CN112821451B - Urban power distribution network photovoltaic access response method based on demand side management and energy storage - Google Patents
Urban power distribution network photovoltaic access response method based on demand side management and energy storage Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
- H02J2300/26—The renewable source being solar energy of photovoltaic origin involving maximum power point tracking control for photovoltaic sources
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
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Abstract
The invention provides a distributed photovoltaic access response method of an intelligent town power distribution network based on demand side management and energy storage, which comprises the following steps: step S1: modeling is respectively carried out on the time-sharing electricity price, the load which can be reduced and the translational load in the demand side management; step S2: modeling is carried out respectively for service life characteristics and operation characteristics in the energy storage technology; step S3: establishing an intelligent town power distribution network operation optimization model considering safety and stability constraints; step S4: converting the optimization model in the step S1 into a condition risk value for minimizing photovoltaic waste light; step S5: based on the clustering process, the photovoltaic output mass scenes are reduced, a typical scene is generated, and the typical scene is used as an input variable of a power distribution network operation optimization model; step S6: and (3) performing second-order cone relaxation on the power flow equation of the power distribution network, converting the optimization problem into a mixed integer second-order cone planning problem, and solving. The photovoltaic power generation system can solve the problem of safe and stable operation of a power system caused by the randomness, intermittence and fluctuation of the photovoltaic power generation.
Description
Technical Field
The invention relates to the technical field of intelligent town power distribution network operation, in particular to a distributed photovoltaic access response system of an intelligent town power distribution network based on demand side management and energy storage.
Background
In recent years, along with the continuous improvement of the photovoltaic permeability in the smart town power distribution network, the randomness, intermittence and fluctuation of the photovoltaic output bring great challenges to the safe and stable operation of the power system, and adverse effects such as node voltage out-of-limit, line tide overload, overlarge system network loss and the like can be caused, so that the safety and the economy of the smart town power distribution network are weakened. After the high-proportion distributed photovoltaic is accessed, the complicated space-time distribution characteristic of the high-proportion distributed photovoltaic further increases the regulation difficulty. The intelligent energy management system is different from the traditional power distribution network, intelligent measurement units, communication links, energy management systems, data processing centers and the like widely existing in the intelligent town power distribution network, the positive effects of the Internet of things technology, cloud computing technology, big data technology and spatial geographic information integration technology in the construction of the intelligent town can be fully exerted, and the clean energy utilization, the quality improvement and the efficiency improvement and the green intensive town construction are promoted.
Under the support of the information technology, the adverse effect brought by the high-proportion distributed photovoltaic access intelligent town power distribution network can be dealt with by optimizing an energy storage output scheme and a demand side management scheme. The prior researches mainly deal with the adverse effects through battery energy storage and demand side management, wherein the battery energy storage achieves the effects of peak clipping, valley filling and standby provision through energy storage and energy release processes, and the demand side management can fully utilize the responsive resources of users to promote the energy utilization. Under the high-proportion distributed photovoltaic access background, the potential of battery energy storage and demand side management in the smart town power distribution network in the aspect of coping with the adverse effects is fully excavated, the distributed photovoltaic digestion is further promoted on the premise of ensuring the safe and stable operation of the system, the scientific problem to be solved is urgent, and the system has great theoretical research value and engineering practice significance.
Disclosure of Invention
In view of the above, the invention aims to provide a distributed photovoltaic access response system of an intelligent town power distribution network based on demand side management and energy storage, which can solve the problem of safe and stable operation of a power system caused by randomness, intermittence and fluctuation of photovoltaic output.
The invention is realized by adopting the following scheme: a distributed photovoltaic access response method for an intelligent town power distribution network based on demand side management and energy storage comprises the following steps:
step S1: the demand side management is used as a distributed photovoltaic access coping means, and modeling is conducted on time-of-use electricity price, load reduction and translational load in the demand side management respectively;
step S2: using an energy storage battery as a distributed photovoltaic access coping means to respectively model service life characteristics and operation characteristics in an energy storage technology;
step S3: aiming at photovoltaic absorption, establishing an intelligent town power distribution network operation optimization model considering safety and stability constraints;
step S4: introducing a conditional risk value theory, describing tail risk brought by uncertainty of photovoltaic output to operation of the power distribution network in the smart town, and converting the optimization model in the step S1 into a conditional risk value for minimizing photovoltaic waste light;
step S5: based on the clustering process, the photovoltaic output mass scenes are reduced, a typical scene is generated, and the typical scene is used as an input variable of a power distribution network operation optimization model;
step S6: and (3) performing second-order cone relaxation on the power flow equation of the power distribution network by using a cone optimization method, and converting the optimization problem into a mixed integer second-order cone planning problem to solve.
