CN111563831A - Source network load storage cooperative scheduling method for ubiquitous power Internet of things - Google Patents

Source network load storage cooperative scheduling method for ubiquitous power Internet of things Download PDF

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CN111563831A
CN111563831A CN202010420608.5A CN202010420608A CN111563831A CN 111563831 A CN111563831 A CN 111563831A CN 202010420608 A CN202010420608 A CN 202010420608A CN 111563831 A CN111563831 A CN 111563831A
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李鑫
王锡龙
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Nanjing University of Aeronautics and Astronautics
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Abstract

The invention discloses a source network load storage cooperative scheduling method for a ubiquitous power Internet of things, which comprises the following steps: s1, acquiring all node information in the distribution network of the transformer area; s2, obtaining PDG、PDES、PSC、PCLCurrent value and value range array lb [4 ]]、ub[4](ii) a S3, executing an optimization algorithm according to the objective function to obtain an optimal scheduling result; s4, according to the optimal scheduling result, adopting the corresponding resource allocation strategy to DG [ n ]]、DES[n]、SC[n]、CL[n]Carrying out adjustment; and S5, descending the obtained power value to be adjusted by each node to all nodes, and operating each node according to the specified power value when the node reaches the next operation period. The invention can be used for ubiquitous electricityThe power distribution network under the power internet of things carries out power dispatching, makes full use of convenience of data and communication brought to the power distribution network under the ubiquitous power internet of things, and the collaborative dispatching becomes simpler, and meanwhile, the construction requirement of the ubiquitous power internet of things is well met.

Description

Source network load storage cooperative scheduling method for ubiquitous power Internet of things
Technical Field
The invention belongs to the technical field of power grid power dispatching, and particularly relates to a source grid load storage cooperative dispatching method for a ubiquitous power Internet of things.
Background
In 2019, a conference held by national grid company Limited indicates that the construction of ubiquitous power Internet of things is an important content and key link for promoting the construction of three types of networks and two networks, and simultaneously, a ubiquitous power Internet of things white paper 2019 is published. The target and the development direction of the ubiquitous power internet of things are clearly put forward in the book, namely, the ubiquitous power internet of things is built to internally realize 'one source of data, one graph of a power grid and one line of business', the internal and external and upstream and downstream resources and requirements are externally and widely connected, and an energy internet ecosphere is created.
In a traditional power distribution network, source network load and storage cooperative scheduling means that power, a power grid, load and energy storage are subjected to multiple interaction means, so that the power dynamic balance capability of a power system is improved more economically, efficiently and safely, and the maximum utilization of energy resources is realized. Along with the increasing environmental pollution and energy crisis problems, new energy power generation, electric vehicles, micro-grids and other technologies are developed rapidly. Therefore, a large number of active load nodes appear in the power distribution network, the traditional one-way power flow structure and the voltage level of the power distribution network are changed, the short circuit capacity of the power distribution network is improved, and the relay protection strategy complexity of the power distribution network is increased. The problems of insufficient renewable energy consumption capability, weak primary grid structure, low automation level, backward scheduling mode, low power utilization interaction level and the like of the traditional power distribution network are caused, the large-scale application of renewable energy is severely restricted, and the optimization and adjustment of an energy structure are not facilitated and the opening of a power market is promoted. The traditional scheduling means for realizing system balance by tracking load through power generation and adjusting the running state of the system through power generation control cannot meet the requirement of safe and economic running of a power distribution network. Therefore, by utilizing the communication and calculation capabilities provided by the ubiquitous power internet of things and fully utilizing the situation awareness technology of the smart power grid, the research on solving the series of problems by aiming at the source grid load-storage cooperative scheduling of the ubiquitous power internet of things becomes significant.
Therefore, in order to solve the technical problems, a source grid load-storage cooperative scheduling method for the ubiquitous power internet of things is needed.
Disclosure of Invention
The invention aims to provide a source network load storage cooperative scheduling method for a ubiquitous power Internet of things, so as to meet the construction requirement for the ubiquitous power Internet of things.
