle speeds, namely initializing the component speeds in N dimensions and the speeds at m moments corresponding to the component speeds to be 0;
d. the position X of the ith particleiSubstituting the obtained result into an objective function to obtain an adaptive value Fit (i) of the ith particle, which is expressed as:
Fit(i)=（Xi（total）*）max-（Xi（total）*）min；
the smaller the adaptive value Fit (i), the more optimal the position of the ith particle is, so as to calculate the historical optimal position X searched by the particle itselfpiAnd the optimal position X searched by the whole particle swarmpg；
e. The update formula of the particle velocity and position after the (k + 1) th iteration is expressed as:
Vi k+1=ω·Vi k+ c1·（Xpi k -Xi k）+ c2η·（Xpg k -Xi k），
Xi k+1= Xi k+r·Vi k+1；
where ω is the inertial weight, c1、 c2The coefficients are respectively the historical optimal value searched by the particle tracking itself and the optimal value searched by all the particles; and η is a random number with a value between 0 and 1, and r is a position updating coefficient;
f. carrying out load optimization calculation with minimum difference between peak value and valley value by using a standard particle swarm algorithm to obtain the power of the load optimization at m moments in twenty-four hours in the future;
step 3, expressing the set of electricity prices at m moments in twenty-four hours in the future by price, and expressing the set of electricity prices at p（t）Representing the electricity price at time t, twenty-four hours into the future, yields:
price=[ P（1），P（2），……P（t），……P（m）]，
t=1，2，3，……m；
solving an electric charge minimization objective function: min (P)i（t）*·price）；
Solving the electricity charge minimization objective function includes:
a. the power P of the ith household at the t moment within twenty-four hours in the futurei（t）Arranging the electricity prices in ascending order at the t time to form a new set Pin=[Pin1，Pin2，……Pinz，……Pinm]，z=1，2，……m；
b. The power P of each timeinzAll are adjusted to the minimum value of the flexible power adjustable range, and the adjusted power set Pin*=[ Pin1（min），Pin2（min），……Pinz（min），……Pinm（min）]Then from Pin1Starting to regulate the power to the maximum value P of the flexible power adjustable rangein1（max）Up to PinzAdjusted power PinzIs less than Pinz（max）And then, the conditions are satisfied:
Pin1+Pin2+……+Pinm= Pin1（max）+Pin2（max）+……Pinz*+……+Pinm（min）；
c. will Pin1（max）、Pin2（max）、……Pinz*、……、Pinm（min）And rearranging according to the time sequence to obtain the power of twenty-four hours m time points in the future of the optimization of the electric charge.
2. The method of claim 1, wherein the load proactive management decision optimization comprises: step 1 with
Figure 859509DEST_PATH_IMAGE001
Indicating daily loadThe power set of m time instants of the x-th day in the historical data, then Dx = [ Dx =1，Dx2，……Dxt，……Dxm]T =1, 2 … … m, P representing the predicted set of powers at m time instants in the twenty-four hours future, a1~anRespectively and correspondingly representing the fitting coefficients of the current day to obtain a model:
P=a1D1+a2D2+……+axDx+……+anDn，
x=1，2，……，n，
s．t． a1+a2+……+ax+……+an=1；
a is to1~anAnd recording a value set as alpha, and solving the optimal alpha.
3. The method of claim 2, wherein the load proactive management decision optimization comprises: solving the optimal alpha, a by adopting an enumeration method1~anThe value range of (a) is 0 to 1, the minimum interval is 0.05, and the judgment standard of the optimal alpha is the degree of closeness between the predicted value and the actual value of P at the previous moments.
4. The method of claim 1, wherein the load proactive management decision optimization comprises: step 3 with PiRepresents the power set of the ith household at m moments in the twenty-four hours into the future, and is represented by Pi（t）Representing the power at time t within twenty-four hours of the ith household, t =1, 2 … … m, yielding:
Pi=[ Pi（1），Pi（2），……Pi（t），……Pi（m）]，
i=1，2，3，……N；
obtaining the adjustable range of the flexible power at each moment by calculating the flexible power, and calculating the adjustable range of the flexible power at each moment by Pi（t）minRepresents the flexible power adjustable minimum value of the ith household at the t moment in twenty-four hours in the future, and is Pi（t）maxThe maximum adjustable flexible power of the ith family at the tth moment in the twenty-four hours in the future is represented as follows:
Pi（t）min≤Pi（t）≤Pi（t）max，
i=1，2，3，……N；
with Pi（t）Denotes the adjusted power of the ith household at time t in the next twenty-four hours,
s.t. ∑Pi（t）*=∑Pi（t）。
5. the method of claim 1, wherein the load proactive management decision optimization comprises: and 4, obtaining the total power Q of twenty-four hours in the future of the ith family through load prediction, and calculating the carbon emission E according to the total power Q.
6. The method of claim 5, wherein the load proactive management decision optimization comprises: the formula for calculating the carbon emission E is as follows:
E=Q·（ηre·ere+ηfossil·efossil）；
wherein etareRepresents the proportion of new energy in the total power Q of the user, etafossilRepresenting the proportion of fossil energy, ereIs the unit carbon emission of new energy, efossilThe carbon emission of fossil energy unit; unit carbon emission e of new energyreThe percentage of electric quantity occupied by various new energy sources in the total new energy sources is determined as follows:
ere =Σei·ai；i=1，2，……，M；
wherein a isiRepresenting the percentage of the electricity quantity of the ith kind in the M kinds of new energy resources to the total electricity quantity of the new energy resources, eiRepresents the unit carbon emission of the i-th new energy.
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