Further, in the step S1, modeling for the time-of-use electricity price, the reducible load, and the translatable load in the demand side management is specifically:
the electricity price in the time-sharing electricity price can be expressed as a piecewise function related to time, and the formula is as follows:
wherein t is a period number; c (C) t The electricity price is t time period; t (T) valley 、T norm 、T peak Respectively a set of time periods at valley, at ordinary times and at peak; c (C) valley 、C norm 、C peak The electricity prices at valley time, at ordinary times and at peak time are constants;
the load-reducible means that the load is partially or completely reduced by the available and demand conditions during operation, and the upper and lower limit constraints are satisfied when the load-reducible is managed on the demand side;
translatable load means that the load is subjected to a certain period of time on the basis of ensuring the continuity after load translationThe whole translation is carried out, and the specific modeling process is as follows: setting a scheduling period to include T time periods, and for the working duration to be T D A translatable load L of a certain period of time shift The active and reactive power of each period of its operating duration may be:
wherein P is shift 、Q shift A row vector formed by active power and reactive power of each period in sequence in the working duration;
active and reactive power for a t-th time period within the operating duration; record translatable load L shift The acceptable shift interval is +.>
Then L is shift Possible position S of the start period shift The method comprises the following steps:
on the basis, a flag bit vector lambda representing the load shift condition of the initial period is defined, and the dimension is +.>
All elements in the vector are 0-1 variables; after shifting the load, the translatable section S shift All possible load conditions within can be expressed as:
in order to ensure the continuity after load translation, the power of the load translated at the moment t of the scene s is calculated as follows:
t∈S shift the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->
Active and reactive load vectors within the translatable interval; lambda is a flag bit vector representing the initial load shift condition; />
A matrix is formed for all possible load situations after translation.
Further, in step S2, the modeling for the lifetime characteristics in the energy storage technology specifically includes:
the cycle life of the battery stored energy is expressed as:
wherein L is cyc The cycle life of energy storage for the battery;
the energy storage life loss of the battery for single simulation; t (T) yr 1 year; t (T) sim Duration of a single simulation; n (N) C The number of charge and discharge cycles of the battery energy storage for single simulation is determined by adopting a rain flow counting method; d, d j The discharge depth of the jth charge-discharge cycle; g (d) j ) Maximum charge-discharge cycle times at a given depth of discharge for battery energy storage; thus, use L ba ＝min{L cyc ,L cal Calculating the service life of the battery energy storage, wherein L ba The service life of the battery is prolonged; l (L) cal Calendar for storing energy for batteryAnd (5) service life.
Further, in step S2, the modeling for the operation characteristic in the energy storage technology specifically includes:
according to the relation between power and energy and the mutual exclusion characteristic of charge and discharge, the operation characteristic of the battery energy storage is expressed as:
E BES.min ≤E BES ≤E BES.max ；
γ BES ∈{0,1}；
E BES (t s )＝E BES (t e )；
wherein t is the time; Δt is the time interval of adjacent moments; t is t s And t e Respectively starting and ending moments of a scheduling period; e (E) BES Stored energy for battery energy storage; e (E) BES.min And E is BES.max Respectively the upper and lower limits of energy; p (P) ch And P dis Respectively charging and discharging power;
and->
Respectively the upper limit of charge and discharge power; η (eta) ch And eta dis Respectively charging and discharging efficiency; gamma ray BES Is an energy storage charge and discharge zone bit, is a variable of 0-1, and is gamma BES =1 denotes charge, γ BES =0 denotes discharge.
Further, in step S3, a smart town power distribution network operation optimization model is established with photovoltaic absorption as a target, and the model constraint conditions include: load flow constraint, node voltage constraint, branch power constraint, battery energy storage constraint, load shedding and load translation constraint.
Further, in step S4, the condition risk value theory is introduced, which describes the tail risk caused by the uncertainty of the photovoltaic output on the operation of the power distribution network in the smart town, and converts the optimization model in step S1 into the condition risk value for minimizing the photovoltaic waste. The objective function of its final model is: minCVaR (DeltaP) PV ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein DeltaP PV Is the photovoltaic light rejection amount; CVaR (DeltaP) PV ) For its corresponding conditional risk value.
Further, the clustering process in step S5 adopts a K-means clustering method.