In order to achieve the above object, an embodiment of the present invention provides the following technical solutions:
a source network load-storage cooperative scheduling method for a ubiquitous power Internet of things (IOT), the method comprising the following steps:
s1, acquiring all node information in the distribution network of the transformer area, wherein the node information comprises the current power P of the transmission networkGThe power sum P of all feeder line losses of the station area in the next operation periodLOSSActive power P of the conventional load of the next operating cycleLOADAnd the distributed power supply DG output condition DG [ n ] of the next operation period]DES [ n ] state of distributed energy storage device DES in next operating cycle]Output plan SC n of flexible load SC of next operation period]Controllable range CL [ n ] of controllable load CL of next operation period]；
S2, obtaining PDG、PDES、PSC、PCLCurrent value and value range array lb [4 ]]、ub[4]Wherein P isDG、PDES、PSC、PCLRespectively the power of a distributed power supply DG, a distributed energy storage device DES, a flexible load SC and a controllable load CL;
s3, executing an optimization algorithm according to the objective function to obtain an optimal scheduling result;
s4, adjusting DG [ n ], DES [ n ], SC [ n ] and CL [ n ] by adopting corresponding resource allocation strategies according to the optimal scheduling result;
and S5, descending the obtained power value to be adjusted by each node to all nodes, and operating each node according to the specified power value when the node reaches the next operation period.
In one embodiment, the objective function in step S2 is:
Figure BDA0002496845030000021
wherein, min is less than or equal to gi(x) Max i ≤ 0,1,2,3 is PDG、PDES、PSC、PCLIs in the range of aboutRestraint with lb [4 ]]、ub[4]That is, h (x) is 0, i.e., power balance PDES+PSC+PCL+PLOAD＝PDG+PG-PLOSSThe objective function f (x) ═ PGnew+PDGnew-PGnow-PDGnow)2Table now indicates the current, table new indicates the next operating cycle;
in one embodiment, n of DG [ n ], DES [ n ], SC [ n ], and CL [ n ] in step S1 represents an array length, and n may be equal or different.
In one embodiment, in step S1:
DG[n]represents the set of nodes for all distributed power supplies, each DG i]By Type, PDGmin、PDGmax、PDGThe description is carried out; wherein, Type is 0, 1; when the Type is 0, the DG is represented as a photovoltaic or wind power generation node, PDGThe predicted value of the output is taken as the predicted value; when Type is 1, it means DG is another new energy device, and has adjustable power, PDGmin、PDGmax represents the upper and lower limits of its output;
DES[n]set of nodes representing all distributed energy storage devices, each DES [ i ]]By SoC, ξ, Q, Pcharmax、Pdismax, where SoC takes-1, 0,1, respectively, indicating the current state of the energy storage device, -1 indicating charging only, 0 indicating charging and discharging, 1 indicating discharging only, ξ -Q/Qc indicating the remaining capacity of the device, Qc indicating the full capacity of the device, and Pcharmax、Pdismax is the power limit of the energy storage device when charging and discharging, respectively;
SC[n]set of nodes representing all flexible loads, each SC i]By State, PSCmin、PSCmax, where State indicates whether the user wants to discharge, and is set to-1, 0, 1; the user does not want to discharge when State is-1, PSCmin、PSCmax represents the upper and lower limits of the charging power which the charging power is willing to accept, and the upper and lower limits are calculated according to the actual situation; when State is 1, it indicates that the user is willing to participate in discharging and has enough power, PSCmin、PSCmax represents its adjustable range upon discharge; when State is 0, it indicates that the parameter cannot be usedAnd discharging but willing to transfer the charging time, PSCmin、PSCmax represents a chargeable power regulation range;
CL[n]set of nodes representing all controllable loads, each CL [ i ] -I]By PC、PCmin is described, wherein, PCRepresents its power level, PCmin represents its adjustable limit value.
In one embodiment, in step S2:
PDG、PDES、PSC、PCLthe initial value is the array DG [ n ]]、DES[n]、SC[n]、CL[n]The sum of the current powers in (1);
array lb [4 ]]、ub[4]P is expressed in the order of DG, DES, SC, CLDG、PDES、PSC、PCLUpper and lower limits of power regulation of (1), wherein PDES、PSCThere are positive and negative values, with positive values indicating consumption and negative values indicating release.
In one embodiment, in step S2:
for PDES, the calculation of the lower limit of the value is obtained by adding the DES [ i ] lower limits of all SoC-1, 0, and the upper limit is obtained by adding the DES [ i ] upper limits of all SoC-1, 0;
for PSCThe lower limit of the value is set by SC [ i ] of all State ═ 1,1]The lower limits are summed up and the upper limit is set by SC [ i ] with all State 1,0]The upper limit is added.
In one embodiment, the optimization algorithm in step S3 is an algorithm with f (x) as an objective function.
In an embodiment, the optimization algorithm in step S3 is a simulated annealing method, a genetic algorithm, an ant colony algorithm, or a particle swarm algorithm.
In an embodiment, the step S4 specifically includes:
for DG [ n ]]Suppose that there are m (n ═ m) photovoltaic generators and wind turbine generators, and for DGType＝0i, outputting power according to the maximum power of the node, and distributing the rest n-m other nodes according to the ratio of the adjustable power to the sum of the adjustable power of all the n-m nodes;
for DES [ n ]]When P isDESWhen the pressure is higher than 0, energy storage is requiredCharging, if PDES-∑DESSoc＝-1If max is less than 0, charging all the rechargeable energy storage devices with average power; if P isDES-∑DESSoc＝-1max is greater than 0, all nodes of SoC-1 are charged with maximum power, the rest part distributes power to nodes of SoC part according to the equal ratio of ξ, the smaller ξ is, the larger charging power is, when P isDESWhen the absolute value is less than or equal to 0, discharging is required, if | PDES|-∑DESSoc＝1If max is less than 0, setting discharge power for all energy storage devices which can only discharge with average power; if | PDES|-∑DESSoc＝1If max is greater than 0, all nodes with SoC being 1 are discharged at the maximum power, the rest of the nodes are distributed with power to the nodes of the SoC part according to the equal ratio of ξ, and the larger the ξ is, the larger the discharge power is;
for SC [ n ]]When P isSCWhen the power is more than 0, the SC part needs to consume power; first, P is judgedSC-∑SCState＝-1If the value of min is greater than 0, the charging pile part does not need node discharge, and P is continuously judgedSC-∑SCState＝-1If the value of max is smaller than 0, a power value is averagely distributed to all nodes with the State-1, if the value of max is larger than 0, the size of residual power is represented by delta, which indicates that more charging piles are needed to be charged to consume redundant electric quantity, and at the moment, delta is distributed to the charging piles with the State-0 according to an array sequence until all charging piles are completely distributed; if P isSC-∑SCstate＝-1If min is less than 0, it indicates that a node with State-1 needs to release part of electric energy to meet the minimum requirement, the power which is lacked at the moment is uniformly distributed to charging piles with State-1 according to the capacity of being able to output power to participate in discharging, and the node with State-1 is distributed with the lower limit value; when P is presentSCWhen the State is less than or equal to 0, the SC is required to discharge, the discharging power should be the residual value after the State-1 part of the nodes meet the minimum charging power of the State-1 node, the node with the State-1 is charged with the lower limit value, for the node with the State-1, if j nodes exist, the power is evenly distributed to be
Figure BDA0002496845030000051
For CL [ n ]]Each knotPoint should be assigned PCL*(PC-Pmin)/∑(PC-Pmin) I.e. evenly distributed according to the controllable capacity.