Further, the clustering process in step S5 specifically includes the following processes:
firstly, according to the preset clustering quantity K, K data objects are obtained from a data set X through random selection, and are respectively used as clustering centers w of K-class data 1 ,w 2 ,...w k ...,w K The method comprises the steps of carrying out a first treatment on the surface of the Second, for each data x i Respectively calculating the clustering centers w 1 ,w 2 ,...w k ...,w K According to the formula
Calculating a cluster center minimizing Euclidean distance to obtain the class of the data, wherein c i For data x i Class of which ||x is a category i -w k || 2 For data x i To the kth cluster center w k Is a Euclidean distance of (2); then, for each type of data, according to the formula +.>
Recalculating the clustering center of the data, wherein u k S is a new cluster center k The number of data divided into k classes in the second step; finally, judging whether the algorithm converges or not,and outputting the newly generated clustering center as a final clustering result if the clustering center is converged, and returning to Euclidean distance calculation if the clustering center is not converged.
Further, in step S6, the second order cone relaxation is performed on the power flow equation of the power distribution network by using a cone optimization method, and the optimization problem is converted into a mixed integer second order cone planning problem to solve the problem specifically as follows:
according to the flow equation of the smart town power distribution network, introducing an intermediate variable
And->
The original prescription Cheng Huawei->
Relaxing the second order cone of the equation to obtain a standard second order cone form
In U i Representing the voltage at node i;
representing the square of the voltage at node i; />
Representing the square of the current of branch ij; />
Representing the head end active power of the branch ij; />
Representing the head-end reactive power of the branch ij; r is R ij The resistance value of the branch ij; x is X ij Representing the reactance value of the branch ij.
The invention also provides a distributed photovoltaic access response system of the smart town power distribution network based on demand side management and energy storage, which comprises a memory, a processor and computer program instructions stored on the memory and capable of being executed by the processor, wherein the computer program instructions can realize the method steps as described above when the processor executes the computer program instructions.
Compared with the prior art, the invention has the following beneficial effects: the invention can further promote the distributed photovoltaic absorption on the premise of ensuring the safe and stable operation of the system, and has theoretical research value and engineering practice significance.
Drawings
Fig. 1 is a diagram of an improved IEEE 33 node system for use with an embodiment of the present invention.
Fig. 2 is a schematic diagram of a photovoltaic time sequence output according to an embodiment of the present invention.
Fig. 3 shows a node 2 timing active load according to an embodiment of the present invention.
Fig. 4 is a timing reactive load of node 2 according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of active power output of a transformer substation according to an embodiment of the present invention.
Fig. 6 illustrates photovoltaic digestion in various exemplary scenarios within a typical day according to an embodiment of the present invention.
Fig. 7 shows a charging and discharging process of the solar cell energy storage according to the embodiment of the invention.
Fig. 8 is a schematic diagram of photovoltaic light rejection amount comparison under different scenes according to an embodiment of the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
As shown in fig. 1, the embodiment provides a distributed photovoltaic access response method for a smart town power distribution network based on demand side management and energy storage, which includes the following steps:
step S1: the demand side management is used as a distributed photovoltaic access coping means, and modeling is conducted on time-of-use electricity price, load reduction and translational load in the demand side management respectively;
step S2: using an energy storage battery as a distributed photovoltaic access coping means to respectively model service life characteristics and operation characteristics in an energy storage technology;
step S3: aiming at photovoltaic absorption, establishing an intelligent town power distribution network operation optimization model considering safety and stability constraints;
step S4: introducing a conditional risk value theory, describing tail risk brought by uncertainty of photovoltaic output to operation of the power distribution network in the smart town, and converting the optimization model in the step S1 into a conditional risk value for minimizing photovoltaic waste light;
step S5: based on the clustering process, the photovoltaic output mass scenes are reduced, a typical scene is generated, and the typical scene is used as an input variable of a power distribution network operation optimization model;
step S6: and (3) performing second-order cone relaxation on the power flow equation of the power distribution network by using a cone optimization method, and converting the optimization problem into a mixed integer second-order cone planning problem to solve.