Compared with the prior art, the invention has the following advantages:
the method can be used for power dispatching of the power distribution network under the ubiquitous power Internet of things, makes full use of convenience of data and communication brought to the power distribution network under the ubiquitous power Internet of things, is simpler in cooperative dispatching, well meets the construction requirements of the ubiquitous power Internet of things, and can better support upper-layer power grid service application and manage the power electronic equipment at the bottom layer as a dispatching means of a platform layer;
compared with some scheduling means under the energy Internet, the method has the advantages that the considered factors are more comprehensive, and more problems can be solved by managing all controllable resources of the whole distribution area.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a diagram illustrating an operating architecture of a distribution network in a distribution area according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a source network load-storage cooperative scheduling method for a ubiquitous power internet of things in an embodiment of the present invention;
FIG. 3 is a pseudo-code diagram of an optimization algorithm simulated annealing algorithm used in one embodiment of the present invention;
FIG. 4 is a flow chart of a DES resource allocation strategy in accordance with an embodiment of the present invention;
fig. 5 is a flowchart of a resource allocation policy of an SC according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to embodiments shown in the drawings. The embodiments are not intended to limit the present invention, and structural, methodological, or functional changes made by those skilled in the art according to the embodiments are included in the scope of the present invention.
Referring to fig. 1, in an embodiment of the present invention, it is assumed that a power distribution network in a certain area is structured, and the source, the network, the load, and the storage are divided into a plurality of nodes for management. In the distribution system of the distribution area, the construction requirements of the ubiquitous power Internet of things are met, corresponding intelligent devices are arranged at all nodes, have certain calculation and communication capabilities, and meanwhile, the power electronic devices in the area where the nodes are located can be controlled according to instructions. Therefore, the intelligent equipment can collect and upload all data which are interested by people to the power distribution node, meanwhile, the power distribution node is communicated with the central cloud platform, partial prediction information and a scheduling plan are obtained, and finally, source network load storage cooperative scheduling is implemented.
The general operation mechanism of the whole power distribution network is as follows: a day is divided into a plurality of operation periods, scheduling is carried out before each operation period comes, power distribution nodes carry out collaborative scheduling optimization through situation perception technology and communication and calculation convenience brought by ubiquitous power Internet of things by combining the load condition and scheduling plan of each current node and relevant technologies such as load and output prediction, and the like, so that the optimization target is achieved. The invention relates to a source network load-storage cooperative scheduling means designed with the purposes of reducing extra loss of power grid operation and peak clipping and valley filling.
With reference to fig. 2, the source network load-storage cooperative scheduling method for the ubiquitous power internet of things in the embodiment includes the following steps:
and S1, acquiring all node information in the distribution network of the transformer area.
Wherein the node information comprises the current power P of the power transmission networkGThe power sum P of all feeder line losses of the station area in the next operation periodLOSSActive power P of the conventional load of the next operating cycleLOADAnd the distributed power supply DG output condition DG [ n ] of the next operation period]DES [ n ] state of distributed energy storage device DES in next operating cycle]The next transportationOutput plan SC [ n ] of flexible load SC of line period]Controllable range CL [ n ] of controllable load CL of next operation period]，DG[n]、DES[n]、SC[n]、CL[n]N in (1) represents the array length, and n may be equal or different.
The DG [ n ], DES [ n ], SC [ n ], and CL [ n ] arrays in the present invention will be described in detail below.
Distributed Power supply (DG) means photovoltaic power generation, wind power generator, fuel cell, etc., DG n]Represents the set of nodes for all distributed power supplies, each DG i]By Type, PDGmin、PDGmax、PDGThe description is carried out; wherein, Type is 0, 1; when the Type is 0, the DG is represented as a photovoltaic or wind power generation node, PDGThe predicted value of the output is taken as the predicted value; when Type is 1, it means DG is another new energy device, and has adjustable power, PDGmin、PDGmax represents the upper and lower limits of its output;
distributed Energy Storage (DES) refers to the set of all energy storage devices, DES [ n ]]Set of nodes representing all distributed energy storage devices, each DES [ i ]]By SoC, ξ, Q, Pcharmax、Pdismax, where SoC takes-1, 0,1, respectively, indicating the current state of the energy storage device, -1 indicating charging only, 0 indicating charging and discharging, 1 indicating discharging only, ξ -Q/Qc indicating the remaining capacity of the device, Qc indicating the full capacity of the device, and Pcharmax、Pdismax is the power limit of the energy storage device when charging and discharging, respectively;
flexible loads (SC) represent those situations where a user who can both charge and discharge a particular load, such as an electric vehicle on a charging pile, needs to combine the flexible load at that moment provided by the cloud center with his willingness to charge and discharge. SC [ n ]]Set of nodes representing all flexible loads (several partitions of charging piles in a platform area), each SC i]By State, PSCmin、PSCmax, where State indicates whether the user wants to discharge, and is set to-1, 0, 1; the user does not want to discharge when State is-1, PSCmin、PSCmax represents the upper and lower limits of the charging power which the charging power is willing to accept, and the upper and lower limits are calculated according to the actual situation; when State is 1, it indicates that the user is willing to participate in discharging and has enough powerAmount, PSCmin、PSCmax represents its adjustable range upon discharge; when State is 0, P represents a desired transfer of the charging time for failing to participate in dischargingSCmin、PSCmax represents a chargeable power regulation range;
controllable Loads (CL) mean those loads whose power can be controlled to a certain degree of hierarchy in the distribution network, CL n]Set of nodes representing all controllable loads, each CL [ i ] -I]By PC、PCmin is described, wherein, PCRepresents its power level, PCmin represents its adjustable limit value.
S2, processing the data to obtain parameters and formats required by the optimization algorithm, specifically:
obtaining PDG、PDES、PSC、PCLCurrent value and value range array lb [4 ]]、ub[4]Wherein P isDG、PDES、PSC、PCLThe power of the distributed power supply DG, the distributed energy storage equipment DES, the flexible load SC and the controllable load CL are respectively.
Wherein, PDG、PDES、PSC、PCLThe initial value is the array DG [ n ]]、DES[n]、SC[n]、CL[n]The sum of the current powers in (1);
array lb [4 ]]、ub[4]P is expressed in the order of DG, DES, SC, CLDG、PDES、PSC、PCLUpper and lower limits of power regulation of (1), wherein PDES、PSCThere are positive and negative values, with positive values indicating consumption and negative values indicating release.
For PDESThe lower limit of the value is calculated by all DES [ i ] of which SoC is-1, 0]The lower limit is added, and the upper limit is obtained by DES [ i ] of all SoC 1,0]The upper limit is added;
for PSCThe lower limit of the value is set by SC [ i ] of all State ═ 1,1]The lower limits are summed up and the upper limit is set by SC [ i ] with all State 1,0]The upper limit is added.
And S3, executing an optimization algorithm according to the objective function to obtain an optimal scheduling result.
In particular, the optimization can be viewed as an objective function:
Figure BDA0002496845030000081
wherein, min is less than or equal to gi(x) Max i ≤ 0,1,2,3 is PDG、PDES、PSC、PCLUsing lb [4 ] as constraint]、ub[4]That is, h (x) is 0, i.e., power balance PDES+PSC+PCL+PLOAD＝PDG+PG-PLOSSThe objective function f (x) ═ PGnew+PDGnew-PGnow-PDGnow)2Table now indicates the current and table new indicates the next operating cycle.
The optimization, that is, the quadratic programming problem of the hybrid constraint, may be solved by adopting a heuristic simulated annealing method, a genetic algorithm, an ant colony algorithm, a particle swarm algorithm, and the like, in this embodiment, an improved version simulated annealing algorithm for the problem is adopted, as shown in fig. 3, and finally, an optimized P is obtainedDG、PDES、PSC、PCLThe value is obtained.
S4, according to the optimal scheduling result, adjusting DG [ n ], DES [ n ], SC [ n ], CL [ n ] by adopting the corresponding resource allocation strategy.