In this embodiment, in the step S1, modeling for the time-of-use electricity price, the reducible load, and the translatable load in the demand side management is specifically:
the electricity price in the time-sharing electricity price can be expressed as a piecewise function related to time, and the formula is as follows:
wherein t is a period number; c (C) t The electricity price is t time period; t (T) valley 、T norm 、T peak Respectively a set of time periods at valley, at ordinary times and at peak; c (C) valley 、C norm 、C peak The electricity prices at valley time, at ordinary times and at peak time are constants;
the load-reducible means that the load is partially or completely reduced by the available and demand conditions during operation, and the upper and lower limit constraints are satisfied when the load-reducible is managed on the demand side;
the translatable load is to translate the load integrally within a certain time period on the basis of ensuring the continuity of the translated load, and the specific modeling process is as follows: setting a scheduling period to include T time periods, and for the working duration to be T D A translatable load L of a certain period of time shift The active and reactive power of each period of its operating duration may be:
wherein P is shift 、Q shift A row vector formed by active power and reactive power of each period in sequence in the working duration;
active and reactive power for a t-th time period within the operating duration; record translatable load L shift The acceptable shift interval is +.>
Then L is shift Possible position S of the start period shift The method comprises the following steps:
on the basis, definition represents the starting timeFlag bit vector lambda for segment load shift case, dimension +.>
All elements in the vector are 0-1 variables; after shifting the load, the translatable section S shift All possible load conditions within can be expressed as:
in order to ensure the continuity after load translation, the power of the load translated at the moment t of the scene s is calculated as follows:
in (1) the->
Active and reactive load vectors within the translatable interval; lambda is a flag bit vector representing the initial load shift condition; />
A matrix is formed for all possible load situations after translation.
In this embodiment, in step S2, the modeling for the lifetime characteristics in the energy storage technology specifically includes:
the cycle life of the battery stored energy is expressed as:
wherein L is cyc The cycle life of energy storage for the battery;
the energy storage life loss of the battery for single simulation; t (T) yr 1 year; t (T) sim Duration of a single simulation; n (N) C The number of charge and discharge cycles of the battery energy storage for single simulation is determined by adopting a rain flow counting method; d, d j The discharge depth of the jth charge-discharge cycle; g (d) j ) Maximum charge-discharge cycle times at a given depth of discharge for battery energy storage; thus, use L ba ＝min{L cyc ,L cal Calculating the service life of the battery energy storage, wherein L ba The service life of the battery is prolonged; l (L) cal The calendar life is stored for the battery.
In this embodiment, in step S2, the modeling for the operation characteristic in the energy storage technology specifically includes:
according to the relation between power and energy and the mutual exclusion characteristic of charge and discharge, the operation characteristic of the battery energy storage is expressed as:
E BES.min ≤E BES ≤E BES.max ；
γ BES ∈{0,1}；
E BES (t s )＝E BES (t e )；
wherein t is the time; Δt is the time interval of adjacent moments; t is t s And t e Respectively starting and ending moments of a scheduling period; e (E) BES Stored energy for battery energy storage; e (E) BES.min And E is BES.max Respectively the upper and lower limits of energy; p (P) ch And P dis Respectively isCharging and discharging power;
and->
Respectively the upper limit of charge and discharge power; η (eta) ch And eta dis Respectively charging and discharging efficiency; gamma ray BES Is an energy storage charge and discharge zone bit, is a variable of 0-1, and is gamma BES =1 denotes charge, γ BES =0 denotes discharge.
In this embodiment, in step S3, the smart town power distribution network operation optimization model is built with photovoltaic absorption as a target, and the model constraint conditions include: load flow constraint, node voltage constraint, branch power constraint, battery energy storage constraint, load shedding and load translation constraint.
Wherein, distribution network trend constraint represents:
wherein s and t are respectively a scene number and a time number; i. j and k are node numbers; α (j) is a set of head end nodes of a branch ending in node j; beta (j) is the end node set of the branch with node j as the start; p (P) ij 、Q ij Active and reactive power for the flow through branch ij; r is (r) ij 、x ij The resistance and reactance of the branch ij are shown, and U is the node voltage;
for the net active power and net reactive power injected into node j, the expressions are:
in the method, in the process of the invention,
the node j is connected with active power and reactive power of the photovoltaic at a scene s moment t; />
The corresponding light-rejecting active power and reactive power; />
The node j is connected with the active power and the reactive power of the generator at the scene s moment t; />
The node j is connected with the active power and the reactive power of the energy storage at the moment t of the scene s; />
Charging active power and reactive power correspondingly; />
Active and reactive load for node j at time t of scene s.
The node load active power expression and the node load reactive power expression are respectively as follows:
in the method, in the process of the invention,
basic active and reactive loads at time t for node j at scene s; />
Load reduction is carried out correspondingly; />
Reducing the amount of the corresponding load; />
For a corresponding translational afterload.
Wherein, the branch power constraint may be expressed as: p (P) ij,min ≤P ij ≤P ij,max ；Q ij,min ≤Q ij ≤Q ij,max . P in the formula ij,min 、P ij,max For the upper and lower limits of active power of branch ij, Q ij,min 、Q ij,max Is the upper and lower reactive power limit of branch ij.