The specific adjustment strategy in this embodiment is:
for DG [ n ]]Suppose that the photovoltaic generator and the wind turbine have m (n)>M) for DGType＝0i, outputting power according to the maximum power of the node, and distributing the rest n-m other nodes according to the ratio of the adjustable power to the sum of the adjustable power of all the n-m nodes, wherein the uniform distribution according to the capacity is beneficial to prolonging the service life of the equipment;
for DES [ n ]]The adjustment strategy is shown in FIG. 4 when PDESWhen the voltage is higher than 0, the energy storage charging is needed, and the time is possibly in the load valley period, if P isDES-∑DESSoc＝-1If max is less than 0, charging all the rechargeable energy storage devices with average power; if P isDES-∑DESsoc＝-1max is greater than 0, all nodes of SoC-1 are charged with maximum power, and the rest is charged with maximum powerThe power is distributed to the nodes of the SoC part according to the equal ratio of ξ, the smaller the ξ is, the larger the charging power is, when P isDESWhen the voltage is less than or equal to 0, the discharge is needed, and the load peak period is possible, if | PDES|-∑DESSoc＝1If max is less than 0, setting discharge power for all energy storage devices which can only discharge with average power; if | PDES|-∑DESSoc＝1If max is greater than 0, all nodes with SoC being 1 are discharged at the maximum power, the rest of the nodes are distributed with power to the nodes of the SoC part according to the equal ratio of ξ, and the larger the ξ is, the larger the discharge power is;
for SC [ n ]]The adjustment strategy is shown in FIG. 5 when PSCWhen the power is more than 0, the SC part needs to consume power; first, P is judgedSC-∑SCState＝-1If the value of min is greater than 0, the charging pile part does not need node discharge, and P is continuously judgedSC-∑SCState＝-1If the value of max is smaller than 0, a power value is averagely distributed to all nodes with the State-1, if the value of max is larger than 0, the size of residual power is represented by delta, which indicates that more charging piles are needed to be charged to consume redundant electric quantity, and at the moment, delta is distributed to the charging piles with the State-0 according to an array sequence until all charging piles are completely distributed; if P isSC-∑SCState＝-1If min is less than 0, it indicates that a node with State-1 needs to release part of electric energy to meet the minimum requirement, the power which is lacked at the moment is uniformly distributed to charging piles with State-1 according to the capacity of being able to output power to participate in discharging, and the node with State-1 is distributed with the lower limit value; when P is presentSCWhen the State is less than or equal to 0, the SC is required to discharge, the discharging power should be the residual value after the State-1 part of the nodes meet the minimum charging power of the State-1 node, the node with the State-1 is charged with the lower limit value, for the node with the State-1, if j nodes exist, the power is evenly distributed to be
Figure BDA0002496845030000091
For CL [ n ]]Each node should be assigned PCL*(PC-Pmin)/∑(PC-Pmin) I.e. evenly distributed according to the controllable capacity.
And S5, descending the obtained power value to be regulated by each node to all nodes, operating each node according to the specified power value when the node reaches the next operation period, and waiting for next scheduling by the power distribution node.
According to the technical scheme, the invention has the following beneficial effects:
the method can be used for power dispatching of the power distribution network under the ubiquitous power Internet of things, makes full use of convenience of data and communication brought to the power distribution network under the ubiquitous power Internet of things, is simpler in cooperative dispatching, well meets the construction requirements of the ubiquitous power Internet of things, and can better support upper-layer power grid service application and manage the power electronic equipment at the bottom layer as a dispatching means of a platform layer;
compared with some scheduling means under the energy Internet, the method has the advantages that the considered factors are more comprehensive, and more problems can be solved by managing all controllable resources of the whole distribution area.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.

Claims (9)

1. A source network load storage cooperative scheduling method for a ubiquitous power Internet of things is characterized by comprising the following steps:
s1, acquiring all node information in the distribution network of the transformer area, wherein the node information comprises the current power P of the transmission networkGThe power sum P of all feeder line losses of the station area in the next operation periodLOSSActive power P of the conventional load of the next operating cycleLOADAnd the distributed power supply DG output condition DG [ n ] of the next operation period]DES [ n ] state of distributed energy storage device DES in next operating cycle]Output plan SC n of flexible load SC of next operation period]Controllable range CL [ n ] of controllable load CL of next operation period]；
S2, obtaining PDG、PDES、PSC、PCLCurrent value and value range array lb [4 ]]、ub[4]Wherein P isDG、PDES、PSC、PCLRespectively the power of a distributed power supply DG, a distributed energy storage device DES, a flexible load SC and a controllable load CL;
s3, executing an optimization algorithm according to the objective function to obtain an optimal scheduling result;
s4, adjusting DG [ n ], DES [ n ], SC [ n ] and CL [ n ] by adopting corresponding resource allocation strategies according to the optimal scheduling result;
and S5, descending the obtained power value to be adjusted by each node to all nodes, and operating each node according to the specified power value when the node reaches the next operation period.
2. The source grid load-storage cooperative scheduling method for the ubiquitous power internet of things according to claim 1, wherein the objective function in the step S2 is as follows:
Figure FDA0002496845020000011
wherein, min is less than or equal to gi(x) Not more than maxi is 0,1,2,3 is PDG、PDES、PSC、PCLUsing lb [4 ] as constraint]、ub[4]That is, h (x) is 0, i.e., power balance PDES+PSC+PCL+PLOAD＝PDG+PG-PLOSSThe objective function f (x) ═ PGnew+PDGnew-PGnow-PDGnow)2Table now indicates the current and table new indicates the next operating cycle.
3. The source network load-storage cooperative scheduling method for the ubiquitous power internet of things as claimed in claim 1, wherein n of DG [ n ], DES [ n ], SC [ n ], and CL [ n ] in step S1 represents an array length, and n may be equal or different.
4. The source grid load-storage cooperative scheduling method for the ubiquitous power internet of things according to claim 2, wherein in the step S1:
DG[n]represents the set of nodes for all distributed power supplies, each DG i]By Type, PDGmin、PDGmax、PDGThe description is carried out; wherein, Type is 0, 1; when the Type is 0, the DG is represented as a photovoltaic or wind power generation node, PDGThe predicted value of the output is taken as the predicted value; when Type is 1, it means DG is another new energy device, and has adjustable power, PDGmin、PDGmax represents the upper and lower limits of its output;
DES[n]set of nodes representing all distributed energy storage devices, each DES [ i ]]By SoC, ξ, Q, Pcharmax、Pdismax, where SoC takes-1, 0,1, respectively, indicating the current state of the energy storage device, -1 indicating charging only, 0 indicating charging and discharging, 1 indicating discharging only, ξ -Q/Qc indicating the remaining capacity of the device, Qc indicating the full capacity of the device, and Pcharmax、Pdismax is the power limit of the energy storage device when charging and discharging, respectively;
SC[n]set of nodes representing all flexible loads, each SC i]By State, PSCmin、PSCmax, where State indicates whether the user wants to discharge, and is set to-1, 0, 1; the user does not want to discharge when State is-1, PSCmin、PSCmax represents the upper and lower limits of charging power which is willing to be accepted by the battery, and is calculated according to actual conditionsKnowing; when State is 1, it indicates that the user is willing to participate in discharging and has enough power, PSCmin、PSCmax represents its adjustable range upon discharge; when State is 0, P represents a desired transfer of the charging time for failing to participate in dischargingSCmin、PSCmax represents a chargeable power regulation range;
CL[n]set of nodes representing all controllable loads, each CL [ i ] -I]By PC、PCmin is described, wherein, PCRepresents its power level, PCmin represents its adjustable limit value.