Wherein the node voltage power constraint can be expressed as: u (U) i,min ≤U i ≤U i,max . U in i,min 、U i,max Is the upper and lower voltage limits of node i.
In this embodiment, in step S4, the condition risk value theory is introduced to describe the tail risk caused by the uncertainty of the photovoltaic output on the operation of the smart town power distribution network, and the optimizing model in step S1 is converted into the condition risk value for minimizing the photovoltaic waste, which specifically includes: according to the conditional risk correlation theory, the distribution function with the loss function f (x, y) not greater than the boundary value α is:
wherein x is a decision variable and y isRandom variables, ρ (y), are probability density functions of y. Based on the calculation formula, the conditional risk value CVaR is as follows: />
The tail risk brought by uncertainty of high-proportion photovoltaic output is brought into an operation optimization model, and the final model objective function is as follows: minCVaR (DeltaP) PV ) In DeltaP PV Is the photovoltaic light rejection amount; CVaR (DeltaP) PV ) For its corresponding conditional risk value.
In this embodiment, the clustering process in step S5 employs a K-means clustering method.
In this embodiment, the clustering process in step S5 specifically includes the following processes:
firstly, according to the preset clustering quantity K, K data objects are obtained from a data set X through random selection, and are respectively used as clustering centers w of K-class data 1 ,w 2 ,...w k ...,w K The method comprises the steps of carrying out a first treatment on the surface of the Second, for each data x i Respectively calculating the clustering centers w 1 ,w 2 ,...w k ...,w K According to the formula
Calculating a cluster center minimizing Euclidean distance to obtain the class of the data, wherein c i For data x i Class of which ||x is a category i -w k || 2 For data x i To the kth cluster center w k Is a Euclidean distance of (2); then, for each type of data, according to the formula +.>
Recalculating the clustering center of the data, wherein u k S is a new cluster center k The number of data divided into k classes in the second step; and finally, judging whether the algorithm is converged, outputting a newly generated clustering center as a final clustering result if the algorithm is converged, and returning to Euclidean distance calculation if the algorithm is not converged.
In this embodiment, in step S6, the second order cone relaxation is performed on the power flow equation of the power distribution network by using a cone optimization method, and the optimization problem is converted into a mixed integer second order cone planning problem to solve the problem specifically as follows:
according to the flow equation of the smart town power distribution network, introducing an intermediate variable
And->
The original prescription Cheng Huawei->
Relaxing the second order cone of the equation to obtain a standard second order cone form
In U i Representing the voltage at node i;
representing the square of the voltage at node i; />
Representing the square of the current of branch ij; />
Representing the head end active power of the branch ij; />
Representing the head-end reactive power of the branch ij; r is R ij The resistance value of the branch ij; x is X ij Representing the reactance value of the branch ij.
The embodiment also provides a smart town power distribution network distributed photovoltaic access response system based on demand side management and energy storage, which comprises a memory, a processor and computer program instructions stored on the memory and capable of being executed by the processor, wherein the processor can realize the method steps as described above when executing the computer program instructions.
In this embodiment, a simulation was performed using fig. 1 as a test example. In fig. 1, node 1 is connected to an upper level substation, the substation capacity is 10MVA, and power reversal is not allowed; the node 7 is connected with a battery for energy storage, the energy storage capacity is 1MWh, and the relevant energy storage parameters are shown in a table 1; the node 10 is connected with photovoltaic, and the photovoltaic capacity is 10MW; according to the management type of the participation demand side, the load in the system is further divided into two types of load capable of being reduced and load capable of being translated, wherein the load capable of being reduced exists in each node except the node 1, the upper limit of load reduction is 10 percent, and particularly, the node 24 has the load capable of being translated with the duration of 4 hours and the size of 200kw, and the load translatable interval is 18 hours-23 hours.
TABLE 1
| Initial SOC | Upper/lower SOC limit | Upper limit of charge/discharge power (kW) | Charge/discharge efficiency |
| 0.8 | 1/0.2 | 250/250 | 0.9/0.9 |
Under the background of high-proportion photovoltaic access to the smart town power distribution network, certain uncertainty exists in the operation of the smart town power distribution network, and adverse effects are caused on the economy, safety and the like of the system. Based on historical data and a K-means clustering method, typical scenes of photovoltaic time sequence output and time sequence load can be generated. The photovoltaic time sequence output is shown in fig. 2, and is influenced by illumination factors, and the photovoltaic output of the power distribution network connected to the smart town has obvious time-sharing characteristics. 1-5, the output is 0, the photovoltaic output is increased along with the continuous increase of the illumination intensity, the illumination intensity is gradually weakened after the maximum output time is 13, the corresponding photovoltaic output is also gradually reduced, and the photovoltaic output is recovered to 0 after 22. Taking the load node 2 as an example, the time sequence active and reactive loads are shown in fig. 3 and 4, and the load peak sections are positioned at 18-22.
In order to compare and analyze the effect of different coping methods in the intelligent town distribution network in the embodiment on the aspect of promoting photovoltaic absorption, on the basis of improving an IEEE 33 node system in fig. 1, 3 types of comparison scenes are additionally arranged, and the photovoltaic absorption conditions under different energy storage configuration schemes and under different load translation levels are analyzed. The scene set pair is shown in table 2, where scene 1 is the reference scene.
TABLE 2
| Scene numbering | Energy storage capacity (MWh) | Translatable load (kW) |
| 1 (reference scene) | 1 | 200 |
| 2 | 1 | 0 |
| 3 | 0.5 | 200 |
| 4 | 2 | 200 |
First, an example analysis is performed on the reference scene in this example. In the reference scene, the active power output, photovoltaic absorption condition and energy storage charging and discharging process of the transformer substation are respectively shown in fig. 5, 6 and 7.
As can be seen from the combination of the load curve and the photovoltaic output curve, in FIG. 5, the substation output is concentrated at 1-5 hours and 18-24 hours, the illumination intensity is weak in the period, and the photovoltaic output is difficult to supply all loads in the system, so that the substation output meets the electricity demand of users. The maximum output of the transformer substation is 3.29MW when 22, and the load at 22 is not the maximum in a typical day, but the difference between the electricity load and the photovoltaic output is the maximum at the moment, and the power shortage is supplemented by the transformer substation. And when 9-18, the output of the transformer substation is 0, which indicates that the photovoltaic output can meet the power load in the system. From the view of a substation output curve, in the smart town power distribution network with high-proportion distributed photovoltaic access, the substation output is inversely related to the illumination intensity, and when the illumination intensity is weak, the substation output is higher.
Fig. 5 illustrates photovoltaic digestion in different typical scenarios within a typical day. Unlike the unimodal version of the photovoltaic output curve of fig. 2, the photovoltaic power profile was relatively flat at 10-16, with small fluctuations in the power level of about 3.3 MW. At this time, the photovoltaic output can meet the load demand in the system, and the consumption is limited by the load. The maximum photovoltaic absorption occurs at 18, marked by a red box, and the photovoltaic absorption is expected to be 4.02MW in different scenarios. At this time, the load reaches the maximum daily value, and thus the corresponding photovoltaic consumption amounts are the same as the maximum.
Fig. 7 shows the charge and discharge process of the solar cell energy storage in this example. As can be seen from fig. 7, the charging process includes 1-5 hours, 10-18 hours, 21 hours, 23-24 hours, and the discharging process includes 6-9 hours, 19-20 hours, and 22 hours, and the charging average power is smaller than the discharging average power. The charging process at 10-18 corresponds to a period of time with larger photovoltaic output, which indicates that in the smart town power distribution network connected with high-proportion distributed photovoltaic, the photovoltaic digestion can be further promoted by using the energy storage device. The charging process at 21 is performed at 21 and the discharging process at 22 because the system load is small at this time and the system load is large at 22. Thereafter, the state of charge of the battery is restored to the initial value by the charging process at 23, 24.
On the basis of analyzing the photovoltaic absorption condition and the energy storage charging and discharging process of the reference scene, the change condition of the photovoltaic waste light amount in the day under the conditions of different energy storage capacities and different translatable load sizes in the example is further compared, and the change condition is shown in fig. 8. Because the embodiment adopts the processing method based on the condition risk value theory aiming at the uncertainty in the operation process of the smart town power distribution network, as can be clearly seen from fig. 8, the photovoltaic light rejection amount is different under different uncertainty scenes, and in order to avoid the tail risk caused by various uncertainty factors to the system operation, the condition risk value can be used for describing the expectation that the light rejection exceeds a certain threshold under a certain confidence level. Comparing the reference scene 1 with the set comparison scenes 2-4, it can be seen that the larger the energy storage configuration capacity and the larger the translatable load in the system, the smaller the solar photovoltaic waste amount. Based on the results of the comparison scene 2 and the comparison scene 4, the risk value of the light rejection amount condition is reduced from 34.79MWh to 31.94MWh, and the reduction is 8.2%, so that the correctness, the effectiveness and the feasibility of the coping method based on the energy storage and the demand side management measures provided by the chapter are fully described. Based on the results of reference scenario 1 and comparative scenario 2, it can be seen that when the energy storage configuration remains unchanged for 1MWh and the translatable load drops from 200kW to 0, the corresponding risk value of the light rejection condition rises from 33.05MWh to 34.79MWh, illustrating the positive effect of translatable load on photovoltaic digestion.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the invention in any way, and any person skilled in the art may make modifications or alterations to the disclosed technical content to the equivalent embodiments. However, any simple modification, equivalent variation and variation of the above embodiments according to the technical substance of the present invention still fall within the protection scope of the technical solution of the present invention.