5. The source grid load-storage cooperative scheduling method for the ubiquitous power internet of things according to claim 4, wherein in the step S2:
PDG、PDES、PSC、PCLthe initial value is the array DG [ n ]]、DES[n]、SC[n]、CL[n]The sum of the current powers in (1);
array lb [4 ]]、ub[4]P is expressed in the order of DG, DES, SC, CLDG、PDES、PSC、PCLUpper and lower limits of power regulation of (1), wherein PDES、PSCThere are positive and negative values, with positive values indicating consumption and negative values indicating release.
6. The source grid load-storage cooperative scheduling method for the ubiquitous power internet of things according to claim 5, wherein in the step S2:
for PDESThe lower limit of the value is calculated by all DES [ i ] of which SoC is-1, 0]The lower limit is added, and the upper limit is obtained by DES [ i ] of all SoC 1,0]The upper limit is added;
for PSCThe lower limit of the value is set by SC [ i ] of all State ═ 1,1]The lower limits are summed up and the upper limit is set by SC [ i ] with all State 1,0]The upper limit is added.
7. The source grid storage cooperative scheduling method for the ubiquitous power internet of things as claimed in claim 1, wherein the optimization algorithm in the step S3 is an algorithm with f (x) as an objective function.
8. The source grid and storage cooperative scheduling method for the ubiquitous power internet of things according to claim 7, wherein the optimization algorithm in the step S3 is a simulated annealing method, a genetic algorithm, an ant colony algorithm or a particle swarm algorithm.
9. The source grid load-storage cooperative scheduling method for the ubiquitous power internet of things according to claim 1, wherein the step S4 specifically comprises:
for DG [ n ]]Suppose that the photovoltaic generator and the wind turbine have m (n)>M) for DGType＝0i, outputting power according to the maximum power of the node, and distributing the rest n-m other nodes according to the ratio of the adjustable power to the sum of the adjustable power of all the n-m nodes;
for DES [ n ]]When P isDESWhen the voltage is more than 0, energy storage charging is required, if P isDES-∑DESSoc＝-1If max is less than 0, charging all the rechargeable energy storage devices with average power; if P isDES-∑DESSoc＝-1max is greater than 0, all nodes of SoC-1 are charged with maximum power, the rest part distributes power to nodes of SoC part according to the equal ratio of ξ, the smaller ξ is, the larger charging power is, when P isDESWhen the absolute value is less than or equal to 0, discharging is required, if | PDES|-∑DESSoc＝1If max is less than 0, setting discharge power for all energy storage devices which can only discharge with average power; if | PDES|-∑DESSoc＝1If max is greater than 0, all nodes with SoC being 1 are discharged at the maximum power, the rest of the nodes are distributed with power to the nodes of the SoC part according to the equal ratio of ξ, and the larger the ξ is, the larger the discharge power is;
for SC [ n ]]When P isSCWhen the power is more than 0, the SC part needs to consume power; first, P is judgedSC-∑SCState＝-1If the value of min is greater than 0, the charging pile part does not need node discharge, and P is continuously judgedSC-∑SCState＝-1max, if less than 0, distributing power value to all nodes whose State is-1, if greater than 0, using deltaThe residual power is shown, that is, more charging piles are needed to be charged to consume redundant electric quantity, and then the charging upper limit value of the charging pile is distributed to the charging piles with the states of 0 according to the array sequence by delta until all charging piles are completely distributed; if P isSC-∑SCState＝-1If min is less than 0, it indicates that a node with State-1 needs to release part of electric energy to meet the minimum requirement, the power which is lacked at the moment is uniformly distributed to charging piles with State-1 according to the capacity of being able to output power to participate in discharging, and the node with State-1 is distributed with the lower limit value; when P is presentSCWhen the State is less than or equal to 0, the SC is required to discharge, the discharging power should be the residual value after the State-1 part of the nodes meet the minimum charging power of the State-1 node, the node with the State-1 is charged with the lower limit value, for the node with the State-1, if j nodes exist, the power is evenly distributed to be
Figure FDA0002496845020000041
For CL [ n ]]Each node should be assigned PCL*(PC-Pmin)/∑(PC-Pmin) I.e. evenly distributed according to the controllable capacity.
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