Claims (9)
1. The intelligent town power distribution network distributed photovoltaic access handling method based on demand side management and energy storage is characterized by comprising the following steps of:
step S1: the demand side management is used as a distributed photovoltaic access coping means, and modeling is conducted on time-of-use electricity price, load reduction and translational load in the demand side management respectively;
step S2: using an energy storage battery as a distributed photovoltaic access coping means to respectively model service life characteristics and operation characteristics in an energy storage technology;
step S3: aiming at photovoltaic absorption, establishing an intelligent town power distribution network operation optimization model considering safety and stability constraints;
step S4: introducing a conditional risk value theory, describing tail risk brought by uncertainty of photovoltaic output to operation of the power distribution network in the smart town, and converting the optimization model in the step S3 into a conditional risk value for minimizing photovoltaic waste light;
step S5: based on the clustering process, the photovoltaic output mass scenes are reduced, a typical scene is generated, and the typical scene is used as an input variable of a power distribution network operation optimization model;
step S6: performing second-order cone relaxation on a power flow equation of the power distribution network by using a cone optimization method, converting an optimization problem into a mixed integer second-order cone planning problem, and solving;
in the step S1, modeling is specifically performed for the time-of-use electricity price, the load that can be reduced, and the translatable load in the demand side management:
the electricity price in the time-sharing electricity price can be expressed as a piecewise function related to time, and the formula is as follows:
wherein t is a period number; c (C) t The electricity price is t time period; t (T) valley 、T norm 、T peak Respectively a set of time periods at valley, at ordinary times and at peak; c (C) valley 、C norm 、C peak The electricity prices at valley time, at ordinary times and at peak time are constants;
the load-reducible means that the load is partially or completely reduced by the available and demand conditions during operation, and the upper and lower limit constraints are satisfied when the load-reducible is managed on the demand side;
the translatable load is to translate the load integrally within a certain time period on the basis of ensuring the continuity of the translated load, and the specific modeling process is as follows: setting a scheduling period to include T time periods, and for the working duration to be T D A translatable load L of a certain period of time shift The active and reactive power of each period of its operating duration may be:
wherein P is shift 、Q shift A row vector formed by active power and reactive power of each period in sequence in the working duration;
active and reactive power for a t-th time period within the operating duration; record translatable load L shift The acceptable shift interval is +.>
Then L is shift Position S of the start period shift The method comprises the following steps: />
On the basis, a flag bit vector lambda representing the load shift condition of the initial period is defined, and the dimension is
All elements in the vector are 0-1 variables; after shifting the load, the translatable section S shift All load conditions that may occur within can be expressed as: />
In order to ensure the continuity after load translation, the power of the load translated at the moment t of the scene s is calculated as follows:
in (1) the->
Active and reactive load vectors within the translatable interval; lambda is a flag bit vector representing the initial load shift condition; />
A matrix is formed for all load conditions that can occur after translation.
2. The smart town power distribution network distributed photovoltaic access handling method based on demand side management and energy storage according to claim 1, wherein in step S2, the modeling for life characteristics in the energy storage technology is specifically:
the cycle life of the battery stored energy is expressed as:
wherein L is cyc The cycle life of energy storage for the battery;
the energy storage life loss of the battery for single simulation; t (T) yr 1 year; t (T) sim Duration of a single simulation; n (N) C The number of charge and discharge cycles of the battery energy storage for single simulation is determined by adopting a rain flow counting method; d, d j The discharge depth of the jth charge-discharge cycle; g (d) j ) Maximum charge-discharge cycle times at a given depth of discharge for battery energy storage; thus, use L ba ＝min{L cyc ,L cal Calculating the service life of the battery energy storage, wherein L ba The service life of the battery is prolonged; l (L) cal The calendar life is stored for the battery.
3. The smart town power distribution network distributed photovoltaic access handling method based on demand side management and energy storage according to claim 1, wherein in step S2, the modeling for the operation characteristics in the energy storage technology is specifically:
according to the relation between power and energy and the mutual exclusion characteristic of charge and discharge, the operation characteristic of the battery energy storage is expressed as:
E BES.min ≤E BES ≤E BES.max ；
γ BES ∈{0,1}；
E BES (t s )＝E BES (t e )；
wherein t is the time; Δt is the time interval of adjacent moments; t is t s And t e Respectively starting and ending moments of a scheduling period; e (E) BES Stored energy for battery energy storage; e (E) BES.min And E is BES.max Respectively the upper and lower limits of energy; p (P) ch And P dis Respectively charging and discharging power;
and->
Respectively the upper limit of charge and discharge power; η (eta) ch And eta dis Respectively charging and discharging efficiency; gamma ray BES Is an energy storage charge and discharge zone bit, is a variable of 0-1, and is gamma BES =1 denotes charge, γ BES =0 denotes discharge.
4. The method for handling distributed photovoltaic access of smart town power distribution network based on demand side management and energy storage according to claim 1, wherein in step S3, the smart town power distribution network operation optimization model taking into account safety and stability constraints is established with the objective of photovoltaic digestion, and model constraint conditions include: load flow constraint, node voltage constraint, branch power constraint, battery energy storage constraint, load shedding and load translation constraint.
5. The method for handling distributed photovoltaic access of a smart town power distribution network based on demand side management and energy storage according to claim 1, wherein in step S4, the condition risk value theory is introduced to describe the tail risk of the smart town power distribution network operation due to the uncertainty of photovoltaic output, and the optimization model in step S1 is converted into minimumA condition risk value of photovoltaic light discarding is converted; the objective function of its final model is: minCVaR (DeltaP) PV ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein DeltaP PV Is the photovoltaic light rejection amount; CVaR (DeltaP) PV ) For its corresponding conditional risk value.
6. The intelligent town power distribution network distributed photovoltaic access processing method based on demand side management and energy storage according to claim 1, wherein the clustering process in the step S5 adopts a K-means clustering method.
7. The smart town power distribution network distributed photovoltaic access coping method based on demand side management and energy storage according to claim 6, wherein the clustering process in step S5 specifically comprises the following processes:
firstly, according to the preset clustering quantity K, K data objects are obtained from a data set X through random selection, and are respectively used as clustering centers w of K-class data 1 ,w 2 ,...w k ...,w K The method comprises the steps of carrying out a first treatment on the surface of the Second, for each data x i Respectively calculating the clustering centers w 1 ,w 2 ,...w k ...,w K According to the formula
Calculating a cluster center minimizing Euclidean distance to obtain the class of the data, wherein c i For data x i Class of which ||x is a category i -w k || 2 For data x i To the kth cluster center w k Is a Euclidean distance of (2); then, for each type of data, according to the formula +.>
Recalculating the clustering center of the data, wherein u k S is a new cluster center k The number of data divided into k classes; finally, judging whether the algorithm is converged, if so, outputting a newly generated clustering center as a final clustering result, and if not, judging whether the algorithm is convergedThe euclidean distance calculation is returned.
8. The method for handling distributed photovoltaic access of a smart town power distribution network based on demand side management and energy storage according to claim 1, wherein in step S6, the method for optimizing the power distribution network power flow equation by using the cone is used for second order cone relaxation, and the method for converting the optimization problem into a mixed integer second order cone planning problem is used for solving the mixed integer second order cone planning problem specifically comprises the following steps:
according to the flow equation of the smart town power distribution network, introducing an intermediate variable V i ＝U i 2 And
to the original side Cheng Huawei V i -V j ＝2(R ij P ij +X ij Q ij )+(R ij 2 +X ij 2 )I ij Relaxing the second order cone of the equation to obtain a standard second order cone form
In U i Representing the voltage at node i; v (V) i ＝U i 2 Representing the square of the voltage at node i;
representing the square of the current of branch ij; />
Representing the head end active power of the branch ij; />
Representing the head-end reactive power of the branch ij; r is R ij The resistance value of the branch ij; x is X ij Representing the reactance value of the branch ij.
9. A smart town power distribution network distributed photovoltaic access management system based on demand side management and energy storage, comprising a memory, a processor, and computer program instructions stored on the memory and executable by the processor, when executing the computer program instructions, being capable of carrying out the method steps of claims 1-8.